Breast Cancer Classifier.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
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    "def cropImage(r,img): #crops image for given coordinate\n",
    "    imgCrop = img[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]\n",
    "    return imgCrop\n",
    "\n",
    "def resizeImage(scale_percent,img): # resizes image\n",
    "    scale_percent = 60 # percent of original size\n",
    "    width = int(img.shape[1] * scale_percent / 100)\n",
    "    height = int(img.shape[0] * scale_percent / 100)\n",
    "    dim = (width, height)\n",
    "    # resize image\n",
    "    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n",
    "    print('Resized Dimensions : ',resized.shape)\n",
    "    return resized\n",
    "    \n",
    "\n",
    "def show(show_img): #displays image\n",
    "    cv2.imshow(\"Image\", show_img) \n",
    "    cv2.waitKey(0)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import datetime\n",
    "import cv2\n",
    "\n",
    "import pandas as pd\n",
    "import os, os.path\n",
    "import re\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pylab as pl\n",
    "\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "METH_1_DATAPATH_1 = r\"./Data_Breast_Cancer/meth1/1/\" # DataSet path for method1 sick images. \"1\" is for sick\n",
    "filenames = os.listdir(METH_1_DATAPATH_1) # list files in that directory\n",
    "#filenames.remove('.ipynb_checkpoints')\n",
    "\n",
    "col = [x for x in range(173600)]  #total number of pixels 560*310= 173600.\n",
    "Data1 = pd.DataFrame(columns=col)\n",
    "i = 0\n",
    "for filename in filenames:\n",
    "    img  = cv2.imread(METH_1_DATAPATH_1+str(filename)) #read image\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #convert to gray\n",
    "    crop = cropImage((35, 98, 560, 310),gray) #crop image\n",
    "    row =  crop.ravel()  #flatten\n",
    "    row_as_list = row.tolist()\n",
    "    Data1.loc[i] = row_as_list #insert in the dataframe\n",
    "    i=i+1\n",
    "\n"
   ]
  },
  {
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   "execution_count": 4,
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    "Data1.insert(0, \"label\", [1, 1, 1, 1, 1], True) #insert label \"1\" for sick images\n",
    "Data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "METH_1_DATAPATH_0 = r\"./Data_Breast_Cancer/meth1/0/\" #data path for method 1 healthy images, 0 means healthy\n",
    "filenames = os.listdir(METH_1_DATAPATH_0) #list of files in the directory\n",
    "filenames.remove('.ipynb_checkpoints')\n",
    "\n",
    "col = [x for x in range(173600)] #total number of pixels = 560*310= 173600\n",
    "Data0 = pd.DataFrame(columns=col)\n",
    "i = 0\n",
    "for filename in filenames:\n",
    "    img  = cv2.imread(METH_1_DATAPATH_0+str(filename)) #read file\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #convert to gray\n",
    "    crop = cropImage((35, 98, 560, 310),gray) #crop image\n",
    "    row =  crop.ravel() #flatten\n",
    "    row_as_list = row.tolist()\n",
    "    Data0.loc[i] = row_as_list #store in dataset\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    "\n",
    "Data0.insert(0, \"label\", [0, 0, 0, 0, 0], True) #insert label \"0\" for healthy images\n",
    "Data0.head()"
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       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 173601 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  0  1  2  3  4  5  6  7  8  ... 173590 173591 173592 173593 173594  \\\n",
       "0      0  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "1      0  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "2      0  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
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       "4      0  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "5      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "6      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "7      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "8      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "9      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "\n",
       "  173595 173596 173597 173598 173599  \n",
       "0      0      0      0      0      0  \n",
       "1      0      0      0      0      0  \n",
       "2      0      0      0      0      0  \n",
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       "4      0      0      0      0      0  \n",
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       "9      0      0      0      0      0  \n",
       "\n",
       "[10 rows x 173601 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge Data0 and Data1\n",
    "frames = frames = [Data0, Data1]\n",
    "meth1_df = pd.concat(frames,ignore_index=True) #method  1 dataset, that has healthy and sick images\n",
    "meth1_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     int64\n",
       "0         int64\n",
       "1         int64\n",
       "2         int64\n",
       "3         int64\n",
       "4         int64\n",
       "5         int64\n",
       "6         int64\n",
       "7         int64\n",
       "8         int64\n",
       "9         int64\n",
       "10        int64\n",
       "11        int64\n",
       "12        int64\n",
       "13        int64\n",
       "14        int64\n",
       "15        int64\n",
       "16        int64\n",
       "17        int64\n",
       "18        int64\n",
       "19        int64\n",
       "20        int64\n",
       "21        int64\n",
       "22        int64\n",
       "23        int64\n",
       "24        int64\n",
       "25        int64\n",
       "26        int64\n",
       "27        int64\n",
       "28        int64\n",
       "          ...  \n",
       "173570    int64\n",
       "173571    int64\n",
       "173572    int64\n",
       "173573    int64\n",
       "173574    int64\n",
       "173575    int64\n",
       "173576    int64\n",
       "173577    int64\n",
       "173578    int64\n",
       "173579    int64\n",
       "173580    int64\n",
       "173581    int64\n",
       "173582    int64\n",
       "173583    int64\n",
       "173584    int64\n",
       "173585    int64\n",
       "173586    int64\n",
       "173587    int64\n",
       "173588    int64\n",
       "173589    int64\n",
       "173590    int64\n",
       "173591    int64\n",
       "173592    int64\n",
       "173593    int64\n",
       "173594    int64\n",
       "173595    int64\n",
       "173596    int64\n",
       "173597    int64\n",
       "173598    int64\n",
       "173599    int64\n",
       "Length: 173601, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "meth1_df = meth1_df.astype('int64') #convert the values to int64\n",
    "meth1_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "X1 = meth1_df.drop('label', 1).astype('float32') #drop label\n",
    "X1 = np.asarray(X1)\n",
    "print(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "Y1 = np.asarray(meth1_df['label']) #store label\n",
    "print(Y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split( X1, Y1, test_size=0.2, random_state=4) #trai,test,split\n",
    "clf1 = svm.SVC(kernel='rbf') #Svm kernel for rbf\n",
    "clf1.fit(X_train1, y_train1) #train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 1 0 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "yhat1 = clf1.predict(X1) #predict\n",
    "print(yhat1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "METHOD 1\n",
      "Accuracy :  0.9\n",
      "Sensitivity :  0.8\n",
      "Specificity :  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm1 = confusion_matrix(Y1,yhat1)\n",
    "print(\"METHOD 1\")\n",
    "total1=sum(sum(cm1))\n",
    "#####from confusion matrix calculate accuracy\n",
    "accuracy1=(cm1[0,0]+cm1[1,1])/total1\n",
    "print ('Accuracy : ', accuracy1)\n",
    "#####from confusion matrix calculate sensitivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#####from confusion matrix calculate specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "\n",
    "### Method1 - Healthy Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise some labels\n",
    "f, axarr = plt.subplots(1,5, figsize=(20,20))\n",
    "for i in range(5):\n",
    "    image = X1[i].reshape(310,560)\n",
    "    axarr[i].axis('off')\n",
    "    axarr[i].set_title(\"accuracy = \" + str(accuracy1) + \"\\nSpecificity = \" + str(specificity1) + \"\\nSensitivity = \" + str(sensitivity1))\n",
    "    axarr[i].imshow(image, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "### Method1 - Sick Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dsmdTu+yRMlS71K3HN7XNHilDtU3denxT2+yRMlTb1K3HN7XNHilDtc00m0YIdZ8tAOLGmAeMMYuNMWX8R2PMhwB8FsByAEMA/BXAr0QaZwOYAWAKgPMBfDD5/f8H4I8AygBUI6EMp+A4zg8BrAFwl5MI7TtH/P4egJcBnMe+vhjAw47jRMW+c5MfpyTT+g2AnwO4hO22BMBux3Fel3kxxow0xhxMs10sj0kyCcAGlo+tSBjpeMv17kGi/K4wxviNMacAGAXgbx5pK7mJ2mX7tapdKtmE2mb7taptKtmE2mb7taptKtmE2mb7tapt9gIqCHUTx3EaAMxBQkn8EYA6Y8xaY8zQ5C4fBXCn4zhvO44TQ0KhPNEYM4ol8zXHcQ46jvMugD8DODH5fRSJB3C44zitjuMc6YP4IICLAMAYYwBcmPyuK/wSwJJkuCKQCLX7hW1Hx3HedRxnUJrN65wlSCjTnEMABnjs/ysAn0ciVPKvAG5zHGdnF69HyQHULttRu1SyCbXNdtQ2lWxCbbMdtU0lm1DbbEdts3dQQagHSBrg5Y7jVAOYjETo3LeSP48C8P+TcglgPwCDxPxP4n32uRmJhxYA/ie5799NYiX3K48wiw8DOMUYMxzAXCQqlL925cCk6vsigPOMMYMALEZCJe5JmgCUiu9KATTKHY0xEwH8BsBlAPKQUHz/xxhzVg/nSTnKUbvsNmqXSq+gttlt1DaVXkFts9uobSq9gtpmt1HbTEMg0xnobziOs8kY8zMk1FoA2AngDsdxDvvBdhznfQDXAInV5QE8a4x5wXGc/8hdO0nnoEm87u98AMcC+JXjOGmPETwA4GoknpeXHcfZZdvJGDMSwFtp0vmoRzm8iUQII6UzBkA+EiGSkskANjuO80zy/83GmN8hUXn8rrMLUXITtUu1SyU7UdtU21SyE7VNtU0lO1HbVNvsaTRCqJsYYyYaY1YbY6qT/9cgETL3SnKX7wO41SRWN4cxZqAxZmUX015J6SKxCJgDIG7ZdQ+AMZ0k9yASSud5SB/CZ0vrcQDTANyExDxPK8kwvpI0m1dFtQbAOcaY04wxxQC+DOBRx3E6qLYA/glgnEm8DtAYY8YiMS92g2VfJUdRu2xH7VLJJtQ221HbVLIJtc121DaVbEJtsx21zd5BBaHu0wjgZACvGmNCSBjnRgCrAcBxnMcAfB3Ar40xDcnfFncx7RnJdJsArAVwk+M42yz7/QTAcSYRKvi4R1prAYwDsMdxnHQP9BcBPJBM6/zkNbQAeARALYBHu5j3LuM4zptIrH6/BsBeJOZzXk+/G2OeNsZ8NrnvViRW0v9fAA1IrIz/CBJloCiE2mU3UbtUegm1zW6itqn0Emqb3URtU+kl1Da7idpmeuiVc4qSFmPM5wGMdxznkk53VhSlT1C7VJTsRG1TUbITtU1FyU7UNjOHriGkdIoxphzAVUis+q4oShagdqko2YnapqJkJ2qbipKdqG1mFp0ypqTFGHMNEouVPe04zguZzo+iKGqXipKtqG0qSnaitqko2YnaZubRKWOKoiiKoiiKoiiKoig5hkYIKYqiKIqiKIqiKIqi5BhZsYaQMUbDlJScxnEck+k82FDbVHIdtU1FyU7UNhUlO1HbVJTsxMs2NUJIURRFURRFURRFURQlx1BBSFEURVEURVEURVEUJcdQQUhRFEVRFEVRFEVRFCXHUEFIURRFURRFURRFURQlx1BBSFEURVEURVEURVEUJcdQQUhRFEVRFEVRFEVRFCXHUEFIURRFURRFURRFURQlx1BBSFEURVEURVEURVEUJcdQQUhRFEVRFEVRFEVRFCXHUEFIURRFURRFURRFURQlx1BBSFEURVEURVEURVEUJcdQQUhRFEVRFEVRFEVRFCXHUEFIURRFURRFURRFURQlx1BBSFEURVEURVGyiGXLlmHy5MmZzoaiKIrSz1FBSFEURVEURVGyiNbWVowaNSrT2VBykLPPPhvV1dWZzoaiKH2ECkKKoiiKoiiKkkVs27YNVVVVmc6GkoOMHj0a4XA409lQFKWPUEFIUY6QadOm4dprr810NhRFURQlJ5g0aVKms9BnHDp0CIWFhZnOhpJjTJkyBXv27EFdXV2ms6IoSh+hgpCiHCHXXHMN9u3bl+lsKIqieFJUVJTpLChKj3DppZeipKQk09noMxoaGhAMBjOdDSXHmDFjhvq2ipJjqCCkKEfIsGHDsHHjxkxnQ1EUxZNp06ZplIFy1DN79mwAwKuvvprhnPQdoVAI8XgcQ4cOzXRWlByiuroau3btynQ2FEXpQ456Qei4447TRfeUPmfWrFloa2vTURRFUbKajRs3Yv78+ZnOhqIcMeeccw7mzZuHZ555JtNZ6XNCoRAmTpyY6WwoOYLP50NhYSHq6+sznRVFUfqQo14QamhowJQpUzKdDSXHOP7443Ho0CEcOHAg01lRFEXx5ODBg/D5fBg0aFCms6Ioh82CBQswffp0vPDCC9i7d2+ms9PnNDY2ora2NtPZUHKEmTNnorm5GYcOHcp0VhRF6UOOekGopaUFY8aMyXQ2lBxj2LBh2L17N+LxeKazoiiKkpY333wTZ511VqazoSiHxaJFi7B8+XI8+eSTePHFFzOdnYzQ2NiI0aNHZzobSo4wdepU7Nu3D7FYLNNZySkGDhyY6SwoOc5RLwjt378fFRUVmc6GkmMEAgHs3Lkz09lQFEXplO3btyMcDmP69OmZzoqidImFCxfi4osvxvPPP4/XXnst09nJGA0NDSgqKoLPd9S768pRwJgxY/Dee+9lOhs5h9/vz3QWlBznqG9hHMdBfn5+prOh5Bh+vx/vv/9+prOhKIrSJd5++23MmDEj09lQlC5xwQUXYPfu3TkbGUSEQiHEYjFMnjy5y8cEg0HMnDmzF3Ol9FcKCgrQ0NCQ6WzkHBqRpWSao14QAoBwOIy8vLxMZ0PJIYLBILZt25bpbCiKonSJN998E/X19Vi2bFmms6Ioabn11lvR2tqKhx9+OOcHXhoaGtDS0oIlS5Z0+Zhnn30WTz31FEpKSnoxZ0p/Y86cOQiHw6irq8t0VnKOUCiEsWPHZjobSg7TLwShvXv3HtboiaJ0F7/fj//+97+ZzoaiKEqX+cMf/oAxY8bgqquuynRWFMXKnDlzMGHCBLz44otYv359prOTcZqbm9Ha2orjjjsOZWVlne5/ySWXYNSoUWhtbcXLL7/cIVJoxYoV+Ne//oV///vfWLt2LS677LLeynq/pKysDCNHjsScOXNw1VVXYfXq1Vi1ahXmzZt31Atw8+fPRygU0rfnZoB4PI6hQ4dmOhtKDhPIdAZ6gvfeew8zZszAP/7xj0xnRckBZs+ejYMHD2qjqSjKUUVDQwOeeeYZfPCDH8SFF16IX//615nOkqKkcNVVV+Htt9/Gk08+memsZAXhcBiRSASxWAx33303brjhBrS2tnru/+EPfxiRSATGGJSWluKJJ55ALBZDfX09iouL4TgOYrEYIpEIxo8fj9tvvx233347nnjiCdxzzz3Ys2dPH17d0cEXvvAFLFq0CCNHjkReXh6MMfD5fDDGAEgsXdHW1oa2tjZEo1E0NTVh27ZtuOWWW7Bp06YM577rjB49Gps3b9Y3jGWISCSS6SwoOUy/iBDauXMnJk6cmOlsKDnCvHnzNDpIUZSjko0bN+Kpp57CpEmTcNFFF2U6O4rismjRIjQ1NeG5555Dc3NzprOTFZB4E41GMXjwYKxduxZ33303zjvvvA77DhkyBMOHD0c0GkU8HkcsFkNrayui0SiKi4sRiUTQ3NyMlpYWtLa2orm5GaFQCC0tLTjrrLPwpz/9CRdccEEGrvLICQaDWLhwIVavXo1vf/vb+J//+R+MGDGix9J/9tlncdVVV6GmpgZtbW1oaWlxy625udktz3A4jGg0CgAoKSnBlClT8Mc//hFLly7tsbx0hTlz5hzRcddddx1isRiampoQCoV6OFdKV9i7d2+ms6DkMP0mQigej2PWrFl45ZVXMp0dpR9jjMHIkSM1Gk1RlKOWzZs345VXXsGZZ54Jn8+HNWvWZDpLioLjjjsOe/bswT//+c9MZyVroAghihJyHAezZs3CBz7wAcybNw8f//jH3X1//OMfIxKJwO/3w+fzIR6Pp4hD8XgcbW1t7v4U2WKMQTweh9/vx+c//3ncfPPN+Oc//4m//OUv2LJlC8rKynD99dfjpJNOgt/vR1tbG5qamlBXVwdjDIqLi9002traEAqFsGPHDmzatAlr167Fzp07e1xkmDNnDr70pS/hmGOOgTHGjdbx+Xy46aabUFdXhzvvvBO/+c1vjij9srIyrF27FlVVVQiHwwDgll1bW5sbISTf/kZlSvt9/etfRzgcxjPPPHOkl9plpk+fju9973t49NFH8YUvfKFLxyxcuBDLly9HeXk59uzZgwMHDvRyLhUvGhsbM50FJYfpF4LQnj17UF9fjwsuuEAFIaVXWb58OaLRKHbt2pXprCiKohwxf/zjH1FbW4vTTz8de/bswbPPPpvpLCk5TjgcRjgcRjwez3RWsgYSg0jUIcEHAGbNmoV169bh4MGDGDBgAHw+H2KxGIwxiMViiMVi7lSmtrY2OI6Tkjb/n8SctrY2FBUVYf78+Vi0aBGCwSACgYArLrW0tCAajSIajaKoqAixWAzNzc2Ix+OIx+OuMFNbW4uxY8finHPOgd/vd0UqYwzC4TB27dqFt956C9u3b4cxBqNGjcLYsWOxefNmPPvss1ZffuDAgbjooovwoQ99CCNHjgQAtLS0AICbPpAQhcrKynDXXXfhYx/7GO688048/fTTHdJbvHgxlixZgnXr1uHhhx9Gc3MzJk+ejBtvvBFz5sxxRS4qKxLkqNzoWnl58uljJLZ95jOf6VFBqLKyEsuXL0dtbS0GDBiAkpISBAIB1NTUoKWlBYsXL8a8efPw9NNP4/HHH8fmzZut6Xz1q1/F9OnT0dzcjMbGRoTDYV0KIYMEAv2iS64cpfSLpy8ajWL37t0YNWoUVq1apaOdSq8xd+5ctLS06FsYFEU5qolGo3j66adRVlaGSy+9FM8991yHDqOi9CV79+51O/pKO62trYhEImhra3OFIcLv97trA1GUDwkRJE6QUOElCPGooba2NjeaKBaLIT8/3xWEaMpZNBpNyU88Hu9wDoqe8fl8rljj9/thjEEgEMDo0aMxZswYtxNMeZg0aZI7Ha61tRUHDhxAIBBARUUFfD6fez2hUKhD2nQ+ihgyxmD48OH47ne/C2MM9u7di/r6encB3+LiYrS1tWHevHn41Kc+lVI2FAVE18SjrPh18r+UN1nmPp8P99xzDz75yU926zmYOXMmbrzxRowYMcJNm69nFI/HEQ6H4fP5kJeXh6VLl+Lss8+G4zioq6tznw/HcVBSUuJOgaMoskgkooJQBtE1hHKHO+64A7fddlums5FCvxCEAOD9999HKBTCWWedhR07duBvf/vbYR0/f/583HDDDVixYkUv5VDJZm6++WbU1tamhF/bGDx4MDZv3oz9+/f3Uc4URVF6h61bt+K1117DgAED8MlPfhJ33313prOk5DCbN2/GSSedlOlsZB0kwpAYQZE49D8XBbhAwQUgHrniFeFC35EQQsfx9YhaW1sRi8Xc/FCanUH7kahD4g3vBNsE6YEDBwJIiEP8PIFAwM0nF5VIdOJTueLxOHw+H8rLy1FWVuZGO4XD4RTRhgtJdDwXvfi0O5vwI//n13PiiSdizZo1uP/++1OiMYuLi7F8+XKcdNJJCIfD+Pvf/47169dj+/btAIARI0Zg/vz5WLBgAWpqahCPxxEKhdz7DsDNu9/vdyO6uCAYj8dRVFTkinjGGESjUffe0mLY4XBY17HJIMYYnHTSSfp2xX5OcXExFi5ciFNPPRWrV6/OmvvdbwSh3bt3IxQKIRKJ4JZbbkFtbS1+8YtfdOnYpUuX4tOf/jSAxHza5557rjez2qMEAgG3MUz35olsYeTIkbjllluwbds2/PCHP3TDfTPJsGHDMHXqVAQCAXz/+9/HnXfeiR07dnTYb968eQiHw6irq3MXD1QURTma+dOf/oTRo0dj2rRpGDMmGKCAAAAgAElEQVRmDN55551MZ0nJUf7zn/+gvLwcgwcP1kgFBglCUnggYQVAypo2XOjhES7RaNQVVbjwQfvz72ialzEGbW1tKesY8Qgifh5Kg/LD4ev80P42Ick2tY0LXZROLBZzRSESOUgk44IWHUPn4iKITFMKSbQPv16aNsbFNX5PeFlwHMdBZWUlPvvZz+L222+3RnEBwIwZM1yxh6fNo7J4/ugv7c/vny2fPMJJil6hUAj19fUd8q70DY2Njbj++utx1VVXZTorSi8ybtw4AAm727p1a4Zz006/EYQOHDiQMopy/vnno6KiAn/4wx9QUlKCsWPHYurUqQiFQvjb3/6G3bt3o6mpCStXrsS5557rzlu/9NJLUVdXhzfeeCPTl5SWUaNGYenSpZg+fToKCwsRj8exc+dOPProo1m9jtJNN92EiooKlJWV4dZbb8Vf/vKXjAtwF154oesoFRcXY/Xq1fjOd76DLVu2pOz3gQ98QOdYK4rSrwiHw9iyZQtGjRqFJUuW4Dvf+U6ms6TkKK2trSgoKEBJSYm2swwSYrzEEr5+jpfwwqN5SAwgMYWEHy6K0AYgRQyhaBkusPC88M9emw2vyBr6jb/inZ9LRkRx8YfyT9/z81O5ySluMn+2KXd8jSDb7+mukcqOR3jxNbMo+ouLRXzjpMurrYzSlX80GkUoFHIX0Fb6niNZO23BggX485//3Au5UXqLsrIyRKNR7NixAwcPHsx0dlz6jSC0d+9eNDY2uuGsjuNgyZIlWLZsGYLBIPx+vxtGuWzZMrdRozBJWrSvoqICX/nKV7B27Vr85Cc/6RDy+ZGPfATbt2/HXXfdZc0HvQJz1KhRAIB//etf2LVrlzXihCr86dOnY9KkSRg2bBgKCgowdOhQFBYWugv4UVhrfn4+ysrKUFpaivz8/JQG0BiD8vJyTJ06FY2Njfjd736H5557zg07zSTGGKxcuRJnnnkmHMdBS0sL2traUFlZiQsuuAArV65ES0sL9u/fj3379qGurg6xWAyhUAgNDQ146623emX1/QkTJuCSSy5BeXl5yrzrQYMG4ctf/jLq6urwn//8B01NTZg8eTIqKyuxe/duvPvuuz2eF0VRlEzx8ssvY+LEiZg2bZrbSVSUTBAMBlFZWZkVvku2QB11KQhR556vn0OfuZhA/i6fYkabXHuHIoNIJKJ1aei16nyhapsAwqdeyTzZxCr510v0sF23FIO8ooKojOj8gUAgJbqJw6NmvIQeW6QW30eWsW3xaa+1nUgsspWHl5gjo7KkuCZFL3luWqxco4Myz0svvYTx48d3GJD2oqqqqpdzpPQ0e/bsQTgcxj333JPprKTQbwSh1tZW7N+/H+Fw2B3JiEajbqPAGyY+YhKJRFIaOSBRqS5btgwrVqzAwYMH0dLSgoqKCgSDQbS1taG6uhozZsxAXV2d+7apkpISVFZWYuDAgSkV8MKFCwEkKmgK9/T5fCgoKEgJceWjLdQg5OXluW+O4I1pPB5Hc3Oze+2y8S0sLMSFF16Iiy++GG+++SZ++9vfHvaaSl2hpqYG48ePRzgcxvbt27Fnzx4UFRWhqqoKZWVlmDBhAqZNm4by8nJ3hIkicXgjR0JMVVUVhg0b1qFxpHJpbm7G/v37UV9fj507d7pCW1NTU6d5zc/PR01NDY499lhMnjwZQ4YMQX5+vvscAO2NKjWW5eXlmD17tutctbS0IBQKYefOnT1ZjIqiKBmlubkZW7ZsQW1tLc466yw8+eSTmc6SkqO0tbWhoqIi09nIKpqamlLWEOJwQYf7ira1bYB2UYCWG5BiDf9MU8Vo0JT8ZB7JYBODpMDE/ydsggX/n0fJcGzXZotMkuIQj7zx+XwIBoMdzkvI6CcutMhrlvBoJumb8/S52GNbs4m+l8fZkNMGbeXLr0U+F9Sn2LNnj+d1KX3DwYMHMWvWrC4JQtdddx3WrVvXB7lSepLGxkY0NDTgzTffzHRWUug3ghCQWFiaj6JQw8UrZ2pQqaGj12fyhpYEC3p7A72JgCJbqDIdMmQIhgwZkjKPl84vkRV7a2trSqUMdJx3LBto21xvvg9fsC8Wi8Hn82HChAn40pe+hHg8jvr6erz99tt48cUX8eqrr3ZJSOEEAgEsXrwYZ5xxBmpra1MEFR6OzEelSJyTr0CV4bfcCeDf018a1amsrERlZSWOPfbYFAGHFjs0xrj3lULPi4qKUsqe0qdIMsLv96c4OrwcAbiCkC66pyhKf+Pf//43TjnlFNTW1mY6K0oO4/f7UV5enulsZBXNzc2u/ySjR3gECv/OFqVCfmgwGEReXh6CwWCK/8ih87W2troDrV4RSnJBZi4ySUFIToOyRQlxPzCd+MJ9XkKuXcTzSn6mFE7kgCw/hgaRKd1AIOApSNnKRgpkPC8kuknRjl8fv6504pCMRuJpeUUxcT+9tbUV77//vkdJK33Ftm3b8PGPfxw///nP0+5XXl6Oyy+/HN/73vf6KGdKT1FZWZmVazX2K0Ho3XffRVNTU4dXYNoaGv7WAB4dJEcfpJJuE3BkRc7T4t/ZRmvoLxci5Fxw2aDIURzeKMu0qcL3+/0YMmQIqqqqsHDhQvh8PkSjUezevRs7duzAO++8g7q6OoRCIQSDQdTX16OpqQlDhw7FtGnTMGvWLFRWVroiSTQaRWtra4c3Lsi56vSZC3EcOUIjG2bp4Egngf4GAgGUlpamlBvBQ4PlcbwM+T2g89LrVoHEWhuHDh3yDDVWFEU5WmlsbEQkEkEwGMx0VpQcJhgMum25kqC5ubnDgsI84oMPdpJ/yN+2RaIGRQWRGMQXjeYDcOQjx2KxDlPF6JyEl+gkz094rb/D0+NRNvx7DhdUuH9IIpHcV6blNY1LXp8UfKQfSv6hV/SO9DUpfdln4Itj2/JM+9iipmjgm+ePyp/nmwuIXBSKRqNoamrSwc4sYNu2bZg4cWLafYqLi/HQQw+htbUV55xzDp588klMnz4dr732Wq/n75hjjsF//vOfXj9Pf+b444/PihcqSfqVILR79240NDS4UToyPJV/JmGDFsnjFTwdZxOWpHBB6UmBR0L78EXkZJ74Z2pIbecAUhsHeZxMj59ThhZXV1dj5MiRmDdvXodjuZNB88jlfHTZuEvxR+bbNhIl9/Eqc5ugJLFFURFSQLOVHRe2eJgxRX/pCIqi9AwLFizA1VdfjVWrVmU6KwrgrrHXG+u1KQpxzTXX4K9//Ss2bdpk/T0vLw/FxcV9nKvspqWlxRVlKGqFhB2/3+/6syROUOQP2TTfaJoY+TXp/Dk6Z7oFrflgpfQvgfa1e8iPlFHltkigdFFBHBmhA3QUW+S+fD/6XopB/De+phAXtowx7v0g5ICsTbzhcBHN1oeQ0UqybyD9bB6hRfeZXz//y5+baDSKhoYGNDQ0eOZV6RsOHTqEUaNGea7lV1NTgwcffBBtbW1oamrC1VdfjY9//OPIy8vDa6+9htbWVjz00EN4/fXXeyxPc+fOxbJly1BTUwOfz4dwOIwNGzbgsccew+bNm3vsPLnCKaecgvvvvz/T2ehAvxKEmpubUV9f774xjFeQAFI6+7IRlCMSMnKFwytk24gN/9+WphSabPDRH1s++DV1pfHkIxD82kgkojRlA2YTsmQa6QQb2eClaxzTCUF0DenKihwc2yhKV8qIn49Xxj6fz2006+rqOk1HUXKdT3/605g4cSJ8Ph9eeeUVa1jzJZdcgosvvhh33XUXNmzYkIFcKpxBgwYBSLzRSOkec+fOxQsvvJDpbGQljY2N+Pa3v43LL7/cXYORU1RUhKKiogzkLHuhKVvk7wWDQeTn57trUZJ/wpdIsIlBQPtAHxdn5OvU6a8UioDUCHYpAMm1g7ifKs8nB0XlICh9x/96Idc0svnF0v/kvi8hp7bJ30gU4pFPXBSSfjGfLSAHKOUgND+Wli+QUT90Xtnv4H0Kfn9JGJRikCyjSCSC/fv3py1jpW8YNGgQYrEYZs6c2eGN0VOmTMGPf/xjhEIhhEIht62m2RpTpkyB3+9HaWkpbrzxxm7l4+STT8YFF1yA6upq9zmPRqPuczN16lRMnz4d27dvx3333ZeVU6CyjYULF+Lcc89FIBDISp+3XwlCQCJKqKWlxbpeDRdkaOSDL+hMeEXuyEaVCwc2dd/WMHUl4kXOtSaVv6viT2fwBoJEFP4bvw7ZwNnCeL3y1NnoSDrSiUGyUfOiq84ExzYnnZ9T38KgKOm57LLLsGzZMrfumjZtGlavXo26ujr84Ac/wMaNG3HDDTdg6dKlOHToEB599FGMHTs209nOeQYPHoz8/HwMHDgw01k5qrn33ntxwgkn4NChQ7jvvvvw7LPPAgBKS0vdtzXlMhRp/NOf/hR33nknnn/+efe3008/3W2Dg8EgotFo5jKaZdALOfx+P/Ly8lBQUJDSUSMRgAbfyC/lUdN8Gj8JCfJV8tI/TTfQx4UREkpozSCCi0HyXDxdiliRke+UBj+3bZDWNohoE2D4ALDcTw4gy9eAc1GIrpsPDvMIeZlvHm3Ezyfvj+3a5ZITNr+b3ye+P+8/8L9yyti+ffugZJ6qqio0NDTgyiuvTBGEFi9ejK9+9atoaGhAKBRyRSAAbh1A93rYsGGoqKg4ov7KggULcOGFF6KystK1VT5dVdrsiBEj8LWvfQ3xeBybN2/G888/j507dyIcDqOhoQE+n899W/bhcO2112Ljxo099kKkM888EyeeeCIKCwvx+uuv46WXXuqTRdSnT5+OWbNmYe7cufD7/YhEItizZ49OGesLtm/fjkOHDrlvq7IJMHL9IMI2r5e+lw1Guggi3tjYGqp0YhCdVzYcXhxJmK3X/rbj5XXw723TsTo7X1fEo66KSF3Z70hEIX693CHIy8vT9YMUJUkgEMDs2bMRCoXcN1Sdd955OPnkk9HQ0OBOTyDndMiQIbjzzjsRDAYRi8XQ2Njo2tOaNWt06liGKS8vR0VFRb9bv2XhwoU488wzUV5ejnvvvRcbN27stXMVFhZi5MiRaGpqQjAYxKc//Wl87nOfg8/nQ35+PoDEyy9+9KMf5eyb3ObMmeOuVfj5z38eX/ziF/Hee+8hHA5j9OjRaGxsVEHIAomJVK9y4cf20gz6a3vzlJy6xQdHuzJYyaOB+PQkOd1Kikq29YOA9qglWuvI5kOnwzboavuN/+4lysgysg2e0r4+n89t4yhSSA6qSpGNn4/+UjSX4zgp6dA55cCtvCc8ba+pfV79FBKEDh48mLaMlb6B7sP8+fPd6OlrrrkGZWVl7rS+1tZW13fizxmfyfGNb3wDv//97/HCCy+4S10MHz4cixYtwqRJk2CMwbvvvosNGzagqKgICxYscAfmpAgknx1u43Run8+H4447DpMnT+4gFgcCATQ0NOCXv/xlp+3eggULcN1112HAgAH40Ic+hHA4jE2bNuGxxx7rEDGVjoEDB+LYY4/FwoULMXPmzBThdurUqbjyyisRDoexdetWvPXWW1i3bh22bdvWbaGmsLAQ48ePx/Tp0zFnzhwUFBQAgGvbtC5bNtLvBKHdu3fj4MGDiEQiKaMQVPGlC1lNJ9CQ8iojhLjByM3WSMkRF1vF7UU6MSXdb+m+7+x3W8PKI2hkKKyMrrGFyqaLwKFjvByBI4k46qoY5BXOS6JQIBDAzJkzsXXr1sPOQ64xaNCgw3IwiouLcdppp+Gll15y57EXFxdjwYIFeOqpp3orm0on+Hw+TJ06FevXr0/5funSpbj11ltTplPy0V/qyAWDwQ7rUVDHmL/G+IQTTsDvf/97XHrppRqFlyFKSkpQU1OTsu7E0cTw4cOxatUqlJeXIy8vD0VFRQgGg+4z2dzcjMsvvxzf/OY38d577/X4+QcMGIA77rjDfa7pbUTxeNztNNKbMm+77TZcfvnluPfee3tsBNRGdXU1/vvf//Za+odDaWkpbr75ZlRWVroOcTgcRjAYRHV1NYwxKW98DQT6nXvaLWjhXy4UcNGHL0rMNy7UyIgVm0CTTgySQhClzQUhr7RtwhDPi9eyAvI76Vvz83XV15OiE4AUMYqLZTaBiYtiXNjhvq7XekXymLy8vBSBj/c1eL9FlhnPDxcB+SA0+a7S3+d5dRwHkUhE1w/KEt577z23zVixYgXOO+88NDY2IhQKoampyX2jMsdmd36/H8uWLcOKFSsQCATcReS5nU2YMAFnnHFGSoCEjIqz9d9sz5EcRKdnnM5VVFSE66+/Hh/72MfQ1tbmvmmbnn2C+u0kfgeDQZx00kk45ZRT0NraijfeeAM7d+7EmDFjMGLECDQ2NmLv3r1obGxEeXk5qqurUVJSkpIe+aQ2sXz8+PGYMGECVqxYAQDYuXMnXn75ZaxduxaHDh1Ke69Gjx6N6upqzJs3D2PGjEFlZSX8fj+i0agr4JNwl5eXl1Km2Ui/a3EjkQiampo6vP6di0JcKLKJN0Bqo8MrXEqHNxKysfJKx9awpcMrYqczYehwo2K89utKuXjlzWtUh38vBRj5W1dHh7qad/rehox2kg0sfV9dXX3YeclFbr31Vqxfvx6//e1vO913woQJ+N///V9MmDABdXV1WLduHUKhEObPn4+JEyfi+uuvxy9+8YsunXfixIm46KKLcN999/VYOGhlZaWr+A8ZMgS33Xab+9vVV1+NH//4xz1ynmxi/vz5qK2txdlnn42Kigo88cQTWL9+PfLy8nDsscdixYoVrsjD12OTi4+Sc8DXlaD/uTBvjEFNTQ0eeeQRvPTSS/jhD38Iv9+P7373u3juuedw9913Z6wscoX8/Hz4/X4UFRXhgx/8IJ555plMZ6nLzJo1C6tWrUJRUVFKOyunceTl5eFjH/sYfvOb3+CNN97o0Tx84hOfcNd/IAGIbIQPFNFoalVVFT73uc/hwQcfxJo1a3o8+vTmm2/Gqaeeiueeew7/+Mc/8Oqrr/Zo+gBw/vnnY//+/e60OElJSQnmzZuHyZMnY/To0SgpKUnptMgIbd7pPVqFyd6Cjyxz0YYLFPQXSK1zpWDh5Vt6+Uc8bTldinc0KW+2jX7jSH/P5l/bjpP5tqXZGVIU4oO0tn6CPKccJLZNdSP4bAMOL0sSpOQ12IQ1+p5shjrevGMuy4P7sVwgNMboVNYsIxKJuIJ4LBZzRSB64x+f9igXcediMa0LBsAVX7zEVP5s8WeJsPX3uChKx0lb4SJ1LBZz80kDg7QPiUny+eYDjPn5+Tj55JNx8sknu+erqKhATU1NByGXp8v7+vy6uXhFkXnDhg3D8uXLMW/ePDQ0NCASieBf//oX1q1bh0gkgry8PEycOBGLFy9GVVUVSkpKXDvkg6O2tzKSzXUmNGWKficIAcDevXvR3NzcIUIIgHuj+BpDgP0NXdwg5KvpZcUuGz7bb/LtW4eDrTHv7Luuppnud4lXA8Q3r5EqfiyQOl9a/j0cMYjvS5WhHA2T1yNHfHg6Mm3ax+fzoby8HIWFhVk5/zObGDx4MKZMmZJWEFq0aBFuueUWjBw5EgMGDEBJSQkqKiowadIkdyTN5/Ph9ttvTysI1dTU4Fvf+hamT5/u3pv169dj7dq1KftNmjQJ1157LRYtWoTZs2d3Gonys5/9zM0LAHeU5ayzzsJPf/pTjBkzBmeffXa/E4RWrlyJ1atXo6Wlxa2rli9fjpUrV7o2TCHFPESZT0PgTogMeSfHBECKiBSJRFBaWopzzjkHK1euRF5eHowxmDRpEi677DLccsst+L//+7+MlUt/Jz8/370Xn/nMZ3DhhRdi7969qKysxIQJE3D33Xfjsccey3Q2XQYPHoxly5bhuOOOw4ABA1zxBejYdvGo1EGDBuG6665Da2sr3n77bWzatMkNFT+SdTQmT56MK664AmVlZSltjxSmyDmnDiAAFBQU4PLLL8eHP/xhhEIhHDx4EM899xweeeSRbr3tbeLEiTjllFNgjMEZZ5yBM888Ez/5yU861IlHyo033oiZM2cCSNjzRRddhI0bN+Kll15CYWEhqqurceyxx2LkyJHW43kHhL85i+obijpR2uFlQx0ZLiQY0z6VLBgMprxeno6hiE0uTkp/DrC/Vl5OD6NX2HMxSC4cDdgFGm4ftoFAW+dN+mwyAsFL5Ep3bl62/DdbhJCM+KHj00UJUZrpOtN8oyhCgu4tz48UqWgBap5/L1+X9qdnBEj4Nc3NzerTZgmBQAChUAj5+fnu251bWlrQ2tqKSCTiCg7kR3FBUUbdyYEI3v5wAYm+53aRLlAASI0Y5OK9rX/L88GfVfkb/U/nlhFxvP8ohSwZ5CHza7MJmQfCGIOBAwdi0KBBMMZgwoQJOP/8893f+MLyXMgi/4nXHQSvUx955BFr2Waaftni7tq1C/v378fw4cNTHkAAKWFxXiGYBJ+jTcosgA7hd0Dqwm8S/sDIcDyObaTBa9RBNtj8b1eQDUdn4otUinmD7LW/7S0O/Hs5KiSvxaZS266D/6VjD2eUkYtXMm0uMAGJez9hwoQefaVjf6SgoMCdtyy5/fbbsXTpUhQWFnZwWqmi5dEjBw4c8DzPsmXLcN9997mNITWWX/7ylzFjxgwMGDAAo0aNQlVVFQoKChCPx9HS0oK5c+em7dz+6le/wsiRIxEKhVIEIZqKctNNN7kO1axZsw5rbnO2EwgE0NjYmPJGCT49QI7gSoeDvgPa36hI4hH9lpeXlxIRQPVjJBKBz+dDKBRyOxx5eXkoLCzE97//fXzve9/DPffck5mC6cdQBy8cDqOwsBB+vx9jxozBmDFjEI/HEQqFcPXVV2P69OnYsGEDKioqcMwxxyAajeLNN99EZWUlTjjhBFRVVaGpqQmbN2/G17/+detIWCAQwDHHHAMAnq8e51xxxRWYO3duyroJBQUFCAQCKY6YbNOozpYLvwKJZzA/Px8nnngiTjzxxJRns62tDfv378eGDRvw17/+1fPtO3PnzsXJJ5+MsWPHpnTSZQdRnpdGI2XkRnFxMYqLi3HppZfi0ksvxa5du/C73/0Ojz76aMpaOsYYnHzyyfjoRz+KwYMHY+PGjXjiiSfcOujMM8/E+eefj5aWlpT29LzzzkNeXh4efvjhTsvcRkFBAU499VSsWLEChYWFiEQiKc728ccfjylTpqR0nins32s0mYsRspOrEUKpGGPcUXXyWajdBBLTc6ljFwwGkZeX576Wnk9H8hLauHjJzynFIBKbaKP2gA94SoHF63zSb/PyB+X+6aIX5OCo7Rpl+8UHMeUAh+xk8zKS55LTyPi1cGyDuTxNXq50XrnWE+/88ogfXvZe5+W+VkFBgQpCWUQsFsPWrVtRVlbmCkK0UXQQ70fy6CDAOyJMRvbxdpE/s/J4Wx+Lp8s3bsNSkEqXFs+7tHmyIWr7Zd0i39ot05bBAbLfK88l2yLez6XrpCl9tnKSn2Xd1NbWhnfffdd6/ZmmXwpC77//vruOEA9JdhwnRRDyelip0uROTXNzsxvuxkPaeMVtUya5Ycj5yIcj4Nj2541JZ2nZKgzuQHcmBtkcbXmt/BxtbW3uyIU8N5UtH52iY2R0EVejeYMt8Wr8pPgkr8WWjgy95Hnz+/2YOnWqCkKdkJ+fnzKPl3jssccwZsyYlFENqrD5a3UpTDQSiXg6KqtWrcJXv/pVd/9IJIJIJILW1lb4fD6cc845bmcxHo+jqanJfU6uvPJKvPbaa9i5c2dKmiUlJbj//vvdBfy4IELPBs0Ppt9srwc9mhk3bhxaW1tTHFTeeeOdWDkCwh1g6TxL55YaVC6YyzqUBKH8/HwEAgFcccUVqKurwwMPPND7BZFDBINBdxFfshnugJEgMXXqVJx00kkp9efUqVNT1ukBEhEq999/P3bt2oX6+nr4fD7U1ta6r7YH2p+X5uZm7N692506GI/HUVBQgKKiIpSUlLj5KSoqSukI8ahdrzaVP6u0n4xO4YIkPX8lJSU49dRTMWfOHESjUUSjUbcjzOstILG2C0W0yIEar5B6OidPS7aTw4cPxzXXXINrrrnGDUWnepPSiEajGDduHG6++eYOHcZoNJpSHj6fD+eeey7OPvts7Ny5E//4xz+wbt06t+wHDRqEWbNmYcaMGYjFYnjnnXcQCoUwaNAgTJ48GZWVlSnPA/cfZOdV1gtyYMvLh+DHeg025SrU9vCy5s82CWh+v98VhEi0kWKb7CTyCB8AHe4fpUmvuydxiOyVC/u2jiPH5nPzzzZhyMtH5/vLTpj0jdN1RrvyHHL/k9cjvDNNZcXLge8jRSDb+el3qk/oWPKJbG+Eo/qa/28T9vj/lG5+fj5mz56N+++/v0O5KJmhsbExxQemyCDpk3GxQkaO8YE8qgekeCP7bDxdLzGWSCf0cxuWgRJefc10NiFtyXZsOgFUIkUhft0yslH2k73aOULeA17OjuPgwIEDrp+UbfRLQai+vt4NsaP5fUD79AT5SnpbI8g7qtzRov2keCFVRcI2WtIVYQLo2YWnbIZka0S7mgeputI5+HH0PxdlqIy4oXC1m4tBsvG0dThlRSA/83xQGvJ4m3NhE9oonZqaGmuZKAmKiooQjUZx3HHHpXz/0EMPuXN9pVNF4i05qwDcjlhlZSWGDx+eshDstddei0996lOuk0zzrGkkhYdsytEKICH8PPjgg4jH49i7dy/27NmD0tJS1NTUuAKwbBhlRAsJxMcff3wflWzfcPzxx7sOJ9mojKTk4cWyM86P4feZPtO94R1o236O47gOES0+m5eXhxtuuAF//vOfs3aU5WiEypaLDbL+5G2ZvE/cJrijOnToUIwYMcKtV2mdCin+VVVVpYzO0WeK0LNFn8l2VLZn9D+PNpH559fFxSF+HroWenZpH2pL5Gud6RjexsmOJE+LBpYcJ/UV4ZQ32o8LUPw38k24X8PvH+WNvvf7/Rg9ejTGjh2LCynemqQAACAASURBVC64IKUtJ5tzHAfjxo1z88nvnbxGjqwT5Hd0fv69zKssSyVBIBBAYWFhSplxEYb7TlzEAZDyfNMzSyIm7yxSW8rtg0cc5efnu9GdlC7lgQ94ynYjnQhDeeI+dWe+MZC6CLSts8r/yt9kXcKxdeioHiHRWtYdXMShe0XH8wFGmyDEr4Gum+4dF+u48CftiwbEeL3C05Plwp8X7nMp2UEgEEhZmJgvtm8TO2VkCz03chDDZhd0HH3myPZIPidS7PCqt70Em67+ztuKrrYTMt+2fXl9KfvmQMcpajwvfP0hqr+k7cm+biwWw/bt2z3znGn6pSAEAIcOHUJLSwsikUiKMyhfr2mLFCL4A0UPjC3kmT7LNOSDYUtfdqboszRcr79HgnQUeV68jF9+JsdO5lWqrdxY5AiLFGu88spDYW2jNbZjvK6XzifvI9+XV6hy5BZIhM4XFxcjFAqlzXuuQlNJioqKsGbNGmzYsAGnnHIKBg8enFJp0gg5OVmyzPm+jzzyCNasWYN4PI6zzz4b48aNc9V8CqulOdZS8AXsIZz0PFVVVWHIkCGIRqMIhUKuc8sda9szQun3p4XGzz///JSRaG7bUvwG0KGM6b5xO5X784gwW2QE77wCcBtSGinz+/345je/6b4VQuk+tF6TFDd4h5PfG94B5O0p78xRZALZEiHTpO/48ZSHrjh8fKBBthNSKOICis15s4nIQOpgBJUTn/LFHUo6L4+ClPmQghQXSHm6hC3ygs4jr1/6FPLecUHLa5CI78dFMkKOMPPPtrqX1wG83ebp2EQ9pR2/34/JkycDaPc9I5GI+zuJFnQf6DM963wBfz6qzwWhQCCQ8oZe2peEIC4aUyeV+9RAR7/ZVn9Iv5h3xLyEE44xpoM444XNt+XCE5Wd7Aza8sB9WW4n/Dnma7lIYYY/63Refj8pLSkCUd1EZSl9UzqOBtF4+2mD++NtbW1u/a+2lx1UV1ejtbXVvZ9yoIXaVtkX5c8aTe/k00ppX97/koESQHtUJ7VdPJKQCyS255iLJV5tlux3Hg7Slnm6tmv0Orfsq0ukz8rT5u2hvAe8XqHfaYvH43jttdcO63r7kn4rCO3btw/Nzc0pzgx3BG2OrIQ6rGRYvCElZENn+146OfwBkg8sT1s2Cvx7W6OZrgHl+bI1kNIovYQnOpYcYCn6UBnx0R5uJNxo5DXIt6zwDiZdNxdsvNRiWwXDr5fyyq+B54ufj8/Bp/tBUSH9aZpQT1JbW+uWZ0lJCRYsWOCufcBtju4H7wTR/7KjUlJSguuvvz7lN7JlGknhYhDhJaTKTg9/M4BsTLggyTeqVyoqKnqlHPuaiRMnYtmyZSlTTfgorBSDvOpNoKM4TPvL+pI7xzISiYv25PzTtJmBAwfiE5/4BL71rW8d9nWeeOKJOuVTwEf9CVmP0j2zrWVhG1UneD1NNmNrF9KJAV7tmhRz6Du5/gf9palX/Dd5bptzLTvStA8XMWxOoJxawzvI0o7oXNyJ5xG0st2m//m6MNzmZJ5sHXIv+/W6F/QdP84WASHTkGXNy5B3cLnYqKRiW7OCL39Azw75LPTc0TNPzz3dIx4dxBcaDgQCKdFE5P+SaCA7kVwM4vkDOkYQSXuX9Yb8K/1bgtoN7sdxpH9r83nT2Yrcx+Zj255pHoXB/V9+jVIE5eXk5RtTBBBF78nBM96ecn9VRm3x66X1+qgOGTp0qOe6j0rfkp+f7w6ARSIR9xniC8jLqB8Ob6vkgISsP2iwjWyeH8fti79Vz6uvaRNl5G9ettkZdBwXaGztV7q2y6tfzvuw6fwQSgdAim3xvqnsF1NZAono3r///e+dXmum6LeC0K5du9DQ0ODO/ecdC1unTt5MIHWUjxxm7ugBqWH0PB05+mDDy8m1NUA2A/TaT/4mHX2eV/qbTkjyOjcdn0744R1p/h2fckP3gBwRmT/6LDfuqPMOq+yEymvmxs/zT/mSC3g6juO+nYO+q62tVUHIg4EDBwJAin0RMnpH2hPBR+D59E2+LxeE+JROr+ed4B1Ieg753Hy+Hxc6bU4bNQYTJ07s0gK5fcHhRq8Fg0FcddVVOPXUU1OecS662jqQsu6TQp5NFALQoW7g0QH0O6UvO5QkVvn9fnzgAx+AMQb33ntvl691wIABmDlzpgpCgvz8fOTn56fcE6DjyF9bW1uHjiB3oHgnRXac6DNvE3jaXemQyTzJ4235kYMTMppBHm8TqaTI4dUR5XmWZWFri2zOo/yejwhT+vx47sDTNduuiZc3/8vLQV4Ht2c5Zd7WQedlIjvDtrrE7/e7UX8yKkpph+4xXweEd+gApLxhrKCgAHl5ee4+fCoYpcfbUzqO2l3uA5FIRP4WjwySb+uVz4sUjuSWrvMlfV++2XzuzjpyNh+W4M8pPzfZl02YojyQ0CsXiecCp7yXsl4jf4TbuBSWeNnb0qJ96J7KRXhlPcp9GmMM5s+fj1//+tee5af0HSTeykFO6SvzOpMLGvwZBOz9IC4Gkegk96Xnn54tGRkrbcqrL8mfc1t++H5d6Zd6DWRIeJ1g8xdsbaD0HbzS5PvbylkGjsRiMWzatCmlbs02+q0gVFdXh8bGRrS0tLjRCbYIIS+hgeANJjWKHOnU0ndeDZSXsUghxxYF5PU9dzqlkXZVDJJ/0+VZNox0DBdW5MbLmhot2bADcN8cI89nc/rJobSJQvI6veB54PvaOqC8LAcPHpw23VymoqLCfTZ4AyJHFnnFK22AHCy+IB45vFy8o/QoLVsDJf/n+eCCkhQ4uBhEzjAdx1/lG4vF8NnPfhaXXXZZr5VpV1i2bBmuuOIKFBQUIBwO48CBA3j//fexZcsWbN26FZs2bUJdXR1isRjKysowffp0zJ49233jE7dN3qnknT/uDNtGd+mzrc7wcga4AEG/2zba15j2SKHZs2dj+/btad8YN2bMGMycORNDhw7FCSeccESvFu/vFBUVIS8vz/2fi6A2m+HtKJDabnmNWtoGBuhYwtZe2oRF+VeKtbwtp9eayzzw9pR+83IWpQNoy6vMdzqnWW5SXPIqT9mmEjxKh+ePbItHZsmy9nK6bZ1WWb78/DKygdchPO+yrXacxPQj+WYxfctYOwMHDrQu5kz2SD4gvZExPz/fffblq6qB1ClDAFLWGaH1a+i5pOdYRtRLe6N0gdQpTrLOkMfy4yR8AWzbs0Xnkr4hx+v5JuSxXIih55ovj8DrCxnxyAU7irCw5Yl32nl58kEtEnSkUEr7edU/PG1+fbIs+LmBRISwkh0cPHgQpaWlKYKQHCA1pn3NKsJ2r6V4T+IEH9yRx3E75vW6HOTgz6atr2kTgryeW9l3O1JkO82v28uv5Hnln+UUel5GNl9A9mGA9rYxFovh6aef7vb19Sb9VhAKhUJoaGhwDYoechneKgUG/j13iAHvzubhOIm2z4Q0MhkWKg3R9r0tPVt++TXKvxLp9Hvt4zjtIY1yoxFOclApb7KzydebsDnBvKMCdHwzmXTibddBn7kTza+Pp+d1fGlpaYcyUBIMGTIk5RmlZ4KHrvM50baNi7D8eeWj7Xx9C5sN2myRjuOjlvKtg3J/PjrDz8k7PCNHjsRtt92Gb37zm116fWsgEMC5556L0047DTt27MCf//znI4paGT58OD7ykY9gzpw5cJxEGDgtiF1aWorS0lKMGzcOQMcFdXknn1+jHBnhtu0lCHEHnTu6ssG13ZPOxCObw0/3AgBWrlyJ0047DevXr8fOnTtRUFDgvjK9rKwMxcXFbp6i0SjKysoOu5z7OzTNhN8L2dnxWi+Et1lc/AW8ozzpWP7XhuwA2p4h+XzwjitPh+9LDjb95tW5JPj5O8unHKCx5ZNvtuefR9tJcZZHMtpsjKdHAya28pfnls67zA9vf2UZ8VFPW5QgPyfvYHCfgL+xlZ4lJcHAgQNx5plnprSnfDCERJyCggJXDIpGo2hpaXEXpZV25ziOG4kQjUZdwYlEJ9mhsT1HUtih9Lkdys1rqimlLaFnT0ah2Tp1HG4L6Xx3WWdIG+LPu/RTKA0uynHb9fKHef0p88CjDSiKXq7XKcvPdn0yfwSPJOT1CX8DpJJZ6uvrUVhY6EaESaGB2tmCgoKUheOlbdH3dCx/lr0i0sg2bCKtrX3z2iTp+sYEz4NXGkeStlfbK+uedH10r3qKBzh4+bLxeOINrBs3bkybz0zTbwUhIPHqPj6CIkcybB0N7nTaRAGqrLnD69UBtT24spGSDrXX/3x/mxDEzyWVWin6cLpipNKRsF2brExkuXDnQRo9OTT8eP6/FHvkSKSXiON1bfy8nTnjfLoSP14q80o7xcXFABJlSU4+KeR8rR7C9lxTJcudSPkcc2dG2rFEOkdyDRSvDpp8dm3TZOjvvHnzMH78eHz3u9+1zhMePnw4Jk2ahOnTp2PcuHGorq5GIBDA1KlT8cEPfhAvvfQSNmzYgBdffLHTKJZgMIhLLrkEy5cvR0lJSUoYvywDWVdJB5SPNvKOL3cWbOXIHQYuzMrRW697wf/n4rWtrpINuHRWqqqqsGTJkg7f8ymgvI4YO3Ystm7dmraMcwmKECC4M2mzEcA+pYMfa2sP0wkqXu0n/9vZM8HFTq+oA9pfRsNxvI71yp+tHLwEIfmZpyfrQWPaB0n42kPSZ5Ebd/5t+eP58CpfG7a2kMqKT/22OdOyMyzFfV7HUzpKgqKiohSRxhYdRFG0QLtAxyMMHMf+FjsefSDL3uZL8nray9bStZn8N1s7yusX/qyks52u+LE2bH0AmyBks2mv+sdmd7JMZLnayoMfIwcvZRlyqK6wlQ/vvHLhCUBKhKiSWVpbWz0HNvhAayAQQH5+Pnw+X8pi8PzV6dy34/1g23MjbUzW47xtT9f+dTYgIs9r+/9w+qaSrqZh8ymk35HufLx+8kqf7HTfvn0d1snNNvp1i7tv3z53dIRGyrhDwitVmyBE+/DKWDq7XXnA5W9ezqMMB+ROIQ8XtDmNQKqDle6h9lJ+OZQG7+TRX5v4ZHMA5fl4Yyg7D7Is+F9yBmgOq61M+TlsYo+XAyE7NzJKS65twJ+RiooK1NfXd8hPLlNRUYG8vLwOU7z4XGW5Vg+VM322NTZA6nMrw9HlCCZ/drjAR8+Rl6PKj+eOHjlZfOMLzVNeq6qq8JWvfAWO0x6WS+fnThmAlLopGAzi9NNPx5IlS9w3vRw4cADNzc3w+XwoLCxEQUEBCgoK3PORuMbLNN1zzp1emxgmy5gfLxt5WVYyikHC0+H3nTs38h7Rvvy+0l+byMV/444Ur1vp/1mzZqkgxKDoAKBjOdtG82WnguD3nj8bdAx9n67dtIkYElvnUnZAbbbNkVE/Xh0s+o0/PzLPskzkgI50OtM5+by94XWV17Qb2fnmZcjzn67MeV5tHXVbmUh/idcvtmmD/HeeJp2Xr8vo9/tRVFTU4Zy5yoABA1xfNB5vX2CYty8kxPEp0Dw6SNoTf37omaIFpLlIxO0knchhE0hsgovtO0rLZhe8TrEJkl3p9Nn+l3bD82ITwvhfbod0X2S7lM7Pl30Q3n7bOua8PaRjeNSXzd/trG23+bRFRUVobm5OW55K79Pc3JxSz0sb5IvB099IJOK+WZu3A3Tf+csKpG/E+03SvgibHw7YI0Kl3y3h5+fXxdPhz6XX8fKzzGtXfvfax7af175UX3L7pb9k31u2bLGeI5vo14JQY2MjwuFwiloqHVb6KzdZaducU6/KmDuBtgeKO8lS/DncjaeRTgTi+bUhG12eDu9cy9G9zhx3Wb62hk3mme6XdFy4s+p1XdRxl/dJOuVex/IKTS6eJp+XsWPHqiAkmDZtWsrrkwGkiEFyygnv/Hg5T9wJo2eITz/j6XFblWtS0PPTWWcxnY3zKBzuNPNFHCmMnzvbfP0Gfh5KV9ZR9NYPng/uvPJpbzZHWjp7Xh1d/jvPE3cQ5L1IV8941QG2fbidkVPDz+9Vb9uuQYrOfLSX4Pmvra3tNJ+5BC0cC9hHzQkp8HCbkg6cFEe82lv6jp/DVhdwuKMlRaB0Yq9s5/gzwY8/HLxEoHTXbjueO/kAOizGK8u5MyGMlxVde7r6TooFslPplTZPn+9vq9v5vZNQeVHUiwpC7RQVFbkdOR5pC6T6m9QZpDKmNhdon74EpA6M0He8bYvH4ylvH5Ptie35k88K7S+FS9tzRfvyv9zvlG2S7Tnmx9naIaq3+Plku2rzFW1iD7c1gpeRjNDx6j9QO8dfimGrt+g3PgiULtrAVndzO+XrE3FRq7q6+qjouOYCXgN8JAjRNG+y+6amJjQ1NaUIhXSvHcdBXl4eioqK3Clmkq76w/J32YeT/dl0/iL312kfXp9Jn8LmL0hk39u2r609Stc/5D4CP8a2r7xWugfbtm3zzHO20K8FIRod4Qu1EdIptTmo/EGXm5ehdkWYoe/5vGA+GsM32/deDqgXPK+y8bUhRTOpAPMyoc88X3xf6YzKfNK9kenyKBJK18tRJ0dBRjPJMrBVENL5AOwLGdK+/PmgqVFKO5MmTeqwCDQXLXjlKztPvJy5bZCDRRu9Zl6+OYWQ4e+ycraNVPL9vIQOnn++0B8JGnxURk5TtTWyNrvlzqR0gPn3UgTj6dqw1V3yftjKy3a8TQy25YGfx9bgc0GIL5bKr11+tjXEvLHmDoR0KKhuABLrXCnt5Ofnd7AL2f7xkUQg9e2ScmF32T7xwQsvG+TPna3NBVKfKZsYJIUgKWLY2nfbKKbNXjtzmG157oovQO0/F0WpkyjXN5O2a8u3zaa98i3vmVdZp6s3bZ/Jb+CRf7IMuB3zDirtq+v0tVNQUODWYdQGUn1G9ycSiQBIjWKh54ffU54OIYUH/j0XNb2i1Li/ZnsW5DNCf9MNzMg0bH63xBatwL+3CVW2QQS+8XqC72tbB5GXlUxb2hfQLvrS/aTzkP/Ay4Uiw/jUIC+48CbLk9eH3EcBEv6bCkKZZ/jw4R2WQuAC5P9r78vD5Cyq9d+emV5npmfLBklICCFA8CZwDYLgT4XHKzdR9GoQ8CKLgnhB8FFB9KoBDa4ocgUkbIogKGF5AlyEEAVZcwkGhBi2SEhCFhKyzd7d05Pp3x/j+fJ+p0/1TPaZ6Xqfp5/evq+qvqo6Vee8deqULCDEYjFEIhHkcjnkcrmAKOQ+I31L9FWgWJ8FStuEAkuGGZaO6ZoH+TNfJ7qDa77RZdH5W/lY+inLt1Uu6z5rDLL0B/1s3d3dePXVV4uuG2gY8oSQTFQuksClAFsd3iIU+D+tBLpIDJdXEP+mI8pb1/aldGohYsF3Ta48+bKwaOOAjVX9XJahKycu6P9cirKuW12PbNy5fis1Yer8tUEi/2nB5mvl9DqP7Rg9enTgdi7H2HK7W3XMZBATejKQSvuLItTV1RWSbT2ZAGHFVwIzyu/aSJR+XMoA5evYQ0cHv+bJTK+MusYCLdOSlyUbLkNMr1Rayrg13ukVW86H05Oycl2xlyKX2ZpQua653Xnc0/fqurEU3P4YCXJfd3d3qJ3GjBmDNWvWlLy3XCBHzgPFMdp4vuC4Lpq0AOx+65oXtRzqPqzHBoHu9y5j0rrHmtc5roYG90drztFzr5ZfeQZtaOtnZE8MUeLZ+1Er1nI/G42WZ6z13DoNXQY9Drju0eOM1KV8l7SYFOJtiTyvC6mQy+UCUqyurq6ozsoRkUgk8JZynRjW1dWFSCRSNNdpb2u9KKo/s2zyfUwCuY6Rt/qay4jT85C+ludfrcPqPmrVl55HdBn6erl0AZY1vdBszVVMUmuPHrlG6zG6LiU/2RKkySCuI5ZJTQqJvLE860UnOYDCY99C9wEe57mPyrgdj8cDjyHum5rAZJ1V+pVcJ3DNg5acAMVeNC77zvIY1Xq21guljK45Xv/m0t1LgedRq9z6OVmm9DNZ8zTQqysNhtNthzQhlM1mkcvlnB3JUmhY6eMG1rCUXnlnQ4knZ/mfFV3LINRHV/L/2nvFVR7rWXmFl71ptFHVl1HKJJE2xPVEaimSDEvht+raSo+VH35Wq6yljAadLveNUmXypxWFMXz4cCQSidAEpY/IdRn8AEJKkZZJMZLknVfOOT2t7EgfsYxKBvdjlkWXoirlYM8W3irHypgVs0jnqw0zl6y4SG3rN01GSRm0Ei6fLaNVgw0LSxGwyu0aX/U1lmeHRaL1RexzWeVaUYQEFRUVmDBhgieE/gnxQADC8wXLgFYkRb40KavnJ+4n8s4vWbDR0P3Jand9vSaENbh/aVieLJI/G6JWvta7tXjDaepnlHrlLSGauOY0OS9pH6knSzEupcDz2KPbi+tOLwzp/4HtcY+4vnhc06QQG71S/kKh4AmhfyKVSqG6ujogfvjkoUgkEiIatMFnLXLqNhe4DCkt7/p0Ttf2TE7H+mwZmC7jsr/GHT+LlhHO3/WySGmdP8/l1vPJdz3vMgGu8+TnZv1B/pcxQbw9LJ1Vv2tdg6FJZvmeSCR2qJ499gxWr16NSZMmAbDtMSYSRd9uaGhANpsN/mdbD0Bo0cElp1afks+6v/L9kqZeeGXofqvjRlqy75qrdbpyX6lxQpeXf7P0VytvTl/b7rosUp5IJBJ4bw50DGlCyPIiANwGhAVL2bMUTf6dyR7XdZYBankDubaMucohz8f/MTnFHRXoVd7a29uDyaaioiI4throNRQaGhpQW1vbp4LNhoA2DLTg8Wqoq27YYNB1zALI11oTOpfPZSgwNOHF+TH85BnG+973vmDbgxyBK0HsWCnSxKS0iyibFRUVAXkkv4tSZBEbuo9Jf5G0XCsT3D9YOWLjxSIZJI98Ph/0Ez6Vhe+T8okCr0khayJi8G+lFFx514qoVkh5QmcFld39LYOX69dVdh5ftWLBdcz3cv6urT+67JY3k1a2dd1wv5AyTJ06FU899VRRnZcjJFi57m+aBJG20duZrPlMoA1TTl/u6enp6ZOMsJRRTsO63lJwpSyWEaXzZsK6lN6gn0sf/iCy7/KEA7YTKfl8PhQLRrcFl02Xk4kCzqPUM8q1HHONn1vrNjKusgHAnj5cz1JXki6flKWv4zm6p6cnCNq/o2TAUEM8HscHPvCBwKiQsU7PkzIPSX3rfsre8jLHaSMRCI+nHFNIk5XysoiOvqBlSRucUkbW71zy7Eqf4epHlqEr0DHseF7XW+QkqK+UkclyXhDS878eGy0yiNOS6/gdQGictp6J5d4a9+RzW1tb6Ybz2Cv461//ikmTJhX1e9axhPytrKxELBZDTU0NRo0aha1bt6KlpSUgjmUc5RPJgPCYbelr+rvrxdBzrPxm2cbsYWfp2SJHMg7oOV2Xw9KhXeXT86C2XQWuhVwuow51INfxHCmnPg50DHlCiFfZ+jOhWJ4u3IlLGW8AQqtg1sCtDSienPm7XlHT3hTaSNP56PwAYPPmzXjqqaewbNkyvPbaa1i5cmW/63LYsGE4/PDD8a//+q+YPn16sHcVCLvxagWWjQOZ7FgpYa8RXV5dT6zsymCi3eT5Hlb8WWnqD+RZtPLK/1snnpUzDjvssFDfraqqCjyGeIVL93NgO8EirvA8aWkPG8D2OtGTmEWk6ne5VjP7Incacq2O5yPb47RhapEmFsnDn13XsFHIZdflcpFBrhVKrRha46LIkw7SrdvRmrSZwAPCCkBVVVXgaSVKjrXqbPUDLqdWfFmRYLA3yn777Vf0nOUKCTbJipX2mJF3DmrLxK5FBrGxr/9jsNxoZZINI96iItCEEBtw/VEQtZLIaUpZeDzS4wLXl3VKKM/jPJdrMlueTxR5a7HE0mN0GVwKcqm5T8rEyq2sPsuYLfMuz78MTehwW7C8chwL+azncxkbamtr0dra6ix3OSCRSKBQKARGhSY0efwEij1b2PCTdhBDRraZsKxrr1omgywPIWtOEfDY0RchwwSLXvV39fv+QM+fpeRA5yNGt5YjrkuuX5EVTchYhBCPO675XOqFjWc9tln56eexoIkAAD5u1wDB8uXLg36h5xM9X4huFY1GUVdXh1QqhXQ6HSz0c9/hfsYEoyZy+iKBrH7F/VCgxwLW1Zm0tsgglldrPOM+L7C8ja0ySVn70s+1XiIvLqteQGZdVzDQj5sXDHlCSCKuS0dkWEoVf9aGJH+2wIaktRLI17CgW0okK5euIMeAfSIYK1rd3d146623cPPNN+PZZ5/d6brctGkTnnzySTz55JO4+uqrcfnll+OEE04o8qTgFyu8WmkWxY/rVT8jf5ZBQVZLOE3+jydLVqJZEdKGpAXXijZ/9oRQGE1NTaGBW5TOSCQSOhGB+6i0hzYyBS4iQ0MrRCxXvOot7yxzmrAQaKOYjRteuZOyizeUaxLRzyHpWoarhmvS4vq2iBOZoFxkUCmSiMukv3MZ+jLmdYwJmeTlJYQhl11c41lB5mdhDzHdDwS6TaW8ck8ikcD++++PdevWlaz7oY50Oo3q6uoi8p639kidWWSQXKv7placmLy35jHuT9qI5b5hnawnaZb6ziil2Ap4XtFErAbPWXoOr6qqCspvzfWcnyZvBVp5ljbRdSh1x2OrbhPrOTRRIO98OICUmZVbaVeuV8t45nKzlxAr/WyUbNvWe8pVXV1d2RNCAJDJZAAg1KZWO7oIQSEapT2k/vX2PfkserPeGipEkDWuW4adjPea6JDPPK7r+1nuXDIq+VhzqFUWa/7U8xzXH3sy8DVaRlkvtYhvnq+0DsBl1BBZ4ADWWue30tD1bP0v76wT19bWFqXpsfexZs2aovFd21FAWC+S36PRKOrr61FdXR0EmtZEpPQpAE5di/WsvmSQ73N917azfha5Bwh72luLt1xWvrc/cz5fy2OC1nu1LOvn5/RYX+JxVsrPsRcHMgZHKXcBfMpYKfCgqA0vixhypWERGvw/K72lXjqotNyjDUGdpjzHC7l+9AAAIABJREFUihUr8Prrr2PhwoV47LHHdrL23Pj+97+PN954A+edd17oeFjNAOtn14QMgEBhtgYci5ipqqoKua33BT3QsSC72lLy5j7Bz9LXveWGYcOGBYaErmNLWWP50Cv/LK+8wtbXpCR9zooXYylfemLV26aYwNDQBgyTG3qC0+W3+hSX1QVdj9rwthRsF9njmrytSVN/1hNmqTT0KhaXid+l33AfYIXbSk/nydCKri67fB4zZkzZE0J1dXWIxWJFcxeTEEyAsqeeRVTq8Zah51VtjDF03ro/WH24FPpSFkvd05ei69IZLN1BXtrAc8mmVVZLEZa0ZH6U9rNkiMcSbi9WYlnX0cavq4yusrGBYekuVttUVlairq4Oq1evdtZFOSCbzSKTyQQyynNSKRnguVjqXryM9NYknQaP11YA6f4u1OjxwCqna7zQukSpscTSMVxlcY0/DJYV13yun99aANXPwPf0Bb6Hn4H/s/QqKw19jS6bPKvoMP2xmTz2HJjYt/5jRwEgvJggbSsLlHICrl5wYC937pfWAqbVB/VnoFiWXYsafaXDv+n/XPLD84yrXLrPa/3WkqtSYwynxeOJjLOSVjKZLCrvQMSQJ4QkqLRr8LSMLJdCx8fCWrDuZVhkkd7Hzd/5c6l05NXS0oK5c+filltu2SsD+l133YW77roLP/rRj/CRj3ykaPDiwUnXNQ9qoiTKs2oPIFFC5LukK7+xoinKDKehhb2UUSEQpYsVGsBe9fIApk2bFjIe5JScXC4HAEUGPrclex1YW4VKKZ3WgC4Tj8iqVuq0/MrkK9fqSUyglWN+Fu6jWl71BMQy4OpD1nhllVfnwUadnthdq0AWaaThGjs5f91u8pkJIZZdWX1lOWNlxVKe+yqzllVdXm2cH3TQQXj++efNei4XjB49GqlUKuSJKl4c3H5y5LFereb5yOqH3Jd1O1jKnry7+oPllaDzFEheLh3A1XfkXj138H/8bi3oyEtWB7WHENefJbMWtPGpn5PHO0mzlBu9pCVkARsY7Ckk6eg6tkgdGXt5TraMZ61LSZkBBPU2btw4LF261KyLckFHRweA4hhoQN/kJF8nc3KhUEAsFgvaQfontx/PcZoQ0n1VG37swcIvF6Fbiphyzfl63HEZbNZ9or/xXFCqTrkf63mTZcyaY3S96jnYZX/ocrBsSJm0fs3Q87w1Blv56AU5j30DiU/qakeeY4Cwh6fM3/J/NBp1EpjJZBJtbW3o6OhANpsNed/qvi7Qc7fuRzwe8Dyh5wzWMyz90pJVnT6n59KlLegy6/v5Nz2+8PZcqXvxaGWirqurK5iLa2pq+l22fYkhTwh1dHQ4t4PIuzVQupQ7V+fl+63JCrCJHFYaJX35rN21+RlEWFpbW/HWW2/h/vvvx7x58/ZYPZbCt7/9bfz+97/Hddddh6amplAZ9WTHK1uuyVnXndSTeAXx5CvGJSscrIBI27OCrCd3l0HA5dbvLsO5XPGe97wnaItt23qPD+7o6Ajqm4OkSr2JsilGpiYBrEkMsLcv6UGdjSNOT/oBE0CShqRtlUH+49gJPMHxdX15+bBcaFJIG3r63r4UYFY6rfrjOrRIFatPuyZrlyJg5c/b1+S5xWi0JnvL+ORn1OXXCogut0VAVFRUYPz48WZdlxNGjx4dxPoCig1DCXLMMfl4XNbeChapoWWWwURyKUORiSHLuNLjRH+MRX5WKQMTSPyfvofncP6st1qJwR2NRhGNRgOjXNeJLj/Xj56DXOXRc12hUAhWiLmNuM44HT0OcttqvYXvs+Zl+U0bsjp9/XySVzwex5gxYwKPp3KFxBCS+iqlf2qSQ5MOeqstL0Rac6DMd9b8bPUp+a5lRI8LrrnJkgkelyzi1SJ3ShEtWlZFZjRknOPn0mMc14FO3ypHf+ZYPSaxkQkgaAs5UYrtCOs5uMxMUjHJZM2fHvsOra2tyGazqK6uBlAcS1MICIGWP8BefNRjvpwGHI/H0dLSgkwmE9pVY+mmpeZTnb6Ug+VNPJfYK5nLq1+uRScL+n/LztfXS7ktO896Drlez6PihCByKGOkzMGDYR4b8oRQZ2en2UEF3MDyHzeqRdj0pZTxAG6tlui0RVGUia+trQ1r167Fa6+9hqVLl2LDhg2YMGEC3vve96KxsRHr16/HM888gyeeeCLwwNjXWLp0KT784Q/jK1/5Cs444wyk0+ngWQXWACW/S70J6VNZ2XtqA09c8r+lGGg3Zr5HBlH5LIOGNiisgcQaWOTdr6T0ora2Nth73tPTE3gGFQq9HjVikOj4L2JsygSkySJNKJSC9AMg3EZ8fyQSPqJXFEE9Wcp4IX2Gxwguv4AnKim7hp5E9bjDsEgNVu51etL/S0ErDFoBtCZZLq82Blnx1Eae1XZ6JZnlVBui/YlLUYo4cLUNtyXQ64UwbNiwkvVWDhgxYkRw7LzUP59o5CKDtGxw+7vazUXQuIxDebk8E6x2Z5JX92GeT1yKJZNCLhLLUlh5QUde0Wg0FKhZvKys8pYigyzF2FKmBVo+ZCzUxKxFCsnvnB/PlfrZ2ZBnw1zGXO11YBkN0o/4+WKxGOrq6jBt2jQ899xzZjuUA+LxeGjO4b6m5xTdBwR8j54rXH2HjU9eBOGtJi4Z0gsyAk7TNVa4CJO+jEWL9Of0+D55Ptk6J+loWdT9VNcNzz86bSAcQJ3nWi6PJmks3ZNjggEILaRFIpFgrInFYs7ycv/hfGV8kHQ4H499hzVr1uDggw8O9Uvxptdx+/jgAouQ1H1AIPNUIpFATU0NWltb0d7ejlwuFzqlWxP7pWSM+7z0M7FzJT9NQFvgZ7PGOyt/VzrWu/Us1hij7VTJj+WZFzZFBygUCqHnFG+sgYwhTwi1tbWFAtUyKSDfGdwJ9Yofu39r6EmKBZM7ChNBkl4ul8ODDz6IZ599Fs8884yZ/uLFi3H33XfvxprZM7jmmmtwzTXXIJ1O4zOf+QxOO+00TJw4sUhZcRmbXOcyiAhhIHERhGnlY8VF0WHjwSKGJD+ZwF0GCJeT37nsnhDqxZQpU0LEWz6fRyaTCa1kcPsIUSTvmmDRxiX/71IWdduy0qaNHEmX3Wrlf2H6NRHIZeN8gTC5YylT3N+sCUV/d+Wt83UZ1/2FlZ9FOvH4xeMaULy1R5NA1kowP4dFCGllXJfJkldOnxUXy0iS9Core49rPeigg7B8+fIdrr+hgLFjxwYxhIDtxAwH+RYySOSHx2u5RxtHus3k3eqzuo8AKGpf+c3lWWCRsJwnz/uWTLvScI03gB0HkFdBWZGVekskEqHTojRRKulrhV7nY8m91mUkDyFt2OjVBiqPn5wef9fGvVaW5R52na+oqAi2olnGNbcjPxvQa6ykUikcccQRWLx48YBfXd1TaG9vD5EOohexXEmdWyStXBONRoNxWYyzvjz8rLFct5s1rvNYoeWct6L1pXcxWWLJgjWm6LmEt0yKLIgeL88hOqVFlOp5UteDiyy15EPDitOmxzxOT8ZZPrVV0pcxXDxvXQQ6l5PHcPm9vr4eW7du7aNXeuxpvPnmmzjooINCOk8+ny8iWFg3Y5JIz12aFGI5jcViiMViqK2tRT6fR3t7O7Zu3YpMJhMiHy29laHlk+cCrTty3wRsWxxAyObjOcyyCbS8sfzo8aiUzsx1qJ9Z0mV9n20N3hrOhJBs/R3IGPKEUGtra1G8Dw62KJ1ED7wicEzcyARqwTKc9KSrVxFbW1txxx134MYbb9ybVbJX0Nrail//+tf49a9/jfr6evzsZz/DjBkzQoKqIUa6TOKxWCwYjLQ3iSaGZLCQNu2PxxALs2V8aPAAAfR6n3kABx10UCBTsoIRiUQCozIejwftCmxn1LWyyQqcjldQqm3Y4LNW+VgpFBmUtDl+Aiusoni5yBaWdz2O6OvYOJZyWWmyYmiNMzyRaQNXK8wuA1mnw2XRYyCw3Shk8oxPCdJuzH0RQlZZdB1bCjG/6/8tYoCVdwErEjLGx+NxjB49umwJoYkTJ6K6ujrU3yxjUBt4WhHSHn4CrYCxIWcZWIB7C6JFCJXq55KWLs+OgK9nmWcPYJYLmfPF65frLBqNIplMBqeQCMEh79og1gYwK/wAinQXq84rKopj8mlZ43cm0C2D0TKCrfpmxV/mAo49petW0pU8xdDp6elBXV0djjnmGOdi2VBHZ2dnaOt7LBYLCCFpF9032DAS2ZaTPmXei8fjQR9l+bI+63bvixDiNuaxQo8Xrn7E0CS0ZdzJM+tFhEKhEPICkjJJH5NydHV1FRGgko6ebyz9gg1EycOqC11HvKgo7SkyKO3CdSCQ8UPGErlXFjlYj+J20+XSL+0J4rHvsGLFipAuaI29eo7gfqP1V57L5V6G9B05tn7UqFFBH1q2bFmwRVHPuzLGsH3L45FLl+O5XMBzmsiufGdC25qDdL9lgomfmecmDdZJdNlZ32E+gcvLi9dMhvU1xg0UlB0hBNiuozzBaO8ga1sXEN5Xr5VFEa6Kigps2bIFq1evxurVq7Fp0ya8+eabwVawckBzczO++MUv4sQTT8TPf/5zjBgxAgCKBi1+ySQu24+6u7uDIxSFGBLXSfYQEoFlTxAeRNiA18SQntgtA0fyAeBXUf6JESNGhAZL2RIhsqKNA8D2AGBlUSsyco9+12SGVgi5P/CL75U+xPmzlxCXmydguZf/13ApiRa0oWcplboMWhFwrZpqkkhIMZ2uzsMaDyUPljcOPCoTo2W4uwx0SxHXZeHfLGXcUtT5OVj5kOeqqqrC5MmT8dRTT5ltMtQxfvx4pFKpImMDCAcYtVb7tYKklS6rD/NvmuBh6HZ1Gai6T0he/L1Un9P9ygXrOfRKqGzZEEObDXSgV+YSiYS5/YbT5vGJ9QkdJ0SPpZyOVqxF1i1508Yut7lrnLZkWl8XiYSJZD0GWfOuHqsBoLq6GlOnTsWmTZvw+uuv99lWQxHsCSK6JbCdwOP6ci0U6PGdFzg18Wq1aSl5lGslbV6wkP+sBQJLRrmPuuY2vk7Kz949rMPJtSxP8jkejwekKcdK0mlpI1jLHMuGLps1j1tjob7GIr04byGFhPAtFApIJBIlx0uBPJ8ObdHT0zMoPBnKAcuXL0d3dzfi8XiRTOg5Qf4D7JAF8juT8tr+ZXuWtw729PRgypQpgVzIdnL2GuL8ub9JnjxX9/T0YOvWrXj99dfR3NyMnp4ejBgxAqNHj8bIkSNDHkRArxxEo9FQ2oVCIXSSl6VfWjoFjwk6H0sG9f38XC7IGBKPx0PpDR8+fMDb/EOeEGprawv2QgIIOrpWBFkZY6GoqqoK3OlkNYWhySBWDl9++WV89atfRUtLy1595oGKRx99FI8++ih+85vf4KSTTgqtevAgt3nzZvz0pz9FR0cHjjrqKJxwwgloaGhAIpEISCHZ4youlELUiELJXiCVlZXBoCTX8UDIygRQrODKZ63cvP3223u5Bgce0uk0kslkaLKRlygbIkcMHliZBNLEglZQ5V5txFlKj0Abb5qo0qvfXB65Tqclny3CxxpXLFJoR186j0KhUPRM4j3H9cbeUzwps9JuKeSa4NYr0NLm3G5a6deKv64fnaf1uyZ2XEYK10upPsKKTzQaxejRo828hzqGDx+OpqamYOEC2G5MavLMUvZc8QV4jrX6u243Vz763TJKXQapwOprO4q+yCAmS6PRaMjzgutU6ka8hFihlfQ1GaQ9kDiWgtyrZYyNZ7lGk8cWuG51Hfb14ueQ9rTKrg0I7kt8Pxs6iUQC6XQaRx99dNkSQtqLR37j/7m9ZR7Q/YivYwPS1Z478mIwKSVp8Xxv9Rspk5YxHX6B+zH3V6kX6T+aEOJ5kImQSCRS5IHOY5rWK6zn1dDzHdeDLhd/5kUakRttr2ijV7zj2WhmUshaWGN9V8/r5bo1c6Chra0NXV1daGhoCHS6iopeD0GeC7h/SR9nudCLnaz/AMU7H4BwAGlJl23bnp5ez3/uY9KvRB8Ets89kUgE69evxy9+8YuS8eBGjRqF2bNn4+CDDy7KmwkceWny1lqYEvDiv1xv6Qo8/2o57msBS+RRxpGqqqrg2traWk8I7WtkMhm0trZixIgRIZKHOxtQbLhxUEhRmFeuXIm2tjYkEgkMHz48GIDb2trwzjvvBAreli1b8OCDD+KNN97YB0888PGFL3wBDQ0NuP/++4PjyiORXpf6pUuXYurUqcG1t956a/D5y1/+Ms4991wMGzYsIOmEGKqsrAzcZ9lQkYmdPYhkEGVFgo1lrXhpiPLwzjvv7NmKGgSYPHlyIEOaEBBFIxqNBm2ktxXxBNIXGaTbh8FKMis7wHZFi40MJhC1l5hWWi1DU6+ouIwsLoOL4NEGpsuQ1oq8PDdPyOwez/1cr/ZrZUAbGVwObUhIfvl8PkQIcTksRZTbqb/QBoyeoEvJqX5WTTyIEr3//vvvUJmGCiZPnox0Oo1YLBbUMZPlmvDU7cuedKxECVxkEBBuO5Yvrdha6el7LUJIE0tadqxxQhO/peRQG6uyBSeZTIa8N6T/Sf3IqS7JZBLZbBbZbNYcC6Q8nL4mhLjeLWLNSodf2oND1y2D61p/FmjDno0K9hLSLvdyjSaS2dM6lUph+PDhGDt2LFavXo1yg+g2YgRJPEUtcyy7QLG3njasmMh3ja26zUsRQjwOWDqViwTiz1q+mFDU/V/PX8B22eY5jj3PeH4Uj1c9n3Gd6Lnd+m7N/5beoucwPS5ZeoLWLzS5J/OYbAnkurYWZbQ3hbwLseQxcDBv3jxceOGFSCaTQf9hopChxwE9X3Kf4PmP53cZXzSZbJGwuo+xTs+yU1lZiaVLl+K8887r83nXr1+P8847D9dffz2mTp0a9HPu3wyxuYHiExS1/iKQslpH3msZ1c/qGiu5DmQuA3q36cr1iUSiz+ff1xjyhBCw3UtIOmdPTw9eeeUVrFmzBtlsFhMmTMDYsWMxZsyYwIVUJqC2tjbce++9IWLCY9exdetWfOhDHwoMstbWVhQKBTQ3Nzvv+dWvfoVf/epXOOaYY/D9738fkydPDrbyideQkDXa40TICI41xESApfDoVRVgO+nR3t7uPb8AHHjggQAQWlWS+maDRMgKXolzkXcW4aOVSWuV1DJkmLThCZD7h0yu2kBkxUkrbtZvLriUPb3fWhMvVjpWOfi5Lc8Jvs5SzDVB5CKvZEKUd1GgZVVE583GRH/hMip1nXMdiDxb6eh60MZGNBot25PGJkyYgGQyGcinlj/L0NTKEa84AsWknWWwWuMrGzdynbyLkeNalbPGC85fIGOAJY/WfZZBxoqxfBZlVeIHaXkQfUKeRcgjUXC18azz4lPL9DY0ridOg2F5WmiSzjIeNGGk2x6wT5qy5FKv7up+psdxHpd4q920adPKkhDavHkzGhsbQ6cMSZ9gwlu3G/dVTbDyOM5EjmXwWfOHpS8JLGJBX+OSO93nXQGw9bvWC3RevL2Ox52KiorgJDfXdnXOS+rRNT9Z91j6DNcBp6fz1fN+RUVFQCpv27YtGH+E2Be9ihe2uC7YaJXYSUDvFqA1a9YUtYnHvsOmTZuC+UL6losslT5v6aZab3LpUrzYJ79rEsql41nkYyQSwYIFC3D55Zfv0HNfcMEFeOCBB7D//vuHnpFJHD1vsL6u9QoGyygH49fQCxSl9GceM/QYAPTaRBKDaSCj7AihSCSCO+64A3/605+Krjv22GMxffp0HHbYYUin03j11Vfxs5/9rCwVkL2FfD6PVatW7dA9zz33HE488UT84he/wMknnxxisGWC40DT7AUiioEILu+p1QKvlQomMNauXbu7q2JQorGxEQBCSgxPLjJxAduVNFa6tFeRTkPAA6xuE1aINYGhr3NNYux2qvPlAb4v8kffX8rY1Cv22jvIdZ/8z4ayfBbCW95lZdRa7bSU5lLGMpNOIl/chpbxYJFBuhyl6tJavdH16iIDXGSBJuEk3osmBIcyYrEY0ul0sJVTywMbh7o9pU31VrH+yIaLEOD72ZjlayzZ1soZQ3vBWYSqq9wWKaqJIEsxLxQKIa85lkkmgcUIFe9J9lxgZZaJYva24TbjcY3Lbj2PJn5LtZWl2HI9lyKfxeCUd6kHi1zW5RLw/CH1O3LkSGeZhzI2bdqEiRMnFpE4AEKkkJYHJoG4LSzDTuYQgTV2usZUDe6LfK/AMkqBsPeLXiixDDfXOKDJG9YvLF1DPNGE2O3q6grklscO6cda9qz6KiU71u96XLTSlHqRUBayXd+KB2UZyJwf68OFQm9MFq/XDiy0t7eH+j+Pl93d3WhtbcWwYcOCeby7uxvr1q3Diy++iGg0GmwLr6+vRyKRQGtrKzZt2oRIJIKamhoMHz4cVVVV6OrqQkdHByoqKlBbWwtge7+JRCLBQgfPCS5iSN43b96MJ554AlddddVOPfsPf/hDzJo1C+PGjQuRUkCxxyqAkA6v5d+lO7gIHE67r/AVfI0shstvEt9LxuyBjrIghNasWYMDDzwQkUgEr776qkkGAcDChQuxcOHCvVw6j53F17/+dXznO9/BLbfcgg9+8IPBaWQSW0g8GOTFp5KJUSvCq70Z9MTMhk82m8XTTz+9T555oKG2tjZEqmgyCECgbGhCxkUc6MFb0iq1YgeEtzlp5ZPzZkNT+oE2/hgWMcFg93SGJldYmZNVUL36aU1aOi1tIEoduFZv+Pk0dLlL1YGkx1vExOjVbeoi2Fx1WuqZrVVYbmdWbOUaq5/ItVr5j0ajGDt2LFauXGmWdShiwoQJqK2tDUgMMTBZDq3vum8BxeSCS5YsxVGnzWkAYe9CNm71PVxWdm13kT3WZ32NRdhaJJAEkhYlXZQ/Lg8r1JFIJIg1lEgkkM1mQ8QtE75sHGuZLzVeWL9pYstSlLneXGlZpK+Wc5ZX/p/JexlDeEyz+ov8L7GExo8fX1ayCgAbNmwI9S0hOYBiWQG2zwdMcuvxshSBocdyq40ZTD7o3/oDKRPH4hLZEsLe8gZgclSXj/s6P4/W+yQ/SVd7K0taup64/1vkuVzj0lus+Zr1FF7I1GS09hqRdPXiDOu2Lj1Grs1ms9i0aVO/28xjz6O5uTmYowuFAlpbWxGJRPD444/jV7/6VXDdFVdcgerqanz729/eLZ4oxx13HIYPH47a2lrU19dj0qRJGD16NOrq6oq2bhUKBWSzWWzZsgWvvvoqnnjiCSxatGiXT2F+/vnncdJJJ+GBBx7AIYccEvKK5PlC5IJ1eU3kWgshLLsMHkN1QGyLOGI548UPyQ+AJ4QGElatWoVDDjkEFRUVWLRo0b4ujsduRCaTwemnn47Jkydjzpw5mDRpUij4tEUMsWIlk6jeLsGCDmzfn9rd3Y3Vq1dj/fr1+/jJ9z3q6+sDhYS9BWRw1h4lrLyUMsQkDVZgtfeGVrrkne93Xa9/Y08aKYNFQHEZpWzsjcPQExCTQS5XeG3cWWCjsFAohO7jeC48QbGhyXWkjYJSZJBO1zLuNalq1bnk0x9wuXgFm8vIz6A9niyDR67jbT4VFRWYOnVqWRmZBx98cEBi8OqWQBv+/LsmbnQf155uFnnA44MlK5y+9p5zkZUCS8mT63S6ltHK5bBIII5rIKSOxA2SOUfqFECo37K8RiLbYwqJ4inltzwHdRmtMUP3f/nNIvBc9akJolIoZehy3jK+aiOUt55Z2xK4LqLRKJLJJP7lX/6lrGQV6D16vrOzM9jiKR4sAj7VjokcDUs2BVqGWEblWms81wQJ/15qPhEwGSQEPZP1pbZS6/S5r7BHge7r2siTRQL5zs/OBqb1HNyXeYxj0s51v0UKyUt0JmA7OczjkpBYor/qttBl5PlPwIs8uVzOE0IDDB0dHcjn8/jLX/6C3/3ud872mTVr1m7N99lnny35/4gRIzBmzBgAwOrVq7Fx48bdmj/jk5/8JB5++GEccsghiEQiASGlt5KxLcdePawra69hnqeBYpKUZVl+13o3sN3GEbJeyGy2HwdDiJGyIIQymQyWL1+O9evX79CqhcfgwauvvooPfehDGDlyJK655hp84AMfQCKRMAkhOS5RPIqsE5JYqZJBIp/PI5PJYP78+fv4aQcG9t9//yIDT5M+lmKpjTwNMb6YXWdFS6CVH1b6OD/2CNLXssHC5dOkA8NSLl2GlTwrk0BCBJWKi1CKFBLlWSYeXj3kupLnYgKL07DS1c9hKbH9ga5vKy/rN1d+eiVUK7ZsbDLYu4j7otSh1H85BZbeb7/9MGrUqEBh0YqPNkwssGxY5InlIaLT1CSGS+b6Iir5u1wvBER/+rnOS6A9gdh7IR6PB2RQMpksio8m4wmXQWK/yG8yDsTjcXR1dRXVgy4nEyT8nxi0Vr1YBqGVByu9rnphUovjsXGaFngs0mWRdHhBhrcFaMRiscAQKSeIBwcvXOi+z9uG+D4NFyEk310vC7oMuo9xn7EgZeEDXGQrpciHJlF5zJF82XOms7MTS5YswfLly7Fq1So0NTXhAx/4AKZMmYJoNFp0L5dRAr+KrEmepU7ekr7NXkc89/J1/Zk/WR+R5xLC1BprmdTRMZA0GcxtxjKYz+eRzWb3qGHvsXO44YYbnLta9hXeffddvPvuu3stvxkzZuD//u//MHr06BBxLGNeNBoNZJa9eixCR3sNCvT4ZZE/7AGkx7Tu7m5kMhn09PQEi22se3Z0dOyNqtollAUhBPR6CW3duhUHHHDAvi6Kxx7Ehg0bcOqppwIAPvWpT+HUU0/FEUccgXQ6HSjrQgjJ1jI+WYLZZe39kc/nsWzZMr/P+p8YNmxYyANFoMkSDXaL1mSFdo0G7JVGbUzq6zQ55DIwrbLINfpdE1lWefh69grilU9e4RBoIpLrStJno1qfMsLPIumxkmgZeaWUfJchLXlY9VyK0HHBZURakL6mCUhfEKUSAAAgAElEQVSXgW8RDtu2bUNXV1dggMt95UQI7b///qipqSmKRaNXx/QqG7C9D8hnlgneDqnTYe813SfYg03Lm6TjMlyB4jFGystkIEPLvx4ftGcOx5riLWKpVArV1dWBZ4GsEPJWKM5P5JFXEiORCBKJRLA4wc9jGe7WOKb/10YgG7d8r653S+51vWiZ09dY9SsyK31Ab8fRBKHkJfLKeVVWVqK6urrsThtjGZU65PFW2lwHHnfNv/K/5VmiyYVSJJEul4tA1otFwPZ+IsdoS0wcJoR4nty6dStee+01PPzww3jyyScBAFOmTMExxxyDI488Eq+99hruvvtu03v7lltuAQCcffbZ+PrXv466ujqTVNWEtpRTvMktOWQCiL0tmTjlutXtyv/p9rUIe50ej7G8uKm93qXcelwQ2ezo6MDmzZvN9vPYN0gmkwOODNpXmDlzZuC5xIs04t3H5Atgb2+2xky+nmXD0hP4f+2RyfO/zOvy365un9tbKBtCqK2tDVu2bMGECRP2dVE89hLmzZuHefPmAQDGjRuHiy66CB/72MdQU1OD7u5uxGKxEEGkSSErcvw//vGPffxUAwfDhw8HYBv9WnmRQVeMA/a8kutkQOf9ty5yQvKwFCy9gqiNJv5Nfpfycd6a9OmLFBKwgayJoHg8XnQyjA6yzenossrvTArJddr4tRR7l8KuJ0JrRVHXgTWButLsD/pznTZ2I5HtXlK8Yuq6V+Q9m80GSn5FRQX222+/fpVxKGDEiBGhE2m0UsTGBMuttZWLPdV4yw/3a9fR1kzYcn/S5JBleFpkgu4/krZAl5/JVH0fexzwVk/xDkomk6iurg7ieLAxpr0J+DnZeOzu7g7GhmQyGXit6nHRJRc8RrlOH7Pu4brWpLu+1ioH38dtZ9WtXiXlGH6lSCFXuxQKBUSjUUycOLGsCCEOIg2ECQSLiNf1rtuOiRBOQ+SM28faTq8JIC2XFrhfS5+VOTEWiyGVSoUIocrKSmQyGSxcuBC33XYbXn311aI0lyxZgiVLlvS7Hn/729/it7/9LS688EKce+65GDVqVGisYa9AnlczmUyoHnT98bjJ3kIyFlh1Z9WNbmNOk+OM6XGE9VfLO4jLLG0gkHGnnORpsCCZTCKTyezrYgwIrFu3Dt/85jfxy1/+EkCvXIonjnj+ab1diBqtM/C4ZukVAhlHWX/m8dbl3S/f5b5Sp2cPJJQNIQQAK1euxLRp0/Z1MTz2AVatWoVLLrkEl1xyCaZNm4ajjjoK/+///T9MnDgxIIjkJUZjLpcDAKRSKcTjceTz+eA3j173fWvVilfHtQs7gFA9y8BZWVkZbMNghcoFyygUsCJskRel0tJGlk5Xew9YpJUolUwCyUsmLgAhg1lPWqz46XLoSY/jC+jn0oSTZTwI+qpvK/++vAX6Iu305F3qOeUaUX6lngAEsYAsQ1Lyk/oU9/hcLhc8s3h5lAPq6+tD23I0GVRqVZrJTvnMWzu0dxAbK2yYAMXkIsurpFGqXDtCOGrSVJNQDCkDx/tiMiiRSKC6uhqJRALRaLSI6GCwd54olmw0ioEu25vlebieLCJAPms5cnn9yLOyxw6PLboeNBHO75Y890UIWW1otaP0Ez2mSb1Ku5WTRx8A5HK5oP14axKw3ctHzwmA22CxFjM0saG30rtIDeu7hu6nslVSvOzi8XggU6tWrcKKFSvw8MMP47HHHtvdVQkAuO6663Dddddh2rRp+PznP49TTjkl+E+Pb/JZ4oOxjGvZlHFD6kz3fUZfci2fpS2kPDoeJr9cMubKU67JZrN44403dlPteuwuZLNZNDY2YsuWLfu6KAMCd999dyCzIg8Sb8yKywn01iHPzdZYxWMf2wIif3yN6Jh6sUzylzLJNfl8Hu+8885eraedRVkRQmvXrvXBgD2wePFiLF68GHPmzAl+a2pqQnd3txn4KxaL4Y477sCIESPQ0NCwN4s6oCH77TXEIBGFXis3+XweHR0dQUyESCQSxDOR+1jJLUVUuPIHwieQMBnCAz+nzfexYSqQsjHxoA1cJoPEeEwkEqFVT/bAEGVPrzxy+eUZeHLi34FwME2rPrSHRl/kWF8EmrWVgNN1paMJBldZ9DXSFhwQXn4XTyyLZLLqQnsJVVVVlRXRy6fTaNLHUoyAsNcMB1dm122WCReJI3lImhbZYBlK1vaH/o4L+sQP+ay33fA1pcigVCqFVCoVOupXZFhvwYlEIsGpRlKH2gtVxgsZT9mzQEPqgkkerksmhXTbMQEkz1lqKx+nK3lb3kB6HAXs4MXs1WC1nTWWSHlZmY9Eeo9M1uPzUIZ4Mes4QTy3WXGzrAUFNmqYOOT0uK2ssV6TyNwPGbpf8txYU1OD2tpaVFdXo7u7G/fccw/+53/+Z4/UnwusC959990YO3ZsUFbx5pVxrqOjA5lMJrQgYZFDUr/ynyXLrrkVCB8tz2MqLyDJHMYxMrVeocdLLbvcxp2dnVixYsWuV6jHbkVnZyeGDRvmCSHCpZdeimOOOQbvec97QoHjAQS2BC9Stba2BnKr5Y5lQs892muVwQSv6N2sl7CndFdXF958883dXxF7AGVFCO2O4/g8hiZK7Z3u6urCWWedhccee6ystpb0BT4OE3BvLeJVMmB78LVMJhMcRy/v4iHk8hzR3gXW6hd74MgqPCuy2kDjtNlLwdomo1eu9Sq8xEKQYLPiXSZ7nF0u9qw469VJTaJIfeo4BVIGyYv3OLNyaK1W8nVswGtigMsuyiQb7KU8AHgC1eSX1b5c51x3Vh1qLwjXCrgo1eIB2NXVhWg0ira2NpQL+CQnjb6IAU0AWScAlSLkXHmyV2EpA1X3KV1GS1YsolAbtPpavU1MjFiRafEo01s2LFlhY921raOqqgqpVCrwANCyxzIkJJP1vLoNrHHFGlutMUHSYS8TPWbJd/a2cpF8ehxytZ9WxPn55LdoNIphw4YVpTFUkcvlihZPhAhg+WHPFHmXvsftY42/up2sF1/L7wzd75i0F6+g2tpa1NbWYvPmzfjWt76FxYsX7/lKLIGlS5di8uTJuPrqq/GlL30pNL5J2aurq9HW1obOzs6gPfRWPp6nRG5cdaz/Yx2iqqoKbW1tyGQySKfTqK+vD9qASSAOYs8EnrXgxfMut3Mul8OyZctKklQe+w6lApqXK2bMmIF//OMfAfHT2dmJgw8+GO+++y4uu+wyfPnLXw6RuZWVlejs7AzkA0Cw8My6o8zVlkekS6+JRCKBTsWL2UDvVt+2tjZPCA1EdHR0oLu7G01NTT54mscOIZPJ4IUXXvAxqAguo4//Y1KFDQVRbFiBFeWGV8pZeSkFTQy5jBEZ6DlooyaZWInlSUBvF9OGrBiP4kVQU1ODZDIZuJHqlVQdhBfojYkwd+5c/OUvfwkmllGjRmH69On4yEc+gvHjxweeRnwkL9c7rzBapBB7NOh6knq3yCFdn3wN77HWhJKGRQxZfUcTcDJZy4qQ3MuTvqTPW3V0/xBSMJvNIpPJoLKyctDs8d4dcJE31koZsJ0o0Z5BeosYp20REi4Z5r4kbWz93xeRoPNnebWMM0nbukeMMtn2KQRvdXV16FQx9vBjeeKtafKdZdDylqmqqgq2jsnKom4XSVMMfSFjrDqxSFJWbJkg1cSPbkOuZ01O6fLxNfJZewXpfqLHCh5zRP55XKmoqMCIESOKnnuoorOzM+gX4g1ZVVUVbIcAthPeTEro9tNzmCYHXKS+JuHls8CSdyaCJEZQTU0NUqkUVqxYgW984xs7FP9nb+BrX/sannrqKdxyyy1oaGgIeTXV1tYinU6js7MTLS0t6OjoCB1MAhQTrEwS63kTCJNGMu4sXLgQ1157bahc48ePx1VXXRXaMqbjX/J4osk7kR/2iBAdrKOjAy+//PKerViPnQZ7wHj0or29HV/4whdw1113oaqqCq2trcGpZ7Nnz8aNN96IRx55BCNHjgzpydlsFtlsNjTnCmQ+Yx1Ez1kWKcSLt3y/yOH69ev36olsu4Ky6mlbt27Fhg0bPBnksVN44okncNRRR+3rYgw4aEMFCCufMoDqgM0CvaJv/b4zZbLIDlHGZIVdv0SpEsZf3gXsHcQKMZ8klkwmkUwmkUgkghPFpH56enrw+uuv4/nnn8e2bdswffp0NDU1IZvN4uGHH8b3v//9omdZv349br31Vtx6660YN24camtrUVVVhaamJsyYMQPHH3886uvrQ8+tPYREgdQTnTyDtf2E68sy8phc04SbNvAs4qfUSip/Z8NW4jNImkyMST2zhxl7UHFZpJ1zuRyi0WhZeY9qg0ETZ6UMd6lTTfKw7FteOa7VNcmT+4wmOEqRQdYz8G+u/LWhLOVm8ktvGeMjseUerisdt0WTYFquNIktBmE8Hg88EFxeR0w+8TNogkvetecCk0B6i5m+V+5hhZmfT3/ncu5Iu2lyiPPkPiFlTafTznSHGoT4kX7CAdxzuVxoOwSvaAs0SePa3mm1l5475bPV/9g4EtmRmFviYXPvvffi5ptv3qP1tSuYN28estks5syZg3HjxgW/RyK93lnr1q1DU1NT4MUjzy6kkJZxPZ7qxQkeNzZu3FhEBgG98U8fffRRzJgxo2j+dZF4Ak3gSnnEw6m5udlvSRrA8B5CNh555BE8++yzmD59OsaMGRP6b8OGDfj0pz+N+++/H+l0OgicDiDwEAfCOrz2hNVzLFDa+1nS4MUaIYQGC8qKEAJ6Txnw8NgZ3HfffXjggQf2dTEGDLQhyb+z4cgkSkVFRUCUxGKxgJiR1XgrRo9rK0N/IAoXpyPly+fzIVafy82KtxWwmY0r2eomQTElLkI8Hg/yzufzeOGFF/CNb3wDq1atCtL52c9+tkMei3wvgCDo5rRp0/CpT30Kp512WmjLmHgnAdsnOomXo93dBexdo70zNEFkudZqw0Gg+4hVl652lf94wpXfJUYTE4/iwWF5QUn58vl84CFkBT8fqujq6irqv4A7Jg8bkiwvejuCJnaZGOlLkSol49rA4Tys+7ivaA86/UxMggohI8askEDi8VdbW4tUKhXINIBQYOjW1lYsWLAAc+fOxZYtW9DQ0IDPfe5zOOGEE5BOp81yAMUeOXLiWCnPPE3YWrLD24S0HOjruA10O7JcabnXxBGXySKTuVz6et1HLC8XTqOcCCEAQcw9mW8ikd6V6Hg8jo6OjmDbNdcXy632SJWFER0nyEUwcF/UefBcz1vDYrEYtmzZgnvuuQdz584dNLHaHnnkEYwfPx7Tp0/Ht7/9bUybNg35fB5HHnkkli9fDqDXE+E///M/EY/H0d7eHmxDZuLFWhjRkLqMRqO48sornWW66aabcOyxxwbb6rl9rODfFrgN5aj55557bleqymMPorKysqy2s+8oZsyYgZdeeglTp07FrFmzcMUVVwT/rVy5EkcccQSuuOIKnH766aioqEBLSwsikUgwXrJnn154YCKVf3ORSPy/vLq6uszTEQcqyo4Q8vDYFXi2PgxrpVwbgJrgSaVSqKurCwZMAEF8DiFRLC8B67tFPHC55D/9nYO/aqJB3kXB0l5BejuDKL/pdDoIOMuBe1etWoWvfvWreOGFF8w63B0eixIc8yc/+QnuvfdeTJ48OTBuq6urg9VkmQyljrg/8zPp1Uee8HQ9aE8AV1u54CIWOU0hDYHtp9tJWWUfOfeJfD5vGpL8TN3d3cjlcqisrHQGSB+KEJnTBKjlRaO3XVkkDXsacLrsmcL5uAwWTdqWkulS5FKpcmtyhMvJW8XEU0fIoHQ6jerq6lAssJ6eHrS0tGDp0qV48MEH8cwzz4TKsXXrVlx77bXBiv/48eNx7bXXorGx0SQpge0GfE1NTVDH1jHtmpThcUzXuz5JzDLseXzm+tXvrpVTfS0bxLqe9fUuogHY7nnGeUoayWTS2QeGIoR0kLqdM2cOnnzySTQ0NODrX/86xowZg66uLtNLhz2KgHBwfSaDBK4xm9uQSVcOvB6JRLBs2TI89NBDWLRo0Z6vmD2IRx55BI888oj532WXXYbLLrsMZ555Ji688EJUV1ejoqIiWGixgtiybALhUzLXrVuHdevWlSzP2WefjWuuuQY1NTUmgVeKDALCp/Vls1msX79+0MQ3KUds27YNo0ePxuuvv76vizJgccQRR2DOnDmYPXt2iBASzJo1C/fddx9uv/12jBw5MhgfRQ/m3QKWDmPNZfxuLZYCvW2XzWaLFnEHMjwh5OHhsVPgYMYMy1jgLUyySllZWYlMJoNCoXcrUDKZDE4I0EYm4A54u6PgwdtaHRdXajYktcEEIDAaq6urkU6nUVtbGxxFDQDNzc343ve+h/vuu2+Xy9xftLW14cQTT8TNN9+Mk046KWTgCtEmpFt7e3uwNUUTL5rc4Rgg2qDm6/oLTdIJXCQOsP00B+15wkGnC4VC4PllkYpsEPHqqigH5QBNCFng9tXePkBYCXIRPUzGWPm4iEOteOlyWYST6zoph5Une74wGSQkL3sGpdNpJJPJgFCdO3cubrrpJnR2dpr1Z2HlypU46aSTcP/992P06NEhJZTrrrKyEslkMpC5TCYT2urJ13MaesuetYXSaifLy8f1H3sjMRGlXyxrOi9NRFpbGF3eVHy/daLiUIbErQGAFStW4MknnwTQSzzOmjUL06ZNw1lnnRVsH+YYWNb2YdlqZhF3wPY5G9h+QANfI15HsVgMuVwOf/vb3/DQQw+VHcFw++234/bbb8dHP/pRfOtb30JTU1Ow6CR6BBBeaGGPVFno+MUvftGv/L7yla/ghhtuMAlcCxbJ2t3djc7OzkFlrJYrfAyhvnH++edjzJgxuO+++zBz5syi/1966SVMmTIFF154YRA0vqqqKogDxsfSA+4A+6Jv8ImOLi/0bdu2YdmyZXutDnYHfE/z8PDYKfTl/q0NFz4iUpRJMXYikUigvGoD0/JCkfQFlkcB/8flYSKI75GBXRtQTKbwViw5Ore+vh7pdBqJRCKYMB5//HGcddZZO1exuwFf/OIXcdlll+HCCy8M6j2ZTAZbrORZZNWZA9SyocwGmA7UDGwnBYHimCK7A2wAi6GoA3GzAak9QgTaC0H/V05x5bidNUmgCR3tQcP1qAkcF9lQinjS5ZJ33Zcs0kFkjU/24LaXMUeTFJpQZJkWbzPx+qurq0N9fT0SiQS6u7tx0003Yc6cObtU/+eeey4WLFhQdHw4rzCKZ5/UCce4Yq9I3QYCvU1XFFR9n+W1U4qYkzq3iDhJyxo/+OQWPvWKxw9dLk5XvzNZUS7YvHlzsIBieSqLl+ihhx6Kj3/84zjssMNQUVGB5cuXY+3atZg6dSpGjRoV2irGniVc53rs57hDMv62tLRg4cKFmD9/PrZu3bo3q2JAYsGCBViwYAGGDx+OH//4xzj88MNDBmUulwv0H+7L0WgUc+fO7dM7iHHHHXfgjDPOCI1tnK7+rOVVyvPOO+/s0jN77HnsyKJDOeOkk07CI488glNPPRVz5841r7nuuuvw0EMP4dZbb8WIESPQ3NyMtrY2ZLPZ4FAbvV3W2lILhLfFA2Hvc4lPOX/+/L3y7LsLnhDy8PDYKYj7uiZV9Mpvd3d3SBHiQVSMOTFS+UQZSa+/btGWMuTyUGCSga+XvPRWqp6enpDrfTKZRDqdRl1dHerq6pBIJALD50c/+tEuG427A7Nnz8aKFStw9dVXB4onx2oSJV9WSaTOOQaPtAkHpZYtdWLM8ck2XO9W3JL+rGjKdUwMCLgtdb+T37QXBafHZMCaNWvw4IMPBvFeyglWu7g8fDTBpttQ/2fFfnF58ljlsggALg/H8JE8tQeTJo+s39jYFTJIPBRTqRQaGhqQSqWwZcsWLFq0CJdddlk/arZvbNq0CT/+8Y/xve99LxTfhQ3vioqK4GQjKWculwsFw9T1qYk967n1fbqN+BoXQaPJRIYQBkLWVFVVhU41FAKPvYJcY7n1kvylzcoJmzZtQjabRVdXF/L5vPO6119/3bnF5Prrr8eYMWNCcdP0tk6eo3nxJJfLYeXKlXjqqafwwgsv+NgmDmzcuBHnnnsuGhoa8N3vfhdTpkwJ+q94DIl8VVVVYdu2bc5taS4888wz+PSnP41oNIru7u6SWz418S/X5/N5H0x6EEAfiuHhxvTp03H99dfj8ccfx8aNG81rVq5cieOPPx5XXHEFpk+fHoxvYofwiX3sQcmyVWoxTMigpUuXDrox0hNCHh4eO4X169dj7NixodV6IBxnQt45gLMYdLzaKNdp45IHaCaHLE8EvcqpySAXCeEiiywPJzEW6+vrUV9fj7q6OsTjcRQKBSxcuBA/+MEP8OKLL+62Ot5V/O53v8PLL7+MefPmYcSIEUGQXCaEpP141ViMNiHApP3EuNPH7AopBIS3lDG04efyJnJ5oegVGk5D2l8mcF7R0fn29PTgwQcfxEsvvRT8lslkdrhuByu0YmOtfMn/1iqYaxVa/tdyyeSiyxtJl8sihaQ84kHD98jv2ktFjyn8bLxVjANIx2Ix1NXV4a233sIXv/jFHa/gfuCee+5BY2MjvvzlLwdxBgqFAkaNGoWDDz44IFWYIGpvbw/GUsvbx2pL3VYWmSr1ook/TRzxZxcpJGRNLBYLxn8hk1k2ebuxRdhL+0iaVlld28qGKrZs2RIQQjvr0XjBBRfguOOOw4wZMzB+/HikUikAKJp/M5kMmpubsX79erzyyit45plnsGnTpt32LOWArVu34uKLLwYAXHTRRTj++OMRj8eDo+ILhQKi0ehOt+Vzzz2H448/PtjirRdkWD7y+TzWrl2LZDIZbGnLZrNob2/f9Qf12KPw2/p2DBdccAG+9rWv4eqrry553axZs7B48WJ897vfDcgfXuS2bA3Lu1au5zS2bNmCu+++e48/6+6GJ4Q8PDx2CmvWrMG0adOC70wOiDEghnkkEglWt/WWB+01xIMxu2C6yCCBRS5YK2UMvZou13KwTPYaqK2tRUNDA+rr61FbWxuQJLNnz8aNN964s1W5R7FkyRIcdNBBePzxx3HUUUcFx2ezsVlRUYFMJhMihXjrnBinYtxxfbGnENez9gqxvH00uC30JGwRgtpLSLv96nQjkQj+/ve/h8igcgOTZSKnuj5d3hkCF2EDuE8cFIPFRQgxtIcLgCJCSXsj6rLwtherPEK6iHeQxANLpVJ45ZVX8NWvfnWX6rkv3Hjjjc4x46yzzsKsWbMwYsSIYKsnk7cig0yUA8WkEN9jtaPA8vRxXcvX8LtAxk5N6Oix1uoHrr4j3y3SsFxQKBTQ0tKCbDa7S1tcn332WTz77LPB9+rqakSjUXR1dfntKXsIElz+6KOPxsyZMwPSNxqN7rSXzsMPP4yPfvSjRSclscy88847ePzxx0PBvc8991yMGzfOb/PzGLK4+uqr8bGPfQx//OMfS173wAMP4LXXXsNNN90EIBwrk73hrYUp3pYtCx9dXV3o6OjADTfcsAefbs/BE0IeHh47hbfffhv5fB6xWKxo+wIPqtow59NOLGhCyCKDtKcIQ28fcf1v/c4r3EKcyOln6XQaTU1NqK+vRyqVQiQSwapVq3DyySfj7bffdlfUAMEJJ5yAm266CaeeeiqSySSi0SjS6XQwybW0tKCzszMghSKR3lPIOFaHZYixsWmdWsYGYKk2KdUnNGmotxDy9kQpv8sDxXXaW7kgk8kEpBkQ3pYJhJUeFxlkxXDq61o26HUgW4blZaTJRMtLSMBl0d5GrMwJESTeQRIzSE4F3Je47bbbcNttt+Giiy7CFVdcgWQyGRBYssUzl8uFyM++iPL+ErDWvaX+Z0PUla/0L1lF5bgMfcEqP8ezKic0Nzejo6OjX9sv+4tyCqi/r7Fo0aKAnGlqasJnPvMZ/O1vf9uptNrb24Pt63KymZDEf/3rX3HfffeZW1aWLFmCsWPHYuXKlbvyKB4eAxp9kUGCZcuW4cMf/jDmzJmDCRMmoKqqCplMJrAHeJFUfuPFpEhke4y8TCaD+fPnD7qtYgJPCHl4eOwUxBtDB/fkFXtWUoRkKBQKRUabBq94y3t/yCCB5eHQnxVvvYUkmUyiuroayWQSDQ0NqKurQyqVQkVFBTZs2ICzzz57UJBBgvPOOw/r1q3DN7/5zeA5q6urQ3E+stlsSeOSPYZ464p+115CpeqfvRlc0G2oyUL5bm0v5PusgKzlhI6OjqCe2Fi3vLdcBI/ldWORB3K9Jn9c3mKWbPM9khZ7C7GnkPYOEggp5SKDxDuoubkZ3/zmN3emWvcIrr32WkQiEfzkJz8J4pQBCJFhevvYjnjO9CVvkh6/63tdhJ7ImZBATNZqL1Gdp6ucfRFUQx3t7e3BIozH4MbmzZt32ZOAT5Dr6enBmjVr8Kc//QkLFy503pNOp9Hd3e3jB3l4EM4//3xcddVVOPzww0Px6YQUkvlGbAQ+3KSqqirQPwfzgqMnhDw8PHYamzZtwv7771+0yiysuf6dDTjeBmEZlNojqBQZxF4IO7rdQRNBcsIQbxGrrq5GXV0dkskkCoUC5s+fjzPPPHMXa2/f4Hvf+x5efvll3HbbbaipqQkMTSF52tra0NnZGcT90KQLsD3GRzQaDW0VE1hbkPg/wY4ar9yfeGub9dmFiRMnlnU8jI0bN6Kzs9PpdadPGLLIH0veSpFLTNgILCKAvZYETEDJ8dd8r7U1kccXfuktYuIJWFNTg2HDhmHGjBl9np64t3HNNdfgqaeewp///Gc0NjYikUggk8mgtbUV7e3tQbBpjs0DbD8pSuKBcdv1J86X9q7S2/L4nT/L1k1p866uruAl44nkVWos0GSUQPpDOQZbbW5uRj6fR21t7b4uiscAwHXXXYcDDjgAf//73/HKK6/0657JkyejpaUF77777h4unYfH4MLFF1+Mgw46CGOkJG8AABMiSURBVKeddhqOOeYY1NTUoKOjI5i7xHbhsAsyF3V1deGdd94Z1AuOnhDy8PDYabz22msYNmxYsGLJW0LEiADCBh8TQhzHRhv8bDC4VoOtALI6Hf7O+bD7p8QKSiQSqK6uRm1tLerr69HY2Ij6+nrE4/HgxJxLL70Ut9122x6q0b2DefPmYf78+fjzn/+Mo48+GvF4PBQ3SWIKMcFiES5icALhrX7cDmLA9+UpJGSEoBRZxP1MyqS9g1x95rjjjsNzzz2345U2RLBx40Y0Nzeju7sb0Wg01DaaDNJgudEyJSSdKE5aHvvayintViootIwpevuYJpwkDQ4cXVFREfIIkgDS1dXVSKfTSKVSSCQSA44QAoCXXnoJw4YNwwMPPIBPfOITAYnV3t4eHJ3LwWoFVgwhDjirZdLV5hY54/L+kq2brCgzGWSRtbrP6e2h/DyS50Bspz2NjRs3Ytu2bYjH4/u6KB4DAAsXLizpDWRhv/32w5IlS/ZQiTw8BjeWL1+OH/7whwCAj370ozjqqKOC0z47OzuDxbQ333wTK1euRCqVwmc/+1mMHTvWebLZYIEnhDw8PHYaS5cuxVFHHRU6sYq3Lui4JPyfHEWvPRPktx2BdUqO5MvXsEEkHjG8PaympiYggoYNGxYcKQ/0unifeOKJeOONN3a6vgYSMpkMjjvuOFx66aWYPXt2cMy1kEKtra0Atht4YmzqQLZA2KCTutUeXZoY2lFYMYF0HhaJqPtEKpXCxRdfjDfeeAP5fB6PPvroTpVnsKK1tRXvvvsuurq6ghNqrO1AFinEbtN8nSCfzyOXywUBGYVsLeV1pMlCJnGZzHCNK7pPaQ8hyb+qqgrxeDyIC5ZKpQLyN5lMYv78+WhpadnNtb178clPfhL19fU444wzAvL0oosuQlNTE9rb25HNZgNyVNexyCWf+FbK48f63dr6Z73L+MAnjAkZ5GpfnQ73BSaLpb1lfContLS04JVXXkFzc/O+LorHIMWbb76503GLPDzKCQsWLMCCBQv6vG7hwoU48cQTBz0hFBkI+7Ajkci+L4SHxz5EoVAYkEem9Ec2jz/+eBxyyCFBgDW61/QOYPJGVu55ewNQHHeCtyvJu7XqrUkl3qLGZIV4C1RWViKRSATbwxobGzF8+HAMHz4cdXV1iMViKBQKeOWVVzBt2rQhu01h//33x2OPPYZDDz0UhUIBmUwGnZ2d2Lx5MzZv3ozW1lZ0dHQEAYnZuAO2k0aueCFM0pQihZg4YINegv4KOSfEg86HYwfp36RfxGKxgIz8+c9/3icJMJhl04Xx48fj5JNPRkNDQ1GcJyZRNIkjbSFyyx4bXV1daGtrC0iJSCQSeONEo1Ez2DP3B91Weqzga9kjjIlB9kpjj7doNIqqqqpA1mtqalBbWxscMf/ss8/i5JNP3m1ts7dxyimnYPbs2YjH48hms0XB3bu7u4OtZez+7hrPLE8d9sTT91tBxWXrGG9pE2KK0y0UCsjn86aHkyb0JZ5DS0sLHn/8cbzwwgtDTjY9PIYChuK86eExFOCSTfu4HQ8PD49+4vnnn0culys6OUjHDZLPHGeEDXfZcsKGPBt8TApZ20pKxQ0Rw1BesmVEPAQaGxsxatQoHHDAARgzZgyampoQj8exatUqzJw5E0ceeeSQJYMAYN26dTjssMNw0003IRKJIJVKoaGhASNHjsTIkSPR2NiI2tpapFKpYPucDtzLhJs+0r7UNqT+wGpjTQSKpxd7hDAxINcIgfHyyy8PeI+QPYWVK1eipaUldDoNUHximOUhJNdLnerj3YUI6OrqCshDa+FJxwYTWHLLfYpJHh0cWggo2f4pBFB1dTVqamqQTqdRX1+PpUuXYtq0aTj00ENx/vnnD2oyCADuvvtuHHrooZgzZw6qqqpQU1ODVCoVnCao5ZHrUpN0TAZZ28343ZJ1i/jjfGUMFlnktpNFBdf2MZkHcrkcVq9evU/q2sPDw8PDY6jBbxnz8PDYJXR0dGD58uU49NBDEYvFQsYlG5s6Roz8ZgU51caiy5PRIhjY+0AgRop4CsipQnJ62LBhwzBy5Eik02nk83n8/Oc/xw9/+EN0dnbucv0MJnzpS1/CvHnz8Ic//AH19fWoq6sL6kvqrq2tDZWVlcGqP58gJyv84mUChLebSH/QgYCBsOFnbV3R2xLFoASAtrY2/PGPf8Rrr72GWCyGQw45BMceeyxSqZTptZbJZHDvvffukTocLHjzzTfR2NgYMupdW4YYTMoKocBbkrSXSH/IIN6KpgkE3moo24fkGr3djfuJjhck8ZIuvvhiPPPMM0FZ7r///l2vzAGCK6+8EldeeSXOOecc/Nd//RdGjhwJoLcdqqqqsG3btpAnJ9ejQMdikusEHNRbbwnmsZf7lGxVk7Z+9NFHMW/evCDN973vfZg5cyZisRgqKiqKYg2x3Hd3d6O1tXXQu+d7eHh4eHgMFPgtYx4eAwCD3b02Eong9NNPR21tbZHBoY0Ja9WZV63ZU0i2A/HJNJIer26X8kiRrS5iICYSiSBWUF1dHUaNGoXGxkbE43E89dRT+Pd///fdXY2DEmeccQauv/561NTUBEbYpk2bgi1k2Ww2iBeTz+eDF28Z4+1+7BVmbVfR3gryPRqNBp4eyWQyOOJT8srlcrj00kvR0dFhPsOhhx4aMi63bduG+++/Hy+99FK/6mGwy6YLjY2NOPXUUzFy5EjE4/FQvXNwcU2oVVZWBrF4qqqqAiKwq6sLnZ2d6OjoQC6XC0g78dTRZLAV74m3CfKY4OozDCYf+CSxZDKJRCKBxYsX4/zzz9+VKht0GD9+PO68806MGDEikFeO6aNPJtP6oCb3Re5kbNYeYzqWm44j1NPTgzlz5jgD4V555ZVIJBKh67ls27ZtQ2trK55++mk899xzQ1Y2PTwGO7xsengMTLhk0xNCHh4DAENh8pwwYQJOOOGEwFD85/0hAgcIB4CWFWjtDSBGhI4VImlq4oe/s2Ei203EUyCVSiGdTqOpqQmjRo1CZWUlnnzySYwcORIPPfQQ5syZs1vrbyjg3nvvxcyZMwEAuVwOLS0t2LBhA1paWtDZ2Ymuri7kcjlkMplQjBI26vL5PAAUkUICa9uKeHlI3BoJBByLxUJeI3Pnzi3p7fPd734XtbW1gRG7evVq3Hzzzf1+/qEgmy6cdNJJOPzww1FbWxuQMLylJxKJBPGjUqkU6urqirbniaEu5FwmkwnIAr5Ob/3Rnn/S5hyIWkhBvbXU2sLELyF+k8kk2tvbcdZZZ5X1FqOrrroKn/jEJ4rkU58aWKpd5P9t27YF8bvkWhm/Ozs7sWzZMmzcuBGdnZ2oqKjAmDFjcMQRR6CiogLr1q3Dl770pZJlPf300zFlyhTEYrGi8T+Xy2HDhg24/fbb5bSXISubHh6DGV42PTwGJlyy6beMeXh47Ba89dZbGDduHCZNmlQUOJQ9g4BwrBKGdYoNfxZY5IG8s2eJGKQcQ2T48OF4++23ce6552LZsmV7tE6GAk4++WQcffTRuPPOO3HnnXfiyiuvxHnnnYczzzwTNTU1yGQyaG9vR1VVFTKZTNEWMQABccC/6y1i+tXZ2YmVK1cin8/jve99L6qrqxGNRtHZ2YnLL78cI0eOxOTJk/vc+vX000/jpJNOQk9PD9rb2/Gb3/xmD9XU4MNf//pXjBkzJog1I0TM5s2bceutt2Lt2rWhAMWJRAKzZ8/GfvvtF/LmE4NdgrSL14kOTl1qAYpjQUncGyH+JD1XXBqOTyOxaJLJJBYuXIgLLrhgz1biIMDFF1+MP/zhD7j22mvR0NCAfD6PqqqqgLTluG76xfWtt+jx1rHbb7/dKYuTJk3C9ddfj8WLF/dZVhlnPv7xj+OEE04IPNC6u7uRyWTw1ltvld1WXg8PDw8Pjz0JTwh5eHjsNrzwwgs44IADgngdmsQBEHgBMHGg44DoODP6M6fJZJOLDKqtrUVdXR3q6uowf/58fOc739kr9TFUsGjRIkyZMiUwxK6++mo8/fTTuPzyyzFu3LhQLBkdCJyNRvlNx4XRMUsA4Pbbb8fatWsB9B7r+bWvfQ2NjY3429/+hpUrV2LlypVYtGhRn2VfsWJFEL/k8ccfH9LBwXcU69evR3NzMxobG5FMJgNi9Ze//CW2bt1adH02m8Udd9yB73znO0WxZESONQnE8V9KkUI6QLF8ZrC3Ib/Em4y3sy1ZssSTQYTFixfj85//PG644YaA0AO2j60czB8Iewtp70v5XWKHvf766yWJ2WXLluGhhx7C888/3+/yPvTQQzjooINw4IEHBlvcOjs78cYbb+xaRXh4eHh4eHiE4LeMeXgMAAwl99oxY8bg3/7t35BIJBCPx80TbsRoBBAiBuQa2WZkHXHOBBAbjkI0iYeAxJ2pr69HfX09Fi1ahEsuuWT3VpAHAOCSSy7BzJkzkc/n0dLSgvb2dmQymVDMEjE69SlzbPRLOy5evBj33HNPUT7nnHMO3njjjVBQ4L5w7LHH4hOf+ARuv/12vPrqqzv8bENJNi2MHTsWn/zkJ7Hffvuhvr4eDzzwABYsWFDyng9+8IP48Ic/HJzOp+P7iAxrL0COBcYeZLxdTGL/CMHDx8wL+MRA3lpYKBSwZs0aXH/99Xjsscd2R/UMSZx++um49NJLkUwmQ1vHWD6Z2NVkkMj1tm3bUFFRgVNOOQWbN2/eI2W99tpr0dbWhpaWFrzyyit46KGHgv+Gumx6eAxWeNn08BiY8DGEPDwGMIba5Dlu3Di8//3vx7Bhw0JbUfQR8OIxIMae/N/T0xMEqtXBS9kbiN/FK0iIqHQ6jYaGBqxduxaXXnop1q9fv1vrxqMYZ511Fj772c+iUCigvb0dHR0doePHmRQCEHpnw/PXv/71TpE3FqZPn45FixZhy5YtO3X/UJNNC8cffzyOPvpo7Lfffrjzzjt3yJPjkEMOwbe+9S1UVlZiy5YtiEajqK+vRzweB7C9bfWWJB0XTMYECQQthJB4hwhE3jnO0fLly3HllVfixRdf3F1VUhY4//zzcd555yGdToeCwGtvIU28d3V1IZvNoqenB2+99RbOOOOMPVbGa665Bu+88w7effdd3Hnnnchms8F/5SCbHh6DEV42PTwGJjwh5OExgDFUJ8//+I//wJgxY4LtW0zcsJeQgLceiYGSz+fNYLIcgFY8gxKJBKqrq1FdXY1169bhl7/85W4jFjz6jxNPPBGf+9znUFtbG3gKscFpbSMT47OysnKHSQkXjj/+ePzlL3/ZpTSGqmxqnHbaaTj88MPx7rvv4tprr93l9MaPH4+ZM2di2rRpiMfjIaIBCJOATPAkk0lUV1cHJ58JmQggJPOVlZXo6OjA73//+91S3nLGoYceitmzZ+Ooo44KCDjtwdfc3IwXX3wRq1evxvvf/34ceOCBKBQKePjhh/Hf//3fe6RcJ598MqZOnYply5bhiSeeKAoMXi6y6eEx2OBl08NjYMITQh4eAxhDefI85phjcOSRRyKVSgVEEAeO7enpwZIlS7B582ZMmjQJkyZNQmVlZeg4c70dRZ8gJqdQ9fT04OGHH8YjjzyC5ubmXS26xy7i8MMPxznnnBPEAZG2ZC8RaWcOXrx69WpceeWVu5T3IYccslvijQxl2WTU1dXhlFNOwcSJE/HTn/50pz2qLJx55pn4+Mc/Hmw50ifMyclgchLgHXfcgZtvvhmjRo3CDTfcgPHjxwfXdnd34+WXX8b//u//4r777tttZfTo3T542WWX4dhjj0UqlQrG2sceewxnn3126NrPfvaz+MEPfoDf//73mDVr1m4vy8SJE3HOOedg+fLluOuuu9De3l50TbnIpofHYIOXTQ+PgYkBTQh5eHh4eHh4eHh4eHh4eHh4eOw9VPR9iYeHh4eHh4eHh4eHh4eHh4fHUIInhDw8PDw8PDw8PDw8PDw8PDzKDJ4Q8vDw8PDw8PDw8PDw8PDw8CgzeELIw8PDw8PDw8PDw8PDw8PDo8zgCSEPDw8PDw8PDw8PDw8PDw+PMoMnhDw8PDw8PDw8PDw8PDw8PDzKDJ4Q8vDw8PDw8PDw8PDw8PDw8CgzeELIw8PDw8PDw8PDw8PDw8PDo8zgCSEPDw8PDw8PDw8PDw8PDw+PMoMnhDw8PDw8PDw8PDw8PDw8PDzKDJ4Q8vDw8PDw8PDw8PDw8PDw8CgzeELIw8PDw8PDw8PDw8PDw8PDo8zgCSEPDw8PDw8PDw8PDw8PDw+PMoMnhDw8PDw8PDw8PDw8PDw8PDzKDJ4Q8vDw8PDw8PDw8PDw8PDw8CgzeELIw8PDw8PDw8PDw8PDw8PDo8zgCSEPDw8PDw8PDw8PDw8PDw+PMoMnhDw8PDw8PDw8PDw8PDw8PDzKDJ4Q8vDw8PDw8PDw8PDw8PDw8CgzeELIw8PDw8PDw8PDw8PDw8PDo8zgCSEPDw8PDw8PDw8PDw8PDw+PMoMnhDw8PDw8PDw8PDw8PDw8PDzKDJ4Q8vDw8PDw8PDw8PDw8PDw8Cgz/H+uooW+uWB/ywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise some labels\n",
    "f, axarr = plt.subplots(1,5, figsize=(20,20))\n",
    "j=5\n",
    "for i in range(5):\n",
    "    image = X1[j].reshape(310,560)\n",
    "    axarr[i].axis('off')\n",
    "    axarr[i].set_title(\"accuracy = \" + str(accuracy1) + \"\\nSpecificity = \" + str(specificity1) + \"\\nSensitivity = \" + str(sensitivity1))\n",
    "    axarr[i].imshow(image, cmap='gray')\n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "METH_2_DATAPATH_1 = r\"./Data_Breast_Cancer/meth2/1/\"\n",
    "filenames = os.listdir(METH_2_DATAPATH_1)\n",
    "#filenames.remove('.ipynb_checkpoints')\n",
    "\n",
    "col = [x for x in range(173600)]\n",
    "Data21 = pd.DataFrame(columns=col)\n",
    "i = 0\n",
    "for filename in filenames:\n",
    "    img  = cv2.imread(METH_2_DATAPATH_1+str(filename))\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    crop = cropImage((35, 98, 560, 310),gray)\n",
    "    row =  crop.ravel()\n",
    "    row_as_list = row.tolist()\n",
    "    Data21.loc[i] = row_as_list\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>173590</th>\n",
       "      <th>173591</th>\n",
       "      <th>173592</th>\n",
       "      <th>173593</th>\n",
       "      <th>173594</th>\n",
       "      <th>173595</th>\n",
       "      <th>173596</th>\n",
       "      <th>173597</th>\n",
       "      <th>173598</th>\n",
       "      <th>173599</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 173601 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  0  1  2  3  4  5  6  7  8  ... 173590 173591 173592 173593 173594  \\\n",
       "0      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "1      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "2      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "3      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "4      1  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
       "\n",
       "  173595 173596 173597 173598 173599  \n",
       "0      0      0      0      0      0  \n",
       "1      0      0      0      0      0  \n",
       "2      0      0      0      0      0  \n",
       "3      0      0      0      0      0  \n",
       "4      0      0      0      0      0  \n",
       "\n",
       "[5 rows x 173601 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data21.insert(0, \"label\", [1, 1, 1, 1, 1], True)\n",
    "Data21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "METH_2_DATAPATH_0 = r\"./Data_Breast_Cancer/meth2/0/\"  #data  fro method 2 healthy images, 0 is for healthy\n",
    "filenames = os.listdir(METH_2_DATAPATH_0)\n",
    "#filenames.remove('.ipynb_checkpoints')\n",
    "\n",
    "col = [x for x in range(173600)]\n",
    "Data20 = pd.DataFrame(columns=col)\n",
    "i = 0                                                 ##### perform same operations as method 1 #####\n",
    "for filename in filenames:\n",
    "    img  = cv2.imread(METH_2_DATAPATH_0+str(filename))\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    crop = cropImage((35, 98, 560, 310),gray)\n",
    "    row =  crop.ravel()\n",
    "    row_as_list = row.tolist()\n",
    "    Data20.loc[i] = row_as_list\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
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       "   label  0  1  2  3  4  5  6  7  8  ... 173590 173591 173592 173593 173594  \\\n",
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       "\n",
       "[5 rows x 173601 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data20.insert(0, \"label\", [0, 0, 0, 0, 0], True)     ##### perform same operations as method 1 #####\n",
    "Data20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 173601 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  0  1  2  3  4  5  6  7  8  ... 173590 173591 173592 173593 173594  \\\n",
       "0      0  0  0  0  0  0  0  0  0  0  ...      0      0      0      0      0   \n",
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       "\n",
       "  173595 173596 173597 173598 173599  \n",
       "0      0      0      0      0      0  \n",
       "1      0      0      0      0      0  \n",
       "2      0      0      0      0      0  \n",
       "3      0      0      0      0      0  \n",
       "4      0      0      0      0      0  \n",
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       "7      0      0      0      0      0  \n",
       "8      0      0      0      0      0  \n",
       "9      0      0      0      0      0  \n",
       "\n",
       "[10 rows x 173601 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge Data0 and Data1\n",
    "frames = frames = [Data20, Data21]\n",
    "meth2_df = pd.concat(frames,ignore_index=True)         ##### perform same operations as method 1 #####\n",
    "meth2_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label     int64\n",
       "0         int64\n",
       "1         int64\n",
       "2         int64\n",
       "3         int64\n",
       "4         int64\n",
       "5         int64\n",
       "6         int64\n",
       "7         int64\n",
       "8         int64\n",
       "9         int64\n",
       "10        int64\n",
       "11        int64\n",
       "12        int64\n",
       "13        int64\n",
       "14        int64\n",
       "15        int64\n",
       "16        int64\n",
       "17        int64\n",
       "18        int64\n",
       "19        int64\n",
       "20        int64\n",
       "21        int64\n",
       "22        int64\n",
       "23        int64\n",
       "24        int64\n",
       "25        int64\n",
       "26        int64\n",
       "27        int64\n",
       "28        int64\n",
       "          ...  \n",
       "173570    int64\n",
       "173571    int64\n",
       "173572    int64\n",
       "173573    int64\n",
       "173574    int64\n",
       "173575    int64\n",
       "173576    int64\n",
       "173577    int64\n",
       "173578    int64\n",
       "173579    int64\n",
       "173580    int64\n",
       "173581    int64\n",
       "173582    int64\n",
       "173583    int64\n",
       "173584    int64\n",
       "173585    int64\n",
       "173586    int64\n",
       "173587    int64\n",
       "173588    int64\n",
       "173589    int64\n",
       "173590    int64\n",
       "173591    int64\n",
       "173592    int64\n",
       "173593    int64\n",
       "173594    int64\n",
       "173595    int64\n",
       "173596    int64\n",
       "173597    int64\n",
       "173598    int64\n",
       "173599    int64\n",
       "Length: 173601, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meth2_df = meth2_df.astype('int64')       ##### perform same operations as method 1 #####\n",
    "meth2_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "X2 = meth2_df.drop('label', 1).astype('float32')\n",
    "X2 = np.asarray(X2)                   ##### perform same operations as method 1 #####\n",
    "print(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "Y2 = np.asarray(meth2_df['label'])     ##### perform same operations as method 1 #####\n",
    "print(Y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "clf2 = svm.SVC(kernel='rbf')       ##### perform same operations as method 1 #####\n",
    "clf2.fit(X2, Y2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "yhat2 = clf2.predict(X2)\n",
    "print(yhat2)                       ##### perform same operations as method 1 #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "METHOD 2\n",
      "Accuracy :  1.0\n",
      "Sensitivity :  0.8\n",
      "Specificity :  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix     ##### perform same operations as method 1 #####\n",
    "\n",
    "cm2 = confusion_matrix(Y2,yhat2)\n",
    "print(\"METHOD 2\")\n",
    "total2=sum(sum(cm2))\n",
    "#####from confusion matrix calculate accuracy\n",
    "accuracy2=(cm2[0,0]+cm2[1,1])/total2\n",
    "print ('Accuracy : ', accuracy2)\n",
    "\n",
    "sensitivity2 = cm1[0,0]/(cm2[0,0]+cm2[0,1])\n",
    "print('Sensitivity : ', sensitivity2 )\n",
    "\n",
    "specificity2 = cm2[1,1]/(cm2[1,0]+cm2[1,1])\n",
    "print('Specificity : ', specificity2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "### Method 1 - Healthy Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3/gcAbgBwpS+G9h1nfn8dwF8AvF9tPhnAL733fWbfI0v/Hlg6100A/gvAh9Ru7wGwznv/uE2Lc24P59yWKp+T7TElFgJ4QqXjRRSNdEHgfjegmH8fcc7FnHP/BGBPAPdVODeZmNAuB+6VdklqCdrmwL3SNkktQdscuFfaJqklaJsD90rbHAMoCI0Q730ngKUoKonXAHjDOXerc25maZfVAL7ivX/Ge59HUaF8q3NuT3WaK7z3W7z3rwK4G8BbS9v7UHwBZ3vvM9777X0RfwbgJABwzjkAHyxtGw4/BfCeUrgiUAy1+0loR+/9q977KVU+la7ZhKIyrdkKoLnC/j8H8AUUQyXvBXCR937tMO+HTABolwPQLkktQdscgLZJagna5gC0TVJL0DYHoG2ODRSERoGSAZ7mvd8dwCIUQ+e+Wfp5TwDfEuUSwGYADsXxn8J69X8vii8tAJxf2vchV5zJ/aPbmcRfAvgn59xsAEeiWKDcO5wDS6rv/QDe75ybAmAFiirxaNINoMVsawHQZXd0zu0L4CYApwJIoqj4nu+c++dRThPZyaFdjhjaJRkTaJsjhrZJxgTa5oihbZIxgbY5YmibVYiPdwJ2Nbz3zzrnrkNRrQWAtQAu995v84vtvV8P4GNAcXZ5AH9wzt3jvX/B7jrEeba44nJ/JwLYD8DPvfdVjzFcD+AMFN+Xv3jvXwvt5JzbA8DTVc6zukI+PIViCKOcZy8AKRRDJC2LAPzde/+70ve/O+d+g2Lh8ZuhboRMTGiXtEtSm9A2aZukNqFt0jZJbULbpG2ONowQGiHOuX2dc+c653YvfZ+LYsjcA6Vdvg/gQlec3RzOucnOuROGee4T5LwoTgLmAfQHdt0AYK8hTvczFJXO96N6CF/oXLcAOBjAOSiO8wxSCuNrqvKpVFDdAOA459wRzrlJAC4F8Gvv/SDVFsD/AdjbFZcDdM65N6M4LvaJwL5kgkK7HIB2SWoJ2uYAtE1SS9A2B6BtklqCtjkAbXNsoCA0croAvA3Ag865HhSN80kA5wKA9/5mAF8FcKNzrrP024phnvuw0nm7AdwK4Bzv/UuB/a4FsL8rhgreUuFctwLYG8AG7321F/qLAK4vnevE0j2kAfwKwJsA/HqYaR823vunUJz9/gYA7SiO5/yE/O6cu8M597nSvi+iOJP+VQA6UZwZ/1co5gEhAu1yhNAuyRhB2xwhtE0yRtA2Rwhtk4wRtM0RQtusjiw5R0hVnHNfALDAe/+hIXcmhOwQaJeE1Ca0TUJqE9omIbUJbXP84BxCZEicc20ATkdx1ndCSA1AuySkNqFtElKb0DYJqU1om+MLh4yRqjjnPobiZGV3eO/vGe/0EEJol4TUKrRNQmoT2iYhtQltc/zhkDFCCCGEEEIIIYSQCQYjhAghhBBCCCGEEEImGDUxh5BzjmFKZELjvXfjnYYQtE0y0aFtElKb0DYJqU1om4TUJpVskxFChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIRMMCkKEEEIIIYQQQgghEwwKQoQQQgghhBBCCCETDApChBBCCCGEEEIIIROM+HgngBBCCCGEEEIIIeOPcw4HH3ww5s+fjylTpuDqq68e7ySRMcR578c7DXDOjX8iyITnyiuvxO67745LL70Uzz777A69tvfe7dALDhPaJpno0DYJqU1om4TUJrTNnZclS5bggAMOQFtbG+rq6pBKpQAATzzxBG6++eZxTh0ZKZVsk4IQIQD+53/+B3PmzIH3HplMBk8++SR+97vf4Re/+MUOuT4rT0JqE9omIbUJbZOQ2oS2uXNx5JFH4vDDD0dbWxuccygUCgCKUUKxWAx1dcUZZnK5HC644ILxTCoZIRSECAlwxRVXYNWqVairq0N/fz9yuRz6+/uRzWaRz+fR19eHjo4O3H777bjmmmvGLB2sPAmpTWibhNQmtE1CahPa5s7BrFmzcOKJJ2L27NlIJpOoq6uD9x6FQgGiDzg38ChjsRjS6TQuvPDC8UoyGSEUhAgpccopp+DDH/4w9tlnH8Tj8UgNz2azyGaz6O/vR39/P/L5fPR/Z2cnjj322DFLEytPQmoT2iYhtQltk5DahLZZu7S2tuJtb3sb3vrWt2LmzJlwzkFrAYVCIYoQ0vT396Ourg7JZBK9vb343Oc+tyOTTUYJCkKEADj11FNx+eWXR0p4Pp+PCsJ8Po9cLlcmBPX396NQKCCfz+P222/HZZddNibpYuVJSG1C2ySkNqFtElKb0DZrk/e///1YvHgxkskkYrFYJP547wd9gPLoIOdc9InH4ygUCvj0pz89XrdCthMKQmTCc/zxx+PKK69ES0tLNESsr68P/f39cM5FoZKyLZ/PR4JRXV0d+vr6sGTJEvT19Y162lh5ElKb0DYJqU1om4TUJrTN2mLBggX44Ac/iMbGRsRisUjo0UPD7F/ZRwtB8r2urg6JRAIdHR34whe+sKNvh4wACkJkwrHvvvti7733xnve8x4ceOCB2HPPPdHQ0IBYLIZ8Po9MJhNFBInoI6GTesgYgEhJf/TRR3HaaaeNelpZeRJSm9A2CalNaJuE1Ca0zbFl2bJlOOCAA9Df349HHnkEDz74YMV9V65cicWLFyMWiwFANBxMRwLJdyEkAGlhSCabjsfjyOVyePXVV/GNb3xjLG61Ipdffjna29vR3NyM/v5+/PWvf8VvfvObHZqGHcmMGTPQ3t4+4vNQECI7FatXr8ZBBx2Ev//979tcyFx11VV485vfjPr6eiSTScyZMwdTpkwpK8T6+/uRyWSQyWSiiCAAZaq5DqWU47LZLA466KBRv19WnoTUJrRNQmoT2iYhtQltc2w46aSTcPjhhyORSAAotlVyuRw2bdqERx55BH/+85+xdetWAMC8efOwfPlyzJ49G/X19dH+IgiF5gkCEBR/6urqBm2PxWKRyOS9x+bNm3HxxReP6f3vvvvuWL58OebNmxflgSAd+i+88AJuu+02bNiwYUzTojn44IMxefJk3H333WNy/uOPPx6/+93vkE6nR3wuCkJkp+Gkk07CJZdcAu898vk8enp6cO211w65ytdFF12Ed7zjHWhtbY3GxyaTSTQ3NyOZTEYRP7KEoiwxL6uJ5fN5AIiWV5TriyCUSqUAAF//+tdx9dVXj+o9s/IkpDahbRJSm9A2CalNdmXbPPLII3HPPfeMRnIAAHvuuSdeeeWV4G+LFy/GIYccgtmzZ6OtrS1qnwADUTyFQgG9vb3YunUr+vr6kEgkkEqlUFdXh0KhELV3ZPWw4aAFoLq6uqjdVClKSH7r6+vD97//fTz++OOjkjfCe9/7Xuy3336YMWNGWYSTdNrr+5KpP3p7e/HKK6+grq4O//3f/z2q6RHq6upwwQUXYPbs2QCATCaD2267rer7sXz5crzzne/Erbfeivvuu2/Ia+y7775YtmwZvve9741KmikIkZ2Gz3zmMzjzzDMRj8ejeX6899i6dSuuvfZa/PCHPxx0zDnnnIPVq1dHgo+MkY3H46ivr0c8HkdfX180/48UYgAiQaivry8qPKWwk+Fj3nskk0kkk0k89dRTWLFixaje865ceRKyM0PbJKQ2oW0SUpvsyra5bNkyPPzww+ju7q52HRx99NH44x//GPz98MMPx/ve9z5MnTo1mtN0/fr1aG9vRyKRwO67745p06ZFi9rotpC+hrRXZJoLPUF0oVCI2i9WMNHn0Nvlu7ShQoKQ/AWAeDwetbkSiQSSySQaGxvR2NiIRCJR1sF+11134Ytf/OKQ+RuLxbBo0SLst99+OOyww8o69K0IpO/NTogt6SwUCshkMkgmk0in02X54pxDPp/H//3f/2H27Nno6elBT08PDj30ULS0tER5m81msX79eqxZsya6ZmNjI4499tgyQUzO2dvbi8svvxydnZ3Rfc2fPz9amEiCFjo7O3HGGWcgl8tVzI+vfvWruO2224YlHg0HCkJkp+GUU07BJZdcgkQiEa30pQ137dq1+MMf/oArr7wSQHF87Pnnn4+mpqaoEBPjlCghLQjJucSIpTDWcwbp8bNi/IlEAvF4HF1dXdh///1H9Z535cqTkJ0Z2iYhtQltk5DaZFe2zQULFuAjH/kINm/ejKamJqxbtw5//vOfsWHDBuy55554y1vegqOOOgqzZs3CI488gl/84hfYuHEjDjnkECxYsAAtLS2R2KPFC91RLe2OQqEQTWshbaHSfZR1XsuKyPoj26q180OCkLSL9HWkbSXCkE6zbEskEpEolEwmkUqlonaT9x65XA73338/HnjgASxZsgTPPvss7r77bqxfvx777rsv2trasM8+++CAAw5AU1NTmcil718LQSExSOeRbsfp6Cq515AIJu1ALaLV1dVF4lc+ny+7L51vOr/a29vLBLAf/OAHaGpqKhPYCoUC1qxZgwsuuCD4fFatWoVDDz0U3/3ud7F27dqq7+VwoSBEdhqmTZuGBx54AMlkMioQtMFJ1I4ov2KQesI0vb+o1FKgAAOFQzwej4w8VABrI4/H40gkEshms7jrrrtw+umnj9o978qVJyE7M7RNQmoT2iYhtcmubJvxeByXXXYZpkyZErU/GhoaIgFEz60jbZhcLodcLhd1TNvObhFhdESNjvIRccfO6yNiho4O0oKGbePblcNC6OggPTRMix2SD1rckPaUFoW0cKLbYFa8knyRfJJVnrWoJfml80bfY2iybBF97MppQrX8CYlLOh+A8NA6+V/yp6OjA9OmTYvmcbLHyf3ffffduO666wAUp045+uijkU6n8Y9//CMKgBgNKtlmfNSuQCY0y5Ytw1VXXYW99torMtCenh7cddddOO+887Bu3bphn2vjxo2RGAOgrIDTxgqgzBBDYZEy4ZouZPVvWiAS45XtuVwuKqwTiUTZ+ZctW4YzzjgjOHyNEEIIIYQQsmuRz+fR3t6OhoaGqP0h7QWgPLpHxKBsNotcLheJIlrcsOKLnEMLH7Z9owUFPd+pPlb+B1B23kpCkJzXpickolTaJ7RNCzpWFJLvMo9rKKpJR/JUCmKx927vX/KumiAUmjLE5l+hUIhWnZb80lFTsq88/1gshsmTJ0fPSCLAdHokQOGd73wnjj322LJFj7q7u/H8889XfF6jCQUhMip885vfxNSpU3HPPffgscceQzwexzHHHIOVK1fin//5n/GlL31p2KuFTZ48uczA7FhYjQ5ZDBUaMlG0bLeGqwupvr6+srG4utDO5/NROKf0AlxwwQX45S9/iS1btoxGFhJCCCGEEEJqmOeffx5z5syJol+0aCOihrQjstksstlsNE+pHt41XKxIItFCghWMhotuDwGDl5uvJgwNtY9ut4WEGj3CQ5/PToatI6NC2PPbc+q80VFVoTyT64Z+l+06PTpgQa6rhTotesk54vF4mWCk8yqbzUZt0L6+PmQyGTz00EPB+x5tKAiRUeHAAw+suP3GG2/ExRdfjOOOOw6f+MQn8Oyzz1Y91yWXXBIVCHY8rBaF5K+e5V6+y+86zBAoN3bZN1SIicHbsETZX4697bbbcMQRR2xPlhFSMzz99NORYJpKpbDnnnuOd5IIIYQQQmqOJ554Av/0T/8UTTeho3kEGWkgYpAWioDBQsZQgocmtG8oekafVw81AwYEDisCVTpe7yud63KOfD6PTCYT/SZTcdgJl/VHCylaQNPRM1b00v8PNT9SiOGIZpX20dutCFYtOkvakDqqS6KM9OgXOxSuv78fXV1deO2117bpHrcXCkJkTHniiSew33774eqrr8aqVatw33334dHpcbqaAAAgAElEQVRHH8WPfvQj3HTTTWX7Tp06Feeccw7e/e53IxaLDRJ/bJiliDZiOHquH11QyfFyLhmOpucgCiFKLzBQEOgl6wuFAqZMmYK77roLRx999KjnHaltOjs78Z//+Z/47Gc/O95J2W6effZZpNNp5HK5qOJNp9N45pln0N7ejqOOOmqcU0gIIYSQ4VJtaA0ZHdauXYvu7m40NDQAKB8WpSNDstls2YgDOzFyKJplOFQb9hU6V6X9Q0O+hpMOLW7k83msW7cOf/vb36Khc1ZsEuFMtkk7yooqMkXHtGnTMHv2bNTX15ftuy35MZy8rLRPtWvZSC1BghFC+9jhaPYaoeGGmUwGr7766pD3MFpwUmmyw5g9ezZ+/OMfY8mSJdHYyM7OTnR1dQEoDhVrbm6OlpuXglOGc8mEbHbJQGBAqBFjk8nNAAwKz7TqeChsEcAgwUiWU5QlEGU8sPcer776Ks4666ztVnL9LjwB367Kli1b0Nvbi9///vc47bTTxjs5ZZx33nn40Ic+hJaWFvT09GDLli1YunQpHnzwQSSTSTjnoiGSumIHBiprANEEgc3NzXDOIZvNoqWlJbKBtWvX4uSTT8bjjz8+nrc7ptA2CalNaJuElDN9+nS88cYb452MCWGbJ554IhYvXhwtXJNMJssmCs7n84PmDtIN/tDcQMNFd3jbYU1W1BGfTvt7sq/eX/a1/9tj9QTSMn/Sc889h7/85S/RSs7DvafQkLV4PI7ddtsNixYtwsyZM8sCAnS+ybxJNiInNHRMInE0oXafFamGSrfNEz2KxE5hYifdlvcGGDyfUjabxRtvvIEbbrhh1EWhSrbJCKHtYNmyZbj00kuRyWTQ0tKCuro6HHbYYeOdrJrn9ddfx/Lly9HS0oKHH34YTU1NSCaTaG1tBYBodn4t/GiBJ5/PD1JUtTGLwWsl2hZ2cpwtMLWirwtRXTjI7zK2M5vNolAoIB6PY+7cubj++utxzDHHjFHukVojnU6ju7sbS5YswXPPPYcFCxaMSzoOP/xwtLW1YeHChTj++OMxbdo0ZDIZ9Pf3Y+vWrSgUCmhoaMDDDz+Muro69PX1ARi82oStBKW3o6+vDx0dHVEvz9atWyPHp62tDb/97W/hvUd9fT3uvfdevO997xuXfCCEEEImMgsWLKgJQWgi8Nhjj+Gtb30rEokEgPI5Z6RhrydJtu2M0Gpgtr1SSZgYaliTTLsBIBqeJJE2FnvtUJtJ/9VpAwaGiw01P5I+T6X7kvabFoBC5wgRGralJ20OpUm3BW36NHa0SOhc0kYNiXLyvx4yKPdr95XFkDZt2rRDI4QoCG0H73nPewAUI0YymQwnFd5GOjs7sc8++2Dt2rXI5/NlS8OLIYggZMei2oLUDh/T4zClAauHeVl0NIQ2xmrjdbXCLwp1MpnEjBkzcNVVV+Hss88eq6wjO4DVq1fj6quvrrrPnDlz0NHRgVwuF1WCDz/8cCSiNDQ0IB6PRysndHR0IJvN4u6778ZXvvKVEaVv0aJF+N73vofdd98dTU1N0ZAvGa/e09NT1vNUKfrNjgMPRdvZlRO0rYg4qkOjFy9ejDvvvBM33ngjrr322hHd53BYuXIlbr755jG/DiGE7Aw0Njait7c3+NuFF16Irq4ufOc739nBqSKVOPzww/HnP/95u47da6+9sGbNmrJtMoSJjD0vvPACenp60NjYGLUxdKNf/D8rlOgoFzsXTihCp5L/JlgBRDqwQ8cMZyhUpaFmoevoaCgtlgxnHh6gfB4jYGBKj1CaQpE/o4XNm+EMyQMGJpiWZ2h9ZnsvetoTAGVRRbptmc1md9hk0gIFoe1g3333RSaTATB4pncyfB5//HHsvffe6O/vRzKZjGZe10OxgIEGajKZHFTQhEIFQ2GOesyqxjaG9SRfdgIxvSyiHtYm+ycSCSxduhQLFizAc889NxZZtsvzvve9D7feeuug7R/5yEfw4x//eEyv/be//Q0zZsxALpfDUUcdhZNPPrnivjfffDO6u7sHVeYyDKuvr69MjJQouA996EM47bTTovdRJnFOJBL4+Mc/jubm5or3+fnPfx7nnHMOuru70dfXF0XuABjkYITQtmHFUqmUBOs86B4dCdW1K0iIw7PPPvvg8ssvx5VXXokDDzwQ//jHP4aV/4ccckj07Ddv3oxCoYDW1la8+OKLWLZs2aD9L7300h022R4hhOwMTJo0KSgIXXXVVTjggAOQTCaxdetW/OQnPxmH1BHN4sWLcf/99293G+LUU0/FM888g5tuugl1dXVYuXIlnnrqqVFOJanG3//+d7S2tpb5T7ptoIdQhYaK6d8ABAWF0Pw+ociWELYdZEWPbRFWpL2rRRxpV+mJsvX+mpDQZCOXdPtN399QE0hX6sTX222UUEh40tvscLZKIpdeQUyPLAEQTV2i87qSWCf3ms/n0dPTs8OnYqAgtI18+9vfRiqVKotQyefzjAzZDjZv3hxFWGjjtcsU6tnZ5SNhhdrIQqF50mjVYXkaaQwDA3MGhVYBsEauQxpFFZZ0nXLKKbj44ovHNO92VmbPno3XX3+9bNuiRYvwi1/8AvF4HLlcDieccAI+/OEPI5FI4LjjjsPZZ5+NlpYWnHXWWdi8eTNWrFgx6um6++67MWXKFPT29sJ7j6VLl+L444/HLbfcEtxfRJ9q4kuoJ0OvYCf/i4j0ta99DbFYDF/+8peRSCTQ2dkZRc81NDQgnU5j69atwWvZHiabLlsR6/HgIgbp1fkq9dDoiROlUtP3qMvFXC6H+++/Hy+88AK2bt2KJUuW4M4778SnPvWpSMgSFi5ciGuvvTY655QpU6Lf5s+fj4cffhjt7e2YPn06stlsNCRuR0QhEULIzkI6nS773tTUhJUrV2LevHlIp9Po6+vDCSecEBSEjjzySNxzzz07KqmjQmNjI2bMmIGXX355vJOyzXzyk5/Epk2btvv46dOn47DDDsOiRYvQ3NyMyZMn46677hrFFJKhePbZZ3HIIYdEw8aAgY5lK/jo9o2NENLHCtonE6qJh5V+C0UHWbFDGEogsvcj3/VqWaG0bGtET7U5Xu3wLvt9qPMOZ34jLe4M9zw6X8S/1u1bnSdaANSjWERTGI9hnxSEtgHvPe68885B88ykUinMnz8fP//5z3HSSSeNcyp3Hg466KBoPhOJugEwKJJHCpxYLIZEIlHW+LVL0ev9JYpBIo90o1cXjtLoTiQSwbBF59yg1c30OGFgYOxoLBbDO97xjrHKsp2eXC6HW2+9FalUCrNmzUJvb29Z4VcoFDBnzhzceeed0TPK5/PYvHkznHOor6/HPffcEz2bRCKBeDyODRs24N5778UVV1wRvO6kSZNw/vnn49lnn8VTTz2FmTNn4s4778TKlSvxrW99K5rkXD/byy+/HFdddRW6urrQ398fDQ2tr6+PhMwQumDXFZVddcF+pDcJKB++mM/nkU6nB/XyhCrjSuh3WYtBoVUPbM+TjcjTn1DPlU6L9x4zZszAjBkz0NHRgUMPPRQPPfQQent7MWvWrDIBVsJkxaZ1GqdMmYJJkyYhnU5Hw0c7OzuxcePGqvdNCCETie7ubqxevRrPPfccFi9ejEWLFmHSpEnREN94PI5Jkybh05/+NL7xjW9Ex51//vn4xCc+gc9+9rO48cYbx/EOhubf/u3f8Pa3vx1tbW1obW3FtGnTcOedd2L16tXjnbRhcfrpp2PVqlVYuHAh0uk05s2bt12CVktLCxKJBBYtWhT5SbazhYwtTz75JDo7O6NJgvXcN6FIaiuoWMFDdz6HolYqERJ8rF9XbciY9fXseayfKPcp/0sbS8+PI+cNnV9HVOntVqiSa4TyTc5bSbSpNPwuJLRV8+ftvpUQMUe3AeTaem5be286Pf39/Uin03j00UeHvN5os0sKQjfddBP2228/HHjggdusSobYZ5998JWvfAW33377oEYdMDCkaerUqTjzzDPxgx/8YMTXnAhMnToVGzZsKIs2ELUUGAgl1A1XEY2AcINVtsvxAKL5iKSQDY3Z7O/vjyIxxCiz2Wx0Tl3ohSY60ypvS0sLZs6ciQ0bNox+pu3kpNNp1NfXo6mpCd3d3dHwQKA8hFQ/e0GelRZjZJ9Jkybh+OOPxwc/+EGkUil476M5fPScOscee2z0Dnz1q18FgKqiQiaTia6bSqWiiJ7h9DCEei5spJl+t/XvoR4hPbePdjqGW8bZ4ZM2Cshe3zoroUq50n3L9Wz0kvSYNDY2oqenJ9pHD9PUc4jp1Sys0JTP56MVCgkhhAzwr//6r2UdZ7q8zuVyWLlyJfL5PB566CHsscceWLVqFdrb27Fq1aqaFYT22msv3HDDDWhra4sa24VCAT09PTj88MPx8Y9/HN/73vfGO5kV2X333XH33XejtbU1Wn3Ke4/3v//9+PrXv77N52tubi6ro9vb28cg1WQo1qxZgylTpkSTN4sYFIoEqjRkTPtN2heuNJRfR58MVwiyw71Cw6osVqyxkTgSDa4nVK6rqxs0hKxSO836xIKeXHpbopnseULfbQCB3Jvdz+arjgoKXdv6zrK/FpWswKbbOzLvVE9PD/76178G73ks2SUFoZkzZ2Lu3Lk444wzcM0112zTsSeddBJOOumk6KFOmjQJzrmo4JYHZ3vp5YU+7rjjKAgNk5DCK0JNaIyq5LE0jLUaHVK95XnZhmyhUEAymYxWSpJGaD6fRywWQzKZLGuU2vPb8b5i1ACi5QSnTZtGQShAT09PNA8TUB4OatV8/Qn1ruh9ZbvM8yTn1tfS9PX1BcWRUMWpRcDhCEFAuGLQFaPupdDliQ3F1cKRHdYVqpiq9SiFeoiG0/MUup/Qdv3dRjPp/BUHQt+nrij1cywUCkgkEkilUoOimbLZLCf0J4QQwzPPPINly5YNasDZYRgf/ehHceaZZ6K3txe9vb0oFAqYNm0avv3tb+NTn/pUsO5cuHAhLr/8cnzjG9/An/70px1yP01NTfjWt76FQw89FACiod2CNBrPPPNMvOlNb8L5558/rPPuueee6Onp2e5I04aGBpx99tlIpVK49NJLK+531lln4aKLLkJjYyNyuRx6e3ujOs57j1NPPRV33HEHnn766WFd9+1vfzuOPvpoTJ8+PVpJ1HuP9evXb9d9kJHx4IMPYv/9949GI8gnNLLARqVUisyxWN9UR5bofYBycUN3rIZ8Xc1Qgoe0p6Rc0Ksu66jukM8XOpeUT7KP3I/4uqEOUp1vQ/m+lfxbLbiFrmHb+HqbpFnnhU2fFcF0+1aLfLZjtlAoIJ1O45VXXgmme6zZJQWhQqGA7u5uHHnkkdssCJ111llIJpORem/HgQLhCVe1QPC73/0Ol1xyCR544IHRvbFdjNCwG72qmI7IsQ1rbbChyaKtUFdXVzcobNO54hAkGfsrFbQMTevr64uEwEoGr9VruU48HsfixYs5uV8FJKRSClSg3Kbk2egKT0/Sp0NSK6n68o7o40Po6JRQhSv76GFZku5KvRb2/PLXlhPacdAVhN5f0iX3IoJJpXfS9kjZfAkJQiHHwKIrRitKWWdHCzzaQdD5IfcvzoTkaTweL4sa0uVvKpUqS1NXVxdFV0IIMbzwwgvIZrNRxLMt56W8loZcJpNBLpcDUOzUEv8lFoth06ZNuP/++/HKK6/gLW95C5YuXYpkMonrr78er7/+Ol588UW89tpruO+++/Doo4+is7MTsVgMzc3N6O/vH5FIsXTpUnzuc5/DXnvtVbaEt0VHEqxYsQIrVqzA+vXr8dJLL+HFF1/EX//6V2zatAm77bYbVqxYgYMPPhizZs2KfIx0Oo0f/vCHw1oBdMmSJbjwwgux9957o7W1FU1NTYjFYvjYxz6GK664An/84x/x/PPPo66uDgsXLsS5556LI444AkB5h5iIQUBxxeJf/epX+O53vxtcAe7444/HOeecg1gshqampug5ZjKZsrqbiyyMDy+//DI2bdoU+Sh6Kgvt54kvE+oQDRGaZiDU0ab3kf/1OfR57PkqXXuo7fpestls5NdLR71ud4XOGYrc0QEXkmY7gsD61LZT0t6n/A3djx2BoI+37QC7nxaSQtOW2E5u7fuG7lvuNZPJ7PDVxYRdUhDy3iObzWLevHnbdNxFF10UXCLQPsiQGGR7zc877zysWrVqdG5oF0VWatOG75xDQ0MDkslkNHeKdgTEyHTDWv6GFGO9vz4OQDQfkQhAAMp6bXSBaQtuW8DofXO5HE499VT8/Oc/j+6RDNDd3Y2pU6dWVeGlt0CGCumILWtrVnSwlaStWDT6+Qr2XRIHTk+YrHsyZFsI3fsh6DJF3vl4PF4mktn8CAk6tiK0Tod9X+19yzG6XLP3oXuc9Llku15q1N6b2Jbu7dHp089F5k7SYba6spVV2/Sz2bx5M4eMEUKIYf369chms5g0aVLZ9lBDJ5/PD/J5pF6Lx+OYPn06VqxYURYVkMvlkEgkMHfuXMydOxf5fB4nnHBCsHHb29uLhx56CPfddx86OjqwdetWNDY24qGHHiqbULm+vh4HH3wwGhsbsXz5crzrXe9CQ0NDVP7buTtt1K3uPKyrq8OsWbMwZ84cHHnkkWWLikh9r1dic87hox/9KE4//XS88cYb+M1vfoMvf/nLZffx3ve+F2effTYOO+yw6DzS1vDeo76+Hl/60peiSCFZCVT8Bz11gfZlhWQyifPPPx8XXnghstksNm7cGD2Xurq6skmL5fqSdulAXbdu3fBeEDLqPPLIIzj22GPL5jm1bQfrrwnW/7TijhZJ7O+yT6XvVgzSVPJbK+2nRZvQPYkYJJEwcm/VrhMSgXS65fpWiAqlTRMSg0Kdn5V83kr5V+l6djl5nW7r+4pNi/8v2/P5PLq7u8dtgvxdUhByrrgC0NSpU7fpuCOOOKJM7QtFAdgGGTBY5c1ms0ilUrjtttsQi8XwiU98YqdcAWEsOfPMM5FOp8sahiIASSUrQ7lkNSdZLcxGHlgVWiu3IeVYPlrhlkpVrq0dJHssUP7staggPTaNjY342c9+hnPOOQdr167dEVm607BhwwbMnTs3GL6qt8mE0bpitQKtrSSBwcur63PboX660NcVmBYmdE+PnBMYHNZaLdRWzmtFRnkPpRINvW+6HKr0XocqodA+tmdIRz5ZwUkIiVKSHhHsJNJH7lOuJ3kpgpdc1/b26n310DH9TCSP5DvFIEIIGUyhUEBnZydmzJgxKKLW1rN6mLVzLhqim0gkBpXVup6LxWJlEUbVeskPPfRQLFmyJPpNOuGk4Wh9balT9DxBuq6283nYa4oIYxvWlRqd2i9oa2vDRz/6UZxxxhlYt24dUqkUdtttt6juklXcbINT+ymSFh3ZrNNnj9fPI5vNIpvNRnWriEl9fX3Rwif6k0gkIn/ZruBKdhwPP/xwJD5a4c++H9VEktB7UclPDvlrgrWP0PXssfrdDe1vO/uE0LCpkaI7LbcF294L3Yv1szUhP1z76845pNNp5PN5JJPJaF7bSv6/FdPsufW0Ft6P77DPXVIQAhA1HmS55uEgD1e/QLbwrtQDH2qAiap/8cUX4/TTTx/N29upmTlzJk4//fSylbyAgXA5PZmzLVSlcQmUF466x0U3fG3B65yLriuGKIKSXsVMC0zS8BcnxYYtiwMiw8vkGq2trfj2t7+N559/Hl1dXVXHmE8kXn31VRx88MHRd3lmtvIT50f3+mm0U6nFhkqiinYG7f/yjuhnG4oStOfWghBQeaI52V9XKlpgsr0R1dDpqXQ965xqEcj+1Y5zSKTT83qFeqckTTbCSQtOoQmhpSGiozJtfmgHRPdQ9/f3Y/PmzUPmFSGETETa29sxf/78sjpFl+PSyy/lqgzVr6+vR0NDQ1kPtohDth6UYfV6cYhKHRV23j4ROCoNy5A6ys6TAgwssGBX7rF1o06vjkjVHQv6r9T5uVwO8XgcbW1tABANg5PzyP/y13aw6GiBUOSAfR6ycIKkIZfLRcKQ9n8lSkgPyZG8yOfz2Lp1a/hlIGNOX18f0uk0GhoagsPEQgKRYEefiE1IR5p+90J/gfBwLG37VlixEfrDEXS0Pen9baT4cLHH6OgjuZ72mXU6qnWKhoSXSoQ6h63Yq33Prq4uvPHGG/DeI5lMYsqUKWhqaoqmGQm1Q3R6Q+0TKSNisRh6enqGn4GjzC4rCEmhfvPNN0cN84ceeginnXYa2tra8B//8R9417vehRtvvBEHHXQQPv/5zw9qWOpecAmFy2azkVovhX9IGZQXMp/PY86cOeOSB7XKj3/848jAbIPaLlloBR3dkyMVqd6mDU43iG14pox7lTlLJA3iEMl+WkwoFAplYpWo5FJgaKdISKVS2HfffZHNZrnyWIk1a9YMChPVokE8Ho+ig3S0ngwhA8odKVuxaRHE9sjoIVLyDkmPjg7pts6cEHJe5Vq2AtBUeofle8geKvUohHqbbO9tpQpT9tP3Ic6k7KMrQ+1463wO5ZdNo76m3J+u7MVmdCSQTrs+n843Od94TbxHCCG1zmuvvRbVk7ohpxuXOvpGFtSQxTb0PDdSZ4i/BJR3hNmIal3/6PJeriOCjIhEukGsRRZBD0GR/cQXsJ0fGttwlW3V0PO7aF9BOg8lLdJhpdsMkh75a+eRsY1N3djXYpStW3VDWTdQ5VlJmhg1O77oSaV1VF0lwSX0buph9ta/tZ1zcozu4NPnDkXQCbZDXn6vJg5pUUO/l3aqFWsLVmCR37Rvp314HVmn7dCmMVTOhNJqy0GdJpv/IV9enmkmk8GmTZvQ1dUVlYm5XA6pVCp69lb0sWnWZaQ8W7nXRCKB5ubmYN7vCHZJQUgaLBLNsW7dOpxwwgn4wAc+gGQyiUKhgK9//evo7+/Hu9/97mhVKd3wEaQSlJdUerTFAKqpqvICxGIxLkdf4uqrr8bMmTPR29s7aBiMxTbmgQEjFpFOF5y6cawLTV3wWUOVZyo9VeKopFKpSAC0hYrsowtFeef0d2BAOc/n85HQNNF56aWXkM/nI4dOkGcci8WQSqUiQVXyX4t3oTHMdo4BwQoONtw1hJxPV+ihc2onz/bU2IovtN2mDcAgB1Oup9NW7aPfQ+tE63fZOt9acLEOum0I6LSEesLsPUo0kJxHHB1tL7bC1g0RWy7n83m89NJLVZ8hIYRMVKTzSZezQLnQL/VrMplEKpVCfX09kslkWYNMhoXF4/Go4RMSjKwfZ303uTYwUC+I/2brHBsVoes1ey59jK1LbeNO9hsKK2JZrKhjO4pk+LTOB+0v2Aau3JtesSmUJr2Cqvc+mggcQDTdAhkfkskkEolEmQho/TIgPAeNFh4l8l3aOEN1QOq/VkTU17N+mT12KDGoksghHeUytUfIHxwO2uakjKm0n/b5Q0Kw9tft6AFdBkieVeroBcpXGc5ms9EKgToiMpPJoLW1dZAPXintGhGEZFTTjBkzhpVfY8EuKQjpnncrENhCV0cgAChrIEnFqaN99Mpj2uBsxSPIw7/44ospCAFYsGBBFAYLDPTG2DGxuoACBkdYSEUtBaiN5AgZpC4MrGOi5yexCrxcTzdQpSCx70AovfK/nRRworJ58+ZBAgdQ7giK3VnFXT8bW/nZZ6Gfn3UkdaEtz9YKQKEKzVbwNoLHou+tkkCkHfVKPRSyXyVhKSTEWGQVCPt+C1rY1GmWc2kBFxiYP8g6E6F06V5XsTM9R1Qlkc++F/o8b7zxRsV8J4SQiYz0Yg8lXoifK1MmxOPxso4s8Xlteazr2FA9GeogkLpCIqu1T6W3C7pDVnd0hHw7fX5bJ9uGoCUkJul6WhqNViDSker6PnXbolJnib2m7sTWPmcl5Fi5Jy5gMr40NzcP8i2HIwZpn0/7v7YTTM6jfaWQv6ix/lQlQUjvqwnZlWzT/rNelbuSCGTfd5tmGwmko9Ir3ZvO15CvrQUfe6xOh41QqpaHsk23G/QCOGK/1c5l06En47YLAexIdnlBSL7r+UasGGQnJJa/UtjbSi9kNFKpWaQyeOGFF8bkXnc2GhsbkU6nywxZK8J2bLjeTxqTeniNFFI2pFgfq/ezBZxUwroAl1BgmaxPhgjKeeV6qVQqin7QzoruNZNjAAxaNnui8tprryGXy6GxsRFA+bPVYem6BxIon9MGGLwcpT4XUD6OOOSsasfSOpyVJpKWfUNlgX23Qvvqd1DKDF1OhSqeSveg0Y6HdZz1tTW2spTrS7SkPAOp6KT81A0C6bG0Fbekzwp1cl05l8xVIefUPdP6fLq3TCL3OF8CIYSE2bRp0yCRX+pW7SNLOSyru2r/S9cp4gNJ2S2NIJk3IxQVEGoA6XJed7iKqAIMjuSxUwtY/9H6CLZe1vWurXPlfJIum16dZt1pqP0RHXEsdZTki5zHNl61LwMMiEh6NVUtHkke6fyT6HYAHC42zsjKuSGfx/5v30ctCIkPXCgUhygVCgU0NDSUvdehjr2QL6sFGitODQcdSWN9V/EPC4Xiin2yGI8WRWz7Tc5VCdthaAVsfQ/SNqgmEMv/+p6tmKZ9XXuvuvwqFIrDRJuampDJZKLysL6+Hs3NzUgmk4PKqFCkn+24BhCVF/F4HMlkcoinMnbskoIQMPACygstPR/ykLQKbw1Eh5jZsZqCNmb9wtqXXUclzZs3b0KvNvbb3/42EoO0EGcbpHobMLgytYWDbJOC1C6hqtGOhi4EgIHlVxOJBOrr66N3RipoEY6cc0ilUmhoaIh+1z1qIZFRDzWb6Hjvg71ZdXV10RwGAMqENVsh9fX1obOzE11dXaivr8fkyZODE1/a4VxyfdsDZwUMXfFU6lXUFYouW2S7FmbsMXK/IYc5JDrb7/o+QmKW/V3bja3oNLK/CD3SQ6wbAbbnKpTXVhTTIp2kUVayGSof5BgRavP5PCeUJoSQKnR3dwHgeXoAACAASURBVEdRobYO1R1i9fX1SKVSUYSQjdLVK33pxpf4QeI32cmUBdsQ1nVkJULRBLJtqGOrnVPqKOuLhSKEhoPcs204Sx5qoQkYLJDJ5NBSr+l2iUafU9JuBSwKQuPLnDlzghHmVrDQQmLonayrKw7l3LBhA7Zs2YL+/n5MnToVU6ZMiaZSsAIoMNhH1KKMFWZCvp9FfFj9Tkv65FyVyoZQmy4kRoXSIe94IpGIBGp9vO30rCQIWVFZykA5xrbtbZteH6Ptr62tDfX19ejt7Y0mnq+vr498WbnHatPKaEFaC8jiZzc1NaG7u3vIZzTa7HKCkESQ6CgSPYO/Dm0L9WhYkQAYvFRnpbBbvT9QPu9HX18frr76aixfvnxsbrzGuffeewdNOGYLqVCFbwU73UC3jUarmNuC04oy8pse7y2rZsiSgnKcFH763Ugmk2hsbCxbVSCXy6G7uztS9uU+pIAjRbq7u4OVpDw76f3SQzq1kt/Z2Yk1a9Zg8+bNiMfjmDVrFmbMmBGJSXreG+s82t7L0HfbMyDnlLSGjg8JG6EoHStSVRKsQsJKKJ1abNHHV7o/G+5vbU07ELKf9I7qsk/3JOvnF+oJ0o0SYGDyeKlIpQfankfvL+V4LBbDxo0bK75bhBAy0ent7S2LENK+j/gqzrnIL5F6QholUgfrzlE5h/bZZA5OO4eIrd9t49D6z1Lmh8QZfS7bWJNOQHt/+ty2w9b+1X7lcBaS0J1+Oi90o1LqNb1QSqgDWjdUdUS09V11+qxPDGBcVycixdWTQ76jtQNgwMb0KBTdznj99dexbt26qG0i78b06dNRX18fvX96iGWoUzMkoAAY1Ma12M497Y+F/NG2trZo6JjtTNX+svYtbRplG1BsW0mHu2A7CK0PrO0FQFne6ONtenTUe2i7nFNEO+ccpkyZgra2tjKBVovo+rp2dIku3ySP5HnKc5k8eTIFodEgmUyWNdxTqVRkbJlMZlBPhi1cbcMMQJmx2pdMH1Op10IX+HfccQdWrFgxqvdc6zz44IOD8tk2VOWvNPhlm35WQHl0gy0UrDqt0dENwEA4n1VxJWS3r68vendsIeJ9cVhNT08PEokEpk6disbGxkjpTaVS2LRpE7LZbNCZIkVByEZ9iY2IcykOJlAuGGWzWbS3t6O9vR09PT1lFVFbWxsaGhoADK4Q5FpWBA69l7ry1L8JOhpIiyL2PPac1gnWlYyOTAz1+tgK0Vb0ujwLCULW+dXROiEHAsAgR8CKWVp0sw6E3IcVyex1rI0JtoKW3u7+/n60t7eHXyxCCCFRHaqHZtu6TS91LqJ7KFJG/BeJXpBteg44G61g6zxdXwKDp2fQfy3WR9d1u65jbJ2u6yMr2ISGa4XSZfNBdxrrelYiBICB+SKlztJ5Iv/L39CwHr2PTovUmRKVLs+yv7+fgtA4M3ny5DLbqvQcdQSM7ZyTtkVHRwfS6XT0nsn0Fdp+bfCCvXZIELLvraRJI+fVwgowEJFmfdhYLIbddtsNM2fOLAvAkGj/RCIRCWDWV83lctGnr6+vbIqOtra2sgWd9D1W8oElTVp8tnljRR4t1NiAAf3d+r62Y1R8YduhKiJfJX1Ai/byf2trK1577bXg/mPJLicI6fBMCYP13qOvry8a92fD3HRPtK1kRpNYLIYpU6bguuuuw2mnnTaq565V7r777rLGc6jy1vlsw4krRXDpBqouIGUfcV7kOC0I2cpXFzKyvwiHMkwllKZMJoOurq6o0GtsbIzmFkokEtGSqlKwVCoQJiIS3ix5r3tHxOHRtqnzv7+/H11dXejp6SmrNDs6OsrmQRAqiSpWqLC/V0L3soiDHHIe9TVClZF9/0O9kIJ+97SIE6oY7XsdElskX8W5l/TYylX38uiK01amtienWgNBO7Ghcdu2MtdplvStXbu24vMhhJCJjo1SljLXzrcodYCsLpbJZKIyvlAolK0wKfUyMDANg9Qjtr6y9YCNSLVCjf5uRRTdCJPry33oeYhC9WtoWIjuHLQdN5JXInjptOj0h+oo26jUQ8Iq+Rr6f+2D6rpbz6Ekw7i1H5LP5zmp9DjT2NgY9L30e6PFA/mr/UaJdBOb6u/vj4Zyyu/aZ9Kd5VYMColCWhASdPqsDVnRSdscMCCKiM8tw7yk7a3/6gWb5LryLsvIjGw2G43ikbLGDkWt5vfqexIblvu1U5TY+9RCjhyjRV5bjkl+xOPxsv10W1PyKiSQ6+tIPsucndOnT6/6ro0Vu5wgpFeckuEjWoGs1NALFda2IA8ZjB1Gpgn1DPT09GD//fdHW1vbLjsPxqc//Wm87W1vw4IFC6IGvjyX0HCvUIFmK1BbueuKUv9erWFpFXI9cXAookJPbqvfBzlXoVBchrCzsxPee6TTaSQSCWQymTLD16sxkSIyNhoYPMG3VIbaIdJOUSKRiFZ0kAnik8nkoB4Cnd/6mYacL0mD7okQQkJNqIfGhtla9LulhUz93lnn0x4PlC+DGRKHQgKYnNu+vzq/9HZdTord6khJK74K+n6sY6+FP72/5IeOtLICmk5Lf38/1q9fPyh/CCGEFPG+GG1QqXyW7+Kb6AmNpZEjq49lMhmk0+koyl7qYdvzD5T30FsfWaOjjkR80YuKSH2vG6BynO0gGgotPNlocTmHrqPkI/en81T7JZJWaQgnk8myCCjxEUW00c+gWkNW/teCkaTRRrU755DL5ZBOp4fMBzI2SFT6UGKQFT3lfdftHfFvOzo6EI/H0djYiObmZjQ1NQ2yZe0rhfw+va9+7yq1g634UcmX1TaoRRFtBzo6SIQvK8KEIqR020x3Gtp7DHWA6t+0UBxqZ1r0nF9SHsr92XTq5yjllib0nLUt2zJT0iXXbGlpCaZxrNnlBCHpsddRQKI46syv1AstaCOwRiEP2D50LRyEzl0oFKKIhosuugjnnnvuKN31+PPggw9ixowZ6O7ujpaVt0N+tJMQamRXMthKQoqOBtPb5Bh7HV1w2kgI+7z1u2PFJf18C4VCJABls9loyKKe2Foci9BQtonKG2+8USa+WEHGChva1lKpFHbbbTds3boVa9asQX9/PyZNmhSNOa4kzGixoVLFqHtUh0ILQ9KzExKGZV9dIWjnU4s5cq+VjrfiZbXooJCzqdOgK+RKPZg6X3Rvh43AsvembUtXhjp/9CSaMt9C6N5DZUU2mw0+E0IIIUW6urqiRo0dmiTlsEQNAQNzlQjS0dXZ2Yne3t7IpxM/W3q0Q/WHFkZC9aIdOiMNK7u6kBxnG5TiL9hVYuXcWtyxDVDBdu6Kn6frR93g0w1VuY5EQKRSqbJoKjlXb29vtHLnUNgOG0mHLOzgvY86wXS9LZEVZHyQpcJDHXO2A00TEl5SqRRmzJiBRCIxaJEbKyBZW7UiQ8j/sz5vpQ46u0/oo0VRPVxMp9Xan71vQcQfOY/koxyn77UaOt9D4o9+HvredQSRFnH0di0Y6fJBtyvsHGQ6j/S1qj0bGea7o9nlBKFkMom+vj709vZGSpxkvg3hCr0sVvjRL22l0FYdzjkUUnjvv//+uOOOO/Dqq69i9erVo5cBY0xrays+85nPYP78+Zg8eTLS6TTmzp2L/v5+rFu3LpgHOgJEb7PRQqEeE71/CF1wiVHaeU2EUBRQpUpar5xhhQOblkKhEAmOIUFDBKHxMvJaZMuWLWXj64EBMUZXNiFBLxaLobm5GXvttRdSqRR6e3uRTCbR2tqK+vr6QZUQUL3HxIoZQ/WOyL76f0mbDlPV59Tn1u+79LbqMH6bbnsdKwaFekhDdhTaZvPJCmjyV/bRjYpQ3laq5LQzLmPFvfdlQyttvun/bW/SeC7NSQghOwPd3d1R2apX7RT0d2mQSR2Ty+WQyWTQ29uLrq4u5HI5AMU6wi7YUMmP0r6y7UWXxpRuQEpEgT2vrYflXuycoLbT10ZlVKvT5b50e0H/Jr/rIeISESG+p+SRbiSHpguoFKUg6LoeKJ9IWkQ5PUxb8oSMD5UEIevHVROEgAGbkLaCFiPkXdKCkG6bWFEh1I4K+bpWvJD7sP6Y7rDT5Ya1r1A7WYuX2tfUvq+2Fy0mW9+0Woet9fEr2bvsZyPtdL6Kv6vzwYpCNrJJ/HHboR3aP5RW2V5fX1/xHseSXU4QkgaGhMrKHEL2RQ29KHab3deqobpiBKorwfoF1H933313XHbZZbj44ovHJkO2g1WrVuEtb3kL8vk8Nm3ahNWrVyOdTmPjxo3YY489UCgUkEql0NXVFY05H24FBwxWoUONcltI6fzThmML2qFCHkON7koioC5gdcFQKbpMN85DBR9XGRtAR48Bg8fl2+dmKyTnHBoaGjB9+nRkMhk456IVq6pN3h1yiPX5dVlRrUcvZN/63bTvqP4OlDuEofer0v6V7KSakGoFHJ1+fa+htIUcCH28FYKsoCZ5Kj0+3nts2bIFGzduRDKZxNSpU8uWOtZ5WUmEraurQ3Nzc8VnQwghBEin0xUbRaEGn4gsUnZnMploolepo0L1sq6zgMHzc4SuqesePeSkknCjxRHv/SABK1T/yXGhBrlt9NoGm2382jpTztHX1zdoXibptKhWl+rtoXpV7lH/r+tUu/9whs6RsUEa8FaU1P6QfNd/NbrdoefnAlA2XYKOYhNsZ7d9F6r5vfpd0v9ru7bnCv0vx+l70cdXah/LPnq4qBxbKWhDfq/2zmu7rSYM2f20bes06BEu9r5D4py+P2kbSDlRbQoR0RXGg11OEKqrq4sEITEq730UhiZDO+yLpF8E+W4/ugfDjokcShCRfWxDrr+/HwceeCBuu+02NDY24uijj66qgJ5yyik46qijohWw1qxZgz/84Q/IZrN44IEHtifLIs4++2wsX74cLS0t0QsZi8WQTqdRV1eHmTNnIp1Oo1AoRBFYoTlXBNvYtlEzOm9DjdHQeWwhoY8PiXk2zFi+i4FqI9Zhj3qYi3Yc5FyVojLspG06kmw4IcMTBVmKEais+GuRxVaqEgnW1NQULcNpx/qGxBl7Te1gWYEFQFnlINsriTValLTvtb2vUEWknQHBOhfaca/kAA8Hfby88/L+2qijkNgjjQNJn7UHHTUpq0nI33Xr1qG9vR3xeByZTAazZ8+G98VoId0Tah0PscN4PI65c+du971PRFpbW9HR0THeySCE7EC6urqCPeW6E0AiVeW7XupaFmGxQxz0OfRksZV6wG1dro+XJab1/DsaXSeJYGV74m1DXNB1s6yiZht2utEdqnvkWrrO00PndAPWdv7J3KU2Osv6BtYn0L6FbkDqCAUt0FVrM5CxR4a82+cnVPquo6e1HaVSqeg9k99sezO0OFK16CCLbTNV20//b33QStey7WcbJQMMiCXSYShtL32/WjjW4koloaeaMKzTptNvRR9tXxJdqaOYQqKspC20WIptA/b19Q0Sn7b1uYwFu5wgJD0ZlRoW8sBDDywkKIhaJ9FGerI9bZxDFQDWgPRHT6R7yy234LbbbkMul0NjYyPa2tpQV1cXTerX2dlZNpRp3rx5OP300xGLxfDJT34SjY2N6Ovrw5YtWzB9+nQ89thjeOKJJ3DuuefihRdewKxZs9DS0oKlS5fipz/9KTZv3oyWlhY0NjaipaUlmhxNXn7nHGbOnBnlLVCcQG3jxo1ROvTM96FGt620dUPYhtzp/NPPQzsU+hwhoSWkUtvfbSEq55JnK8Yt74p2HmQ/GxYKYJDyq5Xv3t5ekCISWi1YZ1XyXa/kFQpZlXH7ofdOCImFunIJOWK2orARK1JeaCFEzhvqRbDnlfdNzq0FD+vYho4dqsK319f5ps8lv8l7qtMVEtK0eGpFXhv5J0iZLKvybd26FZlMBolEAps2bYrmfpJ06cZHSIyKxYrLnJLhc9RRR+GWW24Z72QQMmxmz56N119/fbyTsVPT0dExaHUdoFywt40YKfO1Dy31nUQoVGq06ShPO4TJ1jmyv507CCgXQuSjVx7VvuRwOmPlunrVL91ICzXAdCeNFnTsgheSP7oxK8fKysa6k0ULO0O1G4CBVcR0ukJCFBk/RMAJiZPWH9L7aMFSR8tpkVIPoRIhyAqG2i6szxYST7b1fakkcul32F5Tlp7XUU1yXfHZbRtN7Nx2Uur80rZnR2sMRwzS96OflX1u2sfV4qvYnaRR7lHPOaTbqjpIQEfDyz7a55Y06Q7zHckuJwhlMpmgGKQflDVEPV5R0NslIkgEIakI9fnlf13JauyLZwUSYPBy1rlcDh0dHYjFYshkMshkMlFPe6UJAqWhnUgk0NXVhQULFmDhwoXYuHEjWltbo5UibrnlFrS0tKC5ublscr9YLIb6+nq0tLTgmmuuwde+9rVgPre2tmLz5s3o7u7Gyy+/HE2cp+9D54UtsHT4aygfpXFsCy7t3NjCrVrFrn8LRUNYQUivXKUFCe8HwjklrTbaxariUnBQEBpAi3khp0Y7g/Z90O8NMHjooRYibSWohSBg8LwJWnTSPYLWmZX/dY+drpxC4nJI9NTns+nXx1dyHLVzqb9XitzT13DOlc3foz86ijIkzOp7t/kjf+3KCgAiYVs++Xwera2tmDZt2iAnyaZZ/nfOYdq0aSDD5+WXX8ZPfvITeO/x0ksvob+/H5s2bcJ3v/vd8U4aIUEuuOACnHPOOeOdjJ2azZs3I5fLDaoD7fBc23Em9a74wTJnmxY+pOGqG7K6o0TX07pODvlptg6Rhq+OULLDp0JCkNyD7ViR6+p6U+p38edt3W3rbUmT+IL2ujq/vPdRXZfL5cqO0VEIId/CznEqPqautyWaVvxRzqk3vkiECzC80SLyfPViJBptTwDKbEkfr4XXkMCo2yL6OqGIP+3Tho637RotSur9pS3Z0NCAv/zlL3jyySexdu1avP7663j55ZcBAEuXLsW///u/Y/r06dEcXFZQlfddt9N02aXzRf/V91PNdw61yeWcOn91WQgMiOmSFok81G1Cvb/+LpFBlQQ5uWYmkwn+PtbscoKQLPGolT15qFK5WIO1D0getu65kIdtIwK0ShlSJ3UFZxs2Q72oenK7UHigbkSGCgJdCerIlUqFxGOPPYbLLrsMXV1dQ+ZzR0dHWcN5/fr1eOWVV6LeEJu2kIprjSaUZ1JAyDbdeHfOlU3Kq4/X1wyFXEpFKnkj15O8kOduh4DJ85ChgwAioU7nrY1ccs5xFQiFdkKtkypOl+2J0w6RUEnw0PvqSkoKc6lEtWOm06bPEXqf5FjrNOpK3IrGtgyoNNdRJVFI/x+ym20htBKCFW8EK/Dqe9ECnRXp9H3oHhUp2/r6+pBIJJBKpcoaKKF7lI9ch3MIbRvt7e1Rw3Du3Lnw3mOPPfbA/vvvj6effnq8k0fIIGwjiWw7EiGkOz3Ft5U6FkDZMGVd/ut62fp1ehiTpppfGkIaxrKiaygqSDfe5JiQH699uNB1LHLekI8o968bhnoF2dD5Zb+6urpo5S8ZNiZp1BEg4r/qji/xSXT9J3Wmjl7X6UilUuM2ES0pUk0Isv6avL/i58j7rv1J7R9bewr9taKGvrb4pfodHQrtmw/10eWKDAE95phjKp77vvvuw3333YfzzjsPJ554IhoaGpBOp3Hvvfdi6tSp2G+//aLpSoRKZYpuH+o8sflgj7dikBWrbVtbzqfbLlooljIk9Ax0ntqoJita9ff3Y8uWLRXzbizZ5QShdDr9/7d35dFxldf9NyNpNo1kbd4DJuAUCK4NbVhDCMsJxiVlCRRKThMaTAhmS5McQigtB+pSoNAYiH3IQkpJGraQ4IQSEKsBE6AxhhCTGDB4wTKyZMtaRzNaZvqHe59+7873RmNrtXR/58yZmTdv3vK97/vuvb+7fHkPnQUL4C88zFEHsg2Aj62Uh8yTN0fVsOGnhZZsLyQcB+u00oEKEUFBpBDvx4KS2d1QKIRHH30UP/rRj/amyVFSUoLnn3/ed99aYOsJ0WXIawZce7V0hJEmCOS7PC/+XT9L3W7Z7O58XUlvEfCSgnr/bDbrTX4s1Jnt1p4pw25IIWh5djwR8wpvmpjRZJtrvHF4p/TzsrIylJeXe4QEE0JCMnJYuO6Psi/3NR3dJMQtE1FyXezhdAkK7UkNEuxaSdTH08LFRZ5x/2WI94LHl2ucCFi48W+6bXhbRUUFqqqq0Nvbi3Q6jaqqKpSXl+d5d+TYLk9wLjd2qzDsq9i+fXue3MhkMliyZAk+/PBD/Pu///tYX6JhHGDmzJm48cYb8fTTT+PnP//5mFzDeeedh8997nN5acWGPceuXbs8xxrLw9LSUt/CDqK/yGcdxdLb24v29nb09vYiEokgFot5izjIXM/yUztVBC7ShmUxy0p27Lqg5R/Lm0LGuUtnkPvl1dPYsaMJGpf+yjKwv7/fI4TY9hC9MBqNAvCnzutjup6HdoiJrlRSUuKtdGUYfbgIVe6X/Gyl/3MaoP4ux2Gnu45m1zqXJkW0c5rtXr4uPT75M/d7jgrilFGZT8RJHo/H8c1vfrOodrv99tsDs1CWLl2Kww8/PO+65Fq0Liyf9f1zm2jwXCXfta2tnZ+yr04pC6pNzJ9ln0LzUy6XQ1NTU+DvI4kJRwgBfkJCWH0xODhCQL4D+UWOuZaMHFMPThkAHOLmMqYELq++S1jK766Oo0khDU24uDo0Rw1lMhk88MADePLJJ/e+wQGcdNJJ+OUvf4ny8nIfgSX3FcSWuphzF7RxKfejI6l40tMkggh7bjvZj1PxeNKV/qHzQ3l/nSfLTDmHO9uyoANoa2vz9UutSHEkHxNCLoWP+w4/V+2xkOLTEikIwPOUChkix9NjVAsMuWZX3R329Mg98bXJfgwmriKRCJLJpDdPdXV1+bxJchwOP9VRTkHn0WQtCz9WHuQ8WjBye2uCTD8XOSa3ZyQSwdSpU1FeXo5cLoeKigpEo1Ffu2oFxiVkXfOJIRicm8+GVzabxfTp0/Hd737XMxLvvfderFu3bqwv2TDKuOuuuxAKhdDd3Y3jjz8exxxzDL71rW+N2vmvuOIKfPKTn0Rvby+6u7tRUlKCZDKJzs7OUbuGiYbOzk709fX5VjiV9AaORhHdRmSQjqRvbm7Gtm3bkMvlkEgkUF5ejpqaGpSXl/sMIx3Vyzo3EGyocUQw18gczHAC8nVDTTjxdy2H9WeOetKGvNY/9bWxQ0hq5knGgvzOcp7bXY7HxFo4HPbSaXK5nEcecWSxtFFJSYlX/9Mw+tB2l3zWOiXrMqKzyTNnW4b1ICFk2R7UYyRIv9SkEB9b/0f215FBwACRqQMlWGeNxWJIJBJ49dVXhyXqWFbe/sEPfuDV0pUXR9wB/hWeef7R9rvWU9lBJmBbX+u9Lt2zv7/fm0/1HMHzkCYNgwIment70dDQsPcNNwRMSEJIjD0mgwB/+CtHEfBLOpyE2AlzK51NjikPNx6P56VHuFCsASPXoYkfNphdRBDgZkqlc7PBJwNj586d+M53vlPUdRWD999/H4cccoivwBpfi24DHrQuI5zbgwciD2I2bGQAcruFw2GfF0sMIyF7OAKov7/f57HhY+pQQlFiWNhLnxBCUVJhUqkU+vr6TLEl7Nq1yxtH0pbyPDk6iPsGT+gu8oYVSP5dxrOQTVK7holbHSmoI2H0dx7vTHq4jqHfdapYKDRQgG/t2rW48MIL89prxYoVWLhwIaqqqrzr7ujoQGdnp6/PFvJQ8Jjhz9L3RXnnlVhYSZF5VSvbLuEp/9OKdTgcRnl5ORKJhGeYsGKkvbKG4UMqlfKIOE309ff3o6urCwBw/vnn44ILLkBnZyc6OjqQTqfxwAMPeL9PJtx6662YP38+Kisrkc3uXob7ww8/RH19PR566KFB///tb38bxxxzDGpraxGPxz058N577+Hyyy8fhTsojJqaGixduhS9vb1eSjPPGXfccQei0Sh27NjhETUrVqzw1cM7/PDDcdRRR6GjowN1dXWIx+MoKytDa2srtmzZgscee8zbd+7cudiyZQv+6q/+CvF4HPPnz0cymfQIgVQq5elp/f392H///S2lcQhIp9OebGWvPuCvE6R1RGCgH/T19aGxsREtLS3I5XLo6upCOp32jMFoNOqdg41WbVxq/U/LcHbgssHr+q/8j50kOnVEEEQG8XG0QcjXpz9z2zHY4SfjiaOw5Ng68oDPIdcidgvrr1w3iPeX65WoI8PoQ8YGEBzdLb+x3qkdcqxTyjvraAKtJ2tyMshW0r+7dGrtlOOoII4IEkd4WVmZRwalUiksXbp0mFp1N772ta/hyiuvxMknn+xdh9TYEd1Xl3LR9r20RZAdqYm8IBubweSxpIwF2bjs9OZzaCdvLrc7cruxsXFvmmrImLCEkPbKM+HDRqIWVtyJmESQ1QJk0k8kEh57z5ElLlKDoZlLJiP0fiw0NNEj16o7tWtyYINL2qelpcVjYIcL++23n+eNCvKgCFysKxuhPBExsyr/4UHLzLAYtWzoSypYLrfby9LT0+N5VaRQuJBB6XQa2WzWOyfnbOsJnK8/Fov5DFyuMSSpLy0tLcPa3vsympqa0Nvb60WHcOFFHreayWfCgLdrwSrQxTOlL0iEl/ZCuoSBHnscyu16L5Qa5vI6St+/7LLLsHr1amd7BRmPCxcuxO233+4p/lqx0NehlVMep7zyg1ZMuZ24jXV7yzn0swhSMuSdnyEr8Dq3XhfcNBSPzs5OL9WO+z4rqTz2otEo4vE4SkpKcP311/tq6ckxZC5lBScWiyGTyaC9vR27du3CtGnT8M477yCRSKCnpwebN29GIpHA9OnT0draijVr1uCYY47Bn/70J1RVVeHtt9/26tgtWLAAU6ZMwSc+8QkceuihmDJlCtra2tDQ0IBYLIZp06YhFot51ynRCDLfR6NRpFIpvPHGG9i4cSNmzpyJWCyGHTt24IEHHsDcuXOxYcMG/5C3LAAAIABJREFU755OOOEEHHHEETjppJM8GZHL5ZBKpVBaWopYLIZPfvKTmD9/Pq699lpvEQaREVyXReqiiN7Q2trq/T5r1iysXLkSqVQKW7ZswSuvvIIXXnhhj2oHnHjiifjYxz6G5uZm1NfXe9sPOOAAzJgxA62trWhoaMCCBQtQVVWFqqoq1NbWoqKiAnV1dQiFQt7Kf4BbX8lms8hkMohEIohGo6ioqMCNN97o87i6lOdQKIQpU6bg4x//OE4++WSn8S/9RohG9lzLeJ8zZ44RQkOA1LAB/E5Crs0j2wC/ASrfgd2GV3d3tzfm5fnyvlyaQZ6lGI4yNvU8ox0I2WzW0wVYzgY5VEUmuOByKHA/dB2Dt8v1uM7BBJbWD0OhkKdnsvOR25b1UV2TiPWMrq4uz9iU1ThlntHRD4lEwtkOhpFHY2PjoCQCG/2i50h0neg4el+Xs62QPSK/s97H/TSIrArSVdkZrlPDhAiSRYimTJmCU045ZY/brhh873vfw4YNG7BkyRJEIhF0dXV51yNReBy8wW3A7cD2P+s+hcDzCMs7Pq48R5e+zZFeLtucnaZCvo8VJiQhlEqlUF1dDcDd2XO5nG9FhCDBwROuMP6SIiShcyIcdXqX65y8XSYDUV614aUVdhebKMdzkUH8H45MyGaz6OrqGnYyCAD+9m//Fg8++KBnNPAA0YNTrlHus6yszIuUkPuSSYiJL24Pfb8yqTIxJ0qJCF0mfYDdClM0GvVIGyELZJCLAcXGKSsKbEzzSnTykjDsvr4+tLe3D3ub78vYtWsXotGo98y0EsiErPaqcb/QfUKTHkwqAAN9hZexBfKNZLkO+Y9L4MvzlXPxeHR5RHn8y3sul8Py5csDyaBCqK+vR319PVatWoXq6mqvbsFghImrvVjJ5LlPty/fi4wTF7RRwUoQb3eNcZ0+oAl7i7bbc7S0tKCmpgYABl26lglX2c5pHezE0ONCnk0ikUAymQQAzJs3D8Du5/fJT37St/+xxx6LUCiEo48+GuFwGOecc45PNrN3MpfLobq62rsPuV5NTAm6u7sRCoUwf/58LFiwwPf7SSed5OtX7Azq6OjwFF5WjLlNRG6LDsAKf9B4EeRyOS/lc7/99sPs2bNx9tlne/uLHBRFUgxHuQ6uTZLL5XDqqac6DXw+H0MifIIMbf0/ns94fguaF/meJY2GzxdE6urzzZ49u+D1GQpDomEFYnQA/j7iIkrkc0lJCaqrq9HU1OSlR8h/XHM2e+dlP9Hv+Lyyn8hd+b+kx/AcxU5DPoY4McQJxzKYnQtAfmSrvk/tSJF7YaKZiSPtUOKxIGQQRz6zDsNtwAapHEMiCXfs2OFFQ9TW1mLGjBl5c4s8E6shNHZobW3N62+MoPnSNXcGzck8B7MdEjSPahuQbSKOwhQw8aMdeGzTRCIRrwxDIpFARUUFpk6dipUrV47oculPPPEEnnvuOdx+++2YNWsWSktL0d3d7RFBsmKuy7Es98fOhiDZ5SLNXJC21c+cZZic10U8yfWwPtzT04M333xzD1tm+DAhCSG9ZJsIDWBACAHuVX50J2EhIQ9fQuUA+ISr7iAuocPH0Z7xICJIE0J8XS5jjT+zt0awffv2PW7TYiFeaL42Ob82kvm+ZMUhHTXAg9fVJtx+3D5iRPKqRoC/YDF7yiKRiO+5yaTiWs5TBrdMoGxMifIj9yHHtBXG8rFjxw5MmzYNgJ+4AQaWduRnLwJXtrMx5zIGWakTo6QQ6SrbXIJisHnBtb+GKK5sDIXDYbzyyiu47777htSWJ554Iq677jqcc845vvbUcBHV+lo1uc33oxVmHnei3Ov9tIDkz7ncQCFPOZZcAyvdPN/194/dKgz7MoSo0cpS0NhxzdWuz4Wg+1EhopIVNp47CylmrnvgsSVzhpbFuj+LYuZyEjE5rSOI+Riu69TyWr/k3FrB1zqMXD/PUZok1wbAniLIOAl61rq9XdBh+zynBx1T3rPZLKZMmbLH92EYgKRJswNLdCNNhLjGvPxeWVmJiooKhEIhzxDkCHk2elg/YmeOJlxYhmiHmzbq9JgTSNo5G3ha/3UZ6bJNp5rw9bEcFEJLX39Q/+f70EY5G4raaOUoqc7OTi89T/TKqVOnevqllu+WMja2EBkSJBOD7LRCBIQ+FtuMg829fA6WiWyP8Tm0fNMpYkwGSVSuEEKNjY245ZZb9qC19g6ZTAZXXnklbrvtNhx44IHeuOeyCRwppNNi5T5dGEyX0WA5zuPR9Txdbc3zrXzfsWMHPvjggz26juHEhCSE2travIEgIW6sOPFqU1pAaq9JSUkJEokEcrmcx34KGSTfeYJnQ5YHHeD3mDOZwB3XpeTJf+SdO7cgyADl/8rx7rrrriG2cDA6OjryvLda6ebr4/ZmkoWfkQhJjvzJ5XI+zxffLwtZYY3FqyXH06H9olhIIWhgoC4Qe6e5aCJ7PlnxkfPxM+GaC4bd+N///V8cdNBBvrBZ9oJrpVE+82/siWSjSSAKsRhYTAiy0ibgPqknbAErpjqcVMDkBv9P3mWeqK+vx0033TTEltyNm266yTvWfffdh4MPPhipVMqpVLsMZH5p4s2lvPLY5Ag/HiNaEMp55ZnrYnx6xRl5VjJvyPG3bNkyLG02mdDS0pInb3h8afCzY8VKz9vcJ1yySe+jtwf9R47H++o+KNtd41bLcnnnfsm/aacCEyw8DtjDGnQ/cj2sB7gMWtf1FbovJnpdDhLZzscYrE1d2/XvQcRN0P0LtCGi5w/eT+YEdgYZITR0tLa2YsaMGd4crgtGSy0glwNBZMGUKVMwZ84ctLe3eysJ8eqqmsTh/0sxe6mpCPiJD+7nrGO5ClRroxUYWLqdySHej++Txz/r43KtrM/pKEomjFmPlHHO41AihJhUYtJX2lXLW3FSptNppFIpdHd3exHt2ezu9M1EIuGLDpRr5MLhhtFHKpVCZWVl3lyu5znuD4Wg52Yd4eYiGfT/uV9K/+I6Yvr/HJHLxc8lZVhqBUlheUk/PvHEE/eorYaKq6++GgsXLsRll12GsrIyT8eX0i4cUStzCUPrHVqeaX1fzxs6glnbIHwMPSdy2/Oc0tPTg9/+9rdjurrmhCSEmpubfYJEQkkltFuEk+RD6/xehvy/oqLCV3+G87K1gSiGEQuOoLojejUleXcprZrl1IRRUKeWe5aCmCMZ1rd69WrMmTMHQD4hpKOEXIYoG4zigdKTH9+3zr2V+2WyjY+r0wBFYZF6NuFw2Cu8ynWA+Dja86OVIrkGVmra2tpGrM33Vbz44ov4whe+4I1PVwgsjx9tbORy/tQxbdyKoiapnUxqsGfSdU4eT0HGKgsDl+EU1C/lvzt37hw2MkhDClO//PLLvntmJZVJgUJzjijxLlJIwIoGp9Cxos8KLBfm52fgWoIY8Ed29vX1Yf369SPSbhMZEtbOBKt+Lqw8sjHkGg96f9nmgmu77musaPE+WjYEpX5w32TZJ32Ij83jlSN9gq5fxgETQpxCxvu5jq+VSP3O+/E8oZ0geozy9uHEYARQEIEYdB16f01KAwPpt3wci3oYOpqamnDggQf6SB8m4kVf5bbXREoikUBdXR0qKysB7O7nUjdR/sNkh+wjqdnslHVFAPE85IrcZRKI5bqcR7Zph5FciyvikMeY1hvS6bRnL4heKGlp0ndFXun2ErtC0li4PTjdjsHXx7qp6D7i2OQ5SNsAtort2KK5udk3Phgu/dS1H+/LY5Ejy9lWHWzeD5K7bLPyPkwGSfmLSCSCRCKBeDyO8vJylJeXI5lMIh6PI5lMIhaLYdGiRXjiiSeKb6xhQH19Pd5++23ccccdKC8vRyqVQiqV8somMCkrhDGPGXbsBJFz3EasgzAGk8uaDNK6sjhCtm3bhs2bNw9jC+05JiQh9OGHH/oGn3wWxlCEnwwugUvp4TojLCQYru/aUGVoxTRI+QoihHRHc00qsh8brR9++CGWLVtWVBvuLaRwsvaSAgOGn64TJJB9+/r6vOcTi8Wc7C4TQtqI4HPxBMyGrRxHfhehK31DlCdRyEW4i6BnRUCHX8r5eBWA5ubm4WvkCYLm5mZs374dU6dOBeBn0tk7KL8xAcfsP4d/s4IZCoU87yQvua2NzKAJvRC0ZwBwp4UwmGAJh8P4/ve/P6T2Kwaf/vSnsWTJEpxwwglIJpOYOnUqenp6kEqlnKlhAHzjVvo2R0Pomgf8LvfHZLgmF5hgYEGtIyX5HNymfX19ePfdd0ekvSYyOjo6nPOiixDkeZufrTamXLLKhUKEEPcF7htaPjP57joO3w9fJxuSLhQis7RSzkqkboug+5b9Za6Sl3xnEsg1b/B1FEMOuT7rew36HvTfoLbjfbURw7/r56idYLKdx760UyQSGVOv6b6OxsbGvKggwO2pBvKJfNmXSwGEQiFvuWnRn7S+y4YXMCCfRZdyOQzk/JoIZTkv+iPLFtbleDzJdl23Tvc7Oa8uBC/EGY9d10IjckzR+cTJKMcMmi901JKAC/eKUZtIJLyai1rnDYVCk3IVyPGEjRs34qCDDsrbrp0WQb8xguZ6ScPk7dwPuF8E9XUZD9KPeL6VdEROC4tEIl40UEVFhRchJItM9PX1Yf/999/j9hoObN26Feeeey6+9KUv4Utf+hISiQQ6OzvR3d3tkUIypmXeEbCTsZB+AORnszDxHGRDuPQQAD4ySJ5Xd3e3bzXOscKEJIQaGxt9zLrkUGezWd8S5JpkYcGgjRk5lqvT6PQR7hhyfJ0+Jvvxu+s3OQZDvrMA18JB3mWfnp6eESeDAOD555/Hd77zHWQymTzSS7PaehBxW7PBwt4Tvj8+ThAj73q5yDXuL9JuvCQ2pxnpUGYmIliZYeVkJOs27ctYtWoVzjjjDK8At8srqIWjvLOyp0kHBs8BAHwrgDAJAeSTlK4JPRQK+YrSy/W4DLogYf3OO+/ghRdeGKZWLIy7774bd999t/f9vPPOwxVXXOG7b+7PAi3gXKSrHkvAQAqCkDcSVanJHlGgZYU0OSZDK8klJSXo7u7GRx99NBxNM6nQ0dHhS1sA3Ionw0XaCAoRIfxf+ew6ht6PZYD2YBYiYFzKGJ83aBzzPvp4rA8wQaoLzMr+LK8Y2kh1vfjagggqbQAEzYmF4JqvXG3J+xVSlF3XF/Sb6zPfO5+LFfSqqio0NTUVdQ2GfDQ0NCCTyaCiogLAgDEjc77IQ1nFSvaROVuTocCATibzvOyXyWTy+oHMOXIedpRpeSDnDpI9emzJeTUhpXVLOQ+Tw9qxIbJKFo/ha5FrEIIySJ8XIkmchy69k6PI5Z60zi5FeysrK70I6tmzZ/sIISGZ5Tqtrt7Y4v3338eJJ54YmLoXNJ9r+ca/CbSjRCNofGh57ZLfHHUmJG8ymfRIoFgshoqKCpSXl3sEETCwquCvf/1r/OAHPxi8gUYQP/3pT/HTn/4U3/jGN7Bw4UL09PR49bd6enoQiUQ8gkgyBoCBKEZNLjOC2pznL9ecA/jHtnznyCBgd83jZ555ZlwslDIhCaHt27f7JmV50CzQXGkobNDr6JtCD50FGb+4npA2mlwIOjcLZD1p8DGZ8ZX75Do8o4X7778fixYtcnpFmOgpBCFhxJjkNnEpzbrNZJsQOOl02qeYaKGsIxIA+PqOTgnTbS79jb1g8jkcDo9pobDxjN/85jdYsGAB5syZ45tcAX+RviCDghl+l3EhE74oZ9KnWCl19R1XWofsIx4UnTscZOiwsA6Hw1i1ahVuvfXW4W3IPcDDDz+M2bNn4/Of/3ye18OlMDBBFzQHyn7yLvcqn/U+gD/dUyK+goxlIeAAjHlY7b4KSRmT6EbXK8jh4VJaXWSClkcMV79xHcNFCriIHI0gQmIwonYw8HGlX3OtEjb4WJ+QdtP9Wc9Zsm0wMmc4oQ2HvYGWvdqAKQZ6nuR2lvl/2rRpRggNAU1NTejs7ERdXZ3vmUnbZzIZz9sv8ywbLLlczqvTwZD5guuNBEV4ilOG9SItX7SMZ52VZW0hw1rPZawLsEff5ZyUSAIhtVifzmZ3l4kQp4RLNorBKYYy2xhidHOEAkcIyLHkc1lZGRKJBEKhEJLJJCKRCKZMmeLT69lh1tfXZ2UJxgGampowa9YsAG6Ch/sbb+dt2oYRaKd4kLwIknUu0kj2lRWVZWXQyspKr5B8NBr1FZHv7e1Fe3s7Vq9ejWuuuWZPm2hEsWzZMixbtgxLly7Fcccd56WQSS2udDrtjVOJIuKxqu2PQnKMy8Dwc2T7G/DbEjJXSuRhT08P1q9fj3Xr1o1gqxSPCUkIdXR0IJPJeOlGTKgw6aOXhJRtevlrYICg0GkWXJxajiEvEa7yYgOIBaJrcPNv7JHUhZZdCiWnRokQ2rVr1wi2uB/33Xcfenp6vOWDtZeGSRWeBHkS47xpTZIVItc0wSdkgLSVVgTkmIB/tRet7Lq8Voyenh6Ulpb6JhfxWmWzWSOEAtDX14ennnoKF110EYB8Q0KnHAH5hI88cxlvQv7I/zniL5vNeuHchQgcqTOml8vN5XYXquffpE9z39IkkAiF1tbWMSWDBMuWLcNhhx2GAw44wLeEtUCTrlqR1uOR67TI7xxdp48h4PBnbiudziDzXk9PD15++eURb5+JiO7u7ry6Udxng5SfYkgil7GmPw/mEGFIn2J5p4mdIMJJg407Pr7chyZmtCzSfT4owth1/iASiFPWXffE/9Pyaqgohhgb7HyFZGEQUVzoegqRiNOnTx83CvO+iO3bt6OpqQkHHHCAT5ZJ7QyenzOZjLewBjtkWFdmUiObzXpEEuu3mpwRkoX1P706WCGnJR9bIHqx6FmceqydF3Ktcizt3GP9X/QEGaNiuMl+El3Mcl9ILyGEdPq6yDdJAWN9Xtfck3EvBrjoNlxPS88b/f39RpqOA6xZswaf//znfZF2LhSa84LkJOu7Lriig4LGDMuX0tJSxONxTJkyBVVVVT4yKBaLeWOpsbERL730Ep555hm8+OKLxTXIGOGGG27A2rVr0d/fj+7ubqRSKa/gdFdXF1KpFLq6utDV1ZUXuci2PKfouaDlINvroidwihjL/t7eXmzbtm3Uay8VwoQkhACgvb0dyWTSlwLEDzdo0g7qDFpYaKNFoL0PMtmLB5z3keOysao7l3Qqrm3DJIXeVxujIoxHu+DcAw88gLPOOsu7NpnIOCpLGxTasGAyJ8hTrMkwbYQwkecioPh8sh/XCCpEWsk2uVZWDphQ7O3txYYNG4a/kScIXnvtNVxwwQWIx+MA8g0I3e7aqyJ9gydcPaYB9ypkfHyZtEtLS9HT04NHH30U7733HqqqqnDEEUfgzDPP9JTOaDTqKWgSdSQEJo9BVvw2bNiASy65ZLibb6+xZMkSvPDCCygpKfFS6riv6xQ+wD9vyn6c2iNtKe3EJKs+lvwu/5X25+Mwstndxdnr6+tHpX0mGlzpYvLOxkgxBnyQ5zIIhQiEQudxGYI8vvR21+cgskrLERcpw/uzbHdF7AL59elkm4vw5Jd2EBW6pr0lh1xEkL5H/l3Pk0Htq/cpRAi5nots57bjvik15gx7h66uLmzbti1vu7QzOzMikYhXADocDvtknNaV0+m0L6JIxquQIiwLtEONnSuAv3h6IZKSZYdL1xUERRQxGaRT+0VWic7Iup2kkfX09CCdTudFFwhRpFda08QYAI8848gB7u/ymes66bp82lbo7OzExo0bi+wRhpHCRx99hJaWFtTV1fnsHoG2OQpFoAD5CyhI/9CyQj5rhwZ/58/St6LRKMrLy1FTU4Pa2lpUV1d7EWmhUAhNTU1Yu3YtbrzxRq8+7L6Ak08+GWVlZYjFYigrK/O4ABnDXV1d6OjoQHt7O7q6unzjWew/LkitxzCDn4k8L9YPZH7k7KS+vj50dnbi17/+9ai2y2CYsIRQY2MjZsyY4U3yIvgkMoCNfh5osi97urUAGQzaGNWGqewjUTCuiBkAvs7EA1lHCcm+ehIA4KVL3XzzzcPWtsXi/PPPx0MPPeTdR6EoG60AS9vrZ+T6H0dpMQpt02QSKwKFastI2/IxhPGVfiP3IGTgzp07reDfIHjnnXdw+OGHBxqbLqMQGIgwkTHE+dCa8GUBrY0jDufs7e3F4sWLfedbvXo1vve97+ErX/kK/v7v/x4zZ85ESUmJl4bD4fXcNxoaGrBq1Sq88sor425lrN7eXnz5y1/G1Vdfjbq6OpSXl/sUT8m11pFBuk3lnjmMnZVXHlva2GNhC+SnjrCQzWQyWLt2LTo6OsamwfZxaINOtrm2A8Fkgf5NCJLBzu3CYP8bCgGiUeg4LmKH53zA31ZyPE1c6v7LskEiMvi4mlhicoj3Y8Job9pDk0xBRvdwIYgECiISZQ5heSz3Pn369BG91smADz/80Ivm0X1YSAwxljgyRaL0WB/il6zeKf2JHWP6+eq5XyAyXxu6GhwxyGNPIpZEBvM4k+tgZxA7/thJwZHdsk0cylzjTlJQxIDkfYOiy9lQ1MQXE3Pa4NcLocj1yr78fBsaGvasUxhGBC+99BJOPfVUVFRUOEnOYsl9/ZseT1pu8NzONiPbkBIRFI1GEYlEUF1djWnTpqGmpgbl5eWIRqNIp9N4/vnnceedd447nbVYPP3007j00ktx2mmn4aCDDsLcuXN9qwXW1NR40UKdnZ0eMSTL1/f19flWLOMi+IJCOpFLzrLsKykpQXNz87hbLGHCEkLr1q3D/PnzfQJIT/46coRDw9hbpRl5PZHrFxNJvEoZQ++nhQErjLI/MCBcXBOMDksXIfWTn/xk2Nu3WFx88cX45S9/6RlxLqXAlSYC+GsTBL1Y6Osin0wuMdho54geNl7luTC0B5MjGgC/MRAK7V6Fo6enB2+99dawtOVExtNPP40///M/9/q2JgS4z/OY1BMur3qiCViewLUwFTKju7sbX/va1wKv895778W9997r23baaafhmGOO8Zb27e7uxsaNG/HEE09gzZo1Q2+cEcSGDRuc93vaaafh2GOPxaGHHupFbrkiKgF/pAl7QjTRqoldPf41mGDq7+9He3t7XtsbigfPTyzzXM9Tj0FBEGHkMvQH837KPjyHuq6Z5/Kgz/ragrYVIlWY8NcKtj4ey3pNHPGxgIHoB5fcZtnnIoz4nC4ym6+LSRV9Xy5DO0hhZR1E36+rXfV41vpRUD9gAkj3S27bmpoa5/8NxeODDz7wagVpCMEgq2Fx9Ep/f78XMQTkzyE8n7PupPuLdiZwH2HSFBhIEQ4iLUW+S8oZEz5caLmQfq7TQ2Qu5FRyHS0k+iEXnuaIIJfOwdesU0nkHAC89mf9nsestDkTUNyO69ev96XyGcYOTU1NHinEjkJN1gADDgA9XuS3QvKLn792YEikOzs6w+Gwt2JYTU0NKioqUFNT4xUub2howIoVK/DII4+MdBONCl588UVfatvRRx+NefPm4eCDD8aCBQvwsY99DFOnTsW0adO8GmepVAqdnZ1eFFF7ezs6OjqQSqV8KwcGPTOXfisIh8OIx+OoqKhAOBwelwTuhCWENm7c6Mz9YyJGG/6aZHA9cD6OVi6ZbBLh4lJAeT9XB+JBzSSH9lAWw0h2dHSMKcvb0dHh8zgFTXBaUAa1uwYrjoX+X2hiFaGvFVdWNID8lCNXip54k2Q1iGw2a4RQEdiwYQMymYxXSJHBHg7A7akXQ6pQJJ82rNj4EyNq1apVe3ztTz75JJ588sk9/t94htzTKaecgm9961sA4EVdAflGoECPqyAjshBEaWJyUPKtx6MQ3Vcg6RkuAshF7gURL4OhGCJoJFCs7AjyzLIM1bJUe3ULnUOTL5wGyeS2S/YF/ca6SbHjqND16Xsc7NiD3fPegmWpnEe2yb0HrdpjKB47d+5EOp1GMpkEkN9HeU5g/TOI7ORxoMl+kc+8EADgX84+FAp5+jHrVUx6su4rYJ1eloVn+S9E0WD6n2xjQjxIv+ayDpIezpGz2qbQcI1zjvQXQ1PakQliAD5SgduR78PSxcYXNm/ejLa2NieZ7XI4FKpTw//jz2x3yDsXLY5EIl59L9mWTCZRW1uL2tpaJBIJRKNRdHZ24sknn8Rtt902Lla6Gim89tpreO2117zvZ511Fk455RQcd9xxHkEmheV7enrQ2dmJXbt2oaWlxVvKXuoQcTQgcwWsIzMBGA6HkUgkUFtbi6qqKvT3949LR8eEJYRkqc3y8nLn764QURaIbHwWA+0JY7Zee+xEuOi0JCC/non2nkpn00oTnyubHchnHo2l5gdDW1ubN1CCDAsWkvJstFfHBW3AiBLjIvRcRJomBzUpJwqKnJ8Hu0vp4Bx1YHdut9U8KQ5vvfUWjj322Lywaq2g8rPg58+knh7PciwBH1cE5oUXXmheNoVnn30WXV1duOGGG/KUfwFHJuqoBoY2ql3bWRmWZ93f34+Ojg788Ic/HLH7nAyIxWK+Z6fHymjA9dyHC3y8Qvcjipv8h/sdRwtq4mSw9DY+Hl+LEHFSY0xeuVzOk+9sADIBxBhOok1fJ8+rQcTQcDy7oHmB701fUzgcRm1tLXbu3LlX5zQAqVQKO3fuRF1dnc+AZBKGyX7RpUTGlpWV+RwnYsQykSH/1cdm0klktByfUyaYjBLoNBn+HA6HPVIIgBfZpAkejrqTa5dj8D4S/aOvgx24Ujg6qM6kJjjlM88nYqSLA0sIJjYqhdQSUo3tESYPcrkcUqmUFV0fh/jVr36FL37xi14dLj1nso0B+MnOwZwbTAaxfiyrBUr9nFgs5q0SNmXKFFRXV6OiogK9vb3YvHkz7rnnnnFV1Hg0sXLlSqxcuRIAcMghh+Ccc87B6aefjjlz5nirW6bTabS1taG1tRXt7e3o7Oz0ag4JOaQXlAHgjeFIJIJkMukV7a6oqEBpaSk6OjowbdobJaIQAAAgAElEQVS0sbx9JyYsIQTsjjo46qijAOSn+7Chz4LBBSaJtFDisGf+P9cFci1TGRQZpI0iPrZW5jUTKV4XKSI4FnWDXGhubkZtba1H9rhCIRmiTDD55fJguZ6fCFy9coNLwWbPj94nSOHVBANP6FJ/KBqNetFfmzdvttUfisSjjz6Ko446yufh0x5MgY5WCfIIasOO+xCnN11++eVGBgXg1VdfxbJly/Ctb30rbzUygTYWuN11Km7QWJNnwp5lKei5evXqfTaffbxAIjUBd4F115jT5Ib+PhgGI2Zc5EOhuVdHKuhr4vmZP+vjBpHDLHe1IVfouvT18zHlHktLS32rnuq5SI4j+wD+1FbepxiPssZg98HPQkcjBM2vrvvX4EhbIXk0ecf9Uu8PALNnzzZCaIjYtGkTDj744DxniKS2cy0hID/9ivuH6FviLZf9AbexyqQKH5Oft3zmiBghogTaQagXkOD9Cul+fA8SVczETCgU8uZL0RG5viS3lT6v9HGeR0QnFaNd7lkKVLMuz/oxy1R9XyJbP/roI7S2tu5lrzCMFDKZDB577DGceeaZvihHHQkH+Guh6mcdJPPk3UUGRaNRxONxVFVVeTWCEokEOjo6sHz5ctxzzz2j1Qz7BNavX4+bbroJN910EwDgmGOOwemnn44TTjgBBx54oK/mkKxS1t3dnbdiscytHKUlK7XxSrn9/f2IRCJjectOTGhC6Omnn8aRRx7pE4BMKLCBWIyXVHsZ+P88yLUwEuHAwkUXLJZ3zv8MOh4LRPY69PX1oaOjA++99x6WL18+gi27Z5DcdDHiXe0jyoUo/fp3/YzYAyXgNgkqxidgY0d7s/g/8j+dPsbGAh+fPU39/f0TLpVoJNHa2or3338f8+bN8/VxHl8MbVQA+X3ARQjJseQ5/+d//id27do1Gre4z6K+vh4XX3yxF2Wi50tpb210MhkuY0P2B4LTZOXZ9PX1YevWrbj77rtH7V4nKvRSuFr26efpwp6QQQJNzMhxBpO3xYD7G8tLOa9rH03WaEKIiSE26vQ+fA59vyIzuJ+LUajPqyODtJHr8hwPV5u5oPtDsfrRnsBFMLlIIZmr99tvP0u9HiI++OADr38B8GoDSZSNrnep5wmex6Xmhug50qe5rg+PHz4m18rRJRE4Kkl0Kq69oyHjg4kTlz5eCKJ7cs0gOR6T6AKXA1Hgmitk3EciEUSjUW/hCiGD2MnC0UGhUMgjEthJKq9QaLcD+I033hjs0RvGCDt27MCjjz6KM844A7FYzCcPXA4RbWOyrAHyi5JzH5NooGg06hFBdXV1SCQS2LFjB/7t3/4Njz766Cje/b6LV199Fa+++qr3/eqrr8YZZ5yBWbNmIZlM+shtJu2EDJbi1PJdxvvatWtxxx13YOvWrfjKV76CsrKyceWIntCE0Pvvv4+WlhbMnj07b2DpGhVBgoO9HUGChQe33l+zvNKReIUDOQ8zi8L+y/F1ypKA2WE55ngigy655BJMnToViUTCUz5YKZA2EiHMEUT6pUkdwF/TQXszdcodv7uYdh7gfGyetLUyrUkJDkHu6OjAgw8+OFJNOyEhxaUjkUgecaqfGyua2pPGhJBrCVrZXl9fj+eff35U73FfxUcffYT58+d7Ci2PNW1g6u9MxLNxwGPVNUfu2rULN95442jd4oSGhK67DP0gb+RQiKFiCQQew4MdV5NLLqKHPehB+wQp3YUIIBdhKdeknQ0MUdg5SkCiLzQZxUQS6wCuc7F84nMVA24D17WzzqHPr49T6ByFdCbXNiaF+PgzZswIPI+hOGzevBl9fX2Ix+MIhXZHwAihIoWSgQHCgyN+ZIUvTZ5wRBGTQuHw7tpPInslYh7wl2vg7XIeTYxyupru39oRIdehHXYuUlpHLnHRWF7oJRKJ+FKYJaJIX4cmeiORiDf2ZVUnAEin00in016bs84pbVxWVoZQKIRYLObNF9LmLHs7Ozvx0ksvDa1jGEYULS0t+OlPf4qzzjrLl7IJ5KfhumQW67a8TWRKWVkZ4vE4KisrUV1djfLyckyZMgWxWAybNm3CnXfeuVe1MQ0DuO2223DbbbcBAObOnYvPfOYzOPDAA1FeXo6pU6eipqYGVVVVKCsrQ1dXF9avX4/3338fmzdvxlNPPYVkMplX//Lee+9FZWWlEUKjifr6elx44YW+0FMRDi5DkZWtoPcg8qiQAiy/sZGrlR5gwIPC53KBhZ5MCrlcbtx50WbOnImKigrv+sSYZK+STocD8sOOuf20wi9wEXuFwi05MkjaUocB62LG2lPFXixW6sVz09bWNmxtORnw1ltvoaysDMlkEp2dnb5oMW04Af6QcZcBqw08Jl7r6+stdHYP8I1vfAOrVq1CMpn0pRgA8I0RbUiy11MMEO2Flmcqv2UyGbS1teGGG26w6K1hApMQg5E1mjjh7YNBy1L9WXtJtZGn/8fRga7zu47NZImLrJR3TfZoAiiIDBqMTJF3PkckEslb3YhJIa3wuyK5GLJvoWcZRNTKOfi/Wg67zlvM89ftXwwxGOQtl/S5yspKxONxdHd3F3V+Qz6ampqQTqcxdepUtLa24je/+Q127tyJWCyGhQsXes5IIRwkCoXndF4wRfoxr6IHIC99hZ8jywCXriaEEuB3sEmEjchuBsv0oN/keIX6onbWcpSaLFnNY1j0Pk4PE2eu3Dsb7rlcziOD0um0b1Uz6f9MynM9TalVIueWV3NzMzZt2jS0jmEYcfT39+MXv/gFFi1ahAMOOMBHtvI8rqOBdJSqfJdUJKkNJPVpysrKkM1msXbtWvzrv/6rLcIxAtiwYQM2bNiwR/8JsgPb29uH45KGDROeEHrmmWfwhS98wVv1SYSMVuxciotma4cC9sK6SA1WelkoaEJDhCVPEOJ5bGlpGXfLMr/77rv47Gc/65v4stlsntDXyrtmywUiCDkNT0OOycSa9iAFQfbjWg9yTEnLk3PraBNRgkKhENLptBXB3Qvkcjn84z/+o0/JmTt3LhYuXIijjjrKUzABP1nInnXAP55cRt27775raUh7iP7+fnzmM5/BSSedhAsuuACf+MQn8rxXbCyw8s/jXcYXz3OyrxBNO3fuxL/8y7+gpaVlzO53oiESieSlhMjzccEl9ziK0xVBMtxwRQcWisYpBE2GaLKYo3f4xZ5Yrk+nI63kHIB//pFzSlg/G9KcRin7FhMpVcx9ur675CvfA+sprm2DnWsocD1L0RdisRiqq6uNEBoCdu3ahUwmg/b2dpx99tm+38LhME444QRPN5NVtIQgkT6pSQyeS3p7ez1jVepncMqTdhrwf1mOcNQQR//o/VyREzy+g/r6YJC+zyRMX1+fb9UmdmrIuYQAkrmEo5Ol3Tg6SOZeXT9I7kuMe46ql2vq7+9HKpXCc889twc9wDDWeOKJJ7BkyRJUV1cjnU57eis71lheSD/KZDLIZDKYOXMmqqqqUF1djWQyifLyckSjUfT29uKtt97Cgw8+iGeffXaM79Kwr2LCE0IAcP/992Px4sV5od4y4FxkkBY+haCjE1g55O0sBLUyyf9hMkgLNxYYEpYrQveVV17ZyxYaOTzwwAM44ogjcOyxxwIYCPEVw17fP7c5E2i5XM4TzDrENigtjD1X+sX7upQGHT0k1+L6rxgK8spms1i/fj3++Mc/jmjbTlRoj5cw8itWrMBhhx2GxYsXY86cOT4jR0eq6PEkylkmk8Gjjz467ojTfQnPP/+8l2Y3Z84cVFRUYNu2bejp6cGiRYtwxRVXAIBHDAEDz4UL5mojNZfbHQL/0Ucf4YYbbhgVwmEyQVJEgAG540p9CCLQdXqmJoUG88AXAx63/FmT8AImXlzHcBmK2uMqXnyRJbwfF4eMRqM+QpqjlzRhouVFOBz2Vn1hnUNkmY6M0ffkupdCDiwXXEaytC33C00G6VTsoHNogom3677C1x/UZ/icJSUlqKmpwbZt25z7GopDY2MjLr/88rztP/rRjzB//nxvSWTWsySlgSNmpE+EwwN1IeV/PT09HhmkCRqBjhBlcKQaR/y4yB4ZS3pMyzYX5DeJUmfyRsa7kFIclZPJZDzHshjsek6U+YAdh9LXZYUyjq7SqddMUAv5I+3P6WpS4JaX0TbsG/jlL3+JsrIybN261ds2e/ZsVFdXezqqrArY1dWV9/+XX34ZlZWVAIBt27bh/vvvx3333Tdq12+YuJgUhNDq1atRV1eHCy64wFfZWwx4nZbk8kq6vJMCTSS4CBytXAtciiDv61Je+beSkhJEo1E0NDTgoYceGlpDjRC+/e1v4/zzz8dll13mI7k42ok9QPIsRLlgIahXdWDChsNuZR+OJuLfB1O4XXA9H8kPl4Ju4XAYqVQKd91110g05aTH22+/jW9+85sAgBNPPBHnnXce9ttvP+e+4lnZvHkz3nzzTbzwwgv44IMPRvNyJzw2b97s+/7zn/8cL774Iu69915veVMdBcRFQIGBuSyTyWDNmjW45557jAwaAYhB50oFYrgMLx3OXgiDychij+Eieopxzsj5NOGoSSHXiz2zMrfH43HU1tbinXfewQcffIC5c+eitbUVf/rTn1BXV4eNGzeivLwc2WwWc+fOxf77749p06ahtrYWPT09XmTQzp07sf/+++OrX/0qLr30UsTjcV/EMOsL8nKly7lInUJt7dqf93EROTo6qFiib2+es7y7yHy+lmQyuUfHNuRj06ZNvqXeGVdeeSVuuOEGfPzjH89LC+N5nKM/xSkppIXobJ2dnd4x4vF4nq7NkYYavA/gdtjJZyZxxDmqI/81GaV1c7kHdtbK/kJ+9fT0eEQPOxV5H/msjy/3pElj/o9O0+TvvCS96MWZTAa7du1CR0fHYI/cMM6wffv2vG0NDQ1Fp3etX78ea9assQh3w7BjUhBCAPDUU0/h7LPP9oXhuWoCuFAsQRDkhdTCTd61guv6v75GSUmSz5FIBJFIBHfeeedwNdWI4KGHHkJNTQ2++MUv5glJLUC5gB4w4LXUK7NpjyeTQixUtTfUpSS44Ho2fK0cFs1LZTc1NeHtt98e9jY0+LFq1SqsWrUKp512Go4++mhMmzbNt1LIe++9h5deeskitUYZ27dvx2WXXYbrr7/eSyvjaAjx3GazWezYsQNbt25FOBxGPB7Hww8/vFdLahuKA5PlOlpVz3euCNk9jQAqhsQZbgSRJDqSgGWzyB2Zx+V7JBJBRUUFrrrqqr1aoeXZZ5/F3LlzsXr1apx55pkAdkdj/OhHP/LqcYnhzVELhYxYlpmaLNKfdXtoGaajmvjzUCO+NMGjz+dCoSgkcSAZ9h6DpeAuX74cy5cv91LGgAECiCN3+DnJWOF5hSOLBIM53VxEtCuqTcD1ejjCTztl9RjRv8m447RmiRCSyB5OI5N6QHJMLgHhivSR73I83k+/6zlZPotM5JpCnZ2dBZ+lYWJi8eLFY30JhgmKSSNhU6kUHn/8cZxzzjk+pU+ESW9vr0ckMAbzwmkFS47JoatMVgB+IkRHIYmAYiGnCSLxaMRiMSSTSVx11VW+JfLGK+6++2489thjuOGGG3DooYfmKYcsRF1GoQhu2VcLe2lXwF8fQysY2lNTSEFmQ5avUby+iUTCSwMAgO7ubtx6663D2GqGwfDkk0/iySefHOvLMBA2bdqEiy66CBUVFbj00ktx8MEHo6+vDxs3bsTvf/97rFu3zhcybRh5aCND3l2EkHx3GW6u9KggMCnAx9MRIULWMwmlyST2vgedx7VdRwNwihjXB9LRBolEAv39/TjooIMC728wnHLKKYG/nXjiiXjhhRcQi8XyogT09WoSSOsdQeTNYEa1nC/I4OffOCrCRfTxd06h4f/Ib7ovunQv/p7NZr3UeMPeY7AC/Tt27MA111yDpUuXIhQKobu7O08fY52IiVN+vqFQyEeqaN1WP3P5jfVbHp/i+JSxy8cSh5zsw9F+Qc4/Kdgs1yLHlvOmUimfQ5bTn7nuD7cFp0dzupomj5hA0hHvMvaF6NIRQ5K6Vlpaig8//HAPn77BYDAEY9IQQgDw8MMP46STTsLMmTO9yb6srMxbOcDF3nPUSSGlk8PNtQeSCSFXSLvLY8H1jfRqBaWlpYjH46iqqsJ9992HZ555ZnQacBiwdetWXHzxxZg1axauvfZaLFiwwLccMkd4iEIhnhtpG9fz0QSR/M6eGX5OGqw86FVgWEFmMi6RSCAej3srqPX19eHZZ5/F2rVrR64BDYZ9CB0dHd5ynYaxBRsxApZvrt8YrnQGjeGKBmKDMSiS1vUS6JRE10sblKILCBnU1NSEhQsXDsv9BOGzn/0sfve733kFv+UetOzRL26jwcgw/Z3PITJXr3wqxy8GLGf5XC6j3xUl5CKD9PmHEqlkGEBQuhhj8+bN+PKXv4xbb70VdXV1eStKCpgEYQcory4m+8ln0ZV0SqT0DSFmOPK6tLQUmUwGO3bsQCKRwIwZM3ykrhR7lvGrU7WYEGJdUfqUOBr7+vrw2muv4bvf/a5HnB1yyCE4/vjjMWfOHCQSCY9IAuBLq9MQRySPYa61xDql3Du3o5Qf4Jp7fX193upkU6ZMwfr164t97AaDwTAoJhUhBAC//vWvceGFF3oTruQdl5WV+XL5dbHIQoqXhvZMaCKIt8tn9miwp1J7McULkkgkUFFRgb/4i78YyeYaMWzbtg1XXnklAODmm2/G5z73OZ+nhT0nHHWlvZk6Skj21x5T9rTqsHStkGqlnJ8d15WQAoNyzc3NzVi6dOkItprBYDDsHdLpNIDgBQ04EsQVcTIYGSQIkpMucmdvoSNkdBSAJoR0sWiWs+wYkuLRvb29I04GCZYsWYJ77rkHALw0G52mUyi6yxXxUwj8f17pjLfvKXSU2XATOHKthqEhHN5d3FzmgkK45pprcM0112DevHm+QtNS7FicZ9px6oL0ByGEAOSlXoXDYTQ3N+O5557DW2+9BQCYPn06urq60Nzc7B2rrq4OX//613H00Ud7Y1YIJCZ85bhMuAADZI3MAeFw2FvIQM4rWL9+vY94mTdvHj71qU/hkEMO8TkPXe0sbcUORhnTTKTJu9gj0WjUI564ZpGQeVOnTkU2m0VjY+Ogz9BgMBiKxaQjhJ577jn8zd/8jTfpspKolUomg7SirL2qekUEAZNKnHakyQ4d4irbhKySFU5isZj3PZfL5RV13Rdx7bXXYvny5bjiiitw2GGHYcqUKZ5AZMGpQ9bZQHGRQrKfbNe57/wfJpeCPLLSP9i7Jb9v2LABN99880g2k8FgMOw10um0M5UH8JML8pm93Lyf67OORNHQhIFrP1dqR6HfNRmkyX2erzUJxE6WaDTqydd4PA4A+Mu//MvA6xhurFmzBj/4wQ/w1a9+NS+lWbc3p27xZyaPNFwRYWyY6kgk135yPoZO7wsC3xOTOq7/ufqU9Etejtywd9jTtLtbb70Vl156KQ477DD8+Mc/xpYtW7D//vtj5syZmDdvHqZPn+6L4BawrgvsJmFkyfW2tjY0NDSgpaXF03Pb2tqwZs2avCLJesVRYHda2z//8z/jrLPOwtVXX414PO6lirH+x/MFv8uy8aWlpejp6cHatWtxySWXFNUe69atw7p16xAOh3HGGWfgyCOPBJBfC0jagHV/IXgY3E6RSATJZBKxWAwAvGLSAHy2xbp16zBr1iy0t7cXdc0Gg8FQDCYdIZTJZNDW1oZEIoFcLud5LPSS9IIg4kF+431cyk2QosSRLax8CwEkHg9RVlnoAbtzwf/pn/5prwpdjkc0NDTg2muv9b4ff/zx3ipFnZ2d6O3txSGHHIKFCxcimUx6Sj3gXrpUe69c+7HxoxVtfsm+TBxy+kVbWxu+8Y1vmMJqMBjGLVwyzPU777O3aTuu1CA57lAjhVypYuxpF+eMjriVz1xvhKOCysrK0NjYOCZFO++++2586lOfwpFHHpkXGeSSX5xGAuQboryP/F6IGAqKRtoTBBGEDE4TYgKymGMXk+5kKIzy8vKiooMY3//+933fW1pa8Oabb+KJJ57wtvF4j8VimDFjBqZPn45QKISGhoYRqXezcuVKVFRU4LrrrvOlqMn1uAhm1vGz2Sz++Mc/Fk0GMbLZLFauXIn/+Z//wfnnn4+DDz64YBaBjCttY4jzN5FIoLy8HPF43FtVV3RMIa6bmppw/fXXAwD++q//etAC7QaDwbAnmHSEEAC88cYbmDZtGrq6urw8ZU7lAvIjS3RhR9knKEefI1K0kiXnyuVyaGlpwYMPPoidO3eitrYWCxYswJFHHon9998f8XgcyWQS8XgcsVgMuVwO27dvx89+9jPccssto9hio4/Vq1fnbXv99dfxs5/9DOeeey6OO+44fOxjH0N5eTkikYhvPyFy5Dm4VoEA/CHz2gsuQlmOw+kF0k+y2Sy6urpw4403GhlkMBjGNYIMCJZ1mnzQUZQMl/Gj5ahr36GmjLkIIQC+iF8mioQECoVCXr0RJoJisRhisRjC4TAWLVo0pGsbChYvXozrrrsO5557rlfTUCD3KKSIODvEQTRYZJD+HuTE0pA2LGR86mjpoOfLz4TryhS6ftalWltbC16rYXBUVFSMyHG5f6TTaWzatMkZ3TPcePzxx3HTTTf5IvE5HUxDzxdD7VN9fX342c9+BgCYM2cODjzwQMyePRsf//jHEY/HAwlPGQdSr0z0WI5uLCkpQSwWQzQaxRtvvIFly5Z5/7eC0gaDYbgxKQmhn/zkJ9iyZQsWLFiAI444wkvNEmGiw7a14un6zGHpHLquvXocxt7V1YUrrrjCtzzn888/DwA47rjjsGLFClRWVnpRTH/4wx9w8sknj1YzjVs88sgjeOSRR3D66afj05/+NObNm+cp9JzmF4lEEAqFkMlkPK8YPx+u06BJIyaJ+PlyWHImk8HDDz+MDRs2jFlbGAwGQzEIShOTz3o/Nq4YOg1Db+dj61UeXfsC+elgg0UQsdzVJJD+jevxCSEUjUY9QkgicC+99NLgxhsl3HTTTZgzZ46XsqbJOhd5B+wmiDgiykX46JRAOU6xUTpBEdR8HFeUhIvY0qRQ0DXItkwm46sjY9g7SDrSRMGXvvQlvPrqq16NIynCzNH03N9Zj5MizVdddRV++MMf7nHklMbmzZt9JRxKSkpw+OGH49BDD0VNTY0XASRzE6esSjFs0UF37dqFjo4ONDc343e/+x1+//vf+85lDkiDwTDcmJSEEACsWrUKq1atQmVlJW655RbPcyIKFUf4uDyS+qXJAjmWizgCgMbGRlx55ZU+Mojx29/+Fvfccw9uvPFGvPzyy3jkkUfwk5/8ZIRbZd/C448/jscffxwAcPHFF2PevHlIJBIIh8OoqKhAIpEAsLuAdV9fH6qrq32KAjCwAoYrTJ+fnatWw44dO7By5cpRvmuDwWDYc3BqtGsZaZ7jZP5z1Y3h33RtN4EuPst18uQ4w5XyEEQGifEnadhCDDEZJNFBF110EV5++eVhuZ6h4pJLLsETTzyBqVOnAnCTNkEFlpnw0YTf3qaCFQtXVIYmnxjF9oFQKIT29na0tbUN38VOUriiZvZVHH/88bj//vtx5513DvlYs2bNQmNj47CmYfX39+P111/H66+/7tsei8VQW1vrKyRdU1ODRCJRdGTVRHqOBoNhfGDSEkKC9vZ2XHbZZTjvvPNw1FFHoaamJo8UYtKHCykC8Lbp4pZaSeVw9tdee83LBS6EFStWYMWKFSN6/xMFskoL4+/+7u9QV1eHX/ziFzj22GPxuc99zismKoozPx8gfyUdJoP0qhH//d//PXo3aDAYDENANpvF+vXrUV5ejtra2jyCQKfNuoo4M3HE+/I8KdvEYy+rCen9XESSdqAERebq75q851Qx8cRLCobU55N08csuu2zckEGCRYsW4aWXXkI0GvW26UibbDbr1dHjbdqxwZGurggjbkd9jkI1EF3fddSPi8zi/wZFCunr3LJly+CNZiiIZDIZ6IDcF7Fjx45hixrbtm0b9ttvv1FJxUqn02hoaBjSMWzFPYPBMNwIjbTXqKiLCIXG/iL+H1/+8pdxxBFHoKqqCoC/lgLXGtKh6pos0kSQLJv+hz/8Af/wD/8wZvc3mfHVr34VJ598MhKJBEKh3auW9PT0oLe3N29ZUK5DJB7ucDiMrVu3Yu3atdi+fbuzztHeIpfLDX0t5hHAeBqbBsNYYKKMzbKyMs8g/PrXv+5FCrmWTuZaaa4UMb3ioibOs9ksuru70d/f74vSYZnpqkmkSSDtbJHr4npu2inD22SBhmg0itLSUsRiMV+qxt13351XNHe84Oyzz8b111+PdDqNTCaD3t5eT1719fV571zvTtKgeRl5XhxBp5CxzJNlxTWR5KqByAQSP3tXVG1QLUWdns0LNTDC4TAee+wx3/LfdIwJMTZHC/Pnz89bWt1gGAnY2DQYxieCxqYRQgE4/fTTceaZZ3rL0ALwKaFCEGgFyOXRzGazaGxsxP3334+nn356TO7HsBvHHHMMFi9ejIqKCvT09KClpQUdHR1Ip9PIZrOoq6tDZWWllxoBAN3d3XjzzTdx//33o7u7e0Suy4SnwTA+MRHH5qJFi3DIIYc4CSEd3fr/58qL0GEixkUAZDIZr4aQkDB8fBfJ4CKE2LGiCSGRw0wC8W9CRMViMe9dagndcccduPfee/e2CUcFX/nKV3DVVVchnU6jp6fHRwjxZ14+3rWcfKFl5eX33t7ePJJIMFixaB3tFUQIMZHE55Z7kN8Y4XAYy5cvd8reiTg2DYaJABubBsP4hBFCe4mFCxfi3HPPRU1Nja9wNNdDYC+ZKEGpVAq///3v8atf/Sovh9gwvlFbW4tTTz0V6XQaK1euHPHaC4AJT4NhvGIijs1YLIYlS5YEEkL/f/w8cka2A25CiAJ/AW4AAAOTSURBVGWiEAH8n6Di0ZpU0OSQi+yRukRMCHEBaSGEpE6QrCxWWlqK//iP/8B//dd/7W3zjSoWL16MK664AplMxiOFXIQQP0v5LKl7TL64yCCO1NFpZy7o1D15dnJeJuiAAZJHp4LJtTIhJP+X/TKZDL73ve85r2Mijk2DYSLAxqbBMD4RNDYnfQ2hwVBfX4/6+nrU1tbirLPOwqGHHopkMul5GgGgt7fXW8nqo48+wlNPPYU333xzjK/csLfYuXMnHnjggbG+DIPBYBgRpNNppNNpL2onCNqA12ROEGEg5Iw+jgtB0bXyrlPC5Ni8nDwTQUwYybLN8XjcSxP7/ve/v8+QQQDw4x//GABw5ZVX5hFr0nZSvFvSADk6GfCnjbnqj7jS94oFkzu9vb3o7e31nlM0GnVGhLlqHPF1cK0qW8XTYDAYDIaRhRFCRWLnzp2eYmYwGAwGw76MTZs24c/+7M8CjX+d4sMolD7k2saE0mC1gzTpoYkgXTDaFR0kBaRlWflIJIK2tjZcd911ePHFF/e4rcYaP/7xj/HBBx9g2bJlHgEkbSEEjETaMFkk5JBrVTn5rFEMGaQLUkuEEUcrDfZ/XduIo5X4GtasWTN4AxkMBoPBYNhrjIuUMYPBYDAYDAaDwWAwGAwGw+ghf91Xg8FgMBgMBoPBYDAYDAbDhIYRQgaDwWAwGAwGg8FgMBgMkwxGCBkMBoPBYDAYDAaDwWAwTDIYIWQwGAwGg8FgMBgMBoPBMMlghJDBYDAYDAaDwWAwGAwGwySDEUIGg8FgMBgMBoPBYDAYDJMMRggZDAaDwWAwGAwGg8FgMEwyGCFkMBgMBoPBYDAYDAaDwTDJYISQwWAwGAwGg8FgMBgMBsMkgxFCBoPBYDAYDAaDwWAwGAyTDEYIGQwGg8FgMBgMBoPBYDBMMhghZDAYDAaDwWAwGAwGg8EwyWCEkMFgMBgMBoPBYDAYDAbDJIMRQgaDwWAwGAwGg8FgMBgMkwxGCBkMBoPBYDAYDAaDwWAwTDIYIWQwGAwGg8FgMBgMBoPBMMlghJDBYDAYDAaDwWAwGAwGwySDEUIGg8FgMBgMBoPBYDAYDJMMRggZDAaDwWAwGAwGg8FgMEwyGCFkMBgMBoPBYDAYDAaDwTDJYISQwWAwGAwGg8FgMBgMBsMkgxFCBoPBYDAYDAaDwWAwGAyTDEYIGQwGg8FgMBgMBoPBYDBMMvwfOf4ayUOAB3oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise some predicted labels\n",
    "f, axarr = plt.subplots(1,5, figsize=(20,20))    ##### perform same operations as method 1 #####\n",
    "for i in range(5):\n",
    "    image = X2[i].reshape(310,560)\n",
    "    axarr[i].axis('off')\n",
    "    axarr[i].set_title(\"accuracy = \" + str(accuracy2) + \"\\nSpecificity = \" + str(specificity2) + \"\\nSensitivity = \" + str(sensitivity2))\n",
    "    axarr[i].imshow(image, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "### Method 1 - Sick Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualise some labels\n",
    "f, axarr = plt.subplots(1,5, figsize=(20,20))\n",
    "j=5\n",
    "for i in range(5):\n",
    "    image = X2[j].reshape(310,560)\n",
    "    axarr[i].axis('off')\n",
    "    axarr[i].set_title(\"accuracy = \" + str(accuracy2) + \"\\nSpecificity = \" + str(specificity2) + \"\\nSensitivity = \" + str(sensitivity2))\n",
    "    axarr[i].imshow(image, cmap='gray')\n",
    "    j=j+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}