SudokuSolver.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import math\n",
    "import sys\n",
    "from dlxsudoku import Sudoku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To simplify the plot of gray-scale image define a function\n",
    "def plot_gray(img, figsize):\n",
    "    plt.figure(figsize = figsize)\n",
    "    plt.imshow(img, cmap = 'gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Before solving the soduku puzzle, training a MNIST digit classification first using tensorflow\n",
    "# Load data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data\", one_hot = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model\n",
    "def model(X, is_train):\n",
    "    conv1 = tf.layers.conv2d(inputs = X, filters = 32, kernel_size = [5,5], padding = 'same', activation = tf.nn.relu)\n",
    "    pool1 = tf.layers.max_pooling2d(inputs = conv1, pool_size = [2,2], strides = 2)\n",
    "    conv2 = tf.layers.conv2d(inputs = pool1, filters = 64, kernel_size = [5,5], padding = 'same', activation = tf.nn.relu)\n",
    "    pool2 = tf.layers.max_pooling2d(inputs = conv2, pool_size = [2,2], strides = 2)\n",
    "    pool2_flat = tf.contrib.layers.flatten(pool2)\n",
    "    fc1 = tf.layers.dense(inputs = pool2_flat, units = 1024, activation = tf.nn.relu)\n",
    "    dropout1 = tf.layers.dropout(inputs = fc1, rate = 0.5, training = is_train)\n",
    "    return dropout1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_epoch: 0, training loss: 0.092201, traing accuracy: 0.969582\n",
      "num_epoch: 1, training loss: 0.038820, traing accuracy: 0.987673\n",
      "num_epoch: 2, training loss: 0.030399, traing accuracy: 0.990200\n",
      "num_epoch: 3, training loss: 0.022998, traing accuracy: 0.992582\n",
      "num_epoch: 4, training loss: 0.014643, traing accuracy: 0.995655\n",
      "num_epoch: 5, training loss: 0.010560, traing accuracy: 0.996782\n",
      "num_epoch: 6, training loss: 0.007781, traing accuracy: 0.997745\n",
      "num_epoch: 7, training loss: 0.007412, traing accuracy: 0.997673\n",
      "num_epoch: 8, training loss: 0.006565, traing accuracy: 0.998055\n",
      "num_epoch: 9, training loss: 0.004854, traing accuracy: 0.998527\n",
      "num_epoch: 10, training loss: 0.003561, traing accuracy: 0.999000\n",
      "num_epoch: 11, training loss: 0.005207, traing accuracy: 0.998345\n",
      "num_epoch: 12, training loss: 0.004140, traing accuracy: 0.998582\n",
      "num_epoch: 13, training loss: 0.002745, traing accuracy: 0.999236\n",
      "num_epoch: 14, training loss: 0.003177, traing accuracy: 0.999073\n",
      "num_epoch: 15, training loss: 0.002215, traing accuracy: 0.999291\n",
      "num_epoch: 16, training loss: 0.002270, traing accuracy: 0.999218\n",
      "testing accuracy: 0.993000\n"
     ]
    }
   ],
   "source": [
    "# Training and evaluation\n",
    "# Prepare data\n",
    "X_train = mnist.train.images.reshape((-1, 28, 28, 1))\n",
    "y_train = mnist.train.labels\n",
    "X_test = mnist.test.images.reshape((-1, 28, 28, 1))\n",
    "y_test = mnist.test.labels\n",
    "\n",
    "# Hyperparameters\n",
    "class_num = 10\n",
    "learning_rate = 1e-4\n",
    "num_epochs = 400\n",
    "batch_size = 10\n",
    "train_size = X_train.shape[0]\n",
    "test_size = X_test.shape[0]\n",
    "\n",
    "# Input\n",
    "X = tf.placeholder(tf.float32, [None, 28, 28, 1])\n",
    "y = tf.placeholder(tf.int32, [None])\n",
    "is_train = tf.placeholder(tf.bool)\n",
    "\n",
    "# Inference\n",
    "features = model(X, is_train)\n",
    "logits = tf.layers.dense(inputs = features, units = class_num)\n",
    "\n",
    "# Loss function\n",
    "onehot_labels = tf.one_hot(indices = tf.cast(y, tf.int32), depth = class_num)\n",
    "loss = tf.losses.softmax_cross_entropy(onehot_labels = onehot_labels, logits = logits)\n",
    "\n",
    "# Define training ops\n",
    "train_op = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss = loss)\n",
    "\n",
    "# Define accuracy\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(onehot_labels, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "# Define session\n",
    "sess = tf.Session()\n",
    "\n",
    "# Global intialization\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# Training\n",
    "new_training_loss = 0.0\n",
    "for i in range(num_epochs):\n",
    "    s = 0\n",
    "    while s < train_size:\n",
    "        e = min(s + batch_size, train_size)\n",
    "        batch_X = X_train[s:e]\n",
    "        batch_y = y_train[s:e]\n",
    "        sess.run(train_op, feed_dict = {X: batch_X, y: batch_y, is_train: True})\n",
    "        s = e\n",
    "    [training_loss, training_score] = sess.run([loss, accuracy], feed_dict = {X: X_train, y: y_train, is_train: False})\n",
    "    print ('num_epoch: %d, training loss: %f, traing accuracy: %f'%(i, training_loss, training_score))\n",
    "    \n",
    "    # Early stopping\n",
    "    if math.fabs(training_loss - new_training_loss) < 1e-4:\n",
    "        break\n",
    "    new_training_loss = training_loss\n",
    "    \n",
    "# Testing\n",
    "test_score = sess.run(accuracy, feed_dict = {X: X_test, y: y_test, is_train: False})\n",
    "print (\"testing accuracy: %f\"%(test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(730, 807)\n"
     ]
    },
    {
     "data": {
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YfD7fS/gVi0W2bt0aaOo0CDXGQWMYBl1dXXieR6FQCDWVGqO8/21tbb20RYXa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e30efdef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Soduku puzzle solver\n",
    "plt.figure(figsize = (5,5))\n",
    "img = cv2.imread(\"manual3.bmp\", 0)\n",
    "plt.imshow(img, cmap = 'gray')\n",
    "print (img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 577,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cd67bf320>"
      ]
     },
     "execution_count": 577,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZcFDy88bLys/P57bbbsPv9+Nyuejs7KSjowNRFJk7d25Cj+b5fD66urqwWCyqRT3/ohEI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\nxAB2R2TQawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e30efd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Blue image to smooth out noise\n",
    "plt.figure(figsize = (5,5))\n",
    "g_img = cv2.GaussianBlur(src = img, ksize = (9,9), sigmaX = 0)\n",
    "plt.imshow(g_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 578,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8cff5cf668>"
      ]
     },
     "execution_count": 578,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7fNJcGTnmCzgD+BpwCXAW8EXg1T2H+UbgMuDB2rk/AvaFz/uA94bPVwP/yGRV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd68f1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Thresholding images\n",
    "th_img = cv2.adaptiveThreshold(g_img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)\n",
    "th_img = cv2.bitwise_not(th_img)\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.imshow(th_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 579,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8c7160fa90>"
      ]
     },
     "execution_count": 579,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ap9N7Ae8E7kizHAWuT68Ppvekz9+lFTn0uNLd88kbG7pNtvG6l+t8Evh14NXp/YXAMxHx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\nrYGzMmo9f5JeBfw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cfd38fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dilate image to re-connect broken outlines\n",
    "kernel = np.array([[0,1,0],[1,1,1],[0,1,0]])\n",
    "dil_img = cv2.dilate(th_img, None)\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.imshow(dil_img, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 580,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contours finding\n",
    "# Note: use dil_img.copy() instead of directy inputing dil_img, findContours will change the src image\n",
    "_, contours, _ = cv2.findContours(dil_img.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "# Sort according to contour area\n",
    "sorted_contours = sorted(contours, key = lambda x: cv2.contourArea(x), reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top left:  (12, 489) bottom right:  (262, 712)\n",
      "top left:  (278, 489) bottom right:  (529, 711)\n",
      "top left:  (541, 489) bottom right:  (791, 711)\n",
      "top left:  (278, 252) bottom right:  (529, 474)\n",
      "top left:  (12, 15) bottom right:  (263, 237)\n",
      "top left:  (12, 252) bottom right:  (262, 474)\n",
      "top left:  (541, 252) bottom right:  (791, 474)\n",
      "top left:  (541, 15) bottom right:  (791, 237)\n",
      "top left:  (278, 15) bottom right:  (528, 237)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7oARkAlhGJoDuhryP15/o4ALKkzo4N39bvP82Sc+N979d0s3DSgSKQSaAZWQCWLHTO9eH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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd5471550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the first 11 contours by the descending order of contour area.\n",
    "# The biggest one is the outline\n",
    "# The second to tenth is the outline for each 3*3 sub-soduku\n",
    "blobs = []\n",
    "plt.figure(figsize = (10,10))\n",
    "for i in range(1, 10):\n",
    "    # Approximate contour with lines\n",
    "    perimeter = cv2.arcLength(sorted_contours[i], True)\n",
    "    epsilon = 0.1 * cv2.arcLength(sorted_contours[i],True)\n",
    "    approx = cv2.approxPolyDP(sorted_contours[i], epsilon, True)\n",
    "    [tl_x, tl_y] = approx[0][0]\n",
    "    [br_x, br_y] = approx[2][0]\n",
    "    box_img = cv2.rectangle(cv2.cvtColor(dil_img, cv2.COLOR_GRAY2BGR), (tl_x, tl_y), (br_x, br_y), color = (255, 0, 0), thickness = 15)\n",
    "    plt.subplot(3, 3, i)\n",
    "    plt.imshow(box_img)\n",
    "    # Store blobs in list\n",
    "    blobs.append((tl_x, tl_y, br_x, br_y))\n",
    "    print ('top left: ', (tl_x, tl_y), 'bottom right: ', (br_x, br_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h/t7ZZ4KPDAIAABjJ1KcaAQAAZoOFFwAAwEhYeAEAAIyEhRcAAMBIWHgBAACM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\nJCy8AAAARsLCCwAAYCT/F0y/8mwF2bDSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd5384a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort blobs by the location of blob in the image in asending order of the up-left corner of each blob\n",
    "def blob_sorting(blobs):\n",
    "    sorted_blobs = sorted(blobs, key = lambda x: x[1])\n",
    "    sorted_blobs[:3] = sorted(sorted_blobs[:3], key = lambda x: x[0])\n",
    "    sorted_blobs[3:6] = sorted(sorted_blobs[3:6], key = lambda x: x[0])\n",
    "    sorted_blobs[6:9] = sorted(sorted_blobs[6:9], key = lambda x: x[0])\n",
    "    return sorted_blobs\n",
    "\n",
    "sorted_blobs = blob_sorting(blobs)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize = (10,10))\n",
    "i = 1\n",
    "for (tl_x, tl_y, br_x, br_y) in sorted_blobs:\n",
    "    sort_blob_img = cv2.rectangle(cv2.cvtColor(dil_img, cv2.COLOR_GRAY2BGR), (tl_x, tl_y), (br_x, br_y), color = (255, 0, 0), thickness = 15)\n",
    "    plt.subplot(3, 3, i)\n",
    "    plt.imshow(sort_blob_img)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DAADolIIOAACgUwo6AACATinoAAAAOjWpgq6qXLUIGB3ZBIyVfIL+dV/Q7RZE\nUwimrXlNYS6wimQTMFbyCaal64Ju+x+sP15gLGQTMFbyCaan24Lu24XQvP/pJcDLkU3AWMknmKYu\nC7rtgdRam3QITXluMDWyCRgr+QTT1V1BtzOQdrbttt2b3scPq0g2AWMln2Daji17APsxtfDZi3eY\noA+yCRgr+QTT11VB11qb/PrvqQctTJFsAsZKPsH0dVXQJZcGz9Yf8RQCabupzQemTjYBYyWfYNq6\n+wzddlN7R2Zq84FVNbW/5anNB1bZ1P6epzYfOIiuCzoAAIBV1t2Sy+221oVP5RT7VOYBq042AWMl\nn2B6uj9D5w8ZGCPZBIyVfIJp6b6gAwAAWFUrV9D58CwwRrIJGCv5BOM2ys/QLSo4FhlQwhD6J5uA\nsZJPsLr2PENXVb9UVc9W1Re2tV1VVQ9U1WPD/ZVDe1XVh6rqYlU9XFU3HGRQbc43YBqWkU/zNu/8\nk4Mwf1PMpkWTgzC7WZZc/kqSd+xouzPJ+dbaqSTnh+0kuTnJqeF2Nsld+x3QIv/AWmtzvS3quWCF\n/UoWmU8LzIxFmdp8YCR+JQvMpmRx+TTFHITe7VnQtdb+R5Kv7Wg+neTc8Phcklu3tX+4bfpMkiuq\n6toDj661o70Bk7LUfJq3o84/OQgLM+lsWjQ5CHs66EVRrmmtPZ0kw/3VQ/uJJE9u67c+tAEsinwC\nxkg2AXNx1Fe53O3Tq7u+FVJVZ6vqQlVd2NjYOOJhAFxipnySTcCCee0EHMpBC7pntpYDDPfPDu3r\nSa7b1u9kkqd2+wattbtba2uttbXjx48fcBgAlzhUPskmYE68dgLm4qAF3X1JzgyPzyS5d1v7e4Yr\nNt2Y5Pmt5QUACyKfgDGSTcBc7Pl/6Krq15P8UJLvrar1JP8hyc8m+WhV3Z7kiSS3Dd3vT3JLkotJ\nvpnkvXMYM0AS+QSMk2wCFmnPgq619u6X2XXTLn1bkjsOOyiAWcgnYIxkE7BIexZ0Y1W122eI43+Y\nAEslm4Cxkk8wTUd9lcule7mwAlgm2QSMlXyCvnV7hm7nu0nCCBgD2QSMlXyCaZrcGTrLBoAxkk3A\nWMkn6NskCjrvMAFjJJuAsZJPMB3dLrlMLg0j7zABYyCbgLGSTzA9XRV0rs4EjJFsAsZKPsH0TWbJ\npaUDwNjIJmCs5BNMR1dn6Pa6OtP2be88AYsim4Cxkk8wfV0VdDu5/C4wRrIJGCv5BNPTdUG3k3eW\ngDGSTcBYySfo3yQ+QwcAALCKxn2GzjIAYFXJPwBZCDMYXUFXSRZ18n+R68atUYf+TfHveIpzglW0\nqL9lmQHjM7qCLtks6gBWkfwPo7QPAAAgAElEQVQDkIWwHz5DBwAA0KlRnqGb9xWXtpYLLOp55v1c\nlj/AYsimgz8PMF/y6eDPA71zhg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgU3sWdFV1XVV9\nqqoeqaovVtX7hvarquqBqnpsuL9yaK+q+lBVXayqh6vqhnlPAlg9sgkYK/kELNIsZ+heSPJTrbXv\nT3Jjkjuq6geS3JnkfGvtVJLzw3aS3Jzk1HA7m+SuIx/1nFXVvm7AUsgm2QRjJZ/kEyzMngVda+3p\n1trvD4//IskjSU4kOZ3k3NDtXJJbh8enk3y4bfpMkiuq6tojHzmw0mQTMFbyCVikfX2Grqpek+RN\nSR5Mck1r7elkM7iSXD10O5HkyW1ftj607fxeZ6vqQlVd2NjY2P/I52j7P7JsrV1ye7m+wHLIJtkE\nYyWf5BPM28wFXVV9V5LfSvITrbU//3Zdd2m75C+3tXZ3a22ttbZ2/PjxWYcB8BKyCRgr+QQswkwF\nXVV9RzYD6ddaa789ND+ztRxguH92aF9Pct22Lz+Z5KmjGe7ybV/37R0mWC7Z9CLZBOMin14kn2C+\nZrnKZSW5J8kjrbWf37brviRnhsdnkty7rf09wxWbbkzy/Nbygp7stkRg535geWTTy+8Hlks+vfx+\n4Ogdm6HPW5P88yR/UFWfH9r+XZKfTfLRqro9yRNJbhv23Z/kliQXk3wzyXuPdMRL5KpMMCqyaSCb\nYHTk00A+wfztWdC11v5ndl/bnSQ37dK/JbnjkOMaNe8wwfLJpkvJJhgH+XQp+QTzs6+rXK4y7zAB\nYySbgLGST7AYCrp98g4TMEayCRgr+QTzpaADAADolIIOAACgU7Nc5ZJYLgCMk2wCxko+wWKMsqBb\n1IdoF/lhXR8Mhv7JJmCs5BOsLksuAQAAOjWqM3ROzQNjJJuAsZJPgDN0AAAAnVLQAQAAdEpBBwAA\n0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABA\npxR0AAAAnVLQAQAAdGrPgq6qXlVVv1dV/6uqvlhVPzO0v7aqHqyqx6rqN6rqFUP7K4fti8P+18x3\nCsAqkk3AWMknYJFmOUP3f5K8rbX2hiRvTPKOqroxyc8l+UBr7VSSrye5feh/e5Kvt9a+L8kHhn4A\nR002AWMln4CF2bOga5v+97D5HcOtJXlbko8N7eeS3Do8Pj1sZ9h/U1XVkY0YILIJGC/5BCzSTJ+h\nq6rLqurzSZ5N8kCSryT5RmvthaHLepITw+MTSZ5MkmH/80levcv3PFtVF6rqwsbGxuFmAawk2QSM\nlXwCFmWmgq619v9aa29McjLJm5N8/27dhvvd3lFqlzS0dndrba21tnb8+PFZxwvwLbIJGCv5BCzK\nvq5y2Vr7RpJPJ7kxyRVVdWzYdTLJU8Pj9STXJcmw/3uSfO0oBguwG9kEjJV8AuZtlqtcHq+qK4bH\nfyvJDyd5JMmnkrxz6HYmyb3D4/uG7Qz7P9lau+RdJoDDkE3AWMknYJGO7d0l1yY5V1WXZbMA/Ghr\n7eNV9aUkH6mq/5jkc0nuGfrfk+RXq+piNt9detccxg0gm4Cxkk/AwuxZ0LXWHk7ypl3av5rNNeE7\n2/8qyW1HMjqAlyGbgLGST8Ai7eszdAAAAIyHgg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACg\nUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p6AAAADqloAMAAOiUgg4AAKBTCjoAAIBO\nKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6NTMBV1VXVZVn6uqjw/br62qB6vq\nsar6jap6xdD+ymH74rD/NfMZOoBsAsZLPgGLsJ8zdO9L8si27Z9L8oHW2qkkX09y+9B+e5Kvt9a+\nL8kHhn4A8yKbgLGST8DczVTQVdXJJP8syS8O25XkbUk+NnQ5l+TW4fHpYTvD/puG/gBHSjYBYyWf\ngEWZ9QzdB5P8dJK/GbZfneQbrbUXhu31JCeGxyeSPJkkw/7nh/4vUVVnq+pCVV3Y2Ng44PCBFSeb\ngLGST8BC7FnQVdWPJHm2tfbQ9uZdurYZ9r3Y0NrdrbW11tra8ePHZxoswBbZBIyVfAIW6dgMfd6a\n5Eer6pYkr0ry3dl81+mKqjo2vJN0MslTQ//1JNclWa+qY0m+J8nXjnzkwKqTTcBYySdgYfY8Q9da\ne39r7WRr7TVJ3pXkk621H0/yqSTvHLqdSXLv8Pi+YTvD/k+21i55lwngMGQTMFbyCVikw/wfun+b\n5Cer6mI213nfM7Tfk+TVQ/tPJrnzcEME2BfZBIyVfAKO3CxLLr+ltfbpJJ8eHn81yZt36fNXSW47\ngrEBzEQ2AWMln4B5O8wZOgAAAJZIQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABA\npxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACd\nUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0KmZCrqqeryq/qCqPl9VF4a2q6rq\ngap6bLi/cmivqvpQVV2sqoer6oZ5TgBYXbIJGCv5BCzKfs7Q/ePW2htba2vD9p1JzrfWTiU5P2wn\nyc1JTg23s0nuOqrBAuxCNgFjJZ+AuTvMksvTSc4Nj88luXVb+4fbps8kuaKqrj3E8wDsh2wCxko+\nAUdu1oKuJfnvVfVQVZ0d2q5prT2dJMP91UP7iSRPbvva9aHtJarqbFVdqKoLGxsbBxs9sOpkEzBW\n8glYiGMz9ntra+2pqro6yQNV9eVv07d2aWuXNLR2d5K7k2Rtbe2S/QAzkE3AWMknYCFmOkPXWntq\nuH82ye8keXOSZ7aWAwz3zw7d15Nct+3LTyZ56qgGDLBFNgFjJZ+ARdmzoKuqy6vqb289TvJPknwh\nyX1JzgzdziS5d3h8X5L3DFdsujHJ81vLCwCOimwCxko+AYs0y5LLa5L8TlVt9f+vrbXfrarPJvlo\nVd2e5Ikktw39709yS5KLSb6Z5L1HPmoA2QSMl3wCFmbPgq619tUkb9il/c+S3LRLe0tyx5GMDuBl\nyCZgrOQTsEiH+bcFAAAALJGCDgAAoFMKOgAAgE4p6AAAADqloAMAAOiUgg4AAKBTCjoAAIBOKegA\nAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p6AAAADqloAMA\nAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADo1EwFXVVdUVUfq6ovV9UjVfWWqrqqqh6oqseG+yuH\nvlVVH6qqi1X1cFXdMN8pAKtKNgFjJZ+ARZn1DN0vJPnd1tr1Sd6Q5JEkdyY531o7leT8sJ0kNyc5\nNdzOJrnrSEcM8CLZBIyVfAIWYs+Crqq+O8k/SnJPkrTW/rq19o0kp5OcG7qdS3Lr8Ph0kg+3TZ9J\nckVVXXvkIwdWmmwCxko+AYs0yxm61yXZSPLLVfW5qvrFqro8yTWttaeTZLi/euh/IsmT275+fWgD\nOEqyCRgr+QQszCwF3bEkNyS5q7X2piR/mReXCOymdmlrl3SqOltVF6rqwsbGxkyDBdhGNgFjJZ+A\nhZmloFtPst5ae3DY/lg2Q+qZreUAw/2z2/pft+3rTyZ5auc3ba3d3Vpba62tHT9+/KDjB1aXbALG\nSj4BC7NnQdda+9MkT1bV64emm5J8Kcl9Sc4MbWeS3Ds8vi/Je4YrNt2Y5Pmt5QUAR0U2AWMln4BF\nOjZjv3+V5Neq6hVJvprkvdksBj9aVbcneSLJbUPf+5PckuRikm8OfQHmQTYBYyWfgIWYqaBrrX0+\nydouu27apW9LcschxwWwJ9kEjJV8AhZl1v9DBwAAwMgo6AAAADqloAMAAOiUgg4AAKBTCjoAAIBO\nKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p6AAAADql\noAMAAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOrVnQVdVr6+q\nz2+7/XlV/URVXVVVD1TVY8P9lUP/qqoPVdXFqnq4qm6Y/zSAVSObgLGST8Ai7VnQtdYeba29sbX2\nxiQ/mOSbSX4nyZ1JzrfWTiU5P2wnyc1JTg23s0numsfAgdUmm4Cxkk/AIu13yeVNSb7SWvvjJKeT\nnBvazyW5dXh8OsmH26bPJLmiqq49ktEC7E42AWMln4C52m9B964kvz48vqa19nSSDPdXD+0nkjy5\n7WvWh7aXqKqzVXWhqi5sbGzscxgALyGbgLGST8BczVzQVdUrkvxokt/cq+sube2Shtbubq2ttdbW\njh8/PuswAF5CNgFjJZ+ARdjPGbqbk/x+a+2ZYfuZreUAw/2zQ/t6kuu2fd3JJE8ddqAAL0M2AWMl\nn4C5209B9+68uGQgSe5LcmZ4fCbJvdva3zNcsenGJM9vLS8AmAPZBIyVfALm7tgsnarqO5O8Pcm/\n2Nb8s0k+WlW3J3kiyW1D+/1JbklyMZtXdXrvkY0WYBvZBIyVfAIWZaaCrrX2zSSv3tH2Z9m8ctPO\nvi3JHUcyOoBvQzYBYyWfgEXZ71UuAQAAGAkFHQAAQKcUdAAAAJ2qzWXbSx5E1V8keXTZ41iw703y\n3LIHsWDmvBq25vx3W2td/6OkqtpI8pdZ3WO4Ssx5NUwpn7x2Wg3mvBoOlU0zXRRlAR5tra0texCL\nVFUXzHn6zLlvrbXjU5rPrMx5NZhz97x2WgHmvBoOO2dLLgEAADqloAMAAOjUWAq6u5c9gCUw59Vg\nzv2b2nxmYc6rwZz7NqW5zMqcV4M579MoLooCAADA/o3lDB0AAAD7pKADAADo1NILuqp6R1U9WlUX\nq+rOZY/nqFTVdVX1qap6pKq+WFXvG9qvqqoHquqx4f7Kob2q6kPDz+HhqrphuTM4mKq6rKo+V1Uf\nH7ZfW1UPDvP9jap6xdD+ymH74rD/Ncsc92FU1RVV9bGq+vJwvN+yAsf53wy/11+oql+vqldN7VjL\npmn9ziarl0+yaZrZlEwzn2STbJrycZ53Ni21oKuqy5L85yQ3J/mBJO+uqh9Y5piO0AtJfqq19v1J\nbkxyxzC3O5Ocb62dSnJ+2E42fwanhtvZJHctfshH4n1JHtm2/XNJPjDM9+tJbh/ab0/y9dba9yX5\nwNCvV7+Q5Hdba9cneUM25z/Z41xVJ5L86yRrrbW/l+SyJO/KhI61bJrW7+w2q5ZPsmli2ZRMOp9k\n04sm9Tu7C9l01NnUWlvaLclbknxi2/b7k7x/mWOa41zvTfL2JI8muXZouzab/xg0Sf5Lkndv6/+t\nfr3ckpzM5h/h25J8PEll87/eH9t5vJN8IslbhsfHhn617DkcYM7fneSPdo594sf5RJInk1w1HLuP\nJ/mnUzrWsmlav7PDuFcqn2TTNLNp5xyG7Unmk2yazu/sjvnKpjlk07KXXG5NcMv60DYpw6nSNyV5\nMMk1rbWnk2S4v3roNoWfxQeT/HSSvxm2X53kG621F4bt7XP61nyH/c8P/XvzuiQbSX55WC7xi1V1\neSZ8nFtrf5LkPyV5IsnT2Tx2D2Vax7r74zSLFcqmZPXySTZNM5uSCRyrvcimyf3Obieb5pBNyy7o\nape2Sf0fhar6riS/leQnWmt//u267tLWzc+iqn4kybOttYe2N+/Stc2wryfHktyQ5K7W2puS/GVe\nXCawm+7nPaxrP53ktUn+TpLLs7kkYqeej3WPY96XVcmmZGXzSTZNM5uSfsc9E9kkm3bofs6LyKZl\nF3TrSa7btn0yyVNLGsuRq6rvyGYo/Vpr7beH5meq6tph/7VJnh3ae/9ZvDXJj1bV40k+ks2lAx9M\nckVVHRv6bJ/Tt+Y77P+eJF9b5ICPyHqS9dbag8P2x7IZVFM9zknyw0n+qLW20Vr7v0l+O8k/yLSO\n9RSO08tasWxKVjOfZNM0symZxrHalWySTckkj/Pcs2nZBd1nk5warvLyimx+QPC+JY/pSFRVJbkn\nySOttZ/ftuu+JGeGx2eyuUZ8q/09w9V8bkzy/Nap5x601t7fWjvZWntNNo/jJ1trP57kU0neOXTb\nOd+tn8M7h/5dveOSJK21P03yZFW9fmi6KcmXMtHjPHgiyY1V9Z3D7/nWnKd0rGXThH5nVzGfZNNk\nsymZaD7JJtmUiR7nLCKblvkhwWFstyT5wyRfSfLvlz2eI5zXP8zm6dGHk3x+uN2SzTWw55M8Ntxf\nNfSvbF616itJ/iCbV8JZ+jwOOPcfSvLx4fHrkvxekotJfjPJK4f2Vw3bF4f9r1v2uA8x3zcmuTAc\n6/+W5MqpH+ckP5Pky0m+kORXk7xyasdaNk3rd3bb/Fcmn2TTNLNpGPvk8kk2yaYpH+d5Z1MNXwgA\nAEBnlr3kEgAAgANS0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0\nAAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtAB\nAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcA\nANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAA\nQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAA\nnZpLQVdV76iqR6vqYlXdOY/nADgI+QSMkWwCDqpaa0f7DasuS/KHSd6eZD3JZ5O8u7X2pSN9IoB9\nkk/AGMkm4DDmcYbuzUkutta+2lr76yQfSXJ6Ds8DsF/yCRgj2QQc2LE5fM8TSZ7ctr2e5O/v7FRV\nZ5OcTZLLL7/8B6+//vo5DAVYlscffzzPPfdcLXscO+yZT7IJpu+hhx56rrV2fNnj2MZrJ+DAr53m\nUdDtNohL1nW21u5OcneSrK2ttQsXLsxhKMCyrK2tLXsIu9kzn2QTTF9V/fGyx7CD107AgV87zWPJ\n5XqS67Ztn0zy1ByeB2C/5BMwRrIJOLB5nKH7bJJTVfXaJH+S5F1JfmyWL6wa2+qsvhz1BW5ggg6U\nT7LpcGQT7MlrpyWRT0zBkRd0rbUXqupfJvlEksuS/P/t3V+sZVd9H/DvTx4MDSmMTa4td8bUIEYE\nVAnjjtBQqirFSRu7EeMHLIGieoRGmj5YLTSRUtM+VJH6AFIVE0uVVQsnDBElOA7UI8sitQZQ1Qec\nDME1fwz1QFJ7aseeBmNSrLSlWX2469rHM9e+587cc+7e+3w+0tHZe+115641+96v7u/sdfb5rdba\nt3b6+wBsl3wChkg2ARdjEVfo0lp7IMkDF/H1Ozia8228mrWs77Po7+XVOZjfxeSTbLrw7wO8Mn87\nvfT7LPp7ySemZCEfLA4AAMDiKegAAABGSkEHAAAwUgo6AACAkVLQAQAAjNSkCrqqctciYHBkEzBU\n8gnGb/QF3WZBNIVg2pjXFOYCq0g2AUMln2BaRl3Qzf7C+uUFhkI2AUMln2B6RlvQvVIILfpDLwFe\njmwChko+wTSNsqCbDaTW2qRDaMpzg6mRTcBQySeYrtEVdOcG0rltm+2PzdjHD6tINgFDJZ9g2vbs\n9gC2Y2rhsxWvMME4yCZgqOQTTN+oCrrW2uTXf089aGGKZBMwVPIJpm9UBV1yfvBs/BJPIZBmTW0+\nMHWyCRgq+QTTNrr30M2a2isyU5sPrKqp/S5PbT6wyqb2+zy1+cCFGHVBBwAAsMpGt+Ry1sa68Klc\nYp/KPGDVySZgqOQTTM/or9D5RQaGSDYBQyWfYFpGX9ABAACsqpUr6Lx5Fhgi2QQMlXyCYRvke+iW\nFRzLDChhCOMnm4Chkk+wura8QldVv1VVz1TVN2faLq+qB6vqsf58WW+vqrqjqk5X1SNVdd0iBw+s\nNvkEDJFsApZpniWXn0ryi+e03ZbkZGvtQJKTfT9JbkhyoD+OJbnzQgbVWlvoY9nfZ9HfC1bYp7LE\nfJJNsgnm9Kn420k+wZJsWdC11v5zkh+c03w4yfG+fTzJTTPtn27rvppkb1VdtVODBZgln4Ahkk3A\nMl3oTVGubK09lST9+Yrevi/JEzP9zvQ2gGWRT8AQySZgIXb6LpebvXt10+vaVXWsqk5V1amzZ8/u\n8DAAzjNXPskmYMn87QRclAst6J7eWA7Qn5/p7WeSXD3Tb3+SJzf7B1prd7XWDrbWDq6trV3gMADO\nc1H5JJuABfG3E7AQF1rQnUhypG8fSXLfTPst/Y5Nh5I8t7G8AGBJ5BMwRLIJWIgtP4euqj6b5OeS\n/ExVnUnyr5N8LMk9VXU0yeNJbu7dH0hyY5LTSZ5P8qEFjBkgiXwChkk2Acu0ZUHXWvvgyxy6fpO+\nLcmtFzsogHnIJ2CIZBOwTFsWdENVtdl7iOOzRYBdJZuAoZJPME07fZfLXfdyYQWwm2QTMFTyCcZt\ntFfozn01SRgBQyCbgKGSTzBNk7tCZ9kAMESyCRgq+QTjNomCzitMwBDJJmCo5BNMx2iXXCbnh5FX\nmIAhkE3AUMknmJ5RFXTuzgQMkWwChko+wfRNZsmlpQPA0MgmYKjkE0zHqK7QbXV3ptl9rzwByyKb\ngKGSTzB9oyrozuX2u8AQySZgqOQTTM+oC7pzeWUJGCLZBAyVfILxm8R76AAAAFaRgg4AAGCkBrnk\nclnruZe5btwadRg/2QQMlXyC1eUKHQAAwEgN7grdMt6a6/UeYKiWdXsCOQgMmb8HYX6DK+iWadF3\ndlrWZ7tYkgDLsczMWBbZBNOwrHyaYg7C2A23oFtEYAgJYCwW9UeTHATGxN+DsCXvoQMAABgpBR0A\nAMBIKegAAABGSkEHAAAwUlsWdFV1dVV9uaoerapvVdWHe/vlVfVgVT3Wny/r7VVVd1TV6ap6pKqu\nW/QkgNUjm4Chkk/AMs1zhe4nSX61tfa2JIeS3FpVb09yW5KTrbUDSU72/SS5IcmB/jiW5M4dH/WC\nVdW2HsCukE2yCYZKPsknWJotC7rW2lOttT/u23+R5NEk+5IcTnK8dzue5Ka+fTjJp9u6rybZW1VX\n7fjIgZUmm4Chkk/AMm3rPXRVdU2SdyZ5KMmVrbWnkvXgSnJF77YvyRMzX3amt537bx2rqlNVders\n2bPbH/kCzX5oZmvtvMfL9QV2h2ySTTBU8kk+waLNXdBV1U8n+f0kH2mt/eiVum7Sdt5vbmvtrtba\nwdbawbW1tXmHAfASsgkYKvkELMNcBV1VvSrrgfSZ1trne/PTG8sB+vMzvf1Mkqtnvnx/kid3Zri7\nb3bdt1eYYHfJphfJJhgW+fQi+QSLNc9dLivJ3Ukeba39xsyhE0mO9O0jSe6bab+l37HpUJLnNpYX\njMlmSwTOPQ7sHtn08seB3SWfXv44sPP2zNHnPUn+cZJvVNXDve1fJvlYknuq6miSx5Pc3I89kOTG\nJKeTPJ/kQzs64l3krkwwKLKpk00wOPKpk0+weFsWdK21/5LN13YnyfWb9G9Jbr3IcQ2aV5hg98mm\n88kmGAb5dD75BIuzrbtcrjKvMAFDJJuAoZJPsBwKum3yChMwRLIJGCr5BIuloAMAABgpBR0AAMBI\nzXOXy90xsHXXlgsASzVnBr6QTAPLTIAd+dtJtsGWBlfQVWb+QFn091piSHhjMIzfMn6Pl5GBs7OQ\nTTANy/pdXtr3yWKzUPIxJYMr6BK/ZMBqk4EAshDmNaiCzrJGYIhkEzBU8glwUxQAAICRUtABAACM\nlIIOAABgpBR0AAAAI6WgAwAAGCkFHQAAwEgp6AAAAEZKQQcAADBSCjoAAICRUtABAACMlIIOAABg\npBR0AAAAI6WgAwAAGKktC7qqek1V/WFV/deq+lZV/Xpvf1NVPVRVj1XV56rq0t7+6r5/uh+/ZrFT\nAFaRbAKGSj4ByzTPFbr/neS9rbV3JLk2yS9W1aEkH09ye2vtQJJnkxzt/Y8meba19pYkt/d+ADtN\nNgFDJZ+ApdmyoGvr/lfffVV/tCTvTXJvbz+e5Ka+fbjvpx+/vqpqx0YMENkEDJd8ApZprvfQVdUl\nVfVwkmeSPJjke0l+2Fr7Se9yJsm+vr0vyRNJ0o8/l+QNOzlogEQ2AcMln4Blmauga639v9batUn2\nJ3lXkrdt1q0/b/aKUju3oaqOVdWpqjp19uzZeccL8ALZBAyVfAKWZVt3uWyt/TDJV5IcSrK3qvb0\nQ/uTPNm3zyS5Okn68dcn+cEm/9ZdrbWDrbWDa2trFzZ6gMgmYLjkE7Bo89zlcq2q9vbtv5bk55M8\nmuTLSd7fux1Jcl/fPtH3049/qbV23qtMABdDNgFDJZ+AZdqzdZdcleR4VV2S9QLwntba/VX17SS/\nW1X/JsnXk9zd+9+d5Heq6nTWX136wALGDSCbgKGST8DSbFnQtdYeSfLOTdq/n/U14ee2/2WSm3dk\ndAAvQzYBQyWfgGXa1nvoAAAAGA4FHQAAwEgp6AAAAEZKQQcAADBSCjoAAICRUtABAACMlIIOAABg\npBR0AAAAI6WgAwAAGCkFHQAAwEgp6AAAAEZKQQcAADBSCjoAAICRUtABAACMlIIOAABgpBR0AAAA\nI6WgAwAAGCkFHQAAwEgp6AAAAEZKQQcAADBSCjoAAICRUtABAACM1NwFXVVdUlVfr6r7+/6bquqh\nqnqsqj5XVZf29lf3/dP9+DWLGTqAbAKGSz4By7CdK3QfTvLozP7Hk9zeWjuQ5NkkR3v70STPttbe\nkuT23g9gUWQTMFTyCaGmSzwAAA18SURBVFi4uQq6qtqf5B8l+WTfryTvTXJv73I8yU19+3DfTz9+\nfe8PsKNkEzBU8glYlnmv0H0iya8l+au+/4YkP2yt/aTvn0myr2/vS/JEkvTjz/X+L1FVx6rqVFWd\nOnv27AUOH1hxsgkYKvkELMWWBV1V/VKSZ1prX5tt3qRrm+PYiw2t3dVaO9haO7i2tjbXYAE2yCZg\nqOQTsEx75ujzniTvq6obk7wmyeuy/qrT3qra019J2p/kyd7/TJKrk5ypqj1JXp/kBzs+cmDVySZg\nqOQTsDRbXqFrrX20tba/tXZNkg8k+VJr7ZeTfDnJ+3u3I0nu69sn+n768S+11s57lQngYsgmYKjk\nE7BMF/M5dP8iya9U1emsr/O+u7ffneQNvf1Xktx2cUME2BbZBAyVfAJ23DxLLl/QWvtKkq/07e8n\nedcmff4yyc07MDaAucgmYKjkE7BoF3OFDgAAgF2koAMAABgpBR0AAMBIKegAAABGSkEHAAAwUgo6\nAACAkVLQAQAAjJSCDgAAYKQUdAAAACOloAMAABgpBR0AAMBIKegAAABGSkEHAAAwUgo6AACAkVLQ\nAQAAjJSCDgAAYKQUdAAAACOloAMAABgpBR0AAMBIKegAAABGSkEHAAAwUnMVdFX1p1X1jap6uKpO\n9bbLq+rBqnqsP1/W26uq7qiq01X1SFVdt8gJAKtLNgFDJZ+AZdnOFbq/31q7trV2sO/fluRka+1A\nkpN9P0luSHKgP44luXOnBguwCdkEDJV8AhbuYpZcHk5yvG8fT3LTTPun27qvJtlbVVddxPcB2A7Z\nBAyVfAJ23LwFXUvyn6rqa1V1rLdd2Vp7Kkn68xW9fV+SJ2a+9kxvA9hpsgkYKvkELMWeOfu9p7X2\nZFVdkeTBqvrOK/StTdraeZ3Ww+1YkrzxjW+ccxgALyGbgKGST8BSzHWFrrX2ZH9+JskXkrwrydMb\nywH68zO9+5kkV898+f4kT27yb97VWjvYWju4trZ24TMAVpZsAoZKPgHLsmVBV1Wvraq/vrGd5B8k\n+WaSE0mO9G5HktzXt08kuaXfselQkuc2lhcA7BTZBAyVfAKWaZ4ll1cm+UJVbfT/D621L1bVHyW5\np6qOJnk8yc29/wNJbkxyOsnzST6046MGkE3AcMknYGm2LOhaa99P8o5N2v88yfWbtLckt+7I6ABe\nhmwChko+Act0MR9bAAAAwC5S0AEAAIyUgg4AAGCkFHQAAAAjpaADAAAYKQUdAADASCnoAAAARkpB\nBwAAMFIKOgAAgJFS0AEAAIyUgg4AAGCkFHQAAAAjpaADAAAYKQUdAADASCnoAAAARkpBBwAAMFIK\nOgAAgJFS0AEAAIyUgg4AAGCkFHQAAAAjpaADAAAYKQUdAADASM1V0FXV3qq6t6q+U1WPVtW7q+ry\nqnqwqh7rz5f1vlVVd1TV6ap6pKquW+wUgFUlm4Chkk/Assx7he43k3yxtfazSd6R5NEktyU52Vo7\nkORk30+SG5Ic6I9jSe7c0REDvEg2AUMln4Cl2LKgq6rXJfl7Se5Oktba/2mt/TDJ4STHe7fjSW7q\n24eTfLqt+2qSvVV11Y6PHFhpsgkYKvkELNM8V+jenORskt+uqq9X1Ser6rVJrmytPZUk/fmK3n9f\nkidmvv5Mb3uJqjpWVaeq6tTZs2cvahLASpJNwFDJJ2Bp5ino9iS5LsmdrbV3JvlxXlwisJnapK2d\n19DaXa21g621g2tra3MNFmCGbAKGSj4BSzNPQXcmyZnW2kN9/96sh9TTG8sB+vMzM/2vnvn6/Ume\n3JnhArxANgFDJZ+ApdmyoGut/VmSJ6rqrb3p+iTfTnIiyZHediTJfX37RJJb+h2bDiV5bmN5AcBO\nkU3AUMknYJn2zNnvnyb5TFVdmuT7ST6U9WLwnqo6muTxJDf3vg8kuTHJ6STP974AiyCbgKGST8BS\nzFXQtdYeTnJwk0PXb9K3Jbn1IscFsCXZBAyVfAKWZd7PoQMAAGBgFHQAAAAjpaADAAAYKQUdAADA\nSCnoAAAARkpBBwAAMFIKOgAAgJFS0AEAAIyUgg4AAGCkFHQAAAAjpaADAAAYKQUdAADASCnoAAAA\nRkpBBwAAMFIKOgAAgJFS0AEAAIyUgg4AAGCkFHQAAAAjpaADAAAYKQUdAADASCnoAAAARmrLgq6q\n3lpVD888flRVH6mqy6vqwap6rD9f1vtXVd1RVaer6pGqum7x0wBWjWwChko+Acu0ZUHXWvtua+3a\n1tq1Sf52kueTfCHJbUlOttYOJDnZ95PkhiQH+uNYkjsXMXBgtckmYKjkE7BM211yeX2S77XW/nuS\nw0mO9/bjSW7q24eTfLqt+2qSvVV11Y6MFmBzsgkYKvkELNR2C7oPJPls376ytfZUkvTnK3r7viRP\nzHzNmd4GsCiyCRgq+QQs1NwFXVVdmuR9SX5vq66btLVN/r1jVXWqqk6dPXt23mEAvIRsAoZKPgHL\nsJ0rdDck+ePW2tN9/+mN5QD9+ZnefibJ1TNftz/Jk+f+Y621u1prB1trB9fW1rY/coB1sgkYKvkE\nLNx2CroP5sUlA0lyIsmRvn0kyX0z7bf0OzYdSvLcxvICgAWQTcBQySdg4fbM06mqfirJLyT5JzPN\nH0tyT1UdTfJ4kpt7+wNJbkxyOut3dfrQjo0WYIZsAoZKPgHLMldB11p7Pskbzmn786zfuencvi3J\nrTsyOoBXIJuAoZJPwLJs9y6XAAAADISCDgAAYKQUdAAAACOloAMAABipWn8f7i4Pouovknx3t8ex\nZD+T5H/u9iCWzJxXw8ac/2ZrbdQflFRVZ5P8OKt7DleJOa+GKeWTv51WgzmvhovKprnucrkE322t\nHdztQSxTVZ0y5+kz53Frra1NaT7zMufVYM6j52+nFWDOq+Fi52zJJQAAwEgp6AAAAEZqKAXdXbs9\ngF1gzqvBnMdvavOZhzmvBnMetynNZV7mvBrMeZsGcVMUAAAAtm8oV+gAAADYpl0v6KrqF6vqu1V1\nuqpu2+3x7JSqurqqvlxVj1bVt6rqw7398qp6sKoe68+X9faqqjv6/8MjVXXd7s7gwlTVJVX19aq6\nv++/qaoe6vP9XFVd2ttf3fdP9+PX7Oa4L0ZV7a2qe6vqO/18v3sFzvM/7z/X36yqz1bVa6Z2rmXT\ntH5mk9XLJ9k0zWxKpplPskk2Tfk8LzqbdrWgq6pLkvy7JDckeXuSD1bV23dzTDvoJ0l+tbX2tiSH\nktza53ZbkpOttQNJTvb9ZP3/4EB/HEty5/KHvCM+nOTRmf2PJ7m9z/fZJEd7+9Ekz7bW3pLk9t5v\nrH4zyRdbaz+b5B1Zn/9kz3NV7Uvyz5IcbK39rSSXJPlAJnSuZdO0fmZnrFo+yaaJZVMy6XySTS+a\n1M/sJmTTTmdTa23XHkneneQPZvY/muSjuzmmBc71viS/kPUPAb2qt12V9c+RSZJ/n+SDM/1f6DeW\nR5L9Wf8lfG+S+5NU1j8kcc+55zvJHyR5d9/e0/vVbs/hAub8uiR/cu7YJ36e9yV5Isnl/dzdn+Qf\nTulcy6Zp/cz2ca9UPsmmaWbTuXPo+5PMJ9k0nZ/Zc+YrmxaQTbu95HJjghvO9LZJ6ZdK35nkoSRX\nttaeSpL+fEXvNoX/i08k+bUkf9X335Dkh621n/T92Tm9MN9+/Lnef2zenORskt/uyyU+WVWvzYTP\nc2vtfyT5t0keT/JU1s/d1zKtcz368zSPFcqmZPXySTZNM5uSCZyrrcimyf3MzpJNC8im3S7oapO2\nSd12s6p+OsnvJ/lIa+1Hr9R1k7bR/F9U1S8leaa19rXZ5k26tjmOjcmeJNclubO19s4kP86LywQ2\nM/p593Xth5O8KcnfSPLarC+JONeYz/UYx7wtq5JNycrmk2yaZjYl4x33XGSTbDrH6Oe8jGza7YLu\nTJKrZ/b3J3lyl8ay46rqVVkPpc+01j7fm5+uqqv68auSPNPbx/5/8Z4k76uqP03yu1lfOvCJJHur\nak/vMzunF+bbj78+yQ+WOeAdcibJmdbaQ33/3qwH1VTPc5L8fJI/aa2dba393ySfT/J3Mq1zPYXz\n9LJWLJuS1cwn2TTNbEqmca42JZtkUzLJ87zwbNrtgu6Pkhzod3m5NOtvEDyxy2PaEVVVSe5O8mhr\n7TdmDp1IcqRvH8n6GvGN9lv63XwOJXlu49LzGLTWPtpa299auybr5/FLrbVfTvLlJO/v3c6d78b/\nw/t7/1G94pIkrbU/S/JEVb21N12f5NuZ6HnuHk9yqKp+qv+cb8x5SudaNk3oZ3YV80k2TTabkonm\nk2ySTZnoec4ysmk33yTYx3Zjkv+W5HtJ/tVuj2cH5/V3s3559JEkD/fHjVlfA3syyWP9+fLev7J+\n16rvJflG1u+Es+vzuMC5/1yS+/v2m5P8YZLTSX4vyat7+2v6/ul+/M27Pe6LmO+1SU71c/0fk1w2\n9fOc5NeTfCfJN5P8TpJXT+1cy6Zp/czOzH9l8kk2TTOb+tgnl0+ySTZN+TwvOpuqfyEAAAAjs9tL\nLgEAALhACjoAAICRUtABAACMlIIOAABgpBR0AAAAI6WgAwAAGCkFHQAAwEgp6AAAAEbq/wOiY9DT\nHYX2fAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd5446438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3k7xtl3X+MsktU5gbwERkEzBW8gmYtcMcoQMAAGCBFDoAAIBOKXQAAACdUugAAAA6pdAB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cbb2cfb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EzNyx/Vd5UWvtd5L8znD/q0nevMc6f5nkthnMDWAqsgkYK/kEzNtRjtABAACwRAodAABA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd43f6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEA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\nAAATpaADAACYKAUdAADARP1/PUr9FwP9vRQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cff88cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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joFsymw/Q9XXR262hHrON43+lAOppTrBsZBMwVvKJsfM3dEtmqzXSPQfSulcas3XhMH6y\nCRgr+cTYOUO3pDav+57CQTuFOcCyk03AWMknxkpBN1E7Xfu827XSi1xjbT039E82AWMln+iVJZcA\nAACdUtABAAB0ypLLibEWGhgj2QSMlXyid87QAQAAdEpBBwAA0KmZCrqqerSqvlJVX66qc0Pb5VV1\nX1U9MtxeNrRXVd1RVeer6sGqum6eEwCWl2wCxko+AYuykzN0/6i19ubW2sqwfXuSs621Y0nODttJ\ncmOSY8PXySR37tdgAbYgm4Cxkk/A3O1lyeXxJKeH+6eT3Lyh/WNtzReSXFpVV+3heQB2QjYBYyWf\ngH03a0HXkvzXqnqgqk4ObVe21p5MkuH2iqH9SJLHN3zv6tD2IlV1sqrOVdW5Cxcu7G70wLKTTcBY\nySdgIWb92IK3t9aeqKorktxXVd94hb5bfST9S64H21o7leRUkqysrLheLLAbsgkYK/kELMRMZ+ha\na08Mt08n+VSStyZ5an05wHD79NB9NcnVG779aJIn9mvAAOtkEzBW8glYlG0Luqq6pKp+eP1+kn+c\n5KtJziQ5MXQ7keSe4f6ZJO8brth0fZLn1pcXAOwX2QSMlXwCFmmWJZdXJvlUVa33/8+ttT+sqi8m\n+URV3ZrksSS3DP3vTXJTkvNJvpfk/fs+agDZBIyXfAIWZtuCrrX2rSRv2qL9L5LcsEV7S3LbvowO\n4GXIJmCs5BOwSHv52AIAAAAOkIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p\n6AAAADqloAMAAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWg\nAwAA6JSCDgAAoFMKOgAAgE4p6AAAADqloAMAAOjUTAVdVV1aVXdX1Teq6qGqeltVXV5V91XVI8Pt\nZUPfqqo7qup8VT1YVdfNdwrAspJNwFjJJ2BRZj1D9+tJ/rC1dm2SNyV5KMntSc621o4lOTtsJ8mN\nSY4NXyeT3LmvIwZ4gWwCxko+AQuxbUFXVT+S5B8muStJWmt/01r7TpLjSU4P3U4nuXm4fzzJx9qa\nLyS5tKqu2veRA0tNNgFjJZ+ARZrlDN3rk1xI8ptV9aWq+mhVXZLkytbak0ky3F4x9D+S5PEN3786\ntAHsJ9kEjJV8AhZmloLuUJLrktzZWntLkr/KC0sEtlJbtLWXdKo6WVXnqurchQsXZhoswAayCRgr\n+QQszCwF3WqS1dba/cP23VkLqafWlwMMt09v6H/1hu8/muSJzT+0tXaqtbbSWls5fPjwbscPLC/Z\nBIyVfAIWZtuCrrX250ker6ofH5puSPL1JGeSnBjaTiS5Z7h/Jsn7his2XZ/kufXlBQD7RTYBYyWf\ngEU6NGO/f5Hkd6rq4iTfSvL+rBWDn6iqW5M8luSWoe+9SW5Kcj7J94a+APMgm4Cxkk/AQsxU0LXW\nvpxkZYuHbtiib0ty2x7HBbAt2QSMlXwCFmXWz6EDAABgZBR0AAAAnVLQAQAAdEpBBwAA0CkFHQAA\nQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAA\nnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACd2ragq6of\nr6ovb/j6blV9sKour6r7quqR4fayoX9V1R1Vdb6qHqyq6+Y/DWDZyCZgrOQTsEjbFnSttYdba29u\nrb05yU8m+V6STyW5PcnZ1tqxJGeH7SS5Mcmx4etkkjvnMXBguckmYKzkE7BIO11yeUOSb7bW/izJ\n8SSnh/bTSW4e7h9P8rG25gtJLq2qq/ZltABbk03AWMknYK52WtC9J8nvDvevbK09mSTD7RVD+5Ek\nj2/4ntWh7UWq6mRVnauqcxcuXNjhMABeRDYBYyWfgLmauaCrqouT/GyS39+u6xZt7SUNrZ1qra20\n1lYOHz486zAAXkQ2AWMln4BF2MkZuhuT/HFr7alh+6n15QDD7dND+2qSqzd839EkT+x1oAAvQzYB\nYyWfgLnbSUH33rywZCBJziQ5Mdw/keSeDe3vG67YdH2S59aXFwDMgWwCxko+AXN3aJZOVfWDSd6Z\n5J9vaP6VJJ+oqluTPJbklqH93iQ3JTmftas6vX/fRguwgWwCxko+AYsyU0HXWvtektduavuLrF25\naXPfluS2fRkdwCuQTcBYySdgUXZ6lUsAAABGQkEHAADQKQUdAABAp2pt2fYBD6LqL5M8fNDjWLAf\nTfLMQQ9iwcx5OazP+e+01rr+oKSqupDkr7K8+3CZmPNymFI++d1pOZjzcthTNs10UZQFeLi1tnLQ\ng1ikqjpnztNnzn1rrR2e0nxmZc7LwZy753enJWDOy2Gvc7bkEgAAoFMKOgAAgE6NpaA7ddADOADm\nvBzMuX9Tm88szHk5mHPfpjSXWZnzcjDnHRrFRVEAAADYubGcoQMAAGCHFHQAAACdOvCCrqreVVUP\nV9X5qrr9oMezX6rq6qr6XFU9VFVfq6oPDO2XV9V9VfXIcHvZ0F5Vdcfw7/BgVV13sDPYnaq6qKq+\nVFWfHrZfV1X3D/P9vaq6eGh/1bB9fnj8moMc915U1aVVdXdVfWPY329bgv38r4bX9Ver6ner6tVT\n29eyaVqv2WT58kk2TTObkmnmk2ySTVPez/POpgMt6KrqoiT/McmNSd6Y5L1V9caDHNM+ej7JL7bW\nfiLJ9UluG+Z2e5KzrbVjSc4O28nav8Gx4etkkjsXP+R98YEkD23Y/tUkHx7m+2ySW4f2W5M821p7\nQ5IPD/169etJ/rC1dm2SN2Vt/pPdz1V1JMm/TLLSWvu7SS5K8p5MaF/Lpmm9ZjdYtnySTRPLpmTS\n+SSbXjCp1+wWZNN+Z1Nr7cC+krwtyWc2bH8oyYcOckxznOs9Sd6Z5OEkVw1tV2Xtg0GT5D8lee+G\n/t/v18tXkqNZOwjfkeTTSSprn3p/aPP+TvKZJG8b7h8a+tVBz2EXc/6RJH+6eewT389Hkjye5PJh\n3306yT+Z0r6WTdN6zQ7jXqp8kk3TzKbNcxi2J5lPsmk6r9lN85VNc8img15yuT7BdatD26QMp0rf\nkuT+JFe21p5MkuH2iqHbFP4tPpLkl5L8v2H7tUm+01p7ftjeOKfvz3d4/Lmhf29en+RCkt8clkt8\ntKouyYT3c2vtfyb5D0keS/Jk1vbdA5nWvu5+P81iibIpWb58kk3TzKZkAvtqO7Jpcq/ZjWTTHLLp\noAu62qJtUp+jUFU/lOQPknywtfbdV+q6RVs3/xZV9TNJnm6tPbCxeYuubYbHenIoyXVJ7mytvSXJ\nX+WFZQJb6X7ew7r240lel+RvJ7kka0siNut5X/c45h1ZlmxKljafZNM0synpd9wzkU2yaZPu57yI\nbDrogm41ydUbto8meeKAxrLvquoHshZKv9Na++TQ/FRVXTU8flWSp4f23v8t3p7kZ6vq0SQfz9rS\ngY8kubSqDg19Ns7p+/MdHn9Nkm8vcsD7ZDXJamvt/mH77qwF1VT3c5L8dJI/ba1daK39nySfTPL3\nM619PYX99LKWLJuS5cwn2TTNbEqmsa+2JJtkUzLJ/Tz3bDrogu6LSY4NV3m5OGt/IHjmgMe0L6qq\nktyV5KHW2q9teOhMkhPD/RNZWyO+3v6+4Wo+1yd5bv3Ucw9aax9qrR1trV2Ttf342dbazyX5XJJ3\nD902z3f93+HdQ/+u3nFJktbanyd5vKp+fGi6IcnXM9H9PHgsyfVV9YPD63x9zlPa17JpQq/ZZcwn\n2TTZbEommk+ySTZlovs5i8img/wjwWFsNyX5kyTfTPJvD3o8+zivf5C106MPJvny8HVT1tbAnk3y\nyHB7+dC/snbVqm8m+UrWroRz4PPY5dx/Ksmnh/uvT/JHSc4n+f0krxraXz1snx8ef/1Bj3sP831z\nknPDvv4vSS6b+n5O8stJvpHkq0l+O8mrpravZdO0XrMb5r80+SSbpplNw9gnl0+ySTZNeT/PO5tq\n+EYAAAA6c9BLLgEAANglBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAA\ndEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQ\nKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECn\nFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S\n0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpB\nBwAA0Km5FHRV9a6qeriqzlfV7fN4DoDdkE/AGMkmYLeqtba/P7DqoiR/kuSdSVaTfDHJe1trX9/X\nJwLYIfkEjJFsAvZiHmfo3prkfGvtW621v0ny8STH5/A8ADsln4Axkk3Arh2aw888kuTxDdurSf7e\n5k5VdTLJySS55JJLfvLaa6+dw1CAg/Loo4/mmWeeqYMexybb5pNsgul74IEHnmmtHT7ocWzgdydg\n1787zaOg22oQL1nX2Vo7leRUkqysrLRz587NYSjAQVlZWTnoIWxl23ySTTB9VfVnBz2GTfzuBOz6\nd6d5LLlcTXL1hu2jSZ6Yw/MA7JR8AsZINgG7No+C7otJjlXV66rq4iTvSXJmDs8DsFPyCRgj2QTs\n2r4vuWytPV9VP5/kM0kuSvIbrbWv7ffzAOyUfALGSDYBezGPv6FLa+3eJPfO42cD7IV8AsZINgG7\nNZcPFgcAAGD+FHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAA\nQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAA\nnVLQAQAAdEpBBwAA0CkFHQAAQKe2Leiq6jeq6umq+uqGtsur6r6qemS4vWxor6q6o6rOV9WDVXXd\nPAcPLDf5BIyRbAIWaZYzdL+V5F2b2m5Pcra1dizJ2WE7SW5Mcmz4Opnkzv0ZJsCWfivyCRif34ps\nAhZk24Kutfbfknx7U/PxJKeH+6eT3Lyh/WNtzReSXFpVV+3XYAE2kk/AGMkmYJF2+zd0V7bWnkyS\n4faKof1Iksc39Fsd2gAWRT4BYySbgLnY74ui1BZtbcuOVSer6lxVnbtw4cI+DwPgJWbKJ9kELJjf\nnYA92W1B99T6coDh9umhfTXJ1Rv6HU3yxFY/oLV2qrW20lpbOXz48C6HAfASe8on2QTMid+dgLnY\nbUF3JsmJ4f6JJPdsaH/fcMWm65M8t768AGBB5BMwRrIJmItD23Woqt9N8lNJfrSqVpP8uyS/kuQT\nVXVrkseS3DJ0vzfJTUnOJ/lekvfPYcwASeQTME6yCVikbQu61tp7X+ahG7bo25LcttdBAcxCPgFj\nJJuARdrvi6IAAACwIAo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p6AAAADqloAMAAOiU\ngg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMK\nOgAAgE4p6AAAADqloAMAAOiUgg4AAKBTCjoAAIBObVvQVdXVVfW5qnqoqr5WVR8Y2i+vqvuq6pHh\n9rKhvarqjqo6X1UPVtV1854EsHxkEzBW8glYpFnO0D2f5Bdbaz+R5Pokt1XVG5PcnuRsa+1YkrPD\ndpLcmOTY8HUyyZ37PmoA2QSMl3wCFmbbgq619mRr7Y+H+3+Z5KEkR5IcT3J66HY6yc3D/eNJPtbW\nfCHJpVV11b6PHFhqsgkYK/kELNKO/oauqq5J8pYk9ye5srX2ZLIWXEmuGLodSfL4hm9bHdo2/6yT\nVXWuqs5duHBh5yMHGMgmYKzkEzBvMxd0VfVDSf4gyQdba999pa5btLWXNLR2qrW20lpbOXz48KzD\nAHgR2QSMlXwCFmGmgq6qfiBrgfQ7rbVPDs1PrS8HGG6fHtpXk1y94duPJnlif4YL8ALZBIyVfAIW\nZZarXFaSu5I81Fr7tQ0PnUliAQj7AAAgAElEQVRyYrh/Isk9G9rfN1yx6fokz60vLwDYL7IJGCv5\nBCzSoRn6vD3JP0vylar68tD2b5L8SpJPVNWtSR5Lcsvw2L1JbkpyPsn3krx/X0cMsEY2AWMln4CF\n2baga63992y9tjtJbtiif0ty2x7HBfCKZBMwVvIJWKQdXeUSAACA8VDQAQAAdEpBBwAA0CkFHQAA\nQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECntv1gcfpS9XKfY8qs1j7fFdhPsmnvZBPMh3zaO/l0\nsJyhAwAA6JQzdBM2z3dLNr6bNbXnAeZrapkhm2A6ppYb8mk5OEM3UYs69T3v55nKPIA1UzmmpzIP\n4AVTOa6nMg9mp6ADAADolIIOAACgUwo6AACATinoAAAAOqWgY3KqypWXgNGRTcBYyae+Kej4vqkd\nyOvhNLV5wbKZ2jEsm2A6pnYcy6c+KehI8kIgTeEAdhldmA7ZBIyVfGIsFHRMkmACxkg2AWMln/ql\noONFHMzAGMkmYKzkEwft0EEPAPbbFJY+ANMjm4Cxkk992/YMXVW9uqr+qKr+R1V9rap+eWh/XVXd\nX1WPVNXvVdXFQ/urhu3zw+PXzHcK7NVUDuJX+iPeqcyRF8im6ZvKcSublo98mr6pHLvyaRpmWXL5\nv5O8o7X2piRvTvKuqro+ya8m+XBr7ViSZ5PcOvS/NcmzrbU3JPnw0I8OTGnJQGvt+1/rBNPkyKYl\nIZvokHxaEvKJMdi2oGtr/tew+QPDV0vyjiR3D+2nk9w83D8+bGd4/IbyahitKe2arYJo8+NMh2ya\ntintGtm0fOTTtE1p18inaZjpoihVdVFVfTnJ00nuS/LNJN9prT0/dFlNcmS4fyTJ40kyPP5cktdu\n8TNPVtW5qjp34cKFvc2CPZvaAetzVJaDbJo+2USv5NP0ySfGYqaCrrX2f1trb05yNMlbk/zEVt2G\n261eBS95xbfWTrXWVlprK4cPH551vOyjqR6wW81raqHLGtk0TbKJKZBP0ySfGKMdfWxBa+07ST6f\n5Pokl1bV+lUyjyZ5Yri/muTqJBkef02Sb+/HYNlf6wfq1A/Yqc8P2TQ1sokpkU/TIp8Yo1mucnm4\nqi4d7v+tJD+d5KEkn0vy7qHbiST3DPfPDNsZHv9s86oYrSnvmldaE07/ZNO0TXnXyKbpk0/TNuVd\nI5/6NMvn0F2V5HRVXZS1AvATrbVPV9XXk3y8qv59ki8luWvof1eS366q81l7d+k9cxg3vCxBtDRk\nE12RTUtFPtEV+dS3bQu61tqDSd6yRfu3srYmfHP7Xye5ZV9GB/AyZBMwVvIJWKRZztDRoUX90e4i\n/zh4qn+IDMtENgFjJZ/o1Y4uigIAAMB4OEM3UYtYCb3+ns88111vfGdpUc8DzNcijuUpZiAwf1P6\nnWaROcjBcoZughb5Z63z/iPaRf2Rrj8GhsVYxLG2qKN5Ec8jm2BxpvI7xyJzQ0KNgzN0UzePg9o7\nxkAvZCCw7OTg5DlDBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtAB\nAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEAAHRq5oKu\nqi6qqi9V1aeH7ddV1f1V9UhV/V5VXTy0v2rYPj88fs18hg4gm4Dxkk/AIuzkDN0Hkjy0YftXk3y4\ntXYsybNJbh3ab03ybGvtDUk+PPTjoFTt/xeMi2zi5clADpZ84uDJwcmbqaCrqqNJ/mmSjw7bleQd\nSe4eupxOcvNw//iwneHxG4b+APtKNgFjJZ+ARTk0Y7+PJPmlJD88bL82yXdaa88P26tJjgz3jyR5\nPElaa89X1XND/2c2/sCqOpnkZJL82I/92G7HzxYW+T/AIv+/8X8bW5BNnVnEcbzopJBNvAz51JlF\nHcsLe56FPAtjsO0Zuqr6mSRPt9Ye2Ni8Rdc2w2MvNLR2qrW20lpbOXz48EyDBVgnm4Cxkk/AIs1y\nhu7tSX62qm5K8uokP5K1d50urapDwztNR5M8MfRfTXJ1ktWqOpTkNUm+ve8j5xW19pL/B/bV+rtL\n836eRT6Xd9m7I5s6JJt2/zx0RT51SD7t/nk4WNueoWutfai1drS1dk2S9yT5bGvt55J8Lsm7h24n\nktwz3D8zbGd4/LNtEa9cYKnIJmCs5BOwSHv5HLp/neQXqup81tZ53zW035XktUP7LyS5fW9DBNgR\n2QSMlXwC9t2sF0VJkrTWPp/k88P9byV56xZ9/jrJLfswNoCZyCZgrOQTMG97OUMHAADAAVLQAQAA\ndEpBBwAA0CkFHUnWLju7/jUVU5oLLCvZBIyVfGIsdnRRFKZp48E7haskb5zPIj/zBdhfsgkYK/nE\nmDhDt+SmFEib3yXbOB/vOEFfZBMwVvKJsXGGjiTTCKR1W82l9/nBsur92JVNMF29H7/yaToUdEts\nqqfUt3pHaapzhSma6vEqm6B/Uz1m5VPfLLlcUlM7jd5aEzgwAbIJGCv5xFg5Q7fkWmsvG1C9HeSb\n57F5/OvrxHubFywj2QSMlXxibJyhW0JbXclou35jt3kduOCB/sgmYKzkE2PmDB1J+r6q0ZSuNgW8\nmGwCxko+MRYKuiW31an1qZninGDqZBMwVvKJsVHQLZnNB+j6uujt1lCP2cbxv1IA9TQnWDayCRgr\n+cTY+Ru6JbPVGumeA2ndK43ZunAYP9kEjJV8YuycoVtSm9d9T+GgncIcYNnJJmCs5BNjpaCbqJ2u\nfd7tWulFrrG2nhv6J5uAsZJP9MqSSwAAgE4p6AAAADplyeXEWAsNjJFsAsZKPtE7Z+gAAAA6paAD\nAADo1EwFXVU9WlVfqaovV9W5oe3yqrqvqh4Zbi8b2quq7qiq81X1YFVdN88JAMtLNgFjJZ+ARdnJ\nGbp/1Fp7c2ttZdi+PcnZ1tqxJGeH7SS5Mcmx4etkkjv3a7AAW5BNwFjJJ2Du9rLk8niS08P900lu\n3tD+sbbmC0kuraqr9vA8ADshm4Cxkk/Avpu1oGtJ/mtVPVBVJ4e2K1trTybJcHvF0H4kyeMbvnd1\naHuRqjpZVeeq6tyFCxd2N3pg2ckmYKzkE7AQs35swdtba09U1RVJ7quqb7xC360+kv4l14NtrZ1K\ncipJVlZWXC8W2A3ZBIyVfAIWYqYzdK21J4bbp5N8Kslbkzy1vhxguH166L6a5OoN3340yRP7NWCA\ndbIJGCv5BCzKtgVdVV1SVT+8fj/JP07y1SRnkpwYup1Ics9w/0yS9w1XbLo+yXPrywsA9otsAsZK\nPgGLNMuSyyuTfKqq1vv/59baH1bVF5N8oqpuTfJYkluG/vcmuSnJ+STfS/L+fR81gGwCxks+AQuz\nbUHXWvtWkjdt0f4XSW7Yor0luW1fRgfwMmQTMFbyCVikvXxsAQAAAAdIQQcAANApBR0AAECnFHQA\nAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdEpBBwAA0CkFHQAAQKcUdAAAAJ1S0AEA\nAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQAQAAdGqmgq6q\nLq2qu6vqG1X1UFW9raour6r7quqR4fayoW9V1R1Vdb6qHqyq6+Y7BWBZySZgrOQTsCiznqH79SR/\n2Fq7NsmbkjyU5PYkZ1trx5KcHbaT5MYkx4avk0nu3NcRA7xANgFjJZ+Ahdi2oKuqH0nyD5PclSSt\ntb9prX0nyfEkp4dup5PcPNw/nuRjbc0XklxaVVft+8iBpSabgLGST8AizXKG7vVJLiT5zar6UlV9\ntKouSXJla+3JJBlurxj6H0ny+IbvXx3aAPaTbALGSj4BCzNLQXcoyXVJ7mytvSXJX+WFJQJbqS3a\n2ks6VZ2sqnNVde7ChQszDRZgA9kEjJV8AhZmloJuNclqa+3+YfvurIXUU+vLAYbbpzf0v3rD9x9N\n8sTmH9paO9VaW2mtrRw+fHi34weWl2wCxko+AQuzbUHXWvvzJI9X1Y8PTTck+XqSM0lODG0nktwz\n3D+T5H3DFZuuT/Lc+vICgP0im4Cxkk/AIh2asd+/SPI7VXVxkm8leX/WisFPVNWtSR5LcsvQ994k\nNyU5n+R7Q1+AeZBNwFjJJ2AhZiroWmtfTrKyxUM3bNG3Jbltj+MC2JZsAsZKPgGLMuvn0AEAADAy\nCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p\n6AAAADqloAMAAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWg\nAwAA6JSCDgAAoFMKOgAAgE5tW9BV1Y9X1Zc3fH23qj5YVZdX1X1V9chwe9nQv6rqjqo6X1UPVtV1\n858GsGxkEzBW8glYpG0Lutbaw621N7fW3pzkJ5N8L8mnktye5Gxr7ViSs8N2ktyY5NjwdTLJnfMY\nOLDcZBMwVvIJWKSdLrm8Ick3W2t/luR4ktND++kkNw/3jyf5WFvzhSSXVtVV+zJagK3JJmCs5BMw\nVzst6N6T5HeH+1e21p5MkuH2iqH9SJLHN3zP6tD2IlV1sqrOVdW5Cxcu7HAYAC8im4Cxkk/AXM1c\n0FXVxUl+Nsnvb9d1i7b2kobWTrXWVlprK4cPH551GAAvIpuAsZJPwCLs5AzdjUn+uLX21LD91Ppy\ngOH26aF9NcnVG77vaJIn9jpQgJchm4Cxkk/A3O2koHtvXlgykCRnkpwY7p9Ics+G9vcNV2y6Pslz\n68sLAOZANgFjJZ+AuTs0S6eq+sEk70zyzzc0/0qST1TVrUkeS3LL0H5vkpuSnM/aVZ3ev2+jBdhA\nNgFjJZ+ARZmpoGutfS/Jaze1/UXWrty0uW9Lctu+jA7gFcgmYKzkE7AoO73KJQAAACOhoAMAAOiU\ngg4AAKBTtbZs+4AHUfWXSR4+6HEs2I8meeagB7Fg5rwc1uf8d1prXX9QUlVdSPJXWd59uEzMeTlM\nKZ/87rQczHk57CmbZrooygI83FpbOehBLFJVnTPn6TPnvrXWDk9pPrMy5+Vgzt3zu9MSMOflsNc5\nW3IJAADQKQUdAABAp8ZS0J066AEcAHNeDubcv6nNZxbmvBzMuW9TmsuszHk5mPMOjeKiKAAAAOzc\nWM7QAQAAsEMKOgAAgE4deEFXVe+qqoer6nxV3X7Q49kvVXV1VX2uqh6qqq9V1QeG9sur6r6qemS4\nvWxor6q6Y/h3eLCqrjvYGexOVV1UVV+qqk8P26+rqvuH+f5eVV08tL9q2D4/PH7NQY57L6rq0qq6\nu6q+Mezvty3Bfv5Xw+v6q1X1u1X16qnta9k0rddssnz5JJummU3JNPNJNsmmKe/neWfTgRZ0VXVR\nkv+Y5MYkb0zy3qp640GOaR89n+QXW2s/keT6JLcNc7s9ydnW2rEkZ4ftZO3f4NjwdTLJnYsf8r74\nQJKHNmz/apIPD/N9NsmtQ/utSZ5trb0hyYeHfr369SR/2Fq7Nsmbsjb/ye7nqjqS5F8mWWmt/d0k\nFyV5Tya0r2XTtF6zGyxbPsmmiWVTMul8kk0vmNRrdguyab+zqbV2YF9J3pbkMxu2P5TkQwc5pjnO\n9Z4k70zycJKrhrarsvbBoEnyn5K8d0P/7/fr5SvJ0awdhO9I8ukklbVPvT+0eX8n+UyStw33Dw39\n6qDnsIs5/0iSP9089onv5yNJHk9y+bDvPp3kn0xpX8umab1mh3EvVT7Jpmlm0+Y5DNuTzCfZNJ3X\n7Kb5yqY5ZNNBL7lcn+C61aFtUoZTpW9Jcn+SK1trTybJcHvF0G0K/xYfSfJLSf7fsP3aJN9prT0/\nbG+c0/fnOzz+3NC/N69PciHJbw7LJT5aVZdkwvu5tfY/k/yHJI8leTJr++6BTGtfd7+fZrFE2ZQs\nXz7JpmlmUzKBfbUd2TS51+xGsmkO2XTQBV1t0Tapz1Goqh9K8gdJPtha++4rdd2irZt/i6r6mSRP\nt9Ye2Ni8Rdc2w2M9OZTkuiR3ttbekuSv8sIyga10P+9hXfvxJK9L8reTXJK1JRGb9byvexzzjixL\nNiVLm0+yaZrZlPQ77pnIJtm0SfdzXkQ2HXRBt5rk6g3bR5M8cUBj2XdV9QNZC6Xfaa19cmh+qqqu\nGh6/KsnTQ3vv/xZvT/KzVfVoko9nbenAR5JcWlWHhj4b5/T9+Q6PvybJtxc54H2ymmS1tXb/sH13\n1oJqqvs5SX46yZ+21i601v5Pkk8m+fuZ1r6ewn56WUuWTcly5pNsmmY2JdPYV1uSTbIpmeR+nns2\nHXRB98Ukx4arvFyctT8QPHPAY9oXVVVJ7kryUGvt1zY8dCbJieH+iaytEV9vf99wNZ/rkzy3fuq5\nB621D7XWjrbWrsnafvxsa+3nknwuybuHbpvnu/7v8O6hf1fvuCRJa+3PkzxeVT8+NN2Q5OuZ6H4e\nPJbk+qr6weF1vj7nKe1r2TSh1+wy5pNsmmw2JRPNJ9kkmzLR/ZxFZNNB/pHgMLabkvxJkm8m+bcH\nPZ59nNc/yNrp0QeTfHn4uilra2DPJnlkuL186F9Zu2rVN5N8JWtXwjnweexy7j+V5NPD/dcn+aMk\n55P8fpJXDe2vHrbPD4+//qDHvYf5vjnJuWFf/5ckl019Pyf55STfSPLVJL+d5FVT29eyaVqv2Q3z\nX5p8kk3TzKZh7JPLJ9kkm6a8n+edTTV8IwAAAJ056CWXAAAA7JKCDgAAoFMKOgAAgE4p6AAAADql\noAMAAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSC\nDgAAoFMKOgAAgE4p6AAAADqloAMAAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADolIIOAACgUwo6\nAACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p6AAAADqloAMAAOiUgg4AAKBTCjoAAIBOKegA\nAAA6paADAADolIIOAACgUwo6AACATinoAAAAOqWgAwAA6JSCDgAAoFMKOgAAgE4p6AAAADqloAMA\nAOiUgg4AAKBTCjoAAIBOKegAAAA6paADAADo1FwKuqp6V1U9XFXnq+r2eTwHwG7IJ2CMZBOwW9Va\n298fWHVRkj9J8s4kq0m+mOS9rbWv7+sTAeyQfALGSDYBezGPM3RvTXK+tfat1trfJPl4kuNzeB6A\nnZJPwBjJJmDXDs3hZx5J8viG7dUkf29zp6o6meRkklxyySU/ee21185hKMBBefTRR/PMM8/UQY9j\nk23zSTbB9D3wwAPPtNYOH/Q4NvC7E7Dr353mUdBtNYiXrOtsrZ1KcipJVlZW2rlz5+YwFOCgrKys\nHPQQtrJtPskmmL6q+rODHsMmfncCdv270zyWXK4muXrD9tEkT8zheQB2Sj4BYySbgF2bR0H3xSTH\nqup1VXVxkvckOTOH5wHYKfkEjJFsAnZt35dcttaer6qfT/KZJBcl+Y3W2v/f3v3FWlbd9wH//sQY\nu3FiA86AKAPFlkeOrUrGdGThuqpSk7SBRoYHI9mKyshCmj6g1m4ipbh9qCL1wZaq4CBVqMg0Hkeu\nY0LsghByisa2qj6YZBxT/Ae7jJ0UphCYxBinRmlLu/pw14XLcOGemXvvuXvt8/lIR2fvdda5d63Z\nZ3/n/s7eZ59v7/TvAThT8gmYItkEbMdufIYurbX7k9y/Gz8bYDvkEzBFsgk4W7vyxeIAAADsPgUd\nAADAoBR0AAAAg1LQAQAADEpBBwAAMCgFHQAAwKAUdAAAAINS0AEAAAxKQQcAADAoBR0AAMCgFHQA\nAACDUtABAAAMSkEHAAAwKAUdAADAoBR0AAAAg1LQAQAADEpBBwAAMCgFHQAAwKAUdAAAAINS0AEA\nAAxKQQcAADCoLQu6qvr3VfV0VX1rQ9sFVfVAVT3a78/v7VVVt1XViap6uKqu3M3BA6tNPgFTJJuA\nZVrkCN2nk/zSaW23JDnWWjuY5FhfT5JrkhzstyNJbt+ZYQJs6tORT8D0fDqyCViSLQu61tp/TvLD\n05qvS3K0Lx9Ncv2G9s+0NV9Lcl5VXbxTgwXYSD4BUySbgGU628/QXdRaezJJ+v2Fvf2SJI9v6Hey\ntwEsi3wCpkg2Abtipy+KUpu0tU07Vh2pquNVdfzUqVM7PAyAl1kon2QTsGT+dgK25WwLuqfWTwfo\n90/39pNJLt3Q70CSJzb7Aa21O1prh1prh/bv33+WwwB4mW3lk2wCdom/nYBdcbYF3b1JDvflw0nu\n2dB+Y79i01VJnl0/vQBgSeQTMEWyCdgV+7bqUFWfS/LzSX62qk4m+VdJPp7krqq6KcljSW7o3e9P\ncm2SE0meS/LhXRgzQBL5BEyTbAKWacuCrrX2oVd46OpN+rYkN293UACLkE/AFMkmYJl2+qIoAAAA\nLImCDgAAYFAKOgAAgEEp6AAAAAaloAMAABiUgg4AAGBQCjoAAIBBKegAAAAGpaADAAAYlIIOAABg\nUAo6AACAQSnoAAAABqWgAwAAGJSCDgAAYFAKOgAAgEEp6AAAAAaloAMAABiUgg4AAGBQCjoAAIBB\nKegAAAAGpaADAAAYlIIOAABgUFsWdFV1aVV9paoeqapvV9VHevsFVfVAVT3a78/v7VVVt1XViap6\nuKqu3O1JAKtHNgFTJZ+AZVrkCN3zSX6ttfb2JFclubmq3pHkliTHWmsHkxzr60lyTZKD/XYkye07\nPmoA2QRMl3wClmbLgq619mRr7Y/78l8meSTJJUmuS3K0dzua5Pq+fF2Sz7Q1X0tyXlVdvOMjB1aa\nbAKmSj4By3RGn6GrqsuTvCvJg0kuaq09mawFV5ILe7dLkjy+4Wkne9vpP+tIVR2vquOnTp0685ED\ndLIJmCr5BOy2hQu6qvrpJL+f5KOttR+/WtdN2trLGlq7o7V2qLV2aP/+/YsOA+AlZBMwVfIJWIaF\nCrqqek3WAumzrbUv9Oan1k8H6PdP9/aTSS7d8PQDSZ7YmeECvEg2AVMln4BlWeQql5XkziSPtNZ+\nc8ND9yY53JcPJ7lnQ/uN/YpNVyV5dv30AoCdIpuAqZJPwDLtW6DPe5P8oyTfrKqHetu/SPLxJHdV\n1U1JHktyQ3/s/iTXJjmR5LkkH97REQOskU3AVMknYGm2LOhaa/8lm5/bnSRXb9K/Jbl5m+MCeFWy\nCZgq+QQs0xld5RIAAIDpUNABAAAMSkEHAAAwKAUdAADAoBR0AAAAg1LQAQAADEpBBwAAMCgFHQAA\nwKC2/GJxxlL1St9jyqLWvt8V2EmyaftkE+wO+bR98mlvOUIHAAAwKEfoZmw33y3Z+G7W3H4PsLvm\nlhmyCeZjbrkhn1aDI3QztaxD37v9e+YyD2DNXPbpucwDeNFc9uu5zIPFKegAAAAGpaADAAAYlIIO\nAABgUAo6AACAQSnomJ2qcuUlYHJkEzBV8mlsCjpeMLcdeT2c5jYvWDVz24dlE8zH3PZj+TQmBR1J\nXgykOezALqML8yGbgKmST0yFgo5ZEkzAFMkmYKrk07gUdLyEnRmYItkETJV8Yq9tWdBV1euq6g+r\n6r9W1ber6jd6+5ur6sGqerSqPl9V5/b21/b1E/3xy3d3CvBSzv1eDbKJ0cim1SGfGI18GtsiR+j+\nV5L3tdbemeSKJL9UVVcl+USSW1trB5M8k+Sm3v+mJM+01t6a5Nbejwmbyw78amE0lznyErJp5uay\n38qmlSSfZm4u+658moctC7q25n/21df0W0vyviR39/ajSa7vy9f19fTHry6viCHM6ZSB1toLt3Ve\nhvMim1aHbGI08ml1yCemYKHP0FXVOVX1UJKnkzyQ5PtJftRae753OZnkkr58SZLHk6Q//mySN+3k\noNk5c9pRNwui0x9nXmTTfMkmRief5ks+MTULFXSttf/bWrsiyYEk707y9s269fvNXuUvezVU1ZGq\nOl5Vx0+dOrXoeNklc9thfY/KapBN8yebGJV8mj/5xFSc0VUuW2s/SvLVJFclOa+q9vWHDiR5oi+f\nTHJpkvTH35jkh5v8rDtaa4daa4f2799/dqNnW+a6w242r7mFLi8lm+ZFNjEn8mle5BNTtMhVLvdX\n1Xl9+a8l+YUkjyT5SpIP9G6Hk9zTl+/t6+mPf7l5RUzS+maZ++aZ+/xWlWyaL9nE6OTTfMknpmjf\n1l1ycZKjVXVO1grAu1pr91XVd5L8blX96yTfSHJn739nkt+pqhNZe3fpg7swbnbInHfYOc+NJLJp\n1ua8/855brxAPs3YnPfhOc9tzrYs6FprDyd51ybtP8jaOeGnt/9Vkht2ZHRwFoTRapBNjEY2rQ75\nxGjk09jO6DN0AAAATMcip1wyoGV9aHeZHw6e6weRYZXIJmCq5BOjcoQOAABgUI7Qzdhung+98R2f\nuf0eYHfNLTNkE8zH3HJDPq0GR+hmalkfbt3t3zOXeQBr5rJPz2UewIvmsl/PZR4sTkEHAAAwKAUd\nAADAoBR0AAAAg1LQAQAADEpBBwAAMCgFHQAAwKAUdAAAAINS0AEAAAxKQQcAADAoBR0AAMCgFHQA\nAACDUtABAAAMSkEHAAAwKAUdAADAoBR0AAAAg1LQAQAADEpBBwAAMKiFC7qqOqeqvlFV9/X1N1fV\ng1X1aFV9vqrO7e2v7esn+uOX787QAWQTMF3yCViGMzlC95Ekj2xY/0SSW1trB5M8k+Sm3n5Tkmda\na29NcmvvB7BbZBMwVfIJ2HULFXRVdSDJP0zyqb5eSd6X5O7e5WiS6/vydX09/fGre3+AHSWbgKmS\nT8Cy7Fuw3yeT/HqSn+nrb0ryo9ba8339ZJJL+vIlSR5Pktba81X1bO//5xt/YFUdSXIkSS677LKz\nHT+vYFn/Dyzz/xv/t6UO230AAA6sSURBVLEJ2TQY2cQKkU+DkU+MassjdFX1y0mebq19fWPzJl3b\nAo+92NDaHa21Q621Q/v3719osCyu7fIN9pps4tXsdgbKQV6NfGIq5OBqWOQI3XuTvL+qrk3yuiRv\nyNq7TudV1b7+TtOBJE/0/ieTXJrkZFXtS/LGJD/c8ZHzipaxg7Ws/e/T2u7/tvV3l3b7d3kXaziy\naUDL2I+X9UfGsnJQNg1JPg1oWfvysv52Wubfg+ytLY/QtdY+1lo70Fq7PMkHk3y5tfYrSb6S5AO9\n2+Ek9/Tle/t6+uNfbst45QIrRTYBUyWfgGXazvfQ/fMkv1pVJ7J2nvedvf3OJG/q7b+a5JbtDZFt\naW3nbzBtsokXyUCmRT6xfHJw9ha9KEqSpLX21SRf7cs/SPLuTfr8VZIbdmBsAAuRTcBUySdgt23n\nCB0AAAB7SEEHAAAwKAUdAADAoBR0JFm7vO36bS7mNBdYVbIJmCr5xFSc0UVRmKeNO+8crpK8cT7L\n/M4XYGfJJmCq5BNT4gjdiptTIJ3+LtnG+XjHCcYim4Cpkk9MjSN0JJlHIK3bbC6jzw9W1ej7rmyC\n+Rp9/5VP86GgW2FzPaS+2TtKc50rzNFc91fZBOOb6z4rn8bmlMsVNbfD6K01gQMzIJuAqZJPTJUj\ndCuutfaKATXaTn76PE4f//p54qPNC1aRbAKmSj4xNY7QraDNrmS0Vb+pO/08cMED45FNwFTJJ6bM\nETqSjH1VozldbQp4KdkETJV8YioUdCtus0PrczPHOcHcySZgquQTU6OgWzGn76Dr50VvdQ71lG0c\n/6sF0EhzglUjm4Cpkk9Mnc/QrZjNzpEeOZDWvdqYnRcO0yebgKmST0ydI3Rz90pXYTqL50yd4IHx\nnf6ZlG3v1xPIM9kE87Dj+bQsr3YE7iyew/Qo6GaoskXBtpO/a4k7vPO5YXxnuh+fzX6/rAxcH5ls\ngnlYRj5t53lTNJ+ZjE1BN1N2MGCVyUBg1cnB1eEzdAAAAINyhG5mhjmfG1gpsgmYKvnE6ByhAwAA\nGNRCBV1V/WlVfbOqHqqq473tgqp6oKoe7ffn9/aqqtuq6kRVPVxVV+7mBIDVJZuAqZJPwLKcyRG6\nv9dau6K1dqiv35LkWGvtYJJjfT1JrklysN+OJLl9pwYLsAnZBEyVfAJ23XZOubwuydG+fDTJ9Rva\nP9PWfC3JeVV18TZ+D8CZkE3AVMknYMctWtC1JP+pqr5eVUd620WttSeTpN9f2NsvSfL4huee7G0A\nO002AVMln4ClWPQql+9trT1RVRcmeaCqvvsqfTf72ouXXT6oh9uRJLnssssWHAbAS8gmYKrkE7AU\nCx2ha6090e+fTvLFJO9O8tT66QD9/une/WSSSzc8/UCSJzb5mXe01g611g7t37//7GcArCzZBEyV\nfAKWZcuCrqpeX1U/s76c5O8n+VaSe5Mc7t0OJ7mnL9+b5MZ+xaarkjy7fnoBwE6RTcBUySdgmRY5\n5fKiJF+sqvX+/6G19qWq+qMkd1XVTUkeS3JD739/kmuTnEjyXJIP7/ioAWQTMF3yCViaLQu61toP\nkrxzk/a/SHL1Ju0tyc07MjqAVyCbgKmST8AybedrCwAAANhDCjoAAIBBKegAAAAGpaADAAAYlIIO\nAABgUAo6AACAQSnoAAAABqWgAwAAGJSCDgAAYFAKOgAAgEEp6AAAAAaloAMAABiUgg4AAGBQCjoA\nAIBBKegAAAAGpaADAAAYlIIOAABgUAo6AACAQSnoAAAABqWgAwAAGJSCDgAAYFAKOgAAgEEtVNBV\n1XlVdXdVfbeqHqmq91TVBVX1QFU92u/P732rqm6rqhNV9XBVXbm7UwBWlWwCpko+Acuy6BG630ry\npdbazyV5Z5JHktyS5Fhr7WCSY309Sa5JcrDfjiS5fUdHDPAi2QRMlXwClmLLgq6q3pDk7ya5M0la\na/+7tfajJNclOdq7HU1yfV++Lsln2pqvJTmvqi7e8ZEDK002AVMln4BlWuQI3VuSnEry21X1jar6\nVFW9PslFrbUnk6TfX9j7X5Lk8Q3PP9nbXqKqjlTV8ao6furUqW1NAlhJsgmYKvkELM0iBd2+JFcm\nub219q4kP8mLpwhspjZpay9raO2O1tqh1tqh/fv3LzRYgA1kEzBV8glYmkUKupNJTrbWHuzrd2ct\npJ5aPx2g3z+9of+lG55/IMkTOzNcgBfIJmCq5BOwNFsWdK21P0vyeFW9rTddneQ7Se5Ncri3HU5y\nT1++N8mN/YpNVyV5dv30AoCdIpuAqZJPwDLtW7DfP0ny2ao6N8kPknw4a8XgXVV1U5LHktzQ+96f\n5NokJ5I81/sC7AbZBEyVfAKWYqGCrrX2UJJDmzx09SZ9W5KbtzkugC3JJmCq5BOwLIt+Dx0AAAAT\no6ADAAAYlIIOAABgUAo6AACAQSnoAAAABqWgAwAAGJSCDgAAYFAKOgAAgEEp6AAAAAaloAMAABiU\ngg4AAGBQCjoAAIBBKegAAAAGpaADAAAYlIIOAABgUAo6AACAQSnoAAAABqWgAwAAGJSCDgAAYFAK\nOgAAgEEp6AAAAAa1ZUFXVW+rqoc23H5cVR+tqguq6oGqerTfn9/7V1XdVlUnqurhqrpy96cBrBrZ\nBEyVfAKWacuCrrX2vdbaFa21K5L8rSTPJflikluSHGutHUxyrK8nyTVJDvbbkSS378bAgdUmm4Cp\nkk/AMp3pKZdXJ/l+a+2/J7kuydHefjTJ9X35uiSfaWu+luS8qrp4R0YLsDnZBEyVfAJ21ZkWdB9M\n8rm+fFFr7ckk6fcX9vZLkjy+4TknexvAbpFNwFTJJ2BXLVzQVdW5Sd6f5Pe26rpJW9vk5x2pquNV\ndfzUqVOLDgPgJWQTMFXyCViGMzlCd02SP26tPdXXn1o/HaDfP93bTya5dMPzDiR54vQf1lq7o7V2\nqLV2aP/+/Wc+coA1sgmYKvkE7LozKeg+lBdPGUiSe5Mc7suHk9yzof3GfsWmq5I8u356AcAukE3A\nVMknYNftW6RTVf1Ukl9M8o83NH88yV1VdVOSx5Lc0NvvT3JtkhNZu6rTh3dstAAbyCZgquQTsCwL\nFXStteeSvOm0tr/I2pWbTu/bkty8I6MDeBWyCZgq+QQsy5le5RIAAICJUNABAAAMSkEHAAAwKAUd\nAADAoGrtc7h7PIiqv0zyvb0ex5L9bJI/3+tBLJk5r4b1Of+N1trQX5RUVaeS/CSruw1XiTmvhjnl\nk7+dVoM5r4ZtZdNCV7lcgu+11g7t9SCWqaqOm/P8mfPYWmv75zSfRZnzajDn4fnbaQWY82rY7pyd\ncgkAADAoBR0AAMCgplLQ3bHXA9gD5rwazHl8c5vPIsx5NZjz2OY0l0WZ82ow5zM0iYuiAAAAcOam\ncoQOAACAM7TnBV1V/VJVfa+qTlTVLXs9np1SVZdW1Veq6pGq+nZVfaS3X1BVD1TVo/3+/N5eVXVb\n/3d4uKqu3NsZnJ2qOqeqvlFV9/X1N1fVg32+n6+qc3v7a/v6if745Xs57u2oqvOq6u6q+m7f3u9Z\nge38z/rr+ltV9bmqet3ctrVsmtdrNlm9fJJN88ymZJ75JJtk05y3825n054WdFV1TpJ/m+SaJO9I\n8qGqesdejmkHPZ/k11prb09yVZKb+9xuSXKstXYwybG+nqz9GxzstyNJbl/+kHfER5I8smH9E0lu\n7fN9JslNvf2mJM+01t6a5Nbeb1S/leRLrbWfS/LOrM1/ttu5qi5J8k+THGqt/c0k5yT5YGa0rWXT\nvF6zG6xaPsmmmWVTMut8kk0vmtVrdhOyaaezqbW2Z7ck70nyBxvWP5bkY3s5pl2c6z1JfjFrXwJ6\ncW+7OGvfI5Mk/y7Jhzb0f6HfKLckB7K2E74vyX1JKmtfkrjv9O2d5A+SvKcv7+v9aq/ncBZzfkOS\nPzl97DPfzpckeTzJBX3b3ZfkH8xpW8umeb1m+7hXKp9k0zyz6fQ59PVZ5pNsms9r9rT5yqZdyKa9\nPuVyfYLrTva2WemHSt+V5MEkF7XWnkySfn9h7zaHf4tPJvn1JP+vr78pyY9aa8/39Y1zemG+/fFn\ne//RvCXJqSS/3U+X+FRVvT4z3s6ttf+R5N8keSzJk1nbdl/PvLb18NtpESuUTcnq5ZNsmmc2JTPY\nVluRTbN7zW4km3Yhm/a6oKtN2mZ12c2q+ukkv5/ko621H79a103ahvm3qKpfTvJ0a+3rG5s36doW\neGwk+5JcmeT21tq7kvwkL54msJnh593Pa78uyZuT/PUkr8/aKRGnG3lbjzjmM7Iq2ZSsbD7Jpnlm\nUzLuuBcim2TTaYaf8zKyaa8LupNJLt2wfiDJE3s0lh1XVa/JWih9trX2hd78VFVd3B+/OMnTvX30\nf4v3Jnl/Vf1pkt/N2qkDn0xyXlXt6302zumF+fbH35jkh8sc8A45meRka+3Bvn531oJqrts5SX4h\nyZ+01k611v5Pki8k+duZ17aew3Z6RSuWTclq5pNsmmc2JfPYVpuSTbIpmeV23vVs2uuC7o+SHOxX\neTk3ax8QvHePx7QjqqqS3Jnkkdbab2546N4kh/vy4aydI77efmO/ms9VSZ5dP/Q8gtbax1prB1pr\nl2dtO365tfYrSb6S5AO92+nzXf93+EDvP9Q7LknSWvuzJI9X1dt609VJvpOZbufusSRXVdVP9df5\n+pzntK1l04xes6uYT7JpttmUzDSfZJNsyky3c5aRTXv5IcE+tmuT/Lck30/yL/d6PDs4r7+TtcOj\nDyd5qN+uzdo5sMeSPNrvL+j9K2tXrfp+km9m7Uo4ez6Ps5z7zye5ry+/JckfJjmR5PeSvLa3v66v\nn+iPv2Wvx72N+V6R5Hjf1v8xyflz385JfiPJd5N8K8nvJHnt3La1bJrXa3bD/Fcmn2TTPLOpj312\n+SSbZNOct/NuZ1P1JwIAADCYvT7lEgAAgLOkoAMAABiUgg4AAGBQCjoAAIBBKegAAAAGpaADAAAY\nlIIOAABgUAo6AACAQf1/l6mmkmMEhmIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e41a83c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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S0AEAAHRKQQcAANApBR0AAECnFHQAAACdUtABAAB0SkEHAADQKQUdAABApxR0AAAAnVLQ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\nAACAkfr/laXOyz4+H8IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cb783a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dAABApxQ6AACATil0AAAAnVLoAAAAOqXQAQAAdEqhAwAA6JRCBwAA0CmFDgAA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\ntN9f2vtXVd1eVaer6uGqumZWgz33lZ5zw2iWp/c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cff42a5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iHywOAADA5Cl0AAAAnVLoAAAAOqXQAQAAdEqhAwAA6JRCBwAA0CmFDgAAoFMK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\nLzHTM41QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd581c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HI/dSb+Jdl6ACxkc2AWO1bvmk0EFnWms/cEw4wBjIJmCsVj2fHHIJI7fKAQT0SzYBY7Vu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\n0AEAAAxKQQcAADCo/w8A7M/C1sKAmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8cd655a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iterate on each 3*3 blob to get each 1*1 unit-blob\n",
    "unit_blobs = []\n",
    "\n",
    "for (tl_x, tl_y, br_x, br_y) in sorted_blobs:\n",
    "    \n",
    "    sub_img = 255 - np.zeros(img.shape)\n",
    "    sub_img[tl_y:br_y, tl_x:br_x] = img[tl_y: br_y, tl_x:br_x]\n",
    "    \n",
    "    # Pad image\n",
    "    sub_img[tl_y-5:br_y+5, tl_x-5:br_x+5] = np.pad(sub_img[tl_y:br_y, tl_x:br_x], pad_width = 5, mode = 'constant', constant_values = 0)\n",
    "    \n",
    "    # Blur image\n",
    "    sub_blur_img = cv2.GaussianBlur(sub_img, (5,5), 0)\n",
    "    \n",
    "    # Thresholding image\n",
    "    _, sub_th_img = cv2.threshold(sub_blur_img.astype(np.uint8), 230, 255, cv2.THRESH_BINARY)\n",
    "    \n",
    "    # The same as previous operations, get contours, sorting contours to get each unit-blob.\n",
    "    # Contours finding\n",
    "    _, sub_contours, _ = cv2.findContours(sub_th_img.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    \n",
    "    # Sort according to contour area\n",
    "    sorted_sub_contours = sorted(sub_contours, key = lambda x: cv2.contourArea(x), reverse = True)\n",
    "    \n",
    "    # Get the contours by the descending order of contour area.\n",
    "    # The biggest and the second bigges ones are the outlines, this needs to be manually adjusted by experimenting repeatedly\n",
    "    # The third to eleventh is the outline for each 3*3 sub-soduku\n",
    "    sub_blobs = []\n",
    "    for i in range(2,11):\n",
    "        \n",
    "        # Approximate contour with lines\n",
    "        perimeter = cv2.arcLength(sorted_sub_contours[i], True)\n",
    "        epsilon = 0.1 * cv2.arcLength(sorted_sub_contours[i],True)\n",
    "        approx = cv2.approxPolyDP(sorted_sub_contours[i], epsilon, True)\n",
    "        [tl_x, tl_y] = approx[0][0]\n",
    "        [br_x, br_y] = approx[2][0]\n",
    "        \n",
    "        # Store location of each sub-blob in list\n",
    "        sub_blobs.append((tl_x, tl_y, br_x, br_y))\n",
    "    \n",
    "    # Sort blobs by the location of blob in the image\n",
    "    sorted_sub_blobs = blob_sorting(sub_blobs)\n",
    "    \n",
    "    # Store unit images to unit_blobs list in order\n",
    "    unit_imgs = []\n",
    "    plt.figure(figsize = (15,15))\n",
    "    i = 1\n",
    "    for (tl_x, tl_y, br_x, br_y) in sorted_sub_blobs:\n",
    "        \n",
    "        unit_imgs.append(img[tl_y:br_y,tl_x:br_x])\n",
    "\n",
    "        sorted_sub_blob_img = cv2.rectangle(cv2.cvtColor(sub_th_img, cv2.COLOR_GRAY2BGR), (tl_x, tl_y), (br_x, br_y), color = (255, 0, 0), thickness = 5)\n",
    "        plt.subplot(3, 3, i)\n",
    "        plt.imshow(sorted_sub_blob_img)\n",
    "        i += 1\n",
    "    \n",
    "    unit_blobs.append(unit_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 0 1 2 9 0 0 7 5]\n",
      " [2 0 0 3 0 0 8 0 0]\n",
      " [0 7 0 0 8 0 0 0 6]\n",
      " [0 0 0 1 0 3 0 6 2]\n",
      " [1 0 5 0 0 0 4 0 3]\n",
      " [7 3 0 6 0 8 0 0 0]\n",
      " [6 0 0 0 2 0 0 3 0]\n",
      " [0 0 7 0 0 1 0 0 4]\n",
      " [8 9 0 0 6 5 1 0 7]]\n"
     ]
    }
   ],
   "source": [
    "# Iterate on the unit_blobs list, each element in the unit_blobs stores a number or empty\n",
    "# Use scikit-learn MNIST model to recognize each digit\n",
    "digits = np.zeros([9,9])\n",
    "\n",
    "for blob in range(len(unit_blobs)):\n",
    "    for unit in range(len(unit_blobs[0])):\n",
    "        \n",
    "        # Resize image to (28,28) to fit the size of MNIST model input\n",
    "        row_half, col_half = int(unit_blobs[blob][unit].shape[0]/2), int(unit_blobs[blob][unit].shape[1]/2)\n",
    "        digit_img = cv2.resize(unit_blobs[blob][unit], (28,28))\n",
    "        \n",
    "        # Check center of image\n",
    "        if (digit_img[6:18, 6:18] < 255).sum() > 20 and np.count_nonzero(digit_img) != 0:\n",
    "            # Thresholding\n",
    "            _, digit_img = cv2.threshold(digit_img, 127, 255, cv2.THRESH_BINARY)\n",
    "            # Gaussian blur\n",
    "            digit_img = cv2.GaussianBlur(digit_img, (3,3), 1)\n",
    "            \n",
    "            # Make digit apprear in the center of image, move the digit patten upward\n",
    "            M = np.array([[1,0,0],[0,1,-3]], dtype = np.float32)\n",
    "            digit_img = cv2.warpAffine(digit_img, M, (digit_img.shape[1], digit_img.shape[0]), borderValue = 255)\n",
    "            \n",
    "            # Invert colors\n",
    "            input_digit_img = np.reshape(cv2.bitwise_not(digit_img), (-1, 28, 28, 1))\n",
    "            \n",
    "            # Predict digits\n",
    "            predicted_digit = sess.run(tf.argmax(tf.nn.softmax(logits), 1), feed_dict = {X: input_digit_img, is_train: False})\n",
    "            \n",
    "            # Store to array\n",
    "            digits[math.floor(blob/3)*3+math.floor(unit/3),(blob%3)*3+(unit%3)] = predicted_digit[0]\n",
    "print (digits.astype(np.uint8))\n",
    "\n",
    "# Format array to string for puzzle solver\n",
    "digit_str = ''.join(map(str, digits.astype(np.uint8).flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 8 1 2 9 6 3 7 5]\n",
      " [2 5 6 3 1 7 8 4 9]\n",
      " [3 7 9 5 8 4 2 1 6]\n",
      " [9 4 8 1 5 3 7 6 2]\n",
      " [1 6 5 9 7 2 4 8 3]\n",
      " [7 3 2 6 4 8 9 5 1]\n",
      " [6 1 4 7 2 9 5 3 8]\n",
      " [5 2 7 8 3 1 6 9 4]\n",
      " [8 9 3 4 6 5 1 2 7]]\n",
      "Solved!\n"
     ]
    }
   ],
   "source": [
    "# Solve sudoku\n",
    "sol = Sudoku(digit_str)\n",
    "sol.solve()\n",
    "print (np.array([int(x) for x in sol.to_oneliner()]).reshape([9,9]))\n",
    "print ('Solved!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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