IMG_net_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try knocking out some frequency bands\n",
    "## maybe Merged456 will work better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "# from URL_download_2 import gdown\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_data_from_GDRIVE():\n",
    "    # imports the custom GDrive downloader library:\n",
    "    # provide file ID:\n",
    "    file_id = '1YPr_K8xSuKghONozyjnVThe47EsiDEZj'\n",
    "\n",
    "    # instantiate class (create a class instance):\n",
    "    G = gdown()\n",
    "\n",
    "    # Download data from Google Drive as necessary:\n",
    "    G.download_file(file_id)\n",
    "\n",
    "def group_frequency_bands(X):\n",
    "    X_ = np.zeros([X.shape[0],X.shape[1],X.shape[2], 2])\n",
    "#     X_[:,:,:,0] = np.mean(X[:,:,:,3:6],axis=3)\n",
    "#     X_[:,:,:,1] = np.mean(X[:,:,:,7:11],axis=3)\n",
    "#     X_[:,:,:,2] = np.mean(X[:,:,:,12:29],axis=3)\n",
    "    \n",
    "#     processing frequencies: 0 16\n",
    "#     processing frequencies: 16 32\n",
    "#     processing frequencies: 32 49\n",
    "#     processing frequencies: 49 65\n",
    "#     X_[:,:,:,0] = np.mean(X[:,:,:,0:16],axis=3)\n",
    "#     X_[:,:,:,1] = np.mean(X[:,:,:,16:32],axis=3)\n",
    "    X_[:,:,:,0] = np.mean(X[:,:,:,32:49],axis=3)\n",
    "    X_[:,:,:,1] = np.mean(X[:,:,:,49:65],axis=3)\n",
    "    \n",
    "    X_.shape\n",
    "    # subplot(1,3,1)\n",
    "    # imagesc(squeeze(mean(X(1,:,:,4:7), 4)))\n",
    "    # title('Theta')\n",
    "\n",
    "    # subplot(1,3,2)\n",
    "    # imagesc(squeeze(mean(X(1,:,:,8:12), 4)))\n",
    "    # title('Alpha')\n",
    "\n",
    "    # subplot(1,3,3)\n",
    "    # imagesc(squeeze(mean(X(1,:,:,13:30), 4)))\n",
    "    # title('Beta')\n",
    "    return X_\n",
    "\n",
    "def im_resize(X_, height, width):\n",
    "    \n",
    "    if width>0 and height>0:\n",
    "        X__ = np.zeros([X_.shape[0], height, width, X_.shape[3]])\n",
    "        for i in range(X_.shape[0]):\n",
    "                for j in range(X_.shape[3]):\n",
    "                    im = Image.fromarray(X_[i,:,:,j])\n",
    "                    X__[i,:,:,j] = im.resize((height, width), Image.ANTIALIAS)\n",
    "        print (X__.shape)\n",
    "\n",
    "        plt.subplot(121)\n",
    "        plt.imshow(np.array(X_[200,:,:,1]))\n",
    "        plt.subplot(122)\n",
    "        plt.imshow(np.array(X__[200,:,:,1]))\n",
    "    else:\n",
    "        print('IMAGES NOT RESIZED')\n",
    "    return X__\n",
    "\n",
    "def downsample_frequency_domain(n_bins):\n",
    "    print('Downsampling the frequency domain from', X.shape[3],'to', n_bins)\n",
    "    xx = np.zeros([X.shape[0],X.shape[1],X.shape[2],n_bins])\n",
    "    print(xx.shape)\n",
    "    a = np.round(np.linspace(0,X.shape[3],n_bins+1))\n",
    "    a = a.astype('uint8')\n",
    "    print(a)\n",
    "    for i in range(n_bins):\n",
    "        xx[:,:,:,i] = np.mean(X[:,:,:,a[i]:a[i+1]], axis=3)   \n",
    "        print('processing frequencies:', a[i], a[i+1])\n",
    "    print(xx.shape)\n",
    "    return xx\n",
    "\n",
    "def parse_array(in_array):\n",
    "    new_array = []\n",
    "    for i in range(len(in_array)):\n",
    "        if str(in_array[i]).find(\"left\")>0:\n",
    "            new_array.append(\"left\")\n",
    "        if str(in_array[i]).find(\"right\")>0:\n",
    "            new_array.append(\"right\")\n",
    "        if (str(in_array[i]).find(\"left\")<0 and \n",
    "            str(in_array[i]).find(\"right\")<0):\n",
    "            print(i)\n",
    "            new_array.append(\"unknown\")\n",
    "    return new_array\n",
    "\n",
    "def find_right(Q, label):\n",
    "    idx = []\n",
    "    for i in range(len(Q)):\n",
    "        if Q[i] == label:\n",
    "            idx.append(i)\n",
    "    return(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat' #0.77\n",
    "# file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat' #2# #0.77\n",
    "# file = 'Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat' #1# #0.76 #0.79\n",
    "# file = 'EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'\n",
    "\n",
    "\n",
    "\n",
    "# file = 'Merged123_1_64_ICA(-eyes)AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=1, TOPO, .mat'\n",
    "# file = 'Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat'\n",
    "# file = 'Merged123-143-202_ICA(-Eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat' #0.56\n",
    "\n",
    "SHUFFLE = False\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "DS_FREQ = 1 #(0 1 2)\n",
    "n_bins = 8\n",
    "RESIZE = 1\n",
    "width = 15\n",
    "height = 15\n",
    "SENSITIVITY = 0\n",
    "knockouts = [0, 1, 2]\n",
    "\n",
    "path = '/home/amplifier/home/DATASETS/' + file\n",
    "mat_contents = sio.loadmat(path)\n",
    "X = mat_contents['X']\n",
    "Y = mat_contents['Z']\n",
    "# Z = mat_contents['Z']\n",
    "Q = parse_array(mat_contents['Q'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(450, 67, 67, 65)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/amplifier/home/DATASETS/Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat\n",
      "Concatenating datasets...\n"
     ]
    }
   ],
   "source": [
    "SHUFFLE = False\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "DS_FREQ = 1 #(0 1 2)\n",
    "n_bins = 8\n",
    "RESIZE = 1\n",
    "width = 15\n",
    "height = 15\n",
    "SENSITIVITY = 0\n",
    "knockouts = [0, 1, 2]\n",
    "\n",
    "X = list()\n",
    "Y = list()\n",
    "Q = list()\n",
    "# 'EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'\n",
    "# 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat',\n",
    "# 'EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'\n",
    "files = list(['Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'])\n",
    "\n",
    "for file_name in files:\n",
    "    path = '/home/amplifier/home/DATASETS/' + file_name\n",
    "    print (path)\n",
    "    mat_contents = sio.loadmat(path)\n",
    "    X.append(mat_contents['X'])\n",
    "    Y.append(mat_contents['Z'])\n",
    "    Q.append(parse_array(mat_contents['Q']))\n",
    "    \n",
    "print('Concatenating datasets...')\n",
    "X = np.concatenate(X, axis=0)\n",
    "Y = np.concatenate(Y, axis=0)\n",
    "Q = np.concatenate(Q,axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downsampling the frequency domain from 65 to 8\n",
      "(450, 67, 67, 8)\n",
      "[ 0  8 16 24 32 41 49 57 65]\n",
      "processing frequencies: 0 8\n",
      "processing frequencies: 8 16\n",
      "processing frequencies: 16 24\n",
      "processing frequencies: 24 32\n",
      "processing frequencies: 32 41\n",
      "processing frequencies: 41 49\n",
      "processing frequencies: 49 57\n",
      "processing frequencies: 57 65\n",
      "(450, 67, 67, 8)\n",
      "Downsampled Frequency Domain shape (450, 67, 67, 8)\n",
      "(450, 15, 15, 8)\n",
      "Resized shape (450, 15, 15, 8)\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Original data type: float64\n",
      "y_test.shape before (90, 1)\n",
      "y_test.shape after (90, 2)\n",
      "Normalized data type: float16\n",
      "Prepped test input shape (90, 15, 15, 8) Noralized MEAN: 0.09375 min -0.02908 max 1.109\n",
      "Prepped train input shape (360, 15, 15, 8) Normalized MEAN: 0.0941 min -0.0571 max 2.4\n",
      "Prepped test labels shape (90, 2) Normalized MEAN: 0.5 min 0.0 max 1.0\n",
      "Prepped train labels shape (360, 2) Normalized MEAN: 0.5 min 0.0 max 1.0\n",
      "EEG in dataset: (450, 15, 15, 8)\n",
      "Labels in dataset: (450, 1)\n",
      "Length of textual labels: 450\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if DS_FREQ==1:\n",
    "    X = downsample_frequency_domain(n_bins)\n",
    "    print('Downsampled Frequency Domain shape', X.shape)\n",
    "if DS_FREQ==2:\n",
    "    X = group_frequency_bands(X)\n",
    "    print('Grouped Frequency Bands shape', X.shape)\n",
    "if RESIZE==1:\n",
    "    X = im_resize(X, height, width)\n",
    "    print('Resized shape', X.shape)\n",
    "\n",
    "if SENSITIVITY==1:\n",
    "    X = np.delete(X, knockouts, axis=3)\n",
    "    print('sens', X.shape)\n",
    "if X.shape[1]<X.shape[2]:\n",
    "    X = np.transpose(X,[0,2,1])\n",
    "\n",
    "if Y.shape[1] > Y.shape[0]:\n",
    "    Y = Y.T\n",
    "    \n",
    "# verify that the model REALLY finds a mapping between the input and the labels. If we get\n",
    "# our accuracy by chance, then we should get the same accuracy on a permuted dataset:\n",
    "# Y = np.random.permutation(Y)\n",
    "\n",
    "print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')\n",
    "x_train, x_test, y_train, y_test, q_train, q_test = train_test_split(X, Y, Q, test_size=TEST_TRAIN, shuffle=SHUFFLE)\n",
    "print('Original data type:', x_train.dtype)\n",
    "\n",
    "\n",
    "# convert to float64 for numerical stability:\n",
    "x_train = x_train.astype('float64')\n",
    "y_train = y_train.astype('float64')\n",
    "x_test = x_test.astype('float64')\n",
    "y_test = y_test.astype('float64')\n",
    "print('y_test.shape before', y_test.shape)\n",
    "\n",
    "\n",
    "# # normalize to unit variance and zero mean:\n",
    "# for i in range(x_train.shape[0]):\n",
    "#     for j in range(x_train.shape[3]):\n",
    "#         x_train[i,:,:,j] = preprocessing.scale(x_train[i,:,:,j], axis=1)\n",
    "#         x_train[i,:,:,j] = preprocessing.minmax_scale(x_train[i,:,:,j], axis=1)\n",
    "# #     y_train[i,:] = preprocessing.scale(y_train[i,:], axis=0)\n",
    "# #     y_train[i,:] = preprocessing.minmax_scale(y_train[i,:], axis=0)\n",
    "\n",
    "# for i in range(x_test.shape[0]):\n",
    "#     for j in range(x_train.shape[3]):\n",
    "#         x_test[i,:,:,j] = preprocessing.scale(x_test[i,:,:,j], axis=1)\n",
    "#         x_test[i,:,:,j] = preprocessing.minmax_scale(x_test[i,:,:,j], axis=1)\n",
    "# #     y_test[i,:] = preprocessing.scale(y_test[i,:], axis=0)\n",
    "# #     y_test[i,:] = preprocessing.minmax_scale(y_test[i,:], axis=0)\n",
    "    \n",
    "\n",
    "# one hot encode the labels:\n",
    "onehot_encoder = preprocessing.OneHotEncoder(sparse=False)\n",
    "y_train = onehot_encoder.fit_transform(y_train)\n",
    "y_test = onehot_encoder.fit_transform(y_test)\n",
    "print('y_test.shape after', y_test.shape)\n",
    "\n",
    "# convert to float16 to save space:\n",
    "x_train = x_train.astype('float16')\n",
    "y_train = y_train.astype('float16')\n",
    "x_test = x_test.astype('float16')\n",
    "y_test = y_test.astype('float16')\n",
    "print('Normalized data type:', x_train.dtype)\n",
    "\n",
    "leng = X.shape[1] # if you work in the FD, this is the height of the sample time-frequency image, othewise EEG channels\n",
    "chan = X.shape[2] # if you work in the FD, this is the width of the sample time-frequency image, othewise time samples of EEG signal\n",
    "\n",
    "if len(X.shape)==3:\n",
    "    streams = 1 # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "if len(X.shape)==4:\n",
    "    streams = X.shape[3] # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "\n",
    "\n",
    "print('Prepped test input shape', x_test.shape, \"Noralized MEAN:\", np.mean(x_test), \"min\", np.min(x_test),\"max\", np.max(x_test))\n",
    "print('Prepped train input shape', x_train.shape, \"Normalized MEAN:\", np.mean(x_train), \"min\", np.min(x_train),\"max\", np.max(x_train))\n",
    "\n",
    "print('Prepped test labels shape', y_test.shape, \"Normalized MEAN:\", np.mean(y_test), \"min\", np.min(y_test),\"max\", np.max(y_test))\n",
    "print('Prepped train labels shape', y_train.shape, \"Normalized MEAN:\", np.mean(y_train), \"min\", np.min(y_train),\"max\", np.max(y_train))\n",
    "\n",
    "# print('Window length', winsize)\n",
    "# print('Step size:', stepsize)\n",
    "# print('Trial length:', trial_len)\n",
    "\n",
    "print('EEG in dataset:', X.shape)\n",
    "print('Labels in dataset:', Y.shape)\n",
    "print('Length of textual labels:', len(Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(450, 15, 15, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'25-30 Hz')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gVrmgnvmfIIB5x/ECgCSOF4D8ytRIMLOyJnf+G939c+2ZEoA8IxcAJJELAJLIBSDfsly1wSR9StIWd/+L9k0JQF6RCwCSyAUASeQCkH9Zzki4VNJbJP2Mmd3V+rqiTfMCkE/kAoAkcgFAErkA5Fz4wxbd/fuSrI1zAZBz5AKAJHIBQBK5AORf5g9bBAAAAAAAx4+2XP4xLXeXj0+Eande84JQ3cSpsfEkyQ4FV0+lGR4zS2/WisFxs2wFnqE2qFRuhGv3jg2E6q4554fhMT/x3p8N1Z31jsdDdd7MsP0tAHdXc3w8VLvtHWtDdS9ZvSVUJ0nf231GqG6wJ55FBYvvaM1mLFS8niGM6sEedYZNtzAen29lf2y+tcXhIXXwNWeG6gZvvitU574AYZ2BN5tqjo2FandvuCBUV1l5KFQnSQ89vCpWWMjwuNTifwsqjsRqKwfjY5aHY3VWDw+pRl88F6qjsUsN+pp41j9+VSwXTvqrI66amEruciHD8cLu37koNuhzgxuuJHuqL1wbVYgfIqtQDdZl2EeLE7FtsDQa33ZLwTElqVCL1TZ64tlZf/5pobrCptix7mxygTMSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAajQSAAAAAABAaqV5Ha2/V80Lzg2VDl0wERuzYbE6SbasGqorFZvxMQseri2XG6G6SqkeHtMsNt9qPb7pZZnvir7hUN3DYyeGx/zll/04VHf7FetDdf69H4bqFooPLlL94heFagfO2R+qu23HulCdJPX2xXIhi4lafH+pjcdqbbQYHrMwEetRB+NksjYWf5OC4xbH40MOrYut3yVnrA3V2fZKqG7B9PdJ550XKh16YXAfPdAbq5NUHIo9nsXx+DFKdLuVpNJYbNxy7ClUktRzIDbh8mj8mKraH/97mVtsHdkjPeExR9bE1pFddE5swM3fjdUtlP4+6bwXhEpHXjIaqos/E0pejD2exYl4LhSCL5ckqTQem29pLD5meSS2f5cy5EKhkSE8o2NW42OOr4g9fw+ujL12sd3pjxs5IwEAAAAAAKRGIwEAAAAAAKRGIwEAAAAAAKRGIwEAAAAAAKSWuZFgZkUzu9PMvtyOCQHIP3IBQBK5ACCJXADyqx1nJLxT0pY2/BwA3YNcAJBELgBIIheAnMrUSDCzNZJ+TtIn2zMdAHlHLgBIIhcAJJELQL5lPSPhLyW9R9JRL+ZpZhvMbJOZbarVRjIOByAHyAUASeQCgCRyAcixcCPBzN4o6Sl3/8mxlnP369x9vbuvL5f7o8MByAFyAUASuQAgiVwA8i/LGQmXSnqTme2Q9C+SfsbM/qktswKQV+QCgCRyAUASuQDkXLiR4O5/5O5r3H2dpKskfcvdf7NtMwOQO+QCgCRyAUASuQDkXzuu2gAAAAAAAI4TpXb8EHf/jqTvtONnAegO5AKAJHIBQBK5AOQTZyQAAAAAAIDU2nJGQlq1gYIef9WiUG2xZzRWVzzqFWVmtG7F06G6VYuGwmNmUW3GHs7+YjU85oqe4VDdSL0nPObT1dg2JEkF83Bt1Ck9+0N1N/1SbK7Ve0JlC6Y2YNr18tj2MB7bRWXD8egbGSyG6kZL8W2+WY2NKUk2HKstH4r3mYtjFisMlkmSL0Bb3OMPi5qVWN3+C5aH6upPzOvTfWa1wYJ2vXowVHvK6t2hut37loTqJKlvWzlUVxwPD5lp+yvUYnWl0fhzaLEaq/VChmDIUFoOXmmwUMswqGLraM+LYvtKfUeGjWgB1AYL2nlZ7Hc986RHQnX7RuNXitj/aKw2uu1JUmk8wz46Easrj8Rfa5VGY7XWjP+emTLFY+NmefnRqMTmO3HmylCd70//fMYZCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAIDUaCQAAAAAAILXSfA7WrEhjqxuh2t5KPVRXKHioTpLWDe4L1f3eid8Jj9mQhWtvHzstVDfu5fCYZ/XsDtUVFX9ctk6cFK7dWx8I1ZUttt1K0rrK3lDd8894PFR3oLcWqlsoXpDq/cHtYbwYq2vG9zMfi41ZGI3vZ5Xx+Hyjm26hNv9jepbWdny6smasLst8x1fEBi3UYoM2K6GyBeMFqTYQy4W9Q/2hukIxnvPVxbG59lbjG25hIlyqYjU230LsUEySVOuL/a61wfg6qsWe8rOJH96o0RurGzsxto6a8/oqILtmWRpfFcvOWjP23F0pxTf65qLYXMuH4k8uPQeDT2iKPxeWh+PZWajFdpjqkvjGW1sUz5TSRGy+zWJ8zOE1se2hOhira/w0/Vw5IwEAAAAAAKRGIwEAAAAAAKRGIwEAAAAAAKSWqZFgZkvN7CYzu9/MtpjZS9s1MQD5RC4ASCIXACSRC0C+Zf2YlY9K+pq7/4qZVSQtasOcAOQbuQAgiVwAkEQuADkWbiSY2WJJr5T0Vkly96qkanumBSCPyAUASeQCgCRyAci/LG9tOF3SHkn/YGZ3mtknzeyIay6Z2QYz22RmmxrDwxmGA5ADs86F5sjI/M8SwHya/fECuQB0u8DrCHIB6CRZGgklSRdJ+oS7XyhpRNK1yYXc/Tp3X+/u64sDC3FBXwDzaNa5UOiPXfMdQG7M/niBXAC6XeB1BLkAdJIsjYSdkna6+22t/9+kyUAAcPwiFwAkkQsAksgFIOfCjQR33y3pMTM7u3XTZZLua8usAOQSuQAgiVwAkEQuAPmX9aoN75B0Y+uTVh+S9NvZpwQg58gFAEnkAoAkcgHIsUyNBHe/S9L6Ns0FQBcgFwAkkQsAksgFIN+yfEYCAAAAAAA4zmR9a8PsFFwaqIdK+3tjl5bdfzD+Ca97xmNXmbigpyc8Zs0b4dqt1dFQ3Z764vCYvVYL1S0tjMXH7I2NKUlVL4bqmhl6bmtL+0N1J/cNherKFt+GcqfooTLvaYaHtIaF6soH49tQaTxcqmZsk1ejNz5mYyD4uJRidVL8cZEkBTeHxknxS56vXHkwVPf02ImhumD0LRhrSqXR2GNaeyD23B3dbiWpuSKWu4Vq/DCs50C4VAo+jTYq8SEnlsYez/GV8celPhh/PrR6bL6Zcqw/doxsjeBxZx7/nBhcvbv2LwnVLemPH6+WV8RqC/X4a5fKUIZjQItt86WR2HYrSdaMPaCNE8vhMUdPim/4heDT/sTy8JAaPzU2aHNbLLCbs3haymOEAAAAAACABUIjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApEYjAQAAAAAApFaa19HMZaVmqHTVwKFQ3ch4JVQnSU+MLA7VffzAqeExe60Wrr1vdHWorqdQD4+5qnwgVDfS7AmP2czQ/3pBZXeo7lCzHB5z1GO72Z7xgVBd3fPVHzRJ4U3QgnWxGMqk0evh2kIt+osqvI4affH51pfHcqzQ0wiP2fT4OioGn5eee9Ke8JjD1VgGlg4Ff88F2OYzC26CXozV9T4Vz876QOxxic5Vkup98VovxOab4alQE8tiD2h9SfwYxXrjmaLh2C9rffExo1lk0SHjMb8wPP67NhqxbX7PvsHYgJKa1dgOProqw3O+4jtpsRrbIHoyHHZWDlRjhRlWUTX28k5S/FiuviL++q5QiW30xfHggLOIoXy94gAAAAAAAAuKRgIAAAAAAEiNRgIAAAAAAEgtUyPBzP6TmW02s3vN7NNm1tuuiQHIJ3IBQBK5ACCJXADyLdxIMLNTJP2BpPXufp6koqSr2jUxAPlDLgBIIhcAJJELQP5lfWtDSVKfmZUkLZK0K/uUAOQcuQAgiVwAkEQuADkWbiS4++OS/lzSo5KekHTQ3b+RXM7MNpjZJjPb1Dg0Ep8pgI4XyoURcgHoZpFcqI+SC0A343gByL8sb21YJulKSadJWi2p38x+M7mcu1/n7uvdfX1xsD8+UwAdL5QL/eQC0M0iuVBaRC4A3YzjBSD/sry14WclPezue9y9Julzkl7WnmkByClyAUASuQAgiVwAci5LI+FRSZeY2SIzM0mXSdrSnmkByClyAUASuQAgiVwAci7LZyTcJukmSXdIuqf1s65r07wA5BC5ACCJXACQRC4A+VfKUuzufyLpT9o0FwBdgFwAkEQuAEgiF4B8y3r5RwAAAAAAcBzJdEbCrNUL8n09odLlZ8Qu+fJoaWmoTpL2HYx9OuwND18SHtPdwrVRy/tGw7X3Dq0O1Y3UKuExdx8cDNdefMqjobpfW/Hj8Jhlq4fqfrrjlFDd2ER83S4Eq0u9e2PbffWEWF1puBiqk6RmxUN19cFmeEwvxHu+hUasrlmO/Z6SpGCMZcm/F659PFx78bIdobrd1cXhMf9955mhuoGdscelWA2VLRirS737Yr/r2EnBdTQR3/5KI7HaRuyQSJLUzBD1HoyURm98zMaiDJkSdO7aJ8K1lULsufu+3SeFx6zXY89N0efQ4OHJgrGGVB6Kbby9fbEQPDA8EKqTJBuNPZ4Ty+P7SrMcz7HieKx2eHX8GKVYLYfqahku4LF4/Z5w7fOWPxmqe3D/yvCYT+6KvZYd2BU77izW0i/LGQkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACA1GgkAAAAAACC10nwOVhlyPefrjVDtk+ctDtUNPx6rkyQ3D9XtqxfDYzar8VorNkN1+8uLwmM2hiqxwuC6laTFW8rh2u+dcl6o7o1X3h0es2yxx/TUm2K75779obIFUx5uatWtI6Ha0VWxbXfJtlCZJGlsZaz/OnpqPTxmoz+2b0tSs2axwvguKhuObbulseBcJa17/r5w7X9d8UCo7q/2rw2PuWPZCaG6iS2xdVscj29DC6E80tCJmw6GakfWLA3V9e4JlUmSmsGnwmidJHl8dwn/GcmLGYIhWFrZGz9U/ZlLY/u2JL17+UOhupeO/HJ4zP2HYs9pq26LPYc+MpKvXKgcamrNt0ZDtdvWDoTq+rfGd9Lo/j1xQvxxqWXYRev9wTFXxI9viv21UF349Yek/3LGv4dr37r4qVDdZSNvCo9ppdiDuvi+A6G64nj61+qckQAAAAAAAFKjkQAAAAAAAFKjkQAAAAAAAFKbsZFgZteb2VNmdu+U25ab2S1mtrX177K5nSaATkIuAEgiFwBMh2wAulOaMxI2Sro8cdu1kr7p7mdJ+mbr/wCOHxtFLgB4to0iFwAcaaPIBqDrzNhIcPfvSno6cfOVkm5ofX+DpF9o87wAdDByAUASuQBgOmQD0J2in5Gwyt2fkKTWvyuPtqCZbTCzTWa2qVaNXZ4GQC7EcqFGLgBdLJQL1XrsEm8AciNVNjwrFzheADrKnH/Yortf5+7r3X19uRK8QCmArvKsXCiTCwCenQuV0qKFng6ADvCsXOB4Aego0UbCk2Z2siS1/n2qfVMCkFPkAoAkcgHAdMgGIOeijYSbJV3d+v5qSV9sz3QA5Bi5ACCJXAAwHbIByLk0l3/8tKQfSTrbzHaa2dsk/Zmk15rZVkmvbf0fwHGCXACQRC4AmA7ZAHSn0kwLuPubj3LXZW2eC4CcIBcAJJELAKZDNgDdac4/bBEAAAAAAHQPGgkAAAAAACC1Gd/a0E52cFQ9X7sjVLv9sheH6nrXDofqJKn68GCorlFphsfM0trxRqy4MRrfDIrDxVBdsye+jqpLw6Vq9sXGPbE0FB7zrd+/JlR31pd+HKozz9n110fHVdi0JVSJ8UjPAAAOn0lEQVQ6eP6LQnVjJ1qoTpKKE7E6q2bYuc3jtcFhrRZfR4VadMzwkPriXReEax8dWRaqe8myHeEx7717bajurB/fFhvQx2J1C8THx+X3PhiqHbwgdrwwsjq+zZeisZth17ZGvFbxXzWsEMyULL/nx+98Vbj266vPDdVN1OLHVKU7YsedduuPYgM285ULGh5T4Qd3h0qXXvDSUN3oSfGdtDgW2+a9FB+z2RsuDR+blweq4TH7+mK1w414iH1yx8vDtd9bGru4SKMZPwZctKUnVNe89yehOvfx1MtyRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEiNRgIAAAAAAEitNJ+DWaGgQl9vqPbsjz0eqtvywRNDdZI0eNaBUN3Q3v7wmGrGS2XBsnqwUFKhEawbjfewGr0eru09aSRUt2HTW8JjnvPHe0J1zUWLQnU2lq/+oJnJyrEoOvmLD4fqtv/eaaE6SRo9oxaqs4lieEwF9zNJ8uDuXciQRRatjUeRFm2rhGvvbK6L1SlWJ0nnfuixUF2zN/YcauMZVu4CMCvIKrHHdMW/bQvVjfyHs0J1knTojNhOWhqO53VxIv6YRnMhyz4a1SzHa3vv6wvXbtuzJlRXPhR/TE//h9i26z09obq85YLMwrlw8me2huq2v+vMUJ0k1c+LHXP6ePzlmWc51qhkeRESU6vH5lsoxl8LPP7YCeHafYdir/GqO+OvDc++PrbtNoO5oFk8t+TrFQcAAAAAAFhQNBIAAAAAAEBqNBIAAAAAAEBqMzYSzOx6M3vKzO6dcttHzOx+M/upmX3ezJbO7TQBdBJyAUASuQAgiVwAuleaMxI2Sro8cdstks5z9xdKelDSH7V5XgA620aRCwCebaPIBQDPtlHkAtCVZmwkuPt3JT2duO0b7l5v/fdWSbGPtgWQS+QCgCRyAUASuQB0r3Z8RsI1kr56tDvNbIOZbTKzTVUfb8NwAHKAXACQRC4ASEqdCzVyAegomRoJZvY+SXVJNx5tGXe/zt3Xu/v6isWufw0gP8gFAEnkAoCk2eZCmVwAOkopWmhmV0t6o6TL3N3bNyUAeUUuAEgiFwAkkQtA/oUaCWZ2uaT3SnqVu4+2d0oA8ohcAJBELgBIIheA7pDm8o+flvQjSWeb2U4ze5ukv5Y0KOkWM7vLzP52jucJoIOQCwCSyAUASeQC0L1mPCPB3d88zc2fmoO5AMgJcgFAErkAIIlcALpXO67aAAAAAAAAjhM0EgAAAAAAQGrhqzaEFEzWF7t0S/PJPaG6c97XDNVJ0vYNzwnV9Z17KDxmvVaM11ZjtV6O95Pqi+LrNyy+ilS5fXGo7rR/2hEes7nv6VCdDfTHBpywWN1CMZNVyqHS5v4Doboz/uahUJ0kPfpbZ4TqRl8wFh6zWc/Q852I7TCNDH1ma8TqCvX4tttoxj/0+4Qfx54KV968PTxm48DBUF1h0aLYgNWc/d3AJCvFHpfm0FCobu3120J1krTrV88M1R08px4es96I7y+F6vw/T3hwEywoPlfPcJR74k9idcu//kB4zOah4VBdoacnNuACbAdZWMHCv2szmLlnfCye84+8LZYLpYtiGSZJjUXxrI9eO6NUir8WKBRigxZLwQMNSfViPBgq3x8M1Z12Y4ZcOBh7XVkIvua2WvpcyNmRBQAAAAAAWEg0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGo0EgAAAAAAQGqleR3NJdXroVIb6A/VNfcfCNVJ0roPPhmqq73y/PCYu17RE65tDjZDdVa38JjFaqyuZ398zFW3jYRrC7dvCdU1e+OPS3Tbje4r8ljZwnGpGZu0BR+X5tChUJ0krf7z22JjXvrC8Ji7Xh7f/uoDwXXbCA+pwkRs/+7dF994V94+FK7V3Q+Eyrwn/rgUFi2KFTaiD0zOgsEl99icC8HHxQ/Gt6FVH4/lwsqXnBce88lLgs8tkqqLw6VhhVqsrndvhly4bX+41jdvjdVVKuExw9tucF/JWyzIJQ9mYKGvN1TXPHAwVCdJa/7HD2NjvurC8Jg7X9MXrp1YHlu39WJ4SCn4GqTvyfjfwp/7reFwrW7dFCrz3tj2J8W33ei+Mptc4IwEAAAAAACQGo0EAAAAAACQGo0EAAAAAACQ2oyNBDO73syeMrN7p7nvP5uZm9mKuZkegE5ELgBIIhcATIdsALpTmjMSNkq6PHmjmZ0q6bWSHm3znAB0vo0iFwA820aRCwCOtFFkA9B1ZmwkuPt3JT09zV3/S9J7lL/PfAWQEbkAIIlcADAdsgHoTqHPSDCzN0l63N3vbvN8AOQUuQAgiVwAMB2yAci/0mwLzGyRpPdJel3K5TdI2iBJvYX4NY8BdK5MuWDkAtCNyAUA05lNNpALQOeKnJFwhqTTJN1tZjskrZF0h5mdNN3C7n6du6939/UV64vPFEAni+dCoXcepwlgHmU4XiAXgC6WOhvIBaBzzfqMBHe/R9LKw/9vBcB6d9/bxnkByBFyAUASuQBgOmQD0B3SXP7x05J+JOlsM9tpZm+b+2kB6GTkAoAkcgHAdMgGoDvNeEaCu795hvvXtW02AHKBXACQRC4AmA7ZAHSn0FUbAAAAAADA8YlGAgAAAAAASM3cff4GM9sj6ZGj3L1CUqd9yEqnzYn5zKzT5rQQ81nr7ifO85hh5EJmzGdmnTYncmEG5EJmzGdmnTYncmEG5EJmzGdmnTanjs6FeW0kHIuZbXL39Qs9j6k6bU7MZ2adNqdOm0/edOL667Q5MZ+ZddqcOm0+edOJ66/T5sR8ZtZpc+q0+eRNJ66/TpsT85lZp82p0+aTxFsbAAAAAABAajQSAAAAAABAap3USLhuoScwjU6bE/OZWafNqdPmkzeduP46bU7MZ2adNqdOm0/edOL667Q5MZ+ZddqcOm0+edOJ66/T5sR8ZtZpc+q0+TxLx3xGAgAAAAAA6HyddEYCAAAAAADocDQSAAAAAABAavPeSDCzy83sATPbZmbXTnN/j5l9pnX/bWa2bg7ncqqZfdvMtpjZZjN75zTLvNrMDprZXa2v/zZX85ky5g4zu6c13qZp7jcz+1hrHf3UzC6aw7mcPeV3v8vMhszsXYll5nwdmdn1ZvaUmd075bblZnaLmW1t/bvsKLVXt5bZamZXz+F8PmJm97cek8+b2dKj1B7z8T0ekQup5kUuHDkPcqGLkQup5kUuHDkPcqGLkQup5kUuHDkPcmEuuPu8fUkqStou6XRJFUl3Szo3sczvS/rb1vdXSfrMHM7nZEkXtb4flPTgNPN5taQvz/N62iFpxTHuv0LSVyWZpEsk3TaPj99uSWvnex1JeqWkiyTdO+W2D0u6tvX9tZI+NE3dckkPtf5d1vp+2RzN53WSSq3vPzTdfNI8vsfbF7mQel7kwpFjkwtd+kUupJ4XuXDk2ORCl36RC6nnRS4cOTa5MAdf831GwsWStrn7Q+5elfQvkq5MLHOlpBta398k6TIzs7mYjLs/4e53tL4/JGmLpFPmYqw2u1LSP/qkWyUtNbOT52HcyyRtd/dH5mGsZ3H370p6OnHz1G3lBkm/ME3p6yXd4u5Pu/t+SbdIunwu5uPu33D3euu/t0pak3Wc4wS50B7kwiRyoTuQC+1BLkwiF7oDudAe5MIkciGj+W4knCLpsSn/36kjd7hnlmmtzIOSTpjribVOfbpQ0m3T3P1SM7vbzL5qZs+f67lIcknfMLOfmNmGae5Psx7nwlWSPn2U++Z7HUnSKnd/QpoMc0krp1lmodbVNZrs9k5npsf3eEMupEMupEMudAdyIR1yIR1yoTuQC+mQC+mQCxmV5nm86TqCyetPplmmrcxsQNK/SnqXuw8l7r5Dk6fgDJvZFZK+IOmsuZyPpEvdfZeZrZR0i5nd3+pcPTPlaWrmeh1VJL1J0h9Nc/dCrKO0FmJdvU9SXdKNR1lkpsf3eEMupEMutA+50PnIhXTIhfYhFzofuZAOudA+5MIxzPcZCTslnTrl/2sk7TraMmZWkrRER56K0jZmVtbkzn+ju38ueb+7D7n7cOv7r0gqm9mKuZpPa5xdrX+fkvR5TZ7KNVWa9dhub5B0h7s/mbxjIdZRy5OHT8Vq/fvUNMvM67pqfQjLGyX9hrtPGzQpHt/jDbmQArmQGrnQHciFFMiF1MiF7kAupEAupEYuZDTfjYTbJZ1lZqe1OlNXSbo5sczNkg5/IuavSPrW0VZkVq33TH1K0hZ3/4ujLHPS4fdWmdnFmlxn++ZiPq0x+s1s8PD3mvzgjXsTi90s6bds0iWSDh4+NWcOvVlHOR1pvtfRFFO3laslfXGaZb4u6XVmtswmP431da3b2s7MLpf0XklvcvfRoyyT5vE93pALM8+JXEiPXOgO5MLMcyIX0iMXugO5MPOcyIX0yIWsfJ4/3VGTnxT6oCY/dfV9rds+qMmVJkm9kv6PpG2Sfizp9Dmcy8s1eXrKTyXd1fq6QtLvSvrd1jJvl7RZk58Me6ukl83x+jm9NdbdrXEPr6OpczJJH2+tw3skrZ/jOS3S5A69ZMpt87qONBk+T0iqabI7+DZNvuftm5K2tv5d3lp2vaRPTqm9prU9bZP023M4n22afB/V4W3p8KcGr5b0lWM9vsf7F7kw45zIhennQC508Re5MOOcyIXp50AudPEXuTDjnMiF6edALszBl7UmBQAAAAAAMKP5fmsDAAAAAADIMRoJAAAAAAAgNRoJAAAAAAAgNRoJAAAAAAAgNRoJAAAAAAAgNRoJAAAAAAAgNRoJAAAAAAAgtf8HYomuZaFu3FsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x1296 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X.shape)\n",
    "\n",
    "f, axs = plt.subplots(1,4,figsize=(18,18))\n",
    "\n",
    "axs[0].imshow(X[100,:,:,2])\n",
    "axs[0].set_title('1-5 Hz')\n",
    "axs[1].imshow(X[300,:,:,4])\n",
    "axs[1].set_title('5-10 Hz')\n",
    "axs[2].imshow(X[300,:,:,6])\n",
    "axs[2].set_title('15-20 Hz')\n",
    "axs[3].imshow(X[300,:,:,7])\n",
    "axs[3].set_title('25-30 Hz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 15, 15, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 15, 15, 30)        2190      \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 15, 15, 30)        120       \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 15, 15, 30)        8130      \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 15, 15, 30)        120       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 30)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 8, 60)          16260     \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 8, 8, 60)          240       \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 60)          32460     \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 8, 8, 60)          240       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 60)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 4, 4, 120)         28920     \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 4, 4, 120)         480       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 2, 2, 120)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 480)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 480)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 100)               48100     \n",
      "_________________________________________________________________\n",
      "batch_normalization_13 (Batc (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_14 (Batc (None, 10)                40        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 138,732\n",
      "Trainable params: 137,912\n",
      "Non-trainable params: 820\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dilation_rate = 1\n",
    "\n",
    "# make sure you don't hog all the video memory\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.Session(config=config)\n",
    "from keras import backend as K\n",
    "K.set_session(sess)\n",
    "###################################\n",
    "\n",
    "def activation():\n",
    "    return ELU(alpha=1.0) # Activation('relu') #\n",
    "\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, BatchNormalization, Dropout, ELU\n",
    "from keras.models import Model\n",
    "from keras.callbacks import TensorBoard, EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "sh = x_train.shape\n",
    "\n",
    "\n",
    "input_img = Input(shape=(sh[1], sh[2], sh[3]))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(30, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(input_img)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv2D(30, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Conv2D(60, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv2D(60, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Conv2D(120, (2, 2), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(100, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(10, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dense(2, activation='softmax')(x)\n",
    "\n",
    "model = Model(input_img, x)\n",
    "optimizer=Adam(0.00006)\n",
    "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 360 samples, validate on 90 samples\n",
      "Epoch 1/300\n",
      " - 3s - loss: 1.0864 - acc: 0.4972 - val_loss: 0.7266 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.54444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 2/300\n",
      " - 0s - loss: 1.0129 - acc: 0.5194 - val_loss: 0.7074 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.54444 to 0.55556, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 3/300\n",
      " - 0s - loss: 0.9691 - acc: 0.5417 - val_loss: 0.6973 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.55556\n",
      "Epoch 4/300\n",
      " - 0s - loss: 0.9168 - acc: 0.5556 - val_loss: 0.7115 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.55556\n",
      "Epoch 5/300\n",
      " - 0s - loss: 0.8488 - acc: 0.5722 - val_loss: 0.6894 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.55556 to 0.56667, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 6/300\n",
      " - 0s - loss: 0.9147 - acc: 0.5444 - val_loss: 0.6851 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.56667 to 0.61111, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 7/300\n",
      " - 0s - loss: 0.9720 - acc: 0.5528 - val_loss: 0.6893 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.61111\n",
      "Epoch 8/300\n",
      " - 0s - loss: 0.9062 - acc: 0.5500 - val_loss: 0.6963 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00008: val_acc improved from 0.61111 to 0.62222, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 9/300\n",
      " - 0s - loss: 0.9622 - acc: 0.5417 - val_loss: 0.7041 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.62222\n",
      "Epoch 10/300\n",
      " - 0s - loss: 0.8646 - acc: 0.5639 - val_loss: 0.7032 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.62222\n",
      "Epoch 11/300\n",
      " - 0s - loss: 0.9509 - acc: 0.5583 - val_loss: 0.6929 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.62222 to 0.64444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 12/300\n",
      " - 0s - loss: 0.8863 - acc: 0.5806 - val_loss: 0.6887 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.64444\n",
      "Epoch 13/300\n",
      " - 0s - loss: 0.8645 - acc: 0.5639 - val_loss: 0.6597 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.64444 to 0.67778, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 14/300\n",
      " - 0s - loss: 0.8078 - acc: 0.6194 - val_loss: 0.6397 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.67778 to 0.68889, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 15/300\n",
      " - 0s - loss: 0.7721 - acc: 0.6056 - val_loss: 0.6277 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00015: val_acc improved from 0.68889 to 0.70000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 16/300\n",
      " - 0s - loss: 0.7979 - acc: 0.5861 - val_loss: 0.6157 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.70000\n",
      "Epoch 17/300\n",
      " - 0s - loss: 0.7888 - acc: 0.6139 - val_loss: 0.6298 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.70000\n",
      "Epoch 18/300\n",
      " - 0s - loss: 0.7759 - acc: 0.6167 - val_loss: 0.6387 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.70000\n",
      "Epoch 19/300\n",
      " - 0s - loss: 0.7818 - acc: 0.6278 - val_loss: 0.6298 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.70000\n",
      "Epoch 20/300\n",
      " - 0s - loss: 0.7591 - acc: 0.6222 - val_loss: 0.6154 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.70000\n",
      "Epoch 21/300\n",
      " - 0s - loss: 0.6959 - acc: 0.6306 - val_loss: 0.6071 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.70000\n",
      "Epoch 22/300\n",
      " - 0s - loss: 0.8180 - acc: 0.6222 - val_loss: 0.6030 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.70000\n",
      "Epoch 23/300\n",
      " - 0s - loss: 0.7872 - acc: 0.5889 - val_loss: 0.6101 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00023: val_acc improved from 0.70000 to 0.71111, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 24/300\n",
      " - 0s - loss: 0.7769 - acc: 0.6139 - val_loss: 0.6078 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.71111\n",
      "Epoch 25/300\n",
      " - 0s - loss: 0.6757 - acc: 0.6722 - val_loss: 0.6019 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.71111\n",
      "Epoch 26/300\n",
      " - 0s - loss: 0.7215 - acc: 0.6389 - val_loss: 0.6007 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.71111\n",
      "Epoch 27/300\n",
      " - 0s - loss: 0.6725 - acc: 0.6611 - val_loss: 0.5920 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.71111\n",
      "Epoch 28/300\n",
      " - 0s - loss: 0.6688 - acc: 0.6333 - val_loss: 0.5945 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.71111\n",
      "Epoch 29/300\n",
      " - 0s - loss: 0.7754 - acc: 0.5972 - val_loss: 0.5894 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.71111\n",
      "Epoch 30/300\n",
      " - 0s - loss: 0.7163 - acc: 0.6556 - val_loss: 0.6097 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.71111\n",
      "Epoch 31/300\n",
      " - 0s - loss: 0.7193 - acc: 0.6444 - val_loss: 0.6058 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.71111\n",
      "Epoch 32/300\n",
      " - 0s - loss: 0.6296 - acc: 0.7000 - val_loss: 0.6017 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.71111\n",
      "Epoch 33/300\n",
      " - 0s - loss: 0.6526 - acc: 0.6639 - val_loss: 0.5910 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.71111\n",
      "Epoch 34/300\n",
      " - 0s - loss: 0.7117 - acc: 0.6167 - val_loss: 0.5783 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00034: val_acc improved from 0.71111 to 0.72222, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 35/300\n",
      " - 0s - loss: 0.6975 - acc: 0.6444 - val_loss: 0.5684 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00035: val_acc improved from 0.72222 to 0.73333, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 36/300\n",
      " - 0s - loss: 0.6267 - acc: 0.7000 - val_loss: 0.5601 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00036: val_acc improved from 0.73333 to 0.75556, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 37/300\n",
      " - 0s - loss: 0.6680 - acc: 0.6667 - val_loss: 0.5550 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.75556\n",
      "Epoch 38/300\n",
      " - 0s - loss: 0.6262 - acc: 0.6917 - val_loss: 0.5538 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.75556\n",
      "Epoch 39/300\n",
      " - 0s - loss: 0.5534 - acc: 0.7250 - val_loss: 0.5509 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00039: val_acc improved from 0.75556 to 0.77778, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 40/300\n",
      " - 0s - loss: 0.6239 - acc: 0.6917 - val_loss: 0.5559 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.77778\n",
      "Epoch 41/300\n",
      " - 0s - loss: 0.5872 - acc: 0.7194 - val_loss: 0.5675 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.77778\n",
      "Epoch 42/300\n",
      " - 0s - loss: 0.5408 - acc: 0.7250 - val_loss: 0.5792 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.77778\n",
      "Epoch 43/300\n",
      " - 0s - loss: 0.6035 - acc: 0.6889 - val_loss: 0.5850 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.77778\n",
      "Epoch 44/300\n",
      " - 0s - loss: 0.5741 - acc: 0.7250 - val_loss: 0.5905 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.77778\n",
      "Epoch 45/300\n",
      " - 0s - loss: 0.5555 - acc: 0.7250 - val_loss: 0.5825 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.77778\n",
      "Epoch 46/300\n",
      " - 0s - loss: 0.5482 - acc: 0.7167 - val_loss: 0.5748 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.77778\n",
      "Epoch 47/300\n",
      " - 0s - loss: 0.5170 - acc: 0.7611 - val_loss: 0.5723 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00047: val_acc improved from 0.77778 to 0.77778, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 48/300\n",
      " - 0s - loss: 0.5450 - acc: 0.7361 - val_loss: 0.5681 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.77778\n",
      "Epoch 49/300\n",
      " - 0s - loss: 0.5794 - acc: 0.7083 - val_loss: 0.5623 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.77778\n",
      "Epoch 50/300\n",
      " - 0s - loss: 0.4929 - acc: 0.7444 - val_loss: 0.5627 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.77778\n",
      "Epoch 51/300\n",
      " - 0s - loss: 0.5205 - acc: 0.7444 - val_loss: 0.5632 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00051: val_acc improved from 0.77778 to 0.78889, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 52/300\n",
      " - 0s - loss: 0.5242 - acc: 0.7417 - val_loss: 0.5610 - val_acc: 0.7667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00052: val_acc did not improve from 0.78889\n",
      "Epoch 53/300\n",
      " - 0s - loss: 0.5423 - acc: 0.7333 - val_loss: 0.5518 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.78889\n",
      "Epoch 54/300\n",
      " - 0s - loss: 0.5019 - acc: 0.7778 - val_loss: 0.5575 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.78889\n",
      "Epoch 55/300\n",
      " - 0s - loss: 0.5358 - acc: 0.7111 - val_loss: 0.5657 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.78889\n",
      "Epoch 56/300\n",
      " - 0s - loss: 0.4906 - acc: 0.7417 - val_loss: 0.5612 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00056: val_acc improved from 0.78889 to 0.80000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 57/300\n",
      " - 0s - loss: 0.5632 - acc: 0.7472 - val_loss: 0.5568 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.80000\n",
      "Epoch 58/300\n",
      " - 0s - loss: 0.4719 - acc: 0.7667 - val_loss: 0.5539 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.80000\n",
      "Epoch 59/300\n",
      " - 0s - loss: 0.4876 - acc: 0.7889 - val_loss: 0.5487 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.80000\n",
      "Epoch 60/300\n",
      " - 0s - loss: 0.5151 - acc: 0.7500 - val_loss: 0.5348 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.80000\n",
      "Epoch 61/300\n",
      " - 0s - loss: 0.4960 - acc: 0.7333 - val_loss: 0.5389 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.80000\n",
      "Epoch 62/300\n",
      " - 0s - loss: 0.5207 - acc: 0.7500 - val_loss: 0.5350 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.80000\n",
      "Epoch 63/300\n",
      " - 0s - loss: 0.4321 - acc: 0.7917 - val_loss: 0.5329 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.80000\n",
      "Epoch 64/300\n",
      " - 0s - loss: 0.4754 - acc: 0.7722 - val_loss: 0.5192 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.80000\n",
      "Epoch 65/300\n",
      " - 0s - loss: 0.4577 - acc: 0.7917 - val_loss: 0.5134 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.80000\n",
      "Epoch 66/300\n",
      " - 0s - loss: 0.4425 - acc: 0.7806 - val_loss: 0.5195 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.80000\n",
      "Epoch 67/300\n",
      " - 0s - loss: 0.4706 - acc: 0.7694 - val_loss: 0.5264 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.80000\n",
      "Epoch 68/300\n",
      " - 0s - loss: 0.4608 - acc: 0.8000 - val_loss: 0.5165 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.80000\n",
      "Epoch 69/300\n",
      " - 0s - loss: 0.4828 - acc: 0.7667 - val_loss: 0.5144 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.80000\n",
      "Epoch 70/300\n",
      " - 0s - loss: 0.4360 - acc: 0.7917 - val_loss: 0.5129 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.80000\n",
      "Epoch 71/300\n",
      " - 0s - loss: 0.4477 - acc: 0.7750 - val_loss: 0.5108 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00071: val_acc improved from 0.80000 to 0.80000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 72/300\n",
      " - 0s - loss: 0.4253 - acc: 0.7917 - val_loss: 0.5135 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.80000\n",
      "Epoch 73/300\n",
      " - 0s - loss: 0.4194 - acc: 0.8194 - val_loss: 0.5153 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.80000\n",
      "Epoch 74/300\n",
      " - 0s - loss: 0.4000 - acc: 0.8333 - val_loss: 0.5134 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.80000\n",
      "Epoch 75/300\n",
      " - 0s - loss: 0.4545 - acc: 0.7944 - val_loss: 0.5254 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.80000\n",
      "Epoch 76/300\n",
      " - 0s - loss: 0.4397 - acc: 0.8056 - val_loss: 0.5434 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.80000\n",
      "Epoch 77/300\n",
      " - 0s - loss: 0.4480 - acc: 0.7861 - val_loss: 0.5603 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.80000\n",
      "Epoch 78/300\n",
      " - 0s - loss: 0.4287 - acc: 0.7944 - val_loss: 0.5383 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.80000\n",
      "Epoch 79/300\n",
      " - 0s - loss: 0.4181 - acc: 0.8083 - val_loss: 0.5176 - val_acc: 0.8111\n",
      "\n",
      "Epoch 00079: val_acc improved from 0.80000 to 0.81111, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 80/300\n",
      " - 0s - loss: 0.4234 - acc: 0.8083 - val_loss: 0.5352 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.81111\n",
      "Epoch 81/300\n",
      " - 0s - loss: 0.3978 - acc: 0.8111 - val_loss: 0.5486 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.81111\n",
      "Epoch 82/300\n",
      " - 0s - loss: 0.4357 - acc: 0.8111 - val_loss: 0.5308 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.81111\n",
      "Epoch 83/300\n",
      " - 0s - loss: 0.4011 - acc: 0.8111 - val_loss: 0.5221 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.81111\n",
      "Epoch 84/300\n",
      " - 0s - loss: 0.3780 - acc: 0.8583 - val_loss: 0.5276 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.81111\n",
      "Epoch 85/300\n",
      " - 0s - loss: 0.3606 - acc: 0.8639 - val_loss: 0.5218 - val_acc: 0.8111\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.81111\n",
      "Epoch 86/300\n",
      " - 0s - loss: 0.3511 - acc: 0.8556 - val_loss: 0.5060 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00086: val_acc did not improve from 0.81111\n",
      "Epoch 87/300\n",
      " - 0s - loss: 0.3828 - acc: 0.8389 - val_loss: 0.5073 - val_acc: 0.8111\n",
      "\n",
      "Epoch 00087: val_acc did not improve from 0.81111\n",
      "Epoch 88/300\n",
      " - 0s - loss: 0.3475 - acc: 0.8500 - val_loss: 0.5063 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00088: val_acc did not improve from 0.81111\n",
      "Epoch 89/300\n",
      " - 0s - loss: 0.3864 - acc: 0.8389 - val_loss: 0.5008 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00089: val_acc did not improve from 0.81111\n",
      "Epoch 90/300\n",
      " - 0s - loss: 0.3558 - acc: 0.8278 - val_loss: 0.5007 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00090: val_acc did not improve from 0.81111\n",
      "Epoch 91/300\n",
      " - 0s - loss: 0.3875 - acc: 0.8222 - val_loss: 0.4896 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00091: val_acc did not improve from 0.81111\n",
      "Epoch 92/300\n",
      " - 0s - loss: 0.3647 - acc: 0.8667 - val_loss: 0.4948 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00092: val_acc did not improve from 0.81111\n",
      "Epoch 93/300\n",
      " - 0s - loss: 0.3022 - acc: 0.8667 - val_loss: 0.4918 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00093: val_acc did not improve from 0.81111\n",
      "Epoch 94/300\n",
      " - 0s - loss: 0.4057 - acc: 0.8028 - val_loss: 0.4951 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00094: val_acc did not improve from 0.81111\n",
      "Epoch 95/300\n",
      " - 0s - loss: 0.3679 - acc: 0.8417 - val_loss: 0.5037 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00095: val_acc did not improve from 0.81111\n",
      "Epoch 96/300\n",
      " - 0s - loss: 0.3484 - acc: 0.8556 - val_loss: 0.4986 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00096: val_acc did not improve from 0.81111\n",
      "Epoch 97/300\n",
      " - 0s - loss: 0.2980 - acc: 0.8833 - val_loss: 0.4958 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00097: val_acc did not improve from 0.81111\n",
      "Epoch 98/300\n",
      " - 0s - loss: 0.3210 - acc: 0.8583 - val_loss: 0.5063 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00098: val_acc did not improve from 0.81111\n",
      "Epoch 99/300\n",
      " - 0s - loss: 0.3607 - acc: 0.8583 - val_loss: 0.5181 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00099: val_acc did not improve from 0.81111\n",
      "Epoch 100/300\n",
      " - 0s - loss: 0.3497 - acc: 0.8500 - val_loss: 0.5166 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00100: val_acc did not improve from 0.81111\n",
      "Epoch 101/300\n",
      " - 0s - loss: 0.3073 - acc: 0.8750 - val_loss: 0.5232 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00101: val_acc did not improve from 0.81111\n",
      "Epoch 102/300\n",
      " - 0s - loss: 0.3166 - acc: 0.8833 - val_loss: 0.5255 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00102: val_acc did not improve from 0.81111\n",
      "Epoch 103/300\n",
      " - 0s - loss: 0.3538 - acc: 0.8444 - val_loss: 0.5383 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00103: val_acc did not improve from 0.81111\n",
      "Epoch 104/300\n",
      " - 0s - loss: 0.2947 - acc: 0.8917 - val_loss: 0.5531 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00104: val_acc did not improve from 0.81111\n",
      "Epoch 105/300\n",
      " - 0s - loss: 0.2994 - acc: 0.8722 - val_loss: 0.5742 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00105: val_acc did not improve from 0.81111\n",
      "Epoch 106/300\n",
      " - 0s - loss: 0.3317 - acc: 0.8778 - val_loss: 0.5480 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00106: val_acc did not improve from 0.81111\n",
      "Epoch 107/300\n",
      " - 0s - loss: 0.2926 - acc: 0.8917 - val_loss: 0.5275 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00107: val_acc did not improve from 0.81111\n",
      "Epoch 108/300\n",
      " - 0s - loss: 0.3147 - acc: 0.8694 - val_loss: 0.5371 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00108: val_acc did not improve from 0.81111\n",
      "Epoch 109/300\n",
      " - 0s - loss: 0.3895 - acc: 0.8111 - val_loss: 0.5388 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00109: val_acc did not improve from 0.81111\n",
      "Epoch 110/300\n",
      " - 0s - loss: 0.3109 - acc: 0.8639 - val_loss: 0.5291 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00110: val_acc did not improve from 0.81111\n",
      "Epoch 111/300\n",
      " - 0s - loss: 0.2828 - acc: 0.8694 - val_loss: 0.4947 - val_acc: 0.7667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00111: val_acc did not improve from 0.81111\n",
      "Epoch 112/300\n",
      " - 0s - loss: 0.3331 - acc: 0.8556 - val_loss: 0.4855 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00112: val_acc did not improve from 0.81111\n",
      "Epoch 113/300\n",
      " - 0s - loss: 0.3183 - acc: 0.8750 - val_loss: 0.4974 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00113: val_acc did not improve from 0.81111\n",
      "Epoch 114/300\n",
      " - 0s - loss: 0.2870 - acc: 0.8917 - val_loss: 0.5019 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00114: val_acc did not improve from 0.81111\n",
      "Epoch 115/300\n",
      " - 0s - loss: 0.3036 - acc: 0.8750 - val_loss: 0.5151 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00115: val_acc did not improve from 0.81111\n",
      "Epoch 116/300\n",
      " - 0s - loss: 0.3090 - acc: 0.8500 - val_loss: 0.5257 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00116: val_acc did not improve from 0.81111\n",
      "Epoch 117/300\n",
      " - 0s - loss: 0.3067 - acc: 0.8667 - val_loss: 0.5221 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00117: val_acc did not improve from 0.81111\n",
      "Epoch 118/300\n",
      " - 0s - loss: 0.2806 - acc: 0.8750 - val_loss: 0.5127 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00118: val_acc did not improve from 0.81111\n",
      "Epoch 119/300\n",
      " - 0s - loss: 0.3040 - acc: 0.8667 - val_loss: 0.4985 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00119: val_acc did not improve from 0.81111\n",
      "Epoch 120/300\n",
      " - 0s - loss: 0.2836 - acc: 0.8972 - val_loss: 0.4970 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00120: val_acc did not improve from 0.81111\n",
      "Epoch 121/300\n",
      " - 0s - loss: 0.2889 - acc: 0.8806 - val_loss: 0.5020 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00121: val_acc did not improve from 0.81111\n",
      "Epoch 122/300\n",
      " - 0s - loss: 0.2911 - acc: 0.8833 - val_loss: 0.5046 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00122: val_acc did not improve from 0.81111\n",
      "Epoch 123/300\n",
      " - 0s - loss: 0.2730 - acc: 0.8833 - val_loss: 0.5206 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00123: val_acc did not improve from 0.81111\n",
      "Epoch 124/300\n",
      " - 0s - loss: 0.2653 - acc: 0.8889 - val_loss: 0.5231 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00124: val_acc did not improve from 0.81111\n",
      "Epoch 125/300\n",
      " - 0s - loss: 0.2930 - acc: 0.8750 - val_loss: 0.5271 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00125: val_acc did not improve from 0.81111\n",
      "Epoch 126/300\n",
      " - 0s - loss: 0.2627 - acc: 0.9000 - val_loss: 0.5267 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00126: val_acc did not improve from 0.81111\n",
      "Epoch 127/300\n",
      " - 0s - loss: 0.2704 - acc: 0.9000 - val_loss: 0.5129 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00127: val_acc did not improve from 0.81111\n",
      "Epoch 128/300\n",
      " - 0s - loss: 0.2636 - acc: 0.9000 - val_loss: 0.4994 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00128: val_acc did not improve from 0.81111\n",
      "Epoch 129/300\n",
      " - 0s - loss: 0.2371 - acc: 0.9056 - val_loss: 0.5014 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00129: val_acc did not improve from 0.81111\n",
      "Epoch 130/300\n",
      " - 0s - loss: 0.2362 - acc: 0.9028 - val_loss: 0.5028 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00130: val_acc did not improve from 0.81111\n",
      "Epoch 131/300\n",
      " - 0s - loss: 0.2056 - acc: 0.9444 - val_loss: 0.5200 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00131: val_acc did not improve from 0.81111\n",
      "Epoch 132/300\n",
      " - 0s - loss: 0.2344 - acc: 0.9111 - val_loss: 0.5345 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00132: val_acc did not improve from 0.81111\n",
      "Epoch 133/300\n",
      " - 0s - loss: 0.2906 - acc: 0.8917 - val_loss: 0.5550 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00133: val_acc did not improve from 0.81111\n",
      "Epoch 134/300\n",
      " - 0s - loss: 0.2544 - acc: 0.9083 - val_loss: 0.5491 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00134: val_acc did not improve from 0.81111\n",
      "Epoch 135/300\n",
      " - 0s - loss: 0.2241 - acc: 0.9194 - val_loss: 0.5285 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00135: val_acc did not improve from 0.81111\n",
      "Epoch 136/300\n",
      " - 0s - loss: 0.2162 - acc: 0.9333 - val_loss: 0.5081 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00136: val_acc did not improve from 0.81111\n",
      "Epoch 137/300\n",
      " - 0s - loss: 0.2210 - acc: 0.9194 - val_loss: 0.5300 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00137: val_acc did not improve from 0.81111\n",
      "Epoch 138/300\n",
      " - 0s - loss: 0.2120 - acc: 0.9167 - val_loss: 0.5362 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00138: val_acc did not improve from 0.81111\n",
      "Epoch 139/300\n",
      " - 0s - loss: 0.1939 - acc: 0.9583 - val_loss: 0.5278 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00139: val_acc did not improve from 0.81111\n",
      "Epoch 140/300\n",
      " - 0s - loss: 0.2055 - acc: 0.9306 - val_loss: 0.5207 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00140: val_acc did not improve from 0.81111\n",
      "Epoch 141/300\n",
      " - 0s - loss: 0.2284 - acc: 0.9167 - val_loss: 0.5270 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00141: val_acc did not improve from 0.81111\n",
      "Epoch 142/300\n",
      " - 0s - loss: 0.2032 - acc: 0.9389 - val_loss: 0.5315 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00142: val_acc did not improve from 0.81111\n",
      "Epoch 143/300\n",
      " - 0s - loss: 0.2569 - acc: 0.8806 - val_loss: 0.5431 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00143: val_acc did not improve from 0.81111\n",
      "Epoch 144/300\n",
      " - 0s - loss: 0.1901 - acc: 0.9472 - val_loss: 0.5183 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00144: val_acc did not improve from 0.81111\n",
      "Epoch 145/300\n",
      " - 0s - loss: 0.2078 - acc: 0.9278 - val_loss: 0.5200 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00145: val_acc did not improve from 0.81111\n",
      "Epoch 146/300\n",
      " - 0s - loss: 0.1976 - acc: 0.9306 - val_loss: 0.5296 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00146: val_acc did not improve from 0.81111\n",
      "Epoch 147/300\n",
      " - 0s - loss: 0.2009 - acc: 0.9333 - val_loss: 0.5457 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00147: val_acc did not improve from 0.81111\n",
      "Epoch 148/300\n",
      " - 0s - loss: 0.1957 - acc: 0.9444 - val_loss: 0.5519 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00148: val_acc did not improve from 0.81111\n",
      "Epoch 149/300\n",
      " - 0s - loss: 0.2001 - acc: 0.9389 - val_loss: 0.5396 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00149: val_acc did not improve from 0.81111\n",
      "Epoch 150/300\n",
      " - 0s - loss: 0.2357 - acc: 0.9056 - val_loss: 0.5368 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00150: val_acc did not improve from 0.81111\n",
      "Epoch 151/300\n",
      " - 0s - loss: 0.1708 - acc: 0.9528 - val_loss: 0.5338 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00151: val_acc did not improve from 0.81111\n",
      "Epoch 152/300\n",
      " - 0s - loss: 0.1992 - acc: 0.9472 - val_loss: 0.5223 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00152: val_acc did not improve from 0.81111\n",
      "Epoch 153/300\n",
      " - 0s - loss: 0.1848 - acc: 0.9417 - val_loss: 0.5168 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00153: val_acc did not improve from 0.81111\n",
      "Epoch 154/300\n",
      " - 0s - loss: 0.2015 - acc: 0.9306 - val_loss: 0.5176 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00154: val_acc did not improve from 0.81111\n",
      "Epoch 155/300\n",
      " - 0s - loss: 0.2030 - acc: 0.9222 - val_loss: 0.5041 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00155: val_acc did not improve from 0.81111\n",
      "Epoch 156/300\n",
      " - 0s - loss: 0.1869 - acc: 0.9333 - val_loss: 0.5466 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00156: val_acc did not improve from 0.81111\n",
      "Epoch 157/300\n",
      " - 0s - loss: 0.2168 - acc: 0.9083 - val_loss: 0.5400 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00157: val_acc did not improve from 0.81111\n",
      "Epoch 158/300\n",
      " - 0s - loss: 0.2146 - acc: 0.9222 - val_loss: 0.5429 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00158: val_acc did not improve from 0.81111\n",
      "Epoch 159/300\n",
      " - 0s - loss: 0.1905 - acc: 0.9472 - val_loss: 0.5613 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00159: val_acc did not improve from 0.81111\n",
      "Epoch 160/300\n",
      " - 0s - loss: 0.1987 - acc: 0.9417 - val_loss: 0.5489 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00160: val_acc did not improve from 0.81111\n",
      "Epoch 161/300\n",
      " - 0s - loss: 0.1932 - acc: 0.9278 - val_loss: 0.5354 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00161: val_acc did not improve from 0.81111\n",
      "Epoch 162/300\n",
      " - 0s - loss: 0.1892 - acc: 0.9333 - val_loss: 0.5209 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00162: val_acc did not improve from 0.81111\n",
      "Epoch 163/300\n",
      " - 0s - loss: 0.2044 - acc: 0.9250 - val_loss: 0.5273 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00163: val_acc did not improve from 0.81111\n",
      "Epoch 164/300\n",
      " - 0s - loss: 0.1626 - acc: 0.9639 - val_loss: 0.5253 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00164: val_acc did not improve from 0.81111\n",
      "Epoch 165/300\n",
      " - 0s - loss: 0.1796 - acc: 0.9417 - val_loss: 0.5341 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00165: val_acc did not improve from 0.81111\n",
      "Epoch 166/300\n",
      " - 0s - loss: 0.1786 - acc: 0.9444 - val_loss: 0.5502 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00166: val_acc did not improve from 0.81111\n",
      "Epoch 167/300\n",
      " - 0s - loss: 0.1741 - acc: 0.9389 - val_loss: 0.5452 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00167: val_acc did not improve from 0.81111\n",
      "Epoch 168/300\n",
      " - 0s - loss: 0.1502 - acc: 0.9583 - val_loss: 0.5209 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00168: val_acc did not improve from 0.81111\n",
      "Epoch 169/300\n",
      " - 0s - loss: 0.1548 - acc: 0.9556 - val_loss: 0.5033 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00169: val_acc did not improve from 0.81111\n",
      "Epoch 170/300\n",
      " - 0s - loss: 0.1561 - acc: 0.9583 - val_loss: 0.5078 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00170: val_acc did not improve from 0.81111\n",
      "Epoch 171/300\n",
      " - 0s - loss: 0.1675 - acc: 0.9472 - val_loss: 0.4930 - val_acc: 0.8222\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00171: val_acc improved from 0.81111 to 0.82222, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5\n",
      "Epoch 172/300\n",
      " - 0s - loss: 0.2085 - acc: 0.9306 - val_loss: 0.4925 - val_acc: 0.8222\n",
      "\n",
      "Epoch 00172: val_acc did not improve from 0.82222\n",
      "Epoch 173/300\n",
      " - 0s - loss: 0.1528 - acc: 0.9583 - val_loss: 0.5077 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00173: val_acc did not improve from 0.82222\n",
      "Epoch 174/300\n",
      " - 0s - loss: 0.1733 - acc: 0.9389 - val_loss: 0.5381 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00174: val_acc did not improve from 0.82222\n",
      "Epoch 175/300\n",
      " - 0s - loss: 0.1592 - acc: 0.9444 - val_loss: 0.5326 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00175: val_acc did not improve from 0.82222\n",
      "Epoch 176/300\n",
      " - 0s - loss: 0.1643 - acc: 0.9528 - val_loss: 0.5516 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00176: val_acc did not improve from 0.82222\n",
      "Epoch 177/300\n",
      " - 0s - loss: 0.1706 - acc: 0.9444 - val_loss: 0.5448 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00177: val_acc did not improve from 0.82222\n",
      "Epoch 178/300\n",
      " - 0s - loss: 0.1455 - acc: 0.9556 - val_loss: 0.5317 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00178: val_acc did not improve from 0.82222\n",
      "Epoch 179/300\n",
      " - 0s - loss: 0.1472 - acc: 0.9639 - val_loss: 0.5328 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00179: val_acc did not improve from 0.82222\n",
      "Epoch 180/300\n",
      " - 0s - loss: 0.1430 - acc: 0.9694 - val_loss: 0.5170 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00180: val_acc did not improve from 0.82222\n",
      "Epoch 181/300\n",
      " - 0s - loss: 0.1314 - acc: 0.9694 - val_loss: 0.5238 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00181: val_acc did not improve from 0.82222\n",
      "Epoch 182/300\n",
      " - 0s - loss: 0.1265 - acc: 0.9639 - val_loss: 0.5230 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00182: val_acc did not improve from 0.82222\n",
      "Epoch 183/300\n",
      " - 0s - loss: 0.1569 - acc: 0.9528 - val_loss: 0.5149 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00183: val_acc did not improve from 0.82222\n",
      "Epoch 184/300\n",
      " - 0s - loss: 0.1480 - acc: 0.9639 - val_loss: 0.5225 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00184: val_acc did not improve from 0.82222\n",
      "Epoch 185/300\n",
      " - 0s - loss: 0.1505 - acc: 0.9472 - val_loss: 0.5557 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00185: val_acc did not improve from 0.82222\n",
      "Epoch 186/300\n",
      " - 0s - loss: 0.1542 - acc: 0.9528 - val_loss: 0.5606 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00186: val_acc did not improve from 0.82222\n",
      "Epoch 187/300\n",
      " - 0s - loss: 0.1412 - acc: 0.9611 - val_loss: 0.5615 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00187: val_acc did not improve from 0.82222\n",
      "Epoch 188/300\n",
      " - 0s - loss: 0.1369 - acc: 0.9694 - val_loss: 0.5488 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00188: val_acc did not improve from 0.82222\n",
      "Epoch 189/300\n",
      " - 0s - loss: 0.1281 - acc: 0.9639 - val_loss: 0.5502 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00189: val_acc did not improve from 0.82222\n",
      "Epoch 190/300\n",
      " - 0s - loss: 0.1596 - acc: 0.9472 - val_loss: 0.5630 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00190: val_acc did not improve from 0.82222\n",
      "Epoch 191/300\n",
      " - 0s - loss: 0.1208 - acc: 0.9667 - val_loss: 0.5599 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00191: val_acc did not improve from 0.82222\n",
      "Epoch 192/300\n",
      " - 0s - loss: 0.1184 - acc: 0.9667 - val_loss: 0.5455 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00192: val_acc did not improve from 0.82222\n",
      "Epoch 193/300\n",
      " - 0s - loss: 0.1332 - acc: 0.9639 - val_loss: 0.5359 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00193: val_acc did not improve from 0.82222\n",
      "Epoch 194/300\n",
      " - 0s - loss: 0.1150 - acc: 0.9806 - val_loss: 0.5406 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00194: val_acc did not improve from 0.82222\n",
      "Epoch 195/300\n",
      " - 0s - loss: 0.1358 - acc: 0.9611 - val_loss: 0.5358 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00195: val_acc did not improve from 0.82222\n",
      "Epoch 196/300\n",
      " - 0s - loss: 0.1230 - acc: 0.9750 - val_loss: 0.5507 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00196: val_acc did not improve from 0.82222\n",
      "Epoch 197/300\n",
      " - 0s - loss: 0.1216 - acc: 0.9694 - val_loss: 0.5288 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00197: val_acc did not improve from 0.82222\n",
      "Epoch 198/300\n",
      " - 0s - loss: 0.1546 - acc: 0.9500 - val_loss: 0.5341 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00198: val_acc did not improve from 0.82222\n",
      "Epoch 199/300\n",
      " - 0s - loss: 0.1275 - acc: 0.9639 - val_loss: 0.5589 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00199: val_acc did not improve from 0.82222\n",
      "Epoch 200/300\n",
      " - 0s - loss: 0.1123 - acc: 0.9722 - val_loss: 0.5696 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00200: val_acc did not improve from 0.82222\n",
      "Epoch 201/300\n",
      " - 0s - loss: 0.1188 - acc: 0.9750 - val_loss: 0.5472 - val_acc: 0.8000\n",
      "\n",
      "Epoch 00201: val_acc did not improve from 0.82222\n",
      "Epoch 202/300\n",
      " - 0s - loss: 0.1164 - acc: 0.9750 - val_loss: 0.5492 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00202: val_acc did not improve from 0.82222\n",
      "Epoch 203/300\n",
      " - 0s - loss: 0.1274 - acc: 0.9583 - val_loss: 0.5493 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00203: val_acc did not improve from 0.82222\n",
      "Epoch 204/300\n",
      " - 0s - loss: 0.1320 - acc: 0.9583 - val_loss: 0.5385 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00204: val_acc did not improve from 0.82222\n",
      "Epoch 205/300\n",
      " - 0s - loss: 0.1194 - acc: 0.9667 - val_loss: 0.5264 - val_acc: 0.7667\n",
      "\n",
      "Epoch 00205: val_acc did not improve from 0.82222\n",
      "Epoch 206/300\n",
      " - 0s - loss: 0.1005 - acc: 0.9750 - val_loss: 0.5475 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00206: val_acc did not improve from 0.82222\n",
      "Epoch 207/300\n",
      " - 0s - loss: 0.1083 - acc: 0.9694 - val_loss: 0.5586 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00207: val_acc did not improve from 0.82222\n",
      "Epoch 208/300\n",
      " - 0s - loss: 0.0988 - acc: 0.9806 - val_loss: 0.5375 - val_acc: 0.7889\n",
      "\n",
      "Epoch 00208: val_acc did not improve from 0.82222\n",
      "Epoch 209/300\n",
      " - 0s - loss: 0.1154 - acc: 0.9639 - val_loss: 0.5302 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00209: val_acc did not improve from 0.82222\n",
      "Epoch 210/300\n",
      " - 0s - loss: 0.1038 - acc: 0.9806 - val_loss: 0.5325 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00210: val_acc did not improve from 0.82222\n",
      "Epoch 211/300\n",
      " - 0s - loss: 0.0986 - acc: 0.9722 - val_loss: 0.5497 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00211: val_acc did not improve from 0.82222\n",
      "Epoch 212/300\n",
      " - 0s - loss: 0.1225 - acc: 0.9611 - val_loss: 0.5422 - val_acc: 0.7778\n",
      "\n",
      "Epoch 00212: val_acc did not improve from 0.82222\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100, mode='min')\n",
    "checkpointer = ModelCheckpoint(filepath='/home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5',\n",
    "                               verbose=1,\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True)\n",
    "\n",
    "train_history = model.fit(x_train, y_train,\n",
    "                epochs=300,\n",
    "                batch_size=BATCH_SIZE,\n",
    "                verbose=2,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, y_test),\n",
    "                callbacks=[checkpointer, early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fbbf3643588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kd/+5a9zuPxCOctdzeymblcGlKwrJT3Nxy9q5PLuzjnJzEPo3ntzOQ28e7XXei3saaPKF+OEHlpPitPGjZ/cAsKu2nRSnjUc/eToFaS7a/GGWFHqYn5eaOLdsVgYL8ozgb39DB7trjeYqHyqb0esxSovSCEZiHGzsJBrTPLa5inMW5JCf5up13JzsZFx2C6U9AsVzF+WwuaJ10K6f42GaBnsOGiXYE0IIIYQQYtT+vauORzYdHbdmI8/sqKXJF+JL71mQGMfysbPm4LRZ+Nc7tTR2BHnozaM8vqWq13mPbqokN9XJ5SsLuWJVEa8dbCYa0+yv9zEvN4VTZmfy2KfP4Avnz+dDZTOYmZmEw2ZhRqabXI+LWVnJ2CyK/fU+Xj3QBMCFS/N7PcbSQqMEc0e1l437G6lrD/QLCAFsVgvfv3wZt5w7L3HduQtzicY0r+xvGtPv11Cm6Z49Jx2BCIFwFJfdOtnLEUIIIYQQ4oTR3BmiMxSl0RckN9U15LHHMj/zkU2VzMxM4rQ5WYnr3A4rZ8zNYsPeBpYVGdmy3bXtRGMaq0VR3x5g/d4GPnnOXGxWC0sLPXSFoxxu6mR/g49zF+YA4HHZ+cL5CxL3e9a8bObmJANgt1qYnZ3MvnofNotiRqabPE+fjF1OCi67hXeq2qhvD5KV7GDd4rwBn8dVq3sPUV85I500t50tR1u5ZHnBqL4nx2p6ZvZSjfELLZ2yb08IIYQQQhw/fvXifm64763JXsaQ4iPMjjT5e13fEQiz5kfr+cHTu6lo7uR9v3yZ7/6rd7nnNfe+wT3rDwx63/GGJx8qK8Zi6R0krl2Yy5FmP398/QgA/lCUI+ag8se3VBHTJLJspWZA+OqBJpp8QRb0KNns6b4bT+ablyxJXJ6fl8L+hg7KK1opm5XZ73irxWjS8sfXK3h2Zx2XryrCYRtZSGWzWvj3l9Zw+yWLR3T8WJiewV6KEezJvj0hhBBCCHE82Vrp5Y1DzRM+j2004gmTI02dva7fU9fB0RY/9248xPl3v8SO6nae2FKdaGYSjWneOtIyZBljouFJn6wYGGWQAO9UeSktMsopd9a0o7Xmr+VVnFKSyexsI0s3LzcFh83C37ZWG5fN/XjDmZebSkWznyZfkLKSjAGP+e5lpXztvYv45sWL+UyPMs2RyE11jTrT+W5M02DPmLkhwZ4QQvSWkjL4H8MjR45QWlo6gasR09FQr0EhpoNWf4hgJDZmFWgtnaFEsDUWukJRusJRAA439w729tcbXSw/ec4clhen86X3LMDbFWZrZRsADR0BYw9dg6/Xec2+IGFzjc/sqOX0uVkUpLn7PfbMrCTmmCWXN589B4fVws4aL5uOtHK4qTMxIgGMksyFeam8fdR47HjnzeEs6BEUDpTZAyNr+Om1c7l5zRwykwee5Xe8mKbBnpnZk/ELQgghhBDiONJqBnm13sCY3N97f7aRbzy5fUzuC6C5sztZUtE32GvoIMlh5WsXLuLxT5/BDWeUYLWoxLiBmrYuwEi4xJ9neyDM2h9v4CfP7+Nos5+DjZ2sWzTwHjiA9yzOw2GzcO6iXBbkp7Czup3/feUQKU4bFy8buJlKksNK4QDB40Dm5xrlnh6XbcQB4vFsWgd70pFTCDHVfe1rX+PXv/514vK3v/1tvvOd77Bu3TpOOukkli1bxt///vdR328gEOCjH/0oy5YtY9WqVaxfvx6AnTt3csopp7By5UqWL1/O/v376ezs5JJLLmHFihWUlpbyyCOPjNnzE8e/sXwN+ny+Qc974IEHWL58OStWrOD6668HoL6+niuuuIIVK1awYsUKXnvttbF9ckKMg1a/EQRVm4HRuxGMRGnoCPJoeVUiu/ZuxTOOdqvicJ89ewcajK6X8b12aW47q2dmsH5PIwA1bd0B7AFzlt2/ttXSEYjwaHklz++qA+DcRbmDPv6t58/n6c+fhcdlp7QwjbcOt/Dczno+dc4ckhy9e08uNfft9VzTcEqyk7BaFCfNyhjxOcezadmN0+2wkuywShmnEGLiPPN1qBu7T1YByF8GF/3fIQ+5+uqr+cIXvsAtt9wCwKOPPsqzzz7LF7/4RTweD01NTZx22mlceumlo9pDcM899wCwfft29uzZwwUXXMC+ffv47W9/y6233sq1115LKBQiGo3y9NNPU1hYyFNPPQWA1+s9xics3rVJeB2O5WvQ5XLx5JNP9jtv165d3Hnnnbz66qtkZ2fT0mLM4fr85z/POeecw5NPPkk0GsXn8w15/0JMtmhM02bOYKsZJNjrCkXZ39DB8uL0Ye+v5zy37/xzJ098+owR/a4/0NBBqsverxMlGJ04AZYUprG/vqNXt8199R2cOS+71/FrF+Xwo2f3UucN9HpO++o7OLkkk0fKK0lyWGnpDPHLFw9QkpWU2Hc3kCSHjXlm9m1poYeHN1VSnOHm42fP6XdsPLMXz9aNhNNm5Yvnz2fljIH3651opmVmD4yOnE0+KeMUQkxtq1atoqGhgZqaGrZt20ZGRgYFBQV84xvfYPny5Zx//vlUV1dTX18/qvt95ZVXEtmTRYsWMWvWLPbt28fpp5/OD37wA/7nf/6HiooK3G43y5Yt44UXXuBrX/saL7/8MmlpacPcu5hKxvI1qLUe8LwXX3yRq666iuxs401mZqaxz+bFF1/k05/+NABWq1Vee+K45+0KE+/LMlgZ5+Nbqrj8nldp7Bg+aeH1G8HemgU5vH20jb9vrRnROj7xwGZuuO+tAff6tZjvn1fPzMAfMjKH8bXXt/fvennOAmPkweuHmqhp6yLFaSPJYWV/vY+9dR1sq2zjC+fPJ9/jwtsVZu3CwbN6fZ08OxOLgtsvWTLgOLXF+R7Sk+ysnjW6wO2z583nrPnZwx94Ahi3zJ5S6j7gfUCD1rrfjn5lfATwc+BiwA/cqLXeMl7r6Ss7xUljx9jUQgshxLCGycCNp6uuuorHHnuMuro6rr76ah588EEaGxvZvHkzdrudkpISAoHR/T4crEvcf/3Xf3Hqqafy1FNPceGFF/KHP/yB8847j82bN/P0009z2223ccEFF/Ctb31rLJ6aGK1Jeh2O1WtwsPOOZY6XEBPhJ8/vJRLTfO29i0Z0fM+mLIOVcTb7QsQ07K/vIMccJxb36T9vZnGBh8+vmw90Z/ZuOrOE1s4QP3xmN+9Zkkeyc/AQIBKNUdHiJxrT/GVTJdefNmvANa6elcF9rx7mR8/uZU9de6IrZd99bgvzUnHaLOysbqfGG6Ao3Y3LbuFAg48H36zAblVceVIx3q4w96w/OGQJZ1+L8j1s/e8L8LjsA97udlh57evn4bJN37na45nZux947xC3XwTMN78+AfxmHNfSz4wMN5Ut774WWgghjndXX301Dz/8MI899hhXXXUVXq+X3Nxc7HY769evp6KiYtT3uWbNGh588EEA9u3bx9GjR1m4cCGHDh1izpw5fP7zn+fSSy/lnXfeoaamhqSkJK677jq+8pWvsGXLhH2uJ44TY/UaHOy8devW8eijj9Lc3AyQKONct24dv/mN8fYiGo3S3t4+Ds9OiMFt2NvIi7sbRnx8m7lfz2pR1A4S7PlDEYB+HS1r2rp4Zkcdv/jPfg6Yt7WZmb2MJAffvnQJ9e1BfvvSwSHXUOs1OmY6bBbufn5vIjsY19wZwm5VLC82MuWPb6liZ0073zSbwPQtmbRZLSzKT2VnTTs1bV0UpLuYl5vK1so2HnrzKFetLiYrxcnNZ8/hGxcv4sy5WYzGYIFeXJLDNiX23h2rcQv2tNYbgZYhDrkMeEAb3gDSlVITM0oemJOTQnVbF12h6EQ9pBBCTIqlS5fS0dFBUVERBQUFXHvttZSXl1NWVsaDDz7IokUj+8S5p1tuuYVoNMqyZcv48Ic/zP3334/T6eSRRx6htLSUlStXsmfPHj7ykY+wffv2RNOWO++8k9tvv30cnqU4no3Va3Cw85YuXco3v/lNzjnnHFasWMGXvvQlAH7+85+zfv16li1bxurVq9m5c+e4PUchBtLSGaJhFJVk8azZ/NyUXs1MYjGdqKjoTAR7Hb3O3bDXaIJisSi+/5QxyDye2TNKGTO5cGkeD755lFhs8Bl+la1G05Vb182n1R9ONE3pXmOQzGQHheluTpqZzsfPms2nzplLqz+My26hOKN/18slhWnsrPFS09ZFYbqb+Xkp+IIR3HYrX75goblGB59YMxebddruMhsXk9mgpQio7HG5yryudiIePL7x80hzJ4sLPBPxkEIIMWm2b+9uypGdnc3rr78+4HFDNbAoKSlhx44dgNEo4/777+93zG233cZtt93W67oLL7yQCy+88BhWLaaSsXgNDnXeDTfcwA033NDrury8vGPqNivEWGn1h/CHooQiMRy24YOYeCfOpYVpPPl2FeFoDLvVwm1PbKetK8Tvri/DHzQSFfvqe/+sbNjbQFG6mxvPKOHOp3fzTlVbIthLcxvZr4uXFfDcznreqfaycsbADV6qzMq3S5YV8LuXDrK5opUPlnXPr2vpDJGZ7MRqUTxxy5kAdAYjPLGlivw014BZtNIiD3956ygAhWkuFuUb2b9bz5+f6JIvxsdkhs4D5VMH/JhBKfUJpVS5Uqq8sbFxTB48Huwdbuoc5kghhBBCCCFGJxCO4jcryEbaAb6l0wjOlhZ6iGmobzeye5sqWjjYaLxn9QWNzN6BHmWcwUiUVw80sXZhDmsXGg1RjjT7E509U81SxzXzc7AoWL9n8NLSqlY/FgVFGW5Wz8qgvKK11+3NnSGy+gwST3baeOjmU/nJB1cMeJ9LC7ubIxWmuzl7fg5/+EgZN55RMvQ3RLxrk5nZqwJm9LhcDAzYIkhrfS9wL0BZWdngeedRkGBPCCEGtn379kSnzTin08mbb745SSsS0428BsVU0NZjr1tDR5DC9P7ljYFwlFZ/iAJz4HebP4TTZmGu2eSk1hsg3+OissVPVrKRAYsHkC2dIZp9QbJSnJQfaaUzFOXchbnkphrjEhraA7R3hfG4bFjNbFtGsoOVM9LZsLeBL75nQeJ+LMooowSobO2iIM2N3WqhrCST9Xv30uYP0RmKku6209IZYkZGUr/nMm+I8QaL8lOxWhTRmKYgzY3Vojh/yeCD08XYmcxg7x/AZ5VSDwOnAl6t9YSUcILxCUS+x8XBRpm5I4QQPS1btoytW7dO9jLENCavQTEV9Oys2dA+8L69n/57H4+UV7Lpm+djt1rMEkkHRelGwFbT1kVeqotwVCcyep2hCA6rhVA0xv4GH6FojO/9axduu5XT52aR5LDisFlo7AjS5g+RltS7gcm5C3P5yb/30dgRJCfVycf/uIn0JAf33XgyAJUt/sS+uzJzZMEzO+q486ndXFSaT4vPWONouOxW5uYks6/eR9EAQa8YP+NWxqmU+gvwOrBQKVWllPqYUupTSqlPmYc8DRwCDgC/B24Zr7UMZnZ2smT2hBDjarARBaI3+T6NL/n+Dk6+N2K8xPffATQOUsb56sEm2vxhdte2J85JT3IkMn3VbV0cbu4u34zFNJ3BCIvNYeH/3FbD5fe8SlVrF7+9fjXJThtKKXJSnDR0BPF2hRP79eLiow027G2goSPAlqNt7KvvbvZS2epnRqaRuVsxIx27VfGdf+7EF4zwz3dq6AhG+pVxjkSpWcqZlyZ79CbSuGX2tNbXDHO7Bj4zXo8/ErNzknl6+4QlE4UQ04zL5aK5uZmsrCyZATYErTXNzc24XK7JXsqUJK/DwclrT4ynnsFeQ3v/YK8zGGF3rRFklR9pZXlxOq3+MJnJdpKdNorS3eysbifZ0f12vTMUoTMYpbQwjYMNPh588ygFaS7++qnTezUczPU4aewI4g9FSHf3DsyWFHiYkenmyberE9fVegNEojEiMU19ezBRpumyW1lamMbWyjbWLMhh4z6jd0ZmyuiDvY+eOZvFBR6c03jm3WSYzDLOSTcnO5k2fziRMhdCiLFUXFxMVVUVY9VYaipzuVwUFxdP9jKmJHkdDk1ee2K8tJplnDaLGjCzt7Wyjag5AqG8ooWbzppNa2eIJWbWbvWsDN483NxrcLovGMEfipDstHHG3Czq2wP87voy8tN6f2CRk+KWriwZAAAgAElEQVTkSHMnEXOPXE8Wi+KDq2dw97/3Jfb/RWOaWm+AcDQGwIzM7nPOW5RLY0eQe/5rFVf+5jX21fuOKbO3rDiNZcVpwx8oxtT0DvZy4k1afGQmZ/a67bUDTby0v5HbLlo8GUsTQkwBdrud2bNnT/YyxDQnr0MhJke8s2ZJdvKAmb3yI60oBectzKX8SCtaa1r8ITLMRiknl2Twj201vHqgKXGOLxChMxQl2Wnjt9etHnRYeK7HyVtHWrBZFB53/6HjV64u5qcv7GNrZRszM5M42uKnstVPOGoEn/EyToDPnTePW9Ya8+8+VDaD7z+1W8YlnECm9dTC2dlGp6NDjf337T35djX3bjxExPyEQwghhBBCiJFq9YdIddkoSHPROMBg9fKKFhbmpbJ2YQ4NHUEqmv14u8JkmFmz1bOMRMT+Bh+pTpt5n2FCkRjJDuuggR5AbqqLNn+YVn+Y9KT+wV6ROf4A4LrTZgLGfL3KFmOges9um0qpxKDz606bxQ8/sIxVMzNG/f0Qk2NaB3tF6W6UMlrM9lXrDaA1NPlCaK3ZVtk2CSsUQgghhBAnola/sU0oN9VFY0fvzF40pnn7aBtlJRmJoO4/exrQGjLN4GxhfmoiyIuXdsbn7iU5hy7Oi5d+RmO6X4OWuE+tmcOyojQ+VDYDizIas+yt6yDZYSU3deDMnctu5ZpTZiZGOYjj37QO9hw2C/keF1Wt/n631XiNALC+PcAbh1q47J5X2V7lneglCiGEEEKIE1BLp1GSmZPqpNEX7NX5dXdtO75ghLJZmSzMT8XjsnHfK4cBEpk9q0WxcmY6AMvNvW7xYC/FOXSTk57BWvogwd4Z87L55+fOSnT/rGzxU17RyqqZGUNmDcWJZVoHewDFGW6q+mT2tNbUthk/TA0dwcR4hrpBZqQIIYQQQgjRU6s/REaSndxUJ+GoprXHkPWXzK6WZ87LxmpR/PTDKxNz+eJ79gDKzKxfaZER7NV5zcyeY+jMXnywOjBoZq+nGZludtd2sKeunbISKdGcSiTYy0iiuk+w5+0K0xU2uhPVtweoaetKXA/w4p76xCcrQgghhBBC9NXaaey/y/UYWbaepZwv7mlgeXFaotxy3eI8HvnkaVxUms+you6OlVesKuKKVUWcPicLgHrzPpKHyez17OA5omAvI4m99R1o3R1giqlBgr0MN7XerkSrWTAGWMY1dAQTwV6bP0Q4GuPmBzbz/149MtFLFUIIIYQQJ4hWf4jMJAc5ZufKBrNJS2tniLePtrJ2YW6v45cXp/Ob61YnyjgBZmYl8dMPryTLvI94siF5mMxedoqD+FjNtAEatPQV775pUSRKR8XUIMFehpuY7k6LA4kSToCG9kAi+GvvCtPmDxONaeq8/Zu6CCGEEEIIEQhH8YeiZmbPKKmsN8cvbNzfSEzD2oU5I74/q0WR5LDSEA/2hmnQYrNaErPwRlrGCbC4wEPKMPctTiwS7JmtZSt7NGmpNQO5nFSnkdnzdpdxtvmNeuqGjv7zUoQQQgghhGj1d++/K0x3YbUoKpqNHhAb9jaSkWRnRfHoMmgpTluif0SSY+gyTiAxC28kwV78/XDZLNmvN9VIsJdhfJLRs0lLjTeA3apYWuih1htIZP28XeHE5lnZsyeEEEIIIfqKxTSt5kD1zGQ7TpuVWVlJ7K/3AfDGoWbOMBuzjEaKy0YgbGw7Gkn2LddjBJkjOXZBbir5HhcXLs0f1ZrE8W/a52kL0oxZe72CvbYu8tNc5HtcvHqgiXDUaJXr7QonOik1tEtmTwghxLFRSr0X+DlgBf6gtf6/fW6fBdwH5AAtwHVa66oJX6gQYlQaO4K8/5evkJdmlG6mm5015+emsK+hg5bOELXeACuK04a6mwGl9gjahpuzB1CY5iIz2YFSwweVaUl23vjGulGvSRz/pn1mb6BZe7VtAQrS3OR6XIlAz6KgrSucSMt3BCN0BiOTsmYhhBAnLqWUFbgHuAhYAlyjlFrS57AfAw9orZcD3wV+OLGrFEIci588v5eGjgDbKtsAyEyOB3upVDT72VrZCsDSwtEHeymuHsGeffgyzs+tm89vr1s96scRU8u0z+xB/1l7Nd4uymZl9BpIOTs72czshRLXNXQEmS2bWIUQQozOKcABrfUhAKXUw8BlwK4exywBvmj+ez3wtwldoRBiRHqO6KpvD/BIeSU3nTmb0iIPf3mrkhnmXrj5eSlEY5p/basFYGmhZ9SPFS/HTHJYRzT0vCjdTVG6e9SPI6YWiVQwZou8ebgFMOqs69sDFKa7yfN0D6RcXODhjUPNtPUYiFnfHmB2dvKEr1cIIcQJrQio7HG5Cji1zzHbgCsxSj2vAFKVUlla6+aJWaIQYjhaay75xSs0+bq39mSnOPj8uvmkue1csao4cf383FQAnttZR1G6O1HeORopTqPRynAD1YXoSV4tQFGGm9qtxqy91s4Q4aimIN2dyOylOm3MyEziuZ11iQYtIE1ahBBCHJOBPpLXfS5/BfiVUupGYCNQDQy4d0Ap9QngEwAzZ84cu1UKMY3d98phHDYL1502a9Bj2vxhmnxBrj11JucvyQNgaYFnwO6Xc3KSsSjoDEU5c97os3oAqWYZ53AD1YXoSYI9ID/NRUxDsy+UCOAKPK5EZq8w3U2a2044qqlp62JGppvKli5p0iKEEOJYVAEzelwuBmp6HqC1rgE+AKCUSgGu1Fp7B7ozrfW9wL0AZWVlfYNGIcQxePDNCrRmyGAvvgXo7Pk5nNtnQHpfLruVmZlJHGn2H9N+PegR7ElmT4zCtG/QApBjziFp6Agkgr08j4vsFAdKQWG6K/EpzZGmTmZmJuG2WyWzJ4QQ4lhsAuYrpWYrpRzA1cA/eh6glMpWSsX/Rt+G0ZlTCDFBGtqDHG7uHLIZX3xGc3wg+XDmmaWcx7JfD7r37ElmT4yGBHsYc0jA+MGuN4el53mc2KwWZmcns6hHSr7GGyAjyUGex5k4VgghhBgprXUE+CzwHLAbeFRrvVMp9V2l1KXmYWuBvUqpfUAecOekLFaI44jWmg//7nWe2V6buM4fivC+X77MaweaRnQf9796mE/9afOQx3QGI3QEI2gNu2vbBz2ussUI9uIDyYezIC8FgKVFxxjsueINWiSzJ0ZOXi2Q2JvX0BGksT2ARUGWme3722fOxGmzUH6kNXF8RpKDXI9LMntCCCGOidb6aeDpPtd9q8e/HwMem+h1CXE88wUjvHm4heKMJC5aVgDAloo2dlS38/KBJs6Ylz3sfTy9vY5NFS34gpFBh43X9Xh/t7OmnbKSzMTlQDjKQ28e5ZpTZlLZ6sfjsg24R28gHzm9hNnZyRSkHVuHzPh6RzIkXYg4ebUA2WZg19gRpKEjSHaKE6vZ0tbjMn6Ae/4gZyQ7yPO42F7VNvGLFUIIIYSYhrxdRkf0Aw0dies2HTG6qR9p6hz2/FhMs7PGm8jYndwjiOup3tsz2Ou9VfY3Gw7y8//sx+O2U9nSxYzMkWX1wOgR8cGyGcMfOIhUV/foBSFGSso4MQarZyTZE3v2cj3Ofsf0CvaS7OSlOqlvD6K17IUXQgghhBhv8fFX+xt8ifdfmyuMyqvDIwj2Klr8dIaiAOysHrDfEdCd2SvOcLOzpruMs7qti9++dNB83BYqW/2JOXoTIT56IVkye2IUJNgz5aa6aDAze3mprn63pyV1B3uZZmavKxzFN8TGXSGEEEIIMbi9dR3c/fzeQT88b/IFueNvO/CHIrSbmT1/KEqNN0AkGuPto0awV9HsH/YD+HiWTil6BXF9xYO9dYty2VffwfM76/jEA+XceN9bACwp8LDpSCvVrV0jbs4yFqRBizgWEuyZcj1OGjuC1LcHB8zspThsmJWdpCc5KEw3frgPNg7/SZIQQgghhOjvyber+cWLB2jyhQa8/YHXjvCnNyp4+2hboowTYF99B3vqOugMRSmblUFXOEr9MCOxdta0Y7cqTp2dyY4hgr16b4BUl42ykkzCUc0n/rSZd6q82K0Wvn95KReV5nOgwUcwEhtVGee7lSoNWsQxkGDPlJPipNbbRXNnkJwBMnsWi8JjlnJmJjk4Y24WFgUv7mmY6KUKIYQQQkwJNW3GrLoqc4xBT9GY5rHNVQA0d4Zo6xHsHaj3JUo4r1pdDAxfyrmj2sv83FRWzcxgf30HwUh0wOPq24Pke1ysmpmO3ao4f3EeL37lHJ6+9Ww+WDajV8OW4oyJy+xlJDtw2S0Upvd/nyrEYCTYM+V44nvwjLELA4nv20tPspOR7GD1rAxe2FU/kcsUQgghhJgy4sFepTmgvKdXDzRRYzZLafEFE5m9VKeN/Q0dvH6wmYI0F2eaXTiPNA8e7Gmt2VXTztJCD0sLPURimv31vgGPrWsPkOdxUZyRxKtfP497r1/dK5u2ckY6NrPca2L37NnY+NVzuXRF0YQ9pjjxSbBnyu2RzcsdILMH3cFeZrIDgHWL89hV2574RSWEEEIIIUau1gzm4jPrenqkvJL0JDtKQUtnCG9XGLtVsbTIw4a9jTy3q45LVxRSmO7GYbUM2ZGzvj1Ic2fIDPbSgP6dNl/cU4/XH6beDPbAeE9oie/jMbkd1sRg9JHO2BsruR5XomO8ECMhwZ4pJ7U7mzdUZs9htSRa3p6/OBeA/0gppxBCCCHEqERjOtEMpW8Zp9aal/Y2clFpARlJDqOM0x8mze1gQV4qDR1BMpMcfOa8eVgtihmZ7iHLOOPD0ZcUpjErMwm33cqeuu4RDnvq2rnp/nJ+/PxeGjqC5KcN/F4w7oKl+Swt9OCWMQjiOCfBnim3R7A3VGYvI9mOUsYnKnNzUpiZmcR6CfaEEEIIIUaloSNANGZ00Kxs6V0l1eoP4wtGmJebQmayg5bOEO1dYdLcNubnpQLw1QsXJuYhz85OHrKMc785m29BXgoWMzis6lE6+simSgAeLa8kGtPke4beF3fL2rk89fmzR/mMhZh40s7HFA/2lILsFMeAx3xyzVzet7wwcVkpxcoZ6bxd2TohaxRCCCGEmCri22A8LhuVfTJ78bLOGRluMpONzJ7DaiHNbefylYUk2a1cvqp771pJVjIv728iFtOJssvdte2UV7Ry/Wmz2F/vIyfVSXqSw7zfpMRjBCNRnny7mhmZ7kTQmTdMsBf/4F+I451k9ky55g91VrITm3Xgb8uy4jTeW5rf67rCdDd13gCxmAxXF0IIIYToKRiJsreugwMNvn7vlWrajBLOk0syqWnrSmT5gETwNyMziaxkB82+IG1dIdKTHKS67Fy5urjX3rXCdDfBSCzRsTMSjfH5v7zNHX/bQUtniH0NPubnpiSOn5GZRFVrF1prXtjVQJs/zHcvK6XIHK2VnyYdL8XUIMGeKdlhxW239irnHImidBfhqKbJN/RsFyGEEEKI6eaOv+3gwp9t5Py7X+Kht472ui2e2Tt1jjHPrt7cvwckSiyLzcxevEFLvFleX/EZdJ3BCAAPvnmU/Q1Gt83yIy0cqO9ggVn+Gb9fXzBCmz/MP7ZVU5DmYs38HD5UNgOLIhH0CXGik2DPpJQiz+MctDnLYArSjF8G1dKRUwghhBBTnLcrzK0Pv82hxoHHFvRV2dLFvNwULArqvIFet9W0dZHqtLEo32Me6+9xnp/0JDupLjtZyQ7ausK0dg4f7LUHjL1+d/97H6eUZGK3Kv71Ti2doSjzemT24l00q1q7eKfKyymzM7FaFLecO5e/f+YsslJG935QiOOVBHs9fO/yUr70noWjOqfQ/OSnts8vMCGEEEKIqeYX/9nP37fW8PuXD43o+LauMCVZySQ7bPjMrFtcjTdAYbqbGZlG4NVz1l5la1dihl1msgOtwReMDBrspTiN632BCIcbO/F2hbnprBJKi9J4dkcdQJ8yTuP927aqNmq9gcQoBbvVwrLitBE9NyFOBBLs9XD2/JxR/4DH0/wya08IIYQQU9nBRh9/fO0IDpuFf26rxR+KDHtOmz9EepKdZKctUWIZV9PWRUG6i8J0F0r1zuxVtfgTAVlmjyzbcJk9XzBCe8DYt5ee5KBsVgahaAygVxlnPMB8bqcRCJYWSoAnpiYJ9t4lj9tGksOa2GQshBBCCDEV3f38Ptx2Kz//8Ep8wQhPb68b9pw2f5iMJDvJTiudfYLDWjOz57RZyfe4+O1LBznjh/9hR7WXqh6Zvezk7i7pg2b2egR7HWaw53HZWT0r07iPFAcZPe7H47KT5rbz+sFmAJaYmT0hphoJ9t4lpRSF6W7J7AkhhBBiSttX38GZ87J5b2k+s7OTedScTTeYQDhKVzhKepKDFKcNXzCauK0rFKWlM0Sh2fXyGxcv5oNlxbR1hbnrub2EojGKzexbZo+RWOlJg2T2nEaw1xGI0N5lBJWpLhurZ2UA9NqvFzcj000kpilKdydGMggx1cicvTFQmO6mxivBnhBCCCGmrrauMOlJdpRSXLaykJ+9sJ/2QDgx2Lwvb1e8nNIo4/T3KOM8aDZ4mZmVDMD7VxTy/hWFhCIxHi2vAowZe2Ds2YsbSWbPZo5k8LiN7N3Z87M5c152v3NmZCSxo7o9sV9PiKlIMntjoDDNJWWcQgghhJjSvF1h0szM2ooZ6QDsqmkf9PhWfwiAdLeDpD4NWsqPtAAkMm9xHz55RuLf8Y6ZGUnDB3tuuxWLMhq0tAeMx0kxs31/+tipfOqcuf3Oie/bKy2S/Xpi6pJgbwwUprtp8gUJhKPDH9xDfXtg1OcIIYQQQky0QDhKKBJLBFvxhiY7a9rxBSPc+dQuvP5wr3PazMsZSXZS+uzZK69opSDN1W+e3UkzM5iTY2T7is3Mnt1qSTxu2iBlnEops1TU2LOX6rT1Gro+kPj9S2ZPTGUS7I2B+PiFvvNjhnPJL17hly/uH48lCSGEEEKMmXjgFg+6clKd5KY62Vnt5enttfz+5cP8fVt1n3OMzF5aohtn9wfcmyta+2X1wAjavvSeBXxwdTEuuzVxfZZZyjlYZg8g1WVP7NnzDHFc3Nnzczh7fjYnz84c9lghTlTjGuwppd6rlNqrlDqglPr6ALfPVEqtV0q9rZR6Ryl18XiuZ7zENxePZt9eOBqjyRfkjUMt47UsIYQQQogxkdh/5+4uqVxa6GFnTTsv7W0EYIP537juzJ4jkXUDqG7rotYboGyAYA/gfcsLueuDK3pdl5nswGW34LRZBzwHMB8jbGT2XMO3pZidncyfPnbqoHsOhZgKxi3YU0pZgXuAi4AlwDVKqSV9DrsdeFRrvQq4Gvj1eK1nPMUze9WtIw/24rNmdlR7CZvzX4QQQgghjkeJLF2PjFlpURoHGn1s3NeIUvDawaZe21Na/b0btIQiMcLRWGK/XlnJyDNqWSmOIbN6YDRpic/ZkwBOCMN4ZvZOAQ5orQ9prUPAw8BlfY7RQLxQOg2oGcf1jJvCdDc2i+JwU+eIz4l/uhWMxNhb1zFeSxNCCCGEeNd6dtaMW1roIRrTdAQjXH3yTALhGG8cak7c3tYVwmGz4LZbSTabpfiDUTZXtJLssLIoP5WRuv60Er5w/oIhj0lx2vAFInQEIiPK7AkxHYxnsFcE9BzAUmVe19O3geuUUlXA08DnxnE948ZhszAvN4XdtUZHqmZfMPFLcTA9O1JtrWwb1/UJIYQQQrwb8fc1PbNrS80mLTaL4ssXLMBlt/Qq5WzrDJPutpvNU4zyS18owq6adpYWpWGzjvxt6Fnzs7nmlJlDHpPistERz+yNYM+eENPBeAZ7A7VA0n0uXwPcr7UuBi4G/qSU6rcmpdQnlFLlSqnyxsbGvjcfFxYXeNhda2Tobrp/E1f+5jW6QoN32vQFJNgTQgghxIkhHuz1DKKKM9ykue2UlWSQneLkzLnZvLinAa2Nt3ttXaHE2IR4Zq8zGKG5M0SexzXma0w1M3vtXZLZEyJuPIO9KmBGj8vF9C/T/BjwKIDW+nXABfSbeqm1vldrXaa1LsvJyRmn5b47iwtSqWsPcLDRx7YqLwcafHz/qV2DHt9hZvYykx1sk2BPCCGEEMcxb1cYizICqjilFD+/eiX//f6lAKxdlMvRFn9iW0urv3suX7Kje+h5ky+Y6K45llKcNtoDYXzBiOzZE8I0nsHeJmC+Umq2UsqB0YDlH32OOQqsA1BKLcYI9o7P1N0wFhcYWw//+NoRAM6cl8WDbx5l476Bn068QcsZc7M40OijPTB02acQQgghxLsxVMXRcNr8Rmmkpc/surULcxPvgdYuMD6QX2+Wcnr9YTLiwZ4ZJHr9YToCETLHI9hz2QiEY0RjWjJ7QpjGLdjTWkeAzwLPAbsxum7uVEp9Vyl1qXnYl4GblVLbgL8AN+p47v8Esyjf+EX32OYqkhxWfv+RMooz3Pz4+b1orfEFIwQj3b9k42WcF5UWoDX84eXDk7JuIYQQQkx9Fc2drPjO82w6cmwjn7xdxv67oczITGJ+bgob9jYA0OoPJUY1JJt79ipb/QDjE+z1yDrKnj0hDOM6Z09r/bTWeoHWeq7W+k7zum9prf9h/nuX1vpMrfUKrfVKrfXz47me8ZST6iQ7xYk/FKWsJJMkh43Pnzefd6q83PXcXk7/wX/49j+6yzrjDVrWLMjmilVF/GbDAfbVS1dOIYQQQoy9Vw40EYrG2F/vO6bz27rCw44+ADh3US5vHmqhMxihrStMerJxTjwQq2wxgr3xKOPsWbopZZxCGMY12JtuFhcYLYRPn5MFwBUnFTErK4lfbzhIRzDCczvriMaMxGWHmdlLdti4431LSHXZufOp3YPed2WLn7+8dXScn4EQQgghpqLNR1oBaOkMHtP53q6RdbhcuyCHUDTGf/Y0EIrEemT2jGDvaMs4ZvZ6lG5KGacQBgn2xtASs2b9tDnGkFC71cKdly/jAycV8d3LltLSGeKdKqMZiy8YIdlhxWJRZCY7WDM/m0NNg3/a9tfySm57Yrvs7RNCCCHEqJVXGMFec2fomM5v7wqTnjR8gFZWkkmqy8bvNx4CSOzZS0kEe12AMSR9rEkZpxD9SbA3hq44qYjrT5vFsqK0xHVnzc/m7g+t5P3LC7Go7k3LncFIr0+g0tx22rsi/e4zrt3MBNZ5A+O0eiGEEEJMRQ3tgURGreUYg702f4g09/DZMofNwi1r57G92gt0D2F32ixYLYqjzUanzsxk5zGtYyiS2ROiPwn2xtCifA/fu7x0wCGhGckOVs3MSGxa7ghGEiUNYHwC1R4IE4sN3J+m3ZxvUyvBnhBCCCFGIZ7Vc9ktxxTsxWLabNAysmzcTWeVMDMzCSCRDVRKkeSw0hmKYlEM2+zlWPQcCyF79o4jsRhseQBe/QXsfXayVzPtSLA3gc5dmMM7VV4aO4L4ApFev5TS3Ha0Bl9o4OxevHyztq1rQtYqhBBCiKmh/EgrTpuFU2Zn0eQbfbDnC0WIaUbUoAXAabPynUuX4nHZKMlKTlwfL7PMSHL0G+EwFiSzd5yqfRv+8Tn49x3w6PVG8CcmjAR7E+g0s3HL9uo2fH3KOOO15V7/wHvy4iWektkTQgghxGi8XdnKiuJ08j3OY2rQEn9vMtJgD4yunNv++wLy01yJ6+IVTeOxXw+6g0mHzYLLbh2XxxDHoOmA8d+TboBoCLqObfyHODYS7E2gwnQ3AHXeoLFnb4Byg8EasCQye17J7AkhhBBi5Cpb/MzNTSEz2UlLZ4jRjjT2mltJ0pJGVxqpVO/sXTzYG49OnGB0OAfwSFbv+NJyEJQFZp1pXO6om9z1TDMS7E2gnFQnSkF9e4COQO89e/FPy+K/UPuKj2qQzJ4QQgghRioQjtLkC1GY5iIr2UE4qukIDt4QbiCJYO9d7rNLMQerZ41DcxYAi0WR4rTJfr3jTfNBSCuG9JnGZV/95K5nmpGPPiaQ3WohK9lJfXsAX7D3nj2P2eGqfZBgL369dOMUQgghxEjF3zfEq4sAWnyhUQVE8WAvfZSZvb7imbfxyuyBUcqZKmMXji8tByFzLqTkGpcl2JtQEuxNsPw0J3VmsNd39AIw4PiFWEwnGrdIZk8IIYQQI1VjNnYrSHcRjBiNMZo7Q5RkJ/c/+NnbYPH7ic04nV+8uJ+N+xoTx4P5XiXcBX//LJz7DciaC+t/CDkLoPTKYdeSMs5lnGA0aZEyzuOI1tB8CJZ/EFLyjOukjHNCyU/DBMv3uDjU2Ek0pkdcxtkRjKA15KY6aegI0hEIkyolCkIIIYQYRrUZ7BWluxPvMQYcv9DVCm/8mkMNXn6knDy7s46TZqaT7LSR7LRxSkkmuakuqHoTdjwGaUVw1hdh448gd8mIgr2keBnnODVoAfjYWbPHZayDOEb+Zgh6IXMOOFPAkQK+hsle1bQiwd4Ey/W4EoPVe5ZxJjtsWNTADVriJZwL81Np6AhS5w1IsCeEEEKIYcUrgvLTXFjNcQcDdeQM1O/DBezev5/nInXcfsliPnbW7H5NVmg+aPx3/7+hYCXoGNTvgPYa8BQOuZbxbtACcM0pM8ftvsUxiL9eMuca/03JA59k9ibS9GvQ0loBr/3KSCtPgnyPi6g5OL1nGafFovC47QNm9uIB4MK8VABqpJRTCCFOaEqp9yql9iqlDiilvj7A7TOVUuuVUm8rpd5RSl08GesUJ76ati6yU5w4bdZEY5TmzhCf/vNm/vxGReK4o/t3AHBKdpg3v7GOj589p3+gB8b+K4CGXVB+H1jNwG3/v4ddS8oE7NkTx5n46yXLDPZS86FD9uxNpOkX7B15BZ7/JlRvmZSHz/N0d6BKcfbOznlcdtq7wmitCUW6B07GO3EuyDeCvToZvyCEECcspZQVuAe4CFgCXKOUWtLnsNuBR7XWq4CrgV9P7CrFVFHjDVCYbsy6czusuO1WtlW28cyOOp7b2Z1haa3cDUCmbjPKNQfTfBDs5n6/Iy/D4kvBUwz7n9zx2/EAACAASURBVB92LYk5e+PUjVMch5rNsQvps4zLKbnSoGWCTb9gb9ElxqdQO5+YlIfP8/QcLtp74Geamdl7flc9J33v34ksX7yMc35uCkrBm4dauGf9AbpC0YlbuBBCiLFyCnBAa31Iax0CHgYu63OMBjzmv9OAmglcnxhvsRjEhvgbrvWoKpC01v1n50WND4pr2rooTOvuxJmZ7ODFPcaeqf31vu7Dm4wMjLWzfujHbjkIs87obqM//wKY/x44tAEi5l7AcMDYl9Xna1VWiNIcK8UZ7kHv/rik9dD/v0Rv0R7NBlsOGq8Vm5nNTck/voM9rcHXaHzFfw5iMeM13Nk0aZWB78b0C/bc6TB3Hex80vifN8F6BnupfTJ78WBvS0UrvmCEg43GL+F2M7OXlewkJ8XJE29Xc9dze3nJ7JIlhBDihFIEVPa4XGVe19O3geuUUlXA08DnJmZpYkL883PwQN/4voetD8Ldi3u/aR5EJBrj9B++yF83V3Vf2dkEP5qN3vEEtW1dFKR3v/fITjFm7QHUtQdoD4SJxjSpnWZJZ9gPwY6BHyzeWTFrLsy/0MjYzFtnBHwhH1S+YQRFvyqDH8/v97XqkVP4V+CjJIeah31ex5VNf4CfLpWAbyTM1x47zKRK4z7Imtd9e2qe8VoJ+gY+f7K9cjf8eJ7x9Z/vGNc981XjNXzXXHj9nsld3zGYfsEewP9n77zD2yqvP/65kuUp753YGR7Ze5CQEJIACSSssMLeu6UDSlsKv5YWSltKS2mZpWzKLHtnkQEhZJE9PZLYjuO9LQ/Jvr8/jq6WJdtJnMRJ3s/z+JF0l957dWXd7z3nfM+Ii6FuPxStkdcVOfDN40dFrad4iD2rjzVwVFgQdc0O9lY2AlBYZQOg3lmzFxkaxN2zBnHLaQMBqLa53bRqbXb+9MUOGg6yUapCoVAojjp+CqHw/QG6EnhF1/U0YC7wuqZpfn+zNU27TdO0dZqmrSsvVzcBez1tdtj+iaRABrKgP7AZ6g+AraLLzZU3tFBS18z24jr3xJxF0FKHY9N7NLa20TfGO7IH0C8uHIDcsgZ2HaijHwdoDZJykYBuiQ2lYG8Us42Z98P1n0FEAgw8XbKmchbC/vVQWwgTb4Vz/+79N/P/REzmdl3f16vY8j/n51F1rEfS+3Gee2x9X87vsm0SCTYw2i/01uhe0XpJS+4zFrZ/LIGh7R9Dv1MhPlueH2ecnGJv8BwICoUt78nrr34j6r1s+xF/65hwC8FBctgDpXHurRCRV1Apj0bvvcjQIK48pR/3zB4EQI3NbeayIqec51fk8+p3e4/0LigUCoXi8CgC0j1ep9ExTfNm4F0AXddXAaFAgr+N6br+vK7rE3Rdn5CYmHgEhqvoUQpXy8UwQO5i/8sYboXd6EdWWifOmmX1HuZtzvo5095lWHCQ6pXGKfVyt0yTG8e5pQ1szc0nWrPRljbJ+/19MZwV4zMgPA4GTJXXIVa5oM9ZBLsXSMTvjAdg4i3ef6ffC5Gp3arv6zXYqqBorTxXLpJdY3y2+ctg1xfyPHu2e35vF3tVeZA6GsZcDVX5UvbVWA7jb5T2IkVrofH4ikyfnGIvJBKGzYMfXhOFbtxh2v2V/+UrcuCda+DDO2HHp4f11pqmuUxafNM4o0JF7O2rkshegTOyV9dsJyLYTJBZPq4wi5ngIBM1HpG9A07TlpdX7qHZrtIMFAqFohezFsjWNG2gpmnBiAHLJz7LFABnAmiaNhQReypsdyKQsxBMQRCRFFj0GG6F3bggLnE6dJc5RR9tDshbAlFpmO2NTDDtchm0gET0okKDuHR8GiFBJnLK6snftRmA0Kxpnb9vlY+NvifZs6F8J2x6C9InQVhsx2U0Ter78pZKhPN4IO9raS8BykWyK1znXl9J1VzxNxH3ySPcy0SmyGNvFHvt7VC1R25mZJ0l0xb9DtDc6crock4cR5ycYg9g1kNgCYX/3SCuUgmD5G6UL7YqeOMyyF8uovDd6w7byTMlSnrdhFq8D39UmIVWRzvNdvmnss8Qe012ojwahGqaRmy4xSuN0+ijU9HQynueefsKhUKh6FXouu4A7gIWADsQ181tmqY9pGnaBc7FfgHcqmnaJuAt4Aa9gwOH4rgkZ7FEwQafE1j0NLjFXm2TnRteXuNqjv7Ah1v4YssB16KldfL7X94gYu/zLz+B5lo44wHaNAszTRu90jhvn57BonumEx4cRGaile/zq6go2AmA1t8ZqQskairzwGSB6PSO84zoTd1+EXSByJ4tkc3C1YGX6U3kLJR9ht4pUHoTRWvl3Jv5gKT1GueCZwsPI7LXG4VzXRG0tcjNjLiBkrZZtx/6jpd05T5jITz+uEtDPnnFXmQyzH5E7taMuw6GX9wxNGtvkohe3X64+j34yXq5E/fpTw/rjlRSVCjWkKAO/WuiPQRdSlSoR82eg0if+r6YsGCvNM4DNc1kJkYwKi2aN1cXHPLYFAqFQnHk0XX9C13XB+m6nqnr+iPOab/Tdf0T5/Ptuq5P1XV9tK7rY3RdP47y3hQUb4Ta/d7T8peJuUPZNqeDpVP0LP0TrH2Bsq+fpnDneqcboPNCuL6UbcW1LNtVzvJd5dhaHWxb8zXvL1/v2mxJnTuyp+s69Vu+wIEJhpzLnojRnGHeRILV3eogtLmC5N1vwtoXOSWukS37a0nnALpmkvQ1c3DHdMWmGlj/CuxZDrEDwOx9TQKICUfsAHnumbbny8DpIp5WPS3GJ2tf6FaPvmNCe7uk2g6eI68DpXG2t0nA4GS/H5OzEDQzDD3PXafney6ExUlk2ziW1fvkHFj3kjhgHgsObILqvR5pys7ItTF249FkkohfziIZc8kW7+2UbpPUTxCjmn3fHfGhd4eTV+wBjL0GrnwHzvwtDDpbhJ+RP99ml6jfvu9g3rPQbxKERsPcx+TD/fD2Qy7UvWRcX66fMqDDdM/o3bTsBErqmmm2t1HXbCcq1DvlMybc4i326prpExPG2PQYiqpthzQuhUKhUCgUPcCbl8Pi37tfN9fCfy+FBfeL0Bk8FzJmyHXFt4/D578gacX9NH7wU3HCtDt/xxtKqW6U3/qcsnry95fzdvDDnFbymquUo9SZ2dNkb6OhxcGY5jWsax9MhSOUFYwlSyvCVOtxE3jlE/DZ3fD5PVze8F8AxkVUoRn2+NbkjgYt3z8Ln/4MijdA2gT/+6xpMOJSSBjsnbbnS2iURHt2fQGf/0L+3pzfO90ZizeArVJ6CYZEBzau2fm57MP+9f7nnyzkLhIjk9BoORfC40Xce2IyQXSauHQCfPlrOQc+uxu++9fRH3N7u2TwffKTjmnKwy8CcwgMPd+9/LB50FQlY/74Lu9tvX8LfHiHPF/yELx6PjRVH/l96IKTW+xpmqRRBEdA6hjp/bH8UXHJevUCqeE79+8w8lL3OsMugBn3S63fM6cekuA7Y0gy98wa1GG6EdkLNpuYlBGPrsP+miYRe2HeYi82PNg7jbOmidToUBIjQ6hrdqi6PYVCoVAojgXNtRK1qNjlnpa3FNrtcNW78Os9EjkIiYR7dsK9Oei/2M2n+jSS7YXegqKhhCrnb31OaQO1O5YQqtlJ0GpZkSNOnUZkD2BPfi5DtH0sbRvDlqJa/lc7TGZ4pp1V7BYx1m8KqXbpADIspBziMmS+NbmjMUzOAug7Ae7NgQufCbzvZ/wf/GiVd9qeP+a/Ltu6NwfmPCY323tjimTOQlz1WtakwIY5xmddvsv//JOBumIJhhgpvGOvgV/sFnHvS8ZMiRI310nEe9x1kDhEPDKONgc2yrm3b5VE+ILCpM4QIH0i3F8MycPcyw+ZC7/aA+NvkPG6evG1QWUuFK6RLMGcRdDu6BX1fSe32PPEZIJL/gOOZnj3WgnFznsOJt7ccdkZv4Yr3pJ/5vtW9tgQopypmulxYQxMEEvkgkob9c0O1zyDmHALNc5m6/a2dsobWkiJDiMxUlI1KhtbUSgUCoVCcZQxUsEq890XgrmLJNqReaaIPIPgcLAmUanFsM3Rl1i9VsQYSASwvpQa5+95Tlk9YXvlwjHR3MgyZ2P0krpm183i2s2fA7C0fQyfbT7ADnsSDeHp3mmSlXmQkA2Jg4huKuBP80YQ11LojmZE+jS9biiTCNfgc0TwmDq5dNQ0MJkDzzcwB8m2rEnulLneKvbSJorzqO9x8aTSmbpnRIZORozMOEPsaZr/dF9w92X85u/gaIIh50sa8LE4fsZ3o90OW96Xmx6e57i/fQiPg6Th0obEOCdqC6GtFdBh1ZNQX+y9/WOIEnueDDxd7kjNeQx+vBrGXBl42YzpktfegwXGxj/rgQkRpDv73xRU2ahrshPZIY0zmBpbK7quU1rXjK5Dn+hQV15+eX1Lj41LoVAoFApFNzFqdlrrxbJd1+WCL/OMgBe/eysa2aOLS2G7UeeTNBQaSl2RvdK6ZtIqvgGgb0gLy3aX096uU1rbzMi+0QBYC5eyX48nT0t3mrhotA48U0zm7M1SolJT4DSgyERrquKqrFa0lnq36LImeYua3CXymNWJ6crh0FvdGRvKoPgHd72WNbkTl1LnZ155Eou9nIXiwpk0rOtljb6M3z8jrdAGnCbnX/Xeo9+4Pmeh1KqGRIl4i8/o3nrGcq6bOx6fvdF4fcA0Z4SvvefGewgosedLaDRMug2iUjtfLihEXHkK1/TYWxtir398BInWEMIsZvZV2qhrdhAV5v0DERtuwd6m09ja5rJdTnGmcYISewqFQqFQHBOMC3/jeclmEQmdmJbsqWhkr1Pste11ir3UUVKz53TZzNSKSWovw65ZiDc1UNXYyqr8Shpb2xiZFo0FB4Ma17GsbQyTM+JpsrcRHGQiatRciZ7s+1bMMPQ2ubA2xJ0ReTAie9YUqVNzODOEchaK0EkZ1WOHyIve6s5oiFwjUmVNDjxGIyJ1skb2HK2Qt6yj82YgQqzQf6pEwgZMkwh3XKa8ri084sN10VghdZaDz4XMmTLNX1sRfxjLuT575/d+wDTZj5RRkp5qq4ADG3p23AdJgPiqoluknwKr/w2OFhF/h0lseDDnjkzlnBEpaJpGv7hwlu0uo61d92vQAlBja3W1XegTE4Y1RD7SigYl9hQKhUKh6BZ7V0L+Uqk56y4lW8SEpd0Bo6+E0VfIdKM9QbtdnjvTuZ4vHsj42CrG94/r+PaVjezTRfQElW6SqEfCYHA009xYQ1xEMDObN8qycdPIql+DpsFbawoAnctK/sGs4PVE0MzqoPFMHhjPytxKhqREEpQxXqInOYu9L2hDJRro6vVniL9Ip/hqLBPhl7dE0uw6S988HHzdGY80uxeIAD/9l/7nl2yFxQ9K/Z2nyI1MlshPS713Km5znURwTRZ36q6myWf/3ZNi7Ge2+H+vrlj0IAw5V643PSnbCetfhrP/1L202SPB9o/FQROg1SaR7M5cWH3Jni3fOWMd4/yrzBNzxM3vdFxnxKUw7looWA07PoHZf/QvLmsK4Yt7pTQr8wyY+jPv+Y4W+PjHzpRpXURqdJrsU3w3xV50uvMz94jsWcJh3PWw9xsxfsw8E9CkT7dvEGn4xTD++u6912GiInuHQ/okUe8HNvfI5kwmjaevHsfEAfJD8KOZmS6XreiwjmmcADU2u6uhekp0KPFWmV5e30JhlY37P9xCq+PYho8VCoVCoejVbH0PVjwmKY7dZdtHYr5QvBHWPO+eXpUndV6aWZ7nLELvM5Y/f1PFZ5sP+N3U3gobzYRQrMehtTtEZDhNIrSGUkb0jWaMeQ9FegJ68gi01gYmpkWwcFspUdjI2Ps2yeY6FrWNZ3/cKYxwpnUO7xMFljBJm8tZ6G0tHzsA0MR7QDNDTD+ZlzBYHvevd/dN66xv3uFiMklbq0BOlz3Nisek3UUgg731L8OeFRDVRwShIXKtRrqpzziNyE7/U71ruNa9JNs61NTO1kZxTvXnULnrc1j9nKQ9HivWvSR9p+1NTsPDuWK80l1GXipul8MvkteuSFk+LPszlG6XbRt/ZTtg2V9ETK96ElY95a5v9WXz22KyWLZTBLcve76BLf8TY6CR88Wkcci5MOISdzP1rjAHyXfIM6oblyG1rSMugTFXQ0Q8TPkJhMV474u9SW4GHSVUZO9wSHPeaSlcLY49PcyFY/pyamY8H23Yz5wR3ncEYp1ir9oZ2YsINhPp7N0XE26hvL6Fz7cc4M3VBcyfkM6Y9JgeH59CoVAoFCcEhj16ziL/xmz+qMqDmP7SQmHbh+7plXni3N1QAkXroGgtLafeg54Pthb/9Uh7KhpJjQ5lry2FPuYqEXvWJACCbGUkpA5lkKWc/NZUhsTL9NkZoawpbCRdqwHgHet1/Kt8LHPi4hiZFk1IkIlTBjqjiNmzIedeMYoJiRZLfE2T6ERtgVykGtGntIkS9ctZKCJMM7sjgkeKSD8OoEeCxkr5TNBFqHu6rYOzvnKh1Cde+ab3POfnQX2Jd/THEHPZs0UkVuZJHaIRMW0ogaQhBz9WQzTmLZM0yaBg9zzjfK3M634kqqepzJfo1SUvHNr61iS47BX368gUsERIS46aAjjvCZhwo3v+D69Je4QDm+SYgBzjxMEdt52zSEqthl4AS/4g0VdPV9CcheK6efNisITKtLAYuPSlg9uH+Ey3OU9lHiQPl6iv53ZmP3xw2zwCqMje4RCZLKq+8Psj9hZJkaHcdnom0eGB0jjtHKhpJjUmzNWkPcEaQnl9C3ll0rPGeFQoFAqFQuEHI8pzMM55xoV2fCY018g2mqqlB5fTAIU9y0Fvp6rPDAAaWh0dNqPrOnsrGxnXP9ZVtyeRPXke2lxBbLiFdL2YAlKIS5Dpp6dJ+l6SU+zpztq39LhwEqwhfHffGcwb01e2Z0Qr8r4WYwkj9c0wmfCsUzIHSfpZziK5KDb6ph1JrJ04XfYkeUsAXdJGDTHmScVuERr+IpmBjGSMWi3jGFflQ9Ued9TpUGsRjfVa6zteZ9qcYu9Y1Qjam6W2rrv1bd1B0+Smg9GqwPczMAyCvn5Yjkmgz9BWJRHp7NluIex5nHRdWolkTHcLvUMlLlM+7zY71Ow7dsK7C5TYO1z6nwb5KyT/9yjiVbNX10xqtPuETbSGUNHQQm65iDzjUaFQKBQKhR+MSMkep2tlV+i6XOQZog7ktXGX39MAJTyeUutQAGwtHcVeeX0LttY2xqbHuBw5iXRH9qLaKkm1NBLW3sjwkeMIipBoXVaknQRrCP2C6wEwR8m66bFhMgRriOsmMHEDIcHZ39fzAt147nuRmj1bRE3p1iObwmng6wB6pMhZCBGJkjqYu7ijS6IhHvzts2Ek4zvOyjxxoYzPlhquqjx3GwJ/y3cXz/V8RY1nZO9YUL0X0Hte3Bg3H5KGSQ2dJ1GpkDxSjq3JAuNvlN54zXXey+V9LemZ2bO9v5sGlXky/u6ma3Y1XkeTiMt2R8+K3x5Eib3DZdgF0FIrtsZHkZgwI43TTnFNEylRHmIvMoQyFdlTKBQKhaJ7NNVIyqLd1r3+uQ1l0ifMU9RV5rkvKj1FYNZZ1DSLqGhs7ZjGuaeiEYDs5EhKLc5InDUFQmPQzSEkaTWkI7V+Y8eMh7BYAEzN1Vw6Po1xsXKzOSRW1k1ztm7qgK8Rhudz34vUrDM7rnckiUwRZ8S2jmK4U3RdDFNabYGXaWmQZZprRShkzYJB54jjaMF3Ms/4273Av9AAOe7mYDH/8LwhUJXvTIN11nCV75Z6sdiBYtjhT+y1t3u/b0u9jNMTY73kkR0jzk0+kT1Hi2yjrYs6MHuTLHcorQBaGtxjNN63p8WNsb1ANxiM6QOmimBvt8sNGs8x7v5K0pT7jJXPBdw3YVptsPOzzt/jUMa7U/pb9tbInqrZO1wyZkhvjh0fw6Cj8A/RSXCQCWtIEBsLayivb3EVY4OkcRZV22h39nLNU5E9hUKhUCgC01Ql9Vsb35LIgKfY8YfnxW7sANBMMk1vBzSZVu80Y8meTU2TtDFo9BPZ21spYm9AfDg14QPAhpiDaBr28GT6tlaS6tjvfj+jV19TNffNGSJRjrWhxMcnAAfoH1DszRJTi/hs9zQj2peQ5b2sNQn6jBNRmzS082PRE1iTAV1cLaNSYeU/YfP/4M5vvZdztMBTE+C0u2HCTfDh7W7XxktfhhEXwxvzZRvn/1P6nS2433sb2WeJkYhmglfO7TgWX+dGA00T05zvnxZzlJsXyjGq2A3D58kyCdlingIw6Q4Rj/5qEV+7QBwbfTn9V3DGA/K8vkRSFUdfDgv/D2r3Q7TzZkCTM+24Mg9qi+CpiXKjIioNfrbRv/tnwWp4eY603siYAdd97H8//bH8MVj6R3k+8wFxd4Xu96TrLsb5GOgGw6Cz4dvHZX76KVJ/mrMQhp4vpjvLH5XlRs4Xl9LgcIjsI9/NHZ/CO9c432ew06DocMfr/C6tekoe47MCL3sMUWLvcAkKgcFzRNWf98Sh2+seAtFhFlbsLgdgxuBE1/TEyBCX0BuVFs324jrsbe1YzCqQq1AoFAqFF45WidJF9YXY/t1z5DQiePEZch0QnSYX3jX7oO94qQUaOF0EyNALqF4lvcNsfiJ7uWUNhASZSIsNpzEyg7+FPMi9Iy4BoDZ+NJPqltPSPFaMUmL7y0U9uKM7DWVgTeL80X2JCQ8mI9Hqf8wDp8Nlr8o1i0HmGTLGgTM6Lj/vGbfT4pHGlSJZIkJt0ztQtk320RnJBMSSv6ZAhODoq+QCfuB0KN8J2z6Q5zkLxSRj7t9gy3siIMZdJ+tbwsW0w2yBK96EylzvcZiCRCgEYt6z4lL69cOw/SMRFM010G+KzD/rD9B/inxWIy8VMxHfyF59iQi9oeeLq7vB5ndh6/tusddQKselz1h5XbHLQ+w5P/vaQmkXYLfBqCvEhbJwtTQp96VorQi97LOlZs153nSLre9B8gjn8/eh32RpmeH52fQEwy8SgdZ/qv/56ZPE1GXQOfIZZs6UqKeuy2edMlKOw7AL3evEZ8p3c+sHEJ4Ap/3c//E5FKLT4JIX5cZOVN/uH8+jjBJ7PcGweXJnac/yjjnAjZVQvqPnTiwPYiMs7K9pYmBCBP3jI1zTjcbqAGcPT2FzUS0FVTYyA/0AKBQKhUJxsmJcOIfFysV1d2qsKvPQTUFUmJNJBIm47V8nTctn/EaWMZkk0gTUNEl6nb/IXk5ZA5mJVswmjfiIYBY1T+DeYInO7U+Yxpi9n9Ja8KW0RjBbwBQlYsIwlWkoAWsKYcFmZg9PCTxmTXNHoAxMZtcYO3A0InoGhvlJfalEqsq2yevKfEgb717OSGcsXC0RNLsNTr1Lnm95H3Z/CejQUidCsPgHmPl/Yn/vi6fo7S4Dpspf3hIZS0gUoLkjwYmD5M/AmiQtAzwx6vmm/1rEiYE5BL78pdv4p6FU1o/zSBPOPEOEja1KIlb1xbD2BUlXnPuYCLGchf6vOavyIDQGZt4vYi93CYy5sut9rt4nYvrsP8nrBfdL27EjkbJoCfUWar5omrtVA0i0evtHInir8mDOYzDpNu914jKkJ1/FLukX6e9cOBx8HV17ISrU0xNkniG5/t88Ll9CA0cL/PdiSRPYvaDH39ZovzB9UKLX9ARnr72IYDNTMuMBVbenUCgUCoVfPMVeZEr3WgBU5dEQlsbpf/uG+ma7XPgaphV+aoFqbYHTOHNKG8hOlpux8dZgKhtbXfN2R51Cu64RXLfXfXGtaTJWz8ie0Qj9eMWIiDSUeten+bpN5iwUQa63SUP7oFARNtmzxaFx+aPuJu2LH5R1joTBTPZsEUAbXpdIbkSC/+X8uYzmLJR0UCNS5tqmc5zG/teXyvpGSwIjmtzaKLVqaRPkdVW+jCc0Snr9BXKUNURkyig5hv6cLP2Ru8i9z0Z6pWFOdKwxAiyLfiuP/j7r+Ez5rhzpfpG9GCX2egJLKMy4T4q6d3/lnr74D3Bgo9x9+ehOqCvu0bc1Gq17pnCCO7KXmWQlK0l+QPLKG3v0vRUKhUKhOCEw6p/C45yRvTLvG7f+qMynPLgvTfY29lXa3Be+EYnSoNmHaptE9mz2Ntrb3dtubHGwv6aJbOdvdVxEMNW2VtcyJXYrm3Q/Jirhce5x15e40yCPVzydLnMWyXUTmrfbZNUeqMyBKT8VsVtTAAOmSdrfwOlSu1hTIHVd/U6V59ZkETc9jSF6ago6N7CxJonIsDfJ6zY75C0V0eGbHhs3UOopPfvzWZPcLQmMY2F87obYA7eIyZ4NZdslOuqLIdBMJjGpyVvSPUOcnEVS3xafJX9GrVtvMCOJTIHU0fI5xGfJMfTF+N5oZqlVPAlRYq+nGHednGiLHpQvz/4fpIh34q1SBGtvgq9+06NvmRwVSpjFzOSMeK/piVYRe1mJViJDLSRHhSiTFoVCoVAoQNwIi9a5X/umcTqaJA0wEM62CyVBUj9VVG1zX/hmnSUX0z4YaZy6Ds0Od91erjPrJispEoC4iBDa2nVqnctX21r5ThsnC3teXBuRPXuz1IxZO0nfPB4ICpF92rcS8pdJimV0ukT22hxSb/XtP2TZwXOkDyC4hVaIVdIrQYSPMT1rlt/P47DxFD3ZnVj4+/bmK1wj51YggZg9G/Z+K+0EGivc68dnuKOcxvkalylpmUFh0gbMWB/c0b2GMijd5uyLV+Q+h7JniQj99nHI8WgT4Yu9WRrFZ88W0alp7veI62FzlkPFGE+gY2rsc7/J0jj9JESJvZ7CbIGzfi85wRv/CyufEJegM38n+duTbpecYc9eH4fJj2dm8cGPphBqMXtNj4sIJikyhIkDpRfPwIQIl7Vzblk9jy3Yid7VXUuFQqFQKE5E1jwPL85yZ9sYtW9hcR4X52WB16/KB3sjBSax5y+sapKUPHOIdz2RBzU2d2pmY4tb7OU4xZ4rjTNCyjCMCxOIXwAAIABJREFUVM7qxlbWhE2VqFWfce4NhsVJY+1G5zh7qTHEQZEwWISevVHMS+Kd0awdH8N7N8IPr0pbhPhMOc5BoTD4HPf6wy+S667MM2DIudImwbdGsacwasdiB0Dq2MDLWX3Op5yFkmI6cLr/5bNnQVuLmM2guyOecc404TaH+3wNj5MU0iHnupuDJwwSoxCjfcjXD8NLc5wN3nV3lCtzJgRbYekj8MYlULbT/3j2rZS6SE8hNWye7IOfCPYxYegFMp5AtX6xA+VGQoDv5smAMmjpSYacJ05BSx6SL+Npd0sONcApt8N3T8H3z0oRbQ8QFxFMnPOHwZMgs4nv7jsDs0lSBJKjQtlQUAPAp5sO8PTSPOZPSPcydVEoFAqF4qSgfJe0SMhZBOOv94nsOUVTfYnbVt0XZ9RkjWk0AIXVNojOgF/vlZRCP9TY7Jg0aNclddMot8gpqyfYbHK1SzB+06ucYq/KZqfWmgW3FXhvOywWSrZIXRe4RerxzHUfSQQsKFT2Z8en4gK5e4H0TbtlifvzGXoe/Cofgj2uY8ZdDyMvk2lhsc7P4whe55zxWzFZ6Sxy6Hk+gZw7/U51Xxv60n+K1Of98LpzfUPsZUjT7toC7/P1qne819c0EXyGy2j5LukFvfENeW20SgiNhru3igHOC2eICE0a0nE8OYvcdZEGA6bCfYUBz/WjTuoouK8g8GdtCYV7drjbRZyEdCuyp2nazzRNi9KEFzVN+0HTtKPXVO54QdNg1sPSqNMcDJPvdM+LSoVR82HDfyU0f4QJMpvQnPngCdYQKhqk6Wq583HHgfojPgaFQqFQKHodRu2TYTzRVCWRgZBIj0hMJ46cOQshPpucVjHlKKxytkLo5OK32tZKSpRcbDa2uuukcksbyEiMIMjZGineaog9+a2usbUSGxHccdtGzV6DU0Qc7zV7AJYwiZS5UhczJdVw5xeSthk30PuC3vfiXtM6n9/TmMwy5s7wTOM0XEY7q/ELCpG6sv3rvNc3UhEr8901e2GxklXm2/IrPlOW03X3ub7BKfY8Uy/DYsXpNGmY+7vgS85CqYv03c/eIvQMuvqsLWFHp4VIL6W7aZw36bpeB8wGEoEbgb90tZKmaedomrZL07RcTdPuC7DMfE3Ttmuatk3TtDe7PfLeSr9JYgM8476OaRVTfirFuR/eDu3ONI72djiwuXtFsgCtNtnGQZBgDcHW2oat1UF5vfyA7CpRYk+hUCgUJyFG7VPeMumxZ/Ry0zRvV0h/tDZKTVX2bKqdqZmF1U2dvp2jrZ36Zgd9Y+WC2bPXXk5Zg8tIDXAJwqLqJnRdp6i6ieRIPxGJsBhJrzN6Ap4IYs8XI+Wwtb5zgdSbCY+X5u2eLqNd7YunY6RxPhrHoirPO7Lnj7hMieZV5YPNGVxorQ/cFy97FuxbJXWCnlTmyfsdr8de4aK7Ys+Qw3OBl3Vd3+Qxzf8KmmYGngbmAMOAKzVNG+azTDbwG2CqruvDgZ8fxNh7L2c/AtPu6Tg9aQjM/av0WFn0OxF8X/wC/j0NHh8Cy/8q4i8Qug4vzoZ/jZU7Xd3EaMVQUd/qivDtLOmk+FyhUCgUihMRW5VcLPebIhfAhd87xZ7UuEu0JCRw+4U930hNVfYsap0Om0XVtk7r4A2zlT4xIvYanO0X9tc0UVhtY1BypGvZeGsIyVEhbCuuo6SumarGVob18ZPyZ1y0l+8ENHEBPdFwGdJ49LE73jCZpTVXfYmIveh+kDi483W8xJ5TxFuTpMauMg+aaiTVMyjE//rGcTP6+RkN3wO5Z2bPllYOe5Z7T3eJ05OzXcGJRHfF3npN0xYiYm+BpmmRQCeqBIBTgFxd1/N1XW8F3gZ8qydvBZ7Wdb0aQNf1TiqiTxDG3yh/q56Cf46BdS/BmKulyHbpI/DRHe67Nr6UboPSLeLk9PaV8NZVUFPY5VsmOGsDyhtaXJG9nR6Rvd2l9by5uuDw902hUCgUit6MYZI24SYxPclZKALQEE+a5r+xevU+ePc6WPh/YInAnjaZ+hYH8RHBNNvbXSUSGwtruO21ddzy6jo+3CD294YTZ1+n2LM5DVr+/MUOgs0mLhmf5vVWw/tEs624lm3765yv/Yk9pzjd+YUIPfMJaMEQ01+iYmkTJW31eCXS2dMub4n/lgu+RKdB0nA5Jw1B52q/kCvna2fHw4gCGi0cJt/hPd2X9EnSHH7x7+UcN1I/nenKftsZKI4ruiv2bgbuAybqum4DLEgqZ2f0BTyVSJFzmieDgEGapq3UNO17TdPO4URH0+C8f8DF/wFHs/zgXPg0XPm2FPtufgceHQBPT4ba/d7r7vgU0ODOVTDrIchfCs/PcDszBcBoxVDhFHtmk8beykaanKkk//1+H/d/uMXLLUyhUCgUihMO40I2dZQYTeQslkiJ58WzNamj2NvwX/kNtoTClJ9Qa5fLpxF9owFJuwT4ZGMxS3aWsau0jrvf2cRfv9pJtdNsxUjjbGx1sGZPFZ9tPsAd0zNdItBgeJ8ocssaWLevGk2Doal+xF7aBLlIj0qFMVce7lHpnQQFi7ndlLuO9UgOj5Hz5ZxKGgZjr+neOqf9XK4PPUkdJbV8jeWdtxCI7S895fZ8I6+zZ8P4G2Dkpf6XN1tg6k+lrm3HZ/DDa1Iy5ExXVhz/dPdW0KnARl3XGzVNuwYYB/yzi3X83brwzXMIArKBGUAa8I2maSN0Xa/x2pCm3QbcBtCvX79uDrkXo2li1jLyMu87PKffK45HBatg+WPw/s1w9f/gwCboM1ZaN/SfAtF9YerPIGOmiL1Fv4MLnwr4dglOsbenopEWRzsT+seybl81u0vrGZ0eQ0ltMwAbCmuYOfgEsG9WKBQKhcIfVXkSLYodIBeyC+6XPmWpHo23I1O8m3mDRDnSJ8FNXwFQ42yZMCotmuW7yymssjGuXywVDS2kxYax5J7p/PbjbTyzLM9VPmGIusYWBy+v3ENiZAh3TO8YbRneJ5p2HT7asJ+BCRFEhPi5VIvpBzcvPPzj0duZ06U9RO9nyl0HL1hHze84Lessuemwb6VEOwNhtsj5Ub0HotJExJ3fxSX76b+Uv1fPl/TN/lNc6cqK45/uRvaeBWyapo0GfgXsA17rYp0iIN3jdRpQ7GeZj3Vdt+u6vgfYhYg/L3Rdf17X9Qm6rk9ITDyB8tL9hfL7TZaWDec/IaLvrxnwyrnw3GlQtl16zxikjoJTfwwbXpfi2gAY7l47DkhKyLRsOYZG3V5pnYi9wu1rjopTqEKhUCgUx4TKPEmTCwpxRy0cTd7GFdZkt8slSHuDAxu9LnyNTJjhfbwjexUNLSRYQwgym/jjvBGkRIXy4QbJ0knzMGgpqm5iWGoUYcHefXJlmxLJK6lrdm1foSBjpkTs7LbA5iwGRn1efEbny/mSNUscQ9e9LHWB/acc2lgVvYruij2HLtXHFwL/1HX9n0BkF+usBbI1TRuoaVowcAXwic8yHwEzATRNS0DSOnuu6/jxzKj5MOM3MOISSftscgY7h5znvdyM+6SB5sIHxMDFDxaziZhwi0vsjesfQ3iw2dV+oaSumWvNC7lm07Xw0jnu91IoFAqF4kSiKs9duxSfJRE+6Cj2mqrBIRE5l9FFlqfYM0xXQkmwBrvaL1Q0tLgao5tNGpeOT8PeJr/NiZGhWMwajS0OSuuaXc6bvqTFhhEdJnb6I/zV6ylOTsJiJCAAXdcwGi0WAtXpBcK4AbL7S8iYHtgERnFc0V2xV69p2m+Aa4HPnU6bls5W0HXdAdwFLAB2AO/qur5N07SHNE27wLnYAqBS07TtwFLgl7quVx7KjpyQzLgPLnpO8rbvXAnXfwox6d7LBEdIU8/962H3VwE3lWANIa+8EYCkyFCyk6zkljXQ5nBwu+0FHra8wnp9CHr1XvjfDd1vBaFQKBQKxfGArkv/MSPqoWluAecp9iKdDogNTs+4nIXSfy9lpGsRo+1CTFgwabHhHpG9VpcpGsBlE8R8xaRBZEgQ4cFB1DbZqWhoITnav9jTNI1hzjo9FdlTeJHt53z1h+uGxkGKvcTB4hjq+V6K457uir3LgRak314JYrTyWFcr6br+ha7rg3Rdz9R1/RHntN/puv6J87mu6/o9uq4P03V9pK7rbx/ifpz4RPWBgaf7nzfmKrmL8/UjAVs3JFiDaWs37i6GMCAhgqKKGuxvXc1NQV/yafg8Lm+5n5JpfxLjlwW/6bCNdXurWL67vMd2SaFQKBSKo0ZDmfQf84x2GJGMiAT3NKOxet1+6Wubt7SDi6LRTiEmwkLfmDCKa5twtLVTbWt11ckD9I+PYHJGHHERwZhMGhHBZvZV2mjXCRjZAxjR1xB7KrKn8MA4X8MTOl8uIUse47MObvua5hZ5WUrsnSh0y6BF1/USTdPeACZqmnYesEbX9a5q9hRHC7NFUj4/uBV+eBUmdDRKNX58gkwaMWEW+sdHkLj1E0LzvuIP9mvpe+o9tH++g6Xh53DVqfukNURkqtQEOsP4j3yxA1tLG9MHnUB1k72Z2iIIjYaQrjKmFQqFQtElRh+xfpPc07LOgnnPQfbZ7ml9x8nj3m+kH25LbQdXwhqbHbNJIzIkiNToUJbsLKXK1oquQ6KzTt7gzxePoqha0jzDQ4LILxdzl+SowClyt56ewYQBccRGBAdcRnESkjwcLnsFBk7vfLmMmXDhM4cm2Kb/CjJmdMwkUxy3dCuyp2nafGANcBkwH1itaVoAD1fFMWHEpRL5W/hbv733DLGXYA3BZNIYFNnKT8wfUBB3Ki+3zeGUgXHERwSzKr9S2joMngtL/gB/HwzfPE6bvZWdB+qpUu0Zjg5tdvj3dOnppFAcDVoajvUIFIojS85CiYikjnVPM5mkdYHFI8pmTRIH7JxFso4pSC5+Pai2tRITZkHTNPrEhNFsbye3VL5DnpE9gIEJES5jtIiQIIqdDtjJnUT2kiJDOXt4yqHvq+LEZfhFXdfsmcww9upD678YmQLDLuh6OcVxQ3fTOB9Aeuxdr+v6dUjD9N8euWEpDhqTCS54EvR2+OQuuRvpQaKzhsB4nFjwAlaaeD5EooAp0aGcPSKFxdtLsTl0uPy/cM0HkHYKLPkD9ufPIM5eQo2tFT2AEYyL1kZV83e47FsJtgpJH1IojjR7V8Kj/SHv62M9EoXiyNDeJkYr2bPk97IrsmdD0VrY9iH0OxVCvdMpa5rsRIeLdUGfGBFtm/fXAhBvDRyxi/Bw30wJULOnUCgUPUl3xZ5J1/Uyj9eVB7Gu4mgROwDO+TPkL4Nlf/aaleBMK0mMDIGKXJJ2vs7bbWfwUXEMZpNGQkQIF47uQ5O9jUXbS+WuUNaZcPW7MP81TDX7+DDkQc7Xl9Oy+iX45u/yV7bDewx1B+DJCfDMJChcy56KRpbuKkNxkOz8Qh5r9kFNwbEdy4lMVzcueiNtDrA3e0+r3gtbPwDHIUTeHa3w+T3Q7oBtH/XIEBWKXsf+9eKwGcB04vv8Sv6+cJd7QtYsuXlasw+yZ7O3opFrX1zN5f9exb+X51FjayU2XH5X+zj7520uEifrBGvg1MvwYIm0WMwaceEqRVOhUBx5uivYvtI0bYGmaTdomnYD8DnwxZEbluKQGXcdjL0WVjwGP7zummyklSRaQ6QJe1AY/wm6nIYWB0mRkto5cUAcfaJD+WSjTzvEYRfy6uDnsGPm8eDnCP3qHljykPw9Mxk+uE0umh0t8O610Fwrz1+aTf47v+ZXb689mkegIysek54xxwu6Djs/dxdW7115bMdzorL9E4lmVe891iPpHntXwtOT4Y9J8PhQsFXJdF2H92+B926Ef46G/OUHt93vn4HyndKEN2dR9wRwVT6U7+p6OYWit5CzUJqpZ57hd/bHG/fz1NJcHG1iclYWNYxq3VkvnT2blXkVfJNTQVF1E39ftJuCKhsxzvYIqdGG2JPInqcbpy/WEInsJUWGYjL56bWrUCgUPUy3xJ6u678EngdGAaOB53Vd//WRHJjiENE0mPs3qd/75C74+C7IWUySRSIBU1q/gV2fo027h8j4voC7bsBk0jh/TB+W7y6nrN47crC8JoFZLY8xp+XP7Ljye3igBO7Ngck/hs3vwOrn4N3rJe1l3jNw53cw5mrOLH+dt9t+Qfu6VzpGI44GjRWw7C+w/NHjJ4pzYBPUFcHUn0NYHOz99liP6ODI+1rMZXo7616SGxNL/9z1ssea9a/Cq+dBWwtMvlMiFCufkHk5C+V7d8rtEBQMX/4qoCtvB3Qdvn8WMs+E038F9cVQurXzdexN8OqF8Ob84+c7pVDkLIL0SQEt68vqWqQzQ6NEx/+2KJcv2yaQ294HR1w2FfUy/amrxtLqaKewqsmVxhkfEUxwkImi6iaCg0xEhgSukwp3zlMpnAqF4mjR7VRMXdffd7ZJuFvX9Q+P5KAUh4klFK75EKb8BDb8F964hBFvT+Jly6NcuPt+SBkFk++kf3w44G3/fNn4dEwmjTteX09Tq9T96brOtuJa+iQlsEPvT5mWAJYwKWKf/UcpXP/qPunzN/dvMHye1Ddc+BS/j3yQJkIwffYzePEsqN53dI/Flv9Jelr9ASjecHTf+1DZ/pHcgR48BwZMFUe444WaQnj9Inh+5pE73kXrJDrdZj/0bdSXijNfeILcrCjd3vU6dcWHZ2JSvgu+e6pDPW23+Obv0Hc83LYczn4ERs2H1c/LuL9+WFK4z34EZtwvUbrcRf63U5kHFbneY2ooke+sq5nuAhFx9mZ3U2lPVv4LagskIlrm57jZmw5+/xSKI0l9KRzY2GnfsPIGOdfL61vILavnf+uLeCbsdi5sfZiqJjuVjS1Eh1kY2y/W1QPPSOM0mTT6OMVbQkQwmhY4YmfU7HXWdkGhUCh6kk7FnqZp9Zqm1fn5q9c0re5oDVJxCJiDRIj9eg9c/ynayEs5LXQP9jHXwc0LwRLGgPgIwNv+OSvJyr+uGMOGwhp++d4mAA7UNlNts3NalvR1qW70qAsymcS2OvNMMXU55VavYXzVMprzWh+hZM5LUnv2/Ay/bqFHjA1vQMIgEU+7vvSeV5UPL805+gK0M+xN8MNrYgMekQADph1fdXs7P5NHzQSvnOduStxT5C6BV8+Hlf+UNiPdxeiVZUS8tr4v9ThXvCGtLZY+0vn6rY3w7BR45lQoWn9oY1/0ICx8AD79afcjbyBRvJp94pBrmERM/zW0tcKzp0LJFpj5gLRgGXExRKXJ8fGlvQ3+e4mkWhvkL5PHgdOlkXTqGIn0PdofHkmGv2bAvu/cy9cWwbf/cNt+7/TJ5i/fLess/2v390+hONLkLpZHn/YJnpTVidgrq29m6/46dB3mT86ikTAq6lupaGhx1eJdPlEs6Y00TnCncnaWwgnixgmQ1EnbBYVCoehJOhV7uq5H6roe5ecvUtd11enzeCAsVlI6L3wKywOFWOY9KVE5cEX2kn3SSc4Zkcr1pw7gq60lNNvb2FYsun6qIfZ82y9EpZJ79qu0DT7Xa7Ku61Q2tgAaxalnwM2LoaUevnvyCOyoHw5shtItcMpt4qa2y+fCdNmjUPAdrH/l6IynO2x+B2yV0t8QpFcOSLTleGDHp5A0HK55D1ob5PXBUrZDRJ0v9SXw9lXSELnvBBEUrY3d2+auL+D1eSK0mutg05uQOhr6TYZJd4hI7awGbev7IrrsNnj5HO/oWHewVckFZ+wAibavOAgxdEBuutBnjHtafCZc/Dyc9Qe4eZFE+kAE3+Q7xc3V11lz15dQvUeicUad4p7lEDsQYvvL67HXiDnT4Llw5oOSRvzZPe4o6oY3wNEszr9pE2HX597v8cOrcoyWPgIb3+z+PioUR5KchdI3NnmE39nt7ToVHpG94lqJTo9KiwagsrGFivpWl8vmhWP60D8+nBF9o13bMExafNsu+BLhNGhRkT2FQnG0UI6aJzEDEiSy5+9HZ3JGHI52ne0H6li/rxqLWWNKZjya5hPZA7bur+Wsx1fwxZYDXtNrm+zY26Smp8bWComDYNTlErmqLxXnvyNpjrHxTTAHw4hLJCWydKs7ileZB1veBc0sAutgIi1HCl2HVc9Imu2A02Ra4iCJTBqiqc0hkaVtH/WOMXvSUCZRoKHny0VVXKY70tddHK3wzjXwzrUdnSV/eF2ExvxXxXW2oRQW3A9NNV1vt2ynPG54HR7LlGjYxFtk2qTbIShM0hMDsfZFSBoGty2ViJrvjYNA2KrkBsfOz6DdDpe+BMPmyXs1VnRvG4bYSx3jPX3kpXDazyH9FO/pE2+G+Gz4+Cdi6vLyXFjxN1j1tIg3gF1fybm05xvv/mGn3Ar37oaLnoNp98CcR6F8h0T7QMRd+ikiDgfPlVTd2v0yr80u36VBc2Sbn93T85FdheJgaXNIVD97ltS0+6Ha1oqjXX6ryupaKK5pIibcQr84uSFa0dBCRUOLGJwBMeHBLP/lTGYOSXJtw2i/0JkTJ0C406BF1ewpFIqjhRJ7JzFj02P49TlDmDUsucO80ekxAGwurGH9vipG9I0mIiSI6DAL1TbvWqn31osZhxEBfGP1PlbmVrjulALUGOtM/ZlcsD89Ef53PTx1Ciz+/cFHSrrC0SpibvBcaT46eK5MN1I5v31chODZj0Ddfti7omff/1DY9SVU7JKonudFydDzxaSlIheeGg8vnCHHbt2Lx26s/tj5OaDLeDVNHveskIiYQZtDGsWvft7/Nta9CJW5YG+E/evc09vbJAKbMVOiWumnwPgbZdo/hsOX93We6lqxG6L7SSRs2DyJMo+7TuZFJMjzze/4N5bZ/4PU+0y4SRwrE4d0vx/dG5fBk+Ph++ckgtZnnKRc2m3dj3AXb5Sxd9VE18ASJmKtvhhemSvrf/2wRLGn3SM3D3Z/CcU/QGs9ZEwPvK3BcySlePlfRdgd2ARDnBH8IefJo7EfOQuhsRzG3wDzX5PorjXJ72YVim6z+X/w3s3w4Z3+SwAK18Ca/3hPy/saNr4lz4vWQEuttFIIQFl9i9fzAzXNpEaHuVIyK+pbKfdI4/RHdyN7VmcaZ2cN1RUKhaInUWLvJCbIbOLOGZlEhlo6zEuJCiUpMoQ1e6vYVFjLxAFyoRkXHuyVxmlva+fTTdKqIbesnrZ2nT9+toMXv91Deb17OZfYSxwkKWdBoXDBUzDsAqkBemo8fPKTntu5nAWSDjnmankdnwkJgyUiY6uCze9Kytr4GyAk2n1hcKxob4elfxJBMOJS73lDzgO9TWrVavdLjWTGDKkBq9pzLEbrn01vQ1wGJA+X10MvEHMcIwXV0Qrv3SDi4MtfdqyhrN4rzqnpk6Tmz7OFQM4icSidcJN72vlPiGHJ4Lmw9j/w7FTp8+iPyhxIyJZI2CX/gfSJ3vNP/bHU8K16xnt6e7u0GLFEuFMlM2ZCwaqu3WVb6qW3V0MZlG2TSJymyXdg5KWw+t/wzBR468rOXS0PbILUUZ2/ly9pE+Ccv8Doq+DnW+S7ln22iNpB50jE78tfgckCA04PvB1Nk9pfuw3eukqmGSIvcZBER1c/C5/dDYv/ANZkyDoLQqPd0WmF4nBY/qj8D9n0lmSF+LLsz/DVb+RGksGi38kfiKETdHo+eoq98voW9tc00TcmlMiQIILNJvbXNFHf7OhUyBlir7OG6gDj+sUyLTuBYX1UJYxCoTg6KLGn8IumaYxKi2HR9lJa29qZ0F/sqmPCLV5ib8XuciobW4mLCCa3rIE9FY002dvYW9HoE9nzSMmb9yzcsxPGXQuXvAB3b4Mx18gPuW+Tdl2XiMLBpixufBOsKd49lYbMlVqm9S9LKt7YayUKMvJS2PoeFK6V6NniP7h7mB0tdn4m9YUz7hNzHU/6jBXTjfpimPFrGHMlXPi01FYtuL/rbbfavC+EuktLg0Tgvnuq615/hWuh8HupjzSikn3GQlRfdwrq6mfl+ayHJCXxg9uhIkfmbXobnj1NInjn/1PmG+Yh7W0SibWmSKTJkz5jRLzdsVIixkse6jg2XZfPNSE78Phj+8t5sP4V+ey3vCe9GZf+EfKXwtl/FAEDkDlT3qtgVefHpHgDoMv5Pu0X0hrBYMZvRCyZLXIDonCN/20010JVnne9XneZdDtc9CxExMt37ep3ZR8Gz5GU0oocuOxlmd8ZiYNk/fpiuWESn+meN+evIurXvSTHZO5jHc9fheJQaXPITaCJN8tNoJyF3vNbGyXrod0Otc6oX12xpGk3lkl9blW+pC93Ehkvd4q9tNgwyuqbOVArkT1N00iwBrO7tB7oXMhlJESgadDfmfoZiPS4cF6/eRJRfm6yKhQKxZFAiT1FQMakR7tq7sY7xV5cRDDVjRKlK61r5tllecRFBHPFxHQKqmz8sE9S9gqqbJTWSeTDbNKoafJI/TSZxcXTIDoNZj8s0ZNvHndP13VY9Ftx8FzxWPcHXlMgd4JHzfe+8Bw8VyJNyx6V+qvU0TL9zN+KKHn3WnjhTBEWL84+Os22q/aI6PnoTqmz8o3qgYinyXdKLdTUu2VadJqIk70rO48KtdTDM5PgybESzexOX7RWmxhxPDVRInALH5B0wNJtgddZ9aQIibHXuKeZTJLyl7tYLsp2fCoCcOrPJM3PbIHXL4av/wgf3i7RqztXQtJQiVzuXyfj//4ZKFwNZz0o6/gjaYgco01vSjTNk7piSQvtTOyBjMveKI6V798Mn/1cWh4Mv1hSRg36T5WIWP7SzrdnRBQGnQ1n/g6sie558Zlw+wq48QsIiYK1L4gg/eSnkuZq9FYs2SKPvvV6h0P6ZIn63bJEUm27w4zfyDhHXOw93WSGS1+GH62Gn22CYRf23DhPcDRNO0fTtF2apuVqmnafn/n/0DRto/Nvt6Zp3ShOPcGoLRQhF58pNXcHNkq9t8GeFXLjDkTUgdt505hWled9g8IPRl/ZYakQ/9ElAAAgAElEQVRR7Ku0Udtkd6dlRoawq0TEXmdpnOlx4Sy5ZzpnDlWpywqFonehxJ4iIEbdXmZihOuOZkx4MDW2Vtbvq2bGY8vYXFTLvbMHMzQ1inYdPt0sKZ2Odp1NRbWYTRppsWHuNE4ffiioZv5zq2i2RMOEGyXCVpEjomTZXyTlz5oiYq8zseHJ138UUTDpdu/pfSdARKI0ph59pTsCFRYrph+2SohMgYtfkNqjrtLrDpXGStnHilx4eY7Uug2/CK54M3BUZMpdcNXb3vNTRkktSmei9OtHpM4l2Aof3AprAtTKGRSshseHwMc/kojPTQvh7u2yvqcQ96Rqjwi5CTdJGwNPhp4vEZ9Nb4n4GeSMzMX2l5qupir5bIdfBNd97HaFzJghwvyzu2HJwzD4XPnMOmPavZJG+NZVUmdnb5K/it0yP2FQ5+snD5dUx+IfxNTnjm/hvH+I86RnDWWIVWoG/TmGerJ/vaS1dlZrFxwBY66S3oqvnieCfPXz8No8EfLfPgGmIBHJPYXJJMI4aUj314lMETE37d6O88xBsq1OeospvNE0zQw8DcwBhgFXapo2zHMZZ0/bMbqujwGeBD44+iM9xlTlyWNcprttgqeYy1koN17ALfa8puVBZb6s3wlldS1YQ4IYkBDhaqruNlwJcU3rqq1CRqK10x57CoVCcSxQYk8RkFF9RexN6O++WI0Nt1Bla+WDH4owmzQW3zOdqyb1IyvJCsDK3AqCzXJard1TRXxEMLE+dX47S+pYnV8JwPvri1izt4qi6iZpAh8cKbVpn9wFy/8iNXd3rpSo0dtXSf+wki3ejamb62DHZxKF2fiWXDBPukOiX56YTBLd08zu+iuDPmPhrnVw61IYdRmc/SexqDciLD1Fm0OiZE9NkL82u/Q9vPApSZc7GIw6rpLN/ucXb4Q1/xYRdsdKuVha9DvphRaIFX+VesrrP4Pbv4F+kyC6r2xj2wfiYurLhtflceKtHef1myIpVEseAnQYfI57Xp+xcO2HcNbv4ZIXvaN26ZPkM9/6vjQTP/+JrsVEaBRc+5EY77xwJjySIgZApVtlfnwXkT2Ac/8Gcx6Di56HlJFOAWvtuNzQ82W7gZqx6zoUrZX2BF0x4WaJTjia4ZbFcO8uOXdfPV+ao8/9m5jIHGvC41SKZs9xCpCr63q+ruutwNtAZ2HRK4FjXFh8DKh0Crj4TPk+WlPcqZy6LrW8g86WrJDKPKkLzlsmN49Avp91RXLTpRPKG1pIigxxuW2CRw1ehDualxCheuMpFIrjDyX2FAGJDrfwzyvG8OOZWa5psRHBNNvb+Ta3gnH9Y+nn7NU3MCECkwbtOpw+SNLVSuqaSbCGEBNuodYjjfP3n2zj1tfW0epoZ2Wu2M/X2FolenDTl4AmvchOuV2MJSISxLI+JErEynOnwaMD4P1b4MM74LEseOdqERQf3QFhMXDa3f536swHJXUuMqXjvNj+EOystxhxsUT81r5w2MfRiw2vQflOmPJTidbd+AUkD+t6PX8kDRfheiCA2Fv8exFaZ/5OhO4FT4ElXFJG/UUsq/ZItGr8jTBwmre4OvUuuVv+7T+812lvE4GddZaIQl/MQVIr2VwLkX0kGulJ+inyWZnM3tMtofDjtfDrvXJOdNfVMXkY3Po1TP25CP7aAomOBUf6/8x9iekHk27rWtSMvEwibpsC9JKrLZLWEH0ndP2eiYPk/L7xS0gZIefdlW/J4+m/lIi34kSjL+BpLVnknNYBTdP6AwOBblrA9mLeuExu2HWXqnzJKrAmy/+j7FkSBX84Cf6YJGme2bNEzFXlSd1waz0Mn4cemUruqo9kOz5pnF/vLGXe0ytxtEkteHldCwmRIV6Nzj3TOA0SIjtvq6BQKBS9EXWbVtEpF47xvv6IDZcfu32VNi4b746chVrM9IsLZ2+ljRmDE1mVV0FjaxsJkSHEhFnIK28AoMXRxoaCGloc7byztoC9lTYAdzuH5OHSy6x4g7gGGoIjYzrc8Y2kJBasklqNnZ87jVaulrS7xKFiwBLVVwSfPyLiuzakADFuGXuN9Bdb8x9J8YxOl3H4Rgy7S0s9LP2zNHif9dDhp71ZQiFxsP/IXsFqqSmb9bD7WEQmw6w/iOtp3teQdab3OutfFhfM8dd33F5ksjg5rn9FTGSMY5C3VIw7zvlz4HEOvUDE+6CzD26fIzu2BOkW1kSp7zPMfQpXSxSxJ9OrIhIk5XOzs73H7gVS12Zx2qkbbSPSxndveyMu8X6dNFT63fmKYMWJgr+TMVDO+BXAe7qutwWYj6ZptwG3AfTr1+/wR3ckqNojUbmqfKmP7dY6eRA30P3dnfYLScXXnYZdljCpc877Gkq2ulM4B06nLWYgWfXfyXI+kb0NBTVsLKyhoqGVlOhQyuqbGdE32hXZM2mQ7BR5hgNneLCZ8GB1yaRQKI4/1H8uxUFhiD2ACQO8a5GykiLZW2ljRN9oBiREsK24jgRrMFGhFlfN3tb9tbQ45If6sQW7XOt6pnkS6cd10SAmXf5GzYfznpAf/SCPu63DLjjMPfRgwk3SiPoLjzql0GgxF8mY0fm6dcViLOKZTrriMXGIu/KtnhMeKaPcrpWeLP8LhCeIi50noy6XFg8r/+kWe001UlO37hU57lF9/L/X1J+KIFz5L2m2XZUvDpthsYE/L5BjNfoqadh9NNE0OP1X8MYlXdfrHQpjrpQm4y879z0uwy2Ut7wnUdXkkYe+fSX0TmSKgHSP12lAcYBlrwB+3NnGdF1/HngeYMKECUeg0LgHMGrtKnMl5bIL0xRZNk/SNw3iBsqNHF/iMuXm364vof8UCLHSHDUAK06x5/Ne9c3iTlxW30xKdCjl9S0kekT2kqNCCXKWIximLF31z1MoFIreikrjVBwUseFSV2Uxa4xJ946eDe8TRajFxJCUSAYmRACQGClpnPXNDhxt7azZI26dZw1Npq7ZQVSo3G/was3QXcxB3kKvp4nLkLq1u9bD/5WJYUdUX3j9IvjbIDHRaLV1XM/RCu9eJ+Yy/z4ddn0ld51XPS3RwrRupPZ1l5SR0FAivdxA2iV88lO50z3lJ2IA4klQiJhz7Fku49r3HTxzKnx1HyRkScpnIGL6iVg0Gpk/OU4u4CbeItsNRFCItAAw+u8dTbLOhMk/6trc5VDIPhvSTpFejckjRdzrOlTvk3YK4284suen4nhmLZCtadpATdOCEUH3ie9CmqYNBmKBLvp8HAfkLJQbQ+BtshKINgfU7OueKIzPFEOnylyXkUtjxAAA6kzR7rYpTuqa5eZjWV0LjS0OGlvbSIoMJTFSIvOp0e6G54bIi+/EiVOhUCh6M0rsKQ6KOGex+qi0GEIt3pGH26dn8NlPTiPUYnaLPaukcQLUNtlZu7eKjMQIbpgyAICZQ5IIMmnuNM7eRsoIEUFBISKsbloAp90j/fvyl0qEzNEK2z4Ul017s7QrKForqZoRifDW5WIYEhotaZU9iWHScmCz9CJ8/SLpVzj159Io3B/jb3CP6+U5IkhuXiy1bomDO3+/ab+QyGqfseJUefs3MPOBHt2lHkXTJMU0c2bPbzsoGG5ZJH0BT/2x1GLmLXHWeWodo6oKhRNd1x3AXcACYAfwrq7r2zRNe0jTNM/0hCuBt3X9SNgCH0XsTZJ6P3I+xGd17Jfnj5p9IuC6cNIEvJdxir26cElnLdQ6Zio0OCN75Q0trobqSZEhRIUGERxkctXrgVvsqcieQqE4XlFpnIqDItYp9iYMiO0wLzw4iKwksd4fEC9iL8Ea4spYrLa1sm5vFXNHpjI5I455Y/pw1aT+rMytPLTI3rEgNEr68oHUC658QhwT96+HoDCpj6s/IIYmU38mJjNb35O+daf+uHMb/kMhdTSYQ2Dtf6C5BorWiBHLuGs72Ydo+NH3kv5ZVyyphz53vgMSnwk/Wd/1cicbIy6BxQ/CO9dJX7Ch5x96bafipEDX9S+AL3ym/c7n9e+P5piOGHtXitts9mxJT177omRFBHfSgLzKw4mzK+KdJmIx/V39NCtD0sgG9rQn45tT4ErjrGvhQE0TACnRoWiaxi2nDWRcP/fvm0rjVCgUxztK7CkOigRrCI9cNIJZQzs3zxiVFo3ZpJGVZKWiQe6crt5TRV2zg4kD4ggym3jiCukdFhtucTVqP66Y9bDUiJTtFHv8AxuhvgQues5d02cJldRNz2bjPUlotLQuWPAbyF8u6YRjru56vYgEacqu6BmCguHyN2Dz22LEM+0Xx3pECkXvIWeh3AwbMFWcgb9/BvZ+I6ZNgXC1TMkKvIxBRIK4/Q4931UPXWbpQ50exg+O/sxt1zGZ3HXS9S3ONM76Zmn7A6THivD81Tne/Sdjw4OJDbeQmeiTEq9QKBTHCUrsKQ6aqyf173KZ7ORINj84m4iQIDYV1gDw3+8LMGkw9f/bu+/4ts773uOfBwAB7r3EIVKiKMmSLMmSbMmW91SWnbROnDh2M5s2jW+SmzRN0/YmbtqmbZaznDRpbhLnxpnNsONtyzOyLUuWtScpUdx7L3DguX8cAFygSEmkuL7v18svEgcHhw+OIRz88Pye32/ZyJ5ho/vwncme8hZyk2PISoyeeOfplpTrlMuPTpywj9O02vzXzuxi6bNw8xedD1Ny4eVf6vwnIkOshRNPwpKrneqZBVudFjAnnjpzsFeyHbLWTK7HpDHOmuphPTHb+t1c5/86rcRzT09/eAkCDM3sNXT4qWjpxmVgUXLka4rLZdj+6WtJiNbHJRGZm/SpUKZNnM+5OCYHi7ocqWnnhouyyE4aeVFNjh2q1nkmPX2D3Pnfr/KNZ05M/WDPVc76mQ30wAnu3vkTp6F40fUzOxYRkeGaSqGlzOmHB87656XXOsHeeEsRe9ucFjuhx0xGXNqIQlHtvQM0kcQg7nB2SUhnuBqnn4rmbhYlxRDlHv/jUGqc94z3i4jMZnr3kmmXHDP0jerdW8bOCoZm9qy1/GTHKZpGXZhDdpQ00tsf4GSwZ58ME500PUVIRETOR6gYy/DArfgmaC2HxuORH3Pyeac4S7DYyrno9A+Ef2/sGHlNGTmz10N+agwiIvOVgj2ZdgnRHlwGlqTHceWysSk5yXHOzN7xuk7u/eNhvvd8afg+ay07ShrpHwyw/ajTXuB0U4R2ByIiMvuceArSV0BK4dC2ZTcN3RfxMU+DL8lpbXKOOnqHskUau4aWCfT2D9I3GMBlgsFec3d4vZ6IyHykYE+mnctluOPSfD67bcWIRfIhKbFe+gYDHKxqA+APe6sZGHQar+8+3cJ7f7iTrz11nGeP1gFQ295LT9/ghXsCIiJy9vq64PSOsemYyfmQcVHkYM9aJ9hbdr3TS/UcdQzr4zp8Zi80q7c4NZa+wQD1HX7yFOyJyDymYE8uiH//s7VsW7Mo4n2hRu2vlzsN1xs7/bx0ohGA1041A/BfL5RS1+7nqmJnZrC8WbN7IiKzWlOJ06ImP8IM3ZKroGrP2HV7tQegs/a8UjjBCeryUmJxu8yINXuhGb+ijKFiLkrjFJH5TMGezLjkWGdN357TLaTHe0mN8/I/eyoB2FXWzOLUWBYlRWMM4WbsZU1dMzVcERGZjN5252d08tj70pZBXyd01o/cHprtW3bjef3pjt5+EmM8pMV5RwR7obV8S4e1UshP1cyeiMxfCvZkxqUEg71jdR0sy4zn1nU5PH24jsZOP6+fbmHrsnS+f/dGvnjbGjYVOE3JyydYt7evopW+gcC0j11ERMbh73B+RieOvS812Cy9uXTk9hNPw6L1EJ855iF7ylv45z8ewo5XxXOYjt4BEqKjSI/30dTZN2I7wNLhM3tK4xSReUzBnsy4UBqntc4F+M7Ni+kbCPAvjxymo3eATQUprM1L5u4tBSTFRpEcG3XGmb3GTj/v+O4OvvHMOJXeRERk+vmDM3u+CMFeWrBlTdOwYK+7GSpfGzeF8+nDdfx4R9mISpvjcYI9D+kJvjOmcXo9LjITfBGPISIyHyjYkxkXSuMEWJoex/KsBLYuS+OhvdUAXFqYOmL/grQ4Tjd189KJBn61q3zM8U43dROw8ODOcrr7BvjpK2U8cbB2Wp+DiIiMEk7jTBp7X9JicHlGzuydfA5sYNxgb3h/vIl09PaTGB1FepyXxmEze+3BYyxKiiYmyk1eckzEwmEiIvPFuZe6EpkiSTFR4d9D37a+/4ol7ChpIiPBN2bxfGFaLC+XNvHJX+6lwz/AretyifG6w/dXtfYA0NbTz1//bA8vHm9gUVI0N6/K0kVdRORC8TsVlvEljL3P7XHaMTSVQtXr8Iv3OM3UY1Ihd0PEw4Vm5erb/SMKrIxmraXTP0C8z4PX46Kh04+1FmNMOGBMiPaQmegjN0XFWURkftPMnsw4r8dFvM/53iG0aP76lZkszYjjquJ0jBkZoBWkxdHQ4aepq4++gQAvlzaOuL86GOwtz4rnxeMNpMd7qWnrZU+w2qeIiFwAve3g9oFnnDTJ1CJoPgUH/gd6WmHD++Bt3wSXO+LuofTNhs4zz+x19w0SsE5Alx7vpW8gEH5saM1enM/DvW9bzaduWn6OT05EZG5QsCezQnJsFFFuE+535HYZHvrYVr70jovH7FuY5uzz5ouzifO6efboyGpuVS09JMVEce+tq7lmeQa/++hWvB4Xj+yvifi33yhv4XfB6p8iIjJF/B2Ri7OEpBU5aZzHn3RaMbz5y7Dq1nF3DwVq9e29AOMWaukIz95FkRbnBJqhIi0dvf3ERLmJcru4bmUmlyxOOeunJSIylyjYk1khJdZLQVoc7mFplgnRUURHjf2G94qidK5bkcE/vWUVW5el8/yxhhEX/arWHnKSY7iiKJ0HPngZi9NiuW5FBo8dqGEwMPbDwfeeL+VzvzsQbuQuIiJTwN8euThLSOpS6O92Ar5J9NULz+x1+Clr7GLF/3mCQ9VtY/YLpXvGR3vCywTaevqD9zmFW0REFgoFezIr3Ll5MR/cumRS+2YnRfPjD1xGTnIM16/MpKq1h+N1neH7q1p6yE0euQ7jrWtzqO/ws6OkcfThOFbXgX8gwKlG9e4TEZkyve2R1+uFpBUN/T6JvnrDg719lU57nUNV7WP2ax+2Li8pdmSw1+lXsCciC4uCPZkV3nPZYu7cvPisH3ftCqcX03efLwnPzFW39pA3atH9TauyyE2O4V8fPUzfQICH9lZxvK6D7r4Bypudnn2Ha8Z+aBARkXPkbz9zGmeo117aspGB3ziGV+MMfTkXKsgFTlrn6aau8MxeYrSH5FEze+29/cRHRyEislAo2JM5LTspmk/cUMxDe6v5mwf30NLVR4d/YMzMXnSUm3++dTXH6zp50zdf5BO/3MuXHjtCSX0noQzQQ9UK9kREpoy/48xpnEl5EJ0MK98yqcN1+EPBXi+nm5wv6WrahoK9naeaueYrz4czOBKio8JpnK3D0jgTNbMnIguI3vFkzvvfNy0nxuvmPx4/yoaCCoCI5bRvXJXFzauyePpIHUvT49h1qjkc4KXERkVc+yEiIueotz1yj70Qlxs++jLEpk14KP/AIH0DTvZGw7CZverW3vA+tW3O738I9miN93lIDAZ77eFgr5+c5Oizfy4iInPUtM7sGWO2GWOOGWNKjDF/f4b9bjfGWGPMpukcj8xfH9haSILPw4/+dAqAnOTIvZO+9Z5LeOZT1/C3t6ygq2+Q3+yuwOdxceNFWRyubh+3upuIiJwl/wRr9gCSciFq4uCryz8IQGqcl5bufkrrnXXa1cNm9jqGrekDZ81edJSb6CjXyDV7PqVxisjCMW3BnjHGDdwPvAlYBbzHGLMqwn4JwMeBndM1Fpn/fB43N63Ooj54kR+dxhkSHeWmKCOeLUudb5L3lLdSnBXPxXlJtHT3U9PWG/FxZ8M/MBix6qeIyIIRCEycxnkWQuv1ioK9WDv8A/g8Lqpbe8Jf0oX2ATAG4rxO8lJSTBSt3aHWCyrQIiILy3TO7F0GlFhrT1pr+4BfArdF2O9fgC8D5/8pWxa0t63NAcDncZEe7z3jvqlxXlZmO984L89KYHWO84Hk8BSs23vTN1/ie8+XnPdxRETmrL4OwJ65QMtZ6PA7M3NL0+PD2zYWpNDbH6C1OzRr1x++L97nwRVs5ZMc46Wtp5+BwQDdfYPEK9gTkQVkOoO9XKBi2O3K4LYwY8wlQL619pFpHIcsEFuXpZMUE0VucgzGmAn3v6IoHYAVWQmszE7EGDhQdX7r9gIBy6nGLnaeaj6v44iIzEnt1bD7x86sHkz5zN6S4MwewBVFToZGqCJnZ+8ASTFRZCdGk+AbCuiSYqJo6+kPt25IUDVOEVlApjPYi/RpO5zbZoxxAfcBn57wQMZ8xBiz2xizu6GhYQqHKPOJ1+Pi0zcvn3QLh6uKnWBvdU4ScT4Pa3KSIvbhOxsd/gGshWO1Hed1HBGROen1B+CRT0LDUef2RGv2JikUqC1Nd4I9l4HNwXT86mCwF0rR/IsrCrgm2JYHIDEmitbufpq7nFTOlFgFeyKycExnLkMlkD/sdh5QPex2ArAGeD44C5MNPGyMudVau3v4gay1PwB+ALBp0yYthpJx/cXlhZPe99oVGfziL7ewZWlq+Pb9z5XQ1t0fbsR7tkIV3+o7/LR09ZESd+Z0UhGReaUpmMJe/Ybzc4rSOEPBXmF6HMY4RbgK05zAL7TWusM/QLzPw99cu2zEY5Njozhc3R+u3LkoKfKabhGR+Wg6Z/Z2AcXGmCXGGC/wbuDh0J3W2jZrbbq1ttBaWwi8CowJ9ESmizGGy4vSwimf1yzPIGDhTxPM7h2ubg8XYHnyUC17K1rD97X3Dq0ZOV43PbN7u8qaufjeJ2nq9E/L8UVEzllzqfOzeq/z03eG1gtnoSOYxpkcG0VqrJcl6XGkxXnxBou0gJPGGan4SiiNM1S5c7wCXiIi89G0BXvW2gHgHuBJ4Ajwa2vtIWPMF40xt07X3xU5V+vzk0mM9vDC8foR23v6BnmltAmA001dvOXbL/H7N6oIBCyf+c0+/vWRw+F923uGqsFNV7B3sKqNjt4ByoJNhUVEZgVroemk8/s0zewl+KL44JVLeM9li3G5DIuSoqkOzux1Bmf2RkuKiaKrb5Dy4HtmVpJvSsYkIjIXTGtJKmvtY8Bjo7Z9fpx9r53OsYhMxON2cVVxBi8cb8BaG57xe3Dnaf710SP86bPXcai6HWth16lmNixOpr13gD3lLbR295Ec6x0xs3d0mtbtNXU6605C609ERGaF7ibwB4tctVc5P6eoQEtHbz9ulyE6ysXHrhtK08xJihma2fMPsCQ9bsxjk4Np+Udr28lI8OHzuKdkTCIic8G0NlUXmWuuWZFBXbt/RFXOPeUtAByqbg/P1u2taGV/pbNPwMJLJ5zUz1Dj3uzE6Gmb2WvqctI3m7uUxikis0hTMIUzKnZo21QVaOl1Zu1GV1pelBw9okBLpLYKSTFOsHekpoOcpIkbuIuIzCcK9kSGuWVVNl6Pi9++Xhnetq/CCeqO1LRzoq4TgOP1HbxS2kR0lIvk2CieP+ZUiQ0VaLl0SSrHajvCzX6nUkOHM6PX2KmZPRGZRZqDKZxLrnF+Gjd4x860TeTxAzVjMhc6xknRzEqMprHTj7WWTn//iJYLIYnBYK+qtYccrdcTkQVGwZ7IMEmxUdyyOpuH9lXjHxikqdMf7uF0ODizl+DzYC08vK+aNTlJXF2cwQvH6wkELO29AxgDmwpSaO8doLa9d8rHODSzp2BPRGaR5lIwLii63rntS4BJ9DwFwl+MHaxq46MP7uHrTx8bcf94xVfS4330D1qauvro7Q9EDAiTY4aqK6sSp4gsNAr2REa5fWMerd39bD9SH07VzEr0caCqjVONXbx13SIAevoHWZuXzHUrM2js7ONwTTvtPf3E+zxsWJwCDKV3TqXGTgV7IjILNZVC8mLIWOHcnmRxlldKm1h771McqWnn17srAHhobzW9/YPhfcYrvpIe77S3KWvsAjhjGidATrLSOEVkYVGwJzLKlcvSyU6M5sGdp9lX2Yox8Ocb8qhp62UgYLlsSSoFac6alHX5SVycmwxAaUMn7b39JEZHsSY3kfzUGB7dXzPm+N959gRfeuzIOY8vVKClUa0XRGQ2aS6F1CJIK3JuT7I4yx/3V9PhH+ALDx3iD29UsSQ9jo7eAZ48VBvep9MfeT1eerxTWfNUKNiLNLMXO9TvVG0XRGShUbAnMorbZfjLq5eyo6SJB14uY1lGPJcWpobvL85MYH2+E+CtzUsmK9H5sFHb1kt7Tz+JMVEYY3jLxTnsKGmkZdQM3P+8XskPXjzJy6VjZ/2Gf5MdSXffAN19zj6a2RORGWUtdNQO/d500gn0EnLAEzOpYM9ay/NH60mM9vBaWTPtvQN88bbV5KfG8KtdFeH9QgVaRgsFe6eDbRUipXomDtu2SMGeiCwwCvZEIvjAFYVsXpJKS3c/a/OSWZXjfGhxGViWGc+fbcjjTWuyKUyLJSE6ijivm9r2Xtp7BsIfLN66dhEDATvi2+ne/kHKm50PJV946BD9g4HwfeVN3ay99yl2lzWPO67QrJ7HZRTsicjMOvoI3LfaSd9sq4S+DkhbBi4XZK2CxEUTHuJ4XSfVbb383baVrMxOYHFqLFuL0vnzDXm8XNpEa7fzPtfhj7xmLy2YxnmqKTSzFzVmH4/bFQ4UlcYpIguNgj2RCFwuw9fetY7sxGiuW5lBZoKP1Dgvi1NjiY5yc83yDL5318ZwGfCspGjq2ntp7+0Prw9ZnZNIYVosjx4YSuU82dBFwMLb1+dwor5zRJrnGxUt9A0GOFzTPu64QqmbSzPiaOrqm5ZqnyIik1KzDwIDcOxxKN3ubFtytfPz3T+HN38VgJdLG/nCQwcjHuK5Y/UA3HhRFj/78GZ++ZEtuGWhC30AACAASURBVFyGFVlOy4aaUMP0cWb2UmK9uMyZ1+yBs24vym1Ij1NDdRFZWBTsiYwjLyWWVz53PW9dm4Mxhrevz+Wta3Mi7pudGD0ijRPAGMPlRekcqh4K3k7UO733/uqaIrwe14jA7kiNc1/ow00koXYLy7MS6BsI0OkfOL8nKSJyrkJ99UqehhNPQ1I+ZKx0tiVkQ6yT/v704ToeeOV0uLLxcM8dreeiRYlkJ0WTHu8Lt0bISHCCsvoOPwODAXr6ByPO2rldhtQ431CwFyEgBCfYW5QUg8s1ueqgIiLzhYI9kTMY3sD3829bxd/esiLiftmJ0dS1+2nvHSAxeugDSX5qDM1dfeGg7ERdJ26XoSgjniVpcZxs6Azve6zWCfxqInwgCmkKzuwtD37rrVROEZkxzcFgr2wHnHweim+K2Gqho9d5/xueom6t5etPHWPnqWZuWZ015jGZCU66ZUOHny6/s055vFm79HgvXcG1zJFSPcFJv1+TO7mCMSIi84mCPZEpkJnopHF2+gdIjBn6sJGf4lTtrAiu0ztR30FhWixej4uizDhKG7rC+x6rnczM3shgr0nBnojMhFBBlsxVEOiHvk4ovjnirh29/QC8frolvO37L57kW8+W8M6NeXzsumVjHjM0s+ekxwMRG6bDUJEWGD/Y+/q71vHNd18yiScmIjK/KNgTmQLZiT4GAs76ueE9nfJTRwd7nRRnOoHa0vR4ypu76RsI0NbTT3UwyBse7HX5B/jGM8fDVTobO/tI8HnCRQZCBVteON7Atm+8SFtP/3Q+TRFZ6OoOwYlnoKvBKciy/k7wJoDbO7Reb5Shmb2hYG/XqWaKM+P58u1riXKP/SgS43WT4PNQ3+6ntt15T8xMjLzeLtRrz2UgJsodcR+P2xXx74iIzHd65xOZAtlJQxXehqdxLg4Fey09+AcGOd3UTXFWPABFmXEMBizlzV0cr3Nm9ZZnxVPb1hsuvPLU4Vq+8cyJcHP2xk4/afFeUuOcDzfNXc5M38uljRyt7eBXu8qn+ZmKyIL24lfhN++D+mCv0IyVsOFuWPdu8MZFfEgojf1obXv49+q2Xhanxo5IlR8tI9FHQ6c//GVZ6Muz0UIze/E+zxmPJyKyECnYE5kCWYnDgr1hM3spsU5bhormbk41djEYsCzLDAZ7Gc7P0oYujgYLtVy3IpO+wUB4Ld7+yjZgqLBLU2cf6fE+0oIV5UJpnJXNzjq/n+woY2BYOwcRkSnV3eSkbO590LmduhS2/Tvc+u1xH9LRO0BqnJeAhTfKndm96taecDGW8WTE+2ho91MRfH8bryF6WjDYS4geW8BFRGShU7AnMgVGzuwNrRkxxpCfGktlS3d4TV4ojXNJuvMteGlDJ0drO0iI9nDJYqdZeyiV80Ao2KtzCrk0dTkzezFeN7FedziNs6KlmwSfh+q2Xh4/ONTXT0RmJ2PMNmPMMWNMiTHm78fZ513GmMPGmEPGmJ9f6DFG1BMssnLwd2DckLx4wod09PZz5bJ0XMZJ5ezyD9DW08+iCXreZSZGU9/RS2VLN5kJPqLHSdEMpXGOV4lTRGQhU7AnMgXS433hInTDZ/bAaeFQ0dzDK6VNJPg84TTOhOgoshJ9lNZ3sa+ylZXZCSxKcr65rmnrZTBgw20bQmmejcGZPYDUOG94BrC8uZu3rsshPzWG37xeOe3PV0TOnTHGDdwPvAlYBbzHGLNq1D7FwOeArdba1cAnL/hAI+lpdX4G+iGlANwTz6a19w6wKCmaoox4DlW3U9N25pm6kIx4Hw0dfipausdN4QRIDxZzGa9ap4jIQqZgT2QKRLld4SAsaVSwl58aQ3lzN88fa+DK4vQRRQKWpsfzyP5qDla189a1OeFvumvbeiht6KSnf5CMBB8l9Z00dfpp6e4jO5gymhbvo6mrj47eflq7+ylIi+XKZensq2hVs3WR2e0yoMRae9Ja2wf8Erht1D5/CdxvrW0BsNbWX+AxRtbdDFHBtXmpRRPu7h8YpG8gQLzPw/KsBE7Ud1Dd6mQuhL7cGk9moo+uvkGO13WSnzL+vhnD1uyJiMhICvZEpkgoCBs9s7c4NZae/kFq23u5dkXGiPuKMuPwDwS4qjidu7cUkB7nw+MyVLf1htfrveOSXPwDAX72ajnWwpXF6QBkJfiobO4Or2dZnBrL2rxk2nr6Od3kFDSobu3hrh/u5F3ff4Vf7SpXECgyO+QCFcNuVwa3DbccWG6M2WGMedUYs+2CjW48A37o74KL3ubcTps42OsMVuJMiPawLNOpQBzqL5ozURpncMauuavvjDN7aaE0Ts3siYiMoWBPZIpkJUbjMhDnHbmuJNRrD+Ca5Zkj7rtyWTpFGXF87Z3rcLkMLpchKzGa2rZeDlS2Eud1c8vqbAAeeKWM9Hgv6/KcdX2XLE7hZKOTAhr6O6H79lW2cqSmnVu/s4N9Fa00d/Xx2d8eYPsRZ3Lg29tP8PrpZkRkRkQqGTn6mxgPUAxcC7wH+KExJjniwYz5iDFmtzFmd0NDw5QOdISeYOuE/Evhhi/Ahr+Y8CEd4WAviuKseKyFP5U04TIjC1tFEuq1ByPfR0cLFawarw+fiMhCpndGkSlSlBHHkZqYMaW/Q99Ir8xOGFHIBWDbmkVsW7NoxLac5GiqWno40T/A6twkVmQ7BV2au/p458Y8XC7n+JcWpgDw+zeqgn8nhnifh+goF/sq2nh4bzUBa/nd31zB4rRY1v3zU+wobWRVTiJfe/o4u05n8NMPXjb1J0JEJlIJ5A+7nQdUR9jnVWttP3DKGHMMJ/jbNfpg1tofAD8A2LRp0/RN34eCvZhUuPTDk3pIx7CZvcJgUaqXSxvJTIiesO9dZsLQ+2Ve6vhpnF6Pi4sWJbI8K2FSYxIRWUgU7IlMkY/fUMz7txaO2Z6fGoPX7eKGizLHPiiC7KQY/rjP+dz3j2++iHifh9zkGKpae0YcY01uEl63i9dONZMQ7SEpJgpjDGtyknj2aB3lzd189NoiioMfgDYWpPBKaRMrgrdfLmmkrbufpFiVKxe5wHYBxcaYJUAV8G7gzlH7/AFnRu8nxph0nLTOkxd0lKN1B7MBYlMn/ZAOfz/gzOwVpsXhdhm6+wZZmT1xYJY5yZk9gMc/cdWkxyQispAojVNkisT5PBELDsR6PTx0z1buua54Use5ODeRWK+bL9++lg9ftQSA4qx4otyGK4uH1vxFR7lZm5cEOB+EQjOK6/KTKWvqJmDhjk1DZdG3LEnjaG0Hf9hbhdfjYiBg2X60DoC69l5+s7uCpk5/eP/23n5u+86fwn2xJutobTvdfQNn9RiRhcRaOwDcAzwJHAF+ba09ZIz5ojHm1uBuTwJNxpjDwHPAZ6y1TTMz4qDwzF7KpB8yfGbP63FRmOYEbYsmqMQJkBwbRZTb4HYZFiWdOeVTREQi08yeyAVw0aLESe/7oSuX8oGtS0akOH3kqqXceFHWmGpzmwpT2X26hfxhKU7r8p1lPVcVp7M4bejb8MuL0uBpePVkM+/alMeLxxv5w95qXjrRyB/2VmEt3L2lgH95+xoAdp5sZl9lG08crOWSxZP7cNfbP8ht39nBR68t4pM3Lp/0cxZZaKy1jwGPjdr2+WG/W+BTwf9mh1CPvZizmNkbFuyB02e0tKFrwrYL4PQpzYj34XIZPBOkfIqISGR69xSZZdwuM2YtyxXL0rlrS8GYfUPr9hYPq1R3WWEqsV43H9y6ZMS+a/OSiQk2Jb5uRSa3rM7ixeMN/GFvFR+5einXLM/gkf3V9A0EANhV5nywe6OiddJjr23rxT8QCDeDF5F55Jxm9obSOAGWB/uMTnamLi81lqKM+LMYpIiIDKeZPZE5bFNBKgk+D2tyk8LbspOiOXjvLeFCLiFej4tNhSm8XNrEFcvSKUyPY/fpFj5zywquXZHJs0fr+OBPGnjheAM3rcritVNOsHegso2BwcCkvlmvbnXaQByt7ZjCZykis0J3M7iiwBs36Yd0jprZWxZcM5wziZk9gPvuWI/HFal4qYiITIaCPZE5LCk2il3/dCM+z8hAbHSgF3LPdcu48aIskmKiSIqJ4tGPDxU1uKo4g7Q4L79/o5Kty9I4WNXG4tRYypu7OVbXweqcpIjHHK4qGOxVtfao+IvIfNPT4hRnMZMPvjr8A0RHucLZClcXp/OOS3LZvGRyqaCTSfcUEZHxKY1TZI6LjnKPafcwns1L03jfFYUR74tyu3jbuhyeOVLPQ3urGQjYcIGYN8pbqWrtobW774zHr27tDf9+tLZ9ck9AROaGnuazSuEEJ40z3jf0pU9yrJf77lhPcqx3qkcnIiIRKNgTkbAPXbmEOK+bf/j9AYyB29bnkhbn5aG9VWy770Xe+u0/UdPWM+7jq1t78AZnGZXKKTLP9LROujjLvzxymO89X0p77wCJ0UoiEhGZKQr2RCQsPzWWH/zFJqJcLlZkJZAUE8Uli5PZVdZCtNdNW3c/7/3vnePO8FW39XBRdgIpsVGa2ROZb7onP7P30N4qfrenks7egfB6PRERufD0DiwiI1xamMr/+9Bl+IKVOy8vSufl0iZ+9L5L6ejt584f7uSxA7XcuXnxmMdWtfawMjuBWK+HwzWa2ROZV3paIPeSCXfr6O2nsbOP5q4+PG4XaXFK2RQRmSma2RORMTYvTWN9sF/fB64o5LV/vJGL85K4vCiNjAQfO0+N7e1sraW6tYecpBhWLkrgeG0HgwF71n/717sq+IffHzjv5yAiU2ySa/bKGrsBCFg4VtuumT0RkRmkYE9EzsjlMuFm7sYYNi9J5dWTTTg9n4e0dPfT2x9gUXIMFy1KpKd/kFONXWf99x4/WMNDb1RNydhFZGq0d7TDQC/9vomDvVNNQ//uA5bw+4eIiFx4CvZE5KxsWZpGXbuf003dI7aHeuzlJkeHZwX3nkVD9pCq1h66+gbp8g+c/2BFZEq8tO84AGVdE6dklgW/5EkIBnmhhuoiInLhKdgTkbOyZalTjW90Kmeox15OcgxFGfHE+zzsrWg5q2Nba6lqcY5T3+GfgtGKyFQor3Rm2+sHJm6oXtbYxaKkaNbmO705lcYpIjJzFOyJyFkpyognPd7LzpPNI7ZXDwv23C7Duvyks57Za+vpp6tvEID69t4J9haRC6W+rhqAan/0hPueauqiMC2O1TkK9kREZpqCPRE5K866vTQe2V/DO767gxeONwBQ09aL1zNUeW99fjJHajroCQZvk1HZMtTDbzIze4MBy/3PldDSdeZm7yJy7gIBS1LzPgCO9GePu9/LJY109w1Q1thFYXocq3MSAUhUGqeIyIxRsCciZ+1/3bCMP9+Yy8mGLv7fK6cBONXYRV5KDMYYANbnpzAYsBysbpv0cc822Ntb0cJXnjzGE4dqz/IZiMhklTd3c4Xdw4FAIce7I6dx1rX3cucPd/KpX+2jpbufJemxbCpMxedxsSRj4tRPERGZHsqtEJGztjI7kX//s7X4+wO8eKIRay1vlLdydXF6eJ9QkZY3ylu4tDD1jMf7ypNHWZuXHF73Z8zk0jhDvfzqlPIpMm2OlZVzoznOz7y3h9O1Ryut7wQIf/FSmBZHbnIMB+69Ba9H3yuLiMwUvQOLyDlbvziZxk4/r55sprHTz4aCobLsGQk+8lJieO1U8xmOAI2dfu5/rpQfvnSSqpYeYr1ucpJiJjWzd6SmHVAxF5Hp1Hv0GdzG0rfkRqrbesa0XYGhdgtxXjcAS9Kd2TwFeiIiM0vvwiJyzkKzd//3T6cA2FgwsgfX29bl8MyRen61q5wvP3GUt337TzSMCsxeDK75e6O8lRP1HeQmx5CZ6KO+Y+LZunCwp5k9kanV1Qgtp6HlNNnVT9NuEvDkb6K3P0BLd/+Y3csau/B5XHzpzy5m1aJECtKUuikiMhsojVNEztnK7ES8Hhfbj9YR7/OwPCthxP2fumk5e8tb+exvDwDgdhn+9jf7+PH7L8Xlctb2PXesAWNgIGDZUdLI1csz8HlcnGw4c0P2QMByrDaUxjm5mb0HXi7jiYO13LQqi/duWYzP4z7bpywy/9Udhu9dATgzeJuB15NuYlGKE8BVt/aQGjey396pxm4K0+K4bX0ut63PvcADFhGR8UzrzJ4xZpsx5pgxpsQY8/cR7v+UMeawMWa/MWa7MaZgOscjIlPL63GxJicRa+GSxcm4gwFcSJTbxXffu4E3X5zNt95zCfe+bRUvHG/gp6+UAU41zZdONPDmNYvwul0ELOSlxJCZED1haubp5m66+waJjnJNes3ez3eW8/rpFr74yGG+93zpuTxlkfnv6KOAhbd+g4Yb7uPTfX/N6Y3/QE5yDOD01PzKk0d59mhd+CFlTV0UpsfO0IBFRGQ80xbsGWPcwP3Am4BVwHuMMatG7fYGsMlauxb4H+DL0zUeEZke6/Od1M0Ni1Mi3p8S5+W7793IretyuGtLAVuWpvLfL50iELDsrWiltbufbWuy2VDgpITmJseSmeCjraefypZuvvrksYjtG0IpnFuWptHY6WdgMHDGcXb09nO8voO/ua6IjQUpPHes4XyedkT9gwE+9eu94bGJzEklT0POBtj0AZ6PuYnfBq5mzYpiFiU5wd4f91Vz/3OlfOiB3fx4xykGA5byJmdmT0REZpfpnNm7DCix1p601vYBvwRuG76DtfY5a2138OarQN40jkdEpsEli50gbVNh5GBvOGMM77lsMVWtPew81cyj+2twGbiqOJ0ripxKnrkpzpo9gP984hjfea6Eb2w/PuZYR2racRm4clk6AQtNo3rtWWs51djFQ3urqG3rZX9lW3AGMoUrl6Wzv7KV1u7I/fm+tf0ED+2tOqvzAFBS38nv9lTxzOG6iXcWmY26m6FyFxTfBMDrp1tIjPawLCOetDgvXo+LRw/UEOd1c8PKTP75j4d58lAtfYMBCtMV7ImIzDbTGezlAhXDblcGt43nQ8Dj0zgeEZkGb1qTzX/dtYErl6VPvDNw86ps4rxuvvt8CT979TTvuCSP5Fgv29Zkkx7vY21uEpkJ0QA8ur8at8vww5dO8a3tJ9j2jRf5rxdK6e0f5JXSJpZmxIcLQdS3+9l5somSYAn4b20v4bqvPs8nfrmXLzx8kDfKWwBYn5fM1cvTsRZeLm2iormbiubu8PhON3Vx3zPHeXBn+Vmfi9Dfrm6LXJ5eZNYrfRZsAIpvBmD36RY2FqTgchlcLsOipGishbeuzeFr71qPz+Piq08dA9DMnojILDSdwZ6JsG1svWbAGHMXsAn4yjj3f8QYs9sYs7uhYepTr0Tk3HncLratWRRupj6RGK+bbWsW8dKJRtwuw2duWQHA8qwEdv/TjRSmx5GR4MzsBSx88bbVpMZ5+frTx2nt7uc/Hj/Kln/fzu7TLdy+MY+s4CxgTVsPf/Wz1/nEL9+gt3+QH798iquK03nXpjyeOVLPk4fqKMqIIyk2inV5yST4PPxmdwW33b+Dm+97kScO1gDwk5fLsBZONnSe9bkIBXvDm8OLzCknnoLYNMi5hJauPkrqO9k0rE9mTjCV812X5pMUE8WbL14ULqa0RDN7IiKzznRW46wE8ofdzgOqR+9kjLkR+EfgGmttxIoM1tofAD8A2LRpU8SAUUTmjj/fkMtv91TyV9csJTspesz9oTTOhGgPf74hj40FKTR0+NlalM73XzzJQ3uruO+O9Vy3IpPaNqc4y8ulTbR299Pa3c8XHjpEa3c/f3V1EfmpMfx6dyUHqtp450YnU9zjdnF5URpPHa4jwedheVY8f/2zPdy1ZTF/eKMar8dFY2cfrd19JMd6x4xvPCXBAHG8xtMis5q1ULETlt0ILjevn24ERrZU2VCQjH9gkA3B9O13bcrn929UERPlDn/xIiIis8d0Bnu7gGJjzBKgCng3cOfwHYwxlwDfB7ZZa+uncSwiMotcXpTGL/5yC5eOs84vLc5HrNfNretyiI5yszI7kZXZzn0fvbaIj15bFN43Pd6LMfB4cGYuzuvmV7sryE+N4YqiNFwuw1XF6bx0opFLhhWRueGiTJ4+Usd9d6znyuJ0/uPxo/z0lTICFj5+/TK+9WwJpQ2dbCxIZbJKQ2mcrb1Yayc92ykyKxgDH9sFfqfA0J7yFjwuw7q85PAun7ll5YjX9palqRSkxRLr9ej1LiIyC01bsGetHTDG3AM8CbiBH1lrDxljvgjsttY+jJO2GQ/8JniRKLfW3jpdYxKR2cEYw+VFaePe73YZ/vCxreQGS72ficftIj3eR127n/R4L++7vJCvPX2cOzblh3v5fejKJew82Tzib96+MZ+rijPC5eTvvXU1t2/M41htB5sKU5xgr75r0sHeYMBysrGLmCg3Pf2DtHb3kxI3+VlBkVnB4wWPs/62pL6TwvQ4Yrwj+1EOD+qMMdx/5wYGA0q6ERGZjaa1qbq19jHgsVHbPj/s9xun8++LyNw1ukH7mWQl+mjo8LOxIIX3by2k0z/A3VsKw/dfuyKT/ffeTHTU0IdWt8uEA72QNblJrMlNYjBg8bpdlDZ0cqSmnUf311CQFst1KzNJj4+cqlbR3E3fQIDrV2by7NF6qlp7FOzJnFbW1EVh2sS989bkJl2A0YiIyLmY1mBPRORCyEqI5iDtbCpIJSE6is+9+aIx+wwP9CbidhmWpMdR2tDJvz56mB0lTQAkx0bxb2+/mLesXTTmMaHiLFcXp/Ps0XqqW3v0IVjmrEDAcrqpm6uLM2Z6KCIich6msxqniMgFESrosnESvf4mqygzjt2nW9hR0sTHbyjmj/dcSUFqLB/7+R72VbSO2T9UnOXq5c6H46rWHu75+R7+6Q8HRuxX0dxNe2//lI1TZDrUtPfiH1DvPBGRuU7BnojMeSuzE0mP97ImZ+pm0ooy4mnt7scYePel+Vycl8RPP7gZl4HtR8fWkyqp7yQjwceS9Dh8Hhf7K9t49EANP99ZzukmpzT9Ewdruf5rz3PvQ4embJwi06GsUe0URETmAwV7IjLn3b2lgJf+7nq8nql7S1uWGQ/A1cOKuCTFRrEuP5kXj4/s99nc1cdTh2rZuDgFYwy5yTE8ur8GG6xZ8d8vneTXuyr42M/3MBiwvHC8AWtV0EJmr7LgFxSa2RMRmdsU7InInOdymTEVA8/XmtwkXAbu2lIwYvtVxRnsr2yltbsvvO0bzxynq2+QT928HICc5Bj6BgMUpMXyzo35PLiznL/77X4uX5rG59+6iqauPo7VdUzZWI/VdvDHfWPamIqcs7LGLnweF4sSx/bBFBGRuUPBnohIBEUZ8ez6xxu5aVXWiO3XLE8nYJ0m7n860ch/PnGUB3eW897Ni8MVRHOSnQ/I21Zn89fXFpEW5+Wj1xbxkw9cyk2rnYaBoaIvkfQNBHjvD1/l8QM1I7ZXNHdHnBH81rMn+PSv9zEwGJj08+vtH+T4FAacMr+cauymIC023L5ERETmJlXjFBEZR1qENgvr8pJJ8Hn4P384SFNXHx6XYePiFD554/LwPqG0z1vWZLMkPY5d/3hjuDdZbnIMhWmxvFzSyIeuXBLx7x6paWdHSRN7y1tZuSiRvoEAX37iKNuP1vOFt63iA1tHPm5/ZSt9gwHKmrrD6acT+dGOU9z39HF2/sONpKpFhIxS1tTFUqVwiojMeQr2RETOgsft4poVGTx+sJZP3bScj1y9dExbh3dckovHZViflwyMbEINcMWydB7eW80TB2vxuAw3XJQ5Yp+9wWqfxhje9u0/0ekfIN7noTAtlvufK+Xdly4Op602dfqpaO4B4Hhdx6SDvT2nW+kftOwqa+aW4GyjCMBgwFLe1M0NKzNneigiInKelMYpInKW/u3tF/P8317Lx28ojti/ryAtjnuuLx43BW5rUTqd/gH++mev8+Gf7ubu//saNW094fv3VrSSmeDj/vduYGV2Av/45ot48e+u48u3r6Ox08+DO0+H991f1Rb+/WzSMg8GH/faqeZJP0YWhurWHvoG1XZBRGQ+0MyeiMhZSoqNIik26pwff/PqLL70jospyojjeF0H//74UT796308+OHNGGN4o7yF9fnJXLM8g2uWDzW1vmxJKluXpfGd50pYmhHH9Suz2F/RhjGQHu/jRF3npP5+Q4ef2vZeQMGejBWuxJmmYE9EZK7TzJ6IyAUW5XZx5+bFbF6axt2XF/LZbSt5ubSJZ4/W09LVR1lTN+sXJ0d87BdvW0Nmgo8P/mQ3//nEUfZXtlKUEc+6vORxZ/bq2nv51vYT9AcLuBysdmb1Ni9J5VB1Gx1q8i7DqMeeiMj8oWBPRGSG3bl5MUsz4vi3x47wykmnSuf6/MjBXlFGPI/8r6u4Y1M+33u+lJdKGlmXl8zyrHhONXbRN+AEdF998hh/8+DrAPx4Rxlff/o4//N6JQAHK51g7wNblxCw8Prploh/y1rL7/ZU8tNXyqbw2cpsd6qxm+goF5kJYwsUiYjI3KJgT0RkhkW5XXz+ras41djFJ375BsbA2rzIwR6A1+Pii29fzcW5SfQNBFiXn8TyrAQGApaypi6aOv384KWTPHagllONXTx1uBaAb28/gX9gkANVbSxNj+Pq5el4XCZiKmeXf4C//OluPvXrfXz+oUMcrm6ftucvs0tZUxeFaXFquyAiMg8o2BMRmQWuXZHJgx/aTFKMl3V5ycT7zryk2udx8507L+Ga5RlcvzKT4iynCuex2g4e3FlO30AAY+CrTx3jZEMXt6zOorqtl28+c4L9lW2syU0i1uthY0EKjx2oIRAY2b/vvqePs/1oPZ+5ZQWJ0R6+/vSxST+XkvpOXiltOqu+fzJ7lDV2ab2eiMg8oQItIiKzxBXL0nnhM9cyMDi2cXokBWlxPPDBywBIjx/EZeCBl8soa+ri2hUZ9A8GeHS/05j9n29dQ3ffIN99vhSAtXlJANy1pYD/9Ys3rBBn3wAACw5JREFUeO5YPevykzlS005qnJcfv1zGuy9dzMeuWwbAV548xp7yFjYsTplwXL94rZz/9+pp9n/hZjxji5XKLDYwGKC8uZub1Y5DRGReULAnIjKLxE0wozee6Cg3f/+mlXzv+VJauvv5y6uWUt3aw46SJtblJ5OdFM2P3n8pB6raKKnrZNvFzof5bWuyyU6M5tvPltDS3cfppm6MgcToKD5zywoA3n9FIT948SQ/fblsTLDX0OHn+WP13L4xL9wr8KUTDWxekhqxLYU4jDHbgG8CbuCH1tr/GHX/+4GvAFXBTd+x1v5wusdV1drDQMCyJD12uv+UiIhcAAr2RETmiY9cXcT7r1hCRUs3RRnxdPT286XHjvBnl+QCztrADYtTRgRsUW4Xd19ewFeePEaCz8O/vn0Ne8pbuHlVNqlxXsAJQN98cTYP7a2mp28w3NAd4L5njvPzneXE+zy86eJF1LT1cLyuk9s35l3YJz+HGGPcwP3ATUAlsMsY87C19vCoXX9lrb3nQo7tVKPaLoiIzCcK9kRE5hGvx0VRhrN+LyE6ilf/4Qa87jMvz75rcwEl9Z28/4pC1uUnc9eWgjH7vG1dDr94rYJnj9bT3ttPT98gd1yaz8N7qwH498ePcv1Fmbx0ohGAq4ozxhxDwi4DSqy1JwGMMb8EbgNGB3sXnNouiIjMLwr2RETmMd8kFs0lxUZx3x3rz7jP5iVpZCb4+I8njlDR3APA3opWOv0DfPyGYr61/QTfe76U0oYu0uN9rMxOmJLxz1O5QMWw25XA5gj7/bkx5mrgOPC/rbUVEfaZUmVN3cR53WSo7YKIyLygapwiIjIht8vwlrWLqGju4dLCFFZkJfDwvmqWZcbzv28s5i1rF/GNZ07w2IEari5OD6/fk4ginZzRVXn+CBRaa9cCzwAPjHswYz5ijNltjNnd0NBwXgM71dhFQVqc/v+JiMwTCvZERGRSPnzVUu7aspjv372J++5YT5zXzYevXIIxhm/esZ6/umYpgwGrSo4TqwTyh93OA6qH72CtbbLW+oM3/xvYON7BrLU/sNZustZuysg4v/TZ001dSuEUEZlHjLWTK/E9W2zatMnu3r17pochIrLg9fYPjqm42dTpJy1+6lIAjTGvW2s3TdkBZwFjjAcnNfMGnGqbu4A7rbWHhu2zyFpbE/z9HcBnrbVbJjr2+V4jyxq7CFjL0uC6TxERmZ0me33Umj0RETknkVorTGWgN19ZaweMMfcAT+K0XviRtfaQMeaLwG5r7cPAx40xtwIDQDPw/gsxtkLN6omIzCsK9kRERC4wa+1jwGOjtn1+2O+fAz53occlIiLzi9bsiYiIiIiIzEMK9kREREREROYhBXsiIiIiIiLzkII9ERERERGReUjBnoiIiIiIyDykYE9ERERERGQeUrAnIiIiIiIyDynYExERERERmYcU7ImIiIiIiMxDxlo702M4K8aYBuD0eR4mHWicguEsdDqPU0PncWroPE6N2XYeC6y1GTM9iLlC18hZRedxaug8Tg2dx6kxm87jpK6Pcy7YmwrGmN3W2k0zPY65Tudxaug8Tg2dx6mh8yh6DUwNncepofM4NXQep8ZcPI9K4xQREREREZmHFOyJiIiIiIjMQws12PvBTA9gntB5nBo6j1ND53Fq6DyKXgNTQ+dxaug8Tg2dx6kx587jglyzJyIiIiIiMt8t1Jk9ERERERGReW3BBXvGmG3GmGPGmBJjzN/P9HjmEmNMmTHmgDFmrzFmd3BbqjHmaWPMieDPlJke52xjjPmRMabeGHNw2LaI5804vhV8fe43xmyYuZHPLuOcx3uNMVXB1+ReY8ybh933ueB5PGaMuWVmRj37GGPyjTHPGWOOGGMOGWM+Edyu1+QCp+vjudP18dzpGnn+dH2cGvP1+riggj1jjBu4H3gTsAp4jzFm1cyOas65zlq7fljZ2b8Htltri4Htwdsy0k+AbaO2jXfe3gQUB//7CPC9CzTGueAnjD2PAPcFX5PrrbWPAQT/Xb8bWB18zHeD//4FBoBPW2svArYAHwueL70mFzBdH6eEro/n5ifoGnm+foKuj1NhXl4fF1SwB1wGlFhrT1pr+4BfArfN8JjmutuAB4K/PwC8fQbHMitZa18EmkdtHu+83Qb81DpeBZKNMYsuzEhnt3HO43huA35prfVba08BJTj//hc8a22NtXZP8PcO4AiQi16TC52uj1NP18dJ0DXy/On6ODXm6/VxoQV7uUDFsNuVwW0yORZ4yhjzujHmI8FtWdbaGnD+kQCZMza6uWW886bX6Nm7J5g+8aNhaVI6j5NgjCkELgF2otfkQqf/z+dH18eppfejqaHr4zmaT9fHhRbsmQjbVI508rZaazfgTFt/zBhz9UwPaB7Sa/TsfA8oAtYDNcDXgtt1HidgjIkHfgt80lrbfqZdI2zTuZx/9P/5/Oj6eGHodTp5uj6eo/l2fVxowV4lkD/sdh5QPUNjmXOstdXBn/XA73Gm/etCU9bBn/UzN8I5ZbzzptfoWbDW1llrB621AeC/GUpF0Xk8A2NMFM6F7EFr7e+Cm/WaXNj0//k86Po45fR+dJ50fTw38/H6uNCCvV1AsTFmiTHGi7NA9eEZHtOcYIyJM8YkhH4HbgYO4py/9wV3ex/w0MyMcM4Z77w9DPxFsMLTFqAtlDogY43KjX8HzmsSnPP4bmOMzxizBGfx9GsXenyzkTHGAP8XOGKt/fqwu/SaXNh0fTxHuj5OC70fnSddH8/efL0+emZ6ABeStXbAGHMP8CTgBn5krT00w8OaK7KA3zv/DvAAP7fWPmGM2QX82hjzIaAceOcMjnFWMsb8ArgWSDfGVAJfAP6DyOftMeDNOAumu4EPXPABz1LjnMdrjTHrcdImyoC/ArDWHjLG/Bo4jFNd62PW2sGZGPcstBW4GzhgjNkb3PYP6DW5oOn6eF50fTwPukaeP10fp8y8vD4aa2ddaqmIiIiIiIicp4WWxikiIiIiIrIgKNgTERERERGZhxTsiYiIiIiIzEMK9kREREREROYhBXsiIiIiIiLzkII9kXnCGHOtMeaRmR6HiIiIiMwOCvZERERERETmIQV7IheYMeYuY8xrxpi9xpjvG2PcxphOY8zXjDF7jDHbjTEZwX3XG2NeNcbsN8b83hiTEty+zBjzjDFmX/AxRcHDxxtj/scYc9QY86AJdvkVERERkYVHwZ7IBWSMuQi4A9hqrV0PDALvBeKAPdbaDcALwBeCD/kp8Flr7VrgwLDtDwL3W2vXAVcANcHtlwCfBFYBS4Gt0/6kRERERGRW8sz0AEQWmBuAjcCu4KRbDFAPBIBfBff5GfA7Y0wSkGytfSG4/QHgN8aYBCDXWvt7AGttL0DweK9ZayuDt/cChcCfpv9piYiIiMhso2BP5MIywAPW2s+N2GjM/xm1n53gGOPxD/t9EP0bFxEREVmwlMYpcmFtB243xmQCGGNSjTEFOP8Wbw/ucyfwJ2ttG9BijLkquP1u4AVrbTtQaYx5e/AYPmNM7AV9FiIiIiIy6+lbf5ELyFp72BjzT8BTxhgX0A98DOgCVhtjXgfacNb1AbwP+K9gMHcS+EBw+93A940xXwwe450X8GmIiIiIyBxgrD1TtpiIXAjGmE5rbfxMj0NERERE5g+lcYqIiIiIiMxDmtkTERERERGZhzSzJyIiIiIiMg8p2BMREREREZmHFOyJiIiIiIjMQwr2RERERERE5iEFeyIiIiIiIvOQgj0REREREZF56P8DX9UpT8BdRXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(np.array(history.losses))\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "f, axs = plt.subplots(1,3,figsize=(15,5))\n",
    "\n",
    "loss = train_history.history['loss']\n",
    "val_loss = train_history.history['val_loss']\n",
    "plt.subplot(121)\n",
    "plt.plot(loss)\n",
    "plt.plot(val_loss)\n",
    "plt.legend(['loss', 'val_loss'])\n",
    "plt.title('Loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(train_history.history['acc'])\n",
    "plt.plot(train_history.history['val_acc'])\n",
    "plt.legend(['acc', 'val_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 2)\n",
      "(90, 2)\n",
      "Accuracy on the test data: 0.8222222222222222\n",
      "Accuracy on the training data: 1.0\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('/home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5')\n",
    "# let's predict a TRAINING sammple:\n",
    "decoded_train = model.predict(x_train)\n",
    "decoded_test = model.predict(x_test)\n",
    "print(decoded_train.shape)\n",
    "print(decoded_test.shape)\n",
    "\n",
    "# now test sample by sample\n",
    "cor_test = []\n",
    "L = x_test.shape\n",
    "for sample_no in range(x_test.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_test[sample_no,0] < y_test[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) and (y_test[sample_no,0] > y_test[sample_no,1]))):\n",
    "        cor_test.append(1)\n",
    "    else:\n",
    "        cor_test.append(0)\n",
    "\n",
    "cor_train = []\n",
    "L = x_train.shape\n",
    "for sample_no in range(x_train.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_train[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_train[sample_no,0] < y_train[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) and (y_train[sample_no,0] > y_train[sample_no,1]))):\n",
    "        cor_train.append(1)\n",
    "    else:\n",
    "        cor_train.append(0)\n",
    "\n",
    "# Report accuracies\n",
    "print('Accuracy on the test data:', np.mean(cor_test))\n",
    "print('Accuracy on the training data:', np.mean(cor_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save cor_test as csv for future analysis:\n",
    "import csv\n",
    "with open('img_test.csv', 'w', newline='') as myfile:\n",
    "    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n",
    "    wr.writerow(cor_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = '1+2, see desc.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>algorithm</th>\n",
       "      <th>SHUFFLE</th>\n",
       "      <th>BATCH_SIZE</th>\n",
       "      <th>TEST_TRAIN</th>\n",
       "      <th>DS_FREQ</th>\n",
       "      <th>n_bins</th>\n",
       "      <th>RESIZE</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>SENSITIVITY</th>\n",
       "      <th>dataset</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.961111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.m...</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>0.973118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, D...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.963889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged123_1_64_ICA(-eyes)AUDpreproc.mat, DS2=6...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.881944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2...</td>\n",
       "      <td>0.591716</td>\n",
       "      <td>0.599702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.655556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.980556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.969444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.738889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>1+2, see desc.</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.741477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>1+2, see desc.</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>0.773611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   algorithm  SHUFFLE  BATCH_SIZE  TEST_TRAIN  DS_FREQ  n_bins  RESIZE  width  \\\n",
       "0       TOPO    False          20         0.2        1       8       1     15   \n",
       "1       TOPO    False          20         0.2        1       8       1     15   \n",
       "2       TOPO    False          20         0.2        1       8       1     15   \n",
       "3       TOPO    False          20         0.2        1       8       1     15   \n",
       "4       TOPO    False          20         0.2        1       8       1     15   \n",
       "5       TOPO    False          20         0.2        1       8       1     15   \n",
       "6       TOPO    False          20         0.2        1       8       1     15   \n",
       "7       TOPO    False          20         0.2        1       8       1     15   \n",
       "8       TOPO    False          20         0.2        1       8       1     15   \n",
       "9       TOPO    False          20         0.2        1       8       1     15   \n",
       "10      TOPO    False          20         0.2        1       8       1     15   \n",
       "11      TOPO    False          20         0.2        1       8       1     15   \n",
       "12      TOPO    False          20         0.2        1       8       1     15   \n",
       "13      TOPO    False          20         0.2        1       8       1     15   \n",
       "14      TOPO    False          20         0.2        1       8       1     15   \n",
       "15      TOPO    False          20         0.2        1       8       1     15   \n",
       "16      TOPO    False          20         0.2        1       8       1     15   \n",
       "17      TOPO    False          20         0.2        1       8       1     15   \n",
       "18      TOPO    False          20         0.2        1       8       1     15   \n",
       "\n",
       "    height SENSITIVITY                                            dataset  \\\n",
       "0       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "1       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "2       15           1  EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.m...   \n",
       "3       15           1  Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, D...   \n",
       "4       15           1  Merged123_1_64_ICA(-eyes)AUDpreproc.mat, DS2=6...   \n",
       "5       15           1  Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2...   \n",
       "6       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "7       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "8       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "9       15           2  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "10      15           3  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "11      15           3  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "12      15           3  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "13      15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "14      15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "15      15   [1, 2, 3]  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "16      15   [1, 2, 3]  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "17      15   [0, 1, 2]                                     1+2, see desc.   \n",
       "18      15   [0, 1, 2]                                     1+2, see desc.   \n",
       "\n",
       "     val_acc       acc  \n",
       "0   0.766667  0.961111  \n",
       "1   0.788889  0.805556  \n",
       "2   0.580645  0.973118  \n",
       "3   0.800000  0.963889  \n",
       "4   0.666667  0.881944  \n",
       "5   0.591716  0.599702  \n",
       "6   0.722222  0.591667  \n",
       "7   0.755556  0.655556  \n",
       "8   0.755556  0.666667  \n",
       "9   0.777778  0.708333  \n",
       "10  0.777778  0.980556  \n",
       "11  0.755556  0.725000  \n",
       "12  0.722222  0.969444  \n",
       "13  0.800000  0.733333  \n",
       "14  0.766667  0.738889  \n",
       "15  0.788889  0.616667  \n",
       "16  0.788889  0.666667  \n",
       "17  0.787879  0.741477  \n",
       "18  0.688889  0.773611  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_ = pd.DataFrame(columns=['algorithm',\n",
    "                           'SHUFFLE',\n",
    "                           'BATCH_SIZE',\n",
    "                           'TEST_TRAIN',\n",
    "                           'DS_FREQ',\n",
    "                           'n_bins',\n",
    "                           'RESIZE',\n",
    "                           'width',\n",
    "                           'height',\n",
    "                           'SENSITIVITY',\n",
    "                           'dataset',\n",
    "                           'val_acc',\n",
    "                           'acc'])\n",
    "\n",
    "df_.loc[0] = ['TOPO',\n",
    "              SHUFFLE,\n",
    "              BATCH_SIZE,\n",
    "              TEST_TRAIN,\n",
    "              DS_FREQ,\n",
    "              n_bins,\n",
    "              RESIZE,\n",
    "              width,\n",
    "              height,\n",
    "              str(knockouts),\n",
    "              file,\n",
    "              np.mean(cor_test),\n",
    "              np.mean(cor_train)]\n",
    "# df.to_csv('/home/amplifier/home/NEW_DL/weights/blank_document.csv', index=False)\n",
    "df = pd.read_csv('/home/amplifier/home/NEW_DL/weights/performances.csv')\n",
    "df = df.append(df_, ignore_index=True)\n",
    "df.drop_duplicates(subset=None, keep='first', inplace=True)\n",
    "df.to_csv('/home/amplifier/home/NEW_DL/weights/performances.csv', index=False)\n",
    "df = pd.read_csv('/home/amplifier/home/NEW_DL/weights/performances.csv')\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # to CLEAR all the data in the dataframe and save\n",
    "# df = df[:0]\n",
    "# df.to_csv('/home/amplifier/home/NEW_DL/weights/performances.csv', index=False)\n",
    "# df.head()\n",
    "\n",
    "# df.drop_duplicates(subset=None, keep='first', inplace=True)\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample: 0 Prediction: [[ 0.96013433  0.03986574]] Ground Truth [ 0.  1.] right\n",
      "Sample: 1 Prediction: [[ 0.25404075  0.74595928]] Ground Truth [ 0.  1.] right\n",
      "Sample: 2 Prediction: [[ 0.32668161  0.67331839]] Ground Truth [ 0.  1.] right\n",
      "Sample: 3 Prediction: [[ 0.74391818  0.25608185]] Ground Truth [ 0.  1.] right\n",
      "Sample: 4 Prediction: [[ 0.60861325  0.39138672]] Ground Truth [ 0.  1.] right\n",
      "Sample: 5 Prediction: [[ 0.3831735   0.61682653]] Ground Truth [ 0.  1.] right\n",
      "Sample: 6 Prediction: [[ 0.49828056  0.50171942]] Ground Truth [ 0.  1.] right\n",
      "Sample: 7 Prediction: [[ 0.41862848  0.58137155]] Ground Truth [ 0.  1.] right\n",
      "Sample: 8 Prediction: [[ 0.35809729  0.64190269]] Ground Truth [ 0.  1.] right\n",
      "Sample: 9 Prediction: [[ 0.47699139  0.52300864]] Ground Truth [ 0.  1.] right\n",
      "Sample: 10 Prediction: [[ 0.37415457  0.62584543]] Ground Truth [ 0.  1.] right\n",
      "Sample: 11 Prediction: [[ 0.39874229  0.60125768]] Ground Truth [ 0.  1.] right\n",
      "Sample: 12 Prediction: [[ 0.44023103  0.55976897]] Ground Truth [ 0.  1.] right\n",
      "Sample: 13 Prediction: [[ 0.44007784  0.55992216]] Ground Truth [ 0.  1.] right\n",
      "Sample: 14 Prediction: [[ 0.58019108  0.41980895]] Ground Truth [ 0.  1.] right\n",
      "Sample: 15 Prediction: [[ 0.5972892   0.40271077]] Ground Truth [ 1.  0.] left\n",
      "Sample: 16 Prediction: [[ 0.38457316  0.6154269 ]] Ground Truth [ 1.  0.] left\n",
      "Sample: 17 Prediction: [[ 0.635369    0.36463097]] Ground Truth [ 1.  0.] left\n",
      "Sample: 18 Prediction: [[ 0.48101315  0.51898682]] Ground Truth [ 1.  0.] left\n",
      "Sample: 19 Prediction: [[ 0.7774868   0.22251318]] Ground Truth [ 1.  0.] left\n",
      "Sample: 20 Prediction: [[ 0.66724747  0.33275253]] Ground Truth [ 1.  0.] left\n",
      "Sample: 21 Prediction: [[ 0.57681233  0.42318767]] Ground Truth [ 1.  0.] left\n",
      "Sample: 22 Prediction: [[ 0.65336633  0.34663364]] Ground Truth [ 1.  0.] left\n",
      "Sample: 23 Prediction: [[ 0.55167353  0.44832647]] Ground Truth [ 1.  0.] left\n",
      "Sample: 24 Prediction: [[ 0.61871529  0.38128471]] Ground Truth [ 1.  0.] left\n",
      "Sample: 25 Prediction: [[ 0.70293671  0.29706326]] Ground Truth [ 1.  0.] left\n",
      "Sample: 26 Prediction: [[ 0.8275004  0.1724996]] Ground Truth [ 1.  0.] left\n",
      "Sample: 27 Prediction: [[ 0.91670561  0.08329436]] Ground Truth [ 1.  0.] left\n",
      "Sample: 28 Prediction: [[ 0.58379686  0.41620314]] Ground Truth [ 1.  0.] left\n",
      "Sample: 29 Prediction: [[ 0.54316795  0.45683205]] Ground Truth [ 1.  0.] left\n",
      "Sample: 30 Prediction: [[ 0.46432355  0.53567642]] Ground Truth [ 0.  1.] right\n",
      "Sample: 31 Prediction: [[ 0.48211145  0.51788861]] Ground Truth [ 0.  1.] right\n",
      "Sample: 32 Prediction: [[ 0.21449119  0.78550881]] Ground Truth [ 0.  1.] right\n",
      "Sample: 33 Prediction: [[ 0.21206328  0.78793675]] Ground Truth [ 0.  1.] right\n",
      "Sample: 34 Prediction: [[ 0.22660996  0.77339   ]] Ground Truth [ 0.  1.] right\n",
      "Sample: 35 Prediction: [[ 0.46444872  0.53555125]] Ground Truth [ 0.  1.] right\n",
      "Sample: 36 Prediction: [[ 0.69434208  0.30565789]] Ground Truth [ 0.  1.] right\n",
      "Sample: 37 Prediction: [[ 0.24021384  0.75978613]] Ground Truth [ 0.  1.] right\n",
      "Sample: 38 Prediction: [[ 0.53315288  0.46684715]] Ground Truth [ 0.  1.] right\n",
      "Sample: 39 Prediction: [[ 0.51542366  0.48457634]] Ground Truth [ 0.  1.] right\n",
      "Sample: 40 Prediction: [[ 0.40901107  0.59098893]] Ground Truth [ 0.  1.] right\n",
      "Sample: 41 Prediction: [[ 0.86995429  0.13004577]] Ground Truth [ 0.  1.] right\n",
      "Sample: 42 Prediction: [[ 0.40727648  0.59272355]] Ground Truth [ 0.  1.] right\n",
      "Sample: 43 Prediction: [[ 0.64499575  0.35500422]] Ground Truth [ 0.  1.] right\n",
      "Sample: 44 Prediction: [[ 0.24289264  0.75710738]] Ground Truth [ 0.  1.] right\n",
      "Sample: 45 Prediction: [[ 0.5658223   0.43417764]] Ground Truth [ 1.  0.] left\n",
      "Sample: 46 Prediction: [[ 0.39136311  0.60863686]] Ground Truth [ 1.  0.] left\n",
      "Sample: 47 Prediction: [[ 0.40332261  0.59667742]] Ground Truth [ 1.  0.] left\n",
      "Sample: 48 Prediction: [[ 0.50617278  0.49382722]] Ground Truth [ 1.  0.] left\n",
      "Sample: 49 Prediction: [[ 0.57524401  0.42475599]] Ground Truth [ 1.  0.] left\n",
      "Sample: 50 Prediction: [[ 0.5554865   0.44451353]] Ground Truth [ 1.  0.] left\n",
      "Sample: 51 Prediction: [[ 0.7292304  0.2707696]] Ground Truth [ 1.  0.] left\n",
      "Sample: 52 Prediction: [[ 0.5645346   0.43546537]] Ground Truth [ 1.  0.] left\n",
      "Sample: 53 Prediction: [[ 0.52866584  0.47133416]] Ground Truth [ 1.  0.] left\n",
      "Sample: 54 Prediction: [[ 0.8364619   0.16353813]] Ground Truth [ 1.  0.] left\n",
      "Sample: 55 Prediction: [[ 0.63456202  0.36543792]] Ground Truth [ 1.  0.] left\n",
      "Sample: 56 Prediction: [[ 0.69037157  0.30962849]] Ground Truth [ 1.  0.] left\n",
      "Sample: 57 Prediction: [[ 0.58467048  0.41532946]] Ground Truth [ 1.  0.] left\n",
      "Sample: 58 Prediction: [[ 0.64185321  0.35814676]] Ground Truth [ 1.  0.] left\n",
      "Sample: 59 Prediction: [[ 0.60528386  0.39471614]] Ground Truth [ 1.  0.] left\n",
      "Sample: 60 Prediction: [[ 0.43205687  0.56794316]] Ground Truth [ 0.  1.] right\n",
      "Sample: 61 Prediction: [[ 0.43844175  0.56155825]] Ground Truth [ 0.  1.] right\n",
      "Sample: 62 Prediction: [[ 0.54402059  0.45597941]] Ground Truth [ 0.  1.] right\n",
      "Sample: 63 Prediction: [[ 0.28261474  0.71738523]] Ground Truth [ 0.  1.] right\n",
      "Sample: 64 Prediction: [[ 0.58668691  0.41331312]] Ground Truth [ 0.  1.] right\n",
      "Sample: 65 Prediction: [[ 0.57500339  0.42499664]] Ground Truth [ 0.  1.] right\n",
      "Sample: 66 Prediction: [[ 0.45714927  0.54285073]] Ground Truth [ 0.  1.] right\n",
      "Sample: 67 Prediction: [[ 0.70555103  0.29444894]] Ground Truth [ 0.  1.] right\n",
      "Sample: 68 Prediction: [[ 0.38692731  0.61307275]] Ground Truth [ 0.  1.] right\n",
      "Sample: 69 Prediction: [[ 0.43439525  0.56560475]] Ground Truth [ 0.  1.] right\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0688fa4898>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06970382b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sample_no in range(70):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    print('Sample:', sample_no, 'Prediction:', pred, 'Ground Truth', y_test[sample_no], q_test[sample_no])\n",
    "sample_no = 2\n",
    "\n",
    "plt.imshow(x_test[sample_no,:,:,2].astype('float64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Topographies ')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0688f4c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_nos = [2, 30]\n",
    "I = 2\n",
    "J = x_test.shape[3]\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        axs[c].imshow(x_test[sample_nos[i],:,:,j].astype('float64'))\n",
    "        axs[c].get_xaxis().set_visible(False)\n",
    "        axs[c].get_yaxis().set_visible(False)        \n",
    "        axs[c].set_title('Sample number ' + str(sample_nos[i]) + ' (' + q_test[sample_nos[i]] + '), Freq. band ' + str(j+1))\n",
    "        c = c + 1\n",
    "plt.suptitle('Topographies ', fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Topographies Averaged within Classes ')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z9xEK184gvMdWV/YdmJCxD0NN9JYlPxcQPkNmEwbqVZL8x4ZxbE9MLf8Lw32vZhyLrETvBsIlRWcRPnNnEC7sP53wZaDyeXJyxrz/ncS7CGeDDk9eYwsJX2Q+SmjT5qfmmUDo+XPgIeCPCb3n05P5X0/o6bshY33vTu1LN6HHcUjvieT/U5n3a8AJyf9rDqET593AHcAFkeP4dcJn3LZkukF70/fUn3HfgD3hh11I9AgNyvJk2j+Q3WicTH9v3//LiD+YWt93I+v5VuoNe3BV7B9T8+/Ui5W8MX9Hf6M1oSp+QxLbQXbjejgDe6fyEj0nVaNpGMe+IbWtH86IpxO9LuClke3tjB3PquN9WUa8jnAa0ck49UhOopcc6w1J7Pqcffx1Ms01VbFK45uZbI3Ca3s2/cnHP1bFvp48v41Uo1s1zfeTaaK9FIRLDZxQFDz9fAv9p/KvjszbCCxNpvl2RryS6OUmm0M4Do3Aw8lyPpAR/yL9Dc7xGfF59CeCOyV6hN6HSk/J3WTUukym+WEyTXX9sBmp99ytZJxOT23jcBO9TyXzPp6x7jIhUf9OMs1fV03z8eT5JzOWG62jR9Up05xtSyc37WSc+mTg5/RbMuJDTfTWAPtkTHNxaprc058Z86Z7zd893NdpxrEYTh29K5J5f5ARW5HELt2F5Z2W2p7oZRaR1/tI3hOVNjDzchD99P+M5Z0x9hanEXruILxZtlZP4O6/pL/i9duTUT9ZSoRTn1k+TGigjYEjlSB8+wT4ibv/pCqGh5pBH0weTiX0LABgZpMIA0kgXOD5TMb8jxKSgKFYQbgGbVg8XFBbGawy2G3ZvuFhWH31Mh4lfLgB/HFOva5NhCS5ev4+wikzCL1Eu+ICQkPZQ7jObifJPlbW+0YbeJPxyq2IVrN7XEBIZCE04mmVx5OI3w+z8jo+wcx2GoWW3HXl6KppKy4k9Kp1EXpnduLhYvXLkod/nPNeeZakcv1wJOu5MXk44HWWjGx7e/LwOnf/dcb8z5N/X83XEU5TAbwneU1VL8MJyRbAyWaWLjXxVvovQv+Qu5cz1vEJQs/ncN2V/D6kcqeExCmEz5lfEXqkISRVaa+qWsbudIWHEiADeLgYvXIj+V19n6Zd7mGAQrXrCJ+5w1n+1NTf24a1VaOn8r4+NaP0yHA+byrz9BC+1A7VSN8Tu/uzsWaM6U2HGwpN3lyItbE5I5tD9p4dKmd93iWxSROisZ7JOesrDlxf3ewZ9K0LA4+K06d8s3Hh/G/GZi1MbqW8bQcA9fvNvaFxYaqiSl//OuumTadv8yaAiXUzZ3a3TuqfrlCop1zupVBoKE6YOHP1pHArwQFamUoHOyjRR5G6SycXZ1wKUPb+EdZN1vL6yXUzMw9ea2EabeUtOE69NX5nct2M7wAUqaf0wmcZP4rtJ+Fb1lAKef4s0igNkNQAupgwgmsu4bqJagcNspgf5MRuIoxCbiR8I74zY5o7PT4K7qnk9xQza0iSs6Go3EpoKVDKSTKfSH43Ek63LE0eP0hoaC9JRmT/MOsDcQQqo2p/6+5PVsXuIlxMvS8h0bkuY/4fExquyYRei8uq4hcmvzcTTsOkVY7N/UBewdzKsWkmlIXIKmHz06RRyGVmryW8zo4jnOIZyutsMf1FSwd7T/xTJFbZ1+eANTn7+gzhC16RcIlDpcDqScnvp7KSHAB3bzez2wmnzobjPkLS3URI5Cr/7yXJ7zvpf9+cYmbm7p6UlDopNc3u9rOc2FP0n1If1eW7e6eZrSKcltzV5acbnEFfpyOVlCD5M8JnxwGE3vPqjp1JhEtI1kBon+uop49ejMI3JxYnf7NIPWaW2zy32FTafAtAQx0N3a2FqRSSW+Xmtc/1DS309rRhVmRCy6w/tEzqH5jaM6l/hfXz5tL7fMjl6qZPX9F4QLhTWXHqJEpbtgNcVDdz6kWFlmayS+YNUSne79WwLf4vs20d8Vghpy8tLxb5KOss76Cn3LXLOzmiRM/MziD01hSBr3u8IDAAzYUWTmyJlNwpFrOfB7w7PuK53BE/yD2viH/pWnF6fH3lyQPb0a4nnmH9578GwOQ3vIaWk+NlmDZ9/Qba7/0tdfvMZM4n/3pArG5L/+Hu3byJlZeHMjnTXncGh64/7IXY0nu/RHvbGmbt81IOPvw86u/IrrjyePm3PM8y6q2JE5pDaabNpbUs7Qpfuo9sfTVT6mZlb2ipxG/abmFLaS2TizN5+cRQYu+u7ddR8hcSvT9EdzSMrBqKZ/OCSe3A/yZckD+YwWoSPTHE2ILINGty5k+/0JoZem2rg5PfJxFOhQ9Fum7jPxF63WYS6jxuNbO7CRdc3+HuvxviMneSNAaV3rbq3jySRvwawgX+Z1i4V/OGqmm6zex7hOthshK9ShHmGzKS48qxeSXDOzZpQ3md/Q8hyRtM9ess/XrJe0/kxSr7uoDh7evC5Hfea3wo8ajkf/lrQu/cEjISPXdfYWbPEpKdIwjXZB1Lf8I8Fj16Q3mfNr/Ilr859feUXZx3l5jZ+4D/JAyQGcxkkv1tLrRy+MRT+PX2mylToqO8nQZrZlrdHKY2zGF6/Xwm1u286d7dw1NdS3m25xH66KHNe2i1aUytm83kvqlMZzZ1GR3xv5v6CJvWPYZ7ifYdVYc88g6Z/JpTaT37ZQCUu3pY/fEv07duE6WNWynv6KDx4AU0HbyApsMPpHHRvjslfnl5YHlrvIravrfGE72mn9wfjRVa4jd7saam+Mb0ZX+Xv3fbTfF5cgz71K2ZFQllLM4kfMu+wMKNgvdq5SQpLTTml/pJx8uRRLZQzCvfB8UkT08lZpRSnT11g+TxdRY+B/pS81d92WzPmb0td+H9OgeJ/wX9Sd6thIuqDyL0oFTuOPJ3SXywLyZD3d7Yu2+oRaJ35RvVrhTMrHjhxZGcQno54XZI7YRG4g2EQQcPmNnjZnbuMNYBA2vkPW5mR1X/EK5bg3Dsz48sp3JK9tBkHuCFumEHVk2TNqJjU2Ww19n76U/yfky4XKH6dfaJJF79Okv3+kVfY0lPa1ckPNJ9rWxD3mschv6+jLkz+b0EwMymEQa3dBIGxkB/Mrck+V05bfu0D7xDwu4ylPfpCLp2dsvy019EDolONUJmdjRhIEY94cvoRwlf5h4lJOSt9P+/oOq1PqluOidMOpdZ9QsxCvR4J2t7lvF42//xyy3Xcd+Wm9jSu/NZ7cVNx3B408m0FMIZ6h3lzazoeZxHyr/i7vLNPF6+n96q73l9vbG3SpynEqBCUwNzPvM+Wk8/icLEZry7h66Hn2LrDT9n7Wf+m1V//S/suOu3u7yOWjSSHr3jCG/sZQBmdi3h2q7M0wp7i0oCF0veKsrd/S/6WFJYLuV3GpUIL/qi9X9xK6ZuxddH/hm+SoJXZ+kvfgM+vyYSb1hG676Uf5b8vsXdX581gZnlfPUZYCI736+wIr29I20Md0Xl+F3l7hcPZwEeCnC/x8z+nHDa+STCbXJeTWg0bjKzt7t7VjKVKbk2J33Ls+rTqlneTugpqHYnYXT2vGSZlVOrldO2Kwi3C6xWOTb/4+5D6dEdicrr7IfunnlaIbkNUpb0eyDrVG9l/jrCac+8Zdzh7q/J29BB5o+uPzHS92X1dXonkVyfl+qRvZOQNC8hnNFZUjWv7Oy3hGS5mdCDvbu8l/7biz3s7l8AMLPvAq9196U517kC0Fo3jZe1vpaS97G1bx1b+9axsfd5tvatZVvfeu7f+iOOnXI2U+sH3nJ2XsNi5jUspqvcxtbSerb0rWN97wq66eB5X8Y238RxhddRSG4AVUw6MqZMX8RLjx94+fLKnDNu6ZrGxdaJTH/H65l24Zn0LF9N99Mr6Xz0GboeforSpq1suuL7lLe3M/nsU4Z29GrUSAZjzGPgDXpXJc8NYGbvNbOlZra0p7zrGfyepjgjfKPp27gZ740nWr3r+r8V1U2dmjlNR3v+da3tSf92s/V/9jenLv1pL+00DmTg/OUQT183Wbm2IpF3Tdxg18sNKukVrvQCX58z6UuGuMi8b8rp2HPRqUbfsuT3y0a6IHfvcfdfebhl0hmE/8GKJPypnFmznEL8FHbMcWa20/89uQbz2uTh+RYUCaVTIIyozTr3MWrHJk/SsFVOnQ7ndZZ+veS97hfnxCr7euQgtzqMWZ78Hqw3aKS9RffS35Iuob/3587UNJW/T0mO7Vhen7dHSpLkW5KHJ+zGs19HJL8fpv81AwPb5/Tr/LwX2mcf2D4XrY7p9fM4sPlojp96DidMeRN11oBTZllH/IqRpkIL+9QfwKHNJ3Jy4Sz2T3a1jW1soL/Dt2lCuPa8bccahnAZdy4rFGg8YD6TTj+R2R98O/P/429pWDAHgK0/vAsv7d139BxJopfVdb3Th7m7X+Hux7j7MQ2FoXbM7LmaFi0Mf/SV6Hz48eh0bY+EM2LW0EDj3J3yYwC2b19Fd3f2ILoe72IbYYDIlGL/dXjNhRYaLQxCWdcTv2xpc98aKm/sKcX+64qLYSBT5VzuWdEFxEdh7ooG+l9HmV/hzGwq4fKAocg7hVmJddM/0GEs3J78PsLMRjWhcfdngcrAoMW7mEBUTtt2E+6qYbEfQgNR+SR+e+bS+k/N7ktIIl9D/wXrV2fO0X9sXmZmR0SmGQ1Zo5gHMLPpRO4FTbjAv3IbqOG+Jyr7OoP+e53uil8lvxfHkgQzmwi8dhjLfoG7d9F/inYJAwdiVKZ5jpBETCOM8K98UxxOj176upG8bpxakB6V/Z8ZI14z7WJSWHmtZ30WVNrndE/+jS+0z4OcOJlcP5OZDfsB0N6X34lQYWZJohd2td3727MpM8L3or6edjavH/alpZmKk1tofW24M5t3dFHaOtTLYmvTSBK9VYQP9Yr5aJgzjYccSHF66KHb+oOfUe7cuReza/mztD0Qrh1oednLsbrIGXQvs+wPt2SGnvKH8eR9O7duYFWLeXWLANjQu4KNPSt3mrfkfTzZGT7L66yB2fX9HTtJrlC5J+5fREpmHMrQBk/kcvdOQoFaCEWlq9djhJIZ8eHTA73bzF6asZzD6C/f8T13H8tTt9+m/3TyN5PENaq6x8zCjb7zVK6B2zqU0c3JMhvpv3/mLe6eW5LD3VcT6lxBKAe0UwOVDAqpfLN5G/2NycNZJW8SVxEKrRrh2ORex5bVmzgUHu6ZWfkfxF5nXyVygX3SG1lJZN9qZjuNxjKzOcRLIUHozakM1viymc3PmbZSlibtOvp72v41ktT/I2Ek5Ujdmfx+PWEEePr6vIpKUle5fnZZconBrkpfajFnGPPvMdz9PsL1cxBGYV9VVUppADOrM7MP0l/2Zygq3+4PZWCP/XxgtZn9JZHSMO5OZyk/IarE6wv9m91ZbhtwbfhO87xwwxSoT902ftqsQ2ieGAazP/PozXR35iePvevWD3y8Ov+MV9+6ZPyLGYUJIxmbs+cbyTV69xO+Xe5PuD7nfAZ+U8hg0dG1eaNnizOmR2NPfn6ndv0FbzwpPhrmyJwcd3nbwPVt3L6Bykts2vR2Zu8buwwM6oslJn/gFH7/yZvpW7uBLV/8Tw74k5NpPWQfJk/bzup7nmPFVfdDuUzjlCZO+cQimqb9gZ4T+v8VxXf3wNPQvE8r69c+xL1HwJRXnRpKsmzZzNa776T9wXA2ad8JL6F1yr4DtuGA8gms2fAsnaUd/K7tdg6seymzCwsoWj07ypt5uu8htnvYh0MmnECxLv1ZYxAuSj+LcOHuXWb2EULh3gKh1+NyQlK/UxI4DDcQ7lN6rpn9F2GAzxrCKaiPJdvxOOGDazBrgDvM7GOEgR0k2/t5wrVTHfRfcD8m3L3NzN5DKO9yJPA7M/sC4b6g6wnJxf6EItrnJ9t4cmoR95rZw4QP+3vpL2i6H+E6qUoP27UM3RvoHxxwTd6EKZWbnO9PuAtC1jV33yWMuj2P/hF/0esG3X27mf0pYd+OBh40s88TXmuVY3MA4XhcQHKLsiFub7UbCNfpnWdmXyE0uGsIr6tLCb3Gea+zzxHqVU4FfmZmfwf8hNAjdTLhriXdhAvgd7oGyt1LZnYxIYnajzCQ5p8JCeDqZJ79CHdQeDOhYd4/Nf9GM/snwv0DTU5wAAAgAElEQVRdzwB+ZGb/SEge5xHuFf2nhJ62hbt0ZHZ2F+FG8JUPlvT1eRV3Eu55uiA1z3A8QjiG9YSbxH+CcDzKhBy71s65/Q3h/3oGoVf9lWb2ZcKdGZ4n9GrOJ/TMvhtYRCi5M1Q3EMrr7EMoBXUe8H+Ez4r7CJ8Xma9zN+eebdcxo2FfZjXsz+T6mTQWJuKUad+xnhW9T7C1FL6X71NYgHeE8U/r69fy7Pal7DPhIGY0L6Clfjr1hUZ6y9089sYiW2+5HzaA1dfT9qkzeWpqGLn7Ryc8yMyHj+bm9/2c7q6tPPzgv3HUOw5jwSvmsnDGREq9ZdrXtrPukQ08e8cK2te3c/7Nb2JdZ/guc+cnr6HQWMe8Uxcx9aVzmDh3Et3U072xjXV3Pc32ZDTsjBMXcvCBoXRhS3382vkFh2yOxlpOjs/3rTecFI0d9tmscoxBeV08UbXW1khgeGOMhp3ouXufmV1CqDlUJBSrfXS4y6slM046kMV/dRpPfeV/aX92I498fOfybo3TmnnFv5xJ07R4Z9U+J+9PqbuPFT96iPaHH9op3rxoMYe0n7zT83WFBl4+/Q0sXf8Dumjnqb4HeSqj9Nii5pczr7G64wDc/QkzezvhlNt8dj71tp7wIX9P8ngkdd0+Tbg1z2JCQ/xnVfErCSUcvjiEZb2TcKeG/8mIdROq5eeW4dgd3P1mM3srYeTsAkIyG/OLqsdGOBWadzXxPYSkeKgqyWEbYQTqUNxI6F2tT+bPSvSuJiR6lcKPziCJpLt/z8wuIBTgXkh/j0eW23Jig/kkofE8kDDS+y+q4v9DKE3yz5HtXG9mbyQkd9MIJYHS2gg9YLcSkras4q/3mtmZhKR8JiE5/EJke7NKtVxGaKDfCvxR8pP2I8L/5fORZQ7VrxiYsN6ZMU31c1nTDMrdt5nZtwi3LbyQ/gE8EAYwxGtZ7YGSEjZnA58l3A5zIfmFtjeya//P6wmvjzcSvtzekIodRDimHyV80dx5+3A29KxgQ8+KrDAAs4sL2K9u4ImGnnInK9oeYkVbVTt1VfK7ro7p73grdVMHlmfZ54iZvP5Lp3L7pb+ka0s3933pd9z3pezr/ybvt3Nn9bYn1rPtifUZUwct+0/nsA8OVme/9o2ojp6730L/BaaSMu/co5hy1HxW3vgAWx9cSffGNor1BVrmT2bOyQtY9JaX0tCaX4IF4Mi/PZXO5iPZ/ut76V2/Du8rUT9rFq3HHMukE06i+J/Z4wpa6qZyUuNZrCg9yfrSSjp8OyVKNNLE1MJsFrQcyeS6WEkycPcbzexJwq2NTiWU9VhNaMg+x8BrNId9AYS7bzazEwg9bZV7f24jfNP/urtfbWYfGOLiHiGUIrmU8I15DqF+1S8It/ca3QtBdoG732BmdwJ/Tug9OojQq9ZB6KW7j5CkVn8Av5LQK3kaIUmZQ+jp2kgY3XoN4bZuQz1tO53+ax5vTk6fD2X7N5vZbYRk5i1m9v7qXh53f9bMfkX/xfn3DOV0nrtfa2a/ICRfZxCS/sqxeY5wbL5H6PUYlqRH7HhCwncuoSj3NsJF619LtiHv1CvufldyacAnku2cQfjScweh3uFT9J/+zXxPuPsvzGwRYXTkWYTb800hfBFZRbh+9Cb6e6TT85YIg11uJVyK8FJ44VaLVxG+QHywer5dlRQGvp/QcwsZSZy7Lzez5xh5jx6E//szhJ7MxYSRxSMpj/KilpTh+WjSk/dOwvt7EeH11Eu4nOV3hMT9RncfrKROetluZm+mv5zQwYTX1jOE0///Tnjd7MQwjp9yLpt6nmdz72o6SzvoKXdQpkyjNTOpMJ25dQcyq27gGaR9W15Cc91kNnWtYFvPOrpLbfSUOilYkcLcGTQdtIjWV51M/YydC/8DzD92Hy686Rweu+kpnrvneTYv20b3jh6KDUUmzprAjEOns/DUfdn3pIHXsh/72TNZ/5uVbPztStpWbKV7Uwd9nb3UtTbSesAMZp2yiHl/dDiF+lq/9HNwNoRi8qNmct1MP3HyGzNjwz11+/jH9o3G8k7dlnbh1G3a5q78y8Xqi/EzDdOb4u/XnlJ/zn3Xu69l+9Ob2P+8I3jJ+1/Jo/fGz5Ae9B/xDqrS5i3RWGFC9n7cu+0mtvVtGPRD1syWAP+bPFzkGbdKGwtJDblKFcmpnnHLOZGxkCRwlTuoLHF3lRuRPcbk+pl+4tTsm6pUTtNmKUzPTuAAHv9I9kBDCKduY3o93j5XTt1m6Srl913lnrqdkHPqti7n1O2vx+7U7b2bb2Rb7/pd/hKke93KcFUuat9Kf+kIkb1Z5T1RJvs2bSIiY06JnmRKKuLHYi8lnIKEcGph7LqFRcbJIO+J+fSPQL3N3cf7xvUiIsAIr9GTmvY1MysTRk3+lnDN0RzCdUWXEq5F6mTkF36L7Cn+3swWEwYI/ZpQGmYGYZDHJwkDLMrA34/XBoqIVBvbRM8dj9warDgtXl5s9Vfj5+Q/d0i80P3avnhZrrZSvDjkjsZ4rKecf2FnU7E3GptYF7+lWZ31X0tfsNBBVlcoM6Guh9e+Ol6F/Kdz47U0D/1ofDvL2yKl0/o754qE+86elz0hncD57v50fC0iNcUII8RPj8RLwJ8l9dJE9ixlx7uz26i86/DWfCV+V75PHXRTNPZ01+xorK0UH6jYVBdvYwdrnytta5ZeH96gjS+f9u1o7N8PjNcvb3h3fDBkeXPkUvPy8O4goh49ibkMeJJwC6T5hN6KbsIoyNuBf3f3+Bh8kdrzVUKB39MIo01nEpK75wkDk/59PEd2i4hkUaL3InT6Vdkjn8aSuy9lbG8VNizu/gNquBSDvHi4+2OEuo+fHu9tEREZKg3GEBEREalRSvREREREapQSPREREZEapURPREREpEaN6WAML5ejtzr7w+eOiM730YN+GI1t6IuXXjmhOX5Xru3leAmVkWit74rG5jXGb0m2rS9+a7Vfb1oYjR2x4Plo7PG/jN86beEnsytA+DCHb4uIyJ7Ly2XK7dnt85Ofi5fx+vjim6OxDX3Zt/ICeNmE7Pu0A2wqtURjeQrk1+6f1hC/1erCpk3RWEsx3q4vbY+3sx9YcHs0dsmn3h6NHfQnqzKfH277rB49ERERkRqlRE9ERESkRinRExEREalRSvREREREapQSPREREZEapURPREREpEaNbXmVSRPoPvnYzNh5r8ou9wGwrdQcjR3b/Gw0tiin1Mnmcjz2+0JPNDaYlmJ3NLagYWM0torp0VhjsS8aW7FtSjQ257g10Vjvq4/OfN7v+1V0HhERqU0+eQLdr8xuF84/+d7ofNtK8dJgx0+Ilzjbp9geja3MKZu2vGtGNDaYxkJvNDajbns01pQzX3ddfTSWV3rloyfeGo1999yzMp8v/yL+f8ijHj0RERGRGqVET0RERKRGKdETERERqVFK9ERERERqlBI9ERERkRqlRE9ERESkRo1peZWeKfDcOZYZe3Pjpuh8RcrxZXoxGttQyl4XwLpSSzzWGx/avaOvMRoDaC3F46t7p0ZjK7qnRWPbu5uiMTOPxoqF+HFb+eqGzOd7H4sfMxERqU09U+C5N2a3J+fntM9Tih3RWF4JlQk5bVevx1OTtpw2tqMvu12r6C7HS6F0eXzecjneJ9aVs631VhpW7Plzskuq9T4QP2Z5RpTomdlyYAdQAvrc/ZiRLE9ERERGh9pogdHp0TvV3eOVgEVERGS8qI3ey+kaPREREZEaNdJEz4HbzOy3ZvberAnM7L1mttTMlpba4ufrRUREZFTlttFqn/cOIz11+wp3X21ms4DbzewJd787PYG7XwFcAdC4YP7wriQUERGRXZXbRg9onxeqfa5VI+rRc/fVye/1wE3AcaOxUSIiIjIyaqMFRtCjZ2YTgYK770j+fh3wD7nz1JdpnpE9FPvhtvnR+TZ1T4zGDmmNlyVZ1LQuGttamhCNPdcRX+ba9njpFYCyx8uTtOeUZlnX1RqNlXKWWciphrJ2W3yZ5ch/PmdVIiKyh9jVNrquocTsuVszYzvK8RJf23La0rzSK9OKbdHY2r7J0dj67ni7tq4jXjYNoK4QL2kyvSFe/mxqXfy0dmuhKxrb5vFj85sd+0dj9c29mc9bYezLq8wGbjKzynKudvefjmB5IiIiMjrURgswgkTP3ZcBR47itoiIiMgoUBstFSqvIiIiIlKjlOiJiIiI1CgleiIiIiI1SomeiIiISI0ajXvdDpmXjd6e7FWubI8Pbe7sq4/GHi7Pi8a6YzVEgJZidzRWZ+VobDBNxb5hzdfe2xCN5ZVsaeuMl2zpXhsf2j1xQ/YyC8PbfBER2ZM5lMrZfT9tpXh5lZLH+4se6YqXTTugYcOwllkgXmKkp68YjQF0leK5xLa+5vh85fh8k4ud0djDO+L5yVObZ0RjPe3Z+YCXh1f/TD16IiIiIjVKiZ6IiIhIjVKiJyIiIlKjlOiJiIiI1CgleiIiIiI1SomeiIiISI1SoiciIiJSo8a0jl6ejpw6cl198c3csKMlGquzUjR2xsxHo7GDm9ZEY1unxGvTAezbsCka29A3KRpb19UajT23flo0ZqvitX9a18Rr7jSvz64VWOiJziIiIjWq7EZ3pA7d6q4p0fnqC/F2dl13vM2rz2mfFzeujcZeMfXpaGxW045oDGBWfTzeVOiNxh7Yvl809ovNB0VjXb05tXyb4rV8o6UC4yUEc6lHT0RERKRGKdETERERqVFK9ERERERqlBI9ERERkRqlRE9ERESkRinRExEREalRL5ryKkdMez4a++lTh0Vjve310diW1ngplIMacoZvN8WHWbeVc4ZEA5vL2WVLAFoLXdFYS13Oclc3RUPTHonPVtcVH77etKkv8/lC7zDHb4uIyB7L3ejuzm5PFzTHy4bds2FRNFYsxNvDuU1bo7Hpze3R2L4T4vMd2LA+GgOYWcwvvxLzaNvcaGzLtonR2MSJ8TZ/8ZQN0dj6J2ZmB8rxkml51KMnIiIiUqOU6ImIiIjUKCV6IiIiIjVKiZ6IiIhIjVKiJyIiIlKjlOiJiIiI1KhBy6uY2TeAs4D17v6S5LlpwHXAQmA58BZ33zLYsuq3Fph1U2NmbJ9Pbo/ON31KWzS2tn1qNLZyQzz2xLw50diS5niplwmFeDkXgEd64/Gyx/PqOU3borG6jviQ6qYt8RIqpaac+ZZnD5cv9GSXXRERkRef0Wqj67YUmP797JJksz4Tb5+nNnVEY+s6WqOxu9fHy7JMLnZGY2e1xmuKHVC/ORoDmJbTtTWjGC+TclTrymjs4cnx0iuNdfH2tKOvIRrb72fZ7frGbcMrfzaUHr0rgTOqnvsYcIe7LwbuSB6LiIjI2LoStdGSY9BEz93vBqrT5HOAq5K/rwLOHeXtEhERkUGojZbBDPcavdnuvgYg+T0rNqGZvdfMlprZ0t7u+ClYERERGRVDaqMHts/xu1HInm23D8Zw9yvc/Rh3P6a+sWV3r05ERESGYGD7HL9GTfZsw0301pnZHIDkd/4N5kRERGSsqI2WFww30fshcFHy90XAzaOzOSIiIjJCaqPlBUMpr3INsASYYWargE8DlwPXm9l7gBXAm4eyssKWdlpu+HVm7JvHnBadb/aR66Kxus3xcibF5+PDl69oOTkaO+Cw70djhzXkX2dY9uzyMQAFK0djx7Y8G419f97R0Vj7PvH9n/pkVzRWXhv5gter8ioiInuK0WqjC1vaab3h/szY5SefHZ3vuKOfisaWL4tevk/d1nj68d3D4u3oYYfGy58d35hf5a3e4n1b3d4bjZ3e8lg0tmLutGissRBvT6+7LScHue03mc9bKV7KJs+giZ67XxAJvXpYaxQREZFRoTZaBqM7Y4iIiIjUKCV6IiIiIjVKiZ6IiIhIjVKiJyIiIlKjlOiJiIiI1KhBR92OJisUKDRPyIwd9MVl0fmWv+vAaKzR4+tr2hgPtpenR2P/OvH0aOxd8/8vvkKg3krR2JRi/BYzC+s3RmOvP+KRaOyuJ18e35ZH4seUusi/3uKziIhIbbJCgcLE7Pb50M+vis73wF8cHI019cQblOa18fa5rS/ePn9jarwsycx5t0ZjAHOLPdFYXts9t1iMxj49695o7Izfvy0aO+hLK6KxcuT/YG3D65tTj56IiIhIjVKiJyIiIlKjlOiJiIiI1CgleiIiIiI1SomeiIiISI1SoiciIiJSo8a0vApmWGNjZqi8fUd0tgVfezIa6zp6/2isd2J8SLSV4jnuU4/Pi8aurTsuGgM4cVq8pMmRzfHh1Mv7JkVjt/7yZdHYId+Or6/c0xuNWUN9LBKdR0REalTBsMaGzFB5y9bobIv+I97OtB+7MBrrbYm3wV6Ix5Y+HV/m9yYeE40BvGHy76KxIxri5VXKlKOxY+/902jsgL/dFl/m5i3RmE2cmB3oUHkVEREREUlRoiciIiJSo5ToiYiIiNQoJXoiIiIiNUqJnoiIiEiNUqInIiIiUqPGtrwKDqXsIczxch/g3T3RWOPdv4/GmmfPjMbqF8+KxnpbskvAADxcipdzAXh05pxorK99STS2383xsiaLfnxfNFaeMCEai5WyAaL/B/D4PCIiUpscKGWXEbHmpvh83d3R0IT/fTQasznxNrju8Hjb3fNkvF271vLLq/xynwOjsc3t8bZ00nWt0dh+1+W0z7EyKeSUUAHo68t+3ofXPqtHT0RERKRGKdETERERqVFK9ERERERqlBI9ERERkRqlRE9ERESkRinRExEREalR5sMcrjuslZltAJ5LHs4ANo7ZyvNpW/otcPf42HYREak5Ve0zjH9blKZtCYbVPo9pojdgxWZL3T2/6M0Y0baIiIj0ezG1RdqWkdGpWxEREZEapURPREREpEaNZ6J3xTiuu5q2RUREpN+LqS3StozAuF2jJyIiIiK7l07dioiIiNSocUn0zOwMM3vSzJ42s4+NxzaktmW5mT1iZg+a2dIxXvc3zGy9mf0+9dw0M7vdzJ5Kfk8dy20SEZG9l9rnAeuviTZ6zBM9MysCXwHOBA4DLjCzw8Z6O6qc6u5HjcOQ6SuBM6qe+xhwh7svBu5IHouIiOxWap93ciU10EaPR4/eccDT7r7M3XuAa4FzxmE7xp273w1srnr6HOCq5O+rgHPHdKNERGRvpfY5pVba6PFI9OYBK1OPVyXPjRcHbjOz35rZe8dxOypmu/sagOT3rHHeHhER2TuofR7cHtdG143DOi3jufEc+vsKd19tZrOA283siSSLFxER2Zuofa5B49GjtwrYN/V4PrB6HLYDAHdfnfxeD9xE6LoeT+vMbA5A8nv9OG+PiIjsHdQ+D26Pa6PHI9G7H1hsZvubWQNwPvDDcdgOzGyimbVW/gZeB/w+f67d7ofARcnfFwE3j+O2iIjI3kPt8+D2uDZ6zE/dunufmV0C/AwoAt9w90fHejsSs4GbzAzCsbja3X86Vis3s2uAJcAMM1sFfBq4HLjezN4DrADePFbbIyIiey+1zwPVShutO2OIiIiI1CjdGUNERESkRinRExEREalRSvREREREapQSPREREZEapURPREREpEYp0RMRERGpUUr0RERERGqUEj0RERGRGqVET0RERKRGKdETERERqVFK9ERERERqlBI9ERERkRqlRG8cmNlsM7vbzHaY2b+O4nL3M7M2MysOYdolZrZqtNYtIiLyYmVml5rZ14c47WfM7Dsvsm260swuG856xjTRM7M7zWyLmTWO5Xp3l2R//mQYs74X2AhMcvcPDeVFZWbLzew1edO4+wp3b3H30jC2SURE9hI12h53JZ0dG83s+2Y2pxJ398+5+3Da66x15bbHQ+1IGc1tyjNmiZ6ZLQReCTjwht20jrrdsdzdYAHwmLv7aC1wD9p3EREZRzXcHl/i7i3AIqAF+Jdx2IYhGcvjM5Y9eu8E7gOuBC6qPGlmJ5jZ2vTpRjN7o5k9nPxdMLOPmdkzZrbJzK43s2lJbKGZuZm9x8xWAL9Inr8hWea25BTp4allTzezH5nZdjO738wuM7NfpuKHmNntZrbZzJ40s7cMZ2eT/fqVmW01s4fMbEnyfGX/P5J88zgLuBR4a/L4oYxlfRvYD/hRMs1HsvY99VxdMt+7zOzx5BTxMjN7X872ftTMnk+mfdLMXj2c/RYRkRe9mm6P3X0r8APgqNSyBpw5M7N3mtlzyX58MqOXrsHMvpW0iY+a2THJfDu1x+l1m9lE4FZgbhJvM7O5yfpvNLPvmNl24OKMbYoeq6p1zDCzHyf5xWYzu8fMovncWCd6301+Tjez2QDufh/QDpyWmvZtwNXJ3+8HzgVeBcwFtgBfqVr2q4BDgdOTx7cCi4FZwAPJOiu+kqxvH8ILPP0inwjcnqx7FnAB8NXYwY4xs3nAT4DLgGnAh4HvmdlMd7842Z4vJKdZfwx8DrgueXxk9fLc/R3ACuDsZJov5Ox72nrgLGAS8C7gi2Z2dMb2HgxcAhzr7q3Jspbvyj6LiMgeo6bbYzObDrwJeDoSPwz4KnAhMAeYDMyrmuwNwLXAFOCHwJdh0PYYd28HzgRWJ/EWd1+dhM8BbkyWmT4OFXnHKu1DwCpgJjCb0FkUPUM4JomemZ1MOF15vbv/FniG8OKpuIbwT8TMWoE/Sp4DeB/wcXdf5e7dwGeA82xgt+dn3L3d3TsB3P0b7r4jNf2RZjY5+Zbyx8Cn3b3D3R8Drkot5yxgubt/09373P0B4HvAebu4y28HbnH3W9y97O63A0uT/RptA/Y9zd1/4u7PeHAXcBuhu75aCWgEDjOzendf7u7P7IZtFRGRcVTj7fGXzGwb4Rr4GcBfRaY7D/iRu//S3XuAT7FzovTLpA0vAd8GduqEGYZ73f0HSV6Q1WZnHquM5fQSEtQF7t7r7vfkXQo2Vj16FwG3ufvG5PHVpDL35PGbLFwU+ibgAXd/LoktAG5Kuii3Ao8TEpPZqflXVv4ws6KZXZ50LW+nv2dqBiH7rUtPX/X3AuD4yrqS9V1I+LaxKxYAb65azsmEf8xoWxkLmNmZZnZf0rW7lfCGnVE9nbs/DXyA8MJab2bXmtnc3bCtIiIyvmq5PX6/u08GjgCmAvMj081Nr8vdO4BNVdOsTf3dATTZyK+ry2uv845VtX8m9FbeZuGyrI/lrXS3XwxoZs3AW4CimVUOXCMwxcyOdPeH3P0xM3uO0N2Z7iaGcGDe7e7/l7Hshcmf6Uz2bYTu0dcQDtRkQveyARuAPsI//w/J9PtWresud3/tsHZ24HK+7e5/OsTphzIoIzZN5vPJm/R7hC76m92918x+QDgOOy/E/WrgajObBPw38HngHUPYLhER2QPsLe2xuz9ioRTJV8zs6IzerjXAwaltbwam78oqhhnPmy/vWA1ciPsOwunbDyWnsv/XzO539zuyFjwWPXrnEjL+wwgXRh5FOH9/DyEJqbiacP7/FOCG1PP/D/ismS0AMLOZZnZOzvpagW5Cdj6BcP0bAEkX7PeBz5jZBDM7pGobfgwcZGbvMLP65OdYMzs0Z311ZtaU+qkHvgOcbWanJ1l6k4Xh1rFvF+uAhXkXUybTHJATr9ZAeANvAPrM7EzgdVkTmtnBZnZakhx2AZ2E/5mIiNSOWm+P064iXOuWNar4RkIbfZKZNQB/T6QTJGKw9ngdMD1y2jUmeqyqmdlZZrbIzAzYTvifRtvssUj0LgK+mdR4W1v5IVzYeGGqK/QaYAnwi1SXMsB/EC6EvM3MdhBGCh2fs75vAc8BzwOPJdOnXULIlNcSzrtfQzi4lSz5dcD5wOpkms8TEqaY/yIkRpWfb7r7SkJmfikh0VoJ/C3x4115I20yswci0/wT8ImkC/vDOdtDal/eD1xP+FbwNsJxzNIIXE64rmEt4c1x6WDrEBGRPUqtt8cvSK69+xLwyYzYo4Tr964l9O7tIAxe7B7KshmkPXb3J5J9WZZMM5RLoQY7VmmLgZ8DbcC9wFfd/c7YxDaKpdz2SGb2eWAfd79o0IlFRERktxiv9tjMWoCtwGJ3f3Ys1z0W9rpboFmoy3OEBccB7wFuGu/tEhER2ZuMZ3tsZmcnp4wnEgorP0KNlhXbG++m0EroUp1L6Kr9V+Dmcd0iERGRvc94tsfnEE4XG6H82fl5JUr2ZHv9qVsRERGRWrXXnboVERER2VuM6anbBmvy5kLLLs+X2+uYE7Pmpmisb0IxGvNdGWS900pzQjm7UezIqWbS0ZWz0PgKLScW01luo8e7RnIERERkDzPW7TMThtc+767uKSvHY3t6+zyiRM/MziAMty4CX3f3y/Omby60cELz67ODxZzEq6dnWLHCwYdEY5uPmBKN9TVHQ7nJGkA554gWcwZuT//d9mjMf/dofHsa4yPNraEhvsJS9gv3vs6fxOcREZE9wouife6ON3p2SPyWtRuPnhSN9U3ISZ5GcCVaXUd85j29fR52bmzhPnVfIVTPPgy4wMKNgkVERGScqH2WtJF0gh4HPO3uy5LChNcSRrGIiIjI+FH7LC8YSaI3j4E36F2VPDeAmb3XzJaa2dIezzmXLSIiIqNB7bO8YCSJXtaJ8p1Ocrv7Fe5+jLsf02Dxiy9FRERkVKh9lheMJNFbBeybejyfcD86ERERGT9qn+UFIxl1ez+w2Mz2J9yE93zgbYPOFRm9413x0TmFSfEh3+vfdHQ0tm1RfDMKffFYXVt8VE/efAClnC9FPZPio3o2HdkajU0+8aRobM51T0Rj5e1t0Zg1Dem+0CIisud5UbTPa9+V0z4fnFMarRSPFTujIYo9+ZVH8qpi5LXdm1+S0z6fcGI0ts/1T8a3ZQzb52Eneu7eZ2aXAD8jDN/+hrvHxxmLiIjIbqf2WdJGVEfP3W8BbhmlbREREZFRoPZZKnQLNBEREZEapURPREREpEYp0RMRERGpUUr0RERERGrUiGMhffQAACAASURBVAZj7Cp3j94AOW+I9rOXHBKNdS2I31C5sD2+e1aOD8P2nPTX4/d2BqDUEB8WXs6JeUM5Gtt2XG801jUrfmz2/8+c0itt7dnb4SO4K7SIiOyRhts+L3t/vA3qWZhzt422+nisO6dMSl77PEjXlRdz2uC8WF08tuXYeM21jn3ix+aA/8wpvbIju/TKcNtn9eiJiIiI1CgleiIiIiI1SomeiIiISI1SoiciIiJSo5ToiYiIiNQoJXoiIiIiNWpMy6vgjnd3Z4bW/MnLo7OVDskuBQJAZ3yIdnliKWdb4nVSCvFqJngpZ9g3UJqQM/x5cnzBhbp4eZXy9vg+ds2Pl5dZ89b40O5ZX/1VdkDlVURE9j7DbJ/LB+W0z905KUZLvD0sF+PzlUrxttstv/3KK3FWas3JF3LKq1hnfHu6981pn88/OBqb9eXRbZ/VoyciIiJSo5ToiYiIiNQoJXoiIiIiNUqJnoiIiEiNUqInIiIiUqOU6ImIiIjUqLEtrzKhCTv8JZmhrcdkD+sGaMopPdI4tSMa6+xsiMbKPfEc1wvxEiqWMwIboJxzROubc8qrFOLDprtyhpoX2uKxrS/ti8ZmHffS7MDvI8O6RUSkduW1z0fHy4Q05LRdk6fGS6/05pRJ6fCcMmbt8bbb8uYDPKdry5rijXshp7xKKafkmrXH93HrS8aufVaPnoiIiEiNUqInIiIiUqOU6ImIiIjUKCV6IiIiIjVKiZ6IiIhIjVKiJyIiIlKjxrS8Sm9rkeeXTMqMzZi5MTrfxIb40O6+ck6ZlJyh1u319dFY3hhsi4+IBqDYHV9nb2d8nVaMl5ChHF+m5wxtJ2ek+frjWjOf73tWub+IyN6mt7XI6lOy2+eJU7dF55s8oTMea+yKxtp6GqOxrq689jkeK3YNUl4lJ9zbFS+FUm4cXvuc25WW03avO2F02+cRJXpmthzYAZSAPnc/ZiTLExERkdGhNlpgdHr0TnX3eHeciIiIjBe10Xs5nacTERERqVEjTfQcuM3Mfmtm782awMzea2ZLzWxpqTN+OxQREREZVblt9ID2uUPtc60a6anbV7j7ajObBdxuZk+4+93pCdz9CuAKgOZ99s0ZOSAiIiKjKLeNVvu8dxhRj567r05+rwduAo4bjY0SERGRkVEbLTCCHj0zmwgU3H1H8vfrgH/Im6dcD52zs780zGqKD8OuL5SisVJOKZS+pnisvb4pGiuU4sOl6zuioWTm+Lylxviw8Nzh28NUty0+XDxvmLmIiOzZdrWNLtdD5z7Z7fP81rboeqY2xhvFhmK87W4q9kZjG+pborFSb077HN9MAKwvp31uzimvMswyKZ7TrBd35KwvkpkNt90eyanb2cBNZlZZztXu/tMRLE9ERERGh9poAUaQ6Ln7MuDIUdwWERERGQVqo6VC5VVEREREapQSPREREZEapURPREREpEYp0RMRERGpUaNxr9shszIUI1VUuvrimzIzZ2j3lq4J0VixEB/bbHnDlHPKRhZ6cuYDCvER4xR64istF4c3brrYEc/VJ6yLL3PC+uxjk7f9IiJSm6wcb98KFm8Up+eUV9nWGy9jlld6pa4up6RaNAKF7pwgUIhXOMtt93Nj5Xg7W2yPl1Bp2hSfr2lz9goLfTnbkUM9eiIiIiI1SomeiIiISI1SoiciIiJSo5ToiYiIiNQoJXoiIiIiNUqJnoiIiEiNUqInIiIiUqPGtI5eoS9e223N0zOj83XuFy9+090b34WuzoZozLvjOW6pOV40p3tKfr27vHm9Ph6znBp7DdtyauWtjS+z5fl40Z3GLdkFkwq98dqDIiJSmwp9MGFtdju07A/7ROfrOiDeBpfK8bYrr+3u6YnH+lribVTXjHjdOoBSU0773JDTPufkC/Vb47GmjTm18rbE19e0ObtaYKEvr6BfnHr0RERERGqUEj0RERGRGqVET0RERKRGKdETERERqVFK9ERERERqlBI9ERERkRo1puVVrAR17dnDgxvXx4dFd26cHo2VcoZEF3LSWG+ND9HunZw9tBmgrzk/N/aGnPIkOTHriO9/08b4Iqc/3BGN1W3vis/Y05u9Hb3xfRcRkdpkJahvy25Pm9bGU4WNm2dHY30ThldujNZ4aTBvice640sM8w63fW6L73/T5ngJlcnL49ta6I3vf8PW7PJnpvIqIiIiIpKmRE9ERESkRinRExEREalRSvREREREapQSPREREZEapURPREREpEYNWl7FzL4BnAWsd/eXJM9NA64DFgLLgbe4+5bBllXsLDH9we2ZsbYFk+Pz5VQJKfTEhzaXjtoRjR06e0M0tnzL1GisbVtzfGMAi28OhWJ8aHQpZz4rxfPxuo3xfaShPh7bsi2yISqvIiKypxitNrquo8T0B7PbhR0Lp8Tna483XsWueKzv8PZo7KA566Ox57fFc4VthQnRGOS3wcViTsm1ck4DTbw0Wn1bvD0tNcTb9fqVmzKft954uZY8Q+nRuxI4o+q5jwF3uPti4I7ksYiIiIytK1EbLTkGTfTc/W5gc9XT5wBXJX9fBZw7ytslIiIig1AbLYMZ7jV6s919DUDye1ZsQjN7r5ktNbOlvX3xuziIiIjIqBhSG51un3vUPtes3T4Yw92vcPdj3P2Y+rr88+ciIiIyNtLtc4Pa55o13ERvnZnNAUh+x6+cFBERkbGkNlpeMNxE74fARcnfFwE3j87miIiIyAipjZYXDKW8yjXAEmCGma0CPg1cDlxvZu8BVgBvHtLaOrrwBx/LDE0+/PjobFsOjg9ttvhoac5Z/Eg0dsGUX0dj/9H4mmjsN6X94isEOnc0xYM5Q7sL9TnDsJsa4rGpE6Oxug3ZpWwAym2Roe2l+BBzERF5cRmtNto7uyg/9HhmbPJLctrnQ+Ptc6E3HnvF/suisYtm/l80dlXTK6Kx3/QN0j63N0ZjXsgpE9MUb5/7ciqudU+Np1gtz8WviSytzy7/5sMsrzJooufuF0RCrx7WGkVERGRUqI2WwejOGCIiIiI1SomeiIiISI1SoiciIiJSo5ToiYiIiNQoJXoiIiIiNWrQUbejycwoNGYPb572kyej83VNPTQa274oXg7kgc37RmPvmRYfvn3mtIejsae2zozGIL+8iudULinWxYNtC+KxrqfjY7sn/P6Z+AoLkRzf4kPMRUSkNg27fZ6W0z4fGG+7nt0+PRqbuU+k/Bdw1rSHhrVMgBU55VXK5ZzyKnXx8iod+8VLnrSsLEZj9sTyaIxi9nw2zPZZPXoiIiIiNUqJnoiIiEiNUqInIiIiUqOU6ImIiIjUKCV6IiIiIjVKiZ6IiIhIjRrT8iqYQX19Zqjc0RGdbe53Ho/G/OL40O5DX7E2Gvv/7N17tF1lff/7z2evfQtJCCQhIeESpERuKpZG9FRb8VKEFoVatYhVtLT4D3V0DK3l0Fpph78Wbf311FNsy3AgqAXEC4KICsUqonIOkVNUEDBAICGEcM9tX9f6nj/m3LCyWc+zd9beWSuZ+/0ag7HXXs+c83nm3Dt8v/uZ8/mul/bNT7Yd0LMp2fY/y9cl2yTpBzoq2bZ9OL20e2ioPz2ee9NLquf/6FfJtmjk6rmkl30DAOaYduPzF9PxufGBdHx+xWsfTbYd358uG7a89liy7WfL0/FQkn6g1cm2bSPpGLwzE7v3uzudRi258d5kW2N0NNnm3tlNzZjRAwAAqCgSPQAAgIoi0QMAAKgoEj0AAICKItEDAACoKBI9AACAiupseRVJqtdbvu3+9NLm3NLug//5x8m2n93/qmTbr5/3a8m21658KNm2btvSZJskPfHMwnTjQ/slm4741lCyzT++PdnWyFy37BLtiHQbAGDumeX4vOJ/Z+Lzfen4fOIHj0y2/fYhDyTb1m07KNkmSZufTcfnxoMLkm2rbkjH557b0ufYGEiXZelkfGZGDwAAoKJI9AAAACqKRA8AAKCiSPQAAAAqikQPAACgokj0AAAAKsrRwTIbtp+Q9HD57VJJT3as8zzG8oJVEZFfow4AqJRJ8VnqfixqxlgKbcXnjiZ6u3Rsr42INV3pfBLGAgDAC/amWMRYZoZbtwAAABVFogcAAFBR3Uz0Lu1i35MxFgAAXrA3xSLGMgNde0YPAAAAexa3bgEAACqqK4me7VNt32d7ne0LujGGprGst/1z2/9je22H+77M9hbbv2h6b7Htm23/qvx6YCfHBACYu4jPu/RfiRjd8UTPdk3SJZJOk3ScpHfbPq7T45jkDRHxyi4smb5c0qmT3rtA0i0RsVrSLeX3AADsUcTnF7lcFYjR3ZjRO0nSuoh4MCJGJV0t6YwujKPrIuJWSU9PevsMSVeUr6+QdGZHBwUAmKuIz02qEqO7kegdImlD0/cby/e6JSTdZPunts/r4jgmLI+IxySp/Lqsy+MBAMwNxOep7XMxurcLfbrFe91c+vvaiNhke5mkm23fW2bxAADMJcTnCurGjN5GSYc1fX+opE1dGIckKSI2lV+3SLpWxdR1Nz1ue4UklV+3dHk8AIC5gfg8tX0uRncj0btD0mrbL7HdL+ksSdd3YRyyPd/2wonXkk6R9Iv8Xnvc9ZLOKV+fI+m6Lo4FADB3EJ+nts/F6I7fuo2IcdvnS/qupJqkyyLi7k6Po7Rc0rW2peJaXBkR3+lU57avknSypKW2N0r6uKSLJV1j+1xJj0h6Z6fGAwCYu4jPu6pKjOaTMQAAACqKT8YAAACoKBI9AACAiiLRAwAAqCgSPQAAgIoi0QMAAKgoEj0AAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIoi0QMAAKgoEj0AAICKItHrAtvLbd9qe5vtT8/icQ+3vd12bRrbnmx742z1DQDA3sr2hbY/N81tL7L9pb1sTJfb/kQ7/XQ00bP9fdvP2B7oZL97Snk+f9LGrudJelLS/hHx4en8Utleb/vNuW0i4pGIWBAR9TbGBACYIyoaj4fLyY4nbX/d9oqJ9oj4+4hoJ1636isbj6c7kTKbY8rpWKJn+whJvyUpJL1tD/XRuyeOuwesknRPRMRsHXAfOncAQBdVOB6fHxELJB0laYGkf+rCGKalk9enkzN675N0u6TLJZ0z8abt19je3Hy70fbv2/5Z+brH9gW2H7D9lO1rbC8u246wHbbPtf2IpO+V73+lPOZz5S3S45uOvcT2N21vtX2H7U/Yvq2p/RjbN9t+2vZ9tt/VzsmW5/Vj28/avsv2yeX7E+f/0fIvj9MlXSjpD8vv72pxrC9KOlzSN8ttPtrq3Jve6y33+4DtX5a3iB+0/cHMeP/S9qPltvfZflM75w0A2OtVOh5HxLOSviHplU3H2uXOme332X64PI+PtZil67f9hTIm3m17Tbnfi+Jxc9+250v6tqSVZft22yvL/r9q+0u2t0p6f4sxJa/VpD6W2r6hzC+etv1D28l8rtOJ3n+W/73F9nJJiojbJe2Q9Mambc+WdGX5+kOSzpT0ekkrJT0j6ZJJx369pGMlvaX8/tuSVktaJunOss8Jl5T9HaziF7z5l3y+pJvLvpdJerekz6YudortQyR9S9InJC2W9BFJX7N9UES8vxzPp8rbrDdI+ntJXy6/P2Hy8SLivZIekfTWcptPZc692RZJp0vaX9IHJP2z7RNbjPdoSedLelVELCyPtX53zhkAsM+odDy2vUTS2yWtS7QfJ+mzkt4jaYWkRZIOmbTZ2yRdLekASddL+ldpynisiNgh6TRJm8r2BRGxqWw+Q9JXy2M2X4cJuWvV7MOSNko6SNJyFZNFyTuEHUn0bL9Oxe3KayLip5IeUPHLM+EqFT9E2V4o6XfL9yTpg5L+KiI2RsSIpIskvcO7TnteFBE7ImJIkiLisojY1rT9CbYXlX+l/IGkj0fEzoi4R9IVTcc5XdL6iPh8RIxHxJ2SvibpHbt5yn8k6caIuDEiGhFxs6S15XnNtl3OvVlEfCsiHojCDyTdpGK6frK6pAFJx9nui4j1EfHAHhgrAKCLKh6PP2P7ORXPwC+V9GeJ7d4h6ZsRcVtEjEr6G704UbqtjOF1SV+U9KJJmDb8JCK+UeYFrWJ2y2vV4jhjKhLUVRExFhE/zD0K1qkZvXMk3RQRT5bfX6mmzL38/u0uHgp9u6Q7I+Lhsm2VpGvLKcpnJf1SRWKyvGn/DRMvbNdsX1xOLW/VCzNTS1Vkv73N2096vUrSqyf6Kvt7j4q/NnbHKknvnHSc16n4wcy2DakG26fZvr2c2n1WxT/YpZO3i4h1kv5cxS/WFttX2165B8YKAOiuKsfjD0XEIkmvkHSgpEMT261s7isidkp6atI2m5te75Q06Jk/V5eL17lrNdk/qpitvMnFY1kX5Drd4w8D2p4n6V2SarYnLtyApANsnxARd0XEPbYfVjHd2TxNLBUX5o8j4kctjn1E+bI5kz1bxfTom1VcqEUqppct6QlJ4yp++PeX2x82qa8fRMTvtHWyux7nixHxp9PcfjqLMlLbtHy//Ef6NRVT9NdFxJjtb6i4Di8+SMSVkq60vb+k/5D0SUnvnca4AAD7gLkSjyPi5y5KkVxi+8QWs12PSTq6aezzJC3ZnS7abM/tl7tWux4kYpuK27cfLm9l/7ftOyLillYH7sSM3pkqMv7jVDwY+UoV9+9/qCIJmXClivv/vy3pK03v/7uk/2V7lSTZPsj2GZn+FkoaUZGd76fi+TdJUjkF+3VJF9nez/Yxk8Zwg6SX2n6v7b7yv1fZPjbTX6/twab/+iR9SdJbbb+lzNIHXSy3Tv118bikI3IPU5bbHJlpn6xfxT/gJySN2z5N0imtNrR9tO03lsnhsKQhFT8zAEB1VD0eN7tCxbNurVYVf1VFjP5N2/2S/laJSZCEqeLx45KWJG67piSv1WS2T7d9lG1L2qriZ5qM2Z1I9M6R9Pmyxtvmif9UPNj4nqap0KsknSzpe01TypL0LyoehLzJ9jYVK4VenenvC5IelvSopHvK7ZudryJT3qzivvtVKi7uRJZ8iqSzJG0qt/mkioQp5d9UJEYT/30+IjaoyMwvVJFobZD0F0pf74l/SE/ZvjOxzT9I+utyCvsjmfGo6Vw+JOkaFX8VnK3iOrYyIOliFc81bFbxj+PCqfoAAOxTqh6Pn1c+e/cZSR9r0Xa3iuf3rlYxu7dNxeLFkekcW1PE44i4tzyXB8ttpvMo1FTXqtlqSf8labukn0j6bER8P7WxZ7GU2z7J9iclHRwR50y5MQAA2CO6FY9tL5D0rKTVEfFQJ/vuhDn3EWgu6vK8woWTJJ0r6dpujwsAgLmkm/HY9lvLW8bzVRRW/rkqWlZsLn6awkIVU6orVUzVflrSdV0dEQAAc0834/EZKm4XW0X5s7NyJUr2ZXP+1i0AAEBVzblbtwAAAHNFR2/d9nsw5vUs2P0dM7OOuflI96VPLzJt6tmdVda7MZ7MeXg0Xc0kxkbT++VWhHv3z2OosV2jMdz+BQAA7HP2THzOxLz+/mRbYyATuzPTU57JDcpGuqlndDw9ntG9Pz7PKNGzfaqK5dY1SZ+LiItz28/rWaDXzPu91MHSO9YzSVA9/dOpHbws2TZ2aLo2YmOglmybqtJOZJLEnvH0WPsfeTrZVt/waHo4vZkfYS1zHol/nLcPfSu9DwBgn7BXxOfxdILUc/hhybahX0vH5/pgOtNzJlkrBpRuqg2nz2Peg5M/NKNpPI9sTI9nL4nPbd+6dfE5dZeoqJ59nKR3u/igYAAA0CXEZzSbyTN6J0laFxEPloUJr1axigUAAHQP8RnPm0mid4h2/YDejeV7u7B9nu21tteOxvAMugMAANNAfMbzZpLotbpp/6IbyxFxaUSsiYg1/R6cQXcAAGAaiM943kwSvY2Smp+mPFTF59EBAIDuIT7jeTNZdXuHpNW2X6LiQ3jPknT2lHslVu9kV+cMpD/DuP7yI5NtOw9KL992eoGNesbaX6Pd6EuvThqf15ds275yRbJt/hHpFUj9P12XHstI+vOZs6uBAAD7ss7F53npmcCh/+PlybYdK9IxKLd6tjaaKdmSievFBummkQPS817bD03H5/0ePyjd9pP7k22NofSt8tmOz20fLSLGbZ8v6bsqlm9fFhF3z9rIAADAbiM+o9mM0saIuFHSjbM0FgAAMAuIz5jAR6ABAABUFIkeAABARZHoAQAAVBSJHgAAQEV1tsZGRPIDkHMlVHa+9uhk2+ii9AcD9w6n12i7kVminWlTrk2SetPjyfbZk173ve2w9LUZXHBMsm3+D+5NtsXoaKKh/dIyAIB9VC4+Z0qoPHPascm24cXpuaTenelY0zPeXgmVnno+fkUmzr64nHRTU2a3534tXTZt5MD0tTnwxl+m+xtOlEZrMz4zowcAAFBRJHoAAAAVRaIHAABQUSR6AAAAFUWiBwAAUFEkegAAABXV0fIqISnqrUuejL/iyOR+w4vTJUtqY+n+6n3tLaV2bml3bnm2JGWaG73pxkb6FNU7nB7PyP7pXL3vxKPSx7ztZy3fj9yFAQBUUigU4+Mt24Z+8+XJ/YYOSsegWiZ2RSbmRT0dKz2DEmC5ODs+mInP/en9ekbT4xlamr42/a9Ll42b9507W77fbnxmRg8AAKCiSPQAAAAqikQPAACgokj0AAAAKopEDwAAoKJI9AAAACqqo+VV3Nen2iEHt2zbeshger/MiuLxgfSS6J7WK8WnbMtxfYrlzZnmyKTVkSnbEpkL0DuSbtu5PL0m/MBDV7Z8349m1pEDACrJ/f3qWXV4y7atq9KpguvpY9az8Tkdu3oyZdPcyJQ/y4xFktSXbmpk2zLlXjIl1XLlZbYdlq71Mv/IVa37Wj+Q7iyDGT0AAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIoi0QMAAKiojpZXaQz2auily1q2jc3PrFFuW6ZkSc8UZVJSR8ws7ZamWjKeq72S6bO9oWaNrlrSehhPpJd8AwCqqT6vV9uPX9q6bTBX/it9zMiFk5H24rMb6UPWRjONkqInPbdVG82NJ31MRy54Z8aSqcuy4+jW8bmxub34PKNEz/Z6Sdsk1SWNR8SamRwPAADMDmI0pNmZ0XtDRDw5C8cBAACzixg9x/GMHgAAQEXNNNELSTfZ/qnt81ptYPs822ttrx0b3THD7gAAwDRlY3RzfB4fIT5X1Uxv3b42IjbZXibpZtv3RsStzRtExKWSLpWkhYsO3QPLCgAAQAvZGN0cnxccSHyuqhnN6EXEpvLrFknXSjppNgYFAABmhhgNaQYzerbnS+qJiG3l61Mk/V1un6hZo/u3Xh48vl97y7dz5UymGks7PEV/PT3p5d255dv5Ttu7NrVMOZdGrXWOn1vyDQDYN+xujI6aNXxA6/g8tl+6n8hkEa5n2jKVULJlWTKyJcwk1TK1ynrG0vu5P71f9KRjpjN102ojmfjc2/qY7cbnmdy6XS7pWhcd90q6MiK+M4PjAQCA2UGMhqQZJHoR8aCkE2ZxLAAAYBYQozGB8ioAAAAVRaIHAABQUSR6AAAAFUWiBwAAUFGz8Vm30xf55dYp9cFM43B6uXGu9Eq9P33IRl8m/+3Jn0Cu3In2QOWS3BL12lCm1MvQeOJ41MwEgLkoFZ9z5U7q8zIlRIbSQa+RyT7qg+n9xjNtuf4kqdHf3nhyHO2VbOnbkd6vd2frH0S78ZkZPQAAgIoi0QMAAKgoEj0AAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIrqaB0910N921rXbxt8Ol3fZmRRrlZeur9cDZt6X66/TP7rvnSb8vWGxgfay6t7h9P18Pq2pev69W0bTbb1bB9u+b7rmcJ8AIBKcj00sLV1PJn3RDqwjRyYjqW5urm52D2+X7ptyOk42ujN19GrZ8J3rnaf6+n6df1b0219O3e/Vp4k9W1rnbzkxpHDjB4AAEBFkegBAABUFIkeAABARZHoAQAAVBSJHgAAQEWR6AEAAFRUZ8urRKg20rp8R9+OdFmP/q3pY+bKmdQz5UxyJVvGM0u03cjnxrnl3Y3M1c4tNR94Lr2keuCx9MXxcLq8isYSHVJeBQDmHDek3qHW///v35qOe7n4lCtjliuhMnJAJo4uSO/nyJdXaWTKq2Tj81imxNsz6fMffGIk2eZGer/a1tb75fbJYUYPAACgokj0AAAAKopEDwAAoKJI9AAAACqKRA8AAKCiSPQAAAAqasryKrYvk3S6pC0R8bLyvcWSvizpCEnrJb0rIp6Z8lijdfVveKpl2+iig5P71UbTJT9yJVSePjZTXmVpPdm236Ppmi1O7yYpX+6lkWvLLPvO8fahdGMtff6xY2diIJRXAYB9xWzF6J6RcQ0+2Do+Dy1ZntyvNpou+ZGrdrLtJZnSK0vTpcH6Hk8Hy9povrxKLj5HZtprrD/dNj4v3WdtOF03rdGXHkzPk4kf1XimDlvGdGb0Lpd06qT3LpB0S0SslnRL+T0AAOisy0WMRsaUiV5E3Crp6UlvnyHpivL1FZLOnOVxAQCAKRCjMZV2n9FbHhGPSVL5dVlqQ9vn2V5re+1oPXG7EAAAzJZpxehd43PmMSDs0/b4YoyIuDQi1kTEmv5a5jNPAABAx+wan+d1ezjYQ9pN9B63vUKSyq9bZm9IAABgBojReF67id71ks4pX58j6brZGQ4AAJghYjSeN53yKldJOlnSUtsbJX1c0sWSrrF9rqRHJL1zOp3F2Jjqj25u2TZ48AHJ/bYflp5SHl2YzlUXvOrJZNvrDno02fZf/9/xybbacP6S9W1LLzVXm0u7s23zBpJt3rYjvd9Q4nkMyqsAwD5jtmJ0jI6p8XDruDj/sAOT+21dlY5B45m7wQcen47Pa5ZtSLZ9p/e4ZNvY0GC6Q0m9mWUCudIrzoR1Zdrq89OlYPq2bE/v98yzrbsan6K+W8KUQaN43gAAIABJREFUiV5EvDvR9Ka2egQAALOCGI2p8MkYAAAAFUWiBwAAUFEkegAAABVFogcAAFBRJHoAAAAVNeWq29lkSa61zi1990PpHQ9LL6fe+pL0bosya6J/b/FdybZnjk+vCf/5U6vTHUrqS6+YVk9mZXQj85MYWpJuHNySHqs3PpYZTCLHt9P7AAAqKRef+3+6LrlfzyHp+LxtVTqeLMnE51MO+EWybeil6ZIlP3gmXRpNknqH03Nb2ficKb0yuih9jvW+dH+9j2xKtrnWukO3GZ+Z0QMAAKgoEj0AAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIrqaHkV2VJi2XCMjiZ3W3Tzvcm2HSvSy6nfsPJXybYz56froPStuC3Z9pfHLk+2SdI27Z9s638uvTS6ZyR9zAMeSzf23P9Isi0ajfRBEz8HAMAclInPjZF0DFr8rfuSbUMHHZtse+OK+5Ntufh8QM+Pkm33H31Qsk2SHutJt/duy5RCGUrH7oUb0nVZBtemy9JEPVPPZZbjMzN6AAAAFUWiBwAAUFEkegAAABVFogcAAFBRJHoAAAAVRaIHAABQUZ0tryJJES3fdm96KI2h4WTb8s/8ONl2+4MnJdtOPO/oZNtRi59Mto2OTrHsOb0KWwNPtT53SVr2k6eSbY37H0y2Rea6ZZdoJ34OAIA5qp34vHNnsu3gf07H5x+te02y7TfOOybZdvzSzcm2rUODyTZJ8ng6QO/3WLptxa3PJNvi7nQZt70lPjOjBwAAUFEkegAAABVFogcAAFBRJHoAAAAVRaIHAABQUSR6AAAAFeXoYJkN209Ierj8dqmkdB2TzmIsL1gVEQd1sX8AQIdNis9S92NRM8ZSaCs+dzTR26Vje21ErOlK55MwFgAAXrA3xSLGMjPcugUAAKgoEj0AAICK6maid2kX+56MsQAA8IK9KRYxlhno2jN6AAAA2LO4dQsAAFBRJHoAAAAV1ZVEz/aptu+zvc72Bd0YQ9NY1tv+ue3/sb22w31fZnuL7V80vbfY9s22f1V+PbCTYwIAzF3E5136r0SM7niiZ7sm6RJJp0k6TtK7bR/X6XFM8oaIeGUXauNcLunUSe9dIOmWiFgt6ZbyewAA9iji84tcrgrE6G7M6J0kaV1EPBgRo5KulnRGF8bRdRFxq6SnJ719hqQrytdXSDqzo4MCAMxVxOcmVYnR3Uj0DpG0oen7jeV73RKSbrL9U9vndXEcE5ZHxGOSVH5d1uXxAADmBuLz1Pa5GN3bhT7d4r1u1nh5bURssr1M0s227y2zeAAA5hLicwV1Y0Zvo6TDmr4/VNKmLoxDkhQRm8qvWyRdq2Lqupset71CksqvW7o8HgDA3EB8nto+F6O7kejdIWm17ZfY7pd0lqTruzAO2Z5ve+HEa0mnSPpFfq897npJ55Svz5F0XRfHAgCYO4jPU9vnYnTHb91GxLjt8yV9V1JN0mURcXenx1FaLula21JxLa6MiO90qnPbV0k6WdJS2xslfVzSxZKusX2upEckvbNT4wEAzF3E511VJUbzEWgAAAAVxSdjAAAAVBSJHgAAQEWR6AEAAFQUiR4AAEBFkegBAABUFIkeAABARZHoAQAAVBSJHgAAQEWR6AEAAFQUiR4AAEBFkegBAABUFIleF9hebvtW29tsf3oWj3u47e22a9PY9uTyQ5oBAKg02xfa/tw0t73I9pf2sjFdbvsT7fTT0UTP9vdtP2N7oJP97inl+fxJG7ueJ+lJSftHxIen80tle73tN+e2iYhHImJBRNTbGBMAYI6oaDweLic7nrT9ddsrJtoj4u8jop143aqvbDye7kTKbI4pp2OJnu0jJP2WpJD0tj3UR++eOO4esErSPRERs3XAfejcAQBdVOF4fH5ELJB0lKQFkv6pC2OYlk5en07O6L1P0u2SLpd0zsSbtl9je3Pz7Ubbv2/7Z+XrHtsX2H7A9lO2r7G9uGw7wnbYPtf2I5K+V77/lfKYz5W3SI9vOvYS29+0vdX2HbY/Yfu2pvZjbN9s+2nb99l+VzsnW57Xj20/a/su2yeX70+c/0fLvzxOl3ShpD8sv7+rxbG+KOlwSd8st/loq3Nveq+33O8Dtn9Z3iJ+0PYHM+P9S9uPltveZ/tN7Zw3AGCvV+l4HBHPSvqGpFc2HWuXO2e232f74fI8PtZilq7f9hfKmHi37TXlfi+Kx819254v6duSVpbt222vLPv/qu0v2d4q6f0txpS8VpP6WGr7hjK/eNr2D20n87lOJ3r/Wf73FtvLJSkibpe0Q9Ibm7Y9W9KV5esPSTpT0uslrZT0jKRLJh379ZKOlfSW8vtvS1otaZmkO8s+J1xS9newil/w5l/y+ZJuLvteJundkj6butgptg+R9C1Jn5C0WNJHJH3N9kER8f5yPJ8qb7PeIOnvJX25/P6EyceLiPdKekTSW8ttPpU592ZbJJ0uaX9JH5D0z7ZPbDHeoyWdL+lVEbGwPNb63TlnAMA+o9Lx2PYSSW+XtC7Rfpykz0p6j6QVkhZJOmTSZm+TdLWkAyRdL+lfpSnjsSJih6TTJG0q2xdExKay+QxJXy2P2XwdJuSuVbMPS9oo6SBJy1VMFiXvEHYk0bP9OhW3K6+JiJ9KekDFL8+Eq1T8EGV7oaTfLd+TpA9K+quI2BgRI5IukvQO7zrteVFE7IiIIUmKiMsiYlvT9ifYXlT+lfIHkj4eETsj4h5JVzQd53RJ6yPi8xExHhF3SvqapHfs5in/kaQbI+LGiGhExM2S1pbnNdt2OfdmEfGtiHggCj+QdJOK6frJ6pIGJB1nuy8i1kfEA3tgrACALqp4PP6M7edUPAO/VNKfJbZ7h6RvRsRtETEq6W/04kTptjKG1yV9UdKLJmHa8JOI+EaZF7SK2S2vVYvjjKlIUFdFxFhE/DD3KFinZvTOkXRTRDxZfn+lmjL38vu3u3go9O2S7oyIh8u2VZKuLacon5X0SxWJyfKm/TdMvLBds31xObW8VS/MTC1Vkf32Nm8/6fUqSa+e6Kvs7z0q/trYHaskvXPScV6n4gcz2zakGmyfZvv2cmr3WRX/YJdO3i4i1kn6cxW/WFtsX2175R4YKwCgu6ocjz8UEYskvULSgZIOTWy3srmviNgp6alJ22xuer1T0qBn/lxdLl7nrtVk/6hitvImF49lXZDrdI8/DGh7nqR3SarZnrhwA5IOsH1CRNwVEffYfljFdGfzNLFUXJg/jogftTj2EeXL5kz2bBXTo29WcaEWqZhetqQnJI2r+OHfX25/2KS+fhARv9PWye56nC9GxJ9Oc/vpLMpIbdPy/fIf6ddUTNFfFxFjtr+h4jq8+CARV0q60vb+kv5D0iclvXca4wIA7APmSjyOiJ+7KEVyie0TW8x2PSbp6Kaxz5O0ZHe6aLM9t1/uWu16kIhtKm7ffri8lf3ftu+IiFtaHbgTM3pnqsj4j1PxYOQrVdy//6GKJGTClSru//+2pK80vf/vkv6X7VWSZPsg22dk+lsoaURFdr6fiuffJEnlFOzXJV1kez/bx0waww2SXmr7vbb7yv9eZfvYTH+9tgeb/uuT9CVJb7X9ljJLH3Sx3Dr118Xjko7IPUxZbnNkpn2yfhX/gJ+QNG77NEmntNrQ9tG231gmh8OShlT8zAAA1VH1eNzsChXPurVaVfxVFTH6N233S/pbJSZBEqaKx49LWpK47ZqSvFaT2T7d9lG2LWmrip9pMmZ3ItE7R9Lnyxpvmyf+U/Fg43uapkKvknSypO81TSlL0r+oeBDyJtvbVKwUenWmvy9IeljSo5LuKbdvdr6KTHmzivvuV6m4uBNZ8imSzpK0qdzmkyoSppR/U5EYTfz3+YjYoCIzv1BForVB0l8ofb0n/iE9ZfvOxDb/IOmvyynsj2TGo6Zz+ZCka1T8VXC2iuvYyoCki1U817BZxT+OC6fqAwCwT6l6PH5e+ezdZyR9rEXb3Sqe37taxezeNhWLF0emc2xNEY8j4t7yXB4st5nOo1BTXatmqyX9l6Ttkn4i6bMR8f3Uxp7FUm77JNuflHRwRJwz5cYAAGCP6FY8tr1A0rOSVkfEQ53suxPm3EeguajL8woXTpJ0rqRruz0uAADmkm7GY9tvLW8Zz1dRWPnnqmhZsbn4aQoLVUyprlQxVftpSdd1dUQAAMw93YzHZ6i4XWwV5c/OypUo2ZfN+Vu3AAAAVTXnbt0CAADMFSR6AAAAFTWjZ/Rsn6piuXVN0uci4uLc9v0ejHk9C3a/o8zt5dyNZ/emTy/6ask2eXfK6UzeN9OWGazHxtO7ZdqyI23jPIYa2zUawzO4AACAbts74nO6zf39ybbGQCY1aTPGTsW5+DySi8+j6f1yg+1gfG77GT0Xn1N3v6TfUfHhundIenf5eXUtLaotjdfM+73UAdOd1dO1e6PeSLbVlrX65JDykAcfmGxr9Lf5SyYpaulJUo+nx9q3cfKnr7ygvnlL+piZ/lTLJLOJn/vtQ9/Sc/UnSfQAYB+118Tn8XSC1HPEYcm24SPTH1LR6MuMJR1iJUlupPOd2kh654GHM/F5w6Pp/jKTTZ2MzzO5dXuSpHUR8WBZmPBqFatYAABA9xCf8byZJHqHaNcP6N1YvrcL2+fZXmt77WgMz6A7AAAwDcRnPG8miV6r6cMXzTdGxKURsSYi1vR7cAbdAQCAaSA+43kzSfQ2Smq+yX6ois+jAwAA3UN8xvNmsur2Dkmrbb9ExYfwniXp7Cn3SjzUmXtoM7c6Ry9blWwaWpz+C8Xj6Ycye8ameKIzo9Gfzp3r+6cv98iS9GceDx6yONnWc8/69FhG0p/PnH1IFACwL+tYfO6Zl46zw69+WbJtx4q+9DDS6zvUO5KL3fnFpc4sPh1bmF4csfPgFcm2eS9JL/ocuHNdsq0xlL5VPtvxue2jRcS47fMlfVfF8u3LIuLuWRsZAADYbcRnNJtR2hgRN0q6cZbGAgAAZgHxGRP4ZAwAAICKItEDAACoKBI9AACAiiLRAwAAqKjO1tiISH4uXq6EythvrE62jc9PL4nu3Zleo+1MCZWezGfSKvNZeWWvyZbIfEJd1NKNw0vTy9f7f/3X0iPJLO3W2FhiIDP4VGgAwL4pE59zJVSeO+XYZNvQ4nRc69uRHkotUyYlV0JlqvIqObmP81XmY2m3rkrnLoMHHJNsW/i9e5NtMZwojdZmfGZGDwAAoKJI9AAAACqKRA8AAKCiSPQAAAAqikQPAACgokj0AAAAKqqj5VVCUtQTpUtefkRyv7GF6WHWhtIlVHKcWaXseqZxiuXNTp2fpJ56ev12pqCL+kbHk23jg+l137WjDk8f9Be/avk2xVUAYO4JhWK8dawZfvXLkvsNLUnHtdpwpsNcOZNcfM7E4FybJDV6MzG4L9eWPmZtNN3n0OL0XFrfmqOSbf3fv6vl+9FmhGZGDwAAoKJI9AAAACqKRA8AAKCiSPQAAAAqikQPAACgokj0AAAAKqqj5VXc26va8oNatu04aDC5X89Yeklx1DJrtNstk9LIFDvJ1UGR5PH0Bu5L59V2bq15Wm0k3d/YgelrOrh0cetxPJEu1wIAqCb396vn8MNatm07LF1fpGcsfczIhZNMZTQ3MiVU0tXG5KmqrWUynkZmrI1MnpEr1VYbSbdtP6Q/2XbQ4Ye27mtjep8cZvQAAAAqikQPAACgokj0AAAAKopEDwAAoKJI9AAAACqKRA8AAKCiOlpeJfp7NXb40pZtjf708uVceRXX2ytLosySaOfKsuRKr0hSbhn2ePq4PZm6LdFm6RVnSsg0lh3YuuGZjv5KAAD2AvV5vdp+fOvyZ2PzM/E5U+6kNporcZZuypUsycXnXHkzSeqppee2ejKlWSLTZ7vxWZndho5K5ElPtBefZxTVba+XtE1FRZzxiFgzk+MBAIDZQYyGNDszem+IiCdn4TgAAGB2EaPnOJ7RAwAAqKiZJnoh6SbbP7V9XqsNbJ9ne63ttWPjO2bYHQAAmKZsjG6Oz+MjxOeqmumt29dGxCbbyyTdbPveiLi1eYOIuFTSpZK0/4JDMo9YAgCAWZSN0c3xecGBhxKfK2pGM3oRsan8ukXStZJOmo1BAQCAmSFGQ5rBjJ7t+ZJ6ImJb+foUSX+X2ydq1viCvpZt9f50zlnvS/+h0TucKVky2uay55wpqqvklne7N1NCpaeWPmimKSu3sr239fWOPXDJAACdtbsxOmrWyKLWwaYxkO6n0TqkT4wi2ZIrm9ZuHPKU8TmTL2TaGpmyadFmFpXrL5kPtXldZnLrdrmka13UkOmVdGVEfGcGxwMAALODGA1JM0j0IuJBSSfM4lgAAMAsIEZjAuVVAAAAKopEDwAAoKJI9AAAACqKRA8AAKCiZuOzbqcvJNdbLynOLadu9KUbc8upa0OZoSTKi0hSoz9dz6SnMcX67U7LXLee0Uypl7F66/cpmQkAc09IbiQCQCZAN/rTh0yEe0lSbTSz30CmLMt4Onb35DqUFD3t1SfJxUVHro5Zuqk2min1MpaI3W3GZ2b0AAAAKopEDwAAoKJI9AAAACqKRA8AAKCiSPQAAAAqikQPAACgokj0AAAAKqqjdfQckazt1rcjXfOtPpipqZOrm5OpmTO+X7pWngfS+W9vrf3cuDGQ7jNXD8/juXo7revhSVLP9nShIg+PtG7Y2+oEAgD2OIfUO9Q61vRvTcegsQWZOrfj6f5ytXPH9ks31jN1dSMTYqdS72+vxl7vcCY+Z2J37tr0Difq3KbqHE6BGT0AAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIoi0QMAAKiojpZXUaTLgfSMpddF9+7MLFGOdFs4U15lYXvrsLPlXCTVMyVU6gOZMjFj6eP2P5Muk9K7ZWt6MGOZ9dvjiTbKqwDAnONGqHeo9f//e0bTc0LztmRiYqZiSWSmmUYXpnds9Ge6a+Tnrhp97bX1ZEJpbTR9/n3b0uXPcuVlajvGWjdQXgUAAADNSPQAAAAqikQPAACgokj0AAAAKopEDwAAoKJI9AAAACpqyvIqti+TdLqkLRHxsvK9xZK+LOkISeslvSsinpnyWKPj6t34VMu28fkHJ/frGU0vUa4PpsuZPH7SQLJtbP/0MuX9H0g2yemhlONJr5mu96fbcku0+3NXdiRdekU96Tw+hoZaN7S5fBsA0HmzFaN7hse1369ax+ehpcuT++Vi13gmHj55YqaU1/7peiYD69NxXY1MzRK1X17FmbBYG86UnnkifY6N3vR+tceebj2ORHm6qUxnRu9ySadOeu8CSbdExGpJt5TfAwCAzrpcxGhkTJnoRcStkianl2dIuqJ8fYWkM2d5XAAAYArEaEyl3Wf0lkfEY5JUfl2W2tD2ebbX2l472kjcLgQAALNlWjF6l/hcJz5X1R5fjBERl0bEmohY098zb093BwAApmGX+FwjPldVu4ne47ZXSFL5dcvsDQkAAMwAMRrPazfRu17SOeXrcyRdNzvDAQAAM0SMxvOmU17lKkknS1pqe6Okj0u6WNI1ts+V9Iikd06nsxgfV/3xJ1q29S9dlNxv6JD5ybadS9OncOBvb062HbnoyWTbj2vHJ9t6xtLlXCRpYGtmyXhmiXajN70sPDLLsDXQn95vx8502/BIYiCZ8QMA9iqzFaNjdFSNhx5p2bbg8AOT+z33knQMGt0/Hdde96pfJtteuXBjsu2S3tcn2zyev/3cvzXdliuhErmqLZm2sf3S+cm8x3Yk2+pPti5zE+PpsjM5UyZ6EfHuRNOb2uoRAADMCmI0psInYwAAAFQUiR4AAEBFkegBAABUFIkeAABARZHoAQAAVNSUq25nkyW5lsgtH9iQ3K9xxLHJtq1Hptc2H5hZE336kruSbVt+Y2GybcMzq5JtktSXXjGtnnq6rZH5SQwvTS9f731uML3j5kyNzJ7Ez8G5deQAgCqyLPe2DkQDd/wquV9tZboc2c4V6Zolj+44INn2x8tuS7b93jFLkm03PHNisk2S+rany6M5V7kkE59HFqVj5rzW1eQKmZzHtdbjdJvxmRk9AACAiiLRAwAAqCgSPQAAgIoi0QMAAKgoEj0AAICKItEDAACoqI6WV5EtJZYNx+hocrcF378v2bb1sPTS7jccnF4S/q4FzyXbaof+MNn2f758abJNkp6N+cm2/nSX6h1OL0NfsGEkveNDj6bbGo10W6q8CgBg7snE58ZIOgYtueHeZNvwknRptDVvfiTZdvK8dOzq8/+TbLvjqMOTbZK0ZeygZFv/1nRM7MmE4AMeSNdl2e8n9yfbYmwsfdDEz6FdRHsAAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIoi0QMAAKiozpZXkaRoXUbEvemhNIaGk23L/+8fJ9t+9NBrkm0nnpde9n3kgU+lx9LI58aN/nTbwNb0kvHF/+8T6WM+tCF90FpmPJRQAQBMVzvxeefOZNvB/1c6Pt++7qRk26//6THJtmOXPp5s27pzMNkmSc5UHJv3eLrE2fIfPZNsi3vWpdsy1y1bQiXxc2gXmQAAAEBFkegBAABUFIkeAABARZHoAQAAVBSJHgAAQEWR6AEAAFSUY5aX8WY7s5+Q9HD57VJJT3as8zzG8oJVEXFQF/sHAHTYpPgsdT8WNWMshbbic0cTvV06ttdGxJqudD4JYwEA4AV7UyxiLDPDrVsAAICKItEDAACoqG4mepd2se/JGAsAAC/Ym2IRY5mBrj2jBwAAgD2LW7cAAAAVRaIHAABQUV1J9Gyfavs+2+tsX9CNMTSNZb3tn9v+H9trO9z3Zba32P5F03uLbd9s+1fl1wM7OSYAwNxFfN6l/0rE6I4nerZrki6RdJqk4yS92/ZxnR7HJG+IiFd2oTbO5ZJOnfTeBZJuiYjVkm4pvwcAYI8iPr/I5apAjO7GjN5JktZFxIMRMSrpaklndGEcXRcRt0p6etLbZ0i6onx9haQzOzooAMBcRXxuUpUY3Y1E7xBJG5q+31i+1y0h6SbbP7V9XhfHMWF5RDwmSeXXZV0eDwBgbiA+T22fi9G9XejTLd7rZo2X10bEJtvLJN1s+94yiwcAYC4hPldQN2b0Nko6rOn7QyVt6sI4JEkRsan8ukXStSqmrrvpcdsrJKn8uqXL4wEAzA3E56ntczG6G4neHZJW236J7X5JZ0m6vgvjkO35thdOvJZ0iqRf5Pfa466XdE75+hxJ13VxLACAuYP4PLV9LkZ3/NZtRIzbPl/SdyXVJF0WEXd3ehyl5ZKutS0V1+LKiPhOpzq3fZWkkyUttb1R0sclXSzpGtvnSnpE0js7NR4AwNxFfN5VVWI0H4EGAABQUXwyBgAAQEWR6AEAAFQUiR4AAEBFkegBAABUFIkeAABARZHoAQAAVBSJHgAAQEWR6AEAAFQUiR4AAEBFkegBAABUFIkeAABARZHodYHt5bZvtb3N9qdn8biH295uuzaNbU8uP6QZAIBKs32h7c9Nc9uLbH9pLxvT5bY/0U4/HU30bH/f9jO2BzrZ755Sns+ftLHreZKelLR/RHx4Or9UttfbfnNum4h4JCIWRES9jTEBAOaIisbj4XKy40nbX7e9YqI9Iv4+ItqJ1636ysbj6U6kzOaYcjqW6Nk+QtJvSQpJb9tDffTuiePuAask3RMRMVsH3IfOHQDQRRWOx+dHxAJJR0laIOmfujCGaenk9enkjN77JN0u6XJJ50y8afs1tjc33260/fu2f1a+7rF9ge0HbD9l+xrbi8u2I2yH7XNtPyLpe+X7XymP+Vx5i/T4pmMvsf1N21tt32H7E7Zva2o/xvbNtp+2fZ/td7VzsuV5/dj2s7bvsn1y+f7E+X+0/MvjdEkXSvrD8vu7Whzri5IOl/TNcpuPtjr3pvd6y/0+YPuX5S3iB21/MDPev7T9aLntfbbf1M55AwD2epWOxxHxrKRvSHpl07F2uXNm+322Hy7P42MtZun6bX+hjIl3215T7veieNzct+35kr4taWXZvt32yrL/r9r+ku2tkt7fYkzJazWpj6W2byjzi6dt/9B2Mp/rdKL3n+V/b7G9XJIi4nZJOyS9sWnbsyVdWb7+kKQzJb1e0kpJz0i6ZNKxXy/pWElvKb//tqTVkpZJurPsc8IlZX8Hq/gFb/4lny/p5rLvZZLeLemzqYudYvsQSd+S9AlJiyV9RNLXbB8UEe8vx/Op8jbrDZL+XtKXy+9PmHy8iHivpEckvbXc5lOZc2+2RdLpkvaX9AFJ/2z7xBbjPVrS+ZJeFRELy2Ot351zBgDsMyodj20vkfR2SesS7cdJ+qyk90haIWmRpEMmbfY2SVdLOkDS9ZL+VZoyHisidkg6TdKmsn1BRGwqm8+Q9NXymM3XYULuWjX7sKSNkg6StFzFZFHyDmFHEj3br1Nxu/KaiPippAdU/PJMuErFD1G2F0r63fI9SfqgpL+KiI0RMSLpIknv8K7TnhdFxI6IGJKkiLgsIrY1bX+C7UXlXyl/IOnjEbEzIu6RdEXTcU6XtD4iPh8R4xFxp6SvSXrHbp7yH0m6MSJujIhGRNwsaW15XrNtl3NvFhHfiogHovADSTepmK6frC5pQNJxtvsiYn1EPLAHxgoA6KKKx+PP2H5OxTPwSyX9WWK7d0j6ZkTcFhGjkv5GL06UbitjeF3SFyW9aBKmDT+JiG+UeUGrmN3yWrU4zpiKBHVVRIxFxA9zj4J1akbvHEk3RcST5fdXqilzL79/u4uHQt8u6c6IeLhsWyXp2nKK8llJv1SRmCxv2n/DxAvbNdsXl1PLW/XCzNRSFdlvb/P2k16vkvTqib7K/t6j4q+N3bFK0jsnHed1Kn4ws21DqsH2abZvL6d2n1XxD3bp5O0iYp2kP1fxi7XF9tW2V+6BsQIAuqvK8fhDEbFI0iskHSjp0MR2K5v7iogIzMVnAAASg0lEQVSdkp6atM3mptc7JQ165s/V5eJ17lpN9o8qZitvcvFY1gW5Tvf4w4C250l6l6Sa7YkLNyDpANsnRMRdEXGP7YdVTHc2TxNLxYX544j4UYtjH1G+bM5kz1YxPfpmFRdqkYrpZUt6QtK4ih/+/eX2h03q6wcR8Tttneyux/liRPzpNLefzqKM1DYt3y//kX5NxRT9dRExZvsbKq7Diw8ScaWkK23vL+k/JH1S0nunMS4AwD5grsTjiPi5i1Ikl9g+scVs12OSjm4a+zxJS3anizbbc/vlrtWuB4nYpuL27YfLW9n/bfuOiLil1YE7MaN3poqM/zgVD0a+UsX9+x+qSEImXKni/v9vS/pK0/v/Lul/2V4lSbYPsn1Gpr+FkkZUZOf7qXj+TZJUTsF+XdJFtvezfcykMdwg6aW232u7r/zvVbaPzfTXa3uw6b8+SV+S9Fbbbymz9EEXy61Tf108LumI3MOU5TZHZton61fxD/gJSeO2T5N0SqsNbR9t+41lcjgsaUjFzwwAUB1Vj8fNrlDxrFurVcVfVRGjf9N2v6S/VWISJGGqePy4pCWJ264pyWs1me3TbR9l25K2qviZJmN2JxK9cyR9vqzxtnniPxUPNr6naSr0KkknS/pe05SyJP2Ligchb7K9TcVKoVdn+vuCpIclPSrpnnL7ZueryJQ3q7jvfpWKizuRJZ8i6SxJm8ptPqkiYUr5NxWJ0cR/n4+IDSoy8wtVJFobJP2F0td74h/SU7bvTGzzD5L+upzC/khmPGo6lw9JukbFXwVnq7iOrQxIuljFcw2bVfzjuHCqPgAA+5Sqx+Pnlc/efUbSx1q03a3i+b2rVczubVOxeHFkOsfWFPE4Iu4tz+XBcpvpPAo11bVqtlrSf0naLuknkj4bEd9PbexZLOW2T7L9SUkHR8Q5U24MAAD2iG7FY9sLJD0raXVEPNTJvjthzn0Emou6PK9w4SRJ50q6ttvjAgBgLulmPLb91vKW8XwVhZV/roqWFZuLn6awUMWU6koVU7WflnRdV0cEAMDc0814fIaK28VWUf7srFyJkn3ZnL91CwAAUFVz7tYtAADAXEGiBwAAUFEzekbP9qkqllvXJH0uIi7Obd/vwZjXs2D3O8rcXs7deHZf+vQi07Zb1XRetG9m50Z6tB4bT7ZFpi071NxYEoYa2zUawzO5AgCALts74nMm5vX1p/cbyMTuPRWdMslEz2gmPo+OJtuci9AdjM9tP6Pn4nPq7pf0Oyo+XPcOSe8uP6+upUW1pfGaeb+XOmC6s3q6dm/UG8m22sHLkm3jhyxOtjX6aumxTCFq6fPoGU2fR9+GyZ++8oL65i3JNtcyk7K1zHkkfu63D31Lz9WfJNEDgH3UXhOfx9MJUu2wQ5JtI0ekP6SiPjCD+Jw5jdpoOpcYfCgTnx/ZmGxzb2ZCqYPxeSa3bk+StC4iHiwLE16tYhULAADoHuIznjeTRO8Q7foBvRvL93Zh+zzba22vHY3hGXQHAACmgfiM580k0Ws1ffii+caIuDQi1kTEmn4PzqA7AAAwDcRnPG8mid5GSYc1fX+ois+jAwAA3UN8xvNmsur2Dkmrbb9ExYfwniXp7Cn3SjzUmXtos2cg/RnG9ZcfmWwbWpxe1ePx9CKU3EOZzqyclaR6fzp3HluYvtzDy9KfeTz4ePrB1N670x/L1xhJfz6zcw+CAgD2ZZ2Lz/PSM4Gjv3F8su255X3pY6a7U89YZiXvFPG50ZtexzByQDom7lhxcLJtvyPT8Xlw7br0WIbSt8qzizja0PbRImLc9vmSvqti+fZlEXH3rI0MAADsNuIzms0obYyIGyXdOEtjAQAAs4D4jAl8MgYAAEBFkegBAABUFIkeAABARZHoAQAAVNTsruGdSkTyc/FyJVSGfvPoZNvo/ukl0X070p/Blyuh0jOD8irtip70su/h5elr0z94VLKtb+2v0h2OjSUGsmfODwCwF8vF50wJla1vOibZNnxAei6pf0c61vRkyp/NpLxKTmSmvRot608Xth2eLuM2tn/62iz83r3psQwnSqO1GZ+Z0QMAAKgoEj0AAICKItEDAACoKBI9AACAiiLRAwAAqCgSPQAAgIrqaHmVkBT11qVL6i8/MrnfyAHpEiq1kfRy41zJkkg35ZcwT7F825ll4a5l9nV6QL3b02Vixuelr03vSw9P9/ez1qVXKK4CAHNPKBTj4y3bRtYcn9xvaGl6vqh3KNNfZpopG59zZhLAMjE4U11FvUPpTkf2z5SXOTFTGu3Wu1q+H22eIDN6AAAAFUWiBwAAUFEkegAAABVFogcAAFBRJHoAAAAVRaIHAABQUR0tr+K+XtVWLm/ZtmPZQHK/2miuhEqbg8mUXnGuCkqiPMzz46m1uS48V9IlM9bacHo8owcOJtvmLVva8n0/3tFfCQDAXsD9/aodemjLtu2H9if36xlLH7ORCSfOhdJcpZNMrMzF7qn6zJV0yZZ7ybTlyr/tWNGXbFty2CGtu9qY/jnkMKMHAABQUSR6AAAAFUWiBwAAUFEkegAAABVFogcAAFBRJHoAAAAV1dFaGjHQp5GXHNSyrT6QKXdSzyynzlc7SWtMsQ57b7IHxlpfdmDL9+Pp2qz3BQDYuzUGerXj6NbxeTxdqSsbg3PlThq96ZjfyIShcK40Wj4hyI41E2edra+S6zDdFJmyacNHti5/1niivZRtRome7fWStkmqSxqPiDUzOR4AAJgdxGhIszOj94aIeHIWjgMAAGYXMXqO4xk9AACAipppoheSbrL9U9vntdrA9nm219peOzq6Y4bdAQCAacrG6Ob4PEZ8rqyZ3rp9bURssr1M0s22742IW5s3iIhLJV0qSfvvf+g+tAICAIB9WjZGN8fnhYuIz1U1oxm9iNhUft0i6VpJJ83GoAAAwMwQoyHNYEbP9nxJPRGxrXx9iqS/y+0TPdLYgtZdju2XW76cbusdbq/0Sk8tfczcsmdn2ooNMu1T7Zs6ZObvLEemMbMkPAYS69dz4wcA7BN2N0Y3eq2RA1rHhfH5mViSCRm10UxbTyZ213MxP31MZ/IBKR/be+rp/RqZTCky02W5HKQ2lh5roy9x0DbD80xu3S6XdK2LxKBX0pUR8Z0ZHA8AAMwOYjQkzSDRi4gHJZ0wi2MBAACzgBiNCZRXAQAAqCgSPQAAgIoi0QMAAKgoEj0AAICKmo3Pup2+kFxvvaQ4t0S7PphpzKymro1mli/3p3PcxBAlSW5MUVMykzrnzjFy5V5yFVQy+6WutSR5PLHuO9cZAKCSrHQpr1x5kfpAui0SVbxe6DHRkomz46PpIFsbaT9+5eJzu7LlVTL5SW2kda2XXKm1HGb0AAAAKopEDwAAoKJI9AAAACqKRA8AAKCiSPQAAAAqikQPAACgokj0AAAAKqqjdfTcCPXtHG/ZNu+ZdM45vChTNydTi8atS9FIksYztfnqA+niP9GTL7aTq//T6Mvk1bnDju9+vR1Jqu0YS7b17BhpPYyp6gQCAKqnEeodal34bfDpdIAaWZRu62kd7ou2sXSsqfenjzm6MH1MN/JzV7m6dvWB9grp9Q3l4nO6w9pwpi0Vu9uMz8zoAQAAVBSJHgAAQEWR6AEAAFQUiR4AAEBFkegBAABUFIkeAABARXW0vIokOVEqJLfUeOH2dAkR1zPlTHrTy6WHF6dPPdLVVeR6PjfO7Vvvyy1DT59H3/b0tenbvC3Z5pHR9GDGE9e0nll/DgCoJDek2kgqPqf3W5CJT7lyJo1M9jFyQKb8WaY02thYPj43+tNtuZIuufjcmymv0v90OgY7E2tr2xPlz9qMz8zoAQAAVBSJHgAAQEWR6AEAAFQUiR4AAEBFkegBAABUFIkeAABARU1ZXsX2ZZJOl7QlIl5WvrdY0pclHSFpvaR3RcQzUx5rdFx9G59q2Ta28ODkfrWRdHmVRm86V9386vRa6tEDMuVcHkofs2c8v7w5t0Q7t5y8Zyy9nxvp5dvemVn3XkufR2zf3rqhQXkVANhXzFaM7hkZ17x1T7ZsG1qyPLlfb6IkiyQ1MuXGnjohHZ/Gl6bLkgyuz9RIaaTjqCTV52V27cvsGOnj9m9Nt9WGxtKH7MtdnGdbv58qizaF6czoXS7p1EnvXSDplohYLemW8nsAANBZl4sYjYwpE72IuFXS05PePkPSFeXrKySdOcvjAgAAUyBGYyrtPqO3PCIek6Ty67LUhrbPs73W9trR+lCb3QEAgGmaVowmPs8Ne3wxRkRcGhFrImJNfy1zgxwAAHQM8XluaDfRe9z2Ckkqv26ZvSEBAIAZIEbjee0metdLOqd8fY6k62ZnOAAAYIaI0XjedMqrXCXpZElLbW+U9HFJF0u6xva5kh6R9M7pdBZj46pvevz/b+9uYqMqozCOP6fTD6GAUmgBoYKJLECMRgjRGCIGY9ANbjSwwsQEN27csXPrzo3GxAWBjRo3RBKNQlhgAiYCxkRIwKK0UFooBrCln5Y5LhhCqXNuTSn30nf+v83M3JN739OZxTm50/dM1VhTy4LwvOHlzWFsqDXeorxyc1cYa5szEMaOldeGsbrx7O3bjf3xVnOV4nM9Y6f1rYaMMSlzmsKYDQzG542MVg8wXgUAZo2ZqtE+9o/KXZeqxuatWBie178qrkFjC+Ka9+KmU2HsmfnV85Ckz+pfDmPSIxkxqSEuiVkTVKSMWGbtbo5HwdRfjXsQ/7s/uOD0xqtM2ei5+44gtGVaKwIAgBlBjcZU+GUMAACARNHoAQAAJIpGDwAAIFE0egAAAImi0QMAAEjUlLtuZ5JJslLQW/7eGZ7nTzwdxm6ujPc9l0biSd/bHz8exq5tiMe5dIysCmOS1DAY51OXsTP6VkMcG26Ng43X4+3k1t0bX7Qu+Bwse3wMACA9WfW58Zdz4Xl1y+NxZDfb4/WuDMUj1Xa2HQ1jb6xZHMa+HXw2XlBS/fm45cmqz+WMESojLfH9sube+MTSpcvxRWe4PnNHDwAAIFE0egAAAImi0QMAAEgUjR4AAECiaPQAAAASRaMHAACQqFzHq8hMKlXfbuzj4+Fp846cDWMD7fHolU1L/whj7yzoC2PtDQfD2Afr3g5jknTDHwtjTTeyRsHE13y0cyyM1XVcDGNeLscXjbZvAwBqT0Z9Lo+Ohqe1fBfX5+G2NWFs/asXwtiWOfGsk+ZFx8LY6aeWhjFJ6irH8fqBuCaWhuPaPf9MnGvTyXgsjd/KmOcSfA7TRbUHAABIFI0eAABAomj0AAAAEkWjBwAAkCgaPQAAgETR6AEAACQq3/EqkuRe9bDVx6mUh+PZI22fxFutf+7cGMbWv7c6jK1ZFI9eGRvLfss8Y1d00/Xqf7sktf70Vxgrd5yP18t43xihAgD436ZTn4eGwtjSj+P6fLTjhTD2/K54LMu61t4wdm1wbhiTJI3HY1Lm9sSxZUeuhzE/3RHHsupz1giV4HOYLjoBAACARNHoAQAAJIpGDwAAIFE0egAAAImi0QMAAEgUjR4AAECizGd4G2/mYmZXJXVVXi6WFM8UyRe53LXS3VsLXB8AkLNJ9VkqvhZNRC63Tas+59ro3bOw2Ql331DI4pOQCwAAdz1MtYhc7g9f3QIAACSKRg8AACBRRTZ6nxe49mTkAgDAXQ9TLSKX+1DY/+gBAADgweKrWwAAgETR6AEAACSqkEbPzLaa2VkzO2dmu4vIYUIunWb2m5n9amYncl57j5n1mdmpCcdazOyQmXVUHhfmmRMAoHZRn+9ZP4kanXujZ2YlSZ9Kel3SWkk7zGxt3nlM8oq7P1fAbJy9krZOOrZb0mF3Xy3pcOU1AAAPFPX5P/YqgRpdxB29jZLOufuf7j4m6StJ2wrIo3Du/qOka5MOb5O0r/J8n6Q3c00KAFCrqM8TpFKji2j0lku6OOF1d+VYUVzSQTM7aWa7CszjjiXu3itJlce2gvMBANQG6vPUZl2Nri9gTatyrMgZLy+5e4+ZtUk6ZGZnKl08AAC1hPqcoCLu6HVLap/weoWkngLykCS5e0/lsU/Sft2+dV2kK2a2TJIqj30F5wMAqA3U56nNuhpdRKN3XNJqM3vSzBolbZd0oIA8ZGbNZjb/znNJr0k6lX3WA3dA0s7K852SvikwFwBA7aA+T23W1ejcv7p193Eze1/SD5JKkva4++m886hYImm/mUm334sv3P37vBY3sy8lbZa02My6JX0o6SNJX5vZu5IuSHorr3wAALWL+nyvVGo0P4EGAACQKH4ZAwAAIFE0egAAAImi0QMAAEgUjR4AAECiaPQAAAASRaMHAACQKBo9AACARP0LEMLY5hTa8nMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f068e21bc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "avg_right = np.zeros([height, width, x_test.shape[3]])\n",
    "avg_left = np.zeros([height, width, x_test.shape[3]])\n",
    "\n",
    "for b in range(x_test.shape[3]):\n",
    "    avg_right[:,:,b] = np.mean(x_test[np.array(find_right(q_test, label='right')),:,:,b],axis=0)\n",
    "    avg_left[:,:,b] = np.mean(x_test[np.array(find_right(q_test, label='left')),:,:,b],axis=0)\n",
    "\n",
    "I = x_test.shape[3]\n",
    "J = 2\n",
    "\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "\n",
    "c = 0\n",
    "for b in range(x_test.shape[3]):\n",
    "    axs[c].imshow(avg_left[:,:,b].astype('float64'))\n",
    "    axs[c].set_title('Average Left trials')\n",
    "    c = c + 1\n",
    "    axs[c].imshow(avg_right[:,:,b].astype('float64'))\n",
    "    axs[c].set_title('Average Right trials')\n",
    "    c = c + 1\n",
    "#         axs[c].get_xaxis().set_visible(False)\n",
    "#         axs[c].get_yaxis().set_visible(False)        \n",
    "#         axs[c].set_title('Sample number ' + str(sample_nos[i]) + ' (' + q_test[sample_nos[i]] + '), Freq. band ' + str(j+1))\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Topographies Averaged within Classes ', fontsize=26)"
   ]
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   "execution_count": null,
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