Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "## Deep Learning\n",
    "\n",
    "## Project: Build a Traffic Sign Recognition Classifier\n",
    "\n",
    "In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary. \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission. \n",
    "\n",
    "In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) that can be used to guide the writing process. Completing the code template and writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/481/view) for this project.\n",
    "\n",
    "The [rubric](https://review.udacity.com/#!/rubrics/481/view) contains \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the \"stand out suggestions\", you can include the code in this Ipython notebook and also discuss the results in the writeup file.\n",
    "\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "import numpy as np\n",
    "import random\n",
    "from pandas.io.parsers import read_csv\n",
    "import cv2\n",
    "\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = '../data/train.p'\n",
    "validation_file = '../data/valid.p'\n",
    "testing_file = '../data/test.p'\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 1: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
    "\n",
    "Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the [pandas shape method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html) might be useful for calculating some of the summary results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of validation examples = 4410\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "\n",
    "# TODO: Number of training examples\n",
    "n_train = len(X_train)\n",
    "# y_train.shape[0]\n",
    "\n",
    "# TODO: Number of validation examples\n",
    "n_validation = len(X_valid)\n",
    "# y_valid.shape[0]\n",
    "\n",
    "# TODO: Number of testing examples.\n",
    "n_test =len(X_test)\n",
    "# y_test.shape[0]\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train[0].shape\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "# sign_classes, class_indices, class_counts = np.unique(y_train, return_index=True, return_counts=True)\n",
    "n_classes = len(np.unique(y_train))\n",
    "# class_counts.shape[0]\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of validation examples =\", n_validation)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. \n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wd/ntV+S/PJz4r2dmqZltzX/eMbO/MrMlMztoZr96KttZlZl91Mx+oMKifwDgvz2Y2yLyUDKzu/Pjb9nMFvJx7JfM7Jwft0XkwaXxpb7O6RfYzGYBfAjA6wHMA9gF4NUABmdyuzaYXwTw9hBCAIAQwu/mvzxMhxCmAfw/AG4MIRzJl38VgMsB7AbwDAC/ZmY/+GBsmJlNAXgcgJsqLP4BAM8ws/MfjG0ROUOeH0KYQXa8/T6AXwfwhjO7SQ8NfRso8qDT+FJD5/TEH8DDACCE8M4QQhJC6IUQPhpC+AoAmNlL878AvD7/i8DXzexZJ+5sZnNm9gYzO2Bm+83sNeOn4JjZz5nZ7WZ23Mz+wcx2j/3s+/P1LZrZnwKwKhtc/hNY/uey1+S/ja+Y2QfNbIuZvT3/1v3zZrZn7P7BzC4zs18A8FPIJuYrZvbBdR7yOVhnYm1mBuBnALx5rPxiAL8TQjgeQrgdwP8H4KXr3P/lZnabmV1w4jQgM/s1MzuU79MXmtlzzeyb+V9jXlFaxbMAfDqEcOIXtbaZvSX/huJWM3v8iQVDCH0AXwRQ5a8DImeVEMJiCOEDAH4MwEvM7NHAd8aot5jZYTPba2avPPGNXfn0uHX+vH5pfprcopn9nZnNs8c/2VhYWraR/+XwjvxY/aKZXcge38ZOBxgbj//YzI4B+J38m8hHjy2/Lf+Wcnt++4fM7Etj31heear7WKSuNL58Z/lajC/n+sT/mwASM3uzmT3HzDaTZa4FcCeArQB+G8Dfjr053wwgBnAZgKuRTSpPvIleCOAVAF4EYBuA/wPgKT60mwAAIABJREFUnfnPtgL4GwCvzNd7B4DrTuN5/DiyCfguAJcC+AyANyL7K8bt+XYXhBD+AsDbAbwu//b++eVlLPtG/WIA31jncZ8G4Lz8uSDffzsBfHlsmS8DcOfWm9lvIvuF4OkhhBPn/e8A0M2fx28h+6Xhp5F9q/80AL9lxXzCcwF8eOz2CwC8C8AmZN/w/2npYW9HdtqSyDkphPA5APcgO16A7K+ZcwAuAfB0ZL+Y/+wDWOWLAfwcsuM6BvA/1llu3bGQ+FUAP4Hs+J3N179WcXtOjMfbkZ2697f5uk74UQA3hRAOmdk1AP4K2V8ttwD4cwAfMLNOxccSkTEaX+oxvpzTE/8QwhKApwIIyCaZh83sA2Z23thihwD8SQhhFEJ4N7JJ8PPyZZ4D4P8KIayGEA4B+GNkk3AgezP8Xgjh9hBCDOB3AVxl2bf+zwVwWwjhvSGEEYA/AXDwNJ7KG0MId4QQFgH8bwB3hBA+nj/uXyM7UE7Fpvz/y+v8/CUA3htCWMlvT+f/XxxbZhHAzNhtM7M/AvBsAM8IIRwe+9kIwGvzffIuZL8U/fcQwnII4VYAtwIY/436OQA+Mnb7UyGEj+T5h7fCT/KXx56TyLnqXgDz+TdiPwbgN/Jj6G4Af4jsS4Kq3hpC+FoIYRXAbwL40fI3bRXGwrKXAXhlCOEbIfPlEMLRqs8thPD6EEIcQugBeAeKH8w/mdcA4OcB/HkI4Zb8L7pvRnYa55MqPpaIeBpfMufs+HLOn+OUn47yUgAws0cAeBuyifiJF3v/ifPbc3uR/Xa6G0ALwAGz75ylEwHYl/97N4D/bmZ/OHZfQ/Zt9s6x5RBCCGa2D6fuvrF/98jtaZyahfz/MwD64z8wswkAPwLgh8fKJ34BmB1bfhbFXxw2AfgFAD+W/6Iy7uhYaLmX/58+FzN7DIClEML4fhv/5WkNQNfMmvkvQCeexwJEzm27ABxD9otzG9mYdcLe/OdVjR9fe5GNeVtLy5xsLCy7ENlfOU9FeZ3/CGDCzK5FdvxfBeB9Y9v1EjP7lbHl28jGXxE5NRpfvrtd5+T4ck5/418WQvg6gDcBePRYeZeNvdsAXITsN959yH672xpC2JT/NxtCOHFayz4Avzj2s00hhIkQws0ADiB7cwL4zrnyF+KhF+73h9lv4Xcgz0KUvAjZwX/j2PLHkT238W/aH4vsm/oTjgP4IQBvNLPTOb2pfJpPFY9E8TQkkXOKZVck2wXgUwCOIPsr2u6xRS4CsD//9yqAybGf7SCrHB+XLsrXd6S0zMnGwrJ9yE5JLFvN/39/21QYs0IIKYD3IPui5icBfCiEcOKLhn3I/oI4PgZPhhDeuc52icj90PhSj/HlnJ74m9kjzOw/mdkF+e0Lkb3Anx1bbDuAl5tZy8x+BNnk8SMhhAMAPgrgD81s1swiM7vUzJ6e3+9/AfgNy68dn4dTfiT/2YcBPMrMXpQHTV4OflA82O5Ddm7e/fkIsnP3yl4C4C2lv4YAwFsAvNLMNud/Qfl5ZL9MfUcI4UZkweL35b9Jn4rnoXiaz/3Kz7t7HICPneLjiWxY+Rj0Q8hOkXtbCOGr+V/P3gPgtWY2k59m+KvI/qoJAF8CcL1lvUHmAPwGWfVPm9kVZjaJ7JzX94bSpYQrjIVlf4ksOHe5Za40sy35aX/788dsmNnPgX+Al70D2SkHP4Xv/hkeyE7f/CUzuzZ/nCkze56ZzdC1iAil8aVe48s5PfFHdgrKtQBuMbNVZBP+rwH4T2PL3ILs8pRHALwWwL8bO1/sxcj+tHMbsm+y3wvgfAAIIbwP2aUu32VmS/l6n5P/7Aiy02R+H8DRfP2fftCe5freAOAKyxLp719nmb8A8FPjf/Uws10Anolskl/228j+SrAX2dWA/t8Qwt+XFwohfAxZCOgDZva4B7LR+SDySAA3P4C7vQDZZUfvfSCPJbLBfdDMlpF9+/RfAfwRiuG6X0H2TdedyL6leweyQNqJY/DdAL6C7IpXHyLrfyuyX9wPIgvev3yd7Vh3LCT+CNmE4aMAlpCNQxP5z34ewH9GNi4+ChWO8RDCLflz3Iks43Si/oV8fX+ab9O3sM4VxkSE0vhSw/HF/Be69WFmLwXwshDCU8/0tpxJZvYOAO8JIaz3y8FDysx+FNkvYD/6AO5zC4B/H0L42oO3ZSIiIiJnr3M+3CsnF0L4yTO9DSULyFL9lYUQTvWUIhEREZFa0MRfNpwQwkfP9DaIiIiInGtqfaqPiIiIiEhdnOvhXhERERERwVlwqs9PvOw/+j9JxLErNSNzteLl+YE0Td0ywd8NEVlXkiR+wdRvWhQ1/HLkryr9waBwezgaVVk9Rmw72F9t0r4vkfu2Wr73Vyj9PhjHPbdMOvK1Tqvras2m/90yJe0Fmt0JX+tMulqK4v5dW1lyy6ws3udqlvrnPjfnm/x+6P1/S94Rcq569g3XuzfjaFTuOwdMzbVdrR8X7xqP/PjSW/FjVYe8/yem/PqjVsvVjBzrlvjHbTaL6xvGZNyAH3MSNubEfv1J6o/rmbbf3qdc80hXe8zTnlG4fbc/hPGJv/lrVwu9w672pEfucbW7jgxc7da933K1iAz+3dJzmGj6MW1t6Me+ZsvvuOlpf9/3/t1nNL7UyDOve7x/Y7CTLII/xso1A5njkFqz4ad1gcxL2IjQ7fhxqEG/Hi5uWxT5+03NzLtau+PHiDQhY2Tbr6/Z9M8riYeuNiwdn8Oh37ftCX9Fzlan42qDkV//5NSU346hH3M2zcy52sxssdbr+zE4BP9aLS0ecrXlRT8evvnd1eYv+sZfRERERKQGNPEXEREREakBTfxFRERERGpgw5/j3yC/m6QNfw5UTM7fb5ZOpjMj6yLnfscVz6NnJ1Ol5HzbIckkLC0vF26vLPnzigP8usrnuANAkrIt8eecheGqq3Was/6epRN9B6MVt4yl/jlNTvpz8pvktVpd8+trT/j7Tk37c+RGafEtu0LO8R+s+PPh2sFv71R7w7/95UHWbK+5WqvpjycS+8GgVzo/k5y72236938U/PhSziMBABmaEJNiO/Lj2nQpv7I68O//NPbHYUQec63njzFy2j/M/J23z+9xtbsOFPNHn7/lY26Z+bbfmbNdP1a96FlPcbV//KY/n//Ld37d1dqRP/4nJorn+bbI6zLX9ufuW8uvKyLLSb2w89cDnTl4Vj4EyBwkkHkP+z6XzUvYoDYa+sdIyfiSlvMHRuY9I/85TKZgaDZITqFJ8k3+rmiQx03SUem2f9DBwGcgG2Q7Rok/x//YEb+Pui2fSRgsH3O1wwdKBfPjxnDkx9HRyM/nhgOfNapK3/iLiIiIiNSAJv4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgMbPt3IwjEJaXZRDpsAPgtD8hFYXiXdY0hjmwYJwgQStGO9OfqkucPiwkLpIf26mi0feB0mfl29gQ/pxQkJr5BmF+2GX1+/FO4dxSwIwwIz/rWKaYMNv76pab9cv+drg1Hxdej1SaOygQ8xb+r6sFBvzd9X6oVkw9Bq+0Au65E3ERWDoFHTN4Bh40aPhNtT8gCk9x2s4UNkIxIWXhkUQ8spicYlLJBHugZOtvz+mCfNblqk+c8nP/dZV9u6/YLC7V3Tm90yfdJwbDNpEnTexU/y64dfH5qf9DUS+I+apccN/rOg3fTNBqMO+XwgwW6pFyPfrbI5QsK6dZZrJMibkvvRCwWQ8SUiF95gTb1SkAsDuHCvX5eRKwXQ5qhkLjEa+sdskJAxu1CCa7pFQvxGjn12wZKYzTVdBRiav0hEnzQca5WeQ0TSzglpLMgaicWxH5uq0jf+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAhg/3siDviIRqR0MffgijYlhjsObXtbDgu6tZ5EMTgURyYhJSbTZYJza/vqXlYri3ZT582m7738v6JKHc6/uA8nDou7o1yb7swT+HuPRUAwkGNUgwaET2W0rCNyH1y/VKnYwBYEDCt8O4GHxhXZYt9e8FH70BksNHSVXqpDfyx4m1fXgTpPtj1CyGTQMZTq3hj502CQ83SYA2Ia0qU3IRgOBafAKDfvEdzzp8jkYkQEfCZpfN+bHpx5/5NFfbuuViV7vz6J2uNn/Z1cXC9ie6Zf76DX/gaps2+9dqoe0Dv/uPz7uakfE1MjIOrRTHnE2TPsTcbpEu8V3/2h85dtzVpF4COZ7YsRhISLdcI4c5QIK8I/KZSHKxNHjMPk/ZhVOsNB8ysnERuR+CP05oQJnMt4b0Ai6k03BpORZsZuHeiDxmWrHWJPMh1s3Yvc5kfjQkFzZgr+mIBH6r0jf+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDG/4c/17fn4c5IOfW93ukKU7pvP9Bz5/Xtbzkz/FPgz93ip3nlpAGChHZpeSULdfEKo78Y46Gfv0j0tBsNPD7I0nI+fbk3MImabIRSucNslMLY7KuiKyLnx9Izldj+xwkt1FqbsHO8QvkvLle39f6p3GOnJwb+kP//oyDz9GU33cAOZ4Sv65um51bTs7LJdvBzvGPgj+nvZzJAYA4LY4Jxs63Jcfw+RP+XNXnXnuNq115gT8vf/FTH3a1p113pavdvuWKwu3333iLW+ZfD+51tanI78uP3/JpV/uHf/qKq4URaULY9d97TZSabj3i4j1umX7Hj60H7/OfP6OBzy1JvSTk2DTSTC8iNdZ0r4x9NrMGUywfEMjGsTEnkIBAuedWEpNxlMyPmhGbN/g5U2Dn85Nz5nnfs2KRrcvIeM6+BU/YHqYN0liuguQ7Sndl+41lHkaxn/cNyX2r0jf+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAhg/3riwtutpaz7djGg58c5dQCm8O+z7M2eutulrMmvqQdAwL1gTaoIKEQUoNGVigeEBDhizMSgIzrGkFCaUkpBlaKIeKWPcP1piLBQhpOMaXWOMQprw28jRhJN6UkNeA3llqpdv1zbqMhHRnJshyzWJzpy5puLd5x3mutv/b97na4oof05rkPXvlYx7nahNbH+tq37z1Y4Xbvf4ht0xj6Bv/PXbXnKs98Sk+3Dv6wmd97V+/4WrJtllX2/X4rYXb3UnWnMaHce/xHwXof/1rrtZa8+trR35fdkjw2krZ5h4JI66xMZ4MX5MTPogt9UI/h8ly7GIZ5SmHkWZg7POVz0vIY7IieYiIFMuNqNiFQ9jzZGHcEWlwmpIDysgxHJF94uYSZN7DAtYpWY49CTaPYgsmrGliWgzpsoB1TD5/2AViYpYcr0jf+IuIiIiI1IAm/iIiIiIiNaCJv4iIiIhIDWjiLyIiIiJSAxs+3Ms68g7WWLiXdGEthStYJznWfbccvAUAsO5vJPTKQsCsa2YobVtKOvKWl8lqLMxDlqMdcwnW0a/0vCz13TxpwIWGb8j6aTM8vyANMldYF3uiLHzkknxSO7PTPoCZ9vyYsHvXHlfb9bCrC7e/dNs/u2WO9nxINZgfdmdIF9kw9GNC1PQXI9g658e+6LJthdt37fdB3slF31n26dde5WrNwaSr3fuFL7raBOk+3L/9dleb3n9z4faWtt+OqEH20VzH1fact93VFg/5/bFy2Ad549S/zu3S++Hwok8UN8ttSwHEZCCd3uQD4VIv7JtV1rk7Iu+pcgfXQN5jtCMv+VBkXYBZV1rSHNd16c22xd3TPybZDtbNNqFdev3x32Adc0kIOCl1ubWIzF/YBUvIvKFB53ikRpZjFyyJSyndtGK4l01fUrJcVfrGX0RERESkBjTxFxERERGpAU38RURERERqQBN/EREREZEa2PDh3tXlBVeLRz70FpMgXDlbEo986CvQIC8Jx5AghZGgbZOkWQO5bzIalJapFu7laVaa8PE1EkqhAdry47LED+uGxwJKNARMlqua0nVO9X4i9G0Ma3VdbRT8e/a+hWJgdpT4ZQbL/uIEgB9zWi0fQOtO+Vpz0m/boeN+jJyemSrc3jwx45a5eqsPNl+253pXO/KhD7ma+ebmGE5vcrX4qA8VT3z1c4Xbj73UdyP++Ca/rjv373O1HdvucbVW6gPVTdJVmY3fE6VOzqsDHwAOJMR8dOG4q022NvzHqzzoyOcr7aLLLtBRHCdYqJQGecn7OmX39ZtBL8bBNrj8uKyDLgsjN0nQtkGC/Owb6QGZqw0Sf3yWP/6jSlFkPt9iGeAo8tubxGyuRuZ9pbngaMSCvOR+rOXxqTfu1Tf+IiIiIiJ1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAhk8fjYa+U+WI5DmGQx/cTV2XNNLlLfWhWrYcC9GwrnkjEjZhHYPTuLi9rAMdC/Owznc0QEQ76bEk48mDO/y5kzAS6SDcaPgwT0hJMJg8L5r3dXckYWra3ZjctcLq5RxH3j/NFjnGhr67bO/wHYXbM5N+OJ2a2epqyaoP2i4cI8HVTX47prZf6Gr7j/iOtqsHvlq4vdV84PXp1z3Z1bD/K65k9/juu+c99VpXiy59mqsdfPcbXG1w652F2zuvebZbZu68C1ztwCG/j1iotp/417RBUosW+XDzUrlRLxmXI/jPjG5jytWmaFJS6oR/DvtPI9LA1S3XZJ1r2Wc/u/AGu4pB1YuCVOgOzMK97O3fIHOEpL/mai1yMYX29JyrxSN/fJbnffQCDuRJsc7ArMtyuTNwVmPhbBLcLe23qkFethwLcVelb/xFRERERGpAE38RERERkRrQxF9EREREpAY08RcRERERqYGzINzrAwwjEq4Yka68rlMvC32ykCrrmEuw5RIW5CVdeVEKubAQbKPRcrWIdLljHXOZlMVZK3Tujcn+5p2GSXiarJ8078Opxm9ZeKq6aq+znLvYsdnu+jfovuPl1CfQbhRDaZMT/nhF5N/Xg5G/YEG37Y/r/qofS9aOH3a1Zuwfd3DsYOH2NVde4pbZsnWXqx342F+72vQ230V38w+80NXS3Ve52rEvfc3Vjn/t7wu35/Z+1S1zfnfgave5CjDT9EHpNfgQYKPp7z3q+deh1SmOCRduP88t02z5MeeeQ/79MUk6NEu9sFBtQucX7CIVrgVtpfXTx6x0pQy6GVR52hTIZ3qbzEtaIFdmSX1383jN76N2x3ctn276B+6X9hsNO5MnaiSNTLss04vEkEAuDfcWazT8zeZRCveKiIiIiMgDpYm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAhg/3xiMfaohHpNsuCaCWO/AaC7JWrLEwSFwx4BpIcDUqhXkbLd9FstHwNSMhYN5az5doboctV+qi2yJddUmGGWnMOs6RgFvw+y1qkrciSQG7RyABGvpMWa75NMIxcm4IqX9vT7T9cdfp+CBsK9peuN1fu42sywdvW10fUjuy5LtXtsnxtP8e3712puUf4+qdxdDr1Zf7TrvHP+679OKQ74Q7+8LnuFr0yCt9bWLa1TY/8ymuds9X/6lwe/Qvt7hlHrVjt6t9Y8p3KP7Gvfe42mrfB6DZWJ0O/dj0iIc9pnC7O+3fC/v3/aurJSPfGdk6fn9IzbALipCgJvssKn9k0XkJ/VwjFx1JfK38OZ/dmZXYnObkG9Jpkosd9Pxx2Ej9cR1FPVeLV3xAf2LWX3gglOYNq3R/++fUNDIHod2SPRaeZuHbcudeGh6ucMGVbF1kQyrSN/4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1MCGD/cmsQ9NxSRZyrrLpqVAC29Kx9IsJFxBIh20Iy9La5AQTaNZCo2RIGtKts1YeJgFdCt29EuN3LkcJCHLNEkYl2QRKwVcAMBIeIWGj8roa0UYCzIp3Ft7tHmzP06GI9+ZtdsuhnTnpyfdMk976vNc7dsrfvz6x0+839VWSIdfG/rQ2+553732GU96QuH2zOpRt8yBWz/nanMP3+NqU6RLLyarBVenH+VDwM1djyjcXiKde6+89FGudtNm35H3yDEfRo4T3wk0jf0L3SCdRZeOFQOEg5Hft8cXSZf4vn9NF8h2SL2wjqssyEsDv6VPspR2/K12YQ9WZJ/D7DMxJvdtlS4y0iXzgVHfHyeDVf8czt9+uavNTvpx7tjCna7W6/tjrNnZXLid9HwXcNr1lnVGZnMw1vW36nyr/LhVpyA0xa3OvSIiIiIicj808RcRERERqQFN/EVEREREauAsOMffnzsZEtIUijaKKp5PVvl0KlILpAFG1eZRUcPv5lA6Z4s1JavacIs19miRpj4gzb8S1timVGPnuTXIc2qQhh0p2Udp7M/9i9i+ZLXSfqt8lpvO5xeGNG1Z6fnvQ4Yjci75avG8/0su3OGW2bFtq6stT/r3dZc0+lpa8mPfJhKkedj8ea62fdfFhdtH/vfH3TKkZyC2POMFrtbYfolfsOJ3Ru3z5l1t8xOvLty+7z3/7JbZdNA35rrmMr8dH73li37LGn5fhoEf52bIELmpXfwc6ZMRptcjeSSSISKxAqmZOGaZPHI+Pw/qFW6yJlEujwegSd6LEZlNJGR1CWskZn7e0ELx4OkM/fF1bGnB1TZtvcjVdlz6cFeLmst+fUO/vjZIE9WoOKY3G34c7ae+lpDXxci0ksUio6oZitLrRSOhvsRfe53jLyIiIiIi90cTfxERERGRGtDEX0RERESkBjTxFxERERGpgQ0f7k1ZkJd03WH9DTwWhvB35JmMaoGciDSdikhzrirrokFhsv5kRIIqrCEWeYSY3LcRlZf092QN05pN8txJoDiN2T5n4RX2Op98X1IK9wox9G9/tDv+/dlyxwTQbRffiwtrvuHW+z7+D67WJ6m6tLfmarNTU652wZRPpF6x2zfAWfvygcLtlTv2umV2PeUaVwtXP9HV/uWbd7vappY//i9+2BWuBrLfZp54XeH23o9+zC2zeNd+V7v6qTe42v+ZuMPVlklTnyj4F7rb8mPJ5vnthdsHYv8ROUGal0WRb94WWr4m9cICnrxZF1Gh1xO9EAmd07B7V/xMNHLRjlGxFrMGh11/fM2ft82vv+2vMtCe2elq03NLrnbo+L/69TWKFw/Z3CEXToAfD3q0OSppzMUasPmt4K9z6b60KVvFZmDlBm8PhL7xFxERERGpAU38RURERERqQBN/EREREZEa0MRfRERERKQGNny4N2aBCxaIoIrhBx5w8ViXO9ZtjwUuGqRjboMEXMtbwoKx7H4JCTuzkAfbNhZKaZDn2mwWwzZst7FQMNtHjQYJI7MkdmCvs79vlRA3XYY8CdZtU+plNPJvlrUVHw6dnfTHYqdRvG+DvO+OHPEdaJcWfJA3HQ5cbcu0f38+6VLfHXhn22/bwi03FW7P7vBB05kffqGrHWhNuNqf/cUfudr3XepDensuvszVrNVxtcnLix14Jx/vQ8arn/iAq80sHHG1Sy7wIcAv3/YVV5toksB2y9eODoqvzYLPa+Oyi7a72iD412ol9uFsqRc252AX3mDcxzW5sIfRjr9s/exCIWQxEsaPSK1R+rxm3YJnt+1ytT6Zvxw8etDVLt3qLxRwwYW+Njjix4Tjh+8u3J6b9GNf2vCB4j6Za9KLupB5FNtvKe3QXKqR9bPL2TBV57OMZj4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1MCGD/eyzr2nE2qogq2fd8irhoVIy+FbFqBh4sSHAFlGlYZqRyxswjrClcKCtGtctdeFPXfezbdap+FqWOCJhW/Uzbfu0tQfT4EENQckBNxpFN+zraZ/DzdJ4rdNQqWNyA/Fl23xAbTHX+EDtGt3HXe10dpC4fau7/93fjsufZyrJYd9d8ze/n2ulu703WurdseO2sULIJz3uCe7Zb5x86f9dtxxl6s9Zs8jXe0rX/+aq7HPkYmpWVcLoVu4vXb8PrfMt4/7QGH5gggAYN2trib1wnK8KfnMZZ9ZSSlASzO7rIssWZBe7IOETyfIhQK6LX/sREkxBN+Z8OPBtu2+o/ht9/hjuNX2F1No2qNcDQ0f0t198aWutny8+BjDge8yjo4/9iMMXS0NvsYupsIuOEODwaV9zuYgKXlNU/aYpzF90Tf+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAhg/38gADC5uePOnAgqZVg7y08ytZLGIhGhbM8BvnlmEhD7Y/Gg3fLThUjMay7n3lGgugRPDBlYiljMl9Ty+cXe7GzJapGsU+9fiwnBsa7H1M3heNpg+9xVHxuBskfl39oe9wbakPjG1p+fs+4XIfXJ0a+M69+79yi6tt23NR4fbE9c90y6A740rDUigYABpDv21TpBsmSGh/RAKEzdL+3XzVVW6Z9sP9cx997TOutnv3xa524Xm+Y+g99/gOyqQxOI4uLBZurywtumUm2/5jszHlx+DlpWP+AaRW2Gcn+2zm85ficlU/N2lfYDp/IRcsiHxIvRv7cO/asF+4PbPjErfM1h2kc++/3uq3Lfbh27XD/njtr/ntbZJn29lc7Kwd91l3XD9WReSiDlWxIG+Vy7/w6QuZl9D30anTN/4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1MBZEO6tGMC0k4dIWRiCBVJpZzYayPFYBzeWIXWNekkILiHdJiPalNYHVYz9Tmek2y4JEKPcMdAvgXUiRH5VJETDMkosaET3b6VHrebB7gAtG1+LHCathh8WWWg/Kh2MzZZ/r6PnOwN3yUH88O1Trvao7Ttdbe3zn3K1ubYPx215/g8Wt+3iR/htI1ZWfBi5R8K9C4f88/rsjT64d/t9X3S1R1xQDB4//vHXuWXOv+ZJrrb3a//iatP3+c66V+723Y2PH/Xddi3ygVxrFkPLzUbXL+MqAEhz0HmbYEtKjbDP8EDGEv5JVFquYsCTXpyAzHODOOiQAAAgAElEQVTazY5fX+zHvkF/zdUmZ4sB2h2X+HBva9ofX8PQd7XQ8+v/xu2fc7Wo4YPHKXxtfktxW44e/LZbxmL/mHOka/Fi4vdwP64atGWvcymwTQYTlu21tNpFY6rSN/4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1MBZEO5lIdJqnVnL4U3eEM2vn+SE17kz2TYWGK0QymGd35LYB+0ilwoGmqRjJt1eut+qdYk71XXx7sMVY7vVdmW1dalLrxDNrg9gsgaO/Z4/FsOoeMw2mn44Ze//rV2/3FMefbmrtXq+a+zwwF2utu3JT3G1qWueXSw0yfN0FWBxacXVjg19cvU9n/JddHHLba60tLrP1Z5x1e7C7Sse5cO428lz2vvxD7qa7dvrao++zAcNv7F1s6uRBryIUXydd26adstc/ohHu9rxFb8dy4dJ4ldqhXbu5Ve3OOly/KPPV+kcgdyzQ8am4cCHbydICHjnxVcUbne3+o7iPbJtzdlZV1s5vOS349C9rhZI9/RGd6urXXbpYwu3V4/4dfUXfIfyuSm/bSPze24t+M7rVS8UYqV5mVW4KE22nF8Xu+BEVfrGX0RERESkBjTxFxERERGpAU38RURERERqQBN/EREREZEa2PDh3nIYAuCBuUpRU5ofrRgEJYGZQDq4JQkJdMU+lBKVgoC+vx+QkG1rRaRLL231RraXhIBjsr1hVKzR9ROs+y4LLfPwiv8d1KJT+72Uh6cU7hWPNdqOSbdNS32tWRoT4pEPAHdTX3vcrgtc7YKZh7va/k/8vavNzvuQ6uwNz3Q1bNruayVs5Bs2/XgwbJDulau+82Ug3bwtJWPOYjFAnMQ+LNfdvsvVdlz5BFdb+PC3XG3r0cOudumFu13twGHf0RNJ8fWamvTjxvS0rw2jba72xa9+wa9faoWGe8lyPBt68nAv697K1sUuABIlvvs2zB+LW7b78P3slmJX8VHkLx4wTP26LrrEdxBf7Pj7TgU/Dt11cL+rDchznZgthpEv2u3H27WjPowfL/dczVozrhYC6cZMtpe/9uxiNRV8j+c0+sZfRERERKQGNPEXEREREakBTfxFRERERGpgw5/jT8/XrnjeXJV1Ve1zxc43j8h56YGcNJwm/jxflO4byIOakYYVDfKS0XP8/XNtNMg5+KzpUFw8X439dtgk96NN1EiGgJ2XaCS7UN5HAGvKRrIMgXVDIav3JamZ+RnfoGlt6M99TVP/Pi6/Y9uJP/Yvmm652hMu9OecDm/9kl//sm/gNfdvf8DVWlc92dXAmvqVsOP68kdc7GpP+0G//pmBP891/kJ/Dv4/fehdrjbRLuYDGpEfS6zddrVdT73B1RZv/IirpXf683evffJjXO3m5pyr3bW32ExoouW37eDisqvtP3jE1ZpN/9pLvdDpS4VmXXn15A9QMd/XIM2vWmSIiMn8ZXrTJr9gVDw+WVbKSO3C7Ttd7eFbt/j7rvimXocWfW2VjB1JVHzgyfl5t8zctvNdbeXAcVcLpJtjk8zLfJoBSOiLf9ICn9N8jzOK+sZfRERERKQGNPEXEREREakBTfxFRERERGpAE38RERERkRrY8OFeGhjlnbgcn4eo1qwrBNKIhoQraCCVNBcLpCFQsFKAloRUyk2+8g3xtYpNPCJy3xZ5jFAOMrNQNNlHcexDzCzsTPPULNDCW52cbNMo/pj6vbfuhuT92SQB+pi80Vql9Nr8ZNct84TdPlg2P+HHjUN3f9PVNu3xTazmrv83roYJEr47xej6xVv9ul7xH37Fr50cd/sP9l3tMx/5sKttmizeud0+eRAZACYv9vtj05VXudry5/7J1fYs+G3bd96VrnbbNz9dLAz9/fpHfOj66JGjfrn+qqtJvbDPMPaRlZLP0/KHG7uYCKt1SZC3QRpMJbF/zG5n1t+XzE3SYfG9PVyi8VZXaTX8Y7bht22NXNhgx2Y/Nq01O66WDEvbEvt9NDXlQ8Yr8I/ZbvrnNU2auS72fI01F7NyWJjNNcn6IzLXZA3CqtLMR0RERESkBjTxFxERERGpAU38RURERERqQBN/EREREZEaOAvCvV71JmblBauGIap1g2WddRGRzr0pCffGxdBIRO4Xsc6PFZ981V1ElysVA+lampAgb0pCzDRUS7og0+fFQsWV7sZSNSfvAiz1Mxj48Fbwb2M02v790yi9f3aTLr2PufAKV7vvy7eedF0AMP/0610t2nWp37jv4fc3DXJATU/6UB07cpaie13tGBk7JjfPFG6329U63Brpsjx3/bNd7d4vfs5vxz33udrW83e42jD0CrdXln1Ad468VoNBz9Vi0slZ6oUFMBPyvmAXFCl/uLFlOuRzbZLNNwb+fTzskS6981N+ueO+o+3ykWOF2zObZtwyo5EPxk90/JSzR8K9LTJz2E4uRNIj+3J1792F292mHzfiFT9/YYNavHLM1aZmfQB62PCvQz+pdtGVMvaeCad4sYb16Bt/EREREZEa0MRfRERERKQGNPEXEREREakBTfxFRERERGpgw4d7WTCWdnmt1Ln3e4w+AAuuspBHsZYkPuDCuvlFFfcHwzrVpqRzabnbLgv3VgneAkDEgrykM2rlLnSh3Lm3WmdE1kHRvseBGTkLkWNiSILrIMfn5lZxuSt3bHfLtA/6UGnY922/ric+zNU613+/346271R5JrAjZzPWXO26G65xtW1bi8d/VDHcy8bbzY+83NU2XXaJqy3ceZtf7mF7XG1yYrJwe2XBh/tWfGYRfRJkTEngT+oljv24Eejn9ckvbsE+IyMjn98DP35FqX8vtjv+uOuvLrnawTUfDI5KFzZZuo9tG7kgCk7eoRgAAlmOzhHI+O3mOeVuudnWucoo9Rd6YN2NseaP9RYZlxsReV5Wnlv57YjZnIzNX05j+qKRSURERESkBjTxFxERERGpAU38RURERERqQBN/EREREZEa2PDhXqZqx1W/XLU0BF09zd6Q8C0Lm7Bfr8ohVRLaTUmgkC13On16aRC2tG0RCSezQLFF/u3EuvTyIC/bNtYtucJrXzF4LEKDdpGvNUjgd8+2YkfICzf77pXHvuBDpWk8cLWYdJfsff1OV2sf9wE0zJDOup2Jwu0GOdIjErI30h0TLHxLuorvuegiV/vll/60q8WhOK5ZY8Itw0cm/7q0tmxxtflrnuFqd339dlfr3LnX1fbsnCvcPnjfPrfMiOT9EhLS6/d8N1+pl4QENennJPtccxey8Iv0U1+cmPDHU5NdEIV8NrNaWgryZttSXp/fjgbZXvpNc8omV37uk5DAL7kMA4LbJ+SAJRcsScnWRRN+TE/JuDkgIWA67ytvG/msMbdv+UVYToe+8RcRERERqQFN/EVEREREakATfxERERGRGtDEX0RERESkBjZ+uJd2a2MLnmJ8s2pQmD4i68jL7s1CqqXbJDACGuQlSBiEbTEPy5L7lkvVmhHTdSVVk9IsfEiCL251ld8fZDElfmsvkBCZmT/u5lo+XHXx1q2F24fuPuKWGR046mozJKd17JOfd7XFz/tAarNLgrBtX1uZK4ZUZ0kIsDU96WrxjA+z9eY2++VK6weAqN32j0G6g05NFbfX5nw4uT3vH9Nm510NLf/cN+0539VmN/nw9Mrd+13tosc+pnD7S21/v5D6SGGDjCVp8r0N5MnZh36+stkEvSCFlW57IxL6PLpKOu2Sz9IW6SzdiHyQNzI/NynXDH7MbJGuws3g129kGtqM/PoSkhYekl1Zzgqz16DR9NuRjPxjRk1/35gEg4csLMyC3a6rcMULp3yP5yr6xl9EREREpAY08RcRERERqQFN/EVEREREakATfxERERGRGtjw4d6q0dAqS1bvyMuCNlW7BbMtO3molmRvABL45b+psRAsCZbRHUDWSDemzK8/nMY+Yp0LK3VoZqkXGpipsmVSN60WOcZIKKvT9kHYxtwFxQIJgiWXH3O1hbUueUwfLGvGPjDWGfhusJ0VH+ZrH76vcLtHwr3LJJA2IuNGnwXo2JhGgoEgnUBbpf10pONDwaHb8auannW1wcSUq3US0gV9zXc87q/6kO7U8WJAe9ec37Zv3+dfg+k26cY654PBUi8Ref8zLvQJIC0ds6zLeDkADPDPYdZBmCLjRIN8NrswK7kgAgvjtkmX7ibpZJySAH2S+mM4kDGn/FTZXGgQyBjBOvyS1sBRgwSDSbjZdxBep1N8eRny+rHAb6X50Tr0jb+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1IAm/iIiIiIiNbDhw71VnWrQgYZxK66L5zR8kWdlS8Gdiu1mWSdcGjwmgRYeBGIh4Ki0zKmrui9poLpCoIV3RmSBX7IUaz8stTLb9kHbHumEvUzCcZ+9+9uF2/GV290yU0+5wtU6nYtcbX7Wh4e3tf12dFZJYG6VdL4cFFNpgYSHU/iQapKuuNpwYcnVFo8suNoyCcv2er7WHxVDeoORD+2xEHNr5aCrRaOBqw3JkNYnr+kqGTs6h4uPcfmmnW6ZhUmyrtg/h3Ton4PUS8W+vbTLq8uGkpXRjtHks5R9vrLlSDwfaUrex1HxvvM7/Ng3M+u7gM/M+OVmJ7e62uoSuSjCou+C3uuRsWmxeF8Wlh2RMcJf6oDft0UmdCTHu858q9SNmbx+fN5Tbbmq9I2/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAWXmOPz3fiS2H8vngFddVsakXu2+DnP9V5bcrel4eOe/P2HJ0jdWWo/utfO47aS5CsQYjFU9DY/uy3MAEAKIKzcX4uW8V3zRSK4O+Pzd7beDPG+/1+662tLRYuH33kTvcMu5YAtBp+fP5u6SR2BSpdVu+Ac5k259Lu3mq+BjnbTnPLTMx65tfJbN+/Utd0pxqp2+mNTfjG1Z1J30jrpnJYlOsbU2/j6bWFl2t1fd5gdDzzcuGI/9aDWJ/3wmQ53qo2Pis3/NnPVtz2dWQkOZt7ERiqRUjDbzY5zqPwlVo9sQaddL7kc/wwD7X2fzFb9zstrnC7cnNfjxoT/nxIJryy60GMg2d3uxKLfJU08hvmy0Xm/C1zK+fZSVHpPEfe/3YufusARsb+5PSY7SaZGyteN7/6Xxvr2/8RURERERqQBN/EREREZEa0MRfRERERKQGNPEXEREREamBszLcy1RpgMUDNFWbdVVLgkYs5EHDPEnpNgv3VmwwxcK3VUO1rFbaXjOSqmFBXhIWouGm0+ibFcLJG2Cs86y+p9sh54YhCX02Sag2in0tLTXF6pNlkuADY8OhDw8vk0Z69yU+pBoPfBObdssHUFvt4ra07Xa3zOy0DwUvB9/G5s57/GOCBKAvOH+Lq3UmWr7WLI4Jncg/9ybZH522Dwo3mn58mez65aanfJC50fDN2w4sFYO7+xd9Q7PJ1O/viAy4s1N+/0q9sI+YhF20gwZGT/5ZF9ErkVTbNtb8q9sk7+2+vxhB1LywcPv4ml/Z8YG/cEI5eAsAk10f+B30fWOu0Peh+vlJfww3m8VjvRn7MbhJxhxLWDNTUmLNv2JfTEjTwHKjtjj24y1rGsbmeKfRv0vf+IuIiIiI1IEm/iIiIiIiNaCJv4iIiIhIDWjiLyIiIiJSA+dMuJcphzdpR7SK62LL0W5tJLiTpj7AUd442petQUKGpHOtRT6QwzvVnjxsAgBpKeQSUtLRjiRcaKdh0pkuItvLOumxQLWVnxd9mv5+LMhLQzRSK+y9zUJZCVkuHhaDwY2Gf/+z47XRJOsnx0Q88o+ZkoEiJt/fDEsL9klQeKpBOvcOyXFIjusGGbEC2Y7+0N83HhTHw2MDEmIe+tB1t+ODwjEL6ZFt65COx0MyLC/2iqHCdss/pxHpSNpu+21rk5Cx1AvressCuXQu4eYv7HOtYude8gEYkXEIJNwfwYfUjy0Vx47etF//9h1+fDlyxId7l3rHXK3d8Mf/XNePkU0S+J+enC/cXj6439+v1D18Pex1YddcSSsEedl92TfvCZurkNeZhbOr0jf+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNTAORTurZJ0qNgRja69WmAmTap1YiuH/hokyNto+ABK1GBBXqJy596TB4YS0l0uGfmufCAByJD4WtVALlO+K7sX64LMnUY6Rs4Jna4/7li36cbQB1BbpY65DRJkZ8dXgwVGyWEyIsdYl9w3Io8bj4rhuCYJGYN0vV3rkfGLXRSh4Y+xhs+30qBaGhefV5Psb2v4WoeEZY10+O2SoG2DjJvTTb++ZLH4QiweP+6WiYIPHs5v2eZqawPffVTqxchnETsWU/aZVZqb0HkJOXZYqDSQ44S3pfWlhvniRZdeVLh9X9uPVY2OH0vmGvOuNuytku3wY9phcixObfNB+5npYjffAXlSqZEu6+QCCBHZb4GkexMy0LHXtDw3iUm3YCNjFXtNaQvhivSNv4iIiIhIDWjiLyIiIiJSA5r4i4iIiIjUgCb+IiIiIiI1YNWDkCIiIiIicrbSN/4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1IAm/iIiIiIiNaCJv4iIiIhIDWjiLyIiIiJSA5r4i4iIiIjUgCb+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNSAJv4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1IAm/iIiIiIiNaCJv4iIiIhIDWjiLyIiIiJSA5r4i4iIiIjUgCb+IiIiIiI1oIm/iIiIiEgNaOIvIiIiIlIDmviLiIiIiNSAJv4iIiIiIjWgib+IiIiISA1o4i8iIiIiUgOa+IuIiIiI1IAm/mPM7E1m9pozvR3nAjN7qZl96kFc/yvM7C8rLvsqM3vbg7UtIue6MzE2mtkNZnbPQ/mYIrLxmdndZvZ9FZf9N2a2z8xWzOzqB3vbzgYbfuKfv8C9/EU7mH8ATZ/p7ZLvMrM9ZhbMrPlQPWYI4XdDCC/7XqzrgQwiIhuFxkYReSiVPyvN7MfN7LiZPf1MbtdJ/AGAXw4hTIcQ/kWf92fBxD/3/BDCNICrAFwN4DfO8PacFjZBfignzWc77SuR7zinxsYTdIyLbGxm9hIAfwbgeSGEm8709tyP3QBuPdMbsZGcLRN/AEAI4SCAf0D2IQcAMLM5M3uLmR02s71m9kozi/KfXWpm/2hmR83siJm93cw2jd33ajP7ZzNbNrN3A+iu99gnTl0xsz/If8O9y8yeM/bznzWz2/N13Wlmvzj2sxvM7B4z+3UzOwjgjayWL/vzZvYtMztmZh8ws515/dVm9vr83y0zWzWz1+W3J8ysb2abzaxrZm/Ln/OCmX3ezM5b5zk90sxuzJe71cxeMPazN5nZn5nZh/PndIuZXbrO7vlk/v+F/NvHJ4+tZ739NWdmbzCzA2a238xeY2aNdbbzVWb23vx5LQF4afn0HTN7cf76HzWz3yS/1bfz98ly/lwfn9/vrQAuAvDBfNt/bZ3nKLJhneGxca+ZPS7/909b9te/K/LbLzOz9+f/7pjZn5jZvfl/f2JmnfxndDwsPc7Lzew2M7vge7HPROTUmNkvAPhDAM8OIdw8Vn+Smd2czym+bGY3jP1s3c98y+ZXnzaz15vZopl93cyeVXFbIjP7L2Z2Rz6evcfM5vPxZgVAA8CX85/r8x5n2cQ/H/CfA+BbY+XXA5gDcAmApwN4MYCfPXEXAL8HYCeARwK4EMCr8nW1AbwfwFsBzAP4awD/9iSbcC2AbwDYCuB1AN5gZpb/7BCAHwIwmz/+H5vZNWP33ZE/zm4Av8BqZvbMfHt/FMD5APYCeFe+7E0Absj//QQAB/PnCwBPBvCNEMJxAC/J98eFALYA+CUAvfITMbMWgA8C+CiA7QB+BcDbzezhY4v9BIBXA9iMbJ+/dp39cn3+/035n9M+U2F/vRlADOAyZN9U/gCA+zt154cBvBfAJgBvLz2XKwD8TwA/hWy/zQHYVbr/C5Dty00APgDgTwEghPAzAL6N/JvTEMLr7mcbRDakMzw2jo9N1wO4E98dm67Pfw4A/xXAk5D9cvJYAE8E8Mqx9bAx8sTz+00ALwXw9BCCzvsXOXP+A4DfAfCsEMIXThTNbBeADwN4DbLj+P8G8Ddmti1f5GSf+dciGzu2AvhtAH9rZvMVtuflAF6IbMzZCeA4gD8LIQzyv4YCwGNDCJfq8z4XQtjQ/wG4G8AKgGUAAcAnkE0wgew3uQGAK8aW/0UAN66zrhcC+Jf839cDuBeAjf38ZgCvWee+LwXwrbHbk/n27Fhn+fcD+I/5v28AMATQHfs5q70BwOvGbk8DGAHYA2ACQB/ZZP6/AHgFgHvyZV4N4H/k9/m5/HlceZL9+jRkvzxEY7V3AnhV/u83AfjLsZ89F8DX11nXnnxfNKvsLwDn5a/bxNjPfwLAP62z/lcB+CSpvS3/928BeGfpsYYAvm9s2Y+P/fwKAL3Se+z7zvR7Xf/pvwfy3wYaG/89gA/k/74d2Yf5u/LbewFck//7DgDPHbvfswHcnf+bjYc3ANgP4I8AfArA3Jne5/pP/9X5v3zMWQLwd+Nzh/xnvw7graXaPyD7MvJ+P/Pz+UJ5zPkcgJ+5n+048fl+O7JfQk787Hxk86ZmfjsAuIzdt67/nS3f+L8whDCD7IPgEch+I0T+/zayD5cT9iL/ttfMtpvZu/I/Ky0BeNvYfXcC2B/yd8LYfe/PwRP/CCGs5f+czh/rOWb2WctO0VlANlHeOnbfwyGEfml95drO8W0IIawAOApgVwihB+ALyH6rPfEt2s0ArstrJ75Veyuyg+1d+Z/TX5d/u1+2E8C+EEJaev7j35QfHPv32onn+gCst792A2gBOJD/SXABwJ8j+8vDevbdz892jv88f6yj620LsufSNZ1HLGe/jTA23gTgaWa2A9kvHO8GcJ2Z7UH2F4cvja23vD07x26zMXITsm//fy+EsHg/2yAiD41fAvAwAH859hd8IPtc/5ETn+n55/pTkU3Eq3zmszFnfHxYz24A7xtb7+0AEmS/bAhxtkz8AQAhC5C8CVlKGwCOIPvNbvfYYhch+5YIyP6UHZB9+z0L4KeR/YkbAA4A2FV64150KtuVn6f6N/l2nRdC2ATgI2OPhXw73FMq3b4XY8/FzKaQfcN/4vncBOCZyP5M9vn89rOR/cn8kwAQQhiFEF4dQrgCwFOQnX70YvLY9wK48MQ5v7nxffdAsOd2f/Yh++1/awhhU/7fbAjhUaf4GAcAfOe8XzObQLbfqnqg2y+yoZzJsTGE8C1kv0y/HNlf5paR/aL9CwA+NfblQmF8y9d57/iqyOqPIxvD3mhm1623DSLykDkE4FnIzhr4n2P1fci+8d809t9UCOH3Ue0zn4054+PDevYBeE7pcbshhPXmMrX/vD+rJv65PwHw/WZ2VQghAfAeAK81sxkz2w3gV5F9ewUAM8j+FL6Qn3/2n8fW8xlk55u93MyaZvYiZBPoU9EG0AFwGEBsWYj1B05hPe8A8LNmdlX+y8TvArglhHB3/vObkE3ibwshDAHciOzP6neFEA4DgJk9w8wek4dmlpB9+CfksW4BsArg1ywLC98A4Pn4bqbggTgMIEV2LvFJhRAOIMsW/KGZzebhnEvt1C8J9l4Azzezp+TnJ78axV+6TuY+VNx2kQ3sTI6NNwH4ZXz3L483lm4D2amErzSzbWa2FdkpeiftrxFCuBFZfud9ZnbtyZYXkQdXCOFeZF9C/qCZ/XFefhuyz+Fnm1nDsguN3GBmF1T8zN+ObMxpmdmPIMsefaTC5vwvZOPcbgDIx5cfvp/la/95f9ZN/PMJ7lsA/GZe+hVkE9g7kZ0H+g4Af5X/7NUArgGwiCx08rdj6xkCeBGyc8uOA/ix8Z8/wG1aRvZt13vydf0ksgDpA13PJ5A9r79B9q3bpQB+fGyRm5Gd63/iKjq3ITvv/5Njy+xANhFeQvYnr5tAPlzz5/8CZIHA/7+9O/uRbb3POv6uqcau7t7dezjD3uf4eDie4jg2ju1MJCFWiFBCBAkICSmKAjfcw5+AgkQkBHdEueEGBQQRChgpJLlwJo/Ejmcfn3nYZ8/dXXOtkYttgdZ6HpPOiazTPuv7ueufVlWtVbXWW++uvZ73dy88/Jf7rzRN8803sN/r8DD4+6ff+e+2j5/jYb8SHv6D6evh4Xv2X8LD/xL8K2ua5mvh4Xnw2+Hh+7YID3+V2J3zKX49PJyQnEZR9M/fyD4Ab7Y3eWz8VHj4j4k/+i5/h/Aw9PeFEMKXQwhfCSH8+Xdq5zm23w8Pg8m/G31nBSEAb56maV4JDyf/vxxF0a9/5+9fDA/zh3fDw1/i/0X4f/PMv+w7/7MhhHeFh/ORfxlC+OWmabq37Dr/Njycb/2vKIoWIYTPhIdB4e+m99/3UfuWKuD7X/SwidFpCOFdTdO88GbvDwAA8KIo+tUQwj9tmubH3+x96YPvu1/8ASeKol+IomjynVzEb4SHvyi++ObuFQAAwMXBxB9vFb8YHgaBboaH/134jxr+OwsAAOD/4lYfAAAAoAf4xR8AAADogQvfwOj9732//JfEZDSS7Q5mY6lVcdL6e3VyIts0sfm3T6MrQdZVIbXdLpda2ejKmYNsKLXhqL2/e3v7ss16rvu72XX724SQZPp+DIcT3bcokdo4LaW2XKxbf1969Adkm6bQ5XXvvv661NI97clVbudS2xUbqR3OZlIrVu3Hztf6GbR7kj0UJ/o5Z6m+H9/89nN/lWVA8X3uh370H8v4UtV6XlR6SoXjR9qtIv7+L3xCtvknv/S3pLZaraT23LN3pPaxH3uv1LIBp+f3OT7AHvnwhz8q48vx0ZFslyY6wDS7RevvYnEq27g5zcaML3t7Oh8IezpnWkU6lyiDzl9C3J46Jql+vyaRnoQ0E14AACAASURBVOpmehRqMy+pzFL7salVZl5WFe05gZu7RZXOe7Jady6t9XOpzZdBaeYvTaVztcuH7R6o165rj7HJlRv6/OnbpHb6QD/T//nJf32u8YVf/AEAAIAeYOIPAAAA9AATfwAAAKAHmPgDAAAAPXDhw70mzxGKUov35hry3J+1gyp1rY+LTAClNoGOKNa3KolMsKTRf0u5JVPrbgC12ck2mWZeQu6iG5G+Zp6b4LEJ6YxSPa7uW7I6fUW2KXbaSbuuNBxTl3pcda375jLW1/Y0aHRn3Q48ubRQY/JzkXmBLMn0RdErw7EG3NKBnhfLMw1qNXX7PFsuzmSb5771ktQGhW732T/8lNSefPujUrvxxCWpERcFLqaVGRP29wZSqxP9Hi5X7TlCudR5RJPrJGEY6/dmk+tcZTbQBUWG40OppdGBeY32oFPWGih2C52koz2p1YU7Lp03FGbeoFuF0H3V2iziETU6xg/M3C0zc7fVZq01EyCeDvVYx1n7dSvzXPlcQ9zxRL9/8vUbH/j5xR8AAADoASb+AAAAQA8w8QcAAAB6gIk/AAAA0AMXPtwbRSa8WWnIwwVhF/N2sKYy4dORCXgO3JPVGt5w715VusDFX96FrspNNziT2kszDQa5APRitZTaYGw65JmgdNMJqpQ7De5UO9OCz4RjolLft8R0zatNQHlVa0ip7oR0XTg7Ns+VJnrsMaHI3ptMTMfs7UJqq60ZO4p2bVvoNsmBdp++fngstcc//SdS++P/+J+l9nP/8O9J7dJTV6QWcXIDb7qh+b6uzRyhaUwn2aT93Tw23991pc9VFTqXmM10HMrMwgZLM5kYNNqVdq/qbNfo9/wy0v0octNVuNHjymqN7S7NgiVZZOYvaSdA211IJYRQ5Pp+J2auMh7oXGJ0OJVaaoK2QzM3GXQOtd7ovi3N4g/NQBeJ2BX6mZ4Xv/gDAAAAPcDEHwAAAOgBJv4AAABADzDxBwAAAHrgwod7G9P9zbV5zYLpVNsJubi4W2GCwkmi4Y3MdLhNzDNWQQMipvlbaDqBk+1OO7PVtQk2mycrzWPTUo9r4t4386aUne2SWF+zMsGVzJxNiQkLFS6QZMLTq1y72sVJe99ic0x1Y8JTphvz1oR50C/PPn9Ti+aaiExgvOh0w2waveYuPaIBrP1D7ej4gUe0I+/v/OZvSe2/v/qc1H7qV39Najc+9HTr7zjlN56uwoyRSaeDqlsoADivYqvn2OpMF94oSw3QDuP2Yw9HOjCNjzSg2wTTkT4bSmle6L4tTIB2GGtAuenOh0w4+cR0392W+j28l+kiHntmvrU23XGXhda6sya3AEhkBvk4mBCwWYgkMxOdbKZzxkGix1V23t/1Ws+F7cLUyue1FvQzPS9GNQAAAKAHmPgDAAAAPcDEHwAAAOiBC3+Pf6hdEyvd7dLcN969ub4y93mbW8RtM42yNve5xbofTaX325veXCHqNJSKEnPPvGk6tdvq81drbYoRmSYeu/REHxvpfWhN501x73dcmHvrTSbBNfZwzYVi8zlvl9rIIutkPnx+QouRyRrQ4giN++3DNHyJzP2fZScjst7ouLEz16E7aYu5nuv1qV6vz3/qk1Jb3NWcwk/82j9r/f3+n/qYbJOOLv7w/4a4hj3mfv7FSt+36fRq6+9hpnkM4Lzyrd67X2/13vfI5IOqpJMhqnQOks8OpJaMtCnhttSxabPT18xNQ8/Y5O8Wcbemj9u5cc59z5us3cLkCs2uhV3txrD2hrbJp3uUmQy6+/6zWrfbm+p7nk30s6nW7c++yHXf8o3e4x9q06jMzFPPi1/8AQAAgB5g4g8AAAD0ABN/AAAAoAeY+AMAAAA9cOHTXVliAhcmhFGaZhTSfMGEcaNYA7SVCVJEmqsJmWnQ0JgQWVxq6LWq2/tWm0YxlQnMVLlpOmWabkQuU2i2C4U5BdL2e1KbxhkD02CjqvW9LMxrFub5GhPAyXN934pOgNh9ViYDFMxHEIapaXSCnjlf6NsFxvO8HbQ/OdOA7sn9udTKgb7C/Vde0Nc0zWncdXLvW38htU/+m19v78ervyLbfOTvfEJqw0sTqWUj0yjG7Edt0ne1uYbTyff2a2e30M/h688/I7Wn3/tBqQ1No6NzcasMmPcI/eIaR+amFknbqRDCsH2dNJNDff7pNamViV7DVayB4qrQsamJdd/cPGQrAVpz7ZtFPCLTSLAyc5WtGYVd+DbolCN0X9YFdBOzb0lqGouaAG021PFrbD6bwVgDv8m4/dhsdKTbnJxKbWUWf4gK/UzPi1/8AQAAgB5g4g8AAAD0ABN/AAAAoAeY+AMAAAA9cOHDvaXpkhabREduQrVV036sC3NWJmiaxCbgVWvIo3IpUtNtN3bBr06ixTSbDGWjgZ96Z7r+uXSM0ZgwSLk1+ztqh5Yb874NMg3CuM56dWkCKC7cZELWsQk8p0nnsZluUxT6WTWNOY+SC3/643vNnGPusnbVqtMtfDlfyDZf/t9fltoq6DXx7Defk1o90HFo77FHpTY8mElt/eBe6+8//g//TrZ58Qt/LLUf+aV/ILWnf/rHpRaZIJzrLvlmtMferDQc97nP/L7UFmEstR/74A+2/jYN1YPtUrrS8F061OdP3mh4GN+XIhM+zcY65iSZhkOHe+3a8EiDvMlUw6Flbq7DXIO8SXfxkxBCajrrnuf3YTOMhmDCuFFkFlhxDzaH0LjAr5tbdQK5iQkeD03IODVjmlucZDTS8PTeWLv0jqdTqSVZe241PjRzq0P9nKubt6W2uHdfaufFL/4AAABADzDxBwAAAHqAiT8AAADQA0z8AQAAgB648OnG3AVQgtZ858T2n67xW+o65tamO25turw2GvyoS33srtQwa9rZmco8LpTmOAsN/Lo4TpJpq+HGddfbbXW7TtB25wLLib4fk5F2qtsb6GPLSoNGhUlK1ybhEw/arzs0n6nr7Fw1JoxsQsboGXP9R+dMpDad5F6x0WvpmS/9qdReu61deutbt3Q/pno97T/9g1I7uKFhsNXrL7X+PnnmW7LNS1/8M6lttqaj5VCDazf+xrulVrvuo8Zqvm4XzPU6HWlX9MSE8WsTKB7uXZLatcPHpPbpP/kjqU2y9mf62PGxbPPI1avmNfU9elOSzbhQklSvidm+fjfPLmlofzB9svX3cKbXeTrU51qbLq9NudaaCbMOUg2kN26a2B03XT7XzBtcuDd2gV8b5NXXqM2qKFGnlkW6zdB1/I3NWGIWD6lLrS1PVvp8Zn5xeNwe10Z7ujDDeKKB7TrR8Pe2viu18+IXfwAAAKAHmPgDAAAAPcDEHwAAAOgBJv4AAABAD1z4cG9twr2F6V7rum12A7QuHeJCpUWtz5+Z7rWp6ebr9sN17i07oTS3TbPT8HBUaVgom2nwY3akobTdXDu9rRcaSqnzTtA4NeHesYaK6sYlZrRT5cgEkpJSj8sFu7tZHte0OE71tJZzIYRQNxf+9Mf3mI1fmmIS64k2yjrXcGzOVxPsD7uNlMqtjjmDSxoYHRxqbXr9SamNjtvbJQMN7d3/1jek9trX/0Rq//U3Xpbah37253XfXAfhVK/1PG6POaN9DS3+6I9+VGqzfQ07V2YAWK70/W1MCPDyVK//b3ztL9rbfPxnZJsocmcNQV6o1LR+PjjQjq7HVzQwPp3daP2dJHr+70xH3sIEeePtUmpDc8qmsflOjHXsqDu/GRcmyJqXZkENs8hGXei4aWYSwUzVQpK57/r2o11n4EGs++Gu68TM+6Jc5yrze69LrVrckdq001V4ZhYsiE2498jM55p6T2rnxS/+AAAAQA8w8QcAAAB6gIk/AAAA0ANM/AEAAIAeuPDpxsQELmoTmItiE0DtBDpd8NYFfmMTBAu5BsYq2y3YhHRN8KXuHJfr8hZyDfcG1zH36nWpzS5r98rGdBrerDQIVJft7epcP4OVZoLDeE//HRmbbp6uI+90rCHgQaGvW3Te812hHY8bE7RLB/r8Bweu2yb6JCq0y2Wc6Dk7jM212AnfP7h9KpusYw2uTnd6zpYmtD8ca/faMNTzuDbB9WT/cuvvg6dNXM4E48JIw7inL2m49zO/89tSO3rnB6X2xMd+WGof/9lPtP6+8ugV2SYxCwq4AXxgOoNfvqrv+Uc//ONS+9rzr0pt/1r7fXvsMQ3a+XAvoFITbh+PTUfuA12gY9zpXr1Z65fu+vQVqa0evGS2O5HayITlm4Hrjq3HsM3bF+M614tza9K4ea3jXJmbcK+Zg7l5Q2rGw6wzFxyZOVluks2TiR5nZsLZA9ORuCl1cYb15oHU7pbtz7ApNHQ9e/ztum97N6TWHOqxnxe/+AMAAAA9wMQfAAAA6AEm/gAAAEAPMPEHAAAAeuDCh3tTE6Rw3Vrdv2GabgjLhWxduNd0egvBBYpNyMt0pnPB46jTubc2nTvd/mb7GjabXtauf4kJqgxmGmbNzjTcWC7bgd+oMl16TcC6yPUYXLjXdfkbZ6YzsgnzlJ2uwu4zyE0X4KrUANFyvZAa+qUptfNlbTpyl7UJrg8nnYJ25IzMOVyZjty2K+VkIrV4pIGuqjbdxzuvu4l0qJ+bjt/xk++U2sFAr//5Cy9I7cU//5TUzhYaKnzX+z/U+vvKoxrG/euITNj56LoG5p4wocJor9MNszILDJiAYmZCxkAa6/daHJvO9aaD62bXHptOTjQs+uC+Bu/n9zW0Xpu1Caqp64St1856q+f7etOu7UozzplFNgozd4uDvke1mW+55tjV1swROl3W80ifa5NrbU+Hg7C/Z6bILqBsxu/uXCWEEB48aId7i8p0cTfB5is3tHvyMNK54Hnxiz8AAADQA0z8AQAAgB5g4g8AAAD0ABN/AAAAoAcufLjXdWtzjROzRP8N0w29ucf5cK9umJmOuVmsj93sNPjhwr2h23HWdKCNEg0BTY81CDccawiwNoGWdLInteFUa7t1O9xbmZBKSPX9Lnb6fmSms15jkka7Qt+3fLOVWtkJ6ZYm2NiYWrcbcQghbJb6muiX6XQmtclIU16jgV5PedE+p6Jcw3fbuZ7DuWl77QKpqbmuYxO0LSvd37u377b+/tJXvyTbLHM9/9/7lIZ7H3vPB6SWDbSr8Mlzz0pt/u0vS+33/v1vtf6utr8q27zzo7ofg7Eee+wSf8Z4qo99zwfeK7WTuzdbf68WGv4ejjRol5vU4nKnix1cO3rjgTx8HzKLfTRmoYCVCcHv8nutv0/v35VtTu/fklq+0e+6veElqcWxhnvvn+k5uy1MgLZzDJU7ThN4jdxvzW6NlGC645rnC0HHvjRqH//AdCNOE52X7Mxxns61s2400n0b7+nCKZtqLbVy015Q5OzEbFNqYLvamq7CR9rN97z4xR8AAADoASb+AAAAQA8w8QcAAAB64MLf4+/u4Gzc/bCmAVQo2vermdvIQ+LuLzMNGnJzD34y0NdszL+lKtdxLG/fh9aYplYD16zr+LLU4lQPojLHGg/13tTBTO/zG8xPW3+v13rfX5XrfW6lyTxU5l7gEOk9ztOJ3iPX5Nrcoujchzc0jU9S10zD3CAYZRf+9Mf3WDZwOQ8930/mei52r7H9kV5LQ9MMLJh7vxuTIUpNA6/G5IXunZxK7ZkXX2z9fXqqjfqecPfzP/aY1KYjvU5GI7O/Js9z8txzUnv1L36v9fd/+lfflm3e9pGPSO3aD2nW4AM/8sNSe/JxPQZ3f3BVm/uSy3btua/8oWzzxedf1CczQ/y1d7xbar/4t/+uboi3rMLc+75e6H3dSXNbatt1O1+yvK8Zomqj49dgoLmlKtOxKd/pWLLJ3f38+hqyVeMa2JlmpnZCZ2rn3Cw2E7jZXnvO8eglbVQ4THVsvXeqeZ6791+XWlbpsV7rNv4LIUzHb5PayYP257ya35dtqoXmwtZrHSM3L2ujtvPiF38AAACgB5j4AwAAAD3AxB8AAADoASb+AAAAQA9c+HRjZgKY3cZcIYSQRiZY2sl9NJUGdF2oLpgwi2tQsTNxE7dvsQnuVp3mP3Gs+zE9uiq14XQqNZdci1ws2rxGMtEg0HCvXdtttSFIacLOTazNLna1BpmCaba2c6eiSQIl3ZrZJs602YU5PUIy1mNHv5xt3PVvrqdaA12jYfs8Hg003FptNciebzXcm440HLYpNOT1+S98Tmpffd40zlq1w8hXH70u2wzN+Z+aZjfBBOgHx49K7SjT449G5lr8djuoNr/5vGzzzB9o2PHeq89I7dIlPYYbV3ShgC9/6c+k9tXnNBRd1u1xLS31M5gO9Zy5vKcLJzwy1f1Av+Qm9L2c63fi+oGGPIvO2BGZAO1kaBrCjbVZVz3Q8WW5MU0JzYIadWMOImqPCVGs538U6bXfNK6BlwsG65wjSXRc3j/QkO7RYTvIPDJzyLrQzyAOeuy7nc5pTsx8aBJpgPjqVZ2/HY3b71Ntxsz8gTZqqyqzwEqj+3Fe/OIPAAAA9AATfwAAAKAHmPgDAAAAPcDEHwAAAOiBCx/uTVxnVtMhtsg1bFp3QnpJoiGSxvWDO2d7ucaFAM3T1aZTZ+h08832NZAzvaTBnfVSA2nLjQZVhkMNAe+2GlRpTDA464TSsqU+rtpo6C0yHe1qE9wpzHu5LUyoyIWiO5+N2yaJTeDXfDBN/sbDMXiLaFwAzZxTYSG1uHPtNKWe67Hp3NuUpluwCaDdPtVr/cx07t0ba7Bus2hfs3PTuffsTGurrXYGT1KzoECkvxlFptP43tu1e23UCUHHJhR9+sorUrv7lS9L7Wu/+wdSy0xn1M9+4X9I7dWVXv/NsN3188bjH5JtfuajPy21k/vaofiZ174htQ+Gn5Qa3rpKM0c4nev36bDU+UuWtq+xzITxx7NHpBZf0iD/ffN9vdtop9rSLCgQIp2/JEn7mh0ORrJNbOZuq40epxtLmljH4Kv7Ok5cPdSxaTBoj+k7M54vzXzjwUID1mWuizOUplvw6Vbft3Gh4/yVK+2u4tMDHW9ff1nHktuvaefeKjKf1Tnxiz8AAADQA0z8AQAAgB5g4g8AAAD0ABN/AAAAoAcufLh3ZwKYiQm4VZXrLtd9nB5uY7r0FuapXDi0dv9uqkyYb6fHEHf2ZXKsXd7SsXaIPL19S2qLjR7DeKyBnPVCw3zD2bHUJp3OvaOlBmjyjT5/7UKLqX5Wrk+fyTaGJtMQzXrdfX91m8iEhdJM38so6GeFfqnO2fW6qjW8VkbtWhLrdRLnGuQLJpAeDTVkHA+0NjCBueLBA6lt8vb1ORrq9brdaThsudbtZhPtjtlEpru5Oa51pPu7PbzS+nvwdtkkHJpjX778ktS+/UeflNrL3/i81B79wDul9hM/9HGprQftY9i/pB2Kp0fakffwkXdIbfhFDSijX+qg10TT6LWTme+nQaer68GhBnmPruvFs0z3pfbN57Q7dm4mOm7sC2aMHHT2dzrRsa+JdNGBtVmEJZgg73hPOw0f7us4dGgWBig6c7V7Gw3y3jozHdVX+rnEpvtw2egxrEw4e226fled7sOzA51/hYGeC3mqc5pXX9GO7efFL/4AAABADzDxBwAAAHqAiT8AAADQA0z8AQAAgB648OFe1x23qF33N/fgdjExG8Wx6UBZacijbsxjzb4FE5hzYb6s0+Vy7/I1fX7TabiuTAda1y3Y1Nx2kQnfpaN2sGY40zDbYKGdTLdb87mY7nXus1qtNGwzNsGauu505TSdUXe16RYca2DmcKYBIvRLFJkkv7nW66DnYpK0w72p6RDZmE6YpekMPjPX2NW3aSC1MCn4JtLrbte5LiJzLe3Wes2dnWi34IOxhuoyE+599jkNm33pm9+SWpy237e3PaoB2qtXbkhtmuoxrF5+UWrL269J7aYZ01dnOnZkj7TH5eZ92lE9fv+h1PaPdPzOflD3F/2SmkVBBgO9hl2I9OBKuwPvtafeI9sM93VRkHt3dRGPfKsLjESNmftErvu4lMK1g/Z4dWgWJznZ6APnW+2qPR3qd/PxnoaFpxPtXLxOdSydr9pj2InpgL5bmG7EkY5zIdaxtQ5uTuoWCtH3fNpZsGR/qItGpLGeC9nTGmLe29ex6bz4xR8AAADoASb+AAAAQA8w8QcAAAB6gIk/AAAA0AMXP9zrOrOacEVtOvdWknA1HV1NOCRJ9W0pTai2LkxHXtNpOEo0vDLphHlHEw151CbE3LiErnk/Ehd2do80gcTu/mZTE+6damCm2GkH0cZ1KW30/c2XGjSsTDAqStqfoXs3IvM4FwKOSg34oG9cH2kXSNfaKG2f24PGBLwKPcfiVMeD8YEGRg8f0dDrwnQCbswiAN0EveuMvVnOpXZ6/77UslgHk9VCr/WvfuXLUnv2Oe22W3YWFLh7565s87YbT0jtsQMdhw5uaOfSSfaq1Db3tOP5dqVBw3cf/lzr74+/50OyzcmrGlj++lfNeNhdiCCE8PNvf6/U8NYVm6+igenM+siVx7T25NOtv7Nj7dx7lpugqflOT003+2AWGTA53pCY6rAzRs5Mjj03C2oMNxpSPTKLB1zf1yBvMdHHvjjXRUZu32+PkcVS52SpGfcjc5y1WfylMYs/VGYqvVmZcPP99pg7yfTYR1NddKSKtXb9yXdJ7bz4xR8AAADoASb+AAAAQA8w8QcAAAB6gIk/AAAA0AMXPtzruqQlJqhiMhgh7dSKSsN3lelUl7judS72YoK8jQkZDw72pTY9Om79HZmQcbMzIUMTlnV5X5sBNsew22pYcLVtB0mmI+2iN5xpGHG30ueqdhpQjisN/SRDPRVrE5YsinbNdV6emPBUaT6X5XojNfRLVer5GZmLJzbdFIdZ+zwbVtqlN871+dNMg/zJSENeUabn9nquIfiy0P3dn7XHnNmehuWGIw3L7ZuxapDofrx4W8OyN197XWrbnYab69Cu3XxFA8DLpY4l+TvfLbUPPKGBx8M9fX8XjX4O85v3pPbMp/+09ffR1adkm6c/qh2VH716LLVLl98nNSAdj6V26brpVN2ZI9Sx6fhb6RykMF16g5nTVGYRA1kPJfiO5HHnscPUjEFmRYGDiR77xHSgjQ90PDw50UUATu/pIgPFur1vcaPjgV3Awcw1IxOAdgvOhMjNOXR8PTtrzzni5qZsc3hZ51YHR2ahBz/JOxd+8QcAAAB6gIk/AAAA0ANM/AEAAIAeYOIPAAAA9MCFD/eWhYayQqK7nZyjy2tkgrHdsGgIITSmEWZcaRikKc/XpXd8dE1q2aQdmK1NNzjXpbfxvWqlMhxrIDeuNGh3Zjrmnp2etP4eHF+WbQYzExZc6Gu6br6Ved/qUt83k0eSI3XvRnzObs9l6QI+6BVzkpk8V3DNcYedtpypCffW5hyL9jXgls40zFabMW1jwvLuWn/n1XYYbGD2f2461+aFBt5Pcx0jXr+lQV73fIl54w5m7cUDHn9cg43vftJ07j3Ucahe6vhy77kXpLZ77bbUio2+l8X2262/P/vfflO2eeWlj0rtfX/zE1Kb7j0uNfSL6wabTXShgHqotbLT+XY9P5Ntbr32itTu3dfQehP0+7Ux07/ILGIQNTpehbr9fAMz77l6qKHdgVkUpDLB2JdNkPfWHe0qnq90zE2r9rzPhXEb85t3ZDoNx+bYm8Y91oSnzfxttW53Gq52Oi7lmztSS8ziJOnsqtTOi1/8AQAAgB5g4g8AAAD0ABN/AAAAoAcu/D3+UWPurTc34demyUTZvaff3h7v7gc3m5lmXcE26zqQ2t7xFamlWecYzP4Hd9+/2zvTxCozTbcmpqFGnmsDie2ufZ9vYe5VG473tDbTY9+aRjxVrrmKUJjGZOa44s7hu8+9Nu/RaKj372125jNFrySR3uc9yPS82B/ruXjUuf1ztNQMTVPr+JWO9X7+dKxNZtw4VJiGYLUZw4Z77UZcB2MdM7vXeQgh7M5OpfbgTO8tNqNV+IH3vUdqT11/UmrX9trHHy0Wss3i5eeldutzn5ZavtTHugZs7t2MTYO0bL99D/L4ujbmGj31Dn2uqb7mzjVSQq80kfte13EiMVfU6nTe+vv1l1+Vbe7celZqVdDzbpRpA6idube+ik2zK3MIdefe922u5//YzI9mE52XPFhqNqjMddy8tK/X4uBQx7Xdtv3+npqmh5u1GavN7+CxmTMNGh2/k9h8j6RmzN2285PbXO/nrzcme1GauebxG29Ayi/+AAAAQA8w8QcAAAB6gIk/AAAA0ANM/AEAAIAeuPjhXhPwrE3axAXhqk7Trdg0WYhdgNQ06womVBclQ6lNjjVEM9rTJhDdxh6uWZcN/NqEsmECf6kJuKYmgBI6hxp1E7UhhGSoIaCBCfcOTViwyE+k5pqhxUN934aDdoOR0nxWZaXh4dh8VqOB1tAvtrGbufzjWK/FYdw+z9JaxwgXGMumGoxPTeitNAsbFKW+Rmyuz+GofX3um6ZhTanXSVnoaz5+VRcniJ96Sh9795bUli98W2q37rS3q5YaUovN2OeaF0apfoWNLul7mV3RJmHptX2pVfvtJmHpsX5Wj93Qz/QDH/thqT1yRRs3ol+aYL5fS71el/e06dZ63g5+Prit11e91ceF2AxglbkmEh0TajMfakxjq6bT+DAx84isu4BJCKEIOuZMBvp+PHqk12Yw7+V6ZRYo2LSDu7UJGbtx2c0i3UIhLog9Geh8aDrS+UXdtIPM260e02qhC6I0hS7CUptQ9Hnxiz8AAADQA0z8AQAAgB5g4g8AAAD0ABN/AAAAoAcufLjXBXIrE9YIiW43SNphsNr8OyeNzL99cg2MNN0uwCGE4cGh1KZH2l0uSvQ1qk6YN4rOGdp1m5kOv945uxSbYLDZSErpWANEw5mGdLYrDa8UhQlG5np61kn7c3DBxtiEIpdrDcKk6UBq6BsdN7rB+xBCyEy4Ny3bIriv/AAADr1JREFUIbLYdPduUhOoN517k4Gei+5Sn071sXt7M6kddRYUyMyQeWC6QZ6+9prUTl56Tmqb23elVqy1Q6Zt8dsddQb6HkVHulDAwVU9zubgMamdmc7Ld03wejI1iz10dnh2/G7Z5r0f+Qndt5mG++paP0E3XuGtKwp6XW8Wei7e2rwstXzTPo+3O/3ejDMNy1461OskmV2S2tlaz9ntSq/h0pzHZ9v29+lrd16XbQYb7fhdmvBwaVZTyEt9zbrWx242evzrTfv5isKEe82cyc17mqD7NjQdv68e6vt7YMbqatIe6+aNjlXLB9qheb6cS63MCfcCAAAA+P9g4g8AAAD0ABN/AAAAoAeY+AMAAAA9cOHDvbXpzOriUYXZbpS1gxSRCXQ0rkuvCZpGiQbQJkeXpTacaLe25hwdeF3nXv+489bMu2Rfw3UM7jzWBJtd8jDO9NgHJng4HGvHwCrX8ErtupR2PgfX2dkGd0xn4O12KzX0SxzpOZbEGhjLTCgz64wdVaHnWGK6Q6dTDcEnAx2KUxP4vXGsXXQ397V75+KLn2/9/fILL+rj7mpAt9rtpOYWNnDiTMfIgVkAIX708dbf2WV9P/KhhnsXtV6v87l2tDw1IePIfNUtFjrmVNXt9nPNtfP4avGS1D74ng9L7cljXejhR37yJ6WGt669iYY+R2ZBkXKpQdhi1z6PE7MWxZ4JwV967BGpRWOt1af6PTyvdZzbVAupPdi2x83lnRN9zQd67QRz7JWZ55RmP1wX5KZxU9hBZxvz+7ZbTMXU0kT3bTbRD+J4puP8pQNd2GSza79GvtXO4NuNfi67tX4n7cxiEufFL/4AAABADzDxBwAAAHqAiT8AAADQA0z8AQAAgB648OHeLNNdLE0gtypM+KHTiW3oOrW6gITpDJzum5DOJa25JrqR6SQbOuEVF7KtXKjOhGOyzHWgNJ3vzH643Evc6TTsGvmaZn4hMvuWjjW8Mthz3Xy1C13luvl2OjQ35jirSs+FONHzaGICleiXQaYn9zDR2ijWsSMu22HT2oxBg4mG71ITFN68qiHVs9eel9rJi69IbXv/vtTKTbv7uLtenWxoOg0fXpXa9HHtmDu+rl1un3hUjz/vNLT84tc/J9vcvPNA98ME+etKu6y79RrKXEPLm42OOUVnXC7NgG4+lvD6i7d0P4o7UiPc2y/Tw2tSi3P9XitMZ/mqMydIU/2+cgtlDEdaSyf6nTtc65h2sGfmDbnWis71VFW6TWSuQ6dxHXPNXMLNfdxv19E5fs82Q3wY6DQqjHQ4DEmtYees1gUKmko/r/W6Pa6tdhrqrswb15j5i+tkfF784g8AAAD0ABN/AAAAoAeY+AMAAAA9wMQfAAAA6IELn24sSg06xCYc54K73eBnZQJeialFkaY8xnsaUjO7EaqthnSC6y7b6awbua66Jtx7cHgktSg13UFj83wmgDOdaehnLx61/h6ZA21MJ1wnivUUyyYahMnGI6kV86V53c57kphgc6w1c+ghdl2L0S+1nsdVouG4EOn4EnVSpI0JoxenGlJ97TN/LLVya8YhMza5RQByE44rO0G4xHSR3H/icalNrmmn3TMTbG5mut2dtR5/tdbw7aATgn5wogG30zMdR7NUr+vE/HRVmyRzXuj3SF65HvBtRaGfwfzMdFnNtKvwlWsa7ES/xDP9vp7f0fNnF02kVqadDtRmPpBv9bx2XV7HU71Q4p1eY/smWFpm+thV0069VmaVkMatdGIW4whBt4vciiJuQRFT7NbM9CsMzTFNU32uQaPX/+70damdJfo9sljpnOZ0157TbHL9DFzH9izTRVJGiT7/efGLPwAAANADTPwBAACAHmDiDwAAAPQAE38AAACgBy58uNepTVJzYDr8djvf1lsNmtXdsGgINg2yOtEujJuFdsyMXSjlHLXIBFxcyXW5s8/vHuyYx0adMO/ShW/sU7mQjm5X5RqE2220VpswU9TtyluZbp5u50yrPtcJET3TmC7drnNv0MBc0gm416ZlbJ6bgLrbD3POpkMNb0VTDcbrlRPCqtPV8fgdT8k2c9Ol8xuvvSy1/T0Nmz0+01DawgQNX72pIeDtrr1vy5W2x5xMTMDa/ExV7DRQ7DrydrughuAXceh+OJuFfn5rU0um2sV9agLh6JfZdCa1zUiv67Iw52fWvp6GqY5BaabPXxf6/PlKz9m40A60AzOGzQZm7EvbF2MZ6QIjIZhFAdwiJmYBl6rU16xNbTjU7/BBpzYc6X6MBvq4JNf54eq+BnnLzYnU7ryuizgkE31Pokk7pLu/p+PjgalNRrq4zKYxbYXPiV/8AQAAgB5g4g8AAAD0ABN/AAAAoAeY+AMAAAA9cOHTjS70Wpt4nInohtANqphwSDBhk7rSwNhuqd32nDfcq84GdN2TmXCMfU0XtD1f17zuM9oGt+c8UL+Z2fCcLxJ1P8PEnMIms1dUJih83vccb1mlSYInpntlWmsHx6ps1xqzKEA61ABWevmK1IbHx7pzMw3pnSw0yvvyy69Ibblub3f3lVuyTTIyHbljHSMr0+H2Cy9oEM6F5av8ptTO5u2xdFNo18vYtORNTJDPdTN1j61KHdNLs7BD1On6Wwc9F5x6ros/vLrWwB/65YY5ZafHev3fzvRcrDoLVwwyPYcH5ppLIz3/i7kuRJKaZQGGYw2kZiN9vv24PTalQw2fpqkuRGCvw5WG8Zcneu1sS93fY9NB/OhKuzY+1H2LUh2XFw/0PVqf6diXmc661U7nh81Gx8hR3P6cx2PtqH4w0i69s0uXpXZ/Z5eJOBd+8QcAAAB6gIk/AAAA0ANM/AEAAIAeuPj3+LsmVuaGftPTK0Rx+2bveDyRbRJzj6i979/sW1yf7x4re0+71NyBmvv57b3wyjYEM/vhni/qHG3U/DXuhbf76+7xP1cpNFE3f2ByG+4mf7Mf253eW4yeiU2GyJxTa3M97V9u33c5fuK6bFNmej/oyW4utftzvUd0c1fvy68Kvefc5VeqQfs3nYVpJJZUep24Y4+TldRO51pLTIZouqf3DF+/0X6f6q0+16u39J75YqPH2ZgvA/d+1OZWfTcud8e+EOu9wEPT/GeQ6HvpvpPQL9HZi1I7mt2QWmF+gs3r9vhSN3qPe769pzWTLSmXeo0N9rT5VzTQ2nSktfG4fY//yMytXP5uu9ZxqKxOpbZZv6Cvab7Dx5XmA6ZJewwbD8yFmOm99aNjPc5qq40P79/UMS2fa5apyrXRV7Vu7+8u1jF+nuhgVUc6Lg8OH5PaefGLPwAAANADTPwBAACAHmDiDwAAAPQAE38AAACgBy58uNeFamsT6KoLbQzRbdA0GGhQKzLhPhsrNUHeyjTKsE9ntTd0zbVcv63SNayJTZjVvHGFaZ4Rm6ZDUaeWuOd3bJDXBe1coNo81LyX3c/eh5O15oKHzTnD2XjrqkwTp7OVDotRpNfANrTD4bv5a7LNequhrG6TqO/GndvuWh8MNUA87ATr3KXpxoPaNDSLanP9m8ZBZbdhYggh3+rzLU7bjc/mcw0jLtYaZKzi813rjVmMwI05tXlTsqwbWtQmRKPMNCozTcjKyi08gD65tb4ttajWc3uwd01qdd0OzJ4utCHUcnFXauXmdantFhru3W+OpHY4cI3zdHxJOs2/ykIXLNhtdTxYrHR/57efl9p6q9sNTKh+sV5ILdxt78txrO/b/sHjUqubsdSuXTGfS6nh3lu1mVuZtUPKbTvwu95oADhamnHUrHKT/TXWXOEXfwAAAKAHmPgDAAAAPcDEHwAAAOgBJv4AAABAD1z4cG/sQg2J/nulMG0Su5UmmI5oLmdqwnd5aYJapjPdINH9tfvWaSUZmaCw61pcmcTryOxHVe+ktt5pqMgFd7O0XUtNV0rH9+g1XUVN99Ful+UQvsvx192uwufsDGxCzGnsouPok9HAnQMaGCsLvf5Pivb1FAUNeKXm2qwj03031+u1cUFbE1J313Cats/3ptakWWPGiCjRBRDi1Pw+VOmx7rb6GrutBuvOFu3wXdOYFFxkAtZmP9wYEUyQ140vo5Fb7KHzGuYLojDnwm6n70dlws7ol2/c1HDvdKTfw1eO9fxs4nbYNN9o19vtVq/hnTazDZPxJamVlV5jm6XuW1RrMLjadb6HM33RjQnZ3z/TsXVxqtfOwUgDxXszHfuuPvKk1Lbb9rHe16xz2M5NeHis+7vO9frPzWWdzMwcaTuSUlO1a2Wp77dbEKJKNQQcn+n7dl784g8AAAD0ABN/AAAAoAeY+AMAAAA9wMQfAAAA6IELH+7Ncw06xCbcm5gAajeqEZvgWm26K3aDtw+fTF8zMzWXK20a83yd7eradPg0gdTEdMy0LX5NCLjbyfg7O6elTqCtsN0xTcjQfAaZaWVcu26+JgjkuiV3N0vM8+cmfFeZzs7pQANE6JcsOpNa0+h5HJuwaZq0z5/IBEF3JlBflC7cbhYKMAG3yUi7Rg7d9dntWj7SrpRFocf5YKlhXJftd2OJC/KX7rrL2k+Ymc6gmemO25gxrXTjpvt+MO9vZMacctve33WuoUXX8TdJ9bvFHQP6xeQ0Q2OC4IOljkPdRQa2uYbgUzMuDWdXpea+J908qjRdus8Weg3EnW647ut7ZxY12eRm8ZNCr51kpsHYd71HOw1/6IM/I7WvfK09Rn7+S9+UbW6ePCu10GjnddcFfGe6ve/MOJSY2qDzlkRB3++FCVjHOz0/jhPtKn5e/OIPAAAA9AATfwAAAKAHmPgDAAAAPcDEHwAAAOiByIU0AQAAALy18Is/AAAA0ANM/AEAAIAeYOIPAAAA9AATfwAAAKAHmPgDAAAAPcDEHwAAAOgBJv4AAABADzDxBwAAAHqAiT8AAADQA0z8AQAAgB5g4g8AAAD0ABN/AAAAoAeY+AMAAAA9wMQfAAAA6AEm/gAAAEAPMPEHAAAAeoCJPwAAANADTPwBAACAHmDiDwAAAPQAE38AAACgB5j4AwAAAD3AxB8AAADoASb+AAAAQA/8H5+l5p1aid9GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbd3980400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_figures(figures, nrows = 1, ncols=1, labels=None):\n",
    "    fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(12, 14))\n",
    "    axs = axs.ravel()\n",
    "    for index, title in zip(range(len(figures)), figures):\n",
    "        axs[index].imshow(figures[title], plt.gray())\n",
    "        if(labels != None):\n",
    "           axs[index].set_title(labels[index])\n",
    "        else:\n",
    "            axs[index].set_title(title)\n",
    "            \n",
    "        axs[index].set_axis_off()\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    \n",
    "\n",
    "# Visualizations will be shown in the notebook.\n",
    "# %matplotlib inline\n",
    "\n",
    "signnames = np.genfromtxt('signnames.csv', skip_header=1, dtype=[('myint','i8'), ('mysring','S55')], delimiter=',')\n",
    "# read_csv(\"signnames.csv\").values[:, 1]\n",
    "\n",
    "figures = {}\n",
    "labels = {}\n",
    "for i in range(15):\n",
    "    index = random.randint(0, n_train-1)\n",
    "    labels[i] = signnames[y_train[index]][1].decode('ascii')\n",
    "#     print(name_values[y_train[index]][1].decode('ascii'))\n",
    "    figures[i] = X_train[index]\n",
    "    \n",
    "plot_figures(figures, 5, 3, labels)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbd37226a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbd36d1978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbd533ba20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_train, counts_train = np.unique(y_train, return_counts=True)\n",
    "plt.bar(unique_train, counts_train)\n",
    "plt.grid()\n",
    "plt.title(\"Train Dataset Sign Counts\")\n",
    "plt.show()\n",
    "\n",
    "unique_test, counts_test = np.unique(y_test, return_counts=True)\n",
    "plt.bar(unique_test, counts_test)\n",
    "plt.grid()\n",
    "plt.title(\"Test Dataset Sign Counts\")\n",
    "plt.show()\n",
    "\n",
    "unique_valid, counts_valid = np.unique(y_valid, return_counts=True)\n",
    "plt.bar(unique_valid, counts_valid)\n",
    "plt.grid()\n",
    "plt.title(\"Valid Dataset Sign Counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play! \n",
    "\n",
    "With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission. \n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture (is the network over or underfitting?)\n",
    "- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "- Generate fake data.\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128` is a quick way to approximately normalize the data and can be used in this project. \n",
    "\n",
    "Other pre-processing steps are optional. You can try different techniques to see if it improves performance. \n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RGB shape: (34799, 32, 32, 1)\n",
      "Grayscale shape: (34799, 32, 32, 1)\n",
      "done\n",
      "27.5591963457\n",
      "27.8521424585\n",
      "27.3828201204\n",
      "-0.784693778549\n",
      "-0.782405137043\n",
      "-0.786071717809\n",
      "Original shape: (34799, 32, 32, 1)\n",
      "Normalized shape: (34799, 32, 32, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbd37adeb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# Convert to grayscale\n",
    "X_train_rgb = X_train\n",
    "X_train_gry = np.sum(X_train/3, axis=3, keepdims=True)\n",
    "\n",
    "X_valid_rgb = X_valid\n",
    "X_valid_gry = np.sum(X_valid/3, axis=3, keepdims=True)\n",
    "\n",
    "X_test_rgb = X_test\n",
    "X_test_gry = np.sum(X_test/3, axis=3, keepdims=True)\n",
    "\n",
    "print('RGB shape:', X_train_rgb.shape)\n",
    "print('Grayscale shape:', X_train_gry.shape)\n",
    "\n",
    "X_train = X_train_gry\n",
    "X_valid = X_valid_gry\n",
    "X_test = X_test_gry\n",
    "\n",
    "print('done')\n",
    "\n",
    "\n",
    "print(np.mean(X_train))\n",
    "print(np.mean(X_valid))\n",
    "print(np.mean(X_test))\n",
    "\n",
    "## Normalize the train and test datasets to (-1,1)\n",
    "\n",
    "X_train_normalized = (X_train - 128)/128\n",
    "X_valid_normalized = (X_valid - 128)/128\n",
    "X_test_normalized = (X_test - 128)/128\n",
    "\n",
    "print(np.mean(X_train_normalized))\n",
    "print(np.mean(X_valid_normalized))\n",
    "print(np.mean(X_test_normalized))\n",
    "\n",
    "print(\"Original shape:\", X_train.shape)\n",
    "print(\"Normalized shape:\", X_train_normalized.shape)\n",
    "\n",
    "figures = {}\n",
    "for i in range(15):\n",
    "    index = random.randint(0, n_train-1)\n",
    "    labels[i] = signnames[y_train[index]][1].decode('ascii')\n",
    "    figures[i] = X_train[index].squeeze()\n",
    "plot_figures(figures, 5, 3, labels)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old X_train size: 34799\n",
      "New X_train size: 27839\n",
      "X_validation size: 6960\n"
     ]
    }
   ],
   "source": [
    "## Shuffle the training dataset\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train_normalized, y_train = shuffle(X_train_normalized, y_train)\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(X_train_normalized, y_train,\n",
    "                                                                test_size=0.20, random_state=42)\n",
    "\n",
    "print(\"Old X_train size:\",len(X_train_normalized))\n",
    "print(\"New X_train size:\",len(X_train))\n",
    "print(\"X_validation size:\",len(X_validation))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Define your architecture here.\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.contrib.layers import flatten\n",
    "\n",
    "EPOCHS = 60\n",
    "BATCH_SIZE = 100\n",
    "\n",
    "def LeNet(x):\n",
    "    # Hyperparameters\n",
    "    mu = 0\n",
    "    sigma = 0.1\n",
    "\n",
    "    # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
    "    W1 = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean=mu, stddev=sigma), name=\"W1\")\n",
    "    x = tf.nn.conv2d(x, W1, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    b1 = tf.Variable(tf.zeros(6), name=\"b1\")\n",
    "    x = tf.nn.bias_add(x, b1)\n",
    "    print(\"layer 1 shape:\", x.get_shape())\n",
    "\n",
    "    # TODO: Activation.\n",
    "    x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.\n",
    "    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    layer1 = x\n",
    "\n",
    "    # TODO: Layer 2: Convolutional. Output = 10x10x16.\n",
    "    W2 = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean=mu, stddev=sigma), name=\"W2\")\n",
    "    x = tf.nn.conv2d(x, W2, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    b2 = tf.Variable(tf.zeros(16), name=\"b2\")\n",
    "    x = tf.nn.bias_add(x, b2)\n",
    "\n",
    "    # TODO: Activation.\n",
    "    x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.\n",
    "    x = tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
    "    layer2 = x\n",
    "\n",
    "    # TODO: Layer 3: Convolutional. Output = 1x1x400.\n",
    "    W3 = tf.Variable(tf.truncated_normal(shape=(5, 5, 16, 400), mean=mu, stddev=sigma), name=\"W3\")\n",
    "    x = tf.nn.conv2d(x, W3, strides=[1, 1, 1, 1], padding='VALID')\n",
    "    b3 = tf.Variable(tf.zeros(400), name=\"b3\")\n",
    "    x = tf.nn.bias_add(x, b3)\n",
    "\n",
    "    # TODO: Activation.\n",
    "    x = tf.nn.relu(x)\n",
    "    layer3 = x\n",
    "\n",
    "    # TODO: Flatten. Input = 5x5x16. Output = 400.\n",
    "    layer2flat = flatten(layer2)\n",
    "    print(\"layer2flat shape:\", layer2flat.get_shape())\n",
    "\n",
    "    # Flatten x. Input = 1x1x400. Output = 400.\n",
    "    xflat = flatten(x)\n",
    "    print(\"xflat shape:\", xflat.get_shape())\n",
    "\n",
    "    # Concat layer2flat and x. Input = 400 + 400. Output = 800\n",
    "    # x = tf.concat_v2([xflat, layer2flat], 1)\n",
    "    x = tf.concat([xflat, layer2flat], 1)\n",
    "    print(\"x shape:\", x.get_shape())\n",
    "\n",
    "    # Dropout\n",
    "    x = tf.nn.dropout(x, keep_prob)\n",
    "\n",
    "    # TODO: Layer 4: Fully Connected. Input = 800. Output = 43.\n",
    "    W4 = tf.Variable(tf.truncated_normal(shape=(800, 43), mean=mu, stddev=sigma), name=\"W4\")\n",
    "    b4 = tf.Variable(tf.zeros(43), name=\"b4\")\n",
    "    logits = tf.add(tf.matmul(x, W4), b4)\n",
    "\n",
    "    # TODO: Activation.\n",
    "    # x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Layer 5: Fully Connected. Input = 120. Output = 84.\n",
    "    # W5 = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
    "    # b5 = tf.Variable(tf.zeros(84))\n",
    "    # x = tf.add(tf.matmul(x, W5), b5)\n",
    "\n",
    "    # TODO: Activation.\n",
    "    # x = tf.nn.relu(x)\n",
    "\n",
    "    # TODO: Layer 6: Fully Connected. Input = 84. Output = 43.\n",
    "    # W6 = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))\n",
    "    # b6 = tf.Variable(tf.zeros(43))\n",
    "    # logits = tf.add(tf.matmul(x, W6), b6)\n",
    "\n",
    "    return logits\n",
    "\n",
    "\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "def evaluate(X_data, y_data):\n",
    "    num_examples = len(X_data)\n",
    "    total_accuracy = 0\n",
    "    sess = tf.get_default_session()\n",
    "    for offset in range(0, num_examples, BATCH_SIZE):\n",
    "        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
    "        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0})\n",
    "        total_accuracy += (accuracy * len(batch_x))\n",
    "    return total_accuracy / num_examples\n",
    "\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n",
      "layer 1 shape: (?, 28, 28, 6)\n",
      "layer2flat shape: (?, 400)\n",
      "xflat shape: (?, 400)\n",
      "x shape: (?, 800)\n",
      "Training...\n",
      "\n",
      "EPOCH 1 ...\n",
      "Validation Accuracy = 0.610\n",
      "\n",
      "EPOCH 2 ...\n",
      "Validation Accuracy = 0.796\n",
      "\n",
      "EPOCH 3 ...\n",
      "Validation Accuracy = 0.850\n",
      "\n",
      "EPOCH 4 ...\n",
      "Validation Accuracy = 0.894\n",
      "\n",
      "EPOCH 5 ...\n",
      "Validation Accuracy = 0.912\n",
      "\n",
      "EPOCH 6 ...\n",
      "Validation Accuracy = 0.929\n",
      "\n",
      "EPOCH 7 ...\n",
      "Validation Accuracy = 0.938\n",
      "\n",
      "EPOCH 8 ...\n",
      "Validation Accuracy = 0.940\n",
      "\n",
      "EPOCH 9 ...\n",
      "Validation Accuracy = 0.953\n",
      "\n",
      "EPOCH 10 ...\n",
      "Validation Accuracy = 0.959\n",
      "\n",
      "EPOCH 11 ...\n",
      "Validation Accuracy = 0.964\n",
      "\n",
      "EPOCH 12 ...\n",
      "Validation Accuracy = 0.963\n",
      "\n",
      "EPOCH 13 ...\n",
      "Validation Accuracy = 0.970\n",
      "\n",
      "EPOCH 14 ...\n",
      "Validation Accuracy = 0.971\n",
      "\n",
      "EPOCH 15 ...\n",
      "Validation Accuracy = 0.972\n",
      "\n",
      "EPOCH 16 ...\n",
      "Validation Accuracy = 0.976\n",
      "\n",
      "EPOCH 17 ...\n",
      "Validation Accuracy = 0.977\n",
      "\n",
      "EPOCH 18 ...\n",
      "Validation Accuracy = 0.978\n",
      "\n",
      "EPOCH 19 ...\n",
      "Validation Accuracy = 0.980\n",
      "\n",
      "EPOCH 20 ...\n",
      "Validation Accuracy = 0.979\n",
      "\n",
      "EPOCH 21 ...\n",
      "Validation Accuracy = 0.980\n",
      "\n",
      "EPOCH 22 ...\n",
      "Validation Accuracy = 0.981\n",
      "\n",
      "EPOCH 23 ...\n",
      "Validation Accuracy = 0.980\n",
      "\n",
      "EPOCH 24 ...\n",
      "Validation Accuracy = 0.983\n",
      "\n",
      "EPOCH 25 ...\n",
      "Validation Accuracy = 0.981\n",
      "\n",
      "EPOCH 26 ...\n",
      "Validation Accuracy = 0.984\n",
      "\n",
      "EPOCH 27 ...\n",
      "Validation Accuracy = 0.983\n",
      "\n",
      "EPOCH 28 ...\n",
      "Validation Accuracy = 0.985\n",
      "\n",
      "EPOCH 29 ...\n",
      "Validation Accuracy = 0.982\n",
      "\n",
      "EPOCH 30 ...\n",
      "Validation Accuracy = 0.984\n",
      "\n",
      "EPOCH 31 ...\n",
      "Validation Accuracy = 0.986\n",
      "\n",
      "EPOCH 32 ...\n",
      "Validation Accuracy = 0.982\n",
      "\n",
      "EPOCH 33 ...\n",
      "Validation Accuracy = 0.986\n",
      "\n",
      "EPOCH 34 ...\n",
      "Validation Accuracy = 0.988\n",
      "\n",
      "EPOCH 35 ...\n",
      "Validation Accuracy = 0.986\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# X_train_normalized\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
    "y = tf.placeholder(tf.int32, (None))\n",
    "keep_prob = tf.placeholder(tf.float32) # probability to keep units\n",
    "one_hot_y = tf.one_hot(y, 43)\n",
    "\n",
    "print('done')\n",
    "\n",
    "rate = 0.0009\n",
    "\n",
    "logits = LeNet(x)\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\n",
    "loss_operation = tf.reduce_mean(cross_entropy)\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
    "training_operation = optimizer.minimize(loss_operation)\n",
    "\n",
    "\n",
    "\n",
    "correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
    "accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    num_examples = len(X_train)\n",
    "    validation_accuracy_figure = []\n",
    "\n",
    "    print(\"Training...\")\n",
    "    print()\n",
    "    for i in range(EPOCHS):\n",
    "        X_train, y_train = shuffle(X_train, y_train)\n",
    "        for offset in range(0, num_examples, BATCH_SIZE):\n",
    "            end = offset + BATCH_SIZE\n",
    "            batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
    "            sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.5})\n",
    "\n",
    "        validation_accuracy = evaluate(X_validation, y_validation)\n",
    "        print(\"EPOCH {} ...\".format(i + 1))\n",
    "        print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
    "        validation_accuracy_figure.append(validation_accuracy)\n",
    "        print()\n",
    "\n",
    "    saver.save(sess, 'lenet_1')\n",
    "    print(\"Model saved\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(validation_accuracy_figure)\n",
    "plt.title(\"Validation Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Test Set Accuracy = 0.936\n",
      "Valid Accuracy = 0.943\n",
      "Train Accuracy = 0.998\n"
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver2 = tf.train.import_meta_graph('./lenet_1.meta')\n",
    "    saver2.restore(sess, \"./lenet_1\")\n",
    "    \n",
    "    test_accuracy = evaluate(X_test_normalized, y_test)\n",
    "    print(\"Test Set Accuracy = {:.3f}\".format(test_accuracy))\n",
    "    \n",
    "    valid_accuracy = evaluate(X_valid_normalized, y_valid)\n",
    "    print(\"Valid Accuracy = {:.3f}\".format(valid_accuracy))    \n",
    "    \n",
    "    train_accuracy = evaluate(X_train, y_train)\n",
    "    print(\"Train Accuracy = {:.3f}\".format(train_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7b2876a630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "import glob\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "my_images = sorted(glob.glob('./test/test_*.png'))\n",
    "my_labels = np.array([40, 1, 12, 17, 38, 34,3])\n",
    "\n",
    "signnames = np.genfromtxt('signnames.csv', skip_header=1, dtype=[('myint','i8'), ('mysring','S55')], delimiter=',')\n",
    "\n",
    "figures = {}\n",
    "labels = {}\n",
    "my_signs = []\n",
    "index = 0\n",
    "for my_image in my_images:\n",
    "    img = cv2.cvtColor(cv2.imread(my_image), cv2.COLOR_BGR2RGB)\n",
    "    my_signs.append(img)\n",
    "    figures[index] = img\n",
    "    labels[index] = signnames[my_labels[index]][1].decode('ascii')\n",
    "    index += 1\n",
    "\n",
    "plot_figures(figures, 4, 2, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7drQ97jOQm49efvnlZH327NnMIW0YV/QBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAooA2ao/+JT3wi+WSrV6+2PTNmzEjWc6k3blvuDvfZs2cn63379q16LJcy0NDQYHtc6kwuQWbXXXdN1gcOHGh7XIqR07t3b7vtpZdeStZfeeUV2+OOm0vDkfwxcD25Y+aSEVyShORTjHL77D4DLn1B8qkd06ZNIzEBkHTuuecm55AXXnjB9rjUri5dutgelyDzm9/8xva4pJ7TTz+9qv2SpAEDBiTrbp6S/D7nzjmLFy9O1nPnw86dO9tt1Xr77beT9ddee832uNfTq1cv2+PmhOnTpyfrw4cPt2O5z03uuIwePTpZz31u33vvvWQ9N4eHkJ4qJk+ezBzShnFFHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAJioQ8AAAAUUDoD8EPiohWXL19ue9y2XLShi0McOnSo7XExmi7Ka82aNXYsFxmWi6p0cYy5yK5Zs2Yl6y6aTfIRbC7acerUqXasSZMmJeuDBg2yPVOmTEnW58+fb3vcsXafp1z0qXs/Xcyc5GPwXJSZ5Pd5k03879a5zzQAaeLEicl67md+2LBhyfqTTz5pe84555xk3UVoSv587M4T9913nx3r0EMPTdbHjBljezbddNNkPXcOd/OLO7dL/hyaOzbOdtttl6zvsMMOtmfatGnJ+ssvv2x73Ly3++67J+t9+vSxY7moTvf+Sz4u1L0WSWpsbEzW3fss5Y8b2i6u6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBAGzR1xyWOuMQZSerZs2ey7u78l6Qdd9wxWZ8zZ47tcek+M2fOTNa7dOlix+rWrVuynkvqcakzubvyV69eXdVYkjRq1Khk3d3hP27cODuWS6pxqUOST5ZxqQiS/9wMHDiwqrokvfTSS8n6ggULbE8u5aDaHpduJEkdOnSo+nmAtsSlduXSaHr06JGs59LJJk+enKxfdNFFtmfJkiXJuju3HnHEEXasESNGJOvPPvus7XGpM7k0L3eeys1VToyxqnrueVwKnuTn96OOOsr2LFy4MFl3awI3t0pSfX19sp5LfpoxY0ayXltba3t69eqVrM+dO9f25I412i6u6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAtqg8ZoudjEXr7nNNtsk63369LE9Ljbttddesz3vvfdeVc+fi7Bsbm5O1idNmmR7ampqkvXtttvO9uy2227JuoskzW1bsWJFsj5kyBA71tSpU5P1F154wfZMnz49WXdRnZKPWnNRlbn3pq6urqqxJGnlypXJei6CzsW1urGkfMQoAGnrrbdO1kMItsf9zPXv39/2LF26NFl384TkIzEPOuigqse64YYbkvXcedLNo7l4YBcjmTueLsLR9eTmdzdW7hz+5z//OVl3cZSSdNhhhyXrLib79ddft2O592DevHm2x0Ws5o6N69l8881tTy6uE20XV/QBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAooODuev8wbL311skn69ixo+1xd8XnembOnJmsb7bZZrbH3a2+cOHCZD2X4OPSWI444gjb8+lPfzpZz6ULuWPgEl8kn67jPge5xAa3bdWqVbbHJQnceOONtufhhx9O1rt3717Vfkk+XcelT0it+wzm0jGcTTZJ/9799ttv+wgMoA3Zb7/9kicq9zMqSfX19cl6Q0OD7XEJLrlkFXcOd/PRPffcY8dyKWw57rybO0/169cvWXfn1pxly5Yl63PmzLE9uXQdxyXV5OYdd2498sgjk/XOnTvbsR588MFkPXfMXPKT+2xIPsXIpTtJUlNTU7J+5513Moe0YVzRBwAAAAqIhT4AAABQQCz0AQAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAFlM6p+pC4CMdcxOe0adOSdRflJfkIxcGDB9ueJUuWJOsLFixI1j/72c/asU477bRkfeDAgbbn6aefTtafeOIJ2+OiMnOxce7YuEhQF/El+Qi4XCTo6NGjk/VLL73U9tx///3J+kUXXZSsu+MiSQMGDEjWc/GaLgLORbZJ+YhPx0WwASipq6tL1l2EpuTnig4dOtieqVOnJuvbbrut7XGxxmPHjk3WXUyk5M/HnTp1sj2f/OQnk/Udd9zR9rgYUTdPSH5OcK+/ubnZjjV+/Phk/ZFHHrE9M2bMSNZbczxvu+22ZP3oo4+2Yw0aNChZnzRpku1xn9tcFLaL+HQRmpKPr0bbxhV9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACijkEm/Wt6222ir5ZLk7/BcvXpys5/a7NXe4v/vuu8n6BRdckKyfccYZdqznn38+Wb/33nttz9Zbb52sH3HEEbbnscceS9YfffRR2+NSFtzxzB2zVatWJeu5NAuXftCzZ0/b8/nPfz5ZdwkDZ555ph3LpWn069fP9rjPYI5LpnCvX/IpPnPnzvXRR0Ab8oUvfCF5ourYsaPtmTx5crLuUk0kadSoUcn6iBEjbM9vfvObZN2dJ11dknr37p2sH3/88bZn+PDhyXouzcvtQ+48VW16Xi4Nx53zcukxLoXtySeftD0uIc69freGkKSTTz65qv2SpOnTp1f1/JLUtWvXZL179+62x32mb7nlFuaQNowr+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAmKhDwAAABTQBk3d2XnnnZNPlrvD3SWe5O5Wd9tef/1123PMMcck65dddlmyfsstt9ixXnjhhWR9q622sj3f/va3k/Xc63zkkUeS9U6dOtkely7jUhYWLlxox3JpFq+++qrtmT17drLemqSeQw89NFlfsWKFHcslJjQ1NdmeHj16JOu5n53WpFm4923atGkkJgCS9tlnn+QPXS7Zxf2cDh061PZ88pOfTNYffvhh25M776X06dPHbjvrrLOS9cGDB9uepUuXJuu5OWTZsmV2m5MbL8Ul3kj+fcv1uHPorbfeanvc++ZSf3Kvcffdd0/Wt9hiC9tz0003Jeu5tCi3Xsh91t166e6772YOacO4og8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACmiDxmsOGzas6idzUVL19fW25913303WR40aZXvuvPPOZP26665L1idOnGjHctFgX/rSl2zPyJEjk3UXRyn5Y5CL7HLxW26fa2tr7VjOypUr7baxY8cm63/9619tj4tAc9FwRx55pB1rwoQJyfrZZ59te1z8q9svycezbbrpprbHRYxOmTKFaDRA0nbbbVd1vOagQYOS9e2339729O3bN1m/9tprbY+bS0NI//h+5StfsWONGTMmWX/ppZdsz7x585L1XKxz165dk/Vc7KaLt3Tnr9wc9tZbbyXrnTt3tj3ufctFcl555ZXJ+htvvJGs5z5P7v384he/aHvuueeeZH3WrFm2p3v37sl6bn51x+D5559nDmnDuKIPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACggFvoAAABAAflbyz8E7k7+3B3u7g7z3J383bp1S9Z//OMf254nn3wyWZ80aVKy7u68l6TNN988Wd9uu+1sj0sy6NGjh+157bXXkvUHH3zQ9ixatChZd2kw7s5/SRo9enSyvu+++9qeI444ournufnmm5N1l4bzwAMP2LGOO+64ZP3zn/+87bn99tuT9Z49e9oel8iTS+rJpSkA8D/zS5cutT3vvPNOsp5Loxk/fnyynkupc9v23nvvZH2HHXawY/3yl7+sqp4zdOhQu+2b3/xmsj58+HDb4+a+V155JVn/wQ9+YMeaM2eO3ea4+eVrX/ua7fnCF76QrE+ZMiVZX758uR3LbXNpf5L06U9/Olm/6667bI9LbmtubrY9ubUU2i6u6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAtqgWUwu+slFO0o+drKpqcn2nHfeecl6r169bM+1116brLs4t1zM2qxZs5L1G264wfbsueeeyfqQIUNsz7bbbpusv/HGG7Zn4sSJybp7D9xrkaS///3vyfqrr75qe/bff/9k3UXgSdVHVeai0dy+fe5zn7M999xzT9XP4/bNfZ6lfGQrAGm//fZL1hsaGmzP1KlTk/V+/frZnj//+c/V7Zik2traZN2d8x555BE7lovRdK9fksaMGZOsu7lNki6//PKq6pKPAXY9uUjh888/P1nPzQcuknLgwIG25/TTT0/WXcTp008/bcdy65jcvHvkkUcm68OGDbM9EyZMSNbbt29ve3Lb0HZxRR8AAAAoIBb6AAAAQAGx0AcAAAAKiIU+AAAAUEAs9AEAAIAC2qCpO6NGjUrWGxsbbY9LTBg6dKjt2WuvvZL1xx9/3Pa4O+ldSkouIaW5uTlZz6UsuLv8d911V9uzbNmyZH3atGm2p1OnTnZbSocOHew2lzw0fvx42/PWW28l6zU1NbbH7bN7b3IpTi51Z99997U9++yzT7L+0EMP2Z7u3bsn67kEitWrV9ttAKTRo0cn6+6cK0nz5s1L1nM/b3Pnzq1uxyQNGjQoWe/Tp0+y/txzz9mxDjjggGT9ggsusD19+/ZN1jt27Gh7vv/97yfrM2bMsD1Lly5N1idNmpSsf/Ob37RjHX744cn6woULbc+bb76ZrL/44ou2x+3z9ttvn6znUnfc/OI+Z5Kfq+vr621Ply5dqu6ZOXOm3Ya2iyv6AAAAQAGx0AcAAAAKiIU+AAAAUEAs9AEAAIACYqEPAAAAFBALfQAAAKCANmi8Zvv27ZN1F5MoSfPnz0/WDz74YNvjIgzHjRtne1yMpIvlctGSko8zc3FZkj8GTz75pO1x3HGW/Ot0UXO598ZFjObi3Nxxc/GmkrRixYpkffny5cl67ji7nlzs5e67756s//nPf7Y9bryVK1faHgB5q1atStb79etne3r16pWsu3N7a7l4Tedzn/uc3ebmt9w+u2PTGrnzoTuHOrnz8aJFi6oaS5J69OiRrM+ZM8f2uH3u379/Vc8h+ejP3JqgoaEhWR8wYIDtce91Lj46N1+j7eKKPgAAAFBALPQBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAW0QVN3nn322WS9c+fOtsfdeT5ixAjb49IHcoknLkFm5513tj3OW2+9lazn0gpcykHu2FQ7luRfZ2vGag2XTJBLX6ivr0/WDzrooGT9lVdesWNNnTo1WZ83b57tqaurS9Zrampsj0sxyiUz5JKHAEg///nPk/Xceapnz57Juvu5znE/15JP93Hn3NYkcM2cOdNuu/HGG5P1e+65x/bsscceyfqwYcNsz+zZs5N1l/iSS4LJJcg47j1oTeqQS4jLzbsLFixI1nNzq1vH9O7d2/Z07do1WW9qarI9Q4YMsdvQdnFFHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAJioQ8AAAAU0AbN83NxZsuWLbM9HTp0SNZ79OhhexYuXJistybK6/jjj0/Wc9GKX//615P1XDSbGy8X2ZWLaqxWa2I03fPnXqeLQNthhx1sz+GHH56sDxw4MFnPRZZdffXVybqLTJN87GXuM+Bep/s8S+v3/QSKaMqUKcn6kiVLbM/IkSOT9VzsY26bU+05PHfOdWNNnDjR9vzqV7/K7F3aCSeckKznokfX53nKHZtqY6A/iHs/3XGura2teqzcZ8bFa+Yit138au69yc1JaLu4og8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABbdDUnVwyguPuMM/ded7c3Fz183Ts2DFZd8kI8+bNs2O519maZJtcj7vLP5eKUG2aQWsSFlxagCSNGjUqWT/rrLOq3gf3PDvttJMda8cdd0zWm5qabE+XLl2S9c6dO9sel7rj6gA+WPv27ZP13Pm4oaEhWXdpWlLrUl9cepw7f+VSWtwcsssuu9ieu+66K1m/8sorbc9vf/vbZH2fffaxPe49WJ/Wd9qcS9wIoA9eAAAgAElEQVRzCXG5Oaw13DyeWxO5z4dbq0j5zzTaLq7oAwAAAAXEQh8AAAAoIBb6AAAAQAGx0AcAAAAKiIU+AAAAUEAb9BZtd7f88uXLbU/Xrl2T9VziibvDPpdy4O6Kd3f/u7v1cz2tkdvnDSGXcOC2rVixwva4NAeXWCFJU6ZMSda33HLLZD13/EeOHJmsP//887bHvQe51+n2gVQEoPUWLVqUrOd+rhYvXlx1j5tDcueWuXPn2m0pNTU1VW9z+yVJPXv2TNaPPfZY2/Pwww8n62+88Ybtqa+vt9tScsesNclxbq5uzVzpzuG5FLbce+C4pJz+/fvbnl69eiXruePfqVOn6nYMbQJX9AEAAIACYqEPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACggFvoAAABAAW3QrL/27dsn63369LE977zzTrLe2Nhoe4YMGZKs5yK7lixZkqwvWLAgWXfRV5LUpUuXZL2hocH25PbNcRFs6zPeszVj1dXV2W1PPPFEsn7//ffbnlNOOSVZnzVrVrKeiz6dN29esp57P13UWu797NatW7LuYtak/H4D8OfWnPnz5yfruXObiyl0UZ2S9N577yXrLo7xzjvvtGM9++yzyfo3vvEN29O9e/dk3c1tObno0VwsaEouhtjNFbl9dttc7Kbk93nmzJnJupv3W8u9zr///e+2x8071R5/STrhhBOq7kFxcEUfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAtqgqTvurvjcHfZLly5N1t3d8pI0atSoZL01qTvjx49P1j/1qU/Zsbbaaqtk/dFHH7U9/fv3T9ZdYoTkj2cuMcFpTbqO68mlHzz++OPJ+v777297XCLOn/70p6qeI2e//faz25566qlkvba21vb07NkzWXdJCpK0Zs0auw2A1LVr12Q997Pj5pDcz+KWW26ZrL/yyiu2Z9q0acn622+/nawPHjzYjvWDH/wgWc+dp/fee+9k/ZprrrE9LvHOzWG5fXBj3XjjjXaszTbbLFl3864kvfrqq8n6AQccYHtcitLrr7+erOeSglx6YOfOnW2PO2a55KXm5uaqxvqgbWi7uKIPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACggFvoAAABAAbHQBwAAAApog8ZrLlu2rOqeurq6ZP3NN9+0PYcddliy3qVLF9vjoqz++te/Jut77rmnHeuQQw5J1p9++mnbk4vrdMaOHZusb7rpprZnQ8RvNTY22m0jR45M1o8//njbs3r16mT9ueeeS9YbGhrsWO7z1LFjR9vjYlG7detmezp06JCsL1y40PbkYkkB+POXi0+UpO7duyfrkydPtj2jR49O1l944QXb42KN//jHPybr559/vh3ra1/7WrL+85//3PY89NBDyXruPHXhhRdW3eOcc845yfp3vvMd23PqqadW/TyDBg1K1k888UTbs2DBgmT9pZdeStZz86SLch0xYoTtyUW5Oi6iOYc5BCl8KgAAAIACYqEPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACigDZq6M3jw4GQ9l9LiUlJcwoAknXbaacn6HnvsYXv+8pe/JOvvvvtusv6nP/3JjnXAAQck6yeddJLtad++fbK+44472h6XPDR+/Hjb49IpXMrA8uXL7VhLlixJ1ocOHWp7zj777GTdvX5Juvvuu5P16dOnJ+u51KGDDz44WZ84caLtefzxx5P1Pn362B6X/OOSOaR8cggAqba2tqq65NNL5s2bZ3vq6+uT9d69e9sel6j16quvJuu5OexLX/pSsr7rrrvanpkzZybrufNxv379kvWlS5fanhhjsr7PPvsk61dffbUd629/+1uynktB+8xnPpOsDxs2zPZcf/31ybqbQ3Ln6VWrViXro0aNsj0urSn3eXLHIDe/5bah7eKKPgAAAFBALPQBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAooA0ar7nJJunfK5YtW1b1WM3NzXabiy0766yzbM/DDz+crHft2jVZf+CBB+xYLnZxt912sz25ODPHxcatXr266rFcZJqLN5WkvfbaK1k/6qijbI+L8XTHX/LHukOHDsn6ZpttZscaPnx4sn7BBRfYHsd9niVpzZo1yXouRnTlypVV7wPQlrhzXu5nMRe96bg5ac8997Q9d911V7LuohpvvfVWO5aLXczFLW+11VbJei4i2b3O1pzb3Bw2cuRIO9bo0aOTdTdPSH6uys0hjz76aLLu3hv3HJKP8cxFcr7zzjvJei5G1M0VufeGeE2kcEUfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAtqgqTvurvxcgo67izyXrPKLX/wiWf/0pz9te0455ZRk/YorrkjW999/fzvWlltumaznkgRcgkzu7v/OnTtX/TyrVq1K1l0yw/nnn2/H2mKLLZJ1l8ogSTfffHOy/vjjj9sed2xcKsGxxx5rx3Lv57PPPmt73GetpqbG9rgEndyxce8NgJL+/fsn67n0EneedHXJzzu51Jtx48Yl61OmTEnWc2lzV111VbL+xS9+0fbsuuuuyXouicUlxeSS29z84tJgWpMmlpvDXLrO7bffXvXzOLnXf8ghhyTrc+fOtT0rVqxI1luTCAVUiyv6AAAAQAGx0AcAAAAKiIU+AAAAUEAs9AEAAIACYqEPAAAAFNAGTd1x6SXz58+3Pe6O/cbGRtvjEmS++93v2p477rgjWT/44IOT9SeffNKOtcMOOyTrufSDxYsXJ+suyUCSFixYUPXzuPFcz4QJE+xYc+bMSdYfeugh2zNz5sxkPZc+4BIYTjvttGT96aeftmNdeumlyXrXrl1tj0s+yqXk5N4DpzXpFEBbcvrppyfrCxcutD3u3Dps2DDb88477yTr3bt3tz1f/vKXk/Uf/vCHVe2X5BPqfvnLX9oel/qzxx572J6hQ4cm67lEInfec+fpXKrem2++maw/8cQTtufFF1+s6vklP++5NJy99trLjnXAAQck6y5RbkPKpfSh7eKKPgAAAFBALPQBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAooA0ar/nKK69U3dOhQ4dkfdCgQbbnrbfeStZdLJck7bvvvsm6i4rs06ePHetHP/pRsl5fX2973OvMRS6uXr06Wc9FVbrYx5qammR97Nixdqw1a9Yk6+3a+Y+Vi0AbMGCA7Tn66KOTdRejee6559qx3Ovs1KmT7XFxci4CLzde7th07NjRbgPg4xDduVDy5ykXDyz5yOdcpK47h5988snJ+nXXXWfHamhoqOo5JOm5555L1p955hnb4+ZRF4Ut+fOUOx/OmDHDjjV58mS7zXHn0Nx74z43e++9d7J+3HHH2bGGDBlS1XNIft7LRYI6uTkktw1tF1f0AQAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKKANeov24MGDk/Xdd9+96p5ly5bZnjvvvDNZd+kLkjR16tRk/fDDD0/WL730UjvWd77znWT9sccesz0uMSHGaHtcgkzuTv7cMah2LHeHv3vPJJ9y0K1bN9tz7bXXJus//elPbY9TV1eXrC9ZssT2uLSkTTbxvyfPnTs3WXfvmSR17tzZbgMgvfbaa8l6LnVn+fLlVdUlqX379sn6okWLbI+bkzbffPNk/YILLrBjXX/99cm6S5ST/Pkol8Ti5r333nvP9lQrd55023LzlHvf3Lldkg466KBkvTVpd26fXaKd5N+D3LFx3GdTys8vaLu4og8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACmiDxmu6+MDhw4fbnoaGhmT90UcftT1HHXVUsp6LYHPRl88//3xVzyFJZ555ZrK+33772Z5tttkmWV+6dKntWbBgQbKei4p0cV4uZiwXe+nez9xxHj9+fLJ+xRVX2J5nnnkmWe/fv3+ynosfa2xsTNZzMabumOXiMBcvXpys56LuclGmAKTZs2cn67mfHXc+ykU4uvNu7tzq9sGd81xsryR94QtfSNYnTJhge5566qlk3UVoSvnzUbVcvGRuPnBy5/CddtopWd91111tj4slveeee5L1MWPGZPYuLRdtmZtfHPd5cnNLrgdtG1f0AQAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKKANmrqzcuXKZP13v/ud7Vm0aFGy3tzcbHtcUk8uZcGlGdTW1ibrucSVyy+/PFm/7bbbbM+nPvWpZH3YsGG2x+2bSz+Q/F35Ln1h4sSJdqw333wzWX/66adtj0s/yKU/9O7dO1l3SQZ1dXV2LCd3zJYvX56sz58/3/Z06tQpWXcJPpK0atUquw2Anw9yqSatSTxxP/M57hzmUlJcgpDkz639+vWzPS4JbuHChbbHHc+ZM2faHpdI1KVLl2Q9N+/27ds3WXeJapJPm3vooYdsj0vP69mzZ7L+wgsv2LHc/PLee+/ZHvd5ys0H7nObS9ZZnylKKA6u6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAgqtiR5rrWHDhiWfbMmSJbbHxVK5yMXceLlYqhEjRiTrLmYsF+/pojdzx3rWrFnJuosklaTNNtssWd9xxx1tj4s6e+yxx5L1XIRkhw4dkvXce+MiJHPRkjU1Ncm6i8TMvc/t27dP1levXm173DFrTTRaLrbPPc/s2bP9CwLakAsvvLDqCcv9XK3vn3nHPU8udtKdD5uammyPG69Xr162x50rXUS15M+77jztooYlH++Zi3WeNm1aVc8v+TnZHZvGxkY7losRdfOh5OfxZcuW2R73ucnNIW7bM888wxzShnFFHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAJqtyGfbL/99kvWH3zwQdszcODAZD2XfuDSB3J35bs0A3dXfC5Bx6X+LF261Pa4u/Jdso4kfeITn0jWR40aVfXzvP3228l67g7/Pn362G2OSxLIJeW498315Pa5Nak/rUn3aY0NmYAFfBy5c0EuQafasSSfzuXOBbnxWnOecPNO165dbU9rkl1cus3LL79sezp27JisuzQaV8/tW11dne3Zc889k/XcZ8Cd311Pt27d7FguwSf3eXLn9tw6xn0Gc/NELqUPbRdX9AEAAIACYqEPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACggFvoAAABAAW3QeM0ePXok6yNHjrQ9Q4YMSdZnz55te1w017Rp02zPzJkzk/XWRKOtWLEiWc/Ff7lorubmZtvjYkRzctGTKS7iKzdWLv7LxZy1JjLMvf7WHOfc87vxcj1u39q18z9y6zuuEyga97OYi71057Dcz5uLPaytrbU9LnbSPU/u/OFeT67H7Vv//v1tj5urGhsbbU+vXr2SdTfv5s55kydPTtbdvC/5eM1cRLKbQ9wclptD3Lk9N7e69y0Xh9maaO9cXCfaLj4VAAAAQAGx0AcAAAAKiIU+AAAAUEAs9AEAAIACYqEPAAAAFFDI3cENAAAA4OOJK/oAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAoIBb6AAAAQAGx0AcAAAAKiIU+AAAAUEAs9AEAAIACYqEPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAJiof8xEUJ4IITwxVb2Dg4hLA4hbLq+9+vDEEKIIYQtN/Z+AAAAfJyx0N9IQgiTQghLywvw2SGE60IIde7xMcYDYozXt+a5YoxTYox1McbV5ed+LIRwamv3HQDw8VGeb2aHEDpX1E4NITy2kfblsxv6eYG2ioX+xnVIjLFO0o6SdpH0rZYPCCWtfp9CCO3+hf3b6OMDANaLdpLO2dg78UGYU4D1i4X+R0CMcbqkBySNkv5xxf0HIYSnJC2RtHnlVfgQwiYhhG+FECaHEOaEEH4XQuhW3ja0/NWX/wwhTJH0SEWtXQjhB5I+JenK8l8TrgwhXBVCuKxyn0II94YQ/ju1v+WxzgwhvCPpnXJt9xDC8yGEReV/7l7x+JNDCG+GEJpCCBNDCF9uMd55IYSZIYQZIYRT1s9RBQBU+LGkc0MI9amNuXN44rH9Qwh3hBDmhhDeCyGcXbHtuyGEW8vzUlMI4fUQws7lbTdIGizp3vL8c76Zs+4LIZzV4jlfDSEctj4OBNCWsND/CAghDJJ0oKSXK8onSPqSpC6SJrdoOan8v30kbS6pTtKVLR6zl6RtJO1fWYwxflPSXyX9n/LXef6PpOslHbP2LwchhF6S9pV0U2a3D5O0m6SRIYQeku6T9HNJPSX9RNJ9IYSe5cfOkXSwpK6STpb00xDCjuXn+pykcyXtJ2m4JP6kCwDr3wuSHlPpfPs+63AOr3zsJpLulTRO0gCV5or/DiFUzjWfl3SzpHpJY1Wen2KMJ0iaovJfs2OMl1T0VM5Z10s6vuI5R5ef6/5WvG6gTWOhv3HdHUJokPSkpMclXVyx7bcxxtdjjKtijCtb9B0n6ScxxokxxsWSvi7p6BZ/8vxujLE5xrj0g3YixvicpEUqnbAl6WhJj8UYZ2fafhhjXFAe/yBJ78QYbyjv702S3pJ0SHn8+2KME2LJ45IeVOmvCpJ0lKTrYoyvxRibJX33g/YXANAq35F0Vgihd4t69hzewi6SescYL4oxrogxTpT0/1SaN9Z6MsZ4f/m+sBskjV6Hfaucs+6RNDyEMLy87QRJt8QYV6zzKwUgiYX+xnZYjLE+xjgkxnhGi0X51Exff73/Kv9klb5/2Xcd+1Mqr6Acr9LJOady/Jb7s3afBkhSCOGAEMIzIYQF5V9sDpTUq6J3aos+AMB6FmN8TdIfJf1/LTZlz+EtDJHUP4TQsPZ/kr6h988/syr+/xJJHdfhu/f/mAdijMsl3Srp+PJfEI7RB89JABJY6H90xcy2GSqdbNcaLGmVpMor8Ln+1LYbJR1a/hPpNpLurmL/Wu7P2n2aHkLoIOkOSZdK6htjrFfpz6+h/LiZkga16AMAfDgulHSa3r+It+fwRP9USe+VL1Kt/V+XGOOB6/j8bm5qWb9epb9e7ytpSYzx6XUcH0AFFvofTzdJ+koIYVg5kvNilf6suWod+2er9N3+f4gxTpP0vEpXTe5Yl6/8VLhf0ogQwrHlG37/Q9JIla4ctZfUQdJcSatCCAdI+reK3lslnRRCGBlC6KTSJAQA+BDEGN+VdIuksyvKuXN4S89JagwhXBBCqA0hbBpCGBVC2GUdd+Gf5h+zn09LWiPpMnE1H2g1FvofT9eqdOJ7QtJ7kpZJOivb8X6XS/r3EMLCEMLPK+rXS9pOVZ5UY4zzVbrZ9muS5ks6X9LBMcZ5McYmlSaUWyUtlHSsSjdnre19QNLPJD0i6d3yPwEAH56LJP0jUz93Dm/ZWP7e/SGSxqg0/8yT9GtJ3dbxuX8o6Vvlr/38043BLfxOpTnpxnUcG0ALIcbcNzzQloQQPq3SCXVojHHNxt4fAEDbFUI4UdKXYox7bux9AT6uuKIPSVIIoUal/5jKr1nkAwA2pvJXOc+QdM3G3hfg44yFPhRC2EZSg6TNVPoaDQAAG0U5k3+uSt/n/8NG3h3gY42v7gAAAAAFxBV9AAAAoIA+6D9gsb7x5wOgeuGDHwIU36WXXpqcQzp27Gh7GhoakvVu3XxIzKabbpqsr1njb19asSL9H211PSH4H+uamppkvV07P2W//PLLyfpzzz1nexYvXpysH3PMMbanX79+yfqqVel05/r6ejvW6tWrk3V3LCWpNd9CyL1vKcuXL7fb3HvT2Nhoe9xnbcGCBbanqakpWXfHPzfexRdfzBzShnFFHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQBv6Zlx8RC1atChZ79Kli+3ZZBN+TwSw4Zx44onJ+oMPPmh75s+fn6w3Nzfbnvbt2yfruRtB3c217obXXr162bG6du2arLsbXiVp5cqVVfe45+nQoYPtyd2omjJnzhy7zR3Pzp07256lS5cm60uWLLE9bp9ra2urerzkb/x2N31L/vOUO87u/czdxN2pUye7DW0XKzUAAACggFjoAwAAAAXEQh8AAAAoIBb6AAAAQAGx0AcAAAAKiIU+AAAAUEDEaxZQLhrsmmuuSdbHjRuXrPfo0cOOdcEFFyTrPXv2zOwdALRO9+7dk/VchOOmm26arNfV1dmempqaZN1FHko+brhdu/Q0m3t+FzuZe/5c7KKz1VZbJet9+/a1PUOGDEnWZ8+enaw3NjZWvV+rV6+229z72adPH9uzcOHCZN3Fi+a4uFIXuyn5uNAVK1bYHvd+tiaSE20bV/QBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAoIFJ3Cih3531DQ0Oy7lJ3evfubceaMWNGsk7qDoAPw4UXXpisz5071/bsvPPOybpLtpH8OdQlvkg+DWXRokXJujsXS1KnTp2S9V69etmePfbYI1mvra21Pd26dUvW582bZ3vcMXD1Ll262LFcUlFzc7PtaWpqStZzqUPuvXHPv2DBAjuWS0tyaTyS1L59+2TdpTtJ/n3LfQaXLFlit6Ht4oo+AAAAUEAs9AEAAIACYqEPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACgg4jULaOzYsXbbZZddlqwfeOCByfrbb79txzr99NOT9T/84Q+2Z/DgwXYbAOTMnDkzWR81apTtcVGZuXjLFStWJOsuJlGS1qxZk6x37tw5WR8wYIAda+HChcn6E088YXtmzZqVrLt4T8kfg8WLF9ue1atXJ+tdu3ZN1nMRzTvttFOyPnz4cNvjYieXLl1adY9TX19vt7m40FwkqYtydZ8zycdoLl++3Pa4uFC0bXwqAAAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCBSdz7iXJKDJP32t79N1k877TTbc9ZZZyXrxxxzTLLe1NRkx/rKV76SrB911FG257bbbkvWBw0aZHsAQPLpKQ888IDt2WKLLZL1MWPG2B6XhpI7H26zzTbJukvduf/+++1Yzz77bLLu0ngkn4bTrp2f5nPbnGXLliXrLqln6tSpdqxx48Yl67n35tBDD03Wu3fvbntcupB7/bkEH/cZmD9/vu2pqalJ1nv06GF7mpubk/UFCxbYnlwiD9ourugDAAAABcRCHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAIKMcYN+Xwb9Mk+TlatWpWs//rXv7Y9p59+erJ+3nnn2Z6DDz44WXfxYx06dLBjbbJJ+vfEs88+2/a4OLE77rjD9vTr189uayPCxt4B4KPgwAMPTM4hucjBrbfeOlnfeeedbY+LkMydi9q3b5+su0jh559/3o5VV1eXrLuoTsnHa7q5pbWqXTOE4E9fbt8aGxttT8+ePZP1I444wvZsueWWyfr06dOTdRdtKUn19fXJ+sqVK21P7n1z3LHJ7dvs2bOT9Z/85CfMIW0YV/QBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAoIFJ3NqBc+sE111yTrJ9zzjm258ILL0zWXcKAJM2ZMydZd8kULslBksaNG1fVWJL0zW9+M1nv1auX7bn11luT9b59+9qegiExAZB07rnnJueQxYsX2x63bfPNN7c9++yzT7Le1NRke773ve8l6wsXLkzWc+evNWvWJOvrO0HHjeeeX5LatWuXrLt0ndwc4pLbckk9LiGutrbW9hx33HHJ+ogRI5L1GTNm2LFculJun93rnDJliu3p0qVLst6pUyfb49KnvvWtbzGHtGFc0QcAAAAKiIU+AAAAUEAs9AEAAIACYqEPAAAAFBALfQAAAKCA0rfP41+yfPnyZN0l60jSeeedl6y7ZB1J+uxnP5usz5o1y/a4lIFFixYl6zU1NVWP5VIZJOnyyy9P1s8880zbc/TRRyfrt9xyS7Lep08fOxaAjy+XeJJL3XEpYGPGjLE9brybbrrJ9ixdujRZ32yzzZL1XLKNS2lx9Q8az+nWrVuyvnLlStuzbNmyZN2lzuTmA7fPuQSbnj17Juu5dJ8HH3wwWXfz2/jx4+1YEyZMqGosSdp3332TdfdaJL+OaG5utj2t+Qyg+LiiDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAoIBb6AAAAQAGx0AcAAAAKiHjNVnJRapJ09dVXJ+vf/va3bc9FF12UrO++++62Z968ecl6586dbc/cuXOT9RtuuCFZHzx4sB3r8MMPT9Zz0WyrVq1K1q+44grb46I3jz322GQ9F4HXu3dvuw3AR9uKFSuSdXculKQdd9wxWT/wwANtz8knn5ys52IXXYxmtXGUUj4q0okxVlWX/Pk4F9Po9jv3PE4uLtRx+5aLt5wyZUqy/ve//z1ZHzJkiB3LzaG55+/atWuyvummm9oep7a21m7LrUvQdnFFHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAIidecDLF68OFn/xS9+YXsuvvjiZP373/++7dlpp52S9QULFtieLl26JOtTp061Pbfddluy7hIj5syZY8e64447kvV///d/tz0ukSeXWOASeVwaz/HHH2/HuvHGG5N10niAjz6XEpPjzuF/+ctfbM/TTz+drHfr1s32uEQglyzjHp/rWZ8pNVLrUlrcPrQm9ccl+OTSaFwiUe6z4d63t956K7kguesAACAASURBVFnffvvt7VgjRoxI1nNzZceOHZP1XEJehw4dknWX4iRJAwYMsNvQdnFFHwAAACggFvoAAABAAbHQBwAAAAqIhT4AAABQQCz0AQAAgAJioQ8AAAAUEPGaZY2Njcm6i9H8n//5HzuWi9EcM2aM7Zk/f36y7iI0JWnmzJnJ+s0332x7+vbtm6y7+K+BAwfasSZPnpys33XXXbbHRW/mIsNc1JqL3TzjjDPsWF/84heTdRe7KUk9evSw2wBsOO3apaesXLTixIkTk/UXXnjB9rhoQ3eelPIxlim5CEkXO1ntc0j5SE63Dy7CUvIRyU4uXtO9ntyxac3zuNfj5t0ZM2bYsdy+5aKw3edp4cKFtmfRokXJeu6z3qtXL7sNbRdX9AEAAIACYqEPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACigNpW609DQYLddeeWVyfpPfvKTZP1HP/qRHWvbbbdN1t0d/pLUuXPnZH369Om25/bbb0/W+/XrZ3s6deqUrLskhVzKg0vkcWlAknT33Xcn64ceeqjtWbp0abLevn37ZN29l5J0+umnJ+unnHKK7bn++uuT9W7dutkeAOvf4sWLk3V3XpN8Us8bb7xhe9x5z6WzST4NxaW0tCaNZn2n7rhjk1NTU5Osu/Nx7jlculCuxyUfuf3K9bjUG5coJ0m77rprsp5LyHOpP7m0udYkL+XSgtB2cUUfAAAAKCAW+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAmKhDwAAABRQIeM1FyxYkKxfffXVtudnP/tZsn7JJZck6yNHjrRjzZs3L1nPxW9NmzYtWXcRmpKP0ezQoYPtcRFwLsorx8V8DRo0yPZMmTIlWR87dqztOeSQQ5L1JUuWJOsu5k2SrrrqqmTdxW5K0jnnnJOs52I86+rq7DYArePiLQcMGGB7qo1WlKThw4cn6/X19bbHnXfd+SgXe1lbW1vVc0g+xtONJfkYy1xcqRvPjZWLynTb3GuRWhfj6c7HixYtStavueYaO5ZbX+SOWVNTU7LuPpu5ntznJjf3oe3iij4AAABQQCz0AQAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAF9LFN3XF3vkvSpZdemqzn7qT/6U9/mqxvueWWyfr8+fPtWO4Of5esI0m33XZbst6/f3/b4xIYVq5caXscdyd/jNH2uG0u2UeSBg8enKxPnTrV9tx7773JukvjWbZsmR3LpRL86le/sj1nnHFGsp5L6nGftVwCBoA89/ObS8OZPHlysr7bbrvZni9/+cvJenNzc2bv0lyiWS5ZpjUpaO58nDuHu+fJ9bjz++rVqzN7l9aaHrdvLgUuxyXl5JJtli9fnqwPGTLE9rjPbS75yc3vLm0ut29o27iiDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAoIBb6AAAAQAGx0AcAAAAK6CMfrzl37txk3cVhStJVV12VrF9++eW2Z8SIEcm6i7/KxSROnz49Wb/99tttj4vR7Nixo+1xMZrrM5otN1Zr4txcNNuAAQNsz5QpU5L1P/3pT8n6v/3bv9mxVqxYkay7SFRJ+tnPfpasn3nmmbbnq1/9arL+4x//2Pbk9gGA56IIJX8O79evn+3p2rVrst7Q0GB73LmlNfGaS5cutduq1ZrYyQ01XmvmHRd9meupNhI0F1HdmvjqmpqaZD0XBe0+g7l4zdbElaL4uKIPAAAAFBALfQAAAKCAWOgDAAAABcRCHwAAACggFvoAAABAAX3kU3eef/75ZP2KK66wPb/61a+S9S222ML2LFq0KFlv3759sp678/33v/99sj5kyBDb06lTp2TdpQWsb61J6nHpB7nUHZfYkEtyGDZsWLL+6quvJuudO3e2Y33mM59J1pubm22PS8PJfQZPPfXUZD2XCHT44YfbbQBad/5wiSeNjY22x6Xr5BJXqk2j2VDndneelnzyz/pMb8nNLe3apZcgrZmP3Fi5ba7uEm8kn4iU+zz16tUrWc/NVU7uvXHrFbRtXNEHAAAACoiFPgAAAFBALPQBAACAAmKhDwAAABQQC30AAACggD7yqTu1tbXJeu4O+3HjxiXr22yzje1xSTGu7vZLkvbcc89k/cUXX7Q9gwcPTtZz6QMbIjFhfXOvJ/d+zpkzJ1mvr69P1keNGmXHckkXucSMjh07JuvPPfec7XHvgRsLwAdz5w+XhNJa7vzempSUXOpNtVqTYJPTmhQjd25zrzM3H7kUo9zrdOfw3GfAzeNNTU3J+ooVK+xYPXv2TNbdfJx7/lxCnTueuUSg3H6j7eKKPgAAAFBALPQBAACAAmKhDwAAABQQC30AAACggFjoAwAAAAXEQh8AAAAooI98vOYuu+ySrF922WW25z//8z+T9UWLFtme//qv/0rWFyxYkKzn4sf23nvvZL2mpsb2/O1vf0vWhw4danucXJxbbr/X11itiYCbN2+e7XGRYccdd1yynosfc3FuPXr0sD0PPfRQsv69733P9lxyySXJuvtsAPhgLsKwsbHR9rg4xIULF9oet23ZsmW2p7m5uaqeXOzk8uXLk/VcfKKLnXR1yR83FzuZ2zcXb+nOubnnyR0b93oaGhpsj5uTZs2alaxvscUWdiwX6/z666/bnr59+ybr7lhK/r12n2dp/cfMohi4og8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABfeRTd+rq6pL1E044wfZ07ty56p4lS5Yk61/96leT9Vxigxtrjz32sD0ujeaJJ56wPUOGDEnWc6k3uRSdasdy3GuRfIpRLpnhxBNPTNY7duxY9VguMeG+++6zPRdffHGy/tOf/tT2nHnmmcl67tgAyHPnr0mTJtmezTffPFl/9913bc93v/vdZN2l/kj+vO+SVXJpOK1J0IkxJuu586EbL5fO5p7HySXouPNh7jly4zluHbF48eJkvX///nasTp06JetdunSxPe515lKUXHocyTqoFlf0AQAAgAJioQ8AAAAUEAt9AAAAoIBY6AMAAAAFxEIfAAAAKCAW+gAAAEABfWyz/mpqauy2ww8/PFm/4447bM8xxxyTrDc1NSXrF154oR3LRW82Nzfbnk9+8pPJei7O7dFHH03WBw8ebHvceLk4NcdFhrkITcnHibkITUmqra1N1pctW5as9+3b14511113JeuXXHKJ7bnqqquS9VNPPdX2EKMJrH8zZsxI1idOnGh7tt1222Q9d5784x//mKy7c5Hkz6FurspFSLoY0dx84M45uR63z7nzV7VzRS6i2W3LxYiuT7169UrWBwwYYHteeeWVZL1Hjx62xx3P3DrGxUe7nwFJmj9/vt2Gtosr+gAAAEABsdAHAAAACoiFPgAAAFBALPQBAACAAmKhDwAAABRQIaNB2rdvn6wfcMABtmfs2LHJ+pFHHpmsX3DBBXYsl+CSS6NxiTyf+MQnbI9LU/jLX/5ie4YMGZKsu/QDl/4g+Tv8XbKOJB1//PHJeqdOnWzP8uXLk3WXruOSdSTp0ksvTdavueYa23PSSScl67ljA2D9a2xsTNbdOV/yaSgjRoywPU899VSynkuwWb16dVX7lkuvcdtyCTYuxSeX7OL2uTWJQJ07d07Wc++NS6jLHWd3DHI9bq5yaXfbbbedHWvSpEnJ+hZbbGF76urqkvXccXavJ/c6c+lTaLtYqQD4/9m78zA7yyrf+7+VSk2pVKWSSirzCAkJSTBIXkRm5KhAt9ID7QG0W2xRj9fptp3wbe0+PTm0L85jow2CLbaIgkrT8Qi04gDIEJEpIWQgQ5E5qSmVGpP7/WPvtJt4rxv2NlQqd76f6/KSrGevez97yPOsemrvXwAAQIYY9AEAAIAMMegDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyFCW8Zqe0aP9h3veeedF615U4xVXXOGu9Z73vCda/9SnPuX2dHZ2Ruv79u1ze5YvXx6tpyLY7rrrrmh97ty50boXfyZJfX190fqb3/xmt8eL0fTWkqRJkyZF6z/4wQ+i9U984hPuWjfeeGO07sV+Ahg5mpubo/XUMW/Tpk3R+pw5c9yel73sZdH6gw8+6Pa0tLRE6148cCqe19vmxWGmpHrq6+uj9VQkpxdx6kWCpuIgvW2VPM6hoSF3m/e+WbJkSbS+a9cud63FixdH6xMnTnR7Nm/eHK0PDg66PV4kp1eX/IhTHN+4og8AAABkiEEfAAAAyBCDPgAAAJAhBn0AAAAgQwz6AAAAQIaOq9SdFO/b/2eddVa07qXxSH4izzXXXOP2fPzjH4/Wu7u73R4v/eC0005ze7zH+f3vfz9ab2pqctf60z/902g99c1/L13HS6yQpBUrVkTrXrqOl6wjka4DHMsaGxuj9VQ62M9+9rNofcqUKW7PmWeeGa176SmSf6wOIUTrqWQZ7zidShfy1vPuX0on/5RrYGAgWk+l7niPJ9XT29sbrXvnQ0l661vfGq1756p7773XXWvq1KnR+oYNG9ye/fv3R+up53/GjBnRel1dndvjpdrh+MYVfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMkTqzgvwvv1/+umnuz233nprtH711Ve7Pe9///uj9Y997GOJvYvr6elxty1btixa9779P378eHctb5uXMJDqufvuu92ez372s9H69ddfH62TrAMcX4aGhtxtixYtKrvH2/ZHf/RHbs8NN9wQre/bty9aHzdunLtWJbxjeCrZxUtBSyX1jB5d3tiQSonxnudUgo637ZJLLnF7Fi5cGK23tbVF60uWLHHX2rNnT7S+d+9et8d7njs6OtyerVu3RuupZJ3q6mp3G45fXNEHAAAAMsSgDwAAAGSIQR8AAADIEIM+AAAAkCEGfQAAACBDDPoAAABAhiwVo/USGNY7O1q85/SJJ55we975zndG66moyuuuuy5af+6559web705c+ZE64ODg+5aGzZsKGstSXrggQei9c997nNuj7ftyiuvdHsyY0d7B4CR4Pd///ejB9fUMc+LVmxtbXV7vEjIk08+2e0ZGBiI1m+77bZo/dlnn3XX8mKIzfxDgRcFnYrD9OItDxw44PZ4vPvx9kuSdu3aFa2nIkH/+I//OFpfsGCB2+NFVXr3s3HjRnet7du3R+upWaq2ttbd5vFiPHt7e90e7/X8xS9+wTnkOMYVfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMkTqzgixZs2aaP3P//zP3R4vQecDH/iA2+Ol6EybNi1aT70/du/eHa3/+te/dnu+8IUvROvXX3+923Mcpet4SEwAJL3pTW+KHpBSCTaTJ0+O1hcvXuz21NfXR+vbtm1ze5YvXx6tewk+P//5z921vGNoe3u721NdXR2tp1J3KuGdQ/r6+qL1MWPGuGt5r8F5553n9kydOjVaTyXlePvgncMeeughdy0vESn1PJf7nEl+8pGX+iNJe/fujdYfe+wxziHHMa7oAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIENHNncLFTvppJOi9Ztvvtntectb3hKtf+QjH3F7rrvuumh9aGgoWvdi5iQ/Au5Tn/qU2/OVr3wlWidCE8AL8SIkx40b5/bU1tZG694xT5LM4mmE06dPd3t27twZrXvxmq997WvdtRYuXBitr1u3zu0ZGBiI1g8ePOj2ePGO3lqp9SZNmhStT5w40V1rwYIFZe2X5J93mpqa3J7m5uZofdOmTdG6F20p+ZHTnZ2dbo+33qxZs9weL5Kzra3N7QFiuKIPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGzPsG+UtkWO8sdxs2bIjW3/GOd7g9mzdvjtZvuOGGaH3lypXuWu9+97uj9c9//vNuz1/+5V+62+CKR4AAx5k//MM/jJ5DGhoa3J6amppoPZWs0tLSEq1PnjzZ7fESgdrb26N1Lw1IkqZOnRqtpxJsHn300WjdO+ZLfoLOzJkz3Z4lS5ZE617y0f79+921vH3btm2b2+PtW1VVldvjzTl79+6N1h988EF3Le99473+qZ5Uql1PT0+03tvb6/Z4j+exxx7jHHIc44o+AAAAkCEGfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMjT6aO8AKjdv3rxo/Stf+Yrb88EPfjBaf9Ob3hStDw0NuWt95jOfidaJ0ATwUnjlK18ZrXsxkZLU19cXre/bt8/tMYunEXrxhZI0adKkaL2xsTFaHzXKv87W1dUVrY8e7Z+yf/azn0XrP/nJT9wezwUXXOBuW7x4cbS+ffv2aD31OJubm6P11GvjnZNS5yov3tJ7zU499VR3rV/96lfR+qZNm9weL3ozFW/uRY/OmjXL7Zk7d667DccvrugDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJAhUncy5KXxSNIVV1wRrU+fPr3s+7n00kvL7gGASnmJJ5VIpcF427yUGEmqra2N1hsaGspey0udSaULtba2Rusnnnii2+NJ7VtPT0+03t/fH617qUeSVFNTU1ZdkgYGBqL17u5ut8dLvfFe59T5cOrUqdH6Aw884Pbs3LkzWvceiyQtX748Wq+rq3N7Uuvh+MUVfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMsSgDwAAAGSIeM3jjBfztXLlyrJuL0lXXnlltD537tzydwwAXoAXFTk0NOT2jB4dP82NHTvW7dm9e3e0vmvXLrfHi9H0oiJ7e3vdtQYHB6P1+vp6t8eLkPQev+THdaaiRz1VVVVl3YeUjt70jBkzpuz7MbNo3XucHR0d7lozZ86M1s8++2y3p729PVo/cOCA2+O9n5588km3x4s+xfGNK/oAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMsSgDwAAAGTIQghHex8AAAAAHGFc0QcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9vGhm9pSZnf8ib7vRzP7HS7xLAIAjxMyuNrN7X+Rtx5jZf5pZp5l96wjc9/8ws42/6zov8r4+YmY3Dcd9AUcbg/4Ic/iAbGaXm1m7mZ13NPdLkkIIi0MI9/6u65jZ+WbWdgR2CQCyYWb7Sv530Mx6S/78xqO9f4f5n5ImSGoJIVxRzg8JAIbP6KO9A/CZ2ZslfVrS74UQ7j+K+zE6hDB0tO4fAI4HIYSxh/67eHX76hDCPZWsNQzH7dmS1nBuAEY2ruiPUGb2dkmfkvTa0iHfzM4ws/vNrMPMHiv9KI2ZjTOzG8xsm5k9V/z1ZFVx21Vmdp+ZfaH4q9anzezCxP1vNLP/18wel9RjZqNLf9tgZvVm9vXibxtWm9kHIlfpl5nZ48X7+7aZ1ZlZg6QfSppWcqVq2hF74gAgU2Z2s5n9Q8mfn/dxFzNrM7NrzOwJSftLau81sycOfczGzGpf5P2dbGb3mNne4jnjj4v1j0r6kKQ3Fo/hfy/pi5LOKf55t7Pe1cXzRbeZrTezqyO3+YCZ7TKzrWb2ZyX1OjP7tJltMbMdZvZlM6srbmsxsxXFvnYz+w8zm17SO8/Mfl683x9Jankxjx/IAYP+yPROSR+WdGEI4ZFDxeKB6z8lfUSFX5m+X9JtZjapeJOvSxqSdKKkUyW9RlLpgfQVkjZImijp7yXdbmYTEvtxhaTfk9QcuWrz95LmSJon6dWS3hTpf4OkiyTNlXSKpKtCCD2SLpa0NYQwtvi/rYl9AAC8eJercIwdV1J7gwrH6XmSTpP0py+0iJk1Srpb0r9JapX0RklfNbOTQgh/I+laSd8sHsP/UdJfSPp58c8TnWV3qHBOaZL0NklfMLNTSrbPkFQvaZqk/yXpX8ysqbjtk/rNuWS+CuefvyluGyXpXyXNUuE3DYOSPley7i2SfqnCue/jL+bxA7lg0B+ZXq3CQemJw+pvkrQihLAihHAwhHC3pEckXWJmk1U4uL87hNATQtgp6TMqHPQP2SnpsyGEwRDCtyWtUeGg6/l8CGFLCKE3su0Nkj4WQmgPIbRJ+rzTvzWEsFfSf0ha9oKPHADwu/hcCKHtsOP2Z0MI20MIeyTdqRd3LH69pGdCCP8WQhgKIayU9H1Jl1W6YyGE/wghbAgFP5b0X5LOKblJn6SPFM9Rd0jql7TAzEapcNHq3cVzTpekf1bx/BZC2BVC+F4Iobe47WOSzpMKV/OLj/fvQwj9IYSfSFpR6WMAjjV8Rn9k+l+S/o+k683srSGEUKzPlvQnZva6kttWS/pJcVu1pG1mdmjbKElbSm77XMlakrRJhSsnni2JbdMO2x677faS/97/AvcFAPjdvZhjceo3uYfMlnSWmXWU1EZLuqnSHTOz31fh3DZfhfPTGEkPl9xkdwjhwGH7OlbSFEm1kh4rOb/95j8KHwn9nAq/xW4ulhuL/z9N0p4Qwv6SdTdJmiTgOMCgPzLtlHShpJ9K+rIKH+WRCgfwb4QQ3nZ4g5lNVeHqx8TEl6Omm5mVDPuzJN2R2I+Q2LZNhV+zrir+eWbituWsCwCI61FhOD5kSuQ2R+r4ukXSf4UQLn6Rt0/er5nVS/quClfh/zOEMGhmd6pkYE/YIWlA0kkhhB2R7R9Q4WM9p4cQtpvZcv3mB4htklrMrL7ktxyzJMV+Uw1kh4/ujFDFz62/StJFZvaZYvlmSa8zs9eaWVXxy0nnm9mMEMI2SXdJ+pSZNZnZKDM7wZ4fy9kq6V1mVm1mfyJpkSr/Featkj5oZuOL3x34izJ6d6hw4B33grcEABzya0m/VzzuTpX0rpfwvu6QtNjMriyeM6rN7HQzO8m5/Q5JM8ys2tleK6lG0i5JB4pX991AiFLFq/zXS/qsmU2yghlm9priTRpVuPrfbmYtkv6upHe9pMcl/YOZ1ZjZuUp/ZBXICoP+CBZC2KLCsH+Zmf1z8c+XqpB2sEuFKy7X6Dev45+pcCBdJaldhasnU0uWfFCFX5nulvRRSZcVP7NZiX+S1CbpWUn3FO+r/0U+rqclfUvSBiukB/GRHgB4YTdJWq3CR0/+rwpfMn1JhBA6Jb1Whe+GbVPh4z//rMLAHnO3pLWSdpjZ9sM3hhA6JL1H0vck7VXhs/53lrFL71PhcT8kqVOFC1vzi9s+rcKXj/dIul+FZLdSl0s6q3i/fyPpG2XcL3BMs+d/ZBu5MrOrVMhkPvslWv+dki4PIRz1f9gLAAAAXNFHhcxsqpmdVfyI0EkqXG353tHeLwAAABTwZVxUqkbSV1T4AlSHCr9C/vJR3SMAAAD8Nz66AwAAAGSIj+4AAAAAGRrWj+7s2bOHXx8MgyP5W5qSf5zkiPTwG6TytbS0lP8iABlav379iD2AeMe9cuuSf5wcHBx0e/r6+qL1gYEBt2fMmDHReiXH/VGj4tcNq6qqyu5J3b/XU8m5xevJ7Tw1b948ziHHMa7oAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQOfrDKPUFo4MHD5bdU4mGhoay7qenp8dd60juW+rLT0f6OQCAI807hh04cCBaHxoactfytqWOhd6xfcKECW7P3r17o3VvnyVp9Oj42FBdXe32eCo5tnvnyuH6Aq23z7l9gRf54Io+AAAAkCEGfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMkS85ggxalT8Zy4vSkySqqqqovW6ujq3Z8yYMWXtV29vr7vNi96sJEY0hXhNACOdF6/oHfNqa2vdtbxj+ODgoNuzb9++aH3nzp1ujxeJmYrK9LZ5j8c7t6Wk4j296NFUvGW5+5A651TyeIjexNHEFX0AAAAgQwz6AAAAQIYY9AEAAIAMMegDAAAAGWLQBwAAADJE6s4wSiXOeN/yT33D30tgSCUmTJ06NVp/29veFq17CQeSn4zgpQGltqVSFgAgN/v373e3eSktNTU1bs/48eOj9dbW1vJ2LHH/Kd75LZUU1N/fX9ZaUvr84vEeTyWJbiTo4FjDFX0AAAAgQwz6AAAAQIYY9AEAAIAMMegDAAAAGWLQBwAAADLEoA8AAABkiHjNYZSKyvQiu1JRXqNHl//yffCDH4zW/+AP/iBaT8WceY8n9Ti9GM1UDwBIIzva0IuR9Pa5qanJXauxsbHs+/eOrZXEW6Z45526urpofdy4ce5atbW10Xoqbnn79u3Reioq0zuPVXLePZLvQeI9MRyYrgAAAIAMMegDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyNCwpu543xY/0t88r2Q971v5lSTbeGul9quqqipa37Fjh9szZ86caH3lypVuj5dysGLFimh9wYIF7lrnnHNOtP7ss8+6Pc3NzdF6dXW12zM0NBSte88zCT4ADhmulBLvXOEdc1Pnlu7u7mh969atbs8zzzwTrW/evNnt6ejocLd5ampqovUJEyZE61OmTHHX8ra1tLS4PZMnT47WUwlx3jYv3aeStVIqmUmAI4WJCAAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABka1nhNTyVRmam4qiMZpzY4OBitp+IgvW2pOLUrr7wyWn/kkUfcnsbGxrLv50hGUnqxl729vW7PpEmTovX777/f7dm7d2+07u2zF1Uq+Y+/kvdMKmatkvctgDTvcdLBAwAAIABJREFU73Z/f7/b48VBpnq8Y0vq2NrQ0FDW/fz85z9313ryySej9c7OTrfHO4al9tk7HqWOh9u3b4/Wd+3aFa1v2rTJXcs7V3rnCUm64IILovXx48e7Pd5r473OqfeGJ3Xe8c6VXrxnar3U65laD8cvrugDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJChYU3dGcmJI16CipcKkEo/uPbaa6P1H/7wh27P2LFjo/WWlha3x0sMqCRBxvuGf+o1qySZoq+vL1o/+eST3Z6lS5dG65/85Cej9ebmZnct73Wu5L2Z6hnJ73UgN93d3e62yZMnR+tdXV1uj5fgkkpb85Ja7rnnnmh95cqV7lrecbKSY5u3luSnwXjnI6n8ZJdUCpuX4JN6bbx0nyVLlrg93jnEe2+kUui8x1NfX+/2eK/NwMCA2+Odk1Pndy8lEMc3rugDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJChYU3dGS5e4kkqCaW2tjZa95IUzjzzTHctL7EhlZjgpR9UIpWK4CXieN/k99ICUlKpAF6SQCrlYdWqVdG69xo89dRT7lqVJBV575tK0o0AVM47tqWSvvbv3192j3es3rp1q9uzZs2aaN1L1+no6HDX8o5T3mORpLq6umi9tbXV7WlsbIzWU+eqbdu2Reu7d++O1lMJOt6x1XssktTT0xOtP/roo26Ptw/Lly+P1lPPmZdskzofjBkzJlqvqalxe/bt2xett7e3uz2p9zSOX1zRBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJAhBn0AAAAgQwz6AAAAQIaO2SymVOyjF+Ho1SWpr68vWl+yZEm0PmXKlLL3rZLYyVQkqMeLZpPKj8tM3b/3eFI93v2n9suLWvNiTBctWuSu9atf/Spa9+JVpcriWonkBI48L4Y3dWzv7e2N1lN/571jzurVq92eRx55JFr3oh2rq6vdtbxj2+zZs92ek046qay6JE2ePDlaT8V43nPPPdF6d3d3tJ6Ke64kvtk7tqZiPNetWxete++BpUuXumvNmDEjWk9FZHuxl6keL0bUq0t+jCeOb1zRBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJAhBn0AAAAgQyM+dcdLKUmllwwODkbr/f39bs+5554brU+dOjVaTyUJeN+k37Vrl9vjpR+kkl28dJ1Kkgw8laTEVJL6k7of77n27qelpcVda9myZdH6fffd5/ZMmjQpWk/tM+k6wJHnHdtTxxzv+NHc3Fx2z9q1a8vet7POOitanzt3rruWt807Fkl+skslKS3bt293ezZs2BCt79y5s6z9kvwUpVSPdw4ZN26c2+Ol6j366KNuj8d7DVKJN975IPXaeM9nao5JpSXh+MUVfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMsSgDwAAAGRoxMdrevGSqTi1qqqqaL2zs9Ptqa2tjda9yLT29nZ3rc2bN0frqcjFBQsWlLVfklRfXx+tpyI5y419PJJRnZUqdx9S0Wzjx4+P1vft2+f2tLa2lnX/kjQwMBCtV1dXl70WgILu7u5ovZJjnnf8lKSOjo5ofcuWLW7PvHnzovULL7wwWp82bZq7lncM844rkh/rnIqC9s5jvb29bo/3vHnHydTx24uQ7Orqcnu883vq2Oq9B7zXORWj6sU3L1261O2ZMGFCtO69ZpL/XtuxY4fb4z0Hr3/9690e5I8r+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZGjEp+54UikLXrrOmWee6fZ436T37ufd7363u5aXMuAlRkjSqlWrovWrrrrK7Xnsscei9VRSz3AoN9lHSr+eqdSIcnmJDRdddJHb88tf/jJaHzdu3BHZJwAvTiWpVZWktGzcuDFab2trc3uampqi9UcffTRaTx0/vHSwVBqOd96pqalxe7xUuRkzZrg9r33ta6N1L7kslRLjPTf79+93e7yknNRz451fvDQc73mR/POBN0NI0tSpU6P11Guzffv2aH3lypVuj/dex/GNK/oAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMsSgDwAAAGSIQR8AAADI0IiP1/SiFVPRaK2trdF6Ks7Mi7nq7++P1m+44QZ3rdtvvz1a/9nPfub2dHV1lX0/t956a7T+sY99zO2pr693t8WMHu2/RYaGhqL1VDTZqFHxny29tST/tfEi01JxnF7059ixY90e7/2UepyVxAACSGtsbIzWvUhlSWpoaIjWBwYG3J6enp5o3Tt+SdK2bdui9b6+vmjdi4mUpOXLl0frixcvdnvq6uqi9VSss3c8TB2/5syZE617x9CtW7e6ay1btixa//Wvf+32PP7449H6pk2b3B4verS5udnt8Xiv50MPPeT2vPzlL4/WU+fXE044IVrfsGGD2/PMM8+423D84oo+AAAAkCEGfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZGvGpO1VVVWXVJenqq6+O1lPfcPcSGLz7SaUStLe3R+uzZs1ye9ra2qL1VDLDZZddFq2ff/75bs+FF14YrXuJCV5ageQnNqRSb55++ulo/ZxzznF7duzYEa17KRMpXlJPbW2t2/NXf/VX0fqnP/1ptyf1HACojHecTv1985LTUklb3vEgdczxEs22b98erXvJPpJ/Dtm9e7fbM3/+/Gi9qanJ7fGS6Co5hnvpZKnnrKWlJVo/5ZRT3B7vdUul6nn77J1fU+cDLyFuy5Ytbo/3eo4ZM8btmTt3brQ+b948t2fv3r3uNhy/uKIPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADI34eE1PKvbxv/7rv6L1iRMnuj1e9OaoUfGfhVL378WJpeI9vSitzZs3uz1eNNiECRPcnjVr1kTrXjRbKv7LezxehGWK95pJflxoV1dXtO69ZpK/b14EnyTdfvvt0fq1115b9v0AOPK8aEvJ/7ud+jvq9XhRmZIfY1lTUxOte8cvSVq9enW0vn//frdncHAwWl+6dKnb4+3bvn373J7vfOc70Xpzc3O0vnjxYnetk046KVr3zoeSH6PZ2Njo9njxll7spve8SH60dioK+9lnn43Wvccv+efXhoYGtyd1vsbxiyv6AAAAQIYY9AEAAIAMMegDAAAAGWLQBwAAADLEoA8AAABkaESk7oQQ3G2VpJd4Pak0Fu8b7gMDA9F6JfvsfVtf8r9JP3PmTLfHS+RJpTl0dnZG6xs2bIjWp02b5q7lpfukEok8PT097rZly5ZF67/4xS+i9aGhobLvP/XeqKqqitZTKQvec5N63wBIa2tri9ZTiSve8Th1bvESdLy65B9DUsd9T19fX7S+adMmt8e7n1RSz5lnnhmtp5JdvOPeli1bovXnnnvOXWvVqlXR+tlnn+32eEk1s2fPdntOOOGEsvatktcslaq3cePGaN1LKpL852bt2rVuT+qchOMXV/QBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMjQiEjdSTlw4EC0Xltb6/Z4KSkpXrpOJbxkFe+xSP63/FNpEl6SwOrVqxN7F+cl9aQSEyZNmhStp5IEvOcmlUYzfvz4aN1L10m9/t5rkErd8bZNmTLF7env73e3AaiMlyqS+jvvJcjs3r3b7ampqYnW586d6/bs3bs3WveOH959pHhpPJKfxrJjxw63Z+fOndG6l2wj+c+n9xp4z0tq2759+9ye+vr6aH3BggVuz+LFi6N1Lw3HS3eS/GO7t1+S/zxv27bN7Vm5cmW0njq/H8k5Bvngij4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyNKzxmqkIRY8XO5lay4tQTPVUsm/lMrOye1Kxj3V1ddH6woUL3Z5bbrklWvfi1Lw4O0navn17tD516lS3p7OzM1o/ePCg2+M9Hi9eM7WWJ/XaeO8nL5JU8l+b4XifAbnyYhcHBwfdHi+e99FHH3V7vBjNmTNnuj0bNmyI1ltaWqL10aP906+3LRUr7UUrpqIqf/zjH0frTz31lNvjHcMnTpwYrXvHwtQ279wiSevXr4/WU9Gn8+fPj9bnzJkTrW/atMldq5LIb+9ckYqF9V7P1EzgvddxfOOKPgAAAJAhBn0AAAAgQwz6AAAAQIYY9AEAAIAMMegDAAAAGRrW1B1PKvGkkpQS79vqlaxVSYKL96341FrettQ37D0NDQ3utssvvzxaf8tb3hKtX3PNNe5aXvpCKrHA2zZlyhS3x0tM8JI2vKSmlFQCRk1NTbSeSpMAcOTde++90XoqvcRLVnnuuefcntbW1mh98eLFbs+qVaui9f3790frXmqY5J+rUse2So57S5Ysidb7+vrcHi/Fp5LH2djYGK1v2bLF7fESeVLpQtOnT4/WvaSgSp5n7zwhSb29vdH6uHHj3B7vnJh6nJW8B5A/rugDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJAhBn0AAAAgQyMiXjPFi530ohUl6c1vfnO0fuedd7o9qXjFclUSyVnJWpXcT319fbR+4403Rus//elP3bXuuOOOaN2LN5X8OLNU9Ons2bOjdS9KLBW15/HizyTp4x//eLSeeg8eyfcTgAIvWjEVB9nT0xOtp6INn3rqqWh9wYIFbs/pp58erT/wwAPReipW2ns8qR4vijkV0bxz586y78eLxKwkErSrqytab2pqcnsOHDgQraf2ubu7O1qvra2N1lMR1bt3747WU8d87702YcIEt8eLhU1Fj6airXH84oo+AAAAkCEGfQAAACBDDPoAAABAhhj0AQAAgAwx6AMAAAAZGtZoEC8NJZW44n3DPvVN/muvvTZav/3228vet9Q3+T1eGk5NTY3bMzQ0FK2n0iS8bd5aknTGGWdE69OnT4/Wp02b5q61YsWKaP2CCy5wezyp90Bzc3O07j3OVPqB99p47zNJuvzyy6P1VFKPt2+pBAwAaV7SVXt7e9k9XhqPJE2ePDlab21tdXu8pJgNGzZE6+vWrXPXamlpidZTaTB79+6N1lPHHO+clDoel6uStVKJct45OXV+rauri9a943RnZ6e7lrct9X565StfGa2n5gtvWyrtjfMLYnhXAAAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEPDGq/pxWyl4re8iKn+/n63x4tK7OjocHu8SEavXkksVioq05OKGVu/fn20norX9J6bVLykx4vyOtJxarW1tdF6Jfvs3Y8Xryr5sXWpntQ2AJXZvn17tJ6KHPSiKr3IRck/hm7dutXtaWxsjNZPPfXUaN07fkvSzp07o3UvBlnyI6crOR9UEivtSZ0PvG2pmMj6+vqy98F7f3gRyQMDA+5aXsRpaiaZN29etL527Vq3Z+PGjdF6KsbTi3jF8Y0r+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZGhYU3e6u7uj9V27drk9J5xwQrSe+ia/lzKQup8TTzwxWq+pqYnWUykP5Sb4SP4+p5IhFi5cGK1/97vfdXtOP/30aN1Lo0mlH3gpRo899pjbs2fPnmj90UcfdXu8lAEvjSeVmOClLHgJB5L/vk3xUisqSSQCUOAdc1LHKe/Ylvp7/fjjj0fr69atc3u8xJNzzjknWj/77LPdtVavXh2tp1J/vHOVd5yUjmzamieVqOapJNUu9Vi8c693fk09Ru8Yfv7557s9y5Yti9Zvuukmt8d7D27atMntSc0LOH5xRR8AAADIEIM+AAAAkCEGfQAAACBDDPoAAABAhhj0AQAAgAwNa+rO2LFjo/WGhga3x0ujSX2Tv6qqKlr3El8k6eSTT47W165d6/aUy3ssKalv/3tpCm984xvdHi8tqLq6OlpPPc/lpi+k1kut5aVJeGulEpE+//nPR+sPPvig27N06dKy78dTyXMGoMBLGvPSeCT/72lXV5fb4yW4eOcwSVq/fn207h33L7jgAnet8ePHR+sPPPCA2+OlynnHT8l/nKnjVLlJPamkMW9b6ryzffv2aP3pp592e6ZNmxat9/f3R+up58xL8FmwYIHb4703du7c6fZ4j7OS2QfHN6YOAAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZGhY4zUriVb0eBFXqfvxIiQl6etf/3q07sVuNjY2umuZWVn1lOGK5KxkLe95Tr02XpxaKoKtr68vWvdi80477TR3rUmTJkXrXmyf5D/OVJRZ6vEAqMyECROide8YIflxkD09PW6PdwxLHds8TzzxRLS+f/9+t+fcc8+N1s8//3y358knn4zWN23a5PZ4x9DUcd87j1USlVmJzZs3R+t33XWX2zNmzJho3Xtv1NXVuWudeeaZ0Xoqbvn666+P1lOxsO3t7e42TyXvT+SPK/oAAABAhhj0AQAAgAwx6AMAAAAZYtAHAAAAMsSgDwAAAGTIhjMdpL29PXpnqW/le9/wT6UCeEk1e/bscXsmT54crXvJCK973evctbz7SSXeePucepzea1dJuo93P14qgeR/wz+VFOQlH6VSM7z13va2t0Xrr3rVq9y1TjnllLLuQ6osNaKS18DT0tJy5BYDjmGnnnpq9KCX+vs7ffr0aD2VwtbW1hatd3V1uT1eEpt3bkkd2+vr66P1s88+2+1ZtGhRtL5z50635+GHH47Wu7u73Z5UukxM6vjpncNS50ovEae3t9ftaWlpidZf/vKXR+tz5sxx1/Jez7Vr17o969ati9abm5vdnptuuilaT80x3ryyfv16ziHHMa7oAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEPxfMSXiBezlYoZ8+IdU/GFXsTUxIkT3R4vns2LObvtttvctXbs2BGtX3TRRW5PQ0NDtJ6KGUs9b+VKxdN5vNcmFdk6MDAQraciw3bt2hWt33LLLdH60qVL3bX6+/uj9aqqKrfHk4qNq2Q9AGk1NTXReur41dnZGa2njq1ejGYqWtKL1/QiFFPHvI0bN0brqcfpbZsyZYrbc+WVV0bre/fudXtWr14drT/77LPRent7u7uWdw7zzruS/3yeccYZbo8Xl+lFrHrR0ZJ/Dpk9e7bbM3/+/LLv56GHHorWn3jiCbcn9Z7C8Ysr+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZGhYU3e8xJUUL11n3bp1bs8JJ5wQrae+4d7b2xute+kDLS0t7lpe4sozzzzj9qxZsyZav/jii90eLzUi9Ti9dB/vtUmlPHjpRqnEhA0bNkTrXspEqufSSy+N1r1UBMlPefAShCT/PZhK1vGSh1JpUQCGTyq1rLW1NVpPHdu8Y2vquO/xEuJSCTZ33HFHtD5mzBi3Z+7cudG6lxIjSUuWLInWX/Oa10TrqeOkd35JHY+nTp0araeSgh588MFo/d5773V7PMuWLYvWvWQfyX8/pV6bpqamaN1Ld5L89yCOb1zRBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJAhBn0AAAAgQwz6AAAAQIbMiwF8KezZsyd6Z6n4rVRUpMeLTTt48GDZPamYr3KlHueqVaui9R//+Mduz+te97pofcGCBW6P9xx47wMvQlPyIzm//e1vuz2veMUrovWTTz7Z7fH2zXttBgcH3bVSkXqeSiIxvee5krVaWlrI5AQkvfrVr44eDKqrq92e5ubmaD3Vs2fPnmg9dTz09PT0ROupuGlvW+r45UVVdnZ2uj0zZ86M1r0IS8k/J3vH3QkTJrhreVHYY8eOdXu8KGbvNZOkHTt2ROu7du2K1ru6uty1vPjm1Hn3jDPOiNZnzJjh9nix0ps3b3Z7vMf505/+lHPIcYwr+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZKj8SJvfgZc48uUvf9nt8RITUmk8XjJBKo3FS3CpJD3FW8tLRZCkvr6+aD2V1HP33XdH67feeqvb84//+I9l3c95553nrjVv3rxofevWrW5Pb29vtH7fffe5PbW1tdH65ZdfHq2n3hvea5BKsxjOZCoAvnvuuSdanzhxotsze/bsaN1LnEltmzx5stvjJcV4CTqp4+TTTz8drTc1Nbk93nPQ1tbm9nhSz413PN69e3e0XsnxOHWu9s47dXV1bo93fm1vb4/WU+eD1P2Ua+/eve62SuYYIIYr+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMjQsMZrelGV73rXu9weL0JyzJgxbk91dXW0norMKlcqKtO7n1SPt62xsdHt8aIqn3nmGbfHi0bzIkEvu+wydy1vn88991y3x4uaS8WIevvmRZx6t09JRWimolSP5P0ASGtpaYnWa2pq3J49e/ZE617koiR1dnZG6/39/W7PtGnTyqqPHz/eXeuUU06J1lPnPS928dlnn3V7Ojo6ovUZM2aU3bN9+/Zovaenx12ru7s7WvfOU5J/Dpk6darbs3Dhwmjdi0vdtWuXu5b3PKdem/r6+mjde29K/vk1dX5LPQc4fnFFHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADBkpIAAAAEB+uKIPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEMM+iOQmd1rZlc72/7BzG4u/vcsM9tnZlUV3s8+M5tXZs+3zOwPKrm/w9Y538zaftd1nLX/+zlytj9kZotfivsGAAAYKRj0S5jZ2WZ2v5l1mtleM7vPzP6fo71fnhDC5hDC2BDCgQr7x4YQNkiSmd1kZh9J3d7MTpH0Mkk/KKlNMrN/N7MOM2s3s2+WbKs1s6+ZWZeZbTez91ayny+Wmd1lZq95ETf9pKR/ein3BQAA4GgbfbR3YKQwsyZJd0p6p6RbJdVIOkdS/9HcrxHmHZK+GUIIJbXbJT0sabak/ZKWlGz7B0nzi9umSPqJma0KIfzfI71jZtYg6TRJP5V05gvc/A5J15nZ1BDCtiO9LwAAACMBV/R/Y4EkhRC+FUI4EELoDSHcFUJ4XJLM7KriFf4vFK/4P21mFx5qNrNxZnaDmW0zs+fM7COlH6kxsz83s9XFq94/MrPZJdteXVyv08y+KMlezA6b2RwzC2Y2uvjne4v3e3/xYzn/YWYtZvbN4lX1h81sTkl/MLMTzeztkt4o6QOH+py7vFiFQfpQ/2skzZR0TQihM4QwGEJ4tOT2fybpwyGE9hDCakn/Kukq57G8y8xWmdmMQx/rMbMPmNnO4nP6B2Z2iZk9U/xty4cOW+JCSfeFEA79YFZjZv9mZt1m9pSZLT90wxBCn6SVkl7M1X8AAIBjEoP+bzwj6YCZfd3MLjaz8ZHbvELSBkkTJf29pNvNbEJx29clDUk6UdKpKgyRV0tS8TPtH5L0R5ImSfq5pG8Vt02UdJukvy2uu17SWb/D47hc0p9Kmi7pBEkPSLpR0gRJq4v7/TwhhK9K+qaka4sf53nd4bcpXjGfK2lNSfmM4p+/bmZ7ij9InFe8/XhJ0yQ9VnL7xyT91mfjzez/qPADwHkhhEOf258iqa74OP5OhR8S3qTCVftzJP3dYd8vuETSf5b8+fWSbpHUrMIV/C8edrerVfgYEgAAQJYY9ItCCF2SzpYUVBgqd5nZHWY2ueRmOyV9tnjl+tsqDLm/V7zNxZLeHULoCSHslPQZFYZuqfCRl38OIawOIQxJ+pikZcWr+pdIWhVC+G4IYVDSZyVt/x0eyo0hhPUhhE5JP5S0PoRwT/F+v6PCDyGVaC7+f3dJbYYKP9D8RIXB/FOSflD84WVs8TadJbfvlNRY8mczs09Leq2kC0IIu0q2DUr6aPE5uUWFH4I+F0LoDiE8JekpSaeU3P5iSStK/vyLEMKK4vcXvqHfHuq7Sx4TAABAdhj0SxQH8atCCDNU+Kz5NBUG70OeO+zz6ZuKt5ktqVrStuKXUjskfUVSa/F2syV9rmTbXhU+njO92L+lZB9C6Z8rsKPkv3sjfx6rynQU/790UO+VtDGEcEPxh59bVNj3syTtK96mqeT2TXr+DwrNkt6uwg9BpT8QSNKeki8Z9xb/P/pYzGyppK4QQunzVvrD0n5JdYc+4lTyODoEAACQKQZ9RwjhaUk36flfLp1uZqWfn58laasKw22/pIkhhObi/5pCCIc+prJF0jtKtjWHEOpDCPdL2qbC59wlFS5xl/55GIXkxhB6VPhY0YKS8uNeXwihXYXHVnol/WUqXIk/pF3S70u60cx+l48rHf6xnRdjkZ7/sSIAAICsMOgXmdlCM3ufmc0o/nmmpCsk/bLkZq2S3mVm1Wb2JyoMiyuKyS13SfqUmTWZ2SgzO+HQ59UlXSfpg4ey24tf3P2T4rb/lLTYzP6oeMX5XSp8DGa47ZD0Qpn6KySdV/Ln70kab2ZvNrMqM7tMhd9S3Ffc/m+S/tbMxpvZQklvU+GHp/8WQrhXhS8Cf8/MXlHhvv+env+xnSQzq1Xhs/53V3h/AAAAIx6D/m90q/Bl2wfNrEeFAf9JSe8ruc2DKsRF7pb0UUmXhRD2FLf9mQqRnKtUuFL9XUlTJSmE8D1J/5+kW8ysq7juxcVtuyWG61SgAAAfO0lEQVT9iaSPS9pTXP/QoDycbpB0cvHjRd93bvNVSW889FuNEMJeFb70+n4VPn//15IuLT4mqfDF3/UqfMTpp5I+EYvWDCHcLektku4ws9PK2WkzG6fCD1z3l9H2ekn3hhC2lnNfAAAAxxJ7/kfO4TGzqyRdHUI4+2jvy9FkZv8u6dYQgvfDwLAyszeo8APXG8roeVDSW0MIT750ewYAAHB08Q9moSwhhCuP9j4cpkOFhKMXLYRQ6UeEAAAAjhl8dAfHtOI/avbA0d4PHBlm9rXiP5IW/W2LFXzezNaZ2eNm9vLh3kcAAI4VDPovUgjhpuP9YzvAMLhJ0kWJ7Rer8D2W+SpEs/7LMOwTAADHJAZ9ACNGCOFnKvw7E55LJf1bKPilpGYzmzo8ewcAwLFlWD+j/6Mf/Sj6zd9t27a5PaNGxX8WWbRokdszY8aMaH3fvn3RuiTt378/Wh8YGIjWDx486K7l9Xh1Serv74/WDxw4EK2n9qG7uztal6S+vr5o3ftSdurL2qNHx98+NTU1bo+3bWhoyO3xHqf33ki9Nh5vLcl/nJXs87hx49yehoaGaP1Vr3qVRTccn6br+f+gXFux9lsHETN7uwpX/dXQ0HDawoULh2UHAQA4klauXLk7hDCpkl6+jAvgWBL7ocf7R9u+qkIkrJYvXx4eeeSRl3K/AAB4SZjZpkp7+egOgGNJm57/L0fPUOFfpwYAAIdh0AdwLLlD0p8V03fOkNRZ/JepAQDAYfjoDoARw8y+Jel8SRPNrE2Ff125WpJCCNdJWiHpEknrJO1X4V9UBgAAEQz6AEaMEMIVL7A9SPrfw7Q7AAAc04Z10O/o6IjW6+vr3Z7BwcFo/bnnnnN72traovVUgo1ZecEmqQQdL40lldLiJbukUm88qdQZL13Gu5/Uc+ZtO9I93mtTSbqO9zi91CPJf61T6UJjxoyJ1ltbW92e1D4AAACUi8/oAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEPDGq/pRRumIiS9CMX29na3p6qqquz7ScU7xngxlZK/z6kIT29bqqeS6E1vPS/es6GhwV2rurr6iN1/Y2Oj2+PFWHr3n4ox9V631GPxelLxnl1dXdH6E0884fbs3r07Wj/33HPdHgAAAA9X9AEAAIAMMegDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyNCwpu54ySZeSo7kp76kUm+8BJnU/XhpMN79pFJ6vG2pfa6rqyu7Z3BwMFpPJch49+M9N6nnzHuclTw33mORpP3790frXrLN9u3b3bX6+vqi9d7eXreno6MjWk+l7uzbt6+s+5ekBQsWuNsAAADKxRV9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZGhY4zW9CMtUTKEXu9jf3+/2DA0NlbVWaj1vrdQ+e3GQXl3yYyzHjBnj9kyePDlanzVrltvjRW96j6ezs9Ndy4uk9GIvJam7u7ustaTyIzFTkaTe4wwhuD3Nzc1l38+UKVOi9ZqamrL3DQAAoBJc0QcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQIQZ9AAAAIEPDmrrzxBNPROsHDhxwe7yUGK8u+ek6qVQTL/Wm3JSa1Fonnnii29Pa2hqtT5o0ye0ZP368u80zMDAQrXuPp76+3l3Le55T++zdf0dHh9uzb9++aN1LBPJSeiT/cabeg6mknHKlknoAAACOJKYOAAAAIEMM+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPYEQxs4vMbI2ZrTOzv45sn2VmPzGzR83scTO75GjsJwAAI92wxmtOmTIlWk9FDlYSRxhCKHstL0azt7c3WvdiIiXphBNOiNZT8Zqenp4ed5sXI5mK/vT226sPDQ25a3nPZ+r+R4+Ov+UaGhrcnrq6umjdi73s6upy12pvby9rLcmPS01FcqIyZlYl6UuSXi2pTdLDZnZHCGFVyc3+VtKtIYR/MbOTJa2QNGfYdxYAgBGOK/oARpLTJa0LIWwIIQxIukXSpYfdJkhqKv73OElbh3H/AAA4ZjDoAxhJpkvaUvLntmKt1D9IepOZtalwNf8vYwuZ2dvN7BEze2TXrl0vxb4CADCiMegDGEksUjv8s3hXSLophDBD0iWSvmFmv3UsCyF8NYSwPISwPPWvNQMAkCsGfQAjSZukmSV/nqHf/mjOWyXdKkkhhAck1UmaOCx7BwDAMYRBH8BI8rCk+WY218xqJF0u6Y7DbrNZ0oWSZGaLVBj0+WwOAACHGdbUHS+lJJWGU0mCjpcgYxb7VECBlxTjpbGcfPLJ7lqtra3Ren9/v9vT0dERraeSXWpra6N1LyUmxXs+U89ZJak/XoqP9zpL/mswcWL8Im4qwcd7znbu3On2ePuW2ufU8wZfCGHIzP5C0o8kVUn6WgjhKTP7J0mPhBDukPQ+Sf9qZu9R4WM9V4XUiwEAwHFqWAd9AHghIYQVKnzJtrT2dyX/vUrSWcO9XwAAHGv46A4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADA3rl3ErSXappMdLVtm/f3/ZPUuWLInW6+rq3LV2794drXuJMylNTU3uNm8fvJQaSaquro7WUylGHu/59BKEJKm9vT1a95KSUtu890DqtfESkVLpRnv37o3WU89zJUk9laQlAQAAeLiiDwAAAGSIQR8AAADIEIM+AAAAkCEGfQAAACBDDPoAAABAhhj0AQAAgAwNa7xmKhKzXKmYQi8qMhVVedJJJ0XrDQ0N0boXEyn5UY2pfZ4wYUJZ9y/5UZmpGM+DBw9G65XEazY3N0fr3mOR/EjODRs2uD1tbW3Ren19fbSeevxez6xZs9we7/Xs7e11e7zX+kj+HQAAAEjhij4AAACQIQZ9AAAAIEMM+gAAAECGGPQBAACADDHoAwAAABka1tSdSpJIvG2pBJuBgYFofenSpW7PuHHjovW9e/dG614SiyRVVVVF6xMnTnR7vHQdLyVGkvr7+6P1wcHBsvdt9Oj4W6G2ttZd67nnnovWd+/e7fbMnj07Wl+4cKHb473WXhpP6rXx3k+pdKPW1tZofe3atW5P6nkDAAAYDlzRBwAAADLEoA8AAABkiEEfAAAAyBCDPgAAAJAhBn0AAAAgQwz6AAAAQIaGNV7TixwcNcr/ecOLQ0xFKE6aNClaT8VbdnZ2Rut9fX3RemqfJ0+eHK03NTW5PV7s5Xe+852yey655BK3x4ve9B7Pl770JXetG2+8MVrv6upye5YsWRKtf/jDH3Z7Fi1aFK17r1l3d7e7VnV1dbTe0dHh9owZMyZaT0Wfeq9NJdGfAAAAleCKPgAAAJAhBn0AAAAgQwz6AAAAQIYY9AEAAIAMMegDAAAAGRrW1B0v2aWS1J2DBw+6PdOnT4/W+/v73R4vXSeEEK03Nja6a3nJLl56iyQ99thj0fp73/tet8dLqhk3bpzbMzAwEK3ffPPN0foXv/hFd633v//90fqcOXPcHm+fP/rRj7o93/jGN6L1E088MVpftWqVu5aXeuO9/pL/fDY3N7s9e/bsida95KnUvgEAAFSCK/oAAABAhhj0AYwoZnaRma0xs3Vm9tfObd5gZqvM7Ckz+/fh3kcAAI4Fw/rRHQBIMbMqSV+S9GpJbZIeNrM7QgirSm4zX9IHJZ0VQmg3s9ajs7cAAIxsXNEHMJKcLmldCGFDCGFA0i2SLj3sNm+T9KUQQrskhRB2DvM+AgBwTGDQBzCSTJe0peTPbcVaqQWSFpjZfWb2SzO7KLaQmb3dzB4xs0d27dr1Eu0uAAAjF4M+gJEkFrN1ePTVaEnzJZ0v6QpJ15vZb0UghRC+GkJYHkJYPmnSpCO+owAAjHTD+hl9L3YyxYvXrKurK/t+Ojo63B4vdrK+vj5aHz9+vLuWFxeaik+8/vrro/VFixa5PVdccUW03tXV5faMHh1/yX/4wx9G6+eff7671vve975oPfU6r1u3Llq/7rrr3J5t27ZF697wVlNT467lvc6Dg4Nuj7ctFWPa3t4erXvvZykdM3scaZM0s+TPMyRtjdzmlyGEQUnPmtkaFQb/h4dnFwEAODYwWQAYSR6WNN/M5ppZjaTLJd1x2G2+L+kCSTKziSp8lGfDsO4lAADHAAZ9ACNGCGFI0l9I+pGk1ZJuDSE8ZWb/ZGavL97sR5L2mNkqST+RdE0IIf4vlAEAcBwjXhPAiBJCWCFpxWG1vyv57yDpvcX/AQAAB1f0AQAAgAwx6AMAAAAZGtaP7niJI4XfxMd5STUTJ050e7xEns7OzrLvp7a2Nlr30mtS9//444+7PXfddVe0/olPfMLtmTZtWrTe1tbm9gwNDUXr3uNvampy19q3b1+0nkqP8Z43b78kqb+/P1r30n2qqqrctbzUHe91Thk7dmzZPSmp/QYAACgXV/QBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQoRHxL+N6MYmSH72Zin0cHBwsay3Jj5f0Ig+920v+4/nWt77l9ng2btzobvvmN78ZrV9wwQVujxdvWVNTE60fPHjQXcuLxKwkJjL1fJa7b16Mq+S/Byp5nKmIVe89kNo3AACAI4kr+gAAAECGGPQBAACADDHoAwAAABli0AcAAAAyxKAPAAAAZGhYU3fq6+uj9VTiipeSUltb6/Z4KSmp+yk3QaahocFda/369dH6nXfe6fY0NzdH67fddpvb8/TTT0fr73nPe9yeD33oQ+62mIGBAXeb93ymkmW8BJ1UIpKXopR6PT2p+ym3J5XU471vKkmYAgAAqARX9AEAAIAMMegDAAAAGWLQBwAAADLEoA8AAABkiEEfAAAAyNCwpu54SSSVpKeMGuX/jFJJ6o63b16CTCr15+abb47WR4/2n+6vfe1r0fr8+fPdnne+853R+ne+8x235x3veIe7LSaVLOMlyFTSk3o9vdQd734quf8Ur8d7n73QPnhSzwEAAEC5mCwAAACADDHoAwAAABli0AcAAAAyxKAPAAAAZIhBHwAAAMgQgz4AAACQoWGN1/SiKlOxgiGEsnsGBgbK27HEel49dR+//vWvo/WXv/zlbs9pp50WrdfU1Lg9L3vZy6L1+++/3+3xIka9x9ne3u6u1djYGK2noiV37txZ1n5JfpRpX19ftL5//353Le89mHqevfvfsWOH2+M9n6l4z0piZgEAADxc0QcAAAAyxKAPAAAAZIhBHwAAAMgQgz6AEcXMLjKzNWa2zsz+OnG7y8wsmNny4dw/AACOFQz6AEYMM6uS9CVJF0s6WdIVZnZy5HaNkt4l6cHh3UMAAI4dw5q6MzQ0FK2nEnRSCS4eL0GlkrW8ZJeqqiq3Z8KECdH62rVr3Z4NGzZE695zJkk/+MEPovUlS5a4PVOnTo3WvUSgT37yk+5a11xzTbTuPX5J+tKXvhStv/71r3d75syZE62vWbMmWh8cHHTX8lJ3mpqa3B5vvZ6eHrenrq4uWk+9byp5f2bodEnrQggbJMnMbpF0qaRVh93uw5KulfT+4d09AACOHVzRBzCSTJe0peTPbcXafzOzUyXNDCHcOZw7BgDAsYZBH8BIEvuVy3//YxpmNkrSZyS97wUXMnu7mT1iZo/s2rXrCO4iAADHBgZ9ACNJm6SZJX+eIWlryZ8bJS2RdK+ZbZR0hqQ7Yl/IDSF8NYSwPISwfNKkSS/hLgMAMDIx6AMYSf7/9u43xqr6zuP45+v8Y6AjmzBEVKBDAqioVQliN8aUDWajfQAPNAYTs9Rg9Ynd7Hazxk1NbewD05KmySZuWzc17Zq41pq4jhsaHrSubTaLinFDFgs6UVKhIAgzyDD/h28f3Nvd6fj7nrn3LnPm3p/vV0Iy93vO79zf/O5h+HLm3M99U9I6M1tjZp2Sdkjq/+NGdz/r7r3u3ufufZL2Sdrm7vsXZroAADQvGn0ATcPdpyQ9LGmvpN9KesHdD5rZE2YWv2MbAAB8SqmpOwAwF3ffI2nPrNo3g323lDEnAABaUamNfhRtWBSvOT09nawXRRtG9+MODQ2FY0ZHR5P1wcHBZH3VqlXJuiTt3LkzWX/ooYfCMVu3bk3Wo+9fkrq7u5P13bt3h2OiqMgHHnggWS9as+eeey5ZL4qQvOeee5L1Rx55JBwTvZHy+PHjyXpXV1d4rOhci9ZSko4dOxZui7S3p/9qFUV/unu4DQAAoF7cugMAAABkiEYfAAAAyBCNPgAAAJAhGn0AAAAgQzT6AAAAQIZKTd1ZvHhxsj4+Ph6OidJLzpw5E47p6+tL1ovSfTo6OpL1Tz75JFk/dOhQeKxbb701WX/22WfDMa+88kqyvmjRonDMXXfdlayvWbMmHHP27NlkPVrnxx57LDzW/fffn6wXpe6sWLEiWR8eHg7HHDhwIFmPkpKKEnSWLVtW17GkOHko+l4k6cKFC8l6lDwlFZ+fAAAA9aKzAAAAADJEow8AAABkiEYfAAAAyBCNPgAAAJAhGn0AAAAgQzT6AAAAQIZKjdeMogW7urrCMZOTk8l6USRnFNXY29sbjjly5Eiy7u7J+smTJ8Nj9fT0JOs33HBDOOaWW26p6/mlOBJyZGQkHBNFOEZxkEXrHMVLRlGdkjQ4OJisDwwMhGOiOUQxnkXPH0W8Hj58OBxzxRVX1PX8UryeRa9n0fEAAADqxRV9AAAAIEM0+gAAAECGaPQBAACADNHoAwAAABmi0QcAAAAyVGrqTiNJJFFKTFGyynvvvZesX3PNNeGYJUuWJOtRgk2UICRJJ06cSNbHxsbCMcuXL0/WOzo6wjFFc4hEax2tc1SX4kSk48ePh2M++OCDZL0o3SdKZZqamkrWV69eHR7rrbfeSta7u7vDMVGKUtGco9emKFmnaK0BAADqRWcBAAAAZIhGHwAAAMgQjT4AAACQIRp9AAAAIEM0+gAAAECGSk3diVJFilJ3IkXpJVEayscffxyOWb9+fbL+7rvvJusTExPhsaLElaGhoXBMlMizePHicEyUFFOU1BMl1UTfT1FS0OnTp5P1ou8zUpR6E61nX19fsv7GG2+Ex4qSgtauXVv3mKLUoygVqui1aeTvAQAAQIQr+gAAAECGaPQBAACADNHoAwAAABmi0QcAAAAyRKMPAAAAZIhGHwAAAMhQqfGaUSTm9PT0RX2enp6eZL0oXjOKNrzsssuS9ZMnT4bHOn/+fMHs0qLYy+Hh4XBMtJ5FsY/R80QRklFdKo4YjURz6+zsDMesWLEiWX/ttdeS9dHR0fBYt912W7JedA5Ga1YU8RrFa0Z1qbH1BAAAiHBFH0BTMbM7zOywmQ2Y2aOJ7V83s3fM7ICZ/dLMPr8Q8wQAoNnR6ANoGmbWJukpSXdK2iDpXjPbMGu3tyVtcvcvSHpR0nfLnSUAAK2BRh9AM9ksacDd33f3CUnPS9o+cwd3f9XdR6oP90laWfIcAQBoCTT6AJrJlZI+nPH4aLUW2SXpF6kNZvagme03s/2nTp26iFMEAKA10OgDaCapd2sn3ylvZvdJ2iRpd2q7uz/t7pvcfdPy5csv4hQBAGgNpabuXHJJ+v8VUeJNkaJkmeh5urq6wjHRFb9ly5Yl65dffnl4rHPnziXrJ06cCMdEyS5Fc+7o6Ai3RaK1vnDhQl37S/Gci+YVpRiNjY2FY/bt25esr1yZvmPjqquuCo/ViCiRp5G0qOjcbPR4GToqadWMxysl/X72TmZ2u6RvSPqSu4+XNDcAAFoKV/QBNJM3Ja0zszVm1ilph6T+mTuY2U2SfiRpm7vHObcAAHzG0egDaBruPiXpYUl7Jf1W0gvuftDMnjCzbdXddkv6nKSfm9l/m1l/cDgAAD7TSr11BwDm4u57JO2ZVfvmjK9vL31SAAC0IK7oAwAAABmi0QcAAAAyRKMPAAAAZKjUe/Tb2trqql9s7e3xt9vT05Osnz9/PlmPIjQlaenSpcl6USRnFFVZFMcYbZucnKz7eaK40qJ4zygO8vTp0+GYgYGBZH14eDgcs3HjxmR99erVyXpRVGdRLGskih6dmJgIx9QbYwoAAHCxcUUfAAAAyBCNPgAAAJAhGn0AAAAgQzT6AAAAQIZo9AEAAIAMlZq609nZmZ5EQRpOI6LEkyJRGksj6SkjIyPJ+vj4eDgmSspZtGhROCY6XpSGI8Xfz9DQULJ+5MiR8Fijo6PJepSGI8XJQzfffHM4Jlrrjz76KFkvSipq5NxoRCPnTSOJQAAAABGu6AMAAAAZotEHAAAAMkSjDwAAAGSIRh8AAADIEI0+AAAAkCEafQAAACBDpcZrRvGBRXGIjYiiDYviC6Nt9dYlqaurK1mfmpoKx0Sxi1GEpSSdOnUqWX/55ZfDMS+99FKyPjY2lqxfe+214bEef/zxZP3qq68Ox0SvTRQvWjRmoeMo29rawm3RnJsh+hMAAHw2cEUfAAAAyBCNPgAAAJAhGn0AAAAgQzT6AAAAQIZo9AEAAIAMlZq6EyWOTE9PL+jzS3GCS5SGU5SQEm0rGrNkyZJk/dJLLw3HrF27Nlk/dOhQOKajoyNZ7+7uTtavv/768FibN29O1s+dOxeOida5vb3+U7He10yKz7Wi1yY6XlGKUiOK5g0AAFAvrugDAAAAGaLRBwAAADJEow8AAABkiEYfAAAAyBCNPgAAAJAhGn0AAAAgQ6XGa7a1tZX5dJ8SxTHOtS2lKI6xkeeYnJysq150vLGxsXDMli1bkvWNGzcm68eOHQuPdebMmXBbpJEIyXrXut7XUiqeV/T8FzsOsyj+FQAAoF50FgAAAECGaPQBAACADNHoA2gqZnaHmR02swEzezSxvcvMflbd/rqZ9ZU/SwAAmh+NPoCmYWZtkp6SdKekDZLuNbMNs3bbJWnQ3ddK+r6k75Q7SwAAWgONPoBmslnSgLu/7+4Tkp6XtH3WPtsl/bT69YuStloj78AGACBzpabu3HjjjfxjXIInn3xyoacANOpKSR/OeHxU0i3RPu4+ZWZnJS2T9PHMnczsQUkPVh+Om9n/zMuM0atZa4+LgnWdP6zt/GFt58dVjQ4stdEHgDmkLgbMzjetZR+5+9OSnpYkM9vv7pv+/9PDbKzt/GBd5w9rO39Y2/lhZvsbHcutOwCayVFJq2Y8Xinp99E+ZtYuaamk+j/UAQCAzNHoA2gmb0paZ2ZrzKxT0g5J/bP26Ze0s/r13ZJ+5Y18gh0AAJnj1h0ATaN6z/3DkvZKapP0jLsfNLMnJO13935JP5b0rJkNqHIlf0cNh3563iYN1nZ+sK7zh7WdP6zt/Gh4XY0LYQAAAEB+uHUHAAAAyBCNPgAAAJAhGn0A2TCzO8zssJkNmNmjie1dZvaz6vbXzayv/Fm2nhrW9etm9o6ZHTCzX5rZ5xdinq1orrWdsd/dZuZmRnRhjWpZWzO7p3ruHjSz58qeY6uq4WfCajN71czerv5c+PJCzLPVmNkzZnYy+twXq/jH6rofMLONcx2TRh9AFsysTdJTku6UtEHSvWa2YdZuuyQNuvtaSd+X9J1yZ9l6alzXtyVtcvcvqPJpxd8td5atqca1lZn1SPprSa+XO8PWVcvamtk6Sf8g6VZ3v1bS35Q+0RZU43n7mKQX3P0mVQIT/qncWbasn0i6o2D7nZLWVf88KOkHcx2QRh9ALjZLGnD39919QtLzkrbP2me7pJ9Wv35R0lYz4xO7i825ru7+qruPVB/uU+XzDzC3Ws5ZSfq2Kv95Gitzci2ulrX9qqSn3H1Qktz9ZMlzbFW1rK1LurT69VJ9+vNQkODuv1bx58Jsl/QvXrFP0p+Z2eVFx6TRB5CLKyV9OOPx0WotuY+7T0k6K2lZKbNrXbWs60y7JP1iXmeUjznX1sxukrTK3f+9zIlloJbzdr2k9Wb2n2a2z8yKrqTi/9Sytt+SdJ+ZHZW0R9LXypla9ur9eUyOPoBspK7Mz84PrmUf/Kma18zM7pO0SdKX5nVG+ShcWzO7RJVbzL5S1oQyUst5267KLRBbVPkt1G/M7Dp3H5rnubW6Wtb2Xkk/cffvmdmfq/LZJ9e5+4X5n17W6v43jCv6AHJxVNKqGY9X6tO/Lv7ffcysXZVfKRf9mhS1ravM7HZJ35C0zd3HS5pbq5trbXskXSfpP8zsiKQvSurnDbk1qfXnwcvuPunuH0g6rErjj2K1rO0uSS9Ikrv/l6RFknpLmV3eavp5PBONPoBcvClpnZmtMbNOVd4A1j9rn35JO6tf3y3pV86nBs5lznWt3l7yI1WafO5zrl3h2rr7WXfvdfc+d+9T5f0P29x9/8JMt6XU8vPg3yT9hSSZWa8qt/K8X+osW1Mta/s7SVslycyuUaXRP1XqLPPUL+mvquk7X5R01t2PFw3g1h0AWXD3KTN7WNJeSW2SnnH3g2b2hKT97t4v6ceq/Ap5QJUr+TsWbsatocZ13S3pc5J+Xn1v8+/cfduCTbpF1Li2aECNa7tX0l+a2TuSpiX9vbufXrhZt4Ya1/bvJP2zmf2tKreWfIWLKnMzs39V5Vay3ur7Gx6X1CFJ7v5DVd7v8GVJA5JGJN0/5zFZdwAAACA/3LoDAAAAZIhGHwAAAMgQjT4AAACQIRp9AAAAIEM0+gAAAECGaPQBAACADNHoAwAAABn6A0T6bkfrdNYyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa0a6c4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_signs = np.array(my_signs)\n",
    "my_signs_gray = np.sum(my_signs/3, axis=3, keepdims=True)\n",
    "my_signs_normalized = my_signs_gray/127.5-1\n",
    "\n",
    "number_to_stop = 7\n",
    "figures = {}\n",
    "labels = {}\n",
    "for i in range(number_to_stop):\n",
    "    labels[i] = signnames[my_labels[i]][1].decode('ascii')\n",
    "    figures[i] = my_signs_gray[i].squeeze()\n",
    "    \n",
    "plot_figures(figures, 4, 2, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "My Data Set Accuracy = 1.000\n"
     ]
    }
   ],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver3 = tf.train.import_meta_graph('./lenet_1.meta')\n",
    "    saver3.restore(sess, \"./lenet_1\")\n",
    "    my_accuracy = evaluate(my_signs_normalized, my_labels)\n",
    "    print(\"My Data Set Accuracy = {:.3f}\".format(my_accuracy))\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Image 1\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Image 2\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Image 3\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Image 4\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Image 5\n",
      "Image Accuracy = 1.000\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n",
      "Image 6\n",
      "Image Accuracy = 1.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "\n",
    "my_single_item_array = []\n",
    "my_single_item_label_array = []\n",
    "\n",
    "for i in range(6):\n",
    "    my_single_item_array.append(my_signs_normalized[i])\n",
    "    my_single_item_label_array.append(my_labels[i])\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        saver.restore(sess, \"./lenet_1\")\n",
    "        my_accuracy = evaluate(my_single_item_array, my_single_item_label_array)\n",
    "        print('Image {}'.format(i+1))\n",
    "        print(\"Image Accuracy = {:.3f}\".format(my_accuracy))\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./lenet_1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa0b44208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   40: Roundabout mandatory      (95.38%)\n",
      "   28: Children crossing         ( 4.61%)\n",
      "   12: Priority road             ( 0.02%)\n",
      "    7: Speed limit (100km/h)     ( 0.00%)\n",
      "   42: End of no passing by vehicles over 3.5 metric tons ( 0.00%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BuZ0boZZ09HzQk6+ugjFbVr8Dtb+94x+gFpvSALWMYZI9ImyooIlETF+jYdbPKJ5kn0jqqZERfwi/XxZ/L9EA3yOCRBxqzm792vF7umEMPlMiXhinrEab8He2ef1eqOUG9fZ0sUgUH0dZP46hQT11+ml4xyXEQmhcQiyExiXEQmhcQiyExiXEQiraVQ6HXKmv03vlOAHuATTSpe+i9jq4T9XCBQug1rXm91Ar7tkFtYEPP1DXp7c0w5h/+8FvoeaW8ek7bTnuEfXEg3g8ycRGfVL5nHnz8HEYmiw1TpwKtad/9hTUmqZNUdd37tsGYzZu2QK1sy7+MtTiKbyr3PX79er6aKkAYwydniRajQsJcgl8H3vlsRehVshk1XVDzQX80nKGUSefCB/XqwghRxU0LiEWQuMSYiE0LiEWQuMSYiE0LiEWUuEIEpGQozcIqqk2jJEAU8z39eHUzdKTToFa3pAOiht6G+195El1/dyH74MxN3zvdqg9eu+PoPbrN38FtZoJOCVx19//UI8x9C8ycfMtfwO17//jnVC78dorK/6s5mktUPvaNVdBzT3cCbXet99U142FBAZt2rwToNadwwUUsQAXZNQ16AUxpmyQ4+r3zINpvWDh0/COS4iF0LiEWAiNS4iF0LiEWAiNS4iF0LiEWEhF6SDXdSSV1Md1BAGu8Kitnqau9x7EvZfS57dArWrKdKgFXR1QG1jztrq+/+cvwJib/lIfCSIistQwkT7d0QW15jkzoDa5Sa9UKmTwuTJNgv/iWedCbeVPJkFt/eo16no8loAxp1+AK6LmN+vXgIjIB/feC7WhTr3XU5UhH5T1cSJm3tLToLb2gF49JiLS3KSPDBERqaoCFXMg5SMiEnb1P6B7aHz9yHjHJcRCaFxCLITGJcRCaFxCLITGJcRCaFxCLKSyZnHhsNTX6SmEspc0fMhEdd0v4JgeD0+Jb71sOdR2P/Qo1KpErxzatvKnMKZ6Em621mZoCOfPnA+1YgE31svmc7oQMozwCHBFVG4UpxeOm4cb8i08Tq/OGo0bal7CuIHbrmdfgtr+l16GWjX4s0v6NBkREWlsxY31jjSm8HG8vhsfRxyPEwmH9dSOa/jOwo6uucYOc2NeN65XEUKOKmhcQiyExiXEQmhcQiyExiXEQmhcQiykson0TkLqqhapWiyKUzuJhN4cLRHDFRdeCFcbVZ1/AdQmvLkOarnd+mybqoE+GLPuLtxQrS1zG9Qal18INd+QWogU9K+kKDj/UTaMm3EM6YWih9+zFNeboyWKOOWz/z+fh9qelXheUsrH74nuLAVDu7gl3/ozqG3yBqBWyuHjiMXwRKKgDI7SwccYGCqHxgPvuIRYCI1LiIXQuIRYCI1LiIXQuIRYSEW7yk1Nk+T66/5a1QYG8G5dZkQfqzD92NkwZtce/MB3uK4Jagt/8H2ovXfDDep6eRjvKscG8d+16Xt3QW3ymnehNuXLF0Et1Xqiuh5P4IfjSz4euuEaBmF4I8NQy6zTx7xsfXEVjOl++w2oxQx9sUD7JRERGQIb39MuuRzGzPvG16HW/uyDUIv4uFhjfHPiPxUT4CiTNh54xyXEQmhcQiyExiXEQmhcQiyExiXEQmhcQiykonRQOQhkdFR/+NwzPLDug55IQ114TEfxMNZ6sjjF0V2L0yZz775DXd9x9z/DmHx/L9TqDQ/+F1bhtMmm134DtdjcVnW9YRoe4RFP4IKMQhb3nDpyEE+CH9y5S10PC+6XpZeS/AHTnPWMoX9Uy+WXquttt9wMY6a3zIJayVDQEITxdRUy5awAkQjum4b6VJmKQsbCOy4hFkLjEmIhNC4hFkLjEmIhNC4hFkLjEmIhFaWD8rmCbNm8Q9XKAU4TFEt6SmJfYRTGRMIx/H4d+6A2fAC/pzerRV0/619/DGPeu/9foNa3cQPUcIJGJGlKnW3V+2IdBOufhSmJYfryUVLNkLkRXGMlEqtqhNqSK74GtX3z9SPJxEZgjBvClTdOCKdo3DDWTGmaANQOmdJBUaA5YDTJp+EdlxALoXEJsRAalxALoXEJsRAalxALoXEJsZCK0kGeV5Lenm5Vc128BV8GzcxKhhZcgYvTS35mCGphw3t2bN6prvdMmQBjjr3t21CbtBaPOzm06tdQ69t3AGpxV08ThMv4fJiIGJrFFQznCrVNc6rwKI4ZXzwVak0XnAe113vaofbK00+q60+cfAKMEUPqJlrGx2/KdQURQ4oJfF4mg2uiUBM/3zcl3MbGE0Ksg8YlxEJoXEIshMYlxEJoXEIshMYlxEIqaxbne5Id7tc1wygUNCfFMcxP8fK4yZk4OC4awfUwOV9PIxX6D8GYvYZ0SvNkXPGy6NbvQG1mh55SExHJDernd7ADp5CcYVwpk6iug1pWolBLTW9W1+uOw3Ob2h38dz23+wWordmwEWrTpiTU9Y2b34IxNaF6qLW374VacRQ3knMNE+RRdZApJuIC641zphDvuIRYCI1LiIXQuIRYCI1LiIXQuIRYCI1LiIU4lYy0nzplanDdNddX/CFl8BmBoRIi8HE1TMjwc+MYUkV+Wa9SEsNxFA3akXwWaoEhPzaxrgFqPsjQZAaPwJikIWXlhnHKJ1RdC7Uj+WF1vbNjE45J44Z2qSRO71WlqqFWXz9RXc906zOsRERS5elQS4RxG79SEVwfIlIwNDb0QfXb6CiOGQXvt3p7uwxl8585QIh3XEIshMYlxEJoXEIshMYlxEJoXEIspKIiA9d1JB7TQ3wP78j5YIc1bBjREIvg3kDhsOHh7Sj+k9B0ByeEd7ALpRzU6jJ4h9LP4/NRzOL33Htgj7r+4UfrYUwihs9jMoWLDGpThhnyWb0gozqVhCFtC86HmqmXUtE3FJt4+u5rbR0eUVPlToFaFJ8qcRy8U20qGIjG9J17U8bGK+nX3IcHTYNcxhzPuF5FCDmqoHEJsRAalxALoXEJsRAalxALoXEJsZCK0kFBEIgPtrFDIdzrCU/fxs9Sh0JYSyRxqigWx2kCB4xJCdC8DRFxfJz+KIf0B/FFRAL83LxMnIaPv+To/aNGMy0wpr4J91hKJXEhQcjBuZGOAwfV9WnNs2HM4iVnQ600akiNFHCqyPP082FKL5VK+LL2fPydOXDwikixaEgZFkCvKkP9juPqfnEMaaex8I5LiIXQuIRYCI1LiIXQuIRYCI1LiIXQuIRYSEU9pxzH6RMRPAuDEPJ/ZWYQBHjOy8dUZFxCyNEB/1UmxEJoXEIshMYlxEJoXEIshMYlxEJoXEIshMYlxEJoXEIshMYlxEL+B8hlVlG4UTkVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa115ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1: Speed limit (30km/h)      (100.00%)\n",
      "    0: Speed limit (20km/h)      ( 0.00%)\n",
      "    2: Speed limit (50km/h)      ( 0.00%)\n",
      "    3: Speed limit (60km/h)      ( 0.00%)\n",
      "    4: Speed limit (70km/h)      ( 0.00%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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cDb0ymUyXQ/2hZhUXwImB/yoDBlFcwCCKCxhEcQGDKC5gEMUFDKK4gEEUFzCI4gIG/Q8XWr64+r3IaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa13976a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   12: Priority road             (100.00%)\n",
      "    9: No passing                ( 0.00%)\n",
      "   40: Roundabout mandatory      ( 0.00%)\n",
      "    0: Speed limit (20km/h)      ( 0.00%)\n",
      "    1: Speed limit (30km/h)      ( 0.00%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa255ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   17: No entry                  (100.00%)\n",
      "   14: Stop                      ( 0.00%)\n",
      "    0: Speed limit (20km/h)      ( 0.00%)\n",
      "    1: Speed limit (30km/h)      ( 0.00%)\n",
      "    2: Speed limit (50km/h)      ( 0.00%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa1c86240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   38: Keep right                (100.00%)\n",
      "    0: Speed limit (20km/h)      ( 0.00%)\n",
      "    1: Speed limit (30km/h)      ( 0.00%)\n",
      "    2: Speed limit (50km/h)      ( 0.00%)\n",
      "    3: Speed limit (60km/h)      ( 0.00%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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dLMfPQBSDO2q0vPnW/lf7M0LIjwkKlxCHULiEOITCJcQhFC4hDqFwCXHIXHZQlqYyuNS7yOcZni6vt/TMikYFvzdChqfSexfYvjG6asin9/V2Fp3jV3CfYQ/bB9USzkRqGEW/rPYkeQ7SVyyrxTSLsH0TjP2iQr+fEbA+RESiOi6QlxjXw6jVJ+NUfw5mOS6eV61h6ylN8bXf3sLF3U6PD2Ds7AxlzOFnpwmKz8VGMbu34ReXEIdQuIQ4hMIlxCEULiEOoXAJcQiFS4hD5isWl2cyGen9fnKjk3YQ3VpIJni6/ODVcxg7Oj6CsQj0vBER2fnoV+r2v3qkd6oXEfnqQLe/REQOL17DWCgvwlilgrOipNDfpcG4VYXx/o0MOwhZPt+cD9hBRvm5mWH5jI0qbfUStm9CDfTssfo2Bfy7PvnZIxh78ADbe7/7zWMY2wfPav/sGO6zAGrdpRmLxRHyk4XCJcQhFC4hDqFwCXEIhUuIQ+aaVa5UynL7jj4D2x9M4H55qi8IH4z0dhsiIjO02F5EKjWcSTBO2jAmmT6O3U9wraGlBTzTWHylLy4XEXndxzPmocA1lvIcvEsDnnm1Z5XnTyQQEYkLfXazKHDyR7vbg7GXb/AM6+Iankkdp/oYQwU/ulVjlnr7NnYQTk6wW9HrXeBY91zdHowZ+HKsP99XLDnFLy4hHqFwCXEIhUuIQyhcQhxC4RLiEAqXEIfMZQdFcSwLLb01yMToSJ+CelTTxKi9FOEp/VoJv2/KKbalzl4+VbdnE2xL7XysJyaIiPz6Z+/B2MLBJYw9P8cd01HtrigYFpLR7d2qKxUKHIuRVVQY17eN29D8wbjGZaOVSwZakNTL+NF99OEHMCaghpWIyG/+9V9gLB9hO2g80BNvqgFfqxTYQWLck7fhF5cQh1C4hDiEwiXEIRQuIQ6hcAlxCIVLiEPmsoOmSSJ7+3orhhxkcYiILK/orR1WN1fgPkcdbKe8utyHsaUIt8G4CWo9zdq4pcmzf/8tjN396FMY+5v334ex97axDfbFnl7H6iLBmTdjo4VHFuF3c1rgHXNQqwpUgBIRkWaKLcHktANjlRauz/XwvQfq9s1H+PouL+OaXqfHuE7YPWO/5dt6+xoRkdGbr9Xt0yGuV5aMdessz2kHEfKThcIlxCEULiEOoXAJcQiFS4hDKFxCHDKXHRQkSIj13glFgu2gyUjPhgmgeJuIyHjah7HUyKCot5ZxDLSrH3Rwts7p4R6MZaCgmohIKHA2zMbaJoz93V9/pG5vj3GmyfM3JzB20NYLmYmIdIZ4jDloJ7KCslpEZLMK+mqIyM6DXRjb2LgBYzF43uKAr31lgp+dnVVs+Xxw42MYq8X4+X7z7I/69n08jnYPFDVkdhAhP10oXEIcQuES4hAKlxCHULiEOITCJcQhc9lBIiKlTM8aibGzI9VIn+IuRTg7ZavagrFoAeeoRPUFGHvaRrYJfn9Vb+DMlcsUF0f7/R/+DcbKFdz7aH3jlrr9xjbuYfSr2/dh7G8fPISx2OhHNAPZXolhiyxuYiuuB7JhRES+evIMxh5/rneCD6jHkog8uHcPxrY39Ew1EZG1ZXxfsio+30JDfx7rDZypVq/p+0RG4b9v/d2V/ooQ8qOCwiXEIRQuIQ6hcAlxCIVLiEMoXEIcMpcdVApBVkr6FHdjEdswizU9FpVwpsnlCNsH/SHu/zKZ4Uyfaqy/p2YznJEx6eEiZ1GBL19hHLM3OMTHvNCLwoUjXNDu/I+fw1ie4HG0Gtjq2traVrdnK9gy+W0CMl5E5GI0hLEO+M0iIv2OHpv2cWbTk8efwdidbf13iYg8eogzmLaMrKIcZInVjZ5Iy6t6ocTY0MTb8ItLiEMoXEIcQuES4hAKlxCHULiEOGSuWeXJcCxPPtPr62waSQE3V/WF3RvreMH3fTDrJiKysI5jUbMJYyPQZf38Es8cHx7oLVdEROp1PMO61MQztudt3N08FPq7dHsRn6tcwrWe+l1c96gkeKa3NdWvSTLDM/rv3cDXvtHDNaJKU5xsUvT1di1RhGtwhWqAsUqE27+UA/5t45Hedf6bsejjTzOckFEIHuNV4BeXEIdQuIQ4hMIlxCEULiEOoXAJcQiFS4hD5kwyiGS9pk/5tzL8Dsjb+lR6t4un9OUCWxWlAZ6231jGxa/ut1bBAXFLkLCL22OUQa0hEZFpju2PowhbO92JniSxvIyTOPpjnFhx0n0DY6MEWyNJeqluz0d44XznBNsfS2u4o/vmBq5VNazqdbG6HWxzZSm2l8plXAdqyVjf3+tiCy8DNmMm1vVASQZXkyS/uIQ4hMIlxCEULiEOoXAJcQiFS4hDKFxCHBKKAk+d/68/DqEtIq/e3XAI+X/PTlEUG9/1R3MJlxDy44D/KhPiEAqXEIdQuIQ4hMIlxCEULiEOoXAJcQiFS4hDKFxCHELhEuKQ/wICVueCrcPuLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7aa28804a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   34: Turn left ahead           (100.00%)\n",
      "   38: Keep right                ( 0.00%)\n",
      "    0: Speed limit (20km/h)      ( 0.00%)\n",
      "    1: Speed limit (30km/h)      ( 0.00%)\n",
      "    2: Speed limit (50km/h)      ( 0.00%)\n"
     ]
    }
   ],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "k_size = 5\n",
    "softmax_logits = tf.nn.softmax(logits)\n",
    "top_k = tf.nn.top_k(softmax_logits, k=k_size)\n",
    "signnames = np.genfromtxt('signnames.csv', skip_header=1, dtype=[('myint','i8'), ('mysring','S55')], delimiter=',')\n",
    "\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess, \"./lenet_1\")\n",
    "    softmax_logits = sess.run(softmax_logits, feed_dict={x: my_signs_normalized, keep_prob: 1.0})\n",
    "    top_k = sess.run(top_k, feed_dict={x: my_signs_normalized, keep_prob: 1.0})\n",
    "    for i in range(6):\n",
    "        plt.imshow(my_signs[i])\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.show()\n",
    "        \n",
    "        for j in range(k_size):\n",
    "            sign_name = signnames[top_k[1][i][j]][1].decode('ascii')\n",
    "            pred = [top_k[1][i][j]][0]\n",
    "            print('{:>5}: {:<25} ({:>5.2f}%)'.format(pred, sign_name, 100*top_k[0][i][j]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
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