Dubey_Suprio_LAB1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning LAB 1 \n",
    "Course 2022/23: F. Barbato, M. Mel, P. Zanuttigh\n",
    "\n",
    "The notebook contains some simple tasks to be performed about **classification and regression**. <br>\n",
    "Complete all the **required code sections** and **answer to all the questions**. <br>\n",
    "\n",
    "### IMPORTANT for the evaluation score:\n",
    "1. **Read carefully all cells** and **follow the instructions**\n",
    "1. **Rerun all the code from the beginning** to obtain the results for the final version of your notebook, since this is the way we will do it before evaluating your notebooks.\n",
    "2. Make sure to fill the code in the appropriate places **without modifying the template**, otherwise you risk breaking later cells.\n",
    "3. Please **submit the jupyter notebook file (.ipynb)**, do not submit python scripts (.py) or plain text files. **Make sure that it runs fine with the restat&run all command** - otherwise points will be deduced.\n",
    "4. **Answer the questions in the appropriate cells**, not in the ones where the question is presented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A) Classification of Day/Night"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Place your **name** and **ID number** (matricola) in the cell below. <br>\n",
    "Also recall to **save the file as Surname_Name_LAB1.ipynb** otherwise your homework could get lost\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Student name**: Suprio Dubey<br>\n",
    "**ID Number**: 2013036"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset description\n",
    "\n",
    "The data was recorded using the new **Luxottica I-SEE glasses** in exterior conditions. These devices provide multiple **sensors mounted inside the glasses**, which can be accessed through a bluetooth connection. <br>\n",
    "For the **classification** part of the notebook we will focus on the **UVA**, **UVB** and **pressure** sensors, with the goal of discriminating between **day and night** time.\n",
    "\n",
    "![I-SEE Glasses](data/isee.png \"I-SEE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first **import** all **the packages** that are needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import linear_model, preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change some global settings for layout purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you are in the jupyter notebook environment you can change the 'inline' option with 'notebook' to get interactive plots\n",
    "%matplotlib notebook\n",
    "# change the limit on the line length and crop to 0 very small numbers, for clearer printing\n",
    "np.set_printoptions(linewidth=500, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A.1) Perceptron\n",
    "In the following cells we will **implement** the **perceptron** algorithm and use it to learn a halfspace."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.1.0):** **Set** the random **seed** using your **ID**. If you need to change it for testing add a constant explicitly, eg.: 1234567 + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDnumber = 2013036 # YOUR_ID\n",
    "np.random.seed(IDnumber)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before proceding to the training steps, we **load the dataset and split it** in training and test set (the **training** set is **typically larger**, here we use a 75% training 25% test split).\n",
    "The **split** is **performed after applying a random permutation** to the dataset, such permutation will **depend on the seed** you set above. Try different seeds to evaluate the impact of randomization.<br><br>\n",
    "**DO NOT CHANGE THE PRE-WRITTEN CODE UNLESS OTHERWISE SPECIFIED**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dset(filename, features=[2,3,9], label_id=-1, mode='clas'):\n",
    "    # Load the dataset\n",
    "    with open(filename, newline='\\n') as f:\n",
    "        raw_data = csv.reader(f, delimiter=',')\n",
    "        header = next(raw_data)                # skip first line\n",
    "        print(f\"Header: {header}\\n\")\n",
    "        \n",
    "        dataset = np.array(list(raw_data))\n",
    "        print(f\"Data shape: {dataset.shape}\\n\")\n",
    "        print(\"Dataset Example:\")\n",
    "        print(dataset[0,...])                  # print the first row of the dataset\n",
    "\n",
    "    print(dataset)\n",
    "    X = dataset[:,features].astype(float)      # extract the selected features, default refers to the first part of the lab: [uva, uvb, pressure]\n",
    "    \n",
    "    if mode == 'clas':\n",
    "        Y = dataset[:,label_id].astype(int)    # if we are in classification mode, get the labels from the provided index as indices\n",
    "      \n",
    "        Y = 2*Y-1                              # for the perceptron night --> -1, day --> 1\n",
    "        \n",
    "\n",
    "    else:\n",
    "        Y = dataset[:,label_id].astype(float)  # otherwise get them as floats\n",
    "        \n",
    "    m = dataset.shape[0]\n",
    "    print(\"\\nNumber of samples loaded:\", m)\n",
    "    permutation = np.random.permutation(m)     # random permutation\n",
    "\n",
    "    X = X[permutation]\n",
    "    \n",
    "    Y = Y[permutation]\n",
    "  \n",
    "    \n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header: ['rh', 'temp', 'uva', 'uvb', 'x', 'y', 'blueg', 'blueb', 'worn', 'pressure', 'timestamp', 'hour', 'minute', 'second', 'isnight']\n",
      "\n",
      "Data shape: (2177, 15)\n",
      "\n",
      "Dataset Example:\n",
      "['23.846666666666668' '37.21333333333334' '5.577416666666666' '1.6235333333333333' '615.7777777777778' '686.0' '134.0' '117.0' '0.0' '1.000291690654385' '1650458376' '14' '39' '36' '0']\n",
      "[['23.846666666666668' '37.21333333333334' '5.577416666666666' ... '39' '36' '0']\n",
      " ['23.757272727272728' '38.02303030303031' '5.412893939393939' ... '42' '10' '0']\n",
      " ['23.66571428571429' '38.47761904761904' '4.859261904761905' ... '44' '43' '0']\n",
      " ...\n",
      " ['31.50294117647059' '29.543529411764705' '3.5622254901960786' ... '57' '30' '0']\n",
      " ['31.62666666666667' '30.606470588235297' '5.660215686274509' ... '0' '3' '0']\n",
      " ['31.65148148148148' '32.12518518518519' '7.239' ... '2' '36' '0']]\n",
      "\n",
      "Number of samples loaded: 2177\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "X, Y = load_dset('data/lux.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to differentiate (classify) between **class \"1\" (day)** and **class \"-1\" (night)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data in training and test sets\n",
    "\n",
    "\n",
    "\n",
    "Given $m$ total data, denote with $m_{t}$ the part used for training. Keep $m_t$ data as training data, and $m_{test}:= m-m_{t}$. <br>\n",
    "For instance one can take $m_t=0.75m$ of the data as training and $m_{test}=0.25m$ as testing. <br>\n",
    "Let us define as define\n",
    "\n",
    "$\\bullet$ $S_{t}$ the training data set\n",
    "\n",
    "$\\bullet$ $S_{test}$ the testing data set\n",
    "\n",
    "\n",
    "The reason for this splitting is as follows:\n",
    "\n",
    "TRAINING DATA: The training data are used to compute the empirical loss\n",
    "$$\n",
    "L_S(h) = \\frac{1}{m_t} \\sum_{z_i \\in S_{t}} \\ell(h,z_i)\n",
    "$$\n",
    "which is used to estimate $h$ in a given model class ${\\cal H}$.\n",
    "i.e. \n",
    "$$\n",
    "\\hat{h} = {\\rm arg\\; min}_{h \\in {\\cal H}} \\, L_S(h)\n",
    "$$\n",
    "\n",
    "TESTING DATA: The test data set can be used to estimate the performance of the final estimated model\n",
    "$\\hat h_{\\hat d_j}$ using:\n",
    "$$\n",
    "L_{{\\cal D}}(\\hat h_{\\hat d_j}) \\simeq \\frac{1}{m_{test}} \\sum_{ z_i \\in S_{test}} \\ell(\\hat h_{\\hat d_j},z_i)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.1.1):** **Divide** the **data into training and test set** (**75%** of the data in the **first** set, **25%** in the **second** one). <br>\n",
    "<br>\n",
    "Notice that as is common practice in Statistics and Machine Learning, **we scale the data** (= each variable) so that it is centered **(zero mean)** and has **standard deviation equal to 1**. <br>\n",
    "This helps in terms of numerical conditioning of the (inverse) problems of estimating the model (the coefficients of the linear regression in this case), as well as to give the same scale to all the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in the train set: 1632\n",
      "Number of samples in the test set: 545\n",
      "\n",
      "Number of night instances in test: 261\n",
      "Number of day instances in test: 284\n",
      "Mean of the training input data: [-0. -0.  0.]\n",
      "Std of the training input data: [1. 1. 1.]\n",
      "Mean of the test input data: [-0.04646979 -0.0462295   0.00382748]\n",
      "Std of the test input data: [0.99049025 0.9701474  0.99093923]\n"
     ]
    }
   ],
   "source": [
    "# compute the splits\n",
    "m_training = int(len(X)*0.75) # ADD YOUR CODE HERE\n",
    "\n",
    "\n",
    "\n",
    "# m_test is the number of samples in the test set (total-training)\n",
    "m_test = int(len(X)-(len(X)*0.75)) # ADD YOUR CODE HERE\n",
    "\n",
    "\n",
    "# X_training = instances for training set\n",
    "X_training = X[:int(len(X)*0.75)] # ADD YOUR CODE HERE\n",
    "\n",
    "# Y_training = labels for the training set\n",
    "Y_training = Y[:int(len(Y)*0.75)] # ADD YOUR CODE HERE\n",
    "\n",
    "# X_test = instances for test set\n",
    "X_test = X[int(len(X)*0.75):]  # ADD YOUR CODE HERE\n",
    "# Y_test = labels for the test set\n",
    "Y_test = Y[int(len(Y)*0.75):] # ADD YOUR CODE HERE\n",
    "\n",
    "print(\"Number of samples in the train set:\", X_training.shape[0])\n",
    "print(\"Number of samples in the test set:\", X_test.shape[0])\n",
    "print(\"\\nNumber of night instances in test:\", np.sum(Y_test==-1))\n",
    "print(\"Number of day instances in test:\", np.sum(Y_test==1))\n",
    "\n",
    "# standardize the input matrix\n",
    "# the transformation is computed on training data and then used on all the 3 sets\n",
    "scaler = preprocessing.StandardScaler().fit(X_training) \n",
    "\n",
    "np.set_printoptions(suppress=True) # sets to zero floating point numbers < min_float_eps\n",
    "X_training =  scaler.transform(X_training)# ADD YOUR CODE HERE\n",
    "print (\"Mean of the training input data:\", X_training.mean(axis=0))\n",
    "print (\"Std of the training input data:\",X_training.std(axis=0))\n",
    "\n",
    "X_test = scaler.transform(X_test)# ADD YOUR CODE HERE\n",
    "\n",
    "print (\"Mean of the test input data:\", X_test.mean(axis=0))\n",
    "print (\"Std of the test input data:\", X_test.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We **add a 1 in front of each sample** so that we can use a vector in **homogeneous coordinates** to describe all the coefficients of the model. This can be done with the function $hstack$ in $numpy$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_homogeneous(X_training, X_test):\n",
    "    # Add a 1 to each sample (homogeneous coordinates)\n",
    "    X_training = np.hstack( [np.ones( (X_training.shape[0], 1) ), X_training] )\n",
    "    X_test = np.hstack( [np.ones( (X_test.shape[0], 1) ), X_test] )\n",
    "    \n",
    "    return X_training, X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set in homogeneous coordinates:\n",
      "[[ 1.         -0.48618983 -0.46507992 -0.40688648]\n",
      " [ 1.         -0.24898585 -0.20874012  0.03029423]\n",
      " [ 1.          0.07758538  0.06178465  0.18889549]\n",
      " [ 1.         -0.10219948 -0.11159124 -1.81229807]\n",
      " [ 1.         -0.17971748 -0.18027663 -1.81528676]]\n",
      "[ 1 -1 -1 -1 -1]\n",
      "Test set in homogeneous coordinates:\n",
      "2177\n"
     ]
    }
   ],
   "source": [
    "# convert to homogeneous coordinates using the function above\n",
    "X_training, X_test = to_homogeneous(X_training, X_test)\n",
    "print(\"Training set in homogeneous coordinates:\")\n",
    "print(X_training[:5])\n",
    "print(Y_training[:5])\n",
    "print(\"Test set in homogeneous coordinates:\")\n",
    "print(X.shape[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*TO DO (A.1.2):** Now **complete** the function *perceptron*. <br>\n",
    "The **perceptron** algorithm **does not terminate** if the **data** is not **linearly separable**, therefore your implementation should **terminate** if it **reached the termination** condition seen in class **or** if a **maximum number of iterations** have already been run, where one **iteration** corresponds to **one update of the perceptron weights**. In case the **termination** is reached **because** the **maximum** number of **iterations** have been completed, the implementation should **return the best model** seen throughout .\n",
    "\n",
    "The input parameters to pass are:\n",
    "- $X$: the matrix of input features, one row for each sample\n",
    "- $Y$: the vector of labels for the input features matrix X\n",
    "- $max\\_num\\_iterations$: the maximum number of iterations for running the perceptron\n",
    "\n",
    "The output values are:\n",
    "- $best\\_w$: the vector with the coefficients of the best model (or the latest, if the termination condition is reached)\n",
    "- $best\\_error$: the *fraction* of misclassified samples for the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_errors(curr_w, X, Y):\n",
    "    # This function:\n",
    "    # computes the number of misclassified samples\n",
    "    # returns the index of the first misclassified sample\n",
    "  \n",
    "    pred = np.sign(np.dot(X,curr_w))# ADD YOUR CODE HERE                 # pred must have the same shape as Y, hint: pred = np.sign( ... )\n",
    "    \n",
    "    wrong = np.invert(np.isclose(pred,Y))# ADD YOUR CODE HERE                # wrong has the same shape as pred and Y, contains boolean values\n",
    "    num_misclassified = wrong.sum()             # num_misclassified contains the number of wrong predictions\n",
    "    \n",
    "    if num_misclassified > 0:\n",
    "        index_misclassified = np.where(wrong)[0][0]\n",
    "    else:\n",
    "        index_misclassified = -1 # signaling value for termination\n",
    "    \n",
    "    return num_misclassified, index_misclassified\n",
    "        \n",
    "def perceptron_update(curr_w, X, Y):\n",
    "    # Place in this function the update rule of the perceptron algorithm\n",
    "    # Remember that numpy arrays can be treated as generalized variables\n",
    "    # therefore given array a = [1,2,3,4], the operation b = 10*a will yield\n",
    "    # b = [10, 20, 30, 40]\n",
    "    \n",
    "    \n",
    "    #new_w = curr_w + (Y.reshape(-1,1)*X)\n",
    "    new_w = curr_w + Y*X# ADD YOUR CODE HERE  \n",
    "    \n",
    "    return new_w\n",
    "\n",
    "def perceptron(X, Y, max_num_iterations):\n",
    "    \n",
    "    # Initialize some support variables\n",
    "    num_samples = X.shape[0]\n",
    "    # best_errors will keep track of the best (minimum) number of errors\n",
    "    # seen throughout training, used for the update of the best_w variable\n",
    "    best_error = num_samples+1\n",
    "    \n",
    "    \n",
    "    # Initialize the weights of the algorith with w=0\n",
    "    curr_w =np.zeros(X_training.shape[1]) # ADD YOUR CODE HERE\n",
    "    #print(curr_w)\n",
    "    # The best_w variable will be used to keep track of the best solution\n",
    "    best_w = curr_w.copy()\n",
    "    \n",
    "    \n",
    "\n",
    "    # compute the number of misclassified samples and the index of the first of them\n",
    "    num_misclassified, index_misclassified = count_errors(curr_w, X, Y)\n",
    "    # update the 'best' variables\n",
    "    if num_misclassified < best_error:\n",
    "        best_error = num_misclassified# ADD YOUR CODE HERE\n",
    "        best_w = curr_w# ADD YOUR CODE HERE\n",
    "    \n",
    "    # initialize the number of iterations\n",
    "    num_iter = 0\n",
    "    # Main loop continue until all samples correctly classified or max # iterations reached\n",
    "    # Remember that to signify that no errors were found we set index_misclassified = -1\n",
    "    while index_misclassified != -1 and num_iter < max_num_iterations:\n",
    "        \n",
    "        # Call the perceptron_update function using the found misclassified sample\n",
    "        curr_w = perceptron_update(curr_w, X[index_misclassified], Y[index_misclassified])# ADD YOUR CODE HERE\n",
    "    \n",
    "        # permute the dataset \n",
    "        # **IMPORTANT** use the SAME permutation for samples and labels\n",
    "        per = np.random.permutation(num_samples)# ADD YOUR CODE HERE\n",
    "        X = X[per]\n",
    "        Y = Y[per]\n",
    "        \n",
    "        # repeat the error count and best variables update\n",
    "        num_misclassified, index_misclassified = count_errors(curr_w, X, Y)\n",
    "        # update the 'best' variables\n",
    "        if num_misclassified < best_error:\n",
    "            best_error = num_misclassified# ADD YOUR CODE HERE\n",
    "            best_w = curr_w# ADD YOUR CODE HERE\n",
    "            \n",
    "        # update the iteration number\n",
    "        num_iter += 1\n",
    "\n",
    "    # as required, return the best error as a ratio with respect to the total number of samples\n",
    "    best_error /= num_samples   # ADD YOUR CODE HERE\n",
    "    \n",
    "    return best_w, best_error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use the implementation above of the perceptron to learn a model from the training data using 100 iterations and print the error of the best model we have found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Error of perceptron (100 iterations): 0.03799019607843137\n"
     ]
    }
   ],
   "source": [
    "# Now run the perceptron for 100 iterations\n",
    "#print(perceptron_update((np.zeros(X_training.shape[1])),X_training,Y_training))\n",
    "      \n",
    "w_found, error = perceptron(X_training,Y_training, 100)\n",
    "\n",
    "print(\"Training Error of perceptron (100 iterations): \" + str(error))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.1.3):** use the best model $w\\_found$ to **predict the labels for the test dataset** and print the fraction of misclassified samples in the test set (the test error that is an estimate of the true loss)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Error of perceptron (100 iterations): 0.045871559633027525\n"
     ]
    }
   ],
   "source": [
    "num_errors, _ = count_errors(w_found, X_test,Y_test)\n",
    "num_test= X_test.shape[0]\n",
    "true_loss_estimate = num_errors/num_test# ADD YOUR CODE HERE     # Error rate on the test set\n",
    "# NOTE: you can avoid using num_errors if you prefer, as long as true_loss_estimate is correct\n",
    "print(\"Test Error of perceptron (100 iterations): \" + str(true_loss_estimate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.Q1) [Answer the following]** <br>\n",
    "What about the difference between the training error and the test error  in terms of fraction of misclassified samples? Explain what you observe. (Notice that with a very small dataset like this one results can change due to randomization, try to run with different random seeds if you get unexpected results)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "**ANSWER A.Q1**:<br>\n",
    "answer here\n",
    "    \n",
    "    Training Error of perceptron (100 iterations): 0.03799019607843137   \n",
    "    Test Error of perceptron (100 iterations): 0.045871559633027525\n",
    "    \n",
    "    we see the trainig error is better than the test error.The test error is higher as expected because the test was unseen when we found the best model using the training data.\n",
    " </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.1.4):** Copy the code from the last 2 cells above in the cell below and repeat the training with 3000 iterations. Then print the error in the training set and the estimate of the true loss obtained from the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Error of perceptron (3000 iterations): 0.025735294117647058\n",
      "Test Error of perceptron (3000 iterations): 0.03486238532110092\n"
     ]
    }
   ],
   "source": [
    "\n",
    "w_found, error = perceptron(X_training,Y_training, 3000)# ADD YOUR CODE HERE  \n",
    "print(\"Training Error of perceptron (3000 iterations): \" + str(error))\n",
    "\n",
    "num_errors, _ = count_errors(w_found, X_test,Y_test) # ADD YOUR CODE HERE  \n",
    "\n",
    "true_loss_estimate = num_errors/num_test# ADD YOUR CODE HERE  \n",
    "print(\"Test Error of perceptron (3000 iterations): \" + str(true_loss_estimate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.Q2) [Answer the following]** <br>\n",
    "What about the difference between the training error and the test error in terms of the fraction of misclassified samples) when running for a larger number of iterations? Explain what you observe and compare with the previous case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "**ANSWER A.Q2**:<br>\n",
    "answer here\n",
    "    \n",
    "    Training Error of perceptron (3000 iterations): 0.025735294117647058\n",
    "    \n",
    "    Test Error of perceptron (3000 iterations): 0.03486238532110092\n",
    "    \n",
    "    we see the absolute difference b/w the training error and the test error isnt that significant. However we reach a saturation value for the trainig error and test error on increasing the iteration.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A.2) Logistic Regression\n",
    "Now we use **logistic regression**, exploiting the implementation in **Scikit-learn**, to predict labels. We will also plot the decision boundaries of logistic regression.\n",
    "\n",
    "We first load the dataset again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header: ['rh', 'temp', 'uva', 'uvb', 'x', 'y', 'blueg', 'blueb', 'worn', 'pressure', 'timestamp', 'hour', 'minute', 'second', 'isnight']\n",
      "\n",
      "Data shape: (2177, 15)\n",
      "\n",
      "Dataset Example:\n",
      "['23.846666666666668' '37.21333333333334' '5.577416666666666' '1.6235333333333333' '615.7777777777778' '686.0' '134.0' '117.0' '0.0' '1.000291690654385' '1650458376' '14' '39' '36' '0']\n",
      "[['23.846666666666668' '37.21333333333334' '5.577416666666666' ... '39' '36' '0']\n",
      " ['23.757272727272728' '38.02303030303031' '5.412893939393939' ... '42' '10' '0']\n",
      " ['23.66571428571429' '38.47761904761904' '4.859261904761905' ... '44' '43' '0']\n",
      " ...\n",
      " ['31.50294117647059' '29.543529411764705' '3.5622254901960786' ... '57' '30' '0']\n",
      " ['31.62666666666667' '30.606470588235297' '5.660215686274509' ... '0' '3' '0']\n",
      " ['31.65148148148148' '32.12518518518519' '7.239' ... '2' '36' '0']]\n",
      "\n",
      "Number of samples loaded: 2177\n",
      "[[0.09525    0.00142    0.99178551]\n",
      " [1.397      0.32944    0.99337774]\n",
      " [0.14093902 0.01433854 0.99058884]\n",
      " ...\n",
      " [2.37671429 0.65847429 0.98633722]\n",
      " [0.104775   0.005538   0.99932889]\n",
      " [0.11490476 0.00568    0.99819628]]\n",
      "[0.00142    0.32944    0.01433854 ... 0.65847429 0.005538   0.00568   ]\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "X, Y = load_dset('data/lux.csv')\n",
    "print(X)\n",
    "print(X[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.2.1):** As for the previous part, **divide the data** into training and test (75%-25%) and **add a 1 as first component** to each sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in the train set: 1632\n",
      "Number of samples in the test set: 545\n",
      "\n",
      "Number of night instances in test: 271\n",
      "Number of day instances in test: 274\n",
      "Mean of the training input data: [ 0.  0. -0.]\n",
      "Std of the training input data: [1. 1. 1.]\n",
      "Mean of the test input data: [0.01363305 0.00842718 0.03438657]\n",
      "Std of the test input data: [1.03063626 0.99699788 0.95849453]\n",
      "Training set in homogeneous coordinates:\n",
      "[[ 1.         -0.47189433 -0.45490512 -0.31892471]\n",
      " [ 1.         -0.04783294 -0.11553404  0.07773462]\n",
      " [ 1.         -0.45701056 -0.44153954 -0.61703886]\n",
      " [ 1.          0.14917584  0.13379981  1.06973416]\n",
      " [ 1.          0.05152217  0.01895894 -0.22769455]\n",
      " [ 1.         -0.196513   -0.18972556 -1.41352824]\n",
      " [ 1.         -0.18789386 -0.18091072 -1.44374893]\n",
      " [ 1.          0.2825672   0.24048728  1.1341952 ]\n",
      " [ 1.         -0.46552943 -0.45501813 -0.36150948]\n",
      " [ 1.         -0.15815785 -0.13551433  1.45702726]]\n"
     ]
    }
   ],
   "source": [
    "# compute the splits\n",
    "m_training = int(len(X)*0.75)# ADD YOUR CODE HERE\n",
    "\n",
    "# m_test is the number of samples in the test set (total-training)\n",
    "m_test = int(len(X)-(len(X)*0.75))# ADD YOUR CODE HERE\n",
    "\n",
    "# X_training = instances for training set\n",
    "X_training = X[:int(len(X)*0.75)]  # ADD YOUR CODE HERE\n",
    "# Y_training = labels for the training set\n",
    "Y_training = Y[:int(len(Y)*0.75)] # ADD YOUR CODE HERE\n",
    "\n",
    "# X_test = instances for test set\n",
    "X_test = X[int(len(X)*0.75):]  # ADD YOUR CODE HERE\n",
    "# Y_test = labels for the test set\n",
    "Y_test = Y[int(len(X)*0.75):] # ADD YOUR CODE HERE\n",
    "\n",
    "\n",
    "print(\"Number of samples in the train set:\", X_training.shape[0])\n",
    "print(\"Number of samples in the test set:\", X_test.shape[0])\n",
    "print(\"\\nNumber of night instances in test:\", np.sum(Y_test==-1))\n",
    "print(\"Number of day instances in test:\", np.sum(Y_test==1))\n",
    "\n",
    "# standardize the input matrix\n",
    "# the transformation is computed on training data and then used on all the 3 sets\n",
    "scaler = preprocessing.StandardScaler().fit(X_training) \n",
    "\n",
    "np.set_printoptions(suppress=True) # sets to zero floating point numbers < min_float_eps\n",
    "X_training = scaler.transform(X_training) # ADD YOUR CODE HERE\n",
    "print (\"Mean of the training input data:\", X_training.mean(axis=0))\n",
    "print (\"Std of the training input data:\",X_training.std(axis=0))\n",
    "\n",
    "X_test = scaler.transform(X_test)  # ADD YOUR CODE HERE\n",
    "print (\"Mean of the test input data:\", X_test.mean(axis=0))\n",
    "print (\"Std of the test input data:\", X_test.std(axis=0))\n",
    "\n",
    "# convert to homogeneous coordinates\n",
    "X_training, X_test = to_homogeneous(X_training, X_test)\n",
    "print(\"Training set in homogeneous coordinates:\")\n",
    "print(X_training[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define a logistic regression model in Scikit-learn use the instruction\n",
    "\n",
    "$linear\\_model.LogisticRegression(C=1e5)$\n",
    "\n",
    "($C$ is a parameter related to *regularization*, a technique that\n",
    "we will see later in the course. Setting it to a high value is almost\n",
    "as ignoring regularization, so the instruction above corresponds to the\n",
    "logistic regression you have seen in class.)\n",
    "\n",
    "To learn the model you need to use the $fit(...)$ instruction and to predict you need to use the $predict(...)$ function. <br>\n",
    "See the Scikit-learn documentation for how to use it [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\n",
    "\n",
    "**TO DO (A.2.2):** **Define** the **logistic regression** model, then **learn** the model using **the training set** and **predict** on the **test set**. Then **print** the **fraction of samples misclassified** in the training set and in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: [-6.00578804]\n",
      "Coefficients: [[ -5.96335975  45.56842029 -82.74712862   0.95317835]]\n",
      "15\n",
      "Error rate on test set: 0.027573529411764705\n"
     ]
    }
   ],
   "source": [
    "# part on logistic regression for 2 classes\n",
    "logreg = linear_model.LogisticRegression(C=1e5)# ADD YOUR CODE HERE  # C should be very large to ignore regularization (see above)\n",
    "\n",
    "# learn from training set: hint use fit(...)\n",
    "\n",
    "\n",
    "logreg.fit(X_training, Y_training)# ADD YOUR CODE HERE\n",
    "print(\"Intercept:\" , logreg.intercept_)\n",
    "print(\"Coefficients:\" , logreg.coef_)\n",
    "\n",
    "# predict on training set\n",
    "predicted_training = logreg.predict(X_training)# ADD YOUR CODE HERE\n",
    "\n",
    "# print the error rate = fraction of misclassified samples\n",
    "error_count_training = (predicted_training != Y_training).sum()\n",
    "#print(error_count_training)\n",
    "error_rate_training = error_count_training/m_training # ADD YOUR CODE HERE\n",
    "#print(\"Error rate on training set: \"+str(error_rate_training))\n",
    "\n",
    "# predict on test set\n",
    "predicted_test = logreg.predict(X_test)# ADD YOUR CODE HERE\n",
    "\n",
    "#print the error rate = fraction of misclassified samples\n",
    "error_count_test = (predicted_test != Y_test).sum()\n",
    "print(error_count_test)\n",
    "error_rate_test = error_count_test/m_test# ADD YOUR CODE HERE\n",
    "print(\"Error rate on test set: \" + str(error_rate_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.2.3)** Now **pick two features** and restrict the dataset to include only two features, whose indices are specified in the $idx0$ and $idx1$ variables below. Then split into training and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "new X_trainig: [[0.00142    0.09525   ]\n",
      " [0.32944    1.397     ]\n",
      " [0.01433854 0.14093902]\n",
      " ...\n",
      " [0.95073176 3.25593137]\n",
      " [0.16794727 0.61960606]\n",
      " [0.00370435 0.08834783]]\n",
      "new Y_trainig: [ 1 -1  1 ... -1 -1  1]\n",
      "(2177, 2) (2177,)\n"
     ]
    }
   ],
   "source": [
    "# we define some additional variables:\n",
    "#    feature_names  == name to display on the plots\n",
    "#    feature_scales == scale (linear/log) to use for that metric\n",
    "# this will be referred to in the following cell - to plot the values\n",
    "feature_names  = [\"UVA\", \"UVB\", \"Atm. Pressure\"]\n",
    "feature_scales = [\"log\", \"log\", \"linear\"]\n",
    "\n",
    "# remember that we selected 3 sensors (uva, uvb, pressure), therefore\n",
    "# to make the plot we need to reduce the data to 2D, so we choose two of them\n",
    "# to do so change the following indices (between 0 and 2, inclusive)\n",
    "idx0 = 1# ADD YOUR CODE HERE\n",
    "\n",
    "idx1 = 0# ADD YOUR CODE HERE\n",
    "\n",
    "X_reduced = X[:,[idx0, idx1]]\n",
    "Y_reduced = Y\n",
    "\n",
    "# re-initialize the dataset splits, with the reduced sets\n",
    "X_training =X_reduced[:int(len(X_reduced)*0.75)] # ADD YOUR CODE HERE\n",
    "Y_training = Y[:int(len(Y)*0.75)] # ADD YOUR CODE HERE\n",
    "print(\"new X_trainig:\",X_training)\n",
    "print(\"new Y_trainig:\",Y_training)\n",
    "\n",
    "X_test = X_reduced[int(len(X_reduced)*0.75):]# ADD YOUR CODE HERE\n",
    "Y_test = Y[int(len(Y)*0.75):]# ADD YOUR CODE HERE\n",
    "print(X_reduced.shape, Y_reduced.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now learn a model using the training data and measure the performances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: [2.64335634]\n",
      "Coefficients: [[-78.99370124  13.51414521]]\n",
      "Error rate on training set: 0.0392156862745098\n",
      "Error rate on test set: 0.03125\n"
     ]
    }
   ],
   "source": [
    "# learning from training data\n",
    "# ADD YOUR CODE HERE\n",
    "logreg =linear_model.LogisticRegression(C = 1e5)\n",
    "logreg.fit(X_training,Y_training)\n",
    "print(\"Intercept:\" , logreg.intercept_)\n",
    "print(\"Coefficients:\" , logreg.coef_)\n",
    "\n",
    "##########\n",
    "predicted_training = logreg.predict(X_training)\n",
    "\n",
    "#####   Counting Error for Predicted Training #####\n",
    "error_count_training = (predicted_training != Y_training).sum()\n",
    "error_rate_training = error_count_training/m_training # ADD YOUR CODE HERE\n",
    "print(\"Error rate on training set: \"+str(error_rate_training))\n",
    "\n",
    "#########\n",
    "predicted_test = logreg.predict(X_test)\n",
    "\n",
    "\n",
    "error_count_test = (predicted_test != Y_test).sum()\n",
    "\n",
    "\n",
    "# print the error rate = fraction of misclassified samples\n",
    "error_rate_test = error_count_test/m_test # ADD YOUR CODE HERE\n",
    "\n",
    "print(\"Error rate on test set: \" + str(error_rate_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (A.Q3) [Answer the following]** <br>\n",
    "Which features did you select and why? <br>\n",
    "Compare the perfomance of the classifiers trained with every combination of two features with that of the baseline (which used all 3 features)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "**ANSWER A.Q3**:<br>\n",
    "answer here\n",
    "    \n",
    "    The features I chose are UVA adn UVB beacuse the main source of UV rays entering earth is sun so Uv rays are\n",
    "    better classifier for day and night.\n",
    "    0,1\n",
    "    Intercept: [2.64335634]\n",
    "    Coefficients: [[ 13.51414521 -78.99370124]]\n",
    "    Error rate on training set: 0.0392156862745098\n",
    "    Error rate on test set: 0.03125\n",
    "    On comparing the performance of the classifier with that of the baseline we see that the classification is\n",
    "    better when we use two features rather than using three features which gives a lower error on both the test\n",
    "    and training set in compasrison to that of the baseline.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything is ok, the code below uses the model in $logreg$ to plot the decision region for the two features chosen above, with colors denoting the predicted value. It also plots the points (with correct labels) in the training set. It makes a similar plot for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#firstly we import an additional module to suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X_reduced[:, 0].min() - .5, X_reduced[:, 0].max() + .5\n",
    "y_min, y_max = X_reduced[:, 1].min() - .5, X_reduced[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 500), np.linspace(y_min, y_max, 500))\n",
    "\n",
    "Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# shift all the values to positive, to avoid problems with log-log plots\n",
    "shift = .001 - X_reduced.min(axis=0)\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8,3))\n",
    "axs[0].pcolormesh(xx + shift[0], yy + shift[1], Z, cmap=plt.cm.Set3)\n",
    "axs[1].pcolormesh(xx + shift[0], yy + shift[1], Z, cmap=plt.cm.Set3)\n",
    "\n",
    "# Plot also the training points\n",
    "axs[0].scatter(X_training[:, 0] + shift[0], X_training[:, 1] + shift[1], c=Y_training, edgecolors='k', cmap=plt.cm.Set3)\n",
    "\n",
    "axs[0].set_xlabel(feature_names[idx0])\n",
    "axs[0].set_ylabel(feature_names[idx1])\n",
    "axs[0].set_xscale(feature_scales[idx0])\n",
    "axs[0].set_yscale(feature_scales[idx1])\n",
    "\n",
    "axs[0].set_xlim(xx.min(), xx.max())\n",
    "axs[0].set_ylim(yy.min(), yy.max())\n",
    "axs[0].set_xticks(())\n",
    "axs[0].set_yticks(())\n",
    "axs[0].set_title('Training set')\n",
    "\n",
    "# Plot also the test points \n",
    "axs[1].scatter(X_test[:, 0] + shift[0], X_test[:, 1] + shift[1], c=Y_test, edgecolors='k', cmap=plt.cm.Set3, marker='s')\n",
    "axs[1].set_xlabel(feature_names[idx0])\n",
    "axs[1].set_ylabel(feature_names[idx1])\n",
    "axs[1].set_xscale(feature_scales[idx0])\n",
    "axs[1].set_yscale(feature_scales[idx1])\n",
    "\n",
    "axs[1].set_xlim(xx.min(), xx.max())\n",
    "axs[1].set_ylim(yy.min(), yy.max())\n",
    "axs[1].set_xticks(())\n",
    "axs[1].set_yticks(())\n",
    "axs[1].set_title('Test set')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# finally turn the warnings back on\n",
    "warnings.filterwarnings(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B) Linear Regression on the Atmospheric Conditions\n",
    "\n",
    "As before, these **samples** were **extracted from** a Luxottica **I-SEE** device. <br>\n",
    "For the **second part** of the laboratory we will **focus on linear regression**. We will try to estimate the  **atmospheric pressure** starting from **temperature** and **relative humidity**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we load again the dataset. Notice that the indices of the features were changed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Header: ['rh', 'temp', 'uva', 'uvb', 'x', 'y', 'blueg', 'blueb', 'worn', 'pressure', 'timestamp', 'hour', 'minute', 'second', 'isnight']\n",
      "\n",
      "Data shape: (2177, 15)\n",
      "\n",
      "Dataset Example:\n",
      "['23.846666666666668' '37.21333333333334' '5.577416666666666' '1.6235333333333333' '615.7777777777778' '686.0' '134.0' '117.0' '0.0' '1.000291690654385' '1650458376' '14' '39' '36' '0']\n",
      "[['23.846666666666668' '37.21333333333334' '5.577416666666666' ... '39' '36' '0']\n",
      " ['23.757272727272728' '38.02303030303031' '5.412893939393939' ... '42' '10' '0']\n",
      " ['23.66571428571429' '38.47761904761904' '4.859261904761905' ... '44' '43' '0']\n",
      " ...\n",
      " ['31.50294117647059' '29.543529411764705' '3.5622254901960786' ... '57' '30' '0']\n",
      " ['31.62666666666667' '30.606470588235297' '5.660215686274509' ... '0' '3' '0']\n",
      " ['31.65148148148148' '32.12518518518519' '7.239' ... '2' '36' '0']]\n",
      "\n",
      "Number of samples loaded: 2177\n",
      "[0.99968488 0.9927345  0.99339976 ... 0.99327764 0.99378567 0.98581483]\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "X, Y = load_dset('data/lux.csv', [0,1], 9, mode='reg')\n",
    "print(Y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (B.1)**: split the data in training and test sets (70%-30%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99968488 0.9927345  0.99339976 ... 0.98973847 0.99177367 0.9991113 ]\n",
      "Mean of the training input data: [-0.  0.]\n",
      "Std of the training input data: [1. 1.]\n",
      "Mean of the test input data: [0.04875074 0.05283414]\n",
      "Std of the test input data: [0.97703098 1.08018114]\n"
     ]
    }
   ],
   "source": [
    "# compute the splits\n",
    "m_training = int(len(X)*0.70)# ADD YOUR CODE HERE\n",
    "\n",
    "# m_test is the number of samples in the test set (total-training)\n",
    "m_test = int(len(X)-(len(X)*0.70))# ADD YOUR CODE HERE\n",
    "\n",
    "\n",
    "# X_training = instances for training set\n",
    "X_training = X[:int(len(X)*0.70)]  # ADD YOUR CODE HERE\n",
    "# Y_training = labels for the training set\n",
    "Y_training = Y[:int(len(Y)*0.70)] # ADD YOUR CODE HERE\n",
    "print (Y_training)\n",
    "# X_test = instances for test set\n",
    "X_test = X[int(len(X)*0.70):]  # ADD YOUR CODE HERE\n",
    "# Y_test = labels for the test set\n",
    "Y_test = Y[int(len(X)*0.70):] # ADD YOUR CODE HERE\n",
    "\n",
    "\n",
    "\n",
    "# standardize the input matrix\n",
    "# the transformation is computed on training data and then used on all the 3 sets\n",
    "scaler = preprocessing.StandardScaler().fit(X_training) \n",
    "\n",
    "np.set_printoptions(suppress=True) # sets to zero floating point numbers < min_float_eps\n",
    "X_training = scaler.transform(X_training) # ADD YOUR CODE HERE\n",
    "print (\"Mean of the training input data:\", X_training.mean(axis=0))\n",
    "print (\"Std of the training input data:\",X_training.std(axis=0))\n",
    "\n",
    "X_test = scaler.transform(X_test) # ADD YOUR CODE HERE\n",
    "print (\"Mean of the test input data:\", X_test.mean(axis=0))\n",
    "print (\"Std of the test input data:\", X_test.std(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training \n",
    "\n",
    "The model is trained (= estimated) minimizing the empirical error\n",
    "$$\n",
    "L_S(h) := \\frac{1}{m_t} \\sum_{z_i \\in S_{t}} \\ell(h,z_i)\n",
    "$$\n",
    "When the loss function is the quadratic loss\n",
    "$$\n",
    "\\ell(h,z) := (y - h(x))^2\n",
    "$$\n",
    "we define  the Residual Sum of Squares (RSS) as\n",
    "$$\n",
    "RSS(h):= \\sum_{z_i \\in S_{t}} \\ell(h,z_i) = \\sum_{z_i \\in S_{t}} (y_i - h(x_i))^2\n",
    "$$ so that the training error becomes\n",
    "$$\n",
    "L_S(h) = \\frac{RSS(h)}{m_t}\n",
    "$$\n",
    "\n",
    "We recal that, for linear models we have $h(x) = <w,x>$ and the Empirical error $L_S(h)$ can be written\n",
    "in terms of the vector of parameters $w$ in the form\n",
    "$$\n",
    "L_S(w) = \\frac{1}{m_t} \\|Y - X w\\|^2\n",
    "$$\n",
    "where $Y$ and $X$ are the matrices whose $i-$th row are, respectively, the output data $y_i$ and the input vectors $x_i^\\top$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (B.2):** compute the linear regression coefficients using np.linalg.lstsq from scikitlearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set in homogeneous coordinates:\n",
      "[[ 1.         -1.86015524  2.37742372]\n",
      " [ 1.          0.77270833 -0.24493206]\n",
      " [ 1.          0.13690565  0.02901901]\n",
      " [ 1.         -1.01972845  6.09501228]\n",
      " [ 1.         -0.42812223  1.07346696]\n",
      " [ 1.         -0.70556703  0.15690144]\n",
      " [ 1.          0.10300232  0.01064688]\n",
      " [ 1.          1.32682762 -0.4140911 ]\n",
      " [ 1.          0.77685566 -0.67235367]\n",
      " [ 1.         -0.62761382 -0.65110246]]\n",
      "LS coefficients with numpy lstsq: [ 0.99316304 -0.00322181 -0.00009138]\n",
      "RSS with numpy lstsq:  [0.00865443]\n",
      "Empirical risk with numpy lstsq: [0.00000568]\n"
     ]
    }
   ],
   "source": [
    "#add a 1 at the beginning of each sample for training, and testing (use homogeneous coordinates)\n",
    "X_trainingH, X_testH = to_homogeneous(X_training,X_test) # ADD YOUR CODE HERE\n",
    "\n",
    "print(\"Training set in homogeneous coordinates:\")\n",
    "print(X_trainingH[:10])\n",
    "\n",
    "# Compute the least-squares coefficients using linalg.lstsq\n",
    "w_np, RSStr_np, rank_Xtr, sv_Xtr = np.linalg.lstsq(X_trainingH,Y_training,rcond = None)   # ADD YOUR CODE HERE  # rcond=None avoids warning message\n",
    "#print((Y_training-(np.dot(X_trainingH,w_np))))\n",
    "\n",
    "print(\"LS coefficients with numpy lstsq:\", w_np)\n",
    "\n",
    "# compute Residual sums of squares \n",
    "Y_trainingR = Y_training  # recall that in Python 1D array of size n and 2D matrix nx1 are different\n",
    "\n",
    "RSStr_train = np.sum(np.square((Y_training-(np.dot(X_trainingH,w_np)))))  # ADD YOUR CODE HERE # compute error\n",
    "\n",
    "print(\"RSS with numpy lstsq: \", RSStr_np)\n",
    "print(\"Empirical risk with numpy lstsq:\", RSStr_np/m_training)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data prediction \n",
    "\n",
    "Compute the output predictions on both training and validation set.and compute the Residual Sum of Sqaures (RSS). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (B.3)**: Compute these quantities on  training, validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSS on test data: 0.003844796132936189\n",
      "Loss estimated from test data: 5.887896068814991e-06\n"
     ]
    }
   ],
   "source": [
    "#compute predictions on training and validation\n",
    "\n",
    "#prediction_training \n",
    "prediction_training = np.dot(X_trainingH,w_np) # ADD YOUR CODE HERE\n",
    "prediction_test = np.dot(X_testH,w_np) # ADD YOUR CODE HERE\n",
    "\n",
    "#what about the loss for points in the test data?\n",
    "RSS_test = np.sum(np.square((Y_test-(np.dot(X_testH,w_np)))))  # ADD YOUR CODE HERE\n",
    "\n",
    "print(\"RSS on test data:\",  RSS_test)\n",
    "print(\"Loss estimated from test data:\", RSS_test/m_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TO DO (B.Q1) [Answer the following]** <br>\n",
    "Comment on the results you get and on the difference between the train and test errors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "**ANSWER B.Q1**:<br>\n",
    "answer here\n",
    "    \n",
    "    Loss estimated from trainig data: 5.682489815341806e-06\n",
    "    Loss estimated from test data : 5.887896068814991e-06\n",
    "    we seen that the training loss from the trainig data and the loss from the test data both are small.This means\n",
    "    that the predicted value are are really close to the real values.The model fits the data well.Also we can\n",
    "    predict from the plot that the temp increase results in decrease of the atmospheric pressure.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the data and check our estimation result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
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       "        this.context.msBackingStorePixelRatio ||\n",
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       "            rubberband_canvas.setAttribute('height', height);\n",
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       "\n",
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       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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AAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBAgQIECAAAECBAgQENCNAQIECBAgQIAAAQIECBAgUEBAQC/QBCUQIECAAAECBAgQIECAAAEB3RggQIAAAQIECBAgQIAAAQIFBAT0Ak1QAgECBGZK4J577mkrrLDCTL2c1yFAgAABAgQIEJhDAQF9DrF9KQIECMyWwNVXX9022mij2Xp5r0uAAAECBAgQIDAHAgL6HCD7EgQIEJhNgZNPPrm97nWva7vttls76qijZvNLeW0CBAgQIECAAIFZFBDQZxHXSxMgQGC2BS677LJ2+OGHt3POOaetu+667YILLmhrrrnmbH9Zr0+AAAECBAgQIDALAgL6LKB6SQIECMyFwM0339z23Xffdvvtt7dvfOMb7RnPeEY75ZRT7vWl/+///q/93u/93lyU5GsQIECAAAECBAgsg4CAvgx4PpUAAQLLU+CAAw5oZ555Zttjjz3av//7v7c/+qM/aoccckgv6c4772xf/OIX27bbbrs8S/S1CRAgQIAAAQIEpiAgoE8By4cSIECgisAnPvGJdswxx/RZ80MPPbStt9567Q1veEP7u7/7u/ab3/ymff7zn28vetGLWj5ul112qVK2OggQIECAAAECBJYgIKAbHgQIEJhnAl/96lfbu971rrbSSiu1I488sj3wgQ9sT3/609uxxx7bnvnMZ7ZFixa1HXbYoT35yU9un/rUp+bZd6dcAgQIECBAgMDCFRDQF27vfecECMxDgZ/85Cdtv/32azfddFPbf//927Oe9az2la98pW255ZbtP/7jP9paa63VXvrSl/Yl7zfeeGP/DjOj/gd/8AfNM9LnYcOVTIAAAQIECCwoAQF9QbXbN0tg+gK//e1v2worrGCzsekTzshnfu5zn2v77LNPe/3rX9+XtOfIMvbMnp966ql92fu73/3uvmncxhtvfK+vmT5mw7j00kGAAAECBAgQIFBLQECv1Q/VECgvcMcdd7Rf/epXHuW1HDt1xRVXtKc+9amLK0gg/6d/+qd+L/pOO+3U3vve97a99967/eIXv2g33HBDO/300/vsef5uww037J9nNn05NtCXJkCAAAECBAhMICCgGxoECNxLYHSWdQhyX/rSl9qBBx7YfvzjH7dHPvKR7QUveEHba6+9+tJpx9wIjH1cWpau//7v/37bfffdW5a+/+hHP+qbxZ1xxhm9oL/9279tX/va1/oS9yc96Un9Wem5Z/2Vr3zl3BTsqxAgQIAAAQIECExJQECfEpcPJnDfFkgwT+Abjp/97Gf9nubrrruuh8BNN920vepVr2qXXXZZO+qoo9puu+3W3v72t9+3UebBd/e85z2vfeELX+gh/Oqrr+4Vv+Utb2kXXHBBe9nLXtZe+9rX9j/LLPsHP/jB9ulPf7o99rGPnQffmRIJECBAgAABAgtLQEBfWP323RK4l8B4S52zwVge15UZ23/9139tf//3f9+XtQ87gmdW9sUvfnHfRTw7iuf5247lJ3Dcccf1EJ5w/sd//Mft61//env5y1/ett9++36v+oMe9KBe3JVXXtl3eb/wwgt/Z4l8/s6S9+XXP1+ZAAECBAgQIDAICOjGAoEFJDDZjcH+6q/+qge9hL6VV165vfGNb2yPe9zj2v3vf/8+c56wvt1227U99tijbbbZZvcSPOCAA/pjwBL6pnokPGZn8sz+brXVVlP99GX++G9961vtPe95T18lkGXjq666al/Sn+eN58/zSLOpHF/+8pfbeeed1+8JH4LyVD5/sh/73//93+0BD3hA//Crrrqq/cVf/EV/FvpTnvKUxeH7i1/8Yg/sCfRbbLFF/9h8TG5XyDF2Cf1kv7aPI0CAwPIUmOy5bSbOK3fddVd73/ve189Pkz1H/fCHP+x7g2SlU/4759WHPexh/fGY73jHO9ojHvGIKfHliR1ZCZWVbY9+9KOn9Lk+mACB+gICev0eqZDAjAkkLGYH7+H4x3/8x5bQljcbCdqXXHJJny3P8uf890Me8pD+oc95znPaxRdf3J+rnYD3l3/5l2311Vfvf3f77bf34Dk6A5t7ofNP3nxM9fjlL3/ZHxeWCwRTDcNT/VpjPz7B9k//9E/bE57whL5Det743HLLLf2RZZ/85Cfb+eefP+U3Q+9///v7ruvf//73p/y5k/l+hosgo29Q8yZ01113bZdffnl71KMe1V/m5ptv7r279tpru2+OXHg5+eST27/9278t7pWQPhl1H0OAQCWBPGpy9Mi5Lb8Hs3fK6DET55WcE9Zcc832zne+s+Vi9NKOnAtze1jOk29+85vb4x//+PZf//Vf/fdwQnb2BcnKpqkcWdm2ww47LLcL2VOp1ccSIDB1AQF96mY+g8C8ElhS4Moy9ezwveeee7aPfvSj/ftKUM/V/cysZvY4R96E5I3Ehz70ofbsZz97cRi/9NJL27/8y7/0R3vdF45sqnbaaae1//zP/1w8Gz36fU1nGfhsB/Tx3LOR39Zbb91XImRjvyx9/+xnP9v7lAsv2QE+j2Z7zWte0zf5ywx6LiDk7zfaaKP+koL6fWFE+x4ILEyBzCwnxOapIzN9TDWgJ8jn93CeqLHuuuveq5zp/K4V0Ge6q16PQC0BAb1WP1RDYEYEskw7z8vObPjozOopp5zSsvFb7i/PkTcxmUFNkMvyu8yQ5w1EAuoRRxzR/vmf/7ktWrSoz6BnWd93vvOdPgvw05/+tM+cZ0O5bEyWGdgs1xuO8Za4ZzY6H5tZ3Gwslxozu5sac4FgOMZb4j682Uot2Tk+s/m57/0lL3lJX3b+h3/4h4s/P7MVWU5+7rnn9voSPvP/ufBwwgkn9O95omObbbbpzw9PwJ3MksksV4xbHnuWndIzS5I3Yn/+53+++MJGlvqPPWZimeXSBso111zTtt12236h4Xvf+17vbTaMy/f/zW9+s8aJO/4AACAASURBVOU2hic+8Yl9R/f111+/j4PY7b///u0xj3nM0l7e3xMgQKCswHgB/e677+6rxXJxMhcks0Irv/PzZ5kRH47hiSX5HZrzXv4uFzVPOumkfv4cL2Tn4u6JJ544rkfOebn4mdVhuU1saUf2Csl5JBfA8/Wzouttb3tbP9/lyNfJ7WVjj6Wd35b2df09AQJ1BAT0Or1QCYEZE8jjtLIM/Qc/+EEPnAnQxx57bH/sVu6rzn9vsMEGPaxl47eNN964n/zf9KY39aXO2QQuQTzhMs/NzrL4vCnJfc25/zzP137wgx/c72NOoE74Hl1KOFFAzy7xeVO07777trXXXrsdf/zx7dRTT20XXXRR23LLLfv3P1FAz8WFLL1PyNxkk016SM8yxnyt3MOX48477+x/d9ttt/W/yyPHYpHZ47whW9obmDxP/B/+4R/azjvv3HerH1YTjNeYvMn7m7/5m/bXf/3X/d8rrrhi+8hHPtLOPvvsfnEgIT2BN2/+ct/+Zz7zmcW3DMzEMsslDZbR3fhTz2qrrdZ79NCHPrR/2p/92Z/14B67pz3taYtfKrc7ZJVEep43iDnG7uw/Y4PUCxEgQGCWBMYG9MxS52LtcBtX9hS56aab+jL1/H5MKM7KsRtvvLEH4pzbXve61/XzYC7Y5jySJ2DkY3KOev7zn9/PRdmMM0dC/ERPxsgqszzxJE/byDl28803n/D2rVy8zWvn93KCfWrL7VUJ5cP56+c//3k/d+ZC94c//OF+8TVHvv7ohYZZovWyBAjMgYCAPgfIvgSB2RTIG48cw73lWYadYJ4QmBnkBMdXvOIV7eCDD25nnnlmD92vfvWrF8+mZqlclkPf737361f5X/jCFy4OzHlj8fCHP/x3ljtn9jxfY5111unhLfe15/653Kc9LI+eKKDnPujMwmfTtRz/8z//02fecy9dLhrkmCig5xFhWWafjx2OvOHK7PC3v/3t/kdHH310f1OVUJo3OcOR7zfheWkB/de//nXbaaedeqDPEb98T7nYkVUHw5ufzGpkU5/cr56VCsORXuTNUmb0s7t9juWxxD1fd6JgnXvrY5yLEZlhH45cdDnssMPaoYce2nbcccf+JjWGWRXgIECAwHwSGBvQE3Jz4TW3MGWD0+FIMM/seM4dueUnf59bv7JaKxeuxzumusQ958u8dm4jyzkiq7NygTy/X7MibHSTt/zezUWAXCTP7UfDkRVPeTpHLvrmXG+J+3wajWolMHUBAX3qZj6DQAmB4UQ/3lLsww8/vM+O5up7lpIP95L/7//+b58FzxuCfEzeHORE/9a3vrUv38tMb5Zs503LKqus0kPz6JuZ3EOXGebMlmep3+gu7XkDlGCXY6KAnk3nsnHZ6JHZhMxSJFTnmCigZ7l9gnEe7TYcWfaXpfiZ1c+Rr5/XyVLC0SMz9Nltd2kBfficmGUWPA753LwpyoqB7OyeDX6ytP25z31ut8sM+ugRn8yaZ1f1LGdcXgF9vEGaiwnpZ3qfmZ/0OEcuTKR/uZCTN7bZ+T2zS5kxyhvWXMDJMZ178Ev8sCiCAIEFJTA2oGcGO/uq5Lw19pyZi61ZwZXVZLngm4vbWYmVp5hkJn3sLT9TDegDfH6nnnXWWf28khVg3/3ud/vv4PxZLnLn/3O7Uc4Zw21ow+cm3KeebCyXEC+gL6jh7JtdgAIC+gJsum95/gqMt5lMwmTCdZYsJ0gNy90y053Z77wpyZ8PM6qZOU2IzRLwzErnRJ9Z04T0zDTnyn2W0GU5dO5HHpaeZ7OdhNME5ATjPHYtby7yyJiEvtHwu6R70DOLP3oMj6lJMF9SQB9vw5+xXyf3yudN0PXXX/87XyMz7HlTM9mAPvrJCaW5CJCliZm9zyz+sGRxSSMpqxjyxq9SQE+9udc8S9yHVQz5s1yESDjPss/R+yizzDL3aGYcDIcl7/P394fKCSwUgbEBPRdUc2F1oiObn+YWnxxZBp+LrFluntumEtBzMXsIzdMN6GO/ds4lmdXPBdGcd3MBOL+bl3Qk2OeigYC+UEay73OhCgjoC7Xzvu95JTDezGWu9CdUZzY7s9B5I5HdujMLkDcjOXJVPkvR82dDuE9YTRDPn2cJd5ZzZ+Y5jxhLoEw4z+xvNhIbPTL7mtniBOnRR8IMs8kVAvpEM+jDrPx0AvpgkE3psgIgMxiZXc/yxKw4mOhRclkan9sGqgT0iYJ1Lmbk4kMuKOQ2heHIrQhZVpn7Gof7NXNPZGaWHAQIEKgsMDagJwjnMZm5l3y8Ixe4cwF69MjvzMx25/d8LspmH5TcAjVTAT1fK7cQ5XftsAlrlr7nAvjoyrXRmlJjahXQK48+tRFYdgEBfdkNvQKBORPIPWjZvCtvFvLfmR3PRjXDG4u8ech93vn7zIBntjS7dCeU517vYWlflotng5ksDc+Gatm0bZdddumvk41s8gYkAX001J1xxhn9nuUsUR8NpZlVzpuFCgF9uAc9SwazlH84JnsPenavH579PtrUbKyXixfZ7T4XJLKaIP+fN335mks68uYusy/D0sQ5GyyT+ELZiT8XebJRX2ZvssogR/48qwZycSFjIzNIWamR7zXjIIHdQYAAgaoCYwP6sOopz0sf3RhzMvXnmeW5DWufffZZfPtSNjvNE0gOOeSQpb7EROeVnEdyu1leO8vbc2RlWpa559y+pGM4H4891y21GB9AgMC8EBDQ50WbFLlQBDJTnn+GDd/yfefxXXlWeTaMyc7hub86ATuPjMly8+yGnoCc8JQN2xLCM2OeMJ0AnkeZJSDm3uj8f5a+Z0f2zJhn45rMuufq/XBkyfkQ0Efdb7311v7GIUujs/NtNp8bLhRkFrZCQB/dxf2ggw7qu7hnOX/8sjtvlvRnx/WJjswYJ5xuv/32/ZFwWWGQixsf+MAH+i7weTM0rE5IL7Irfi5QZFOhtdZaq68+yCx0/j08G36Yvc+u8Pn4uA2zIBXGdTaNSz2ZHR+OLHNPQM/y9jgORz4uHz/6sRW+BzUQIEBgVGBsQM/F5vx+z+adWaqeJ3Tkd3H2F8lS9qwOe9GLXtT3XcmqtGxAmnNdNjL9+Mc/3s+xWTmVC9g5Eqxz/s0+HauvvnpbY401fmezt9Fa8vsyF0CzwisrkLIJXM4neWJGLrTn9YfHpqWWXFzOKrV8D7mwnovouUCaJ7LkYmqOfH4unGZT1ze/+c29lpzXs1eKgwCB+S8goM//HvoO7gMC4wXz4dtK6E6QzhuJN77xjT1ojz355x7ihKksVc4biDwmKzO32SQu92kfd9xxPagnjGeX89xfl2N4E5Ml7TkS7icK6Pn7zJ7nzUBCaDZAy5uabFyT+94rBPTUmHvi89zz8847r38/8cgqg+zEnsfNje5cPnbo5HNycSOzLHm0TgJ/dm7PLQSZPRm7nD33A8YyLjFMSM8bsLgmtA9HVivk4kAujuRWg7l4Dvp0fyyGHd7T32E3+7xW7svMRkuZVc9FCRvGTVfY5xEgMNsC4z0HPRe7jzzyyL5nS5aV56J39mlJGH7LW97SL+jmd39+pycM5/d1NljNxdqc9xLwhyP3q+eckJVR2WRzSc9Bz0WBfM081zznp8zIJ9Tn3vNcPB9d7ZXXz3k6e8Xkd/HwSNNsXJdHoeZC73Dke8k/uT0pFyCW5Rau2e6H1ydAYGoCAvrUvHw0gVkVyFK4nGRzhT1vGhJ8M2OeR6PleafZAC0z5sORZXAJo1lmN9yzltB83XXX9dntfH5m2nP/eGaCExpzj9voMd7Gc7P6TS6HF3/Pe97TL2zkjUzekDn+v8DY3meVQWaX8iYyu7dnRiZH3qhmqXvGZx7FljesDgIECBAgQIAAgZkXENBn3tQrEliiwESBOPeWZ1lxwk+WqWU2N1fdE7yz83mWwGVJXJbJ5Yp9nrWd2d7cX50r7rknOsvlho3jsjN7wnuWaY8N5JlZHu/xbPeF1sUoRy5E5LFyWa6YZYhxy60BjiUL5EJGxmD2MxiO3NKQ5Z2Zlcp4cxAgQIAAAQIECMyOgIA+O65elcC9BJb0eKpsyLbnnnu2/fbbb/FztbOcPcE8oTI7aecxWAnleTTLsLw498dlCfbGG2/cZzuz5Pr444/v961nOXZ2Hh+OJS2jvy+1K/fz5Z7xzAbnQkbuI8wGeAme2VXdMb7AREvWsxIj92X+yZ/8SXfNMdnl7cPHLYRVGsYVAQIECBAgQGAmBAT0mVD0GgSmIJDAk3vf8tzVhOscuUc598RlWXGWo+d+5Sxfz/3TWZ6dGc3cF5fl79moLGE8S9cTOHOPeu6tzmPXcq/12PvZplCaD13gAmODd/YayIWh3BaQlQl5MkCOpQXuXCDKGM/S+DzOL4fnpy/wweXbJ0CAAAECBCYlIKBPiskHEZi8wHizi9nVO8vPs9FMNohZZZVV+gZu2XAr95onfOc+84SZ7KieXcQTtvOM1OHIBjN5rEs2O8ts8BCCJqpssrOck//OfORCFMjYzKaDufCTjfDyDN4lHaNBPI8Pyi0W2VBpeH66cbkQR5HvmQABAgQIEJisgIA+WSkfR2ApAhPNEOY+8SxXzzL0hOvsep5HpgyblmVZeu43z26ueQRYZsyHI7uIZ4Y84Saz5bn/N7OS2QV87JFZzYSfsfecaxyB6QiMF6RzgSj39We/hPGC+ujn5CJTdjjOrRa5TSPL4zPGHQQIECBAgAABAhMLCOhGB4FlEBhvqW8ez5IN2IbZ78w6brTRRn039syi5/mpORLME7gT3vP4lDyDNSE8j7bKveYJO3nWdp6/evDBB7c8ZiXPPs3j0kafk74M5ftUAksUGAJ3/p1x/PKXv7zfWjH6+LXRFxguUmU5fML8Jz/5yf5Yu/PPP78/hiibFubPHQQIECBAgAABAuMLCOhGBoFpCIw3u5iN3rIZWR5FlYD9tre9rb30pS9tD3zgA/uzS7ObeMJ2ZtJz3Hrrrf3Zq7fccks744wz2te+9rW+QVyWvm+22Wb9udOZrcyzzRPksyx+OJZ2D/A0viWfQmBSAnkubzYfHDsGh1surrnmmj7Gs3/COeecs/g13/ve9/aVH6effnrLM9YdBAgQIECAAAEC9xYQ0I0KAssgkM3ZEswzO5jZ7myMlSXsmSX86le/2p8pvdtuu7ULL7yw/zsz4fn3cGRJe4LORz7ykR5arr/++j7L/pWvfKU97WlPazvvvPMyVOdTCcycwJI2eRv9u+yan8cE5sLTM57xjHbKKaf0FR+77rpru+KKK9qVV17ZL1o5CBAgQIAAAQIEBHRjgMCUBMabKf/JT37Sl/sec8wxfQlvZhN/9atf9aXnmSHPzuyZHc8mb5n1Pvnkk/vXfOpTn9pnx/PIqvXWW6/lEWmZKc/mW4cccsiEu1wn/CTg3FefWz6lhvjgcgKjPyPbbbddywx6Hg+44oor9gtM2SAxoT17KeSC1UEHHdRXmDgIECBAgAABAgQEdGOAwFIFsjFbAkRmw8ceN998c79f/Nprr+0zgocddlj78Y9/3F7/+tf3GfOf/exniz8lm70lqGRpb3ZqP+KII/py9YTybBaXx6llR/c813x0t/bhBTyWaqmt8gEFBIaAnicSHHDAAf2C1Ate8IK+WWEeF5gVJblQteeee/afmezHMHpkBUkuPrkAVaCZSiBAgAABAgSWu4Al7su9BQqoIJAwnBnxPOos94tnli9Hlq9fd911bfPNN28PechD2q9//et23HHHtX322afvTJ2wniMhO+Hk3e9+d9/sLcfll1/e9t1337bFFlv010u4f/KTn9x22GGHfgFg9dVXb6985Sv7DLyDwHwVSEC//fbb+7L2jPc8nWDYL+F73/tev60jPxsPe9jDFn+LmU3PCpJ11123/5PDvgrzdQSomwABAgQIEJhJAQF9JjW91rwTyP2wCea5f3yDDTboS3IzG54Z8vw794Rnxi//n/vEs4Q3j47aaquteujIx+RIEMkzzldaaaW+7H0IHNncLZ+bAJ+AkvCe5eof/OAHFwcWwWTeDRsFjyOQlSQZ/wnqw5FnqOce9Kuuuqr/0aWXXto3T8zFqlyguvHGG9t+++3X3vCGNzAlQIAAAQIECBBorQnohsGCE8j94pnpzvLbLEnPTul5fFRmxodjp512ag960INalu2uuuqqfSl7gkZmznfccce2++6793tts6HbaBg56aST+o7sw6PUPvaxj/Xd2BNKHv/4x7dzzz23L/nN673whS9ckLOGebzcoYce2u2y43129Y6FY/4KTLSje1akLFq0qPf4oosu6rPp+Xdu7cg96meddVZ7xSte0W8DyYaJw3L5b33rW30FSpbFOwgQIECAAAECC0lAQF9I3fa9tjwKKjPmJ5xwQp/9TgDIMvPsOJ2N2tZff/0eGrJMN/eU5z7a0047rQeLPIM8/87Hf+Yzn2l77LFHD9/DPbUJHlkev/322/cLADnGu4987bXX7o9jSyjJjONCO84+++x22WWX9eX+sZqrgJ5e5GKMY+4Ett56677fQjaHy2x6ep9bOvJzeOCBB7ZNNtmk/3luHdl///17YdmAMRfPcgtJ9m3ITvDz6bjf/e7XV8k4CBAgMBkB56bJKPmYZRHIBfG8n3XMHwEBff70SqUzJJAwMLqLdJafJ5zvtdde/XFp5513Xt9ZPUE8f7fGGmv0za0ywzfMjCes5xnnCedHH310r+y2227rwSLBM5vCjR45AWd2MBvP5RFqG264oWdBZwnPCivMekCP+09/+tN+n7Rj7gQyq54LX7kIlceq/ehHP+r/zj/5Wbnjjjv6z1ZWtOTnY6211urF5c8T0ldeeeW+imW+HQnnua8+Qd1BgACBiQScm4yNuRTI+XSdddaxIetcoi/D1xLQlwHPp85vgWFZbgJBdp1+4hOf2A4//PB+P/k222zTMtP94Q9/uP/5MNP9gx/8oIeJBPXMsmcWPK/jmJ7AXAT0LKNPOE8AzOZldgufXq+m+1lZtZCr99nPIW8Q8hjCHOlJepOfp4c+9KH9nvT8LOaxbAm5eWxhjvEedTjdWmb78/K7II9hzPebR8sZa7Mt7vUJzF8B56b527v5VHnOoXfddVe/pTPn4Gx47KgvIKDX75EKZ1FgCOlZXnvhhRf2gP6kJz2pz5j/8Ic/bFdcccXir/7d7363//3zn//8tu2227Zvf/vb/Rfelltu+TshwqZvk2/YbAf0hL/swp9wPrp52eQr9JHTFRiC9fDvXNxKWH/MYx6zOLhmJ/cE9Sx7z1jIRnOZUc/H5KLYaDifL0E999cnpK+33no9qDsIECAwVsC5yZiYa4Fbb721v2d93OMeZ7n7XONP4+sJ6NNA8ym1BBKI8+Z9OvfXDGH6G9/4Rt8oLru4ZyO4bFL17Gc/u8/svfjFL+73n3/+85/vS9qzwVlCvGPZBWY7oCcApndZ8ZAl047lJ5BeZBY9S7/zRIP8vA49yZvV7Oyepe/5uwT2/Fk+J//Osvgc8yGk5wJDdqfPMveFuMfE8hthvjKB+SPg3DR/enVfqdS5aX51UkCfX/1S7RIElvXNe55PnsCYnduzPDX3in/pS1/qm8Xl3vFsKvesZz1LD2ZQYK4CurA0g02bxkuN/mzmgkneKGSpXVY25Gcr96onoOfPhuelD7Pr+bvcmpBZ9flwDG+8jbn50C01Elg+An5PLB/3hfxVjbn51X0BfX71a8FXO95seZ5L/o53vKP94he/aC95yUv65m15Qz/ZwD7stJ4N3rKEPc8zz4ZwwzF2yfp4O7Mv+MZME0BAnybcPPy00Z/HO++8sy//zmx6NoTLvZgZC3mKwtgjYf6mm27qf5xl4wn0wzF2GX0FFm+CKnRBDQRqC/g9Ubs/98XqjLn51VUBfX71a8FWOzYU5w1+dmPPUtg87iy7sq+22mrt2GOP7Y9ryjL1yR7Dm/xsTpUl7q95zWv6479yDH+3LMvoJ1vHQvm4bASW+/lzbLrppv2iSFYmZJOwrFyYyWOmTki5dzp7EiRUPvzhD5+Tzb923333fn/2Zz/72UmR5N6y7FafWrN0/BGPeER7wAMeMKnPHf2gE088se29995L3fU+H5efvRzZ1C3L0HNvWzZczGPV8vM49hh70Sw/w7k3PUvCn/e857Wrrrqqbw6XMZKfudSfcZGPy5jJrHQuvg0XzcY+kWHK3+wsfMJMjblZKM1LEiBQRGC+/p6Y6nlpprhn87w0UY3DrUo5L+WRoPP9mK9jbr67T7d+AX26cj5v1gUSyvPGf3Qn5Gxysdtuu7XLL7+8bbDBBj3YJZR87GMf6x+b2e+vf/3r7bDDDmtPecpTxn0O+XiFT3a2fda/6QXwBbIZ33i3CuQRdzkJz+SxLCekjIlf/vKXffl1gnIuAt199919s7mE39l+1vVU3gjl3u0sHc8FjlVXXbXvhJ668wSC0UcKTsZ2Km+EEsS/853v9AtZMfryl7/cn2yQn9086z57OCztyMZwubiQxxPm81NvvofMrsc/IT17CORNUgJ6LsoNO8PnZz27wqcfVXZMX5YxtzQrf0+AwH1DYL7+npjKeWkmOzXX56XULqDPZAe91lQFBPSpivn4WRcYO1ueUJ5flNnE7WUve1n/79wvftBBB7Wzzjqrh7osa89x8cUX98efPec5z+nL3ocjm1M99rGPnVSosoR91ls8Z19gqm+CEjQTwjPmEnAzU5sZ3ITybPiVv0sQzsdlPM3mJmBj3whttdVWfZPCfM3jjz++B9hXv/rV7YADDuibGmZmOUH26KOPbp/73OfaBRdc0O/xzsWq/LzkGC6O5HaQ4RnjixYt6isZ8n3lZ2vsxZN3vvOd/WuMPSZ6w5SwnQsDW2+9dfvEJz7RP+2cc87pP6/f/OY3++Zwm2++eTviiCP6kvUcY8P1Flts0S666KJ26aWXtn333bdde+21PfSnznxevq/csx6DXKjLyobR1S6zffFkSQN4qmNuzn4YfCECBMoIzNffE1M5Lw3Y+f0+nJdyDsqzuN/3vveVPC8deeSR/dw+3nnpmc98Zj+H5uk+b3/72/tF41wszuz6Bz7wgX6RufIxX8dcZdPZrE1An01dr71MAldffXVfapvlupk1z9L1/II599xzewDIzF0CSn7Zn3LKKf1rJUC96lWv6m/eTz755B5CTj311Paud72rHXjggW277bab9L3py1S8Ty4hMNkTUoJ4ZoATynNPdGZtE8qzTHs0PH7/ljvbTbfe2Vb6zR1tld/e0WesE+BnY/Z2vDdCeUPwpje9qe2yyy59FUk+5uyzz+615k3FcDHh4IMP7uH3U5/6VPvoRz/arrnmmvaEJzxhqQE9M97HHHNMv7iVn68cmc3OP5MN6Pm4/Nx+/OMf7/tCJJCfdtpp3WjDDTdsuT0lr5+LAfl+8vd5w7PZZpu1008/vdun1tSSTRrzOMM8tzUz5SeccEJ/mkJm59OvhPLccpDfATnSx6yoSXBfY401lssYnOyYWy7F+aIECJQQmMnfEzf8/I520213tUc/+P5t3TXuP6vf32TPS3mf9tznPndx0M05KuelPJb2pJNO6iutqp6XctE6F3mH89IXvvCF/p4z55WcY3NeyqM0s0ozRy6Cn3nmmf0pJdO5rWxWGzby4jM55uaq5oX8dQT0hdz95fy9T3Rfd96EX3nllf3Nd8J53uznl2VmxvPGPM+1Ht6M55d8gvj555+/eDltgkH+ya7rO+20Uw8yubqZ2ffM6jkWjsCSTkgZX/n7hPLMmGeM5U1ETsA5EY8et991d9v701e3S66/ZfEfb77uau21m6zcHrnOGrOy5H28N0KZRb7kkksW15BQm6v6O+64Y59JzpuDXLRKyM7Ga/m+svIkV/Yzg7G0GfTMwE9lKeFE96pnL4js5ZALZZntHntkCX7+PG/Q8sjC0aWE+R5yD3oC+V133dWDeP47ITwbxGWJey64Zb+ILIe/4YYbeoAfltPnAkv+mczy+tn4SfAmaDZUvSaB+5bATPyeyHlpr1MWtYuv//linC3XX7MdtfOmbbVVVpwVsMmel/KY2gTyHLk4O5yXhqKe/vSnz6vz0pLuQc95OeelvBfdZpttZsV9Jl50JsbcTNThNSYnIKBPzslHzbLA6D3gmQ3fdddd+xXLXLlce+21+1fPbGBODplZH3Z7zt+/9a1v7buu537YHLmKmRnG/ELN7KFj4QqMd0LKyTQzuwnmmc3NLHlmW5d05Xv3E69s/4+98wCzorz+/7nb2KWDiAXsIpYYRUUUxS6KGruY6M/8o7FE7IkNS4w1MRpjr4nGHgv2LmADRUVsUbFgxQLSl4Wt9/6fz7uedbjcMjN35t6Zu+/7PNfF3bkz73ved973fM/5nnNe/WyutKVSHcKsTCRk67X7yFnb9AqF8p5JEeKduO666zr6sM8++xjFAGOUAnSqEfA+KEAnPwMeASjvxQLoGAjGjBljYssB1zNmzDAMGEoXaugAsscbvsceeywX66fecTwYarDjXswdoB3a/dixY40hBYohCmBra6v56Yy5L0VuCasEdd79xo7cSsCtBILYJ3777zdk8mdzljuXtlm3n9zx+y3ddsXTdW7PJYzdOEoUoOu5pA875ZRTYncuad85i2CB4UnHCK3n0rXXXmvOvai2INZcVMdWjv2yAL0cZzViY0KBBhDhMXPGd0+bNs3EIUFJxRN41FFHdQBvqLB4xR5//PEOhRvgjUccihQecRrKPrFAxOA6PYsAAQA68bqqpNvY8ogtjCJ0Rw8kPMMwNlgvJFQjZlm95c6yXZm6BK19l3/+7LVOv2b8ydtKTfNC460OkvKeSRFiTRODrW3fffc1BoYTTjihg+KuihDZ0QGzN910k7z77rtGmSBHAx53ZACwpymNjxj0oDzoJ554otAP3nuYCRtuuKFhGZx++unGs81c4DmH0s4YXzfSMgAAIABJREFU1IPOnkCcOY19A4rkwoUL5eKLLzbZ3dkriKcnLp176bvNPM+cOdOEuAwePNjQ5mkWoBfhJbOPsBKwEvAsgULBErT2nf7xUtbnvnDqDqHQ3d2eS4QXatJXDKeZAHpczqX0LO4YlWGBweTiXMIoTF6Vs88+2zA+o9oKXXNRHVe59ssC9HKd2YiMCy/ZfvvtZ8okkfRJQTKePEo0QXPadttt5fLLLzcUVn5us802Jhs715AMSxV2NhcoU3jUtC4yw7zmmmtMFvcHH3ywI+mUDr8UCnpERG+7IWK8yABVEqgB+AClAPNu3dzH6b348Y/y+zveyirPf/92c9lh8IoGSLIuocgHkeXdiyIEi0STxEEth85OQjaUJAAt7xC/w5AFWCbpGj9psEyOPvpokyQOgA5NjzwO0MRztVxJ4rg35dZQyjBcwFDAOEDyNxrJ3/i3AnTi+QYMGGBCW4jr0/cWVgNeCTL8a5Z47ktpPjwwNDzneDQwBmDUQ/78Ww0QxX4RrBJUbInb51kJxE8Che4TL3w8Ww6/7c2sA7/t8KGy4+Dlw4sKlZSXc8kJ0PVc0ucDaON2LmnfOZc4TzU5MSVYMc7jOLIAvdAVZr+vErAA3a6FQCUAIMeLpcoxnjJAtsaqamzv7rvvbgDFQw89ZJ5PXDleNwA88eTQ2EeNGiUXXnihHHHEER19xEuOgo5XkBhUGsCIGsxhJOoKVDj2ZkWRAGsOOjdGG8pwQbfGqwqDw09277we9FNGdHgqgszy7kURArACsLfYYgsDhgHsJI0D8GLUItYb0IyRgt9jGCOrOqwUjGEkhFOATqkzjGSEj2yyySbmPeWT3lC+0suske/hkksuMQAbEA5gZj6QPe8z1HQMJnjA8dwrQAdk8w7jgSBmHuYL/0/sPBR5MutSjo3vffjhh+YZqgjBioBmCJOAhHE8m3mnJBvGmGK3QhXvYvfXPs9KwEqg+BIodJ+Imwedc+nSSy81Dpm7777bnD9xPJc4ZzAs6LlEKdDTTjvNnLXOc6n4Kyr/Ewtdc/mfYK8IUgIWoAcpzU58LxR/gAAKOJnS77jjjg6lHuUfWi3KN1RcvF2jR48WkohoKTSU+EceecQku8LryWZOuSc8kZdddllHHDp/Q0lnk4cS72yWwi7Gqou8kBPxytCh1WtZzssTUAaAAzTywYOKAQfPq3qWCymJli0Gffi6K8h/frfFMqKlL3iEC6W8ewHojJn3inwNgHMMWYBUWCm8l4SGaCPRIt4MwPnQoUONYQwvuwJ0ruPvVD9gDLnKrMGCoWEcA1BjCCFJDsCd/9cG2Oc5JHTjmquvvlooG6cAnesw5FFpgXlkzcKggVqIdx9Fjr2AfpExl0z2GBZQjljrUNq1ZJsCdvJUpCf7KwajxipB5bzT2LFZCQQjgSD2iajEoGcKvUqnuJM7BR0PJlU5nUt4zgHmp556qjEaWw96MO+HvYuIBeh2FQQmAcA22ZtRkEnSBoAGmEOVxcJIDCyecpRqsqkDwEkcRaZ2Gp6xvffe29RcpqwaCUa4580332wSSdmWWwIk0YNyBUjHAwrLANCDXDlE4tpygSoMQ5Q3wVv+3HPPGWBHgrRf/epXxgtLaAVgGep2IQB94dIWOem+d5fJ4j5iUD+56uBNpFdd5my5QVPe3cwfQNkJet18Jw7XONfAZ599ZgwRgG/AOXH2sAI0ORwAHybP2muvbbK+04ppvAtC8Y7DnNg+WglYCfiXQBD7xMIlLXLCvW8XNYu7nxGX67nkRxal/E4Qa66U/e9sz7YAvbPNeIjjhRqLtw5FGevpY489ZmqWY10FnO+yyy7Gqwc9CBorSZ2gPEGjpXENwJ5YcjzkS5cuNUmiKKWW7gUupsIdosgCvfWwYcMMJRhKtzbqSZOECxnGtWHcgQKtawuwhvcXUA5TA+8pBh2MEwBxZ6hDUABdZddeB32JrLFCV1cJeIKkvLuZv3JWhNINNRheYA2QpMcZb07iISj10OtpfI/cAKwhrgW0hxkOY5UgNyvVXmMl0LklEOQ+wbn05dyGotRB9zNr5Xwu+ZFHqb4T5Jor1Rg603MtQO9Msx3yWFGASS6FB4vkUCRxIi4Hjzjxp5SEIhEUZdSg2fJ34nigYZPYCZoQWZzJ3N69e/eQe1tetwcIQuWGlkxSPm1QjSmxRYK+uDbN3s/6woDz1FNPmXJd5DGABr7zzjt3eErTxxg0QPcjw6Ao726eXe6KkBOko2zAUtAyjMiH0AaAO3R3DIX8nf2I7wHi02ujh0F5t0qQm5Vqr7ES6NwS6Ez7RLmfS3FZyZ1pzcVlTnL10wL0cpjFCI2BDOtkXSYBCKAcijpgHQ8nNGvic/CM00goBR2ZjNqUWQKsE6c0cuTIZUZkveX5J1izYGP4GD58eMcXMHogf/IAxLEBoDDaQNe/6667TMKxAw880JQ3IY4tnyc0CgBd5U4WchKkBZXlPY7zGVSf04E164IEgHjUCbEh/px3Aqo7JfVIGKd0d9YETSnxQYN0qwQFNcv2PlYCuSXAe4/hFhYV73++8yBK8rT7RJRmo3P0xa65eM2zBejxmq/I9xagPWbMGEMvhYJM02zP0NXJ2g6FnURyNJRpEkFBiSdhlW3+JKAAnTADypdoo370nXfeKdOnT/d34xJ9C08o5b7IQ0DfYQWQ8I2YY3IS/P3vfzdJ8ABfuVqUADr9LDblvUTTV7LHUloOwE0ZHAwiUNph47BO8KbzYc/BKIhiD3DPlKG+kAFYJagQ6dnvWgnklwDvOO8vOUgw+APOAekY4fjEAbDbfSL/PNsrgpWAXXPByjPsu1mAHraEO+H9zznnHJOBGU868ec0wBb/DzWZEk8AyUzNesv9LZhyoLjjDSHDK4adcePGCfHzgHIyjFPaRBugmzreJAfbeOONl/lbuvSiBtDpXzEp7/5WU/y+hUzZOzDm8C7AUoBhoYkBUUxmzJhhFHmyweM9/+GHHwwlnmRyQYbUWCUofuvH9jg+EuCcAJjzk/ee9x3POf/mdzT+P+qA3e4T8Vlz5dJTu+biNZMWoMdrvmLR2wkTJhgKMpm0SfCljURNZHmH1g74cragaaaxEFTAnSRJ3Oabb26yuGuj9jWx/1FNEse84xWHhk94xI8//mhK7QHMf/nLX2alLGriL0AXACs9OZwTzAeRxT3gqTK3s5T3MKQqsnjxYlMZAgVdG5nfMdag2MPWYb3QqCpBOTjAfFDNKkFBSdLex0rgZwkoAOcdVn1BATqGN238Lv3D3/lEycNu9wm7uostAbvmii3xwp5nAXph8rPfziABKGcALJIzkcUd75QF4OEvFS2zhhEEmjtU8FtuucV4m6H6RqlBT3z66aeNt5ykb/SXNYMxQcvuuekvlGVqZPOdX/ziFx1xxXEA6PQxnfKOZ5e4aWjYlBFLT2rmRiad9ZpMe4x61kk0SJI4wDlhOCj5xKkjZ83ujtyyxbB62b+sEtRZV6Add1gS4P3jnYUlo+8p7yoec/ZQJ0BP74OCdfWu6/f5DqEv6mnnfsWMYbf7RFirxd43mwTsmovX2rAAPV7zFZve4hHl4MRbrsmYtPOa0Ck2g4lRR/GeE5+NVxrA+s9//lO22267SIwARenzzz/vKI+GsqXl0ahj7Vc5AmBBbQZsEZdOssG4AHT6iVwwZtXX1xsFlPcDijbjCDo+OhILocidYB8iNh2vOXJFxrAqYGsga5JXUpbN2ZweOq/r0ipBRZ5g+7iyloBS2nlv0xPBuQHouQC7vueZKPFhA3a7T5T1so3k4Oyai+S0ZO2UBejxmi/bWyuB2EkARsXDDz9sgPkrr7xisvRjuNltt93yJnnzMlhAF0CdUn54R1HmohiD7hwTxgU85l988YWJlwZM4ukFNObyCnmRi71WjGEIRsLAgQM7xDFv3jxjFEHW2UA4DA0ywwPu3dZPt0qQXXFWAoVLQNkv7JFq1E9/T/0AdL+APej92O4Tha8RewdvErBrzpu8Sn21BeilnoEyfj6HZ9hW6DIWX6yHhnL17rvvGlBOgkCoxYDyQw891GT49+qVdCsMEsdBeef5JJADVEUtBl09QlA2aZqJHqaJUt75PbH1muTM7fj9XveXv/xFHnnkEXnnnXfMLagvT4w8v/PbgriH32enfw8wDrUdTzl101l/rI1cbB6UmZkzZ5pkhORDcNusEuRWUvY6K4HMEshGaU+/OgiA7gawk0ySyhB8oMRrlvhC5s/uE/mlV+7nUn4JBHuFXXPByjPsu1mAHraE7f2tBDqRBPA43nvvvSbh23vvvWdiygFq2267bdE8wihtJAUDXK277rqG0owHtFhgN9N0q8Kp2YcB5Rr/mElBhPJ+wgknyOOPP27+DJgk+zjlCUnASJmwIFu6IsQ80mcMK/kawJdyZZRL1KoNfMfLPfI9I9ffncYeQgKI299mm22M/EiaqA2gDVMBubMW8Kane8V22GEHM4bLLrtMZs2aZWjwgwcPNqEGzjh0/k3LZGiySlAhs2m/29klwH6t+2S+2uZhAPRM+/HUqVPNftGvXz/z5/SEc34Ae1z3Cc5zQhjtuZT7TXV7Lrl53/VcuvLKK91cnvWauK65ggYd4y9bgB7jybNdtxKIggRQkiibh7f8gQceMJ5fDnGysUPXLlUjJh3KO15TgLqX5HNB9Vlr9fITJU6BuZv7E5//9ddfC4cyY5g8ebIceeSRJpneDTfcsNwtUGrz1YXP9tx0gJ6rf+Q3wKPEYY+iiqd9xx13XA6guxljENegCGEQ2n333U2fyMxOgkS8/7feeqthbjjBNd50AHp6fD/XMI5NNtlEzj33XGPgIbkia1g97Xof5hOjSaZmlaAgZtXeo7NJgHeL94oP/84HzpFPMQA6zwGga64KZ9I5NdT5Aexx3Sc42zFesudy5hC2FoVzKf19yWY4LtZ75eZcctsXC9DdSqq8rrMAvbzm047GSqAoEkAxwbuIJR0QBGg78MADDTAfMmRIaBR2r4MjxhiKO94Paqk7S295vZfb6zNR2AHOXmMYjzjiCAFMXnrppebRGD7wCj/xxBNG3gqqTzzxRLnooosMhRvv06JFi0x5QwAqSuAWW2xhkgUCPLX97W9/M7/Dqzx69GiTkO6ZZ57JSnFnTHiVqQqA0QCDAUrZ6aefvpw3f/vtt5cXX3xxOZo8+QDo13//+1/TR+3X0KFDTbf4DgB5/PjxcsYZZ8iHH35ovNkognixszUUIXIc7LvvvstcgiGD31PeEZCNwQb5oVAiVxITnnXWWfKb3/zGfM/pGdIbvfzyyzJ8+HA5+uijZeLEiaZ2OkwGSkheeOGFGZP4xVXxdru+7XVWAkFLgP0FYJ6epT3fc0oB0NP7pOXfFKzzdw2hwYjnzBLv/G5c94lMoUtHHXVUyc+lb775xoQvHXPMMXL22Wcvp4NE9VyaO3euHH/88Z7OJZhgnEPOcwkD0pgxY+Skk07K+trEdc3l2wfK9e8WoJfrzNpxWQmEIAEUKErnAcqhXwOu8FDut99+gdOug+i+JokjcRyNOG+Shflucz8Tmf+FSJ+1RVZYZ5nb4E3QUkAoZoDybF5WN88HoOOdHjdunMnyDqjEc/7QQw8ZsAlAv/zyy034AHXuUQSJux8xYoTJVv7nP//ZGCVuuukm+c9//mM8y/z+/vvvl8MOO0yuu+46c+2dd94pV199tTEAZItBBzADzgH1PA8DAewEQPprr71mQOxjjz1m1gPy5TnpihyKw4MPPij/+te/jGeaagN8h3AErleAPmzYMGOUwGjwhz/8wSjtsAe8AnTGgrGI8oMYIZAh4Re77LKLqX3+5JNPyimnnGLuzTOh5JO4kL6h6PATAwZKDf1mjUNx5X4XXHCBGcchhxyyXLesEuRmddtrrATaK1ioQdOZUd2tbIoJ0AFEAMB8zQnYnWPSGuwK2jmbAFqEBxUcfjXnp3Op7/LnUr7+ev17JoCOkZhcM1E6l958803ZcsstjcGX6i5xPpdGjRplqvJw7tA4G1n7GOb32msvcy7BYgSwY9DmvMvU7NnkdbWX9noL0Esrf/t0K4HISwAlAy8koByPOZs8wARgvt5660XGW55JkM4s7gBmMsrjyYbu7ilR3dL5UvHwUZKYMbHjMal1dpKWvW+SlqpuBphrXV2e4+neWVaAAnQAOQ0PLgcv8dUAWw7rSy65xABPLS3HNQBJMo87yxtC8cfbzQEOmMab7qTJb7XVVmZeMwF0WAjc/9prrzWAPL19/PHHsv766xugzn20ORU5MtXjxcZQoKAWmZEb4OSTTzaedacHfeeddza3eeqpp2TPPfc085ZNic3mQWc8zDNgn7Fnatx7gw02MIYO+kNJQhRmwjX4rrMkG4AdEM9YkAWeD4B7eo10qwRFfkuzHYyABJyUdrrjJ6FsFAF6umgzAXbOChgDUMUVoPs6M5bMExl3pMiMCT8/dp2dRQ78t0hdOOFl6QD9jTfekD322EPYszFeYjiOwrmUjeIet3OJiXVLcT/uuOPMmuJcsgA9AptcgV2wAL1AAdqvd14JXHzxxcYLB6jBOou3s5wa4BaaNGAF8LTTTjuZ+GesuQV5oYsopExl1gBQKEcAPrce7op7DhT5/CVJpNp+BuiJSmldfVtpHn2vkYdXCns+MQDQ7777btNPjWXHWn7qqafKCiusYDwWKESffvppx62goZ955pnLxdsDcPkeYBWgfNVVVxkDizY8yS+88EJGgI4ChoeZUmUok86G8qlrIz1JnFMRImEgRgGUJoCuNowJ9AfjjwJ0jAtqcOCem222mTEQQeHL1LIBdMZMnDmeegwAgG2o/cgMowZrgw994HcAbjzoUO9hHTiBN3kAiGuHOQCQJ9s+9PtJkyYt9y5YgJ5vZdu/d3YJOGub8/763TuLBdDfeustEyblxoOeb24VsLP34HFmP2SPVwOFAnVXgP3O/UU+f1HEcS5JolJk7R1EDms37Abd2NfvuuuuZc4lksHeeOONJvQJgM65VcpziTG7AehRP5dgu9GyAXRkDpOL85HzTs8lzuxMzZ5NQb8N4d7PAvRw5WvvXsYSOO+880yWa5JJAWLjBtCnTJmyjMeTqUJ5IPaXTZ9DuHv37oYOTcIyaOKulIYIzXm2OuiALA4rgLXT05yx63M/k8rrt8w6qrYxby5Hdw9CBAB0gCRUdLzyZCfnJ3PE7/FSQGtDydB5AYBfc801BuymN9YqVDivAJ2ydZQZywTQUQxYL3hQcgF0Su4BaNOBNnHjGBvUCEQMOgnoNHu80tShguJt9wLQp02bZrK4k7iQ/AgAdT6AbUIByISP9x4jjcbr83yMEVyjMaV8H8MUNE7o8RgKrrjiCgPOYZRgeHAm57NKUBCr396jHCXgpra5l3ETggLIZU/Lu497uXHatQB0zr+VV165gLss+1XAFDkt2Ncy9T0vYIfWfu3PVSqW69gJ00I5lwDonD8wsJznkj4/U8LRYp9LbgF6HM6lbAAd8M659I9//EO23nprUwIQA/3rr7/eYWhPXxP2bArs9S3KjSxAL4qY7UPKWQLQdlH04wTQSZwGSAFYQdfFg8GGjycTZQRPLQcxiVWKkVgtrPWRDaDzPLwvWJ1p0JkzeXG4pm36M1I77v+yA/Tf3Cey7q6BDyGd4p7+gLFjx8qjjz4qzz33nEkYQ/+ff/55w3AgrjsboIXiDli+/vrrO27JAY8sMlHcOdSJESdO3UlxJ1kcax7FgPh1Mh07y5qlUwm5B/FxToo7HnneHbz76kEPCqDDEEA+GAUA/CR2w8ODMUDnH3o7H5LJoRCPHDnSJKRjrDR+R2I5PBJ4hfB2oZQC5HmHYB1gOHE25Kjem4JjSwNfVfaGVgKlkYDb2uZuewfTBuMhBjZlywDU+fC+B8nyChugp+8TzoRzKo/lAPtn40XuPjC7uA59UGRQ8OdSphh0ZycyAfRinkvaF/ZnjCpxPpcwHNP0XML4ro1zCeP4hAk/hzdgQMZgped4+uKwAN3t7hKN6yxAj8Y82F7EWAJxBOiIG88/nsUPPvjAHGQAcSyyv/71rzvqvcZ4WkzXcwF0HRvX4M1ASVIPtSZ8A6DX1H8tXf89IqsowvSgA4A1Bj29A9RD5wAnKzoNkIwnhjhq4sbxWgA2UVSI5cZbDX0bOjfzDEAn4RvAk+RvuZLE8Sxo8XiWAfj/+9//jBIAnR4FmaRrZM4FwCNHktOlK3IAcbzRAGS80JokbsaMGUapLgSga5k15pJkeFDUkc0dd9zRYRCAxk/CPeTF8/CCY5QCbKsiRIw+4+L3sEcwKsCUYdwwSgDzPAujFpTXjz76qGNaNKYWKn1gyZ/i/gLa/lsJOMqhabnCQphY3IO8F5xfG264oWHg8M6xV2Lc40OeCN5fJ2B3G86UacKKDdDT+5ARsM+bkZPZJSF60JG17pnpfc0E0Ol/2OcSuVmoLIM+8/vf/96EhZXzuQRYJxEsZxWGbk32yr8tQC+PbdcC9PKYRzuKEkogbgAdCyubOd5ygATeU0pPAbAAMX7jAUs4BVkf7Qag82UOc7wwKI4oE+k1y7PFoMva20vykMwJWQqVRz4POqCZZHF4CDTLO8AXI8M555xjwCgKC7RMlCMyveNpp0GPB5RjUT/ggANMbCXZ+bNlcUcp5vtkcgfwoxRrORvux1oiJwP9IDN8pjJrPItkbWRSx4CQrcyaHw+6yhrjAMYmDA9Q0olf10YWfGSKx4HYdMA4LAASv6myCbjHeAH1kfWAYk6/CTMg3wTrg8RyGEL4m1MRQtZ460mciBxQlLiuEDBS6Bqy37cSKKUE1GgFo4f3Eq92Ie8DlHbeTe5LaAnMJ4yr6WcWv2MfUdDO92D6OAG7F2YY4TIwZcKiuHth2jjBesU9B0nii+VzoxCDnggxBt0rQGcNsuejY4R5Lq2yyiqm8gfsMhqheiRTLbdzCb1Nx6rsL8qFYhh/+umnLUAv5aYX4LMtQA9QmPZW8ZcA1l+AT65G+Q7AhbY4AHQ8DGT4xnsJGIHiDBDZf//9jeIC0CABFtRAynnwu3Jo+QB6es1yHTOK3zIK3NIFUvHwkctlcU/u9y+Rut6REBVKE4ATr69S3sPoGAaBTA1KPTHu5daUxs96oH47IB5mAu+IJpIDAPB7DBckuCNeH0YCind6lvdyk48dj5VAJgk4Ke2E4WAkxDDmt3E+4R0FKFM1AlDuNkkcRjb1rvMTAI93VQE7wCaXYRqADiDiE1RzxqB7AejLPH/pAkk8dKRUfP5zdZHk2jtJ2743S6JrHzMmT0nnghqcvU8kJWAp7pGclqydsgA9XvNlexuyBPAu88nVACLOAzWqAB0FCRoglFz6CLjAygowh6qb7slA2QFY4AWkLBfe2EK8HSFPlavb50oSl61mOYoT38tYM33uDJH5n2esg+6qQyFfRN+xrtMUIIb8yLK9fSZgjScGBZ/3Rw04sC8A5yjDAAeUIGIDocHDOtDMzxjJvHjtylawdmBlLwHWOvurUtoB6LBaSMzotXGv6dOnGwMyxmOnF9stQHc+k/cadoyTEs87DEhXwI7xzQnYIwvQdWCOcynVd20zPij9zuSVeZPOeZ0Ye33sJGABerymzAL0eM2X7W0EJRA1gA5Ie/zxx423fPz48cZzASgn8ZubTLcoLsQYY4RAIfJt3Y/AXKXXQdfY8nw1y1EKfddML/G4Ncs7lG6MLHjU425oKbFIOx5PKTZAOjR23gvWE//POwNo5/2CMg8NV+P8ARHQ4mnWmx6VmbT9CEMC2WqbkySMUCriwr20xYsXm3eJ/RpKe7oH3g9AT38+fYYB4/Sw8zvYZArYMcDhuY+cBz2LMBkP4FxzqqRf5vSqWw+7lxUZ72stQI/X/FmAHq/5sr2NkASgEwOCiAOmvAVx3DS8z14VkUKHhUJB4hxAOfHlxPpRGo0SaX484XgUuB+ZcvEKBqmYFDpWL9+HNTBr1iyjXCETvApeapb7qZnupX9hXlssynuYY4jivYmnZV3gcUPGGIF451HoAeywVnh3HnzwQfPuUHudDPIwWWyzEihXCWi4ED9pTg80hmLKF3oJncLwBROF82vQoEFZq2xkikEvRMacpRgGFLDzjjMmznTOQUA7hoJCjZ6BUNxdAPRMBgkL2AtZIfH9rgXo8Zo7C9DjNV+2txGSAFmqqYOc3ii9tMMOOxSlp2SrJTM2Sbqof0nZNDx3O+20kwGjhTYAOkoSXlg8hE7KXKH3Duv7eL9J2EUyMwA6StU666zji15JHz3VTA9rUD7vaynvPgWX4WtO7zfrCiMWISEkv2KNocRjsCNEhDVI7Xi87C+99JIcfPDBpmY9YQfaLOU9uLmxdyqdBHgvnODcGfesvSIxI3lbMGrla7xXnDns4bxD5HTI1oLwoOfrD88gISTsGN5ZGDKcrepd5yc5S7y2UgH09H5mK+nGdUqL1397HaO9PloSsAA9WvORrzcWoOeTkP27lUDEJMCBSqIuvOWUjCImD1BOfDnKTKGW/fTh4iFEYSIL60YbbWQSYUWtIRP6BygnVhEvOV5z+gotOT1vgNf+u6mZ7vWexbreUt6Dk7QTpPNeaLZ23jnWH/XP8a6hfJPFHYBOqTdq1pLlHoDC7w466CDTKef9LP09uHmydyqOBNzWNsdoPWTIEMMyydV4h0hYChhWA1eu69UwEPSZl/7Mt99+2+SS4EwBpGOgUw87/6a/TsDuJpQsTICO4Z4z0I9BPVcNdidQD1vmxVnBnespFqDHa74tQI/XfNnedmIJ4J2jZjXecmLiKI8FMIc6GHZpNAV5PBePIZTeKCS8QskBkAPMiRlXJQpFEAVCY9DXWGMNX16OTMYKZ830OC3HfJR3CxDdz2a6rFh7hLygvAPMAer8xJtOZYgnnnjC1IhnfVLeDoBOEjlnA+zzbtlmJRAHCSg4Zs1n8pofVLagAAAgAElEQVQ7x0DZRWLIAbGZGu/TN998Y0JDeG9gPLkBgOpBd3p6w5CdE6Cn3x+PP151BewYGTDSOUu6AZbTmwL0oM4m5/0LAejp/bSAPYwVVZp7ck7p2RTn3EKlkV7xn2oBevFlbp9oJeBaAiggUGSp50m9yw033NCA8gMPPNAVXdD1g1xeyMFPqRuUko033thTTKHLR+S9DJngFQeUQ4PEKwmw6d+//3K0fpRHgBN/g6YfRNMEchgoOOTcKJJBPDeIe6RT3hkDhh/kCXiMIjsiiHGHfQ/eCy2/RkgEaxN5XnPNNYblctFFFxmDGo0EjqeccorgVdS69PyeuuzEvsKM8UOZDXuM9v5WAkgAwMYeyBmgWdrz7YGcYSQczbS/8L5wpgBw8Zp72YOKBdDx6nOG4EHP1xiPM0M8ewPx607ADkUeGWKUC/Js0r4FCdD9APZ86yGfDO3fw5EA5zxhiyQ5jYKDJZxRls9dLUAvn7m0IykTCaAAoeAT306GeMq+/frXvzbAHCWn1IcfShGlvLDEElMLfbwYfULpQC58eB7KEp98tXWRH8AH+j/AJ4i+Mkd455EFdMY4HXYohj/88IPJXIySjfxgHGDoiNM4ova6I0s8iaxPqKWAkn/84x+y9957y4UXXtjR3fPOO0/uvfdeefnll5cpGcUFABXCSGyzEoiiBNxS2tP7zlrHuNyvX79l/oTnGfBL+TUMvm6o4c4bFBOgc374YbhgFHVmiIdmTLI8ADvjZQyA9KDOJuSDpxSvfTH2czce9iDO3Ci+D3Hpk1YqAJxz1sc16W9c5B1UPy1AD0qS9j5WAgVKAMv7U089ZTxozzzzjGyzzTby29/+1ij4UfSooVxRjg1FAMNBGH0E9AAmAT1QB1GSNLbc7aHP4YTlmO8H3bTeL4CsGMpQIf1XzxfAHIMFyiGKHMqxLcVWiGSX/S4gHdbG+eefbwA42dy1vfbaa+b3GLaokY5R5Oabbzbe98svv7zjOhtuENx82DsFI4H02uZu91+ePmnSJBk8eHBHwjfWNwZeKiIQLuXXyBsHgJ4ufQC6E7BzfrIP4FVn7wgiXA2jgN4vmNkv/C7O9eJl7RT+ZHsHlQDgnDPJyj8ea8IC9HjMk+1lmUoARQUlRcujMcxDDz3UlEdDiY/6RorSBrgARKOAYZkttM/IBAUGUE6JNAAkngsOFj9Jb3TpKC0z6KUE8EcG0N1RNr16gYLuj/N+ePkJA+DDv/FgQSFFIUQRJO5z7NixxotzxRVXmPhP2wqTAAr35MmT5cQTT5T777/flIiisZbPPPNMk/kdLzqVFj766CNTeWHbbbc1AB1avDYL0gubB/vtYCTAOsRQyofmJ96b94H3AE8x4PH99983BtNcceluel8sgE4tdvZOPx70XONAthhJCTOCFo9MOKegxLNH9+zZ05x/XkE7/aU8XbaYfzeyDeIazfDPT93PWD8Ys9WAwL+9ji+IvnW2e8TBidDZ5iTfeC1Azych+/eiSwDLOpTQiRMnGuCHx5Sa3meffbbx1pZDw4v50EMPGWCO8rLbbrsJZdt23XXXQMqjFVtGAEAyvWOhpRybn3lCUdGEbyiDgH3m3kvt3GKPW59Hf6dPn27CEaAo5yoNFHYfUfCgsmHgwNCBdxxZohxn8vID3Hm37rnnHrnxxhtNSbBCjSxhjBGP81lnnSUnnXSSXHnllWE8ItB7omw71+4FF1xgGDLsZccff7x51ogRI4wyzt+GDh263PMBM37epUAHYm/WaSWQXtvcDzhHeFpikLX83nvvGfAJ66oQgyv31VAjv/1yO7EAXgybAwcOdPsVX9cpFdnpYed3nKsaw85+kW9/puQqxmIv8fy+OuzxS07Arl9lLIB1Pgrc843P42Pt5VYCsZSABeixnLby7jT07vvuu8+UDeOQgUZ91FFHGa+ykwYaVSmgVGMRx+PrbBxOxNuR8I0YVA5PKOx4zPGcxf1QYtyAdKjvgNT0eMNM85UOJpEJoByAG0erOgYGvKKMgUQsxRqDsg54Pp5avPn0gTXoJlsr33/00UdlzJgxJt8BANjN94r1Dr755psyevRo41HacccdIw/Q073ft9xyi9x0002y3XbbGaYC7dhjjzVx6pdddpnsueee5ncYSJg/wl1ILKcN4wS0+RtuuKFYIrfP6cQSUCDFOnR6Pv2KBIBOrgsMueyLeHeDOO/KDaCny5fxEY6kgB0vO2eKE7Aj13RZTpkyxTAWogbQM40PI5CGPDAWziwF6grag1grfteu/Z6VQKkkYAF6qSRvn+tJAiixKKfQQ6PeoLWifJPBGSMDgBXv5G233WaSQO27774m4Rsx5sUCcMWSmSa4o1wOXttM2UK5htqxeHhhSGjNcq6PEij0KzPYEVA4GSeJj6AohtV4ltZ+x9iBcqOsAz9KDe8XaxOlCSMZ81fqhoK62WabyfXXX29A66abbhp5gJ4us+eee87sX5RYI96WBJCwFk4//XTjTf/0008N/f3ZZ5+VrbbaygAZxk0yRtYSVODTTjvNlGwLI9dDqefYPj86EvCbCC7bCGDovPLKK+as22KLLYyRLahGXzEM0/zsd277gdcfJlLYHvR8/WFfhpmjgB3dAhDrrMHO/gBAZ+8OqnJJvn4F8Xf2Oa3I4qTEs24A7ErR5t9hznUQY7H3sBIIQgIWoAchRXuP0CVwzjnnmMRpU6dODf1ZQTyAxFB4/TkgOVCJXQP44AXE+l3uDeCIMQLlCSojB296zXIFk/yt3A5cFClyC8ycOdPE5gOagxojXi28rABzDB2wDTBuwFgIwuATNco77w3vEeB2hx12iCVA533nncBDRE3l/fff3yR/BKSjeALOJ0yYYFgMmsUdKv8+++wjhx9+uAE2GChg2lBbnb3wmGOOKfdtxI6vyBLwUtvcTdcI+QHc0qhtTs3vIFtnA+jpssMoyxmggJ1/kwOFMwKdgzKOUcqJkmvu040gzC1NPezK5OCMc1LiLWAP8o2y94qSBCxAj9Js2L5klMCMGTOMB42SRUceeWRkpcQBQuwvpdFuvfVWE09Noje8knjVobd2poY88AAyfoAJAAXjBGAVoBH1rOdBzBXZ4wnRwMNBbL7fmEut/c6awrtKHCKgHCNHGDHKUaG8U0P84osvFijusCviDNBVwbzkkktMLXQMDnjL8a4fcsghct1115n4f5Ru3o1TTz3V0OEB7HfffbepEU2DFo8n/YknngiVnRHE+rf3iIcEWJtea5vnGpkaKDEmse9hTGS/AjAG2YoJ0NnDg+5/kLLgXuRC0eoqAHM1CjprsIdxXgQxDsL/cpWys4A9CCnbe8RJAhagx2m2Yt5XlEpKDOVqKOJ4i7RxsG+//fbmQ+x2FBuHIh4tQPmTTz4pw4YNM7HlUNkBpoAMDh48nH4TqEVx3Ln6BD1Xqddcx+GKwgBVN1/d8riNNV9/8UjDJqCOO5R3LwwK2BeAcj544FFy+QDQi9EwrrCWmb9iU97JMM9eAIBl3dDiDNCd80U8OXG4NDzk5C2AlkrTxHAnnHCC2TtIJDlq1Chj3IE5gXFLQbzeE0AUBHuiGGvKPiNaEgia0k6yT5KqcS7y3pIo8a233jJnoK75oCRgAXpmSRLzT915wquIW1cPO2cQZ4cTsOONjkKDWYTB2W2NbidgZ/9Thpr1sEdhNm0fgpCABehBSNHew5UEoLvxydWIz9Q4ZAAeCaEAvHilo6SAqncYUE48KZQykr0BZkhsl05nBqSR5ZuDEpDuLKfkSngxuMhJvdaa5dDsoCdzgBJny5wSG8fvg6J8x0A0BuDiTYINAqsiV91fDQUAlKNQkX0d1kGpapVTt5cQk2JneX/kkUdkv/32W4ZpATBl3bAX8E7FjYWRCUifccYZJnnk008/3bHHAdaHDx9uYu4B6oAcwDoZ4MnvAF2YhvFi5MiRcXgFbB8jKAHeJ/ZkzV9R6J4Mg4xYYs43zjl9P6dNm2b2L/a9IBv7KgaBsGnOjAnDatQ96CpbKsPAvEk3BnO2ODPEs7eztyhgJ9ysVHsqRhwtp+pnjWjcuiY4dAJ2jV/X0m6FrnM//bPfsRLwKgEL0L1KzF5fFAl8++23Bpxvvvnmctddd5Xs0EgfLKDg4YcfNl4tqKa77LKLiS3ffffd89KXOTjwgAHUUVbWX3/9UOjJRZmgnx7CmJw1y7HOa/bwTHRuKN94k1EKsPDHJT4uKJlCP0TZI5EPCpQaowBuUNdRlpERipKGAkTBw6GU9+OOO85keYeeHXZCP4w8GDWcDW8z7w2gltwG5dAw8BFvDgCnDj3edQx9W2+9taG941FCDtDh99prLyEjPN4m5AOjgMzvRx99dDmIwo6hSBLgfdba5p988okB6Jr7wE8X2L8wHJFzg/uwdzkb6xWwyPoOqqnRlzAqWFnORGl+Q4my9S1uAH3SpEmGrcU5kqsB0J2AHQDPdzRLPP8ulmOE/EIwLDBIB9HSATv3VOMu60MzxDM+C9iDkLi9R9ASsAA9aIna+xUsAaW1s1nfcccdy4Dz9NJlBT/MxQ3Y6IkjhmJPLCjgUsuj+fEEA/KhtALW8DIEdSC5GEpgl+C1YJ7U++KlZjmedsZPKTpAehzHX4ggtWY6gByFFVmSzR6lQeUY1UzdpaS8I/Nyobinrx+qVMDGobIDP8lUT9UHKMLsP4T4oEzjMScLNswC4tj5/QsvvGBuZ2nuhbyVnee7rBP2IEA5DVYPZ5JfgxdxzjBAaKzXTFUroLxzbsIeCqLxLnBPzhKScDo9w7CO1CuMITwIr7BmGA+aoh+ELDLdA4BOzgovGfOVjeAE7KwR5IfxA1ligA8LsL/xxhvmPCQUIoxmAXsYUrX3DFMCFqCHKV17b18SgM6OpyxT07gjXzf2+CUyouLVQlHGA0DW5d/97ncyYsSIgj36jANQhjed2HS8gkFb/T0ON+/lmWqWY6Dwkz3cOX7okNDeo+ApziuEAC5AuYS+jqcUxRKFlvlHCYqDJb9UlPdyBOhOUI1HHIMklHbo/XvssYd5Jyivhtf85ZdfNko38sdj/thjjxkvOmyGE0880axMTUQXwDK1tygzCSj1N722OQAdUKtJCL0Mm30MRhTnAEA5G3gjQzf7nIZmeHlG+rWaGR4gh4FbDQ26d2JsUJCJEVi9wgBM9ljAu1eQGTeATlk7jCVeAHq6nFkvrAtnDDu/c9ZgB7AHdWa9/vrrJjywWLXbswH29Brs1sNeyNtqv1uIBCxAL0R69rtlJwEUZuI/obCTeZ2YOSjsZFcO4+BA2cabjDEgit50DjH6RsgB9Hwo6dAX8fQGQU/He4yChxzw4HhJoBanxYcSifxQaFEeURaRI0rrhx9+WJSa6UHKi3UBiKSGd7Eo70H2P0r3UqOjKrrqGUe5RsaUZMNoCWuHdXT11VebDO8k1GQtAc7Hjh1r4tNtsxLIZthWSjt/Z63peoMiDpsL1obbxjrEuIyRmX07X04VAC4hMYMGDXL7iOWu4z2hdOWXX35pzkpqkudLEuf0CgPWeV/4nZMOzx6cD2TCoON9jIsHHYDOfGKMCKohN5K/qvED4A54dQJ2Qg3yyTJbf0pduz0TYGd86UnnLGAPakXZ++STgAXo+SRk/94pJADdGO8V9FKyRx944IHGW055N6/Wdq8C42AAuBHDh1cAT0Spvel4IegTFHbAc5g1y50J1KiTCw0ybJl7nSM/1zvj80meBG1dS6M547fDrJnup99evlNqyruXvkb92vTM7AAR3gWMIIByGnkvyOh+7rnnGlBOA1xh5LnqqqvMdX4YLVGXje2ffwk4a5trDK7zbuQ3IO8FZ52bBkiD0g67Ay+tm3AcjLCcaTCl/DTOI7zwnEVO4JkPoKc/S0GmgnVAJuNQwI7hNFNujbgBdNg2Q4YMCRSgp8uSdQWDRwE7+5BTlsjUzdrQ+5J5HsML34tCU8DOOLXp++Osw24BexRmqzz7YAF6ec6rHZULCaAQjx8/3njLoYuioOClwmNVrDJWzm6ifKBoowBxUIUVi5VNNBxE0AcB5fzEMg51kRjxYmR25bBHEeLAwyuTKZbRxbSW/BJogVoaTbMj4y3Hm5HLuxBUzfRiC6CUlPdij7WYz8OzeeaZZ5pEcYBuFGFizjFi4WWnKajHuHjUUUcZSmpnCRUp5lzE8VkADDe1zQm1wYDoLG+abbwwqTij8CTjDXdrSOU7nCEYn702wDTx5oBnEtA51zdjdFL2vd6bMw9gybvFcziDAOhKhwcsYljgXGL/5t2LQwOgo88UU49hrcG2U8DOv2HZOdkKuVh3ZJ7n3M+X2K5U8s8G2OkPa0QTzznZKaXqq31ueUjAAvTymEc7CpcSYJNFISGuHNooCu0hhxxigDlxwH7pWS4fn/cy+gdAJrMuwBiPQ9jedGfNchQuwCQfL9bvvANzeQGHPDGRZANGAYTGWOo5cdN1Z4k5FBOMK3jLvXozC6mZ7qafYV1jKe/BSjZTLPk//vEPOfvssw2tGOOZxq+z9sgCD4X5+uuvNxRe555hk8cFOzdxuJsCV2d8drZ9lL2WM2fLLbfMOjTWFiAbwy3Zwb0ajwnjomF4dtsYAwwdPgB7Spylj6FQgJ7eF8apMdcAdk04p8nSOI+LYax2K6Ns18G0oQJOMQF6Jlmq8QPQjvEjV7b9IOLmC5Wbl+8rYGd9cm6zRlmf6THsFrB7kaq91ikBC9DteugUEiBRzKOPPmq85RMnTjTZoIktJxFTELHUQQvR6U0n0zlAL8iGUo+ij2IGQC91re30saEcQYvEi874wy7p5Ue2AB+83njLCZHQEnPEY9bU1Pi5pfmOk/Kfr2a674eE9EWlvHN7Eiz6pbSG1L3Y3pY1ceGFFxqK+4QJE8w4nMAbb9mvfvUrOf/88zvGyH6Hko6Ry7bOIwHWBeedro98Bk7OAMK6hg0bllFIGByhtLMHk0jOz15M+BYgl73cTaP/UNoByFDas3lVgwbo6X2jH5xF7Gua+d6Z1dxPwjk34y/0GvYJGBFRYqGhczgTzjG3nJnqYee8L7VRwY/cyYvA2uasYz3y3qmBVQG71mEHvLtlnfjpi/1OeUnAAvTymk87GocE2CRJZEN5tDvvvNN4hA877DCTTCmTNT5qwqP/UArxphMDXmimc+6HsoFCBqUxX83yUsuDAx3FDq8Nnpd8iYiK1V88AcgQAwcHMJ5yPkF7K1CMUVJRiKH++VGMiyUT53Ms5T0cqUN5pwwbYPyBBx7oYLg8/fTTcsQRRxgQxTuCwsied9NNN8m+++5rvOqlYMOEIwV712wSUEq7Ur7dxsayj7G2tt5662Vuzf0A7uzBlL8iA3s+sJ+tb5xh9MtNrXW8rVDaAcLse7kYZGEDdB0P4BHvL++XswwZYAw2i1Li3SScK8YKfvHFF2Xo0KGRAuiZjB9OWZIwFvnhjAC0B1EerxiyZm3zXqQnQHRS4i1gL8ZMlN8zLEAvvznt9CPCI0wGdhK+kel4r732Mt7yHXfcMRb0tPQJ5OCCXki9WTwQXrPJO2uWo1Bore2gAWVYCw8FEopkKRPoQWFTxgHzgKKGHFHM/CqtbuSlNdMxUqDceqWWunlGGNco5f24446T3/zmN6YUGDIcN26c3HPPPXLXXXcZo5Nt7iSgCh4/zzrrLJM5nwRdtBtuuEEA6RghH374Ybn99tsNsLr22msNbZk1alt5S8ALpT1dEhhrP/30U2P80QaYJu4ajyfrrNA1xP0x3EGPz2VgIGEd1wJ2iPfOt7cWE6ADHqnqok0Tzmn8enrCOa9J0vyu0KUtbdLalpKuNZVSWZEwt3nhhRcMIwKjQlwazEbKrGmmeC2Ppx52suhH0fuMbgILE7ZbrpYO2Lk2PUO89bDHZbUWp58WoBdHzvYpAUpg2rRppmQV2YydhyVgHAo71FqSmxFXDjjAIpvvoA+we6Hcis2dWEGUFzfedC3rhacXxQEZEFfuNSY6lMH4uCnKHV4MwDFelWJkekWGmjQPKjueEkA54LzYibig0aMIMIcor3GIg2Sa6fPJJ59sPHRqZIDBcvjhh5dtST0fy9vVVzDWZFp37HFvvfWWMUKed955csEFFxijJHTkTLHsrh5mL4qNBNinANRuKe3pA+O95D0dMWKE+RPnBR5sAClrqJBwHX0WrA727my11uk/pdhgDUFpd1tus5QAPV2OyJ/+a4Z4/g3ribMKAwdjCkKW+lyA+QffL5av5y0xAL1XXZVssHIPWb1vnQHoW221VWyYM8wjfR4+fLiRGf+PY8HpYdc8ACpPHAxRAOwaiuc03rjZPLJ52J0Z4i1gdyPJ8r3GAvTynduyHRleIkqg7b333qbcEN4jvOUkFzvggAOMoornKAqbd9CToHXD+YlH1enZYMMnKYvSrznogqxZHvRYvN5PKZcYKcgiDOUy6DlWGQKI8ZhDr1QZlpomjIKLEosiiKIbpfhC51yqDJHfpZdeKtAtScCIYe2WW26Rgw8+OPYGM69rN4jrMyV7g9a+5557GkWW0pDEoR900EFBPM7eI+IS4D3LVtvcS9e1esR2221naowDpvFkAjiCMmwTw01okLI+nP3jzGIdA7jwsHsBscUC6DDY8EZ7AWHMzbz58+W72fOkftECaVnaHnOtdHgAu19DazKVkuc/+lGmz2qQ/t1rpFddtSxY2iIViYSMWLevfDztVROyUOozy+06ZG/jnNh2220zzj/zTMy6swY7v1PvOrJEtkGtV7f95jrOZJ5P2GQhzRm/rvdhPE7Azr9LMcZCxuX3uzAo0CH44FjKx1Dw+5wof88C9CjPju1bRgmwmROD+ac//cl45QDjo0ePNgoqNKhybwpUUaQAjxwMJCkDmEMjxsMLgwBZlONmDgUO+iVyQKELgqqPwUNLo3EwIENkSxxclGTI2scQBYWZrLH0MSr9w4CgMsQjhgyRH4wPDCmPPPKIOCnvcYmpj9p+cvHFFxsWCfNOCTYMVn/84x8N7T3oZJJRG7vtT7sEtLa51mguxFAJ6Hn77bfNuwqI9uLBdjsfMGgA4txbG/s3FVWI4cXYSpy7172smAAdsEsf3baZC5bK/76rlx8XN0tVRUJW7VEtq3VtlabF7WXdOKs5oxWwu6VwN7cm5YWP58hzH/0o1VUVhtq+co8usla/rvLDoiYZ2KdWUjPf6/BGu+1vKa/DmEFpOFgcbqrWOMMLkCWsD94BgLKCdgwqXteTHxlgXCL0DJ0ryOYE7M4YdgXs/MTAU4wxBjkuN/diPVxzzTVy9dVXC2Evxx9/vPk35UWffPJJ8/+cgeXeLEAv9xkuk/GxQZEwjdJofNiUUUjZGEmCRBwmMeadRelHMUMegHQ2M63RWqya5aVeVgpUUfDw9uBR93pQITfiLwGVrCcUJQAva8qvZ6NYcolKzXSMGYSbIEMonQBELS+XSYYYF2C40KKU5Z136YwzzjBsHIw1JGQkXIaswlFrl19+uVx00UXGMIXHHIo73k/byl8CqrQXQmlPlxLGPmi6GNTyJWXzK2E881C/SXBIY+/FyMq+C2j3G7IUVYA+a1GTTPh4jixpbpO+3aoNBX1uQ4us1a9Odh7cT6orK8w+o3R45KAJ55TC7Uw4B539+4VNAjj/av4SeefrRTK7oUkG9q6TxpakLGpslbVX6Cor9qiRlKSk+5yPZLttt4lkhZpMa4j1TJm17bff3tfZi+wwLqmHHWMQANZZgz0sNgHMME0U6/f9cPO9TIAdowTnrBo1+FmIsc5NP8K8RlliJDm98sorTe4a9H10Mv6fMM+jjjpKDj30UJPsudybBejlPsMxHx8b9xNPPGGU5eeee85YWIktRzFVMA71CQs9XmTAWlxqZ/uZGs0gDiBiYwZQ0rAyxi0+2c/407/DgYxyyeEL5T+fgYZDju9oJnu+p4drFMvt5ZIR4Bgll/UPk8Bt3GahcicWkHeNNYihQL3kbsvLkU+Aet733nuv3HjjjSWnvLMehgwZYgx8xx57rCk5iCEBOiuevSg21i+MBfY7bTbePIozFVyfCkkEl6kX3A/qOWudtuuuu3o2crodHYZU9gwMXlq2jb2XUJ1C9t2oAvTJM+bJB9/Vy5r9fk7S1tKWlO8WNsrIDfrLGn3rlhFdukd49twF0phMSL++vaWuey/5aEFCFjalDECfMadBunepklRKpF/3GpMYrqG5TdqSKVmtd62s0quLVH3/P9k2RgCds2zSpEmm/G0QAJMzinWmgJ1/s86cgL2QdeecvKlTpxoHAedGMVs6YB87dqzRgwCycW2aZ2X//fc3DMG//vWvss8++xjDIcwxGsksMfIfffTRcR2m635bgO5aVPbCYkmAjQfaJqD8jjvuMEAUaxkl0nLFxWmyG0Aa2c6jGqPrVY5asxwvH2BM6dccNuo15vfEyUGbA6j69Uh47VsUrmdTpxQQ3nBinQHc6Q35KP2aw1tBOcwDr573KIzZCcpQfmFShFkzXQ0byBA5EyeKDKGv+8kUzP2iQnk/88wzZfLkycaDE8dmgXkcZ81bn5XSzt7ltnxaridgJKOEIz9RhEkKN3LkSG+d8nA1XnpiSdkvKH1aaNk25/7nZBN46JKnS0mih17hluL+yLs/GNC8YveaZZ7zxZwlsu26fWWjVXpkfD6x5QD7975dJHMWNkhT41L5bn6DVCWbZbVeVVJV210+ni8iVVVSV10lVZUJ6VNXLZIQQ29fu19X2W2DfvLV+29kjef2NPAiXcw6fPXVV42RNIzzGB0Br7oCdhwdnFtOwO6GWp9JHK+//rox5JYyvIgzYMyYMabygQLZIk1doI9RgL7LLruYEA0SnvJvPpzTGHK22GILwRgBc6zcmwXo5T7DMRofHqEHH3zQAPMpU6bI7rvvbpLB7bzzzq6zZvOCA1YAswAWNqwgLLLFFiMbLvQ3xoHnASCJhxxwnu0g0dh8/6AAACAASURBVJg+xg+LAO9a1KnaQcoV4IiRgjJ0AHUaSqHSr6FJIUP+Hsc1kUtWYdVMR5FRGQISULAB5kHlN4gC5R1j3m677Wbocy+99JKJJUTZgUpnm5VAKSXAng4o51zzm6U9vf8YsgHn7IMYcwG4rHvegTDAEc9XIyL/JlGc11Kh2eZAPeiZEigGOW9uS2npMyd+Mkc+n7NEVu/zs6ccD/c385caivs6K3bL2L2PZy2WiR/PkbqaSuldVy0/LGqU179cIOv06yqD+1bKwkUN8s63i2Th0hbp1qVa+vfsIs2JGmlKVprvHDBkFfnlqt3llZdfjhVAh+6PzgdAL0ZjzRO3roAdAz4hQ86kc24rtbz22mvGyFVoKcJCx41XGfBKGc64Nn2PSS5LTgLKsVKRhMSnVIMBG1A+lGSzjLXcmwXo5T7DER8fBywxPMSc3HfffSbWBAr7IYccYihDfhUGNl/AGkAMBTwuyeMwUkBfBVT6rVnOYQPtm0MIBaxY1OcoLDUOerxByIC1xbyrp9evhTwK43LTh6BqpuPNUFDOeuQ9BJiHZdgoNeVdwyJItIYi8MYbbxhl4KabbjJ7kW1WAqWQQNCUds4TmGmA5Q022MAYojhfYV1R4goPehiGS5J6UgIVQwNZuvOFIXmRdVQB+lfzlhqgXVNVIX27VktrMmU83FDSd99wRamrrlxumHjPH3tvlkkqN6B3rfn77Pommfb1AqmpqpTN1+ht7vX1vKXy3swFIm2tskEfkVkLGkwM+mardpVtBq1oQo44A90mXPMi77Cu5bxmjUBxL0XDM+ss6caZhFNEATsyzebsgJpPiBnXlLKRJBkwe8IJJ5SyG4E8m/ea/QimHgwF9g3o+2AEksVhQA9jrwqk8wHexAL0AIVpb+VeAmyG99xzjymPBuVtv/32M3EllAYJ6sVDISE2nQQ1xAjhUY+iRzlTzXKUp0IAERsccel4KMnyDgUrimN3v2JyX6mx+QBLlE6s4ayxzsgk8FMzPT1hHooJhg3AuVtPQiFzWUrKO0oA1ngoltpOPPFEozDiHbHNSqDYEuBMADSMHz/eAK1CE1yp4ZL3nKRszsoXPGfixImGRhr0u46xGWMxZxmGAM73IFtUATr9+uiHxfL+d/WycGmrVFaIrNyzVrZco5es2KNLRhE0tSbl/mnfmZhyvOe0hqY2mfbNQuMxH7ZWH1m5ZxdJJlPy5lcLTGb4NVboKnXVFbJ6zypZqaZZFi1cYJh3GOfZw5E7P0tVgsztXGPEwVETlWSXAHQnYOcdAYArYHdm3MfTSw4TAH0p26hRo+SII44wn7g3Es9iyLv55ptN7ilYpOjwZG+HZVHuzhadPwvQ476SY9R/ADObGTSVcePGGSs+oByvVZjWRzZ/lASUE7zpUYjP5gAnJgoKO5sRCpjW2/ZSBzbf9OvYUfjwpocp53x9CfrvKHwAck2YBf0fUAnVDJCOVZ4kaowdC3epD9Cgx5/rfm5qpvM+kuQNQM8BSEyesg2C9HJ5GXcpKO+EwZAgCxaPNqpCkCmd99M2K4FiSYBzwVnb/Pnnnzfeo0LyqRD6Q61m9kfO3HRDLc/DEEAoWVCKL3sutHDONhLB8f8kpCOuNMhWLICOE4Fz2WstZrK4z1/SYkA33nNAdbaGB/2J92fJrPqfPehc+9H39Qbsbzygp8nSvmhpq/SsrZJt1+krveqqpLa6UrpUVXTcVjOi01d0DC1BpuCS87FQg0+Qc8i9CNFSr3/Q9y70fqwxDFxOwM561pJurOuhQ4cGUu61kL6SAZ/YbPTpuDbNp4L3/JxzzomMwaZU8rQAvVSS7yTP5YUDANx+++1y2223GSBw8MEHG2DOwe2Xwu5VfBqfDQCArjto0KDAlBEvfcEyizwAlVhl6QvAPKiY3kx9AYjhTecgwQqJNz0oloKXsQdxbXoGcQ5J5JfN0+tkUTBuwFix1lwQ4y3kHplqpqsyxBrEuME6UFAeFQOGk/IOzXz06NGhzhnhNCSxciaJO+WUUwy1zulVL2Qu7HetBPJJIL22OfvUhAkTZNiwYb6Mi9yP5JkYmTDOZkqeSZ+4Di8VnqkgMltjGKU2NIYA4s0BgxgJoNeTgTnIxrnOOcrPMM80ADrGi7CrOnwyu8GUZwNw96mrErzqs+qbpGdttQH4jS1tskqvWlmvfzfpn8UTn16yTEuQaUk3QLszozmAPUingJ/5pU8Y04NeH376ku87rDXWuAJ2cjqw1p0J55wl8vLdL6i/UyGBsmR77rlnULcs+n0UoGNcgl1bqpCHog88ywMtQI/KTJRZP7DKU1MYb/lTTz1lqG2AckomlNJ6iyWU2HQ2WLwJxLyH3TggMUwAyvFYspErqCwm7RxvOocg/YmTN13ZBsgPjwyKkrIN3K4lFAC8SCgmlOxw+72w10Yx7s+aY+woYQoCMGigsDsrARSjL26fUUzKO1R2PHvnn3++MQYQg06COOh11Fu1zUogTAlouSSAlSqoakSEeo7i7ZX5xPmGR5IGSM7lgeeZzz77rKlBXei+iOEPthqhReutt14HaOb8w1gAGyBbI4kaH+K23TaAPwnvoOYrnZs9LSgmgPajWABdqfHvfVsvixpbjCzW7NtVhq7RS7qZ0mqpvMZKLVmWraY4Rm5NkAZoRy9gfQDUkR1G76BDHfLNJ2AXxkXQDIt8zy3078iSBIuEjSBHxqGMBfWwI1PYaWE6BlgX6LOULo1KmEAhsv3LX/5iQjXOPfdcsx7Rk8M0wBXS1zC/awF6mNLthPcm5psMi5RHY/NCwSXREpbnMDcoL6JmMwPsffLJJx0Zv8OwIDtrlnPgZQOVHMQTps+RVz+fZyznu6y/oinFUlXhXlFxO34AGjH5zBPeZCyVUd34MKZoaTSUDi0vh7LqZy2xHlESAflkXQWg+rmPW1mX+jpkxljxlGOgUKUL5kqpM866lU2xKO9PPPGEoQfi5aOUEgnjbBZ3t7Nkr/MrgXyJ4FD+Cc/x8r4qSCaPCfucm/0dDzreS79Ues4VQCznKv1lr3Y2vIwYxjOBh6XNbfLmV/Pl/W8XSXNb0gDSLdfqIwN6L1sr3Hk/5Ea1Es4yDAHsbc6M3DDSAEbIjfPCjQxyzSHnBs8I24OufcBzXt/YagA6dHYvzWtNcQxDyE497DCYVH7IMAj55es/z0Yf22qrrfJdGqm/K1uBda3nazpjAfo+f3N62As1hKULgfcBdiSMGwx6cW9/+MMfjIF8yy23NEYbjG/IjNA7dPUjjzwy7kN01X8L0F2JyV7kVgJnnHGG8dLiLdcsjG6/W+zriGFGqeBw4pAPArCxYSuFPVvNcuc4FyxpkT+N+59gMW9NJiWVEnMoj9qwv/x5z8Emdi2MhvEATwcbO970qGS512RlKHpYotmYmReYDkGxDfDmoCximcXqHIZxJow5c3NPjBAow6xBfmoWey3Pp+WOAKF84mCgKDbl3Y2c7TVWAoVKwE1tc8Iu2KPc1Fh2xn3DEkoHybn6Swy6Xyo9+S6gtNPwJOItTG+weNAL8Oo6Gx7zh9/+Tt6eudAA0erKCpnX0Gzo3AdvMdDQudMbABR2AAZcknOhtGt9eK7lXAfwKeDkTHHGX/uhHwPQOX8oXRr1xvgnT55s6MF+DBPstyo7dCPkp95gDB5hJJzjrMIYyxqMU1NZ56rfztoEpKsBiX87QwxYm4WGlqDHsUfAlMMoF+eGvMgJw3uNYwFmAnJGt+bDe4iDqTM0C9A7wywXcYyafRblgw8AwM8hUcQum/g46FXE4KIMebVusjmigAAqnTXLiS/PRRXje4ff8ba88dXCjMMdtkZPufGQTaVLhpIsQcjHGZ+95pprGsBWirlCDigEgEo8vih4sA2QX6EHVzY5oeQB0jkAMFC4UYCDkHkY90B+HP54yjUEAKMGn0zKclg108MYm96zmJT3MMdh720lwFp2W9ucHAh4bfOBbZRYQDLnjcZ9e5E0njcqGXil0rPfAArYr9dff/2s5wf7E6A6PaZ0xo8NcteUr2Tl5Czp1vCNSFuTJLutJJ8kV5WtBw+UPTdeeZlhYLRlnPQTTz3jRWl3AnTnFzRe2Ak41ZsJ2OTj5oyJI0DPBRrdro30eGvmER0BwK6UePSlQg296E2wIUi2FqeGcYpcJV7qt2PwQO9QwI6zhHPa6WH3GqIBgMWJQb4J3sVybcgOA5KzCkW5jpVxWYBezrNbgrE5k7ZwaPL/msCl0E08zOFwyENvBeRgJac0Wb7+as1ygDljZGPk44Ym+PW8Bjn23vfki7mNeYd1xPCBcuoug/Je5/cCDgi8GzQ8L8VKFgbDANkhc9YKgBL5Fev5GuqA8sVzSRwYlJfe71x4+R5KuSZ7Q34YNJChm4SDQdVM99LfIK4tFuU9iL7ae1gJpEsgH6U9/fopU6aYUKRsCd64H0o5Bmau4+zyY2SlDjreaICXm4ZxF0ryzJkzzZnB3pOrAazffvvt5YDMlM/nypMvTZbBLR+LVFSKVFSJtCyVHyr6S91qQ+S43Tc1t2WcsH94Jvu0M9lnLoCe3if6DThSwI6xkvNa6fDZ4q/jBNABMBh2ggDomeSHvuBMOAcDTcG6X28wThLmFyNRnFoQ5eFYv5oTANCOXgQAVcDuJicADiIcLKztqLAh/c4jugwecowWGM/4sMbKienoVjYWoLuVlL3OlQSUtqeHqibAUW86oDcf8HX1oJAu4uDBs8pmQEm2dEsdwAavAcCSzRCrJeDObc3yeYub5E8PfSCvf5nZa55tWPcfMUR+MdCd8uRHNMwPWd7J9h6mN53DCEAOsOSgx3PtRX5+xpbvOxhaMFAwtyibUT7gtLQc8uMgZ/2hvLtdf+my8FMzPZ88w/47CuhZZ50l//3vf6UYWd7DHo+9f+eQAHsszB1+AqLdnIMkLCSWnE96Y78iTAnlnLwShbCAvMS6a011FGko7W4M0pyVU6dONaXcnO2dDz6Uh154Vdbt3ioVNRpznpKv5jTIagNWld8esJ/ZlxknZzPPSy+T6gWgp8tQ4681Blvjr9W7jrGYucIwwM84UNyZn9dee0122mmn0F8sZ8I5ZMiZ7sbgkd4xdAIMTXGLn9bks7mSH3qdBPYIZ0k31iTrUAE77JF0RwJ6G3sA3y12gj+v48t3Pbr1fvvtZyrz8M7BJuADUGfPxHl2wQUX5LtNWfzdAvSymMboDMIJ0LVXTpAeB286hw6eOkovYZXEWo+lXbOI+6lZPrehWe55/Ru59bWvpanN+3x1r07IG2N38P5Fj99gnChDbIRQv4PwZmu9bQ0B0LhovC5eqVweh+P6cmfyPOYcI4UfT5TrB3q4UOPyUWJQUjmokR0HWBDyc9ZMhzYaB/qYk/JOmbQrrrjCxKzZZiUQNQmwVrW2udfz76233jJGOBJAORv7NFRv1jyKeaFr322sO1RksqZnq6meTfYAN6jAu+yyyzKX1E9/Qf4z8V1ZUL2SrN61WSoTIvObK2VOQ7Psv8ocWWfXY+Wdd981BnOo+5no6CrbIPZrwK2TDs+5wH7LM5AxIXBuDCulXIOMAeaFF9p1UP11GjwAmfQFHUI97NkSzikTDBZHnFoxss8D0J2AHRCOHBWwI18MSBhk2Bf0PXj55ZflsssuE/YQ5Pvwww/Lvvvum1O8GOpIkIoOiOPk9NNPFxK2Odu4ceNMdnV0ZMJvLr74YgOone366683z+a56JFXXnmljBgxouMS9kGqppAIjrGRe+C6664z1+IAA4DzruOE0JwSGPj4N/mtOO87Q7MAvTPMchHHmAmgZwLq/C7q3nRNJsahw6anFHY2RLeHNBvRDS99KTdP/lKafQBz59S9f84OoSWNcz7H6U33C1YZN0qZZmHH4qtx0W48LkVcssspvnjTsULjTc8Uw12MvjEHGpcP/Y9+aL3yQpXxTP3PVDPd7RovhjyyPcNJeb/vvvsM/ZWGokKipFGjRpWye/bZnVwCvFeAO4y+fs48aOEo4hgMaU6qNxU4+ATxnvKu8O5g9Mu2P5A1HU8dSrTXOFeowHh1Sf7kbImvX5UZk8bJUy2byo/NNSZJatfKNtm85mvZuvciea12R2OcoG/ZAHghHvRcy1PPMPZhvLuAA0CDeteZlyjSbv3ERYf1miq4dCbsA1wqYFddCvmSKA4jTJwa7BXei2Ilt2NNYvRwAvYTTzzRsP6Q34MPPmhYCOhblDnmvd5ss83kgAMOyAvQoZWj81C95JhjjjHfHTNmjCndxvdpvMMA7QsvvNCAckD/n//8Z5k0aVKHDDiHDzvsMAGkUxkCltu//vUvw0xVQ+Oll15qgP1//vMfk6D5oosuEgwKhJJkcwpx1p999tly0EEHdfQnTmvFT18tQPcjNfudrBLIBdBVwXB61N1S/Yolcq1ZzoGh3kq8lIAkqDVQ3NzGKTe2tMrp4z6Q8Z/MC6T7lSIy6bRtpVdddSD3y3cT6FtYUhkvSpkbzypKjIJyDhIUPpS5qNbbziQDlGnyEeDx5/CAXhqEEpxP3k6jBt5ynqlx5UEwGfI9n79rtmXmC29REB56N88t5BqlvD/wwAPyu9/9zjBfHn/8cROigpIRhkGjkP7a75a/BPSMy1Tb3MvoSazGvouninthPCReFSDjpfSatDVL4sfpIq1NklphkEidI1wqlZJ3Jj4sa/Volt79+ktq5U1FuvbFGoAV3SRloh88H4q5m3MgfYx4wlDid9ttt2X/tGSeVE66TBqWNsoX1YOkJVUhK1UskB6zp8ln3YbKSiN+mzdBXlgA3dlRPJSaKVvp8BgdkIUC9kzUYy9zHdS1AHRCI9IT8gV1f7/3QX70zclQ4IxT3UDXVzHOWr9jSP8eeiFGq1Ilt0OmeMgB0YBzzflEtYRrr73W6Kw0ZJrPg04Fpscee8zks9CG95x3H2BOO/jgg43xG/CvbffddzdzSB9oGCswCtxwww0d16BL4L3/61//2pGv6eSTTxaeSUNvhJUDcMc4kK3hiSehJed7Z2gWoHeGWS7iGDU7bb5HaiZbfuoGUsqNWSnsACOtWQ6wVOWewxgLIPQiNhtifrO1huZW+e+bM+U/U2bK3IaWfKLw9PceNSKvn7njct/5eFa9TPligXSrqZBdN1xRetXWeLpvtouZT2LTSeCCtwa6f7ong2tgGwBo1aiBtxdwHud4KCzSGChQvAB7YXlLtN47aw9lmIMKYI7iV4p3gjUOEECphvLuNnFUIAvO403YP1hzHPy33367kRfZpG+55RaT9Mo2K4FiS8BJaS/0bOM9xGsLzV3BOu+k2YtSSUn88L4kZn9gkqslB24p0qfd0y4NsyWx6HtJ1faSxKwPpXLavyXRMFuExK213SW50UGS3OJIkVSbVEz7j9S/+V/pWtkiNTWUSEtKqq6vSHVXaei+przbvLrUrj7E7IF+93OAGR4ylPn0lvh2qlT87wFJLJkrbcmkzF9YLwu6rysrjvyTdOvdL+/0FQOgY7ClKUOHf2ussJZ0ox/qHQ6rHFleYTD1DQ3y5ptvRg6gp/dd64Vj8MCozzmo5cfUw+4mw74bmYR1TVRi55988knjkSbshX0CEIv3W9mKbgA6tdw5M6+66qoOcQHqR48ebQwrGOvxgJ9yyinmo+2f//ynobBjqOCdgO2HsdxJez/ppJNM36DQo09icJw2bdoyZ/Q+++xjdI0bb7zRnOmEkvJM9hz+zfo49dRTjS5qAXpYK9ret6wl4BagIwS19jlLshWT9s5mwgYLsNSa5XhL2SQyASP6i3cOShMgCu9quodx/Eez5ZzHpssiP4HmLldG95oK2Xn9fvJ/Ww2UukRCjrj7PZm9eFlDwMDeNXLFARvLLwb0dHnX3JfhtQGsslniTWfjx8OucfkojEph91qmLpAOhnQT1ggWZcaPgoqiHERDmSPWCsUEOYZR772QfiqVlrUetZrp9A2DGu8uHxrKAd6CgQMHyuGHH25+56S8FyIL+10rAbcScNY2D6LEKHsuZxN7BOwtqO7mbGpeIlVPniQVX08WSbZIqqpOpMcq0rbZ4ZJYOk8SM8ZLonGRSMOP5v+NN1zaPeKSaM+W3rbRAZJabbhUvnSxtCxZKBVV1VIlrSItjZKqqpWGHmtLy5JFUtNngFTv+meRlX/hVgzLXYfh8cUXXzQe9IxGx4bZUv/Z6/LlZ9OldqVBsvawUVJZ3cXV84oF0Nl3OPMzNfUOq3fdlCNLiKxc2ywr1CalR68+UrPiupKCmRByw5mAVzW95nzIjy3o9uzfhMThFFEZOhPOaby1XwNRQZ3L8WWYljgnYJaUst1///3GKE3ugUzNDUBnbcNAIwGrNqoBQFNHz0O/Q8+Dlk7eF2333HOPOXPxgnMdOjTMteHDh3dcc8kllxgDOhR2vWd6STio9YBvQDgUes5ynEE4yWBxYnRCZ7r88svlN7/5TSnFXbRnWw960UTdOR7kBaCrRIqZRI5nQePVjRWLN4cCgNvt5o8lD9A2c84imVOzsny1KCVfzFsiM+cvlR/qg/WYF7JqEiJy62G/lGFrZff2e7k/c8sGyyaMrJClUrCRYym8vV767/daxgmQZuxqmHG7VpzPRHnHK8+9ONShrWtceVjeeb9j1u8BhKlzjCeD+LRS0sUBKloBAKUchgbrD+XNyeqwWd4LnXX7fa8SUEYY8eZesrTneg7GQail/IRC28FkSbZJ1cNHSsXn4zvANt70VEW1SE03kapaSXVfuR2oz//c0NpFAOYV7QC9ssb8NKC+uqsklsyR5kStVFQmpKppoflbq1TLou5rSbdVN5SaRV9IcvXh0rb9We3f99FQ3inlNnLkyOUYWMiO+FdiTGG/oJh7OUuiANDTRZJsaZTmj56R1plvS9OSegNeUl16Sdsa20rdmu1zGVb4UBClv3xMcUFfYf7RqzCCa9PyY0qJ14RzubKZF9QJH1/GYYNBgUSNpWy33nqroafjOc/U3AJ0gPbYsWM7bgHQJkM9OgtnLXoKQNsJkO+++275/e9/b9h/CtAB4VtvvXXHffDu33nnnTJ9+vQOgK6gXy868sgjTdnGM88808Sv4wTizOe+nO8YJ4k/x2DQWZoF6J1lpos0TmdCHK+PDJP2rjW32WhoWnPbb8KyVz6dI395Yrr8WN+CzyHSbf9N+stZowZL15oqX/3U0nLIDk8yCUk4LAFrgDa/MvTVmRJ+iTFDOUXZYtxuqN+saWSG7LD+opQpKI+L3EpZM53DWT3lvMOUkkJR4GeuXBDIHXre8ccfb6z9Nst7CV+cMn80a01BIkMNggUGKIGqiiEQ42cHAEi2ScWbN0nVS38VSbaJQEmv7NIOnJsbhFjzVK+Bkuq/kSRmviGJliXG22685z8BcwPmq7sZenuCmPSa7tKUqJWqVJNUNtcbAwNjIBY90X+w8cLTWve5SaRaS6F5m1SMDBMnTjRZ3J3GTeSGARBDIPRaxuq1FQOgwyRCLtk86Ol9rpj1nlR9+qwkew4wRpC2tlZp+vFLWdLYJJ9131LqW6tcZTf3Kguux/NMckEoy3Fp0J5ZIxhosjVnNnPeD84lzWYOJd5L8t6g5KKef/SBUrZrrrnGVEl49NFHM3bDDUCPCsUdA4Bt7RKwAN2uhEAlUAhApyPqTdefhSSRK7RmeSbB0K/zHv9IHnpnliQLkFxVhchOg/rK4pakfL+wSdboWysHbTZAnvvgO3n0f8EklXN2r3/3arnr8M1kYB9iDPM3je1VYElcEUwDwBHeVLzpKC1YPKFeEpvkxeuRvwfRvAK5cCjj7clVLx4wqcnykBWed4B5nJkGjAcLOOMgFtNtskSvM4miRvIdgDnGDZQv1h3hBV69TtmyvHvtk73eSiCTBPzUNs8lSfYX1iwexcGDBxvQQvynAeiplFROuVYqp94ssvhH4xQ3/6moEunSU6S1UaSlQVJ915FUn7Wl4rup7SAeqjunlQHyFSLJVvPvVHWtJJrqDVhvqqiTirZGqWxeJImKCqlIpSS5xrYivQaILPpOpEsPad37+vZn+WicxePHjzd10PUdViDJ2cL4/LKIogjQqz54QBKLZ0mq58CfpZVKScW8T6V10ChZ2ns9E2er3mHOCAy+GnuN8dbveYpcifd1lrXyMWVF/Qq6BDJgzbtp6SEFnBM09a7zk3XlV4Zu+sA1GBYw2JOXqJSN5Gr0Bbp5puYGoJOwjdhuci1pO/bYY81aciaJY3099dRTHddQKYW160wSRyZ5vODaYEYQY+5MEkccO2XcaOxzMOIYB3kqYLkSp46+xDvC8zDiHXjggVkrTZRS/mE92wL0sCTbSe9bKEBXsfn1pqvHEgo7Hks2aWJilJ5TyLRw74ue/kTunfpdIbeRFbtXyw2//qVssErmcm2H3z5NXv9qYUHPyPTlmkqRp48bJqv0zg7SnUwDpbADzLNlEYfeRZwkoJ3Y9FKVJQtcWHluyCGFNx0DkrIIOKg1rhyaIV5ewCw/g6jRW+wxZnpeWDXTUc4UlHM4w9LgncWwUWiiIEt5j8LKKa8+6PmkWdoLMSSrZFin1BjnJzGtvANffvmlodDiXU7M/tDEncvSBZJY/H07+CaePNUmAl0dwN7aKMm+64r0W08S374pCUB702ITpy5Q4AHoqTZJ1fQ03nf+DjW+pXGxJBNV0iW5RBJtLYYqnxy8h0hbiyQWfC1tmxwqyU0P9T2JvN/PP/+8qdUMEOd8BgiQ3wJFvBAgFWQd9GwD9Aogq9+72xhGUj1WWeaWBqCvs6skyZT/U2Mtce4qYAdswjJQsM5PL3sgQAb2RZwAeqYkfF4WGzLkTFaDBzkbMAQpYPcqQ7fP9rou3N7X63WUOmMNUVdcGzoI/aOxf8Ai23HHHc26wqEClZ338I477jDXaJk1sqgTDw4oJ4u7s8wa1HU87VDWAdx47M8555yMZdZI9gbNnT4RH4+eSJJhJZdo5wAAIABJREFUGkAcsH7bbbcZYz8x6uSoIIyQGuish6uvvtr0k7rnjzzyiAHxe+65p8AWQK/qDM0C9M4wy0UcY1AAnS5rEjkOd/6t1MFMh7nGvxDXQh9Q7gGWKDlBtbte/0Yufe4zaWtPPJ+jcUHmWL2qhMjvt1ldTtppnazfv+DJj+W/bxVmBMh2c57/zAnDZFUHSGfjA1giOzZ1PJVsgCQucwMsnWXJ8KaTrKsQhSufdKPyd8ZN+R0OOQwTgFcsyZrB3qu3NyrjytcPZ830QsrQcR/AOJ5ywDnJBXlv+QRt6LGU93yzav/uVgJhUNrJSQHVG2OeM1s6SZP4Gx6pivfuNR50qVuhPXN769Kfk7/9FGOe6rWaoaET7wy9PTFvRjs4pw57qvWn2POuIt1XklSPlaQlVSXNc7+URCopXVJNUk3AVmujLO46UBbVrSa9q9uky8BNpG34ySJdui8noubWpDS1JqV7l8qcez7v+nPPPWdAI0YH3nm85kEk3YwiQK/85jWp/OplSfYd1G4UoTXVS6JxvrRueICkoL5nacgKgKlgE8CNRx2wCbjijMmVA4XrMfQQOxyXxjmKroH+EETjbEaGWi8cmXCmOGUYxPlMv9F1nNn9g+i/13v88Y9/NE4UQLg2AC+APL0BeEn0RkI43kWu00aWdTzbgGn0Z7zqgHRno5wboFyzsQPW999//2WuwXv+97//3bAIcWCQ6d0ZcsEeev7555sa6cwRpdnIHo9hcu+99zb73XnnnWfi1ekPz6B0G951kthRsq0zNAvQO8MsF3GMeBQ4YIJs2ZLIpZf34vDCW86h7wZYeunjG1/MkxPu+5/UN7d5+doy11ZXigzoVSsX/GoD2WINRx3atDu+N3Oh/PrWab6fk++LgPS7j9hMVqpur1mOAoghQ5Pl+T24UCjwipS7N91J/wdYIi/WPQckSqcXb0e+uYry3/3UTFeGCwo6RiFo8grKqSkctmEHjwKKCc1meY/y6opm3zhzMGTiraPsZKHrlbMSbxFAHGDO+UXMd+V7/5WKzyca+uwP3TaUVfc8TSq+eFEqX7taUr3XksTCrySx6FsTcy5tAG+R5CqbS+uvrpOKLyZKxSdPS6K5vt17Tvx5TXeRpfONUPHqplZYV77rN0JmfL9Qhix5SRbPnyWzWuqkR4/e8n63rWXK0oFS35SUpsqustaAlWSH9frLkNV7Sw2xWSKypLlNnvlglrw6Y54sbWmTgX3qZLcN+8tmq2c+13jvn332WbNHcjajiAdV7aMYAJ2wA57jloItjQuk+pMnJbFwpqS69GhnIiRbpG3VzaVtze1/Bu0uljlnizM7PM4Izmutv96jrtpkjCfWnQYwxdgTJ4BO6BTnKGyKMJomnFM5YkxnLSpLwW8Ne5IFc96zF5SyAaKRHd7nuDbeLwxPO+ywg8lVgRHg3HPPNUYExgXbhhKTAHSbxT2us2z7XVIJ4I3lMA6jKa0QpQU6Dko+mzpKDV7LMDJMt7QlZcL0H+XCpz+R+Uu8p4OrkJTUVldIXXWlrNyrVvbfdBU5eIsBUpEjGy7j/MM9b8srM4Knueu89KgS+dvwClltYLvsgkpYxiaLwgnwx6rsNSNvGOsmiHsqhY5xaWkvzWDPQa+J1ACtKNrEU3WG5qZmOrKDmaH5DDQmH/llK2kYpuws5T1M6ZbnvVnDCgQB07zneHkKaSSdhIrMfQGsGKigr0NjNx7y6jppbWmVtqZ6qRnwS2nb5lSpev4s4LZItxUl0bhApGGOJBrnSXLdkdK626UdIA0wnqj/vr2eeV1vkSXzTJz67MXNMndBvWGspJoaZP2NNpYnPpovD7/xmSxsFmlN/JThXRKycGlr+7NEZIVuVbLj4BXlpJ3WlRW718iNL38pk2bMlZ5dqqRLdYXMX9JizrhjtltThqy2PEjHCEzpL84alOwgDejsJwCwIO+ZPq+eATo3aFwglXOmS2L+VybeP7XCIEmusJ7vOH7tE+sGY/jCWV9L8tu3pWvzj9K1rk66rLiW1K61lTRV9TAe0Dhlu8awj8EGEFaMhg7pNHpoDXv1sLtNOIec0Z3IR1PKdthhh8lWW21lMqDHtWn1C8ZAkkN+HnfccaaE22mnnWZi0BkjnvZdd901rsP01G/rQfckLntxLgmgxEBJgRpNrEkYBybPQEEiURcZP9NLLOWdoYbZUjHnU0l26yfSZy1JNPwoKSzPdX2W++qixhY59p735N2ZizwlhMOY3aO2UvbeeCXZa4PeMvHtT43RYsdNB8kma63kyuvS3JaUG16cITdNnrlcv/BhZOIo1FUlpK0tJc057SM/0+9332BFueKgcLKPokBweHHoEpselLck7/wGfAHKkJb2AtgBvFEysbxn8p5xLVZ1rsPb4qccW8BDCP12+k6m10zHS6EZ2JEdzBZAudvQiTA7binvYUq3vO6ttc2VGYahibCWLbfc0vdAYY+Qw4L3gXNMEy5WvHuvVE66zNQ0B6A3NTdL4+LF0ruiXlq3OsE8r/KdO9ozsxsjb0JS/TeU1l0vNqCdlkym5JPZi+WLuUtMoBX//0N9k7w/c6H8WL9UltTXC17X3TZdUxY3tcm9U7+V+qXNkiQWOkeV0D5dq+TAzQbIPr9cWf7+/GfSs7ZKetZWd8hgxpwG2WiVHnL6yEEdeyPvGfsCXjDkB2g0hogAW7EAOiAuV5bxAIeU/1YtS6Vy+qNSsfAbWVLTVxYvaZaWBd/JgtYamddvS5OVX/Ujv8n38nciuCtKCXRZo2r0UEo8I8N4rIA9W8I5mApcR1hfKRsUc2LCqVoS90biYUA5ZRnZLyjPRiw6sfDEn1NSLjLvYcjCtgA9ZAF3ttujvLDZAs4BZm5KUXmVEd5zEqlgiVdvfd7SNq1NUvXqlVL58WOSqJ/dHr8nSUmg4FTVSrLP2pJccQOR3mtK6+BR8knbAPm/298xCky+BmD+9Raryv8NW01SyZQ0J5OyRt86qa1uz3iLYoJRASs83n7irLwAt9ZkUhpbktKtZvk4P1Nbu6FZunepMmXUuPaYu96V175sz2ra3jLHxFcmRB4+Zqis2z9YhUmfimeIGC1AWiGxyvnkH/TfUcTwMGlZOcC4xpW7yVwOGOUdAKASf8Uh3xmaxj4yVta3JsrTDOxuZFdsOVnKe7ElHp/naWiVhm1pIjj2M84gZ51ft6NivyYREgCf89GZ7Cgx632peuRok5TNlETr0lOau64ki5uT0idRL6nVtpbWPf4pie/fkYqvXjExzS39NpIZvbYyVPTV+3DmVAq5Ul77fJ4saW6V7xY2SWNLmwHf/D+Z4FfoWiXrrNRLGluTMqe+SRY2tkhjc5u0tKWkNYdxt7JCZJMBveTgoQPkwWnfybr9ls00Pq+hHeT/bb+NpFuXKpPUiVho9kGSVFEGiljTbAlH3cow/bpOB9CbG6Tqo4el6osXJFnbuz2hX6/VTVx7Yt7n8m3NOjJ9UZ0xirMHYxBROrxfKrffuXH7PYxV0PYBYqVuypZTDzshA5xnCtb5qWxNMpxjeDahKSVsu+22mxx99NEd4Vsl7Epgj54zZ47JyaENYwi61SabbOK74kNgnSvSjSxAL5KgO8tjOJQ5MEkgwYeNC3DmN645k9wAumSsVICO0qO1W7N57ateu0qq3rxZpHmxJJLNOaYjIS2JLvJe6wB5o20D+UjWkGeTm0uzdMmY+K26QuQvew2W/TZdNe8U02e8q1hrKcvh3HzyftnFBRrf++XM7+S4x2fLAheM/F3X7ydXjd7Yxd39XwIdFAobVmjo31H0phtDx5w5BpTzE6UG5dlvFnHm4ptvvjF0f5QO4sPCYJT4n5XgvqkGDYALSg3vOnsAVm7yGkS9Wcp71Geo+P3LlQgOujYg22uML/s/lHYalPZlEiHO+1yqHxsjibmfiLQ2iVRWm3JoqcpaWdBlgPSuXCLJdXeTtl0v6hDG53Ma5M4p38hX85ZIa1tK+nStloF9auX9bxfJSj27mJCsj2fVS1NLm9Q3tgkG2arKCsO+WqFbjfTrXi0zZi+W1qRIWypFeXTJZ45ec4WuctwOa8u4ad/Kan3qpBrU/lP7bmGj9Kqrkov32VAa6heZ8kwAQoyU7AkTJkyQoUOHBpq4lUcjV/SOoMK0Mq02N3W6A12l6CmmBF5XkdpeRm+pqP+uPZadbP7fThVpqZdUjwHtOk1bk7St9EvThfrKPvJOQ39jQEIummyOn0rlVsBejLwfbuSCIQfgW2pPdKa+cpZhfFY5kn8CHYb+ct4RxsenlI3ki2effbYcdNBBpexGIM/mXYP1oTJFH8ObTpx/oWFFgXSwiDexAL2Iwu4Mj3LGoHNwAsyw4qKs40krNKkOMsQry3M4+Gkam65Z35credO0WLrcs48k5n9pPOeJjsi65WcE5SXxkxfB/PunD4rL7FRPuad1F3koOULmS29pki5y4JCV5S97re96XPQR7wnADcsrxotCKWgAfhIXAS45gA2NuP/Kcs3k72XcO7NyLrueXSrlpT9tI12qKkNdnvSLMUfJm85cYB3X2Gg8vIDyIGPyWft4B2is16DpnaFOWo6bo7RwcDKf/EQ5VoMG3gVkijEKgB5mzfSgxm8p70FJMv73UUo7azxT+TRNjrj99tu7Hiz7M2chBmtCX9KNdZWTr5CKd+4UIfv6/C/ak4hRQq2tUZZW9pDa7r2lbZeLJLnOzuaZixtb5eKnP5av5y+VVXvVSnVlQuYsbpZPZy+WFbrWyCar9ZI3v1ogC5c0y/yGZmlOitTVVEiXygqTdb1LdaXJvP79wkYD0Dnn3GSO2WiV7vLvwzaTy57/TL6Zv8R47asTSVnUnJLZ9c1y0GarypC+rcaAAVOM2Fw98ydOnGgyMQfJqoPpBLhjrth3FHgCnryw1PJNZNHqXSdbpfLbqVIx+32R5gZTPi9Fgr/WRqkwSf8WScX8LyXZdQVJtDZLqiflphKSWDJXkrU9TYb/+d3XlfcXdl+O4cEeB5vBGXvNOnR6hktlPMdwhcOi1J7ofOuAv8MMVCo87zX7BawQlWOxWQrMK8CVsmTUJI97I/P8zjvvbGqk8w7jiNP3j+zzxNsHgSPiICcL0OMwSzHqI8k3nI3NQ+Ny2cTwoBZq6Ubxp+GF1qZUdw5q9aZ3lGWb/6XU3LufJJYulEQb1PbszZnfLnOhtHZFBvCeTFRIZXV3SfVfX1o3+70kB40Uqap1NVt47chcClUf4wWeWi+bDoeE1tzmHsT1AobSM9gvbmyRYX+flFP5uuqgjWTXDYqT1Awwh6IKUGUthJHYL98EYDjSuHIMB8gecBlWwjItS0aYA2A1rmXoGAeKCeAbxZjstZqBPdM7HVbN9HzzW8jfLeW9EOnF+7tq6GVv1YRFmfZkjHokPKOmd77GecR5xV6NopmePDLx7VSpfPceqZj+uMnIbspvtTZJYunc9jrnyVZpqaiViq3HSNs2fxSpaDekvvLZXLnx5S9kzb51xiuubcLHP0ptVYVsP6ifTP50tsxb3CRNyQppTqZMBvau1ZWG2t6jS6Usamxtp7UnU64B+u+2Wk3GjhosM2YtlNueeU1m/jjPgOOuNZWyxVr9ZdPV+0n9gnmGIQBYdjbKOUFPDSLkxxnbjh4AKNI62Hg6MVprpnPORrdJv7LNZ7EAesW3U6XqyxclVdtbUl16iiyZK1VfT5JU95Xbs7+T4X/mFKMrpCprJFVV154/h5J6jQulbeVNZU7/4TL924UmoVauxhp3ygwvsdPIwXkYJPMxV19ICsY5HAfGlXMchG3AkGM+1PCB84j1qBnitXJBvr3C7995FzD6PfDAA55ZPX6fGeb3MNRgzKMizl133SUXXnihYeMwPsq1TZkypdNUyrEAPcyV1gnvnQ7QVQQAITzfWByhqpCt0y/ll/heDg68z+lN4wb1p/GANDdIl9tHmpIniVSOLDhuXAj6wAzoPVVRI6muK0hypU2ldcjvJLXGcJHK9jj0TI0+orjhbUCZQNHIBVi5HuUDGQKQ1GsJuMzlhf9/t0+TN7/KnRF+1IYryukj15WVerozMBSytHUtMAbmkEPZi3HCz7M5NBWUo5RorXcOA7/r0Gs/OMDxpkNtJf60FMYJr31WloGWReP7CspZs/nmLaia6V77Xcj1GM/Gjh1ryrBRp3X06NF5x1nI8+x3Sy+BXJT29N6xf6Ak5sskDHsGxRIvEMA03TuZ+PpVqXr2DAOsAFimJFpFhUn2lurWX6RxkcjSefJVjy1k1SP/P3vnAWVXVb3x7742PZlJbwQSSiCU0KVIE6QKUgQEAoIgKE3Bv2ChqFgoooAiKiIiAkrvvQmhmNCbhIQkpPfMJNNfuf/1O5MzufPy3rx737w3hcxZa1bK3Hvu6Wd/e3977zvXBoVra82j7y/W31+dazKENLS2AeThVSWauaxBS9e0aJcRYc1d2awlLW2AHCs5mUMiJh+XzHt1TXGNGFCqNc0JA9ZzXX/l0ZCO2WGULjlsS4Xe+rvi796vDxMj1RCu0sjEQg1tXaiF447RRvt/K+PZRo5llBTpwD3o7HN/YDVH0YpvO+PK/3ljXNigX5aWzDesdZ0/g5693QLQE82Kvn+XhGW8crgZFqdunkIL3zDxCJJj9zQKmzD/5hks5pFSQ4Unv3qqcrgS25+mZc5gE5gPf/8gBcUUyn7GzCo50lORFeuufOutt4wcwN3Sl8prr71mwLFd0zbgnJelwP+hlLI/yG257s0gY0D9KP3ZXyjG+nphfDDiEGj6a1/7mjHkkGaN4HEYszh/Czl+vXm8+gF6b56dPtY2DgqAUGeFCwCAjeDOxkO7HbRwObOJO8uZaQG6jbwbe+5SRd77p7FSrIetc0km6Q3MZlrP0BE3Vq3EJvso8YXvSCO26yBk2ccZM5QX+DZCC0xPTWZTVGG5pF+Whu032M70xWt09F/eyCmADSqP6LZv7KBNhxYnaFz68NBfDmI/yomga4TnsezwDcYNWipabUvD7i7LQHq7bTo22oVCprcKJKw5G4HdsgxoK0JGPpdjPjnT85nzQr3DPrv//vt13nnn6cQTT9Rvf/vbwEJ9odrSX09xR8AGGevMau5tAcyQl156SQRmyrQXrBsTlnOETM709YCN6ypy36kKLX5HbsUIk9e8LU95W0BPt2K4ieLuhqN6acgp2v2ob3X41l3T5uv65z81gdwB3SlXKomEVBKSWluaTUDRoTUDTCR3UquBy2NhR/Fk23OuXPPODmMGygk5+mhBnQHqBIlLtt+HbX/BPo+vOenU9p8wVFceNEyRR89ra2rlMGOtXrWqVjWqU+mw8Up+5QYpQsyWjoUxQzGZz51va0I4x9rK/Y+FjXOcMzUdoHu/zHxYH2KAJ38H1FvAjqU4Fx2eoIAo7rysvYKvyqZVir5/p9zSmvZ0eU7tHIVJueeElNxod7nlgxRa/L6cFR+bdZMa8wU5qxfISTYrsfmhSg3f1rgcEacnKEBP7w/9tUCTcWN/MFbWMlxIoPnGG28YkIkVvS+VKVOmGKUTskWmwtrjLrWKIuRfG3DOUuKDKovSv8O8sKc4b2Do9eXCPsZ16OCDD9aBBx5o/nzmmWdM7IqpU6fq0EMPNet7Qyn9AH1Dmelu6KcfgE4zOFBIk4aWlwMZrVgQP2wuaC6KXDkzrU+6CSLX3KDSh89QmOi3Jnr72lJEcJ5pyFMl1UqN3V3xvX4kDel4mHLwcMhyYHPQIkgALjngrcU33xRV+177spY25I4at+XwCt1/Vv7pg4IuMw5kGAQAVjTRAOh8AGD7dK5lGViLL2NJnYDLnvKvyzQmMCeYa+aT9d9TCgNv22xKOcYOEGLTohWKZeAnZ3rQ9VPs5y3lnbPkr3/96wYXpKbY49uT9TOnNrc57ciZCWRtY2GJEbQIATIdeFMfCmgUUgDIrIFAG1co+s/DjZ+5AzC34Dxlz2hA+jC17HuZnl5YZaz11kI8d2Wjfnj/h/po8Rpzj1WVRhULS7WNcYWU1Fm7DFF0wCB9sJDfE3HF1f8WrTGWdKzgRHrfeFC55qxsi8cytKpEzauWqG7VUi2IV6o2VaawkipRXDElDbhvdaJqdsp0wQGb65tjlyjy3GVKVY/T6voGNTQ0qqamWmXhpJymWsUPu06q3ni9qX355ZfNWce5kk+BOcbYcu+jnLf3RC6Anv4t60NsLcUAUUtJ5jzOFDgNgM75iFGhaCXRstaC3rLOgt64TKG5rxjAntx4bwl/9HijwnNfkVtWY1wioMKnhm3bloXGcQyAweLflTSA6X1kr8BYsGNmgaYF6/yJy1O+Zdq0aSZeQb5rI9/vdvU9lE6wOPwaS5BFcZGxig8UTtatwFrYg8oC3NXIN8iKvVXhH2ScUYyfccYZZn/jj37vvfcaBdw111xjzl0A+4ZS+gH6hjLT3dBPvwDdNoULD5DCYQU4I0CIH3CG/x8Hud+UHNa30E0mFJn6B+n1mxVOrFLYbQuO46v4ftBXbW0PhaJKDpmg+N4/ljtuX5OiBuCG9pux4eDm0uLQ9XtoJ1Ou4smUsZIwlnWNcd33zkLdPGWO6pozZU/v2F6EsRuO3UZf2jI/ISpA7zs8Ct2dtYA1HSEo6GXPRccFBbhkvhkzgHlXfQ/z7Y+f9xD0ETgRfLAsdZX66eeb6c8AmllzjBuCA21g7PCVzWVVyud7zA2++Ok50/Opq9jvIExZwfRb3/qWnnzySbMvizEuxe5Lf/0dRyA9t7lfcE4tALxnn33W+KB7FcsoVKG0owgEnHd6hrXWK3rboVJznZyGJSb9mTGHExjOTUqxKrmVQ9V0woN69pW3TNAk7oCZS+t11VMz9Pa8WmM5B3ST5xyAXhJyNaiyVNcet722GN6RBbWqsVXvzV+t2qa4hlTGTLq0B99dpH+/sUAxt1mVqz/VmkRIdW6ZmlIRhZVSvQDqbXdGi6KqdFp1/+nbaFRJi0JPXqyVrRElnBINHjyobU80tvnNJ464USpflx7Jjvwrr7xiFM/pfvi51qY3NR2uAukgLihAT/+eN3AaABSli5cOzzx2C0BHJFj4liKznzeg2yVgYOtq4ZdOVP/U8ElSKNJGZ68aqcTYPeVEy9uCyHli36Dspr2FBOjpY2aBpgXs3L9Y1C1gB2wGSauJdRTXx0Jntsm1trr6e+IqYN3NN66SdSuwgB1ZwAacYwwxROUaR+YbhRVz8HkJQsu6skYb5gg55fHHHzcGvXzSW3Z1nnvq/X6A3lMj/zn8blCAbocAcAbVGcEGcJZLG8lhjr9SkNQW1pre1BrX9/7xsnZa/qBOSz2gSiezTzqykn/03vXJxJBfHxuu6Rt/QyXbfEWVVVVGC46wwJgAXLOVpnhS1z87U/e8s1hN8ZSxeOCPuM/mNXrm4+Va2ZDbv9Bb93ajq/TXydub3OrdWTiEsaZjAfAT9R/Lh/UrR8hC8AOUIyTk8pVrTaY0fXG9CdlfUxYzgZLqWxImIvKgili3dZt1iW8VEe5Zz1y0uS7krjYOoYDLj7Gz1H9AOZdfECZLV9oBmCGvKcIv0e27SvPrSlu871pQboNNMj8UogzjD8d4+VEiFqo9/fUUdgSs6xMWGf4eBJjblvDeU089pX333desW6t0wk0JkMGPnzUSfvhshT+6Xya/WftlA1APyx08QYrXq/WoW/TUu4vMt9gr1zw9w+Q4X9HQajJvhBxXq+qbRZy4nTcepPq49KODt1gPoGcaxUQypYffW6wnX5qiujX1qlSLDg5N1bPxbTQnNcxY4+tUqbgiiiip0yNP6dtbNmnpPtco/sgFGtQyXyUjtlQoVtoWpKz2M6W2OETJPb+fcdJeffVVc74FoTGjxETpwXxhqcwEhNir3B2FODdt4DT2OSAB0EPMEOrmB8VLIb6TdVWnkgotelPhJTaKe6lSNePkxqoUqv1MSsXlVm+s1NCtDd09U+FsnzNnjgGO3VWYHy8dnnmDlWBp3LnilRDTAeVNV9wfuquv3nMAi+4ee+xRsPvLmxaP8Uwfx0wB55hrFFeduXl099h09XvIRBhcGA/G18952tVv9sb3+wF6b5yVPtoma5XIp/mABqxqWNes3162i5DAHFiWAWNBy9MfLtHFD3yo5nhKu+kd/SN2tfGxW6+kAfRiGNCDtN1Ejg+VqvXwP0oT1qXSWNMc11F/nqaFdR2j5wepO/3ZmrKwfnHERO03YX0rSFfq9fuutaZzwePz57VE2ej1Nuc2YJx1gFXFr2XziQ+X6MqnZhohFz/M9IKv5jHbj9SFB2yqim5SUqA5J4AcewjAmktJ5Xcs7XM2zzvjhgCH0GmDvfUU9d/646OQQQkV1LIWdAyyPZ8NlCMU2DRbKFDINcs85VL+FKpd/fUUdgSCBILL9eWnn37aCI6cTexbKL8IyUFYMJEHv6XQx49J7jpau3HsDkVM1G5SbMVPul9PvfKOWXvNbkQ/fOBDxcIh41u+sr5ZUSUViYTVkgprWFWJJo6q0uWHbWkitvstyX+dpFWr61XStFyD4wv1cctg/SZxrOa5w5R0Qyp3mrVX6H2dH3lITuUQTR37bW20+bYaM+sOhVbOarP+hyPG/zn5xe9ntJ7TlqD3NmOKOxtjCsMo2/leSICePmYWeOKSx95nDVk/bNpVtDzi8SY5rWvaorSXVLU1yygLUeB0Prfcn7S3OwF6+rhlCtJnKdyMG3eOF3ShvOGuL0SEf7/rvqvPse4IzPbFL36xKIptb8A5m9YtUxwAjFu427BfvHfTH//4R0MLB+iyf6677jpzjmQrN954o/7whz8Y5Q7sVPKqn3LKKe2Psxd+/etf67bbbjOpgmG9XnXVVcZP3BbkctZeejn77LNF/ZRTTz3V1OEtxEtASUOBwv6DH/zABIIkLgHtwb3lkksuMd86/vjjuzp1feb9foDeZ6aq9ze0KwDd9g7LGrRJyraTAAAgAElEQVRfNGcI7Zl8kvKlyvGNs+98R89NXxdk4iznPl0cvW/9wc1yBxrqWc3mchqXGt9BJ97gM4NsYecvMWQbxSc/qp88MUsPvLu4oJVHQ9LFB26uE3cdU9B6g1TG/JOGDisGFwHKGgsubfR6AGZQKvyUmct15p3v+2rK2JpS3X/WLiqPdQ+TgP0Da4ILDksTiqquaI654LnYrT8+Aq71x+9NVLieyJmeDZSzMFhrFpzbhTJv3jwj5LAu/bqb+Fpk/Q91ywjkym0etBHPPfecOZdQKqNMI1BUIPbJik8V+/uBUqLJROY2BXp7KGqspAvCozVl5KlaMvYwrZg3Uyd+aUdVD6zSDx/4yFDb6+vXaMayZqVCYYVDIcOcmjiySj84cHNNGpM5YFW2PhLwLbnwXTW2uhrYOEdKtqjFjert1GZaozKNdxZrvLPQGPlbozVq3v+XKtvhaxIgctHbcppWyq0cKXdkGwU7WyElFQJ3rlRallXE+Y9VNdc5WEyAbvsCSCAWDOwIS+vmbOWssFbirvphB12D2Z4HoGPo2HnnnQtVZZfqYT696dxwpeLe9o4brEgU09mCrXWpAUV6GcBKXIW9997bt3GgK02xAecsUwFAftlllxl2JTID1nwbl4kMJOQLB6TvueeeJhsJMVQA85lcQ2+66SZdfPHFuvnmm9sDsuHWdeedd+rwww83zeb3pD3jGRiOsIguvPBCoVyB3UJB+c9+tAXlJfEzaBssIAoAHbe6W2+9tf057lTYE7TvG9/4hvkmdQLUOQdgEvzqV78ywZTvuuuurgxjn3q3H6D3qenq3Y0tBECnhxxECMRsRjZtevqxfKLBNrcm9PfX5uqG52dp3fHRNp7/iFyuL4ZmtP0jq3KaX7T546VG7KDWY24z6XDU2qDw+/9W5L9/VKh+QbdP0GqV6arW43Rn6sCCcfLDjnTZYRN07I6jur0/9oM28i7++NAN0QwTowAaeL7gsjWR1I6/fimj1TxbR0vCjsYNKdeum9To7H020YDSaNHHhIuXiw36LKAwiIXbCkOAXi5B9qS1lCP8dAXwF7Pj3ZUzHeEhnb5OvzKBcm9/EcqxDvBnX7LyFHPO+kLdNv5IrtzmQfpCnQB09hZpInMBSFO3m5Izf6ocInKHogq/c4ecxe+23ynrvh/S2+5muiH8DS2tmignFNHqNWs0fni1Ljxwgh55Z4Fe+GihRlVKlQMGaXljXItWt6imLKpfHjlRE4avtbYG6FBo+mPSf65SfXNS1Y2zPRb9jpUYslHFcCWO/pvcMcHp04AwznB+shWbNx6QmSmXeqb3mAcE+GJSzwHoNlaIbUMmP2zuJuu/znlbzDZlG0POfSjCO+20U4BV0H2PMsc2nRtgE8UH9xKuD9xVPTVuQUeANYexiEBmPXGvsv6wNhNEDf9sWAuAb+JVEBUfqzTA2xbk6COPPNJYwdMLbCCAPBZ3W773ve+ZeohUT0GxhlX9nHPOaX+G+ljzAPdMhToeffRR48JnxwiAzvw/+OCD7a/YDBoEiPvZz35mXMoeeOABXXrppUYWolx77bV66KGHTAaNDaX0A/QNZaa7oZ8cvAhChSocgGjP0MqhSefwYZOjjePy9isoEzjtkoc+0mPvL1bLuhwyHZp5ffgafSX8difsMUjulg/tKDVqZyX2uECp8futqyeVlDPzGUXevlWheVMVSjUHGwov3dovp37tOygdvtfybT2qvYN9M8PTgNLHztlNo6qLnxM9/fMANcAlPza9F4FjoDhxqHPJBPFh9Nb/44f+pwe7wDaA+n7dsdto3y2KT/1nH6GgQtjy44+P8GjTorFvrD8+e6SvULKLlTOdei0wtz7lrItcoNy7dhhT2DwoDoPEvujyZuyvIO8RKCSl3TYCBgXUS5SGWNBR2uQsrfWKPH6hQnNfNUHUlGw21ue266Rj4M5WN6rvuRdq9sBdNX4YAC+kRYsXa43KtcWQUu1UvlIvLCtTXTJqLOfcbdXlUZ26+1jtPj6zT3Kn7WtYqtDHj8p95y6lVs5WSbJ+rU985rfcUTspPvlhE7QsaCFSNwAMK3qmAsCA0s4dj/XMb1yKngLo6X3gvrLWdf7k3146fCHTknU29twDUJB7K0BPbzt7Cissig0s7YybjapfVDeCoAs47XlkFVghAPSeLI888oiuvPJKoywAvALaoarfc889Ouqoo9qb9t3vftfEc4CWn15YK6Qwu+KKK9p/9aMf/ciAYmQLa+G++uqrdfrpp7c/c8IJJxjXFRRYmeYVUI+V/cc//nH7rwHogHMYR+wP0qrxXc6Gv//977r++uvNOXD77bfr97//vUmvRvnhD39ozl6UERtK6QfoG8pMd0M/Cw3QbZPxUYX6AkUXiyJaPfyrOguc5u3utDmrdO6/3lN9S1yJTgKZfzUyRVdF/qGY6n2MVkRuaaWS25+mxK5nSqXVRjkBoAJMQuPaJLRUm352h0prP5UTJ+VNJx/Plu7ND1D3vLsiVabd4jcpofwDnR257TD96qitfYxBYR6xkcQB5bg4AMihYvOntUAgbDO2KGy4tIOm5qOlk37+gjKHBPTfD+ilD39nV20yuNz/S1140gZQpM8oJ7z0aoLkMSbeVHxcct5x68Kne+xVmzOdyxs3l3wo5RaUmxSLqXX7Lggo9w4A6w82D4HtWHv9pXePAPOFUsVaZgph4WJdIiCi9EJoJb+5H2VheMpvFH7jr3LDMTmJZqm1fh2t3TOMTW5M18eP1p3OIQrHykw6tNHVZQo31yrhRLW6qVU//8oWGjdmpF6fvVILVjUbcL7LJjX5nUd18xV56iI5K2cppZBaG+pUlqhNa5vncgmXqHXyI9Ko7fOa/M6yrzC2gAfOL865IEpF5pg7JMg7QTsAhRjwCA3bT2H9AeC8dHjkF290+EAuEX4+uvYZADoyyI477hjgrZ59FNAIJZ/YKHbcrN818+qlw/tV3BS7R1j+33rrLUNx78nyr3/9y9DFUXJQmHtYKgB2LOO2QBHH95tAvOkFAE0dWLtZN+zVww47TMgf1Ic8duKJJxrLNuAaFzxYRF/96leN8puzNr3cfffd5h3cLbxuLdDvsbrDPCLbAFZyZGe+yQ9tgU6PgQJ2AG4E+KdfdNFFpk38bkMp/QB9Q5npbuhnsQA6Tadu/HPZ0FzIu+22m9G++Sl/fnm2bnxxlrE2dAbQSyPS347eSDstukPRt/8hJRrX0sYzoeewiQCucJlaBm+pT8edojmt1SqvHGAOIwSN9S7gZFzOZ1MUfe4yhVfN7Nj0AgF0KgWO7N1yjRYoO5Uw27iNG1Squ7+1S9GDozGfKF44/INEEuciIB0bChAEOb+BxUzwtV+srzn2s37Sn9l2VJX+fUb3+fchfBKXAQERqx0WBoQwGAUILjYtWj5ANp/+d8c7+eRMzwbKEfDsT75tR+DG8odQ0lt8O/Pty4byHgosSlfBOXOPqw13D/uPdYBVx48/NRbz6C37SPVL5LRY67T3sA9B5ZCbcnVd/Eg9nNxdy0LDjPU4mWpz9xocjauqxJFTUqWrjtk2PzCeYdLDL1+j0Af3yq3ZxNyNq2prNdRdLmfNQqXctgzq/NjAdYkv/VSpXc7Me/kAZlByIZjbQv+wvuHLz3meDzulNwL09EHKRIcndoGXDl8oBYNloPUlgA4rEko2AN1bGDeU9ig6AOz8HZcvC9j5029w2LwXbpYXkUFQ2BIkricLvuVYlW1+cAvQAezelGS//OUvjVUaI0d6gb0CdZ3fsydRPE6ePFlYzDECIGfBZMUvHYs9Zyog/YADDjDAHqVKejnooIOMDMzznRXWK2cCioajjz5av/vd74wiAWU6Mg8WdqjvUPf5Vr4p7XpyjvL9dj9Az3fk+t9bbwSKCdDtx9Ba4hPDxse6xkGSSwC79dXP9NtnZyqRdDuzYZtcstuNqdb5+43Xbk0vK/LouSZgzvqFYD4hE8wHywO8+JaKsUpN/KrCu58jlVTkXh0zn1XJ/d9UyNp0swF0I2Hmrq6dfb/2Uar7V2Jv/Tj57YwvV5eG1Zp0TTR76ofW/pVtR+jSQ7dQhJw9RSg2aBkHMppZ5tAGLUu/mDv7PPUAULlosBYjNOeyRhzz59f0vyUBXQ6yNAIr+pPn7WZSsnVHYV/Z3Lasf/rKhcbYBQ2S1x3tLdQ3bPqqznKme0E5z1sKeyFAubcf1Ms6Q/vf0xaTQo1vej0oflCM2D9RhvFvgC4CmKWM46vYFwpt97o05NNm+o7VnLHArcpmV4DFxd2Tja7d/q14o2J/3lOqX9yW3zxTCUVNWrMftJ6hqJPSrOjmanHKVBKWyNIRC0nDBpRoy1E1+tWRExUtxPnsuorecaTc1gapcphSyaQBQQOrKhRa/K65UyNKGLo7Kb6Se12k1I6ntuVqz7NAW0WpbgNZYTWzEfChtOcbIKwvAPT0IbPptKyFnbHwWom5D3PJNdmmAYAGqLKBu/Kcrm57jT3qN10Z42Qt64wdwNKr6IBVWShFR64BoB0YC7xW6lzvFOP3N9xwg2GV4rNNYW2xfoJQ3G27OPtZO8gWf/nLX4y1Oj06PGciRhUMUdDOsbpjQPAWGCcEVARYY2XPVXBhPeOMM/TNb37TKFxwHYDujvWdvkClJ3p7vvGHcn2/t/6+H6D31pnpg+3i8PRGcCxGF6gfTSHadqwaHMgA9c4A3uzl9TrhljdV2xhPx7HrNRFoSkCwP0/eXiULXlP08Qvk1M3t6H/uRBRyvWRpx6TEcUurlZzwFSX2ukiK+QDpkn5x/zRtOvMPOi31dHbZx69MlAHkL0oN1F7xG5TUOp/BLYaW664zdlZppC1dT31LUlsMK1dptDjRygGVVqtvg5ZxAeTKjZpr/SA04/qAVj2XNX3iz1/IVZ3v35Nn/sbjt9U+RfRFZ5wQQFBEoMwAiDNmCLGse/YadEu/bh6+O9cLH0zPmY6CwhvorVig3DsUfAOLFMIQvnqfx3LaaacZYcwG+2SNIbABFBCMWIusPYSvviAo0Xave0PQOUMpBjgn9gB3jNdSB9gEUOX0QXddRf5xqEIL3+z081NS2+mXiZM0PrpSSwfvqhkr4iKoZSgUVoJgdEPK9MPDttbOG/tjjeXsKwD9zqPktqyRKocbgL5i5UqjC65xV8nZ4UQ5dfMVWvhWW9q3iqFKbfM1pSYelTPNV7ZvQ2HnvEJwx0WAMWQvo/jIpWDtrD/dAdABCpxDfinuOcff8wBnC+NhwTp7EDaUlw4fhB3V1wA68/fiiy+aIGVBFc6ARWtd50/q8vr9d0XRkWsOAakoj7H892Qh6Bvr0xuojTbhV04Ud1s4wwDLmYLEZWo/lmuo8kRyz1Q4X5G7jjvuOBNh3Vt++tOfmsjxxGzJxXBgHPkOCgFo7ShYfv7zn/fkkPaab/cD9F4zFX2/Id0B0NEOPv/88yZ1A0I6GxrwB90GYSmb9vSGFz7VzS/PMVZjP2WnMVX66yk7qRzzxRt/Vfj1P8qpXyhXYYWMHd7WA6SH6h5rE2TwRd/pdCV3/Y6fz+i1WSv108ema0lds453ntSX9YaeSu6oinCjfhC5XwYydwGg8zqtvTO+j36dOkVNKtMz539Bo6qL60PNxYlAz9xgfYMiBRUbimMhNdwIN3wDvyoEaayc6cJMPJnSpF8Wht5uJ/Wu03bQpI0KJCyvrZS+QJuzEdgZJxuBHSuBtagghEALhXKLsMu6z9fa4muR9oKH2PfMMW4RpLSyNLdCW8o76yoCJP5yxx57bC8YkcI34Tvf+Y6JqMzYApigksJQYS8TCOjJJ5806xGlmF/3osK30n+N+QJ09hdRhxF6EWozRR0HuDNO3Du5SmjqnxV57rKM6ThJbf2eO14PJ/fQo8ndNGhAhcpKykww05ZQqVY3JxVKJXTpAWP05Z22yPWpQL8Pv/I7hd7/l1JVY7S6oUmt8bgGlyQNtTQ1ckeFPntJbrRCipbJaVltrOnJ3c5Vatv88hDbMeMsszmOsZx19T7oLoDO2czZU+yCXMO3LGBnTwahwxMgDuUSio++UGw+cXJ0B1FEpPctUxoyr98/CrWgCoDOxq835JunfURW537805/+1N5cm2aN/4PmDvglPRqWbth3BIBjnfzjH/8w7yBH47YDsIcZ8Nvf/tYYwvAJt0pIrNq8w7riT0A4MgiuK977gP0ISwarN8HrvIW1zHvHHHOMMTggx+BzzlkLG+GCCy4wSjy+31/wou0qB6x/FPtHYO0IdAdAh9JEQBH8Wywo4UCxFBuCyGWL7v7kB0t05VOfaPHqlpyWdLq07cgq3X3mrgqFHLVM/ZuiL/5aJamGtVRFm3YNoO5IDgDdlRspkyqGqeWw66WRbbkhc5X7316oP7w4S3VNybVW9JTuD12kCc78XK92/H0O3QO/bnSiCteMl7P1kUptf0qbpT9SEuw7WZ5m/rm0AJjMCfPAIQw4z6VFzbcBy9a06MNFa5RKxJVctVCxRL0RqgHrtsxeVq/DbpqW7ycyvjdyQExPnLubYpFwl+vFz8pGYEdYsRHYufQ6A94IcVBEEWqw7ARxE+hyo7uhAhvgzf7JVQU4hEWAkNFVwT5oF1AKfvvb3zZ5XDeUgnXjb3/7m6FPEhQNGiI+l9099vmMdz4AHWUigZBYc5MmTcrKFOC+Yd+RZi1nSbQoeuMOchqXd3gUcH5Hcn/dk9xXq9xKLdYgxRVRxHE0urpUQ6sA6AntOjSpk3cdndtan7MhaQ/UL5H76IVKLvrAzGcimVD5gMFKTfiKwh8/ImFdh5YfKZFbPkROa6NUOUTxr91uQHvQAijn/sYSDdBF2VOI8nkD6OljAlPMGx2e/nrp8CjSvPcE4AlFJuu3L5Ri5RO3ig6bN5x7FqWaZSZwv3YlDV5viZZPKjPWw29+85sO0431HB9y5DHkA3y7rXsWdxjgGOYCBXBMQDeU4JxrRKa/6qqrjMHDFuRulLjEgoJBBZMMAO4NAMezTz/9tJHPqSv9fGT/k5oN9gxMEeRDvoXFnCxN3DNQ888880xzzzCHtKcv3DfF2Gv9AL0Yo7qB1pmPQBR0qKCCEZ3ywAPJ+72uWKsiFEw2PQdDJtocQv7UOat09dMz9MHCNTk/f8H+m+rbe4/T6lXLtfTRX2nLhtflrJ7XFunW+hQCziOxtvyx4RJjaUiN2F7JrY6UO3CMFKtUqmacVFaT9XtvzavVXdMWCMC5tK5R/246Q8OcujZDvV8LOrXnIghkqMstGajkJvsqvueFUvUmbX3xWRh3hHhLxQYkWr/yYkZbrWtq1Zl3vKsPFtZ7kt9J2wwv0dfGtmji2GFmDXC43/raZ7rmmVk+e+T/sbKI9OpFe6skD5DORWUZBvw9X4YBFxjWPmiNXKZcln3Zms56srmr7d+ZEWspJ+4AAqn1Y/U/W11/Enogmn9vHtiu19o7a0DAJ7gPlH7O3PPPP1/nnntu72xsllYFVRjje0nQJ84vIvV3JryzDlmnUDz9FOfDBxR9+KwOj36SGq2L42cq7oa0UEPUpBIlFVZCUeFGU1ka0ZGTRupLQxs1bNBAw5YpVKHt5Mr+9IM3tW1svoamFuvT+Uu10T6nKFY7S5HnLlmreF57YTghuRXDpViZ4kfdInGfBSjIBtzbnFe77rpruy9/gCqyPvp5B+jejlsrsQXsKGmRcyzoBKixjrmT+wpAx/pLXKF99923qECMNWjBOuOH4sObzs3LUvOzLrlzMUj0NFPhrLPOMmmIyR/eV4vNtoFSgWjtKACQ8YnxgUzJD/OHVT5n3I++OggZ2t0P0D9Hk9nTXcE3EXpKMS15aN/Jp0pEx0wFCwjaQLRzCFnZAMvclQ069PeviRhpnZXKkpBuOXlHjSlPafqbL2mvmmUKvXuHnNULJKLyAg8jpW1g3Qm1+aIDcKG8A8wHjJGqxxoLRHLzg+UOWaeRTP/uyoZW/f7F2ZqzokE/XXahtkyunw4j5xznAuhU4AvwO0pVDFdy84OUnDRZ7rCJ7b6HCAnMA5pZQCbgyYJyG0QpZzvzfGD+qibd8+YC3T51gZqzhOQvCUnjqx2tanRVWhrVysa4Vrfm+cEcrw2tiOg/39/LV+UIBAhPjBnjB93fRmDviiafjwOosOohcMAg6IpPp6/OFPChbKAcRQNryzs2ACOULn6oxQVsoqkK4QALBALE57UAarFuYBlBaD755JNF9N9CUkO7a+z8AnRAI9YeBG4sTX4suyjF2M9BfJKjN+8lZ/m6M/1fiX3118ShWq1yrdBA4zoVVdKwsGKRkKrLYkJBvJGWGaUUDIZCFPrLWWGtrJxDnOlPPfWU9t1nH1U+9m2FPn1WipYbty1TEgRLTckdtJniX79bKvefcx3LJTRYCopIv0oNv33tDoCOLy0yRXdQ3P32m+eYS9plATvyD3uVs5Nx7s6gaUHa7X2WfYTyBktqdyqXGSsL2PmT4o0On85MSO8fa4L3tttuu3y7XpD3TjrpJGNt7sv3EuuYe/7yyy9vZzDhpsEc2WCf4Aus+Cj4NpTSD9A3lJnuhn5CDcSXBOsW2v6ugo5MTeZAJNgMh3m2grABEAKooyyA9p4pqNHJf5umqZ/V+RqZqlhIOw1J6erJX1T1wv8oNONpOXNfk1M3ry2iO8A4UirXRHB35OCvN3hzOZFSJYdtrVDjMrkVw5TY+SyppLLjN1d+qsj/HlLTwo/0yYq4lg3aSS3Rah0x42JixAcrBQPoHT/rhkuUGLKVVtVsp1mxiVoZG6Nhw0cYYM6lVuyLtTme0Lf++a7enLc62HgEpiAErF7S7adur53GZmZHWNo/oNxESR44sJ32X2gQzUVmlVPpNP/gvSruG7lAuQXn6a0AGFGwGHR3Of30043yAx86ziGAB7S8z0thfUKJ/MMf/mD8Fq+77jpzdrKG8UNnThCkoIkW42wv9Dj6AeiwA7i36BsWR7/KZWiezH8QK2VoxpOKPPY9CX/uVEL/iB+g3yaPVb3KlJKjMADYnPiOqspiGlQR0xHbjdQ+Q5vMeHvppvmOFf3l/qQ+LH9elhMAfe+dt9GAh78hp3aeRM52lM8onpMJKdGg1BaHKXHUzb4/j9IDZQDyAEDMAkffFfh4sLsAem8AY7mGgzEmcJkN9sjYeIPNATp7W4FBRp7rzmS6YrcZmZH97GUmoOjwMhPS/eMBjLwTRElXjH5AGYfZdfbZZxej+m6vk7nIJE9y96CwL7as2e0d7uSD/QC9N81GH2+LpRBxISMcocH1+gIXontQt6jfT6oj2oBAj6Yzk9JgVWOLdr/q5ZyscG+79xhXrT+csL0qG+bKmfu6QlP/JKd5lVws6OEyuSWVcuINciPlcoduodCKmXLLB7dZIloblNz6OCW3+7oUavNddha9q+gLl8up/Uxuc73cRLPJPdsYG6SF4Y20RdNbnRu8/QBybwd8Wc9zzxTBg1Jjdldil7OU2qR4eUCb4kn9/LHpeui9Jbkb1UNPlEdDeuNH+7R/nYuEdQrDACsVgMYGeysm7Z8GeIPm8U1o/r0FTGUD5bSbNmYD5d5ptRHsCwFWgi4XBCDmFSEBPzssFr/4xS+CVtNrnz/iiCNMypxDDjnEBOGEPosVAwGaH4R/gOnvf//7Hs/962cQcwF0Cx6hTLJPgvg5IpyzFgLlmnZdhd65XeHXbjCZQW6LH6ArkicbcA4wN2nNsIoqogGxkAYPKNfkXTfSTgMbAtHps42NpfAT9I79k95fgkLtscNEVT9ympRsbfOZJw2bCTMaMoroxMHXKjXxyJzDz16HlYA/NEoM5ADYL/w/Sq5CFhu13IKpYrA9eou11M+4YSRh7wIcCcrFOgV48n/cP17QWazYMH7aaZ/BSkqAMijuvaWkMxNQbNlAfRgkULRzBnAmFpoREnQMoILjG37KKacEfbXXPI8CmHzuDz74oFEGI9+fd955xpiRDbD3msYXsSH9AL2Ig7uhVW2pKGwoLjQiQ0Kfg2peKM0tPj+A7iC5eLmYrNIA4YDIxLb87tmZ+tPLc3xPFfh28q5j9IMDN1dJNCxnwVsKvfsPOQvekcKRtqi3CDYVw4wFwmlaodTQraSSAXJWzZZbM17JHU5Ram0AuegTFyg85yUp3iS1rlEq5RpxDZN8S7hSn7oba+vUB5lBelfAuX23QIA9OXQruWP3Uny7E6ShW/oez84efHNurU677W0lgvYza6VBHfr9d+O2UyZpfFVbfnbWKNp2C8p7IiUVgIoAcuxJBLV8cwz7H4HsTyKUA5a8PuU87ReUe2smaix9K7SQn631tBtQSiAerDwIZxdeeKGJ5N4TNPtCzEe2OggAR1Rf8nvTb6zJrF1+EE7pO8LTJZdcYlL49PaCkE0/0gv/D9MEwAplGdp10IL/Ngq4XXbZxfer9S0Jvf7pcn36ycfadMGD+u3S7fWpRqX5HJEnhKSYriaMHqwrj95GiZUL2vex7495HvRGpe8sONtzzz1n+lPz2i8VmvWCNHCsFK+XEq1Sa71UPkTxY2+XUDh3UgAtWOkZe3JxW1YCgJ3/g5VRiEK/kDGYC85a3IYyBQILonjJ1q6+BtAzpYRj7L10eM5RKPAWsHc17Wm+c4oSgWjhpPXqrYU17fVfZyxhwPGDsoszsqcsu8jCUMOxovfVQqwTornbDC1Q2SdPnqybbrqpA8unr/Yv33b3A/R8R67/vfVGwAJ0+wsONS5lBKFcadD8DidCEdEnoWAGKSgN0CwD7gHoaD3RtCcSSR14w6taUIePnb8SCzvaf8JQ/eTQCRpaVSJn5Sw5H9yr0Pz/KlU5wtABndo5chpXyK0aKXfQOGOJcJKtSg3eTO6AjZScdJLUvFol95wgNa2Q07DCgPKUKyVTUkStBqYjqq1QlYZphTrEC88HtBYIjOccJSeixGYHKX74TVJkXf71nO95HiDt3H7XvxbklR59tujgAoAAACAASURBVCYq/WqPdWnRekrY8Q4Ca569AsAkVQoskkIIq34GGuEZIMSPN1FIPqDc+73uoBXSZpgPKFqwPqFcxGKOFQVh8rbbbvMzBP3P9PAIZALoADgo7SjQsOzmy2jhHmIt7rbbbr56+frslbr26elauLJBjhNSS0pa0xIX6tgQ575RwbYd0CElNUBNunKvmPY74DCTe561l4+vK3cw/eVuhtLembLwhRdeMIC6JrVSkaculurmrm2Ta+KpJHf/rlJbHdFpfwGAxDAA9AHEvRbafPz2s32M/tAv+sc82kjPsPgsTZm9yxogWjeGAtrk14Uh/bsoAag3nznwtUAK+JDfM9LmELfjRRPSo8MXsFlZq2JPotAhzVpfKNxnWP1hhLD+WHPcq6wvO375nitB+09b2LMA2fTAyUHr6snnYTB94xvfMCnjKE888YTJ2Q52yJaVqSfb213f7gfo3TXSG8B30gG67TIXAOmROEywfHFZ5lugzPGTb6AILiUOVgRwDgXojZ+taNTJf5uqpQ1J382KhByNG1xmLBzbjBrQRgmc87JCSz6Qu2ahnMXvy2lYqtSgzfBIb4vsPnQLuQPGyIk3K7HjN6Vki0ruPkHi+RYbsd3pECEecmFTqkTL3CoNC69RuVraxLh8ADrvZQLp3roKDuIdueVD1fLFi+ROOlFr88h1Os6fLKnXsTdPyxnAz/dkZXyw8Nb0d3+yt6Lhrqdd61q/1n8bawrWdMAx1nSbQ7zQ37Gg3KZFs/V3FZR721ksQdm6JQDKORsA5VhW+bHjRW5Wonzfe++9hR66XlUfwqcNZMi4cEYyDnZ+AV09ZS0KMlBegM7dw72B5RxlFQrjriirgjC5lqxu1hm3TdPy1c0qK4loTVwmv3nbIc4Jzw3RxpxKKaSoEroodp9O32Sl4ic9YHINwwILGi3axmvJBJYzjSMplwCgPK/6JQrNeErOihnGcp7adH+5w7fJOvyWNYdCnvgQpEFMXyP4RrO2ugpybdA5WB3UxTwie6TPJ21CsWEp3igPLMUbGQTg7pfizbnTVyKjA9BRJAZhKnh9sOkndwZnoDclmd+xCrJHeZa1zblKoLO+VDhLMPJwnjBeVtHB+rRjB7jkp1hjx7whx95///3aY489+tLwdWgrijPODu4aG9Gdvcr/cZZsqKUfoG+oM1+EfqNNzFbYdFwcXNIIe9De8/EVwwqOX2RXKZbUgdIAihIX2ZpUVOff9Y4+WtxgRCa/JRqSth41QDuPrdH2Ywdqu0EpDY81S7VzFX7/LrnxZrmxgUoNGCVn4Bg5q+fLrRyh5LZtfujRpy5S+H8PS4mmtk/iy742fVsqhfhGbN+QGlRi/pzrDtOnqVE6JvKyx+bit7Vrn/OC8GxAv+BAnSB6ZXp521/othUTtaIxqU0Gl2vbUZWavbxRHy9pUDyZUmNLQjOWrx2LgN3qvsczg/sfHbSZTv7CRt3XjABfAqyw9wApXOj4oRYCZHlBOcKCtZbbtGhdAUGZumf9TbEadLUwJghVNi0R55EF5ZksjX/605/0/PPPG+2+LS+99JKuueYaQ9HEqkoeV4L2dFag70GTx+2GLBP4spNf3Vvuu+8+XXrppcZ6CpgkkvpRRx3V4RlS0vBtvssZRlA3rxWKuSD1zl/+8hdDz/zCF76gG2+8sVPBnfElSNxjjz1mQA+CJUpVAuT1NQsNaxPrFnRUxhrgAaDzujjlu4b8xkLh+3947A3d/VGDEgprdYtXCZzp8HU1QI16tfpyVUSl1rPf0mfz5hulkd87j3nnrsVizV4nkKGfvc5aZh0FVaCzj7hLuVNRIhiAn6GwlgGOQQLrpVeD4ggwBxuIfUG//AaJo52Wpsz8oazH9cda1zujKfclgA5rCsVEEICePs7sGS+l246VBexBU5J1ts9QnLB++hrA5ExBeQtA9xbv2DGG1pXAWtcLGVmfvc5dTsaNruyrfM/BQr2HHM75gHsVMgP9Im7FG2+8UbDsFYVqa3fW0w/Qu3O0P+ff6gyg2657Ldho2tGYBRHisSZwoBdKQOdQ4EKjHbTno8X1evj9xXplxgrNWuEfKFbEQho/tEI7jKnW8TuP1mZDKxSa/qicRW8rVT5EREFX4ypjaU9O+Io0fK0f3rLpij3+XYWWfrgWmK8T2ty1AD2usJoVE39iZ7kzub/2C72pSaHP/GVMy3fdFQOkS2pRREtTA/VJaiPdl9pbT6S+4Df3W749yfFeYazpW42o1H1n+vdJLVJnOq3WAgsELIBXPkqy7gbl3g4hoKNk8AtW0geDtltQDuhBMPCC8s6ADP7Zd999t0n1YgtgnRRBBAvDBzAXQOf8gsXwrW99S+Sv5V2Cz911113tPoSvvfaaAdpXXHGFAeXUedlllxkhDJBN+fe//21SoAHS8UH885//bILsIOjayPJXXXWVAfZ///vfDVAjoB0gDKtEpnSIWIWhGUJR/v73v28AENYt3mesAPcEcrIWjp5Yv0G+STtpN1RoLFqA83zWe6Zv+skmglUN6u4Li0J6fHZCdU2JDMSn9UH6rs4n+nfFVSZVZ2rCYVoydE/N0ibaxUd6IcABAJY7ErAchB7K+sKfNkhgV8AH64V9w53cGbU3XyYA44/AjsIBBT3z6I0b4Begp88jbbfWdeYTpo834rk3ywZnjk1LF2QN9sSzxYjTwVhZCzFjxXx76fBdoXRTL2dSULfFnhhb7zfZZ7AwcuXl9roSMHasV2tZt24XfhRomfpr62Jv9NWYKLBqUI6RxpTxRIGBywr31a9//WujgOD85pmDDjqop6e9W7/fD9C7dbg/vx/jAsXi4rdw2SFMcimi6WVj+ild8cfLVj9afbShXEL4pqPFI1jb1U9P131vLdRqHAbXFjBrNqNzSdjRiAEl2mFstX52+JYqVWtbKrZlH5uAcW5ptZIjd1BiyNYKrY1abaqtW6Doi1coPPMpKdnc9qW1n8Q3sUklajUxfsOqd8v0hruFHkjuoWsjf9HI0MqCg/QmRXRJ/JvasqRWJ25dptKGuQrPe60t7U4hStoA8s9XkxN1ZuL7apT/NDAmBR0eAfnS/XP2JRhw33xYuR76dhuA6s0Fqx70PAQjQLqfIFk9Ccq9Y4mVDoVakOBcFpRb+jpWYfrMPg8S3AerNtZltPqZCkJWLoB+8cUX6+GHHzbjbwvWc0AkwJxy/PHHG8qk11J/8MEHG6EOIE8BqKMUwPfQFs4urPcINZzHWOcJbsc3KShQ6TPAHeVAevnnP/9pLPL33HOPAfS2MH4nnniiAV+AdUBgsSibhdw3gC/GlBRf/OQrBGdqEzTW//73vzrggAPW+7WX7s1358SrdOE976s5a7TLjgcYnKlzQg9q79h0RcKOliUrtahya43c93SNGTFC4waXKxRaX3tqfXmZJ6xpQdM4vvrqq0bIZ434KYwvCggCtLH2cina2bcAlKDKdc6r9957z1iFWfPp7JZ8Abq3j9SBMoozkX4hE6DEstZ1Gw29L1gpiwHQ08fKm5LMS4e3rgNBMocw3rC7rPLRz9rrDc+w9lFmASD9Fs4G1pI3nRtg1KvsCLJvqYtzHhaYn3vcbzu78znkbiLQMyasJfrE/1E4L8AV3F1gBJ7ZkEo/QN+QZruIfQ0K0GkKlyKXCUGsSKeAUJjrcCJqKxd2V+hbmYbB+iiiyeWwROCIxkr03vw6/frJT/TRwjUmCU42gG48jx0pGnFUGgnrgC2H6Ou7jNW2owe0RcBNtJhI7m4o0iFwFkKjERzXLFFkyjUKf/qs1LxKqURrO/DGat6kmEnF8647XgvcobovubdmuiN1efhWHRKeqpiT2b08nylvVEwntF6iZeUT9OPt48YHyOS1Xz5dkTf/osgH90ru+tGRc37LB5BGLzEjNVq/SJykKe6kjJb1MC79a+sK4o6Qs33mgWCg3FvniTuP0iWHTvD3mV7wFNRo4jFwsWM5Swdd1pfcBnorNn3dz5D4FeZoO5c7oBxQj8BoQXm+9EwAMznQUSxmKn4AOuljACjXX399exWA+uOOO8745yKsYQG/4IILzI8tv/vd7wyFHeoyAgs+ewBpL+39u9/9rgFMWPg5UwFbb731VgdAROAdBJ1Mge6gwt98882aNm3aet2Dbo9igjHoKwDdxhUoRgYDwCIW53SLjqXT24BiAJam1qQOuP4VLa/vTIHd8XDEH71UcaOUbcvp0XbBlIWl7TYeogsO2EzbjR7YPk82ZRx028022ywvZQRZCjjruYs7Kzb4JKCKe3LMmDF+tq5Zu0Go+lSKsM4ahkqM5Tw9F7WVIzL5oPtqVJaHAAQWRPEna4kzhD2F1bMrFuOutMvPu+z97kz/hTxmo8NzPlswZdkIuZSgnM/IgfnGFfIzJsV4hnXJXsm1Xzr7NuvKKoa4rzJlIehM2cH9xn7nPMo3AGIxxqYYddo9WIy6e2ud/QC9t85MH2tXPgDddhHBFIsShzwgnQs/m7XDWp6KlXuSix6QDpWWgw+BpSme0v/dOVUvzmn0CEvrJqg9/q4jlYRDikVCGjekXJPGDNQR243QqOoypVxXNeUxhUOOsW7xYwEQfQ0v+0jhd+9QeO4UOfVL1djarJJUa3vk9qRCqlWF5rhDVa9KvZCcpC+G3teWoXmqUoPKHYS/lMKuBIDNtyAIznBH64jWX2rCqMH6y3FbGEBi89capgP5fN/8q6Jv3mIi1ZP3vdOodT6Aebb2AsRXqUxfbf2Z5muMasoiGlQR0+wVjUW0nOc3elcftZW+su2I/F7uobeg31n2CNRr6GV2XXrTohXLpzxotxFiOAMy+SvSXs4QC8pps6WvFyKqPvRw6OhYAjOdT34AOufbqaeealLK2ILlEpo6IAthDyUllmqs1rbceeedOu2004zwy3NYbaDHe8fhV7/6lQHenF+2Tqi5WFhsOfPMMw1Qeuqpp9Yb+ieffNL4xhMMD+AJ2GQMAaJE191vv/0M7b6vUNy7ciflWpfsG4Kq4ZdvLcfWgg2NHkurl05/85TZuvbZT9sVi9nrX3dY2nSbKGYpcKhQ1pZGQhpRU6lLD52gUQNLtHz+HK1atng96neuPqT/HkYAd29nFkFLoQdUoGgKovwgVRmWvp133tlX02zedhRWuJ9lkwkKYUHvrEGsI0Av5wp7EysfYMgbQC2IxdhX57vwEG1FjiHOT08U5DkvHd5GOM+Wp55xxXXB77roiT5l+iYKS9ZmIS3XzJv1/edPznv2WDbff+Yady+e867BXPFJ0vuD+xLsMO42+sR5782rjhIGZhb3C3cKCn2YWDC7bEF5TcwTb4GNgyxtS7a4KNRni93n1nhVSOZTb1k7ftvRD9D9jlT/c52OgA3I05Vh4qAGDCLYYCFHqE4vgAm06F4KZle+me1dLhi+xeUCDRhh4dpXlunNxXE1tqY60NwjjpR0pWhYCodCqi6LasSAmLF4NLQmNHJgqYZWlmrCiErtuelgbTq0wny2A0hf/olir/1OoUXvyi2pVGNDvRL1yw34xkrcrBKtcAeqwiGKe9L8X4XiJoRc1ISP4/8cNSuqMrzVHWtzCTY6DW6JLkl8Uw+m9tKR2w3Tr47c2gjkXKC4FyC8obhIt7aGX71OsSnXrOPmt5/Iwb7f2dNQ2Y8c8G8lI6UmPkBzvPD281hIOv2LY3XTS6QYCmZNf+b83TS62j9Fv3Aj07Wa0Ewzv1z2MCWgrFJ6Cyj39i494i97yIJyzg8ucwvKEWwKebkjkGHptt9JH3W/AB2g/aMf/aj9dYA2EYxhNDD2gAAEIXzybLnjjjtMoDaAoQXogHCv3yb+5rfffrthRaSDflsPvu8AJcB4emEcf/jDH5oo9SgHoG9i3Xr55ZfNmOLjnsvfsmsrsbBvFxOgI7CSN3z//fc39xFBxGyE+EwW7PrmhL55+1t6d/7qHJ1cn+7O6U4hujtnPMA9oUg7uwiF7O7jqnXxwVtqi+GVeQ8izAnWX7Y5xkqHv7lVQORiu6U3xG/ueOaNuwaramd52239xQbofIc9x77Hr5+59wab49/pqdwKee4EnVDGjjb1FED3tpe58UY45++wIbzKDc6YrsQVCTo+hXoehRaMikIEnczUJvaB9f1nvfFDsXR47mcYKYcccoi5A+2a8xOfxPs93KRwg4I9hevY1KlTTYwUlMKHH364eZTf4wLFM6wrFLwoc7lnrMsKAJ2749lnn22vHqWBN6ZF0LgohZqrvlpPP0DvqzPXy9pdCIBOlwALNnAb2nw0515aG75oHPDdERDDS8EnSEVZ5UC901Cle95coPmrmpRIrYNwsNTxQY9Gwqoujch1pNaEayKTD6sqUUVJWCMGlGrjwWU6dscxGlVd2j6DHMTJ5npFnrtEkdkvyq0aJUVKNHvebA1LrVJESUN3X6xBBjKO0HLFDCBHcLMpeqguJSzt+KtjUeedNtHOfwHyznOH6qzWC9RQPUEb1ZSrPBrS4MqYth1RppHJpUq2NhtqY/rFFH79D4q+9MuupYHL1tQ0VgDt/NQdpWNaLtMara/Iyd7j7KB7u1EVuvSwLXX2Xe9pWX3c/6BJmjR6gO46fadA7/Tkw9Y6zn6zf0dIAgAjFPcmi5B3nKC8EjEdCyUCMz/sHwvKEZSLJRwDwLAiYynN5G/rB6D3Zoo744wF529/+5tuueUWYwVjPPGzPv/88wP5Wvbk2vZ+20/g0nzayp55+umnTTA/a11lTXYmrNe3JHTx/R/qPzOWK5F0s8QysQC97ZxyhDK4jaPFPcC5l2rnVdHydQcjiuFfHTlRX5owJK89wL5CmLZBBr3jYq3ZNphqLn/zTGPK+QJI78zXGAs9dzx7DH/zTMEM0+vuLoDOGKT7z3P2WIsx9G6AEvKK9V0HiHZ3vAbkJ8bRa5XMZ40X451Myg1kK+4hXBiQ7Yp1fhe6P8S3YIyzZS0o9PdY59b3H7COEtWymXB/QllIW3LFJ0lvFywsGFzEH7GF2CUopGFPUWBhYVU/55xz2p8h3gnuCwB3CgD9wQcfNG5WmUo+cVEKPYZ9rb5+gN7XZqyXtrdQAN12D0EcgZgDCY0d1E8ObjT4CI2kWemuwgWMVhHh1eSRrh6i56cv1z1vzteclU1qTSYVT7gqjYY1sDSiRKrt72taEqoqiWr8kHKtbIyrujyqIRVR7bXZEH154rAOzefwcv73iMKvXCOntUlutEzJlgatrFujUqdVKzRAy91qDVatxjgrFVbCWFJCxp4eUkIhQ4FMmkByJYo5SVU6zYEBum1UrVumQ1qv1GIN7dBO0sqNH1SiXWtatNumg7Tn9lu1xQ2oX6rYTTsrnAoGbH3NYWeUfVda7lZq39brIPpnqa5zS3hVSVhXHL6lDpw4TC/PXGEAOoyIjqXzOh47exeNG5K/9crXOHTxoUygnCrZVwjcUNFsqqQufqrgr7M/sL4g5NNOhGDABMCc8yAfwBC0kVDDUV4gZGYSuv0AdCwRjzzySAc/9u985ztGqPEGiePce/zxx9ubiJWEfnqDxEFthMpoC0wffMy9QeLwYyeNG4Xzi/HKFiQu6Hj0heeLBdDpOywEqM7WouzXL/mzFY3mnKltbNEdU+drZWN6PA/O9TZgbnOj8z3O9zbLubd0PBzLo2GdsedYnbX3OEXCwdSz3K1Y57xpo9h3+JpDffVjze5sTcAQYQ/ttttuGR/DQo9fb9Agdz0J0NM7AtD0+mPbFFsWsOcb/yLIXmO+aEdvBOjefljlBkwJG928s0j6QcagO56F+YQ8GMTNo5DtQplO9g5ih3Cuwz5FgcRd0ll8kvQ2cI8ceuihxn3JFhhe1157rfFttwqnq6++2rC4bIHhxZ3F2UABoAPyGQ/ORBQFuF3ByqPkExelkOPVF+vqB+h9cdZ6YZsLDdDpIgc4wjiUTTSrCKD4V2bT8hdzWNDq28sXrSFtqU+G9cqnK/WfT5Zr+pI1SqZcYy2HzggyXr6mVRsPLtfgihK1JFJqak1os2EV2nRopSZnypfdsFShN/4qLXpPTrxJyebVWrN8kRKppD5MbaLBzmpFlNLGziKVGHp7G2iE9ghIDyupVkW1SpWaktxWpWrRAeG3Va4WYeEP4ppOzfclv6gfJM7OOqzUVx2TTvvCcB3a/JjGv/fbQN/wNV9+G+1KJ7dcqCmy/o2dA2qqHTekTDcct63GD2lzOaDc8spnuuHF2Yqvj9CzNhelxbQf7mNiD/S2kg2U006EIQvO+TcCND9YrnpDYf8DVLFcYSln/2Eh4O/77LNPt1v5oRMibCAYWfcbFIkIxBSEI/y3sbLTTiyRCDooFUjRRrFp1oiiDo0QAYco7t40a9AGsbRDWQdwP/TQQ7rkkksyplkjNzs0dxvgDbcc4mZQAOKA9VtvvdUwkRCW8JvOlmaNd2zeXvqFcIZyEmAP0IBef/TRR/eGpeG7DbTdBjf0/ZKPB6E8cyfg8oM7Vr4KIu6KKx7/WE9+uFStyZQ5p4m7ESMkqZs0KSnbfNCB64B2E47UUzoekAR3B9ZXloa1UQ1srdH6+i7+griRSQAAaQVqxo4+sgZY236s2Z0NHXc56z9TOi0Ug3w/Hwu9lT2KaXllvjNZ0HMtFfaMN5UbbbT0bkB7UDeBXN/j96TcYs0X2w3QT1v8PGNzzKMAsgHTYO9w9iNr2fEC+PUmZhcxSQqxL/yMUbZnyAgCKAaUc3fff//9OvfcczuNT5JeF/FQuCMeffRRc/fDpDnssMPMPWvjomCtZ39iIYe9insPdxN3slWCEkCUs4J1x14hTRqyO3cSaz2fuChdGZvPw7v9AP3zMIu9oA9sVGhVxSjUy6WD7yQXGgJEJhpeMb5t67TCCwIEbeFSoR1Y8rk0Zi9v0FMfLTV/frAQCqyMVR1/85DjmEi+8VRKoweWaq/Nh+iQbdZPZdOaSGnWB6+p8ePnlapbIKd5lTZxlkktdZrTXKmhTq2xm2MZH6pa43veFuG3LbY8glyDSrXcHaDnUzvp18kTNUyr9MvIX7Vf6J3AweMWpWq0R/xGX8N6YfhunRd50Nezvh7yC8xtZWst3ke2XKx3RfT3zsupu2+kr+80WmMHdfQZf+CdRbri8emdpESi3o7gf8KwCj3w7V1zfbJbf4/Qyr7xBnqjAemg3NsoLlXWdb45xgvRQZuGxoJy+gBtGAsBghr/hnbXEwAd4QMfXSu00F8AL4A8vZBPnEBvBITDwsBzthBlHcs2ggvUQazqgHRvwZcPUG6tDoD1dHCM9RyrBoIZlhwivQPs27eE65qgPVhZAN5YNAgGxLOZCkIx5xmCMOsGMGED7wHQoVA+88wzRvgvJhgqxDqydRQaoHPPIXQCNhkH9kqQXOPZ+oZy970FdVq2pkXvLVit56cvU219k5qamtVqVLFe3/NMtWQ/MAms+ccTt9eOYztPZUpeZ+jG+NDDVsGiDigHOGWKnh50ntjTKLOg03rXKKAdWjaKDphyr89epec+XqpFdS3aZHC5YTYRcDVbsRb09owoQRvm43n2GHMeNEWct2qvPzag3QJQa11n3+Wr6PF+B/mEglKuLxTkOlgHrDNv8QZM42yyvv4WsPc0HZ4zHZ9t2tFTBT9x4o5YKrqf+CTpbeVsh7pOPZxpBHabPHmyuVts+jYUaCiUYX+xzwDpuD4B7LkXMxUUvDwHg8v6q3uDodp3OouLwjN96b4p9DroB+iFHtENtL5iAnQ7pFxopILhEuMy5yDpLkExnf6HphcBG8BAW7hkEbLmrWzUv99coAW1TWpsTZqfypKIVjbENaA0oomjqoxVA8u6tySSKT32/mID8pctXaiqlsVKYP2JlOmrpW9pd+d9lSdq1RgaoPKKSiVXzNBQt9bYVyhY0bGp16tUC92hui75Nb2Q2lEVatKuof/pIOe/Ojz0X5WF1qVvy7VUAfq7tP4p12Pm96eEntLPorf5ejbQQ36BuoeS/pPWU3Snuy66aPr3LtxvY52xVxvtKr2sbo7rq3+apqWrW7Lmu09/55KDNteJX/BnpQrU94APIwCyD21aNPt6Z6Dc+4l8cowHbGLGxy0otz7lCGaAcvY3wphXaKVvAFx8fwsBGoK0n/GlPQC0viL8BukfZxnB6RA4UYTyY9OGYTmB3piuSAhSf088W0iAjiCKpYo7h2BhBFWzZ3+h+1bXFNfMZQ2qXD1LI548Q8tbo1ocGaObmr+saUnOrkwHY/bDEnLPE+fuobFp94633R988IGZc6yW3G2Fzh/P+QJ7g4CIFNYW3/xg/irVlY1SbYu0oK5Js5Y3mFSl5bGwGtben2fvM84EWM1UugugF5pdxNpMT+VmA4AhT6AsyaeQipY12lfOKNwekO2yKQ4ZA6+vP2OGwpHz34J1xq0YbIRs4097XnjhBZNFw69rSz5zmesdgrYRsM1m5fCTgjNbnShAAOQoyWBkoThGceK9fy0jBMUyAUWxunNWZCtf/vKXjcKPQHRBKe7Wvx5rPQptmAFkIdmQSj9A35Bmu4h97Q6ATvPRFHJ5oSlE4wzVvDvyP2YKoGOjm6OxBkzg84XvDeD8wXcW6ZMl9VpY12ys51VlEe0ytkYHbj1M24xaP6jZzKX1unnKZ3pnXq2a4kkNLItqRW2D1iQdVcdcHT9ioXavf16DWuZqlTNQ5cM30+hV01RWN0NhpYjbrmY3poQT1uuprXVp4ptqUUxHh17S1qE5SpYOUmNTk3YPva9NnMUa4DSvR5ZMXx4vJrfVaYl10aazLZ+tnTm6PHKrdg7NKDzF3X40F1BP8xlf5A7UTxJn6D+pHdYG05NxAThYL+q40Evaco+vqepL52fs0rQ5q3T+PR+orskPI8TVPceN1lZbbF4Q60fQLWpBOX/yY4tfUO79HhRuLtHuykcLjdqCcmhy7GsLyrNRGXtSMOLbUJqJag5A25AKNPsrr7yy3U++r/QdodO7L/JteJKbKwAAIABJREFUN9ZTwCTzzzmP0IoPKiCokGmWvO3jTsWvVNOf1Obz7jIZPRqa4zq6+RLN1Ji1zCn7hmX1dAbSHf3o4M11/M5jFM3gn46gjUCOEE7ALm/05XzHLf18Ia4MyrXVa+p12zNvasrChD6tc9WacoxbEZk66MGgiqgmjKgyMV3mrWoydP2rj94moxtRXwXo3rGxSkrAJ9Z1DAAAP2tdB4D6pXcD0FmfAKO+UGAYofxClvNbvPnDGTPuEtgeXjp8IdgI2dpjFcUom7pTMZDeHoLDYTyC2m4LTKnO4pP4GWMYapx1WOgzFc5VAgWT1QTXqUyFOx0LOqD6sssuM0oWgL3fuCgWoEON/81vfqOTTz5ZRx11lJ/mf26e6Qfon5up7NmOoA3n0Cp2we8HqwWHMcAY/05omWj7/V5g+bQRawmaRSLLpxcEGgQPLlaEN55B8z9zaYPqmlqNZR0qNRHRS8jFlqEQNOhPL83W3JWNGlIRM7T4BSvWKEE4ODelASVRbVSR0OYNb2tY/DONiTVo29gSNVdvpvcXrNKABCk4HC1zB2iJBunO5AEa7SzXceEXNd8domHhBm3uzjYW9Wo1qklhjXMWqdppzgiqG9yoTo3/UG+4W+Ucru+G79VZ4UdU5hQhQJz36zmCxeVsaIa6EGubz/1YKu9IoYRiev7dH+TMtX74xME6cjR54GUsAF310/TTBy8o59KzPrZdTYvG+mVPZQvi5KdtuZ6B9mZBOdQ6S19HEPW7f7FcIIR0h2LO2x/r13nfffd1oOnm6vPn4fdYVlDcsD56UiANOpZdBejsNay+3DPsb5uCkHbA5sLfn3uh0AXAguDNngAsT3nxOX1pqyEqv+9EpVob9XhqN90T30vTtKWJP0KE9yaVpoH29VvFEbrLxtW65NAJGjekoh3wIkwjCNNffMSLsbc4X/Bpn5EYpDv+O1cr4lHF10aoX8cIaDvk8bqvLgtph42qTQ4TlKVXHrW1obynl88DQE/vE/KUN5Ub8+NN5dYZvZv1yrrpKwAdpTD9A/DlWzKxERgvC9hZz4VkW3KuoKjFpai7o/R7xwjXJ4xVNsYJv7Np1rLFJ0mPi4JChyDI3KmsOeKo4MqEUcoGjCSlHGcgimn+JCAcrikEdWScKf/3f/9n0rLhfsodjw86bDdcZ7oSF8X2d0OkuvcD9HxPhP73OoxAdwF0hHMOCev356Wac8AXWutvO8kBhf85GsBsxeZxR/NtlQh+l8lrs1bqmqdnaHl9i4ZWlpjXAOitbkjxlKuq0ogB7s2JlGLJBm0eWqgjyt7TsthYDR1YrkffX2oo2ZVqVIXTrHuT+xjL+UGhaZrtjtBuoY9MsLiVqtJANSjhEvk9ohK3STuEZqgq1GKEIqjyy90q/T5xlO5KfXltgKLOe3F79JfaI/Rh3hHj/Y5Rh+cygfX1Iq93UnPa+617X6LkbutSiCRSKX35+te0ZE1r1kqO2X6ErjhiKyPUcllhCeBCQ2FUaO19sUC5t3NcztC3MwVxymuO1r4E2GBvAPIA5YBxrI78mY9wg5KOYDZQcbuzICBw9iD4HHjggd356W77Fn20bAy75vg/hDKiykNx70lKZ9CB6ApAZ61Cabfzng5aO1PaBm2n93n2ig1Ah8IXYAGFFTBQfdM2Umt9++NzUsP19+RBejE5SQs1RHFPfvTO2hALOyqLhhUOOYL+Pr4irkPGx7T16Jr1fIG70hf77qK6Zs1bvFx/fXKqpq4sUfPadrbFT8lcShXXlqWrVDVgoJpKh+uaY7btkJ7UvtUdAB0GBUCopwJoelO5cU5zbnpTuXndfQDo/L47UtEWYm0UOi0c+xVFsHUfsKnvAOuMGbJjV92jUCjAoCH+SCGBf9DxJMAbhjFvNg/q6Cw+SXpcFIxLBIFj3TAu9IkAo94sAABtso2gTOHeJeo7jCqvPPz1r39d3M0w8ZDDUfQTGd7LjGBucsVFQZEHnrBuVvzZ1fkKOq695fl+gN5bZqKPt6O7ADr+KATmsFGUGTY2PYFG0ARyAJOWLV//rWzTQLRlgJfXgpLpWcaBCwe/KrSGXJJ+QAgCzGUP/08fLKzTsKpSRUKOFq1crYZkSEnyPFeWGFoiAlXKdTWywtV+zc+rJNmoRRqiuauaTfAyrOLkB38i9QVt73yqI8NTtMqt0C6h6VqhgYbuPVLLhfCD5QVA/nFqjAk8t0pV+jQ1Wi+lttM8DdfAsrC+OH6Qthtdpf/MWKm35tVlDJ72z7UAPRcLvShLvC0+XvCSobEr9vypyvc8y9TFmjrmz9P08dI267i38OrYmhI9cd4eHf6fwErQRLmwgypoMnWgO0C597sou6DxeoM4BR/YtjcANzbQG8KSBeVYzP3sh86+i5sLVkXvGZBvO4O+h/Li5z//eZ+LZu63nygfELAQQJlD5g76KOCU9Y3PY3crRvy2Pdt5nA+zC5CM5YfznvskE7sD6xHr2lqHutJO3mW/E0SNuwNrvdcyj0WLtVf90GSF5r6a8VMvudvrrNYL1OxGAzWFVG5YsGMh6aydBujsw3ZpBx2rm+J6Z36dcdOqKY9pdE2pRlf7941G4fynF2bonVkLtbK+RYtbaRsBTR2F5a5VKLQ1l3Z4AXtUSY2PrDRq413HlOknpx6dEQxxVrNeixkkDoCOkqonA2jaSbWBGy0AZY9aerd1/wPU9BWAXuyo85YOb6PpM17cHda6zt+DKtRRmGC0yRQgNNDm6+LD3/3ud80Z5M1h3sUqe+x17hdyqmOEQwnFvkZWQCHMv1EaFNp40GOd9fnhfoDuc6D6H+t8BLpiqQgytk8//bQBEJkiZ3JJowUEGHA5Yc0MevBma0tQf0OCngDWoL+jQfTjp/jUR0t07TMzVdsYN3nUW1qajACTSLoaWV2qhpakKmJhDamMKRp2VLF6hvZ031V9/RqtTkZVrmatdAfo2dROWqTBGqAGTQ4/rXHOYo12lmmpqlWhVo1xlqrFjWmJW22EoinudhqiOjUrptuSB2lwTY3+MnmS/vPJCs1d2WTo+aQQq21s1euza/XGZ7Ud8oSfF75XF0TuL57/eZAF4vfZLNqE1wccoodHfU+PfrRKa1pT2st5W7fGfmOESUqzG9G+qes0euQ4/fO09dORITyhZUbIRqETdA1a66UN9FYo+rqfYeGCxHJngzj5ecf7DIDO0tcBdQhArHu06V0F5d7vQMWFLVOI6NlB+0gk8/POO8/4w30eCzR2gAjxPRCMbKA4wCrn2PPPP98j457vWAdVHLP/AAxz5841SrbOGFPpacnybSPvcXdRH38SKTxdCdKumG5eoNit+0upDPExnLAWOMN1pPNbLW9YF48iaLs2G1KhIyaN0AeL1uilT5apJQFwbisojnfYaKCuO3ZbDalqY3plK7h2/eSuKXpn1iLVuKu0PFmpeRpq7hxT19q0oOvet1+xNPeURkYbNSk6XxcNeU0jT/i9VL5+oDjOSCjOlGJZM3sTQE8fb9aM9V3nT+4ODBRQjQFvxMXpzaW7febteFkFB3veBufjzmLscq0j7jcUdN6sGT0xxmeccYY5ly+//PKe+HxBvsl6RQGKnzrB6ZDvud/Z08wVZzjsFSzvKOY3pNIP0Dek2S5iX9944w1zsNm0Y8X4FBcxVD8CWHRmIefgJbgOz3N4cUl1teBvxKGBBdBv4fukreIC4uDn/Vz00JdmLNedU+dp/qpmNTas0bgRg4xVIRIOaemaFlWURDS4PKr5tU1KJl0laudqRGKBqlWvZW61ZrqjjaXclvHOQn0l9Jr2Cb1rgsmRIx3g3ujGVOm0aq47VK+7WxuhabvYEu169DkasekO5vUlq1v0zP+Wan5ts/Gj59tbj6xUKOTouuc/1ZzlTQaoD9RqvRI7TxU+fdDJ8wua7xGLux0Y+/EM1nf+69b4l01au8Mj00xuYgrttnmKX9j0Iu32tQuyLgXreoGCCGtYZ1ZHL6WYv3cnKPd2IB+hAwWUBeUopRB0LCgvFi0N31+CcxViX/vdy/Y5fOyg8kH1Zrzof5AzIej3esvzpHBDWILmCHjvKyUIQGcuAckom3FlyMUUgG0C+OlqtGysQ1Dp2TucFZmUWaR0mjRpknkm9PI1irzyW5MnfV2xVCJH7uDNdGTiSr23pKVo0xR2pH+cuqN23mRQ1m+8PWuxLr/zBdWkVqg0EtLc+EB9lhrSDvbbLObYx71xWcj0Tr8cjXRq9b2RH+iA8lka0DJficNukDty/eCMzBcCPJbQQvsa2871ZoDunQDuDpSsgB7uEs5kxsTS4fEXLpTRolCLqycp+d7gfMiN0OHZ097o8JnuMe53lJb5KrMLNXYnnHCC9t13X+P/3VeLBeik2Tv77LPNHdNf2kagH6D3r4SCjAApVBBYuCyhBCKk59JCBv0wGxmq35e+9KWcgYq4nLBiQhmkLbSpK5pkBCS0dxzcQYvXso8wh2a7s0synkyZdG3/eXmKvnbwPpq+rEUvfLJMM5fUa9HqFgNsEXg+WLhaCR/07hK16qDQVO3pfKgyp0VbO7NV6sRV75ZqgTtUtaWjNXSTidp1QK1S20+WO2RCexdpy4LaZrUkUiayrvWP52JbvLpFz09fLiLQb/vR1fq6+0TOyPA0lwB1iGRQGOmLH6De0bYSdAYyPO+TGm/BeXoNJrjc0bfJ3Sy7LzLrFWs61jiCGEKFtfNugbgVpHoKlHv7BfUPRRsKsM4K69mCcqzuCH0WlHdHADF8f2EmFCveRGd9P/bYY42wC1vgiSeeMMFySEezIRRicBDVlyjcfSVgj9/sItD6AeesY5S6fgIW4rvJHcfdkk9hDLmjsNhzL3A+ZLszbXBUo5RqqlX071+WU79USuDatFbbiQYxViUpqfqJJ+hrsw/X9CXru+jk09Zs77zyf3uts6S7KTnLp0vJhFKDN9c9jz6mW99r0saxNQqFI1rWEtb0xAiTcYQCEE+YHO9t/waw43SFZX0TLdZ1pX/RFiMGSOESOckWxY+8WarZpENToBpjyWSeOZc4fxgj62vsZx79jAesPNzodt55Zz+P9+gzGCessQR5DAWQpXfz7/RUboWW04J2nn2EbMYd2dOFdQRIt9Z11peXDo8rAXc4Y0q7SbPWk+WII47Q8ccf3+fSX3rHzEZrh5WG++r552fOrtOT49xT3+4H6D018p+z73Lwc7hZizFWFizGmajo+XYdygsUS3Ir+r14sYoQ+AoBDCEIITMfDTJ+MVAPbcTKfPrAJQntHQsJQmBndaUrI0g1AxCeuaxey9e06tEPFmt5fbCo6Zs783RS6BltF5plxKK54dEaO6hKg0INGjxkuKJDN1Vix9OlEk/wrVRSzpqFhlLplg/t+DtJDS0JvbdgtT6dO1+Hv3W6hifmt/uEe4G3BdirFVPCLdFAp2GtB3xugF5wcB5g8rIBdKqgXfHdL1Bir4s6rdFa01mzlkXhtZQjILEm/a7pAM0P9CigE+t0Jr869p4F5fSH/U1KNEByd4Byb0eILksKmFzxIAJ1vpOHscJiLbnkkkvM+CDgnnPOOQKsdyXycKHaV8h6OHeYawts+TtrlSBB9Jl86FiNPi8AnX6gxCXAI3OZKUtHtvGFGcW9BxU+aLE5wBH0vUFPs9VD3AWCNlmllDN/qqL3nCw1t2XvMNSeaLlUOlBqqZdbVqM1Z7yme99cqH9Om6fFdc0mwKjjuiqLhtQQ96HZ9dGpEQNK9J/v7yVn4dta8tzvNW1pSK2psAZGWrXAGa1/122hYSVxlYZSZk193FxtXK1oM0CcmCglatFeznsa4DQZiL5j6BMdFXpZJWHJrRwhRUqUGrePkgde2dbPtYX7FOYBvvow95hLwBX/z4838jmAvSvW9b4G0OmrjcBtx8sbPI3xsdZibyq3Qroi+Vg+5hEUCpna6/f9Yj6H/GjBuvWL5vxHocAY9rRP9AEHHGDyg38eXK7ADt/73vd00kknGUUYY4yiiT+7W8Yo5poKUnc/QA8yWv3PZh0BBDlrBeTvCD1sOC4JNKOFOPgBEAiKBx10UGDrPACdiwAQhEAVFGhnCk6Xz3KwPspYVhEGURpkolAh+PFNDuD0sYN2fu6/3gv8+Y2dxTos9LpWupXaq3S2JtW0ynXCSjbVqbyqWlUHXCx3VBu9neKsWaTQrOcUqptrrCJuabVSY76g1JhdOwhKPGsE9rmvq+SeE+SkWgx6TQfWeEU2uSWKOcSP9289D9zRAr6QC6DzqeTQiWr9+r1SWc16X7ZAnD1BpHeokkQ3tSnRLDgvYJPzrgphBP9uG5mWNsOMQTgFlGNJsKC8K2yUvBu49kXST9GOzvyDu/oNABR95wdBjHPjySefNH9n31599dVd/USvfP+WW24xa4AzifOWNcCaRSkyefJkk/M26NnZkx3tzILu9fsGJAdNkUgwUFgnQf0icY1gDbOHoK372UuZgpSGX/yFwtNuliKlBsQqFGkb6uY6ueWDFT/9BSlWYXw4UTB9tKBWd86OafGaVjXHk0rk76beYUof+cammvnYtbpl1U4mA0htqkytisgx8BvI7aoslFRzKqLWtdE8ytWo0VqlXZ2PdVb0UY0JLZcc8oisLcYPSlLZYKU221+J/S6VKoa1/xpWEtRo2Avsx3QfdO4j1q8F64BRa12HBQfICiKT9CWAjhEAw0g6QE/fh9ZabK3rjBeKVxvtHBeP7rCuw7xk7xUq2GKxzhvWFC4DAHbORKzrAEgvHT7ImipEO1EQECn985AfHDYRPvXIvRjSUNogJ3EXcc6iHO8Jt7ZCzFO+dfQD9HxHrv+9DiPgBej2F9BfAcUI/mj/sXh15cBHsEF4zDfFkU2HBThG677FFlv41swRnA46Uy6/RL/LggOHsaFPCBnpY9MZW+Dnj/1Pd0xd4PdT7c9t6izQV2PTNGyTbbTxAFflTUsUi9cZS0NLyRBtddRFisTWRuiNNyn83p1yVs+XO2AjKRyT07hMTrxRia2OkjtsYnu9zC8CzGfz5qv5vzfr4MSzKlHSAHT7g+gFnRGATsR46Ix+qO2BO+nnBZ/0dqrqjOLe4VOxSjV9+w1jwcpGX2dOyTfa07lTsw0R7cNSx74AmCLUAsqh/fLjB0j4Gf6uPoOPJUJREGunn2+iFEORB1MAIQwh1/bdpti66KKLjNBw4403+qmyzz1DsKFHH33UnI8IRvSbM4r1AIOA9Dp9qbAXmdf0YnNys45Q2OYjWGN1x6oGs8pvWfT/7J0HmF1Vuf7ffeqUzKT3AiShSZWSQOhNUQSkgyIKCohcQcC/gKIi6BWvClK8KE0EvbSA9BoVUXroLSSBJKT3KZly6v4/v3WyZvacnLLPmTMzGZL1PPOkzC6r7W9971feb+lSkwoGGKFOtd9orlxlPjNe9FMzwBbvOaCWEmzxZikYlsK1io+dqheHnKhQ/QhjDADEU9JzVUtc/5q9Sn+ftaLbQH3qkDY1Na42ddhXpGrVpuh642xhCc9vh4YT+rL+qe8Fp6s2mJZCsLwHpESLGVNy/4uV3v2MDvDOehJaDEBi3lk/PyRxXjDK2nNm2brifrzryASMAqWGuBsj98r3zdqkh22TOUsLWX39bqQC15ULeAHolmyOfc3e9JZy6ykvJsYj1gJQ1l8a+886oKyHnfmz4fDMG0aH7ui7xeaCfc83TWUNHDn9vWFs4LvEEIwBjb9bojiwBCRyG4sO0ltzvRmg99ZMf8rfkwugM2SECAQuWLsBt4QRluqp8AJ+8k9hUu5Ow/LJIQ8AAYyg6BcTpHjPAFbZtXC70w/mBoWNEHwEO2Hv9vkIJ/Lec0UL/M9Ts3XrC5/4fvXAqqCO2WWUDt8ioKbX7lF9/UAlo51ET8E1H6mhbmtNO/wUU8rNANMV7yv43n1yB0/s9Mzw/2s/ljtkslq3PUZzFiwxYCbZ2qQhgwfpr3Okf8xZq68Entb3Qver1okp4QYVU9iEL5J72OJWaYiTqePrG6AXurCcSM1csfdFZjNbp8r12uR2R6nl8Os7ALp9JN5X9heeLAAwOd59Hc7uHa4FpigdKGZ8n3ioAafFSA19b8IKXogCyvcCl0N3G2O3nnIULWQUYcSMPde3DpMsCv6f/vSnLq+m7iylbvieAXy/+93vTK52vgbAv+GGG0xoNeP40Y9+pNNOO63jcvr1y1/+Un/+858NqzoGTsrMHH744R3X4CHD65DdINqxBgRq3vIMb5s6darxRpTSSC265ZZbdPbZZ5v9219C3LMBOv3GQMsPcwooKCb7880TOckYJv2ANvrBGcha4nH3U9XD+17OPYy4XUCM6yr4j8sVfOv/MoRxqXjmp0OyWikVUGLaBXIP6MqVwFy8uqBBN/zzI1NCk2oh5YlTV6O0RlWKa4FGmNKdmVZIcHdaSSEv3d35UHdHfiEnsN6LHowqtcspSh16Zcf5g45BSDvfBvXILVGsH4CevYbZdcUxRnlz17MNNpxzfGvkyPpqlIia+5RCH9wvp400BMmN1iu59ReU/MxxXaMFfD3Q/0WVkI/sV6KmLPjEc+wt5VZOabJ8I4D7gbmvtMHV/4yVfiU6LXuCyBvbAOg2358/ada7zp+VPkvZ90RgPvTQQyYqr783vkGMNeVyevT38efq/2aA/mlc1T4YUz6AbrvCoUpIIFZolAwES6leC8t2W6nakwhYvNhY5VCq89VV5rDCgw5bZqWFLPPD3JDPiNAnHYB8OrzahPN7FXI7ly/PW6Nv3vmGElCo52moP7tPGKhfHbODxg2pyeTntcb0wt8f0simd1RdXatUMKpwvFFLYtWq3fGL2nPnzlzKwOKZCn74qNIewjjmoW3FPC1paNXdyYMVC1SZQ3vymKFqS0q/fGpOhzdme2e+Tg3O0FbOEuMrH63VGh5oUtqV6owHvYRW7OJStcp8zyv0HMvDVKDbbrhWa7/9jrnCgnKvh4x1phrAxuBBtyHc1lsMMIWNnDB8gGVPMbCXsOp5L/WSIJXzPAvKGTsyhbFbkrtiBjhAOAa1e++9t+PV99xzj8kBBKRTIuaPf/yjAbP0M5cR4cYbbzTEcng+UPiJqjjzzDMNARss8TR+T01YrkFhoXrFhRdeaCKIrMcWw4K3xjeKOfwc8GUgq2gAdECk16CAJ6wcsstjjz3WsIxTB94y75Yz/715jxegc0YRfQE4Q7HOJ+/99g95DUjH4FGoYWwFWDJnrF2xPZbrWXlrrkPM9vGzRlYHZj0sJdukVG5ukuTBP1N6am6GZCqE3P/yR3rgzaVa0eYomXZN5Y5gwDElPdsSheLhXYWVNKU6lwoS1WICu2sJDQA6/3ND+AZ9Pvya3BE7KLnHWXJ3OKYDnAMQmQPWDLZnr+5QDkCXm1ZgxbsmfSsVrtPqmkla1dhqAKkN9baAnUgavrVSAHpg1YeK/Juc+fV59HLktK6SE29RbO8LlB7bc2RzgBxC1SthwLR7kW/HW8qN78oLPgtV1Sn2PfFtIH97MmWpWB9K/T3fPWdHvvQW9iReX2vg4O/ecHgiBkrVf7P7yDuYM9Jf+Cb6ezv99NN1/PHH64gjjujvQ6lY/zcD9IpN5ab9IACln8ZBi/easEk8GAgYvx4MS7JWydqTKE0YDvBk5csJB8zMmDHDeO57Erjg0Sd/jMMPoM7fc4XzI5h//PAHmv464Ldrg1jnsi9sqy/tMkY1EW/5msx1b3+yWh+8/arqmuaoSjGtDQ1TaMxO2nfXHTSomvDCTHNWz1Ho7buVqh+nde0Jc9g0Nzepun253gt9RrNGfEFjhwxQIBXTynlva+n8D9Qcl+a7ozTHHSuyzGHp3cJZbsLZV7n1Oj34pD4XfE0TtVhRx1siyMfOKabz8Qi/QN3Ps3x0aYNLAmE1nf9R3rBV9hFszH0F0Hm/N4QboOAN4WbfEbUBCVhPhTOWM63Z9wCQ+Q4nTZrk+3E2nx5F20YJWE95KcolnmmMLI899ljHuwFoePQA3rYRKfTlL3/ZeMGzG6kyAHnAvm2Q48CgT4QFDbmIVx1iNtt4HsYEgHuuxjMIT4cV3MpUADpyBXI3Pw15CIBlr5jUl/V1aAmxJteR/kBK1N8AOmuOpw7ggpGhEnIcwwdnRyEmZ84s3ste88sOn2udADH0HeNtztayQpGb9pXirVI6UxN8wxZQ4msPy4VDJEezPC1b7LCHFje0aeygKg0dEFVNOKgDrv63ViDgN2idQneo1mq1NuTh2PCerjlGISVNqbWjBszSNdW3KTntfKV3O73jNuYZwwpj55vP1hdKBuixJkWf/40CS1+TY4wZjtKDJig+7fvGIO3NXWff2FQP/n/KlCm+gFX47bsUen+60sO7MvwHVs9RcssDlZjyHT+fY1nX9HTIuDcXm/3tBZ8YNQCfpUSIYXghpYaf/tIw1qDLIkv8NOQpe8kCdox2Nt8fQ0c54fDIYELBiQbKKxf8dG4juIax/OIXv9Cdd96p3/72t8YozVmHcYzzeWPWR3py+jYD9J6c3U3k2d4D0s+QTYmuZctMyB8eaRQXP94MPF4onyi3lW4YDDAcIHQRDhwWVhEohz2+3P55y8Pxd6IFcgkn5vCp95br5ucXaElju+qrQjp7v6107GfHFH31orWtmreyRa2xuIbXVWnS8FoNqOoE58bbvmal4jPvlLvsXcUjQzRg4GANCiW0KhHWk4H9NXTcNgqm4xq7dIZWzp2pT5oA3PjKXb2ZnqwZ6d2Vyll0zdV2+kS/D1+jiYEVRfvacYEfUN3HAD1dN1atZ72cd0wcQkRF9KaHmndaUI4yxWEHKCeEPduTx7rjfQVw9ESkiP/FLnwlMoBWrP60BeXIDUAqSpD1lJcCyr29uf322/XAAw+YeaLxDubxvvvu60LUc/755xuvKeud3XbffXeTyw3gte3SSy81igncFDbcFiK6b37zmx3XUPMWbwnGxOxGPwD1eNnmT+rZAAAgAElEQVR/+MMfdvwagA44R4agOBOejiKUL8T6kUceMf2wOYDsHyIOUEjJESTMvj+FoiJDiU6CtLRYKbNS9yfGHoxFuVIZ+JaIRgHAWyIzv4boXP0AoLLPyFvP2RKtCv3vlAxPSCFLZSCk+FcflHKAdMAD77HRF973UEHkyP99yURAdbZsgcsZQIh6MWGdTQLCcxxtE1isv9ReqyG7HqnUoVeYqC+bjoCHMF/VhlIBevjVGxUGPA8YLUUGmLSAQONCpYdMUvsXrs0Q7q1v7H9Cva3HlHcBrLze9VzrGn7tZoU+elrpodt0Wa7A2vlKjdxJ8f0uKXW7+b6eNeT77a2cbgs+LdkcsghZY8nmirHn93ZVDt8TWeBC9iXGy3KreNh8fwvYibTzRiT4ybXGMIIsRg4R/dafG8YLjG/MAbn9RBcwJ3xbnD/o48jSTa1tBuib2or3wHhLBei2Cwh2BJ31XqN8FLKUkd/JtT1V2sIaDlC6sNxhOMCKVygfvAem0zwSoYtlGUFFpAFlpbqj4OXqJ+P11uHGc4a3AuMJ/z96SK0mJOdrQPsiOemU3AEj9EJiG73VNlKTa1o0bM2rGrniBT2+fLDmt2fI5WrUrlHOWk1P7a+P3LF5pwdvy1/Dv9C2gSX+prCYzucXnPO2Ys/y16MNriJ0MTHtorx3W4De0x5q3oOyBDAFnAO2bU55sbKHAE88wuWE4ZY5bSXfxkGN7OC7yG62HJyX5M56yithdACI/+EPfzBh6TTCnPk2n3/++S6eVNjOyf3GCJndANCEnOPtxvOOgkpYH+vF81BGvvKVrxjPK+AaxQVm26OPPrqj3nP2Mwm55x5SiLyhooTfI8MgJQMw/vjHPzZzxztzKYHkml9//fVmTMhirsF4SijnwQcf3JH3W/Ki9dENGFzJt6f/lWaft175bECLQokXk3cTSg+g624jmgrDDZwpuRr7Jv3EpZq48ikRvl2wBcJKnHSX3C3373IZRizY5fOlkJ1/z9t68n1rVM0tcKGIa1FVWUI2oJSGq0mXD52hA771K70ze4ExrPGNVNUMEKVGayNBjRrI8ztbSQC9dY1q7jtJTnuD3MgAU47OrRlm0gIC65YqdvCVSo3dMM8cecL3g6HAAlHWn/PZAlH+tGHLwY//rsjL18utHSEXAr9IrZROCQ96YpdTDdFqT7WeItH001/Wwub3Az5tBIKdIwwH2dEr8Csgn0rlZfDTn566BoMf52yuM6jUd2JERFbYfcXfOX8tYM8XkYCehjywbPKlvndjup59w7nOHDCv6Nw2gssarU888cSNqcu90pfNAL1XpvnT/ZJyAbqdFT5AvNdYqguRtmFZQxEhzKwnGwosAhhll4MDhZfcTwjbeqsxFyjR5MYzN3j8MBiUS7CXr9+sHT+MFU8TYAaAgIegI386ti4TNhkdqJmfNOiTt/+lHZ35Grz2HVW3r9AbTQP0fmyYlihjxZ2sxfqPu6P+nd6l4HSFldAdoSs1JTjX+F2KtkqRxfUIQA9o3flzunhfssdjQ8iJAPFjIS86H54LONRQiCwoB1x5Qblf4w4eX0ivigH5UvpW6WtRlPFA8D3QOMhRoBm7LQdnPeWVAOXe/hPajucbwESzAB354DUc2nA9jH3Zjb4TKk44H98e6wRzLR5zDGT0nfGQl45Hm7UDpMPUC7BHIctuyCbWnOsLNYycyLS7775b5JSX2voLOZwdF/1F2fPLll7KfLDXSEvwkpbi1QLkYhQBzFUqNJMzgJbtsUOmsMdY1122m6jRM86Vs2Rm0WG4AycocdZ/MiXa1jf6XoiEtT2R1C4/f9bz7A1B+u9D1+qq5ClqV0RJSU2qV8qHdA8oqRrF1K6oxgXW6uLJS1Q7eT8Fh2+lnz3xkT5e1WJI6muiIe0/eYi+f9g2GjMo03fWGKNI0b2ZaFP075cp/OHDcmH9dAJkhitdO8J4ugMN8xXb/1Kltjp4g/mzAN2rezD3tu46spfvEmPW0MEDNX75DNW9e4cc6tQHo3KrB8mN1JmQ9/i+F2eMAj3UNibSNRuBYMGnnSML2NFn2HOk9PUnLzC6EnK5WBRXOUvMXvaGw3O+2YgE5s2WvwPQsh+z5VtfEJaSykVkGbIIXZUoPKKtvAaMQoSlNr2zJ6Jjy1mDjeWezQB9Y1mJftyP7gJ0e8iiYNv8UpTvbI8HIJKDkhDR3mhYMlHEUagRmhAw+QU63e2f1zvjNRjA3IyyXkqOl5++4OljjAhU1tOGF2Xf27h4tua/MF2NTp3GpxZqQOsizW2UmlpjesedqGbVaGst0nPuzvpPeueirz4kMFPnBh/UeGeZhjqtxZ3bNjoyG2T3sQe97YR7lJpQOPXChpBXCqCjIKIYAurwlOOZsKC83Bq25MjjsapUOcGiG6CMCwi35kBHWUEeAJTwUlpPeaWNH94ukqN/3nnnmcgfZEE5Ie72eXxvrB0GsZtuuskQwzEuL5hE+UK5xUh4ySWXGK+7NQ7Y5zAfKLgoSHjZizWUSurN8r7sZsmN8ARipGGP0Rhrb8m+Yv0v9fd++VFKfW522U8MyABp1oKfSs4XgID9gsHWNsZFGgXnQwf5HMzuD52j4AcPFB5OqFqJ426XO/GgjusYDykUnHPZzTKof+2xpvW/yi1wZ0e+pttSX9CD6X3V4lIPPagm1SohMs035ETJPCxTKz2TIOUooLQmhhu15aRtNGPWqg36EgpInx0/SLd87bOqCgd9A/TQh48q8uI1ctrWyEnFMp7tdFJOMqb0wAlyw9VqP/zqTOWSrJYLoGdfY8OWnXena9j8h5QORBV1EqpOrFUwHVNqxI6Kff63cus7I8tiybQCjjqqp5S6B3Ndz55AFhIFs7E15Jm3lBvfCDIG+QapXU/K7krOBd85feU778mGPPaGw6MXovs9/vjjRj7fcccdpjKElTV9RVgKmfHJJ59sSE+RR/CnEEUEUao19hciLCVCDLljzxvvnHKuXXTRRSaCZVNrmwH6prbiPTDefLVmy3kVFlc+RH6ya5XzfyiwpdSdLacP3nts6A0edSy8eDB6w7vIIYaAI2fUNjwcKOcoSxgwOIQr1QDozD3js2HvVin3KpqBec+qcf6bmhUfLjV8ojFNb2pVaoAa1q7UnPRYNapWg7VO09P7a76bIX0JrgfTOC2SWdGX2zsLdF7oAS1zB2usluqg4DsK5RgUt1lsnhaejwzzry/Pe/bz1vcHFTOtkFoC9apLryluHMjRr9SA0Wo7499SuGvYZb51IYS4OznefGsc0haUA6Qs0Vs5RDPZ/YSkjHBgP5wQldp7fp8DIMGIBxBCacGAZz3lvaXYEdpOKDnhhfa7ICUAoyGeC9v4PgHLuUjico2X7xyFGib3XA1wxrdJmB/h8952+eWXG+Z48mSLMQMD9nkPBgFvWTf7POTLt7/9bTPGc845xyhMPeF99rvmlbiuWIWRct/BHiTihMgGDMt8k9Ql7glPIOcP77NszbaiCZFOgPZsg23g75cr9Ernfuw6xoz3OL3tF5Wa9j25IzMM0ESy8f1nR4px7pBuhRHsaw+vVqJrInqXR5/mPKmfRe/UwvRwvetuqbBS+owzX39NHqKb00coIbhOMjnnxVv+a+qqgrrqyzvo0O1H+Abo0Rk/UnDxy3IjtQqsniuH6LFgyDCr491O7Hqa4ntfkLNOOQZQjHJFo/eS7ap68kKpvVHt0WEmuqe9vU2KNSuquJbtdpGGTNpdaxIhzfhwlWYvb1Eo6Gj38QN16HbDNNBD1Fp8fnJf0V9Y0W1oNx5/Ik3wrmMYtt519tvGKnuQk+iBOEx6szFnfI8QjFL6Fz4WzmvIhPkBGHMW9TVhKQYtzmbkoyV1zkVYyppztj388MMmMo1KJBhxcDYg09gXVDLhbCUyaVNrmwH6prbiPTDeSgJ02z0+XJQegCoeHwhPAOgoEflKW/TA0MwjMQqgoOCdxFoJYyaW00p7sb39RyHASptNQITBAEUcjwqKIARElQjj9YZQ8g7W1P7p9aYH5jytQMM8JWF3b40psvItNS6arZWrlmlJarA+SY/QK+52+nd6J+MXwdtx/GfH6INlzZq1rFnxVFeydZjezww+pimBWWpwaxVQQrsFZmmUmhVyMnV5Y25IQSdpYHmjW2MYf6mlDkj3A9B5Rouiej+NwpjQ9kODSrQ06uVhx2rm8GPVkAhqzKp/66w1VyuaaiqoOmb8RgG5dWPVfvAVSk/e0NtUaB+S403N0lJIyiwoB5xy8LHvLNFbJUC5t7/kUsNMW4m82Up8jxzWNnwdxYR+Acb5/96KpPGOA8UMSz9ee6s8Wq8FuemEuQN+USq4lnByCOCQG3g7aHy7AH2APUDr6quv1jPPPGNSWqzCB2s695DDzJ+AcOQfcsgbWcTeQB5BIHfVVVd1mXI8otx33HHHGWMn/B3kvxOJxPeeK12Gvp111lnGGwJQLxo2XIlF7uFn9BRAt+ShzCN7gbUq5bsuZdisPXsOAwDyH4Mq6WB4HXN66l1X4T8fLmdpAaUWD3IgrNS+31dqytnGAIBCDUC3zyR0HqXZRgV8/tdPa0FLfqmLfD03+JA+dCdouNOgE6Kvasf0LM1I7aqLEt9Wu8KKKimkOeHseMxzt8IAPhIK6PyDJupb+25ZIkB/RelBW8hpXa1A8xIp0WpI4lLjp6n9yD/kTVHiPCakuFhJPWfdclU9dZHSkM9F64m/V3usXU+vHqkXGuq1smZrBcI1WhNzlA6EDUlrMBxWUyylnUbX6Zz9tzBRAd1pABl0lf5Stoz0IAyaAF5vKTfLUm5zsTcmXpSeZsr3s/6cGT/4wQ+MwZYywPybs6KvCUvpuyXlZJ4s030uwlJrxAakI9NOOukkI0tZa4w16Pt414k6pILKptY2A/RNbcV7YLw9AdBtN1HOUSYBJXyweIi8YX49MJwNHun1ZqMkoXgTxoNHq5JebO+LGTcCC0KxXA3LPAYMrsOAkVdR8zlBzDFKGYDfNksix580fh9Y8Z6C8/4hd9CWmRq16ZTmf/Sh3nn3Nb2p7fSWs73mpoYrlZb5qY0Gdcbe43XriwsVCjimZE97IqWGtqRsGfchoZimpV8zID2qhOa5o/VaerKmBd7XAYG3NNxp1FA1KaGAGlWjAU5MNWpTuABAp8f8oEryZ5siej29jdapWgdOHqxgw3wtnXSiFk/+isKhgF7/pFHt7a3aOfW+4uvW6qHFddot9op2Cy1QNCTFQ7VaEJqoZwL76qsH76adxtT5nNmul/klYWPOAW8WlDP3FpTj3a5k+Ky3h4S4sgcg8+mrBlCwoJw0E6+nHIs63msUkb4A6AAlgBgKhddAh4WfHHIADQrJNddc08VzADgmPJ7Gt4aHmu8bTwGkXNn5egAlPNh47ZB7sL4DwLOVbhQzAJUFbN41Yx4pzYbCjpERkM678FT0FsNzX+0h73t7CqCzD/FWEpHAmdST3j7SGACJ7H/+ZA8W/UbjLQr97ZsKfEzFgayQdABkuDYDUB3Krz2i9vqtTHUCPHHIF4w1GAMwCthz7rLrbtF9q7cosqyYZjPGU/LKLwxN1+eDr+j42OVaoUHm/whmx2ia8ajnakUAetDRz4/+jI7eZbRvgB764EFFXr5O6boxnbn3sWYF2lYrtv+PlNqqM9w/u0d+Abo8HnS3aqAWLV2uaxr21fvJMRrstGjo8JGaHR+hta1x7TgsrConoUQyIScYVkMypG9NG6/9t+usIFPO98P3DuN9fylbRtQGkSFeozDnH+DM5q4jv3BEWO96JeqIlzO39p6NIY0AAlHOGYy2tI2FsJS1A3ijv1CS1LZchKUYkSHZRNbwg/MCHZuzC7mNTEUeURUFo9Om1jYD9E1txXtgvFg6Aaw91TAAoOASzoNljTyXSniN/fY3u5yO14tNiCGAptKeEz81dum/rV1rDRflej7JFQJwZLOSWhI51sCEu6baBUNtYO3HUqTOMAY3NTfpF68FNSOxk9rTQaXTGc83LRiQRtdXaUVzTINrIgqScLee2Gd5c8yEvNvQdbIUQ0oZgqHOEEhXn3Xm6MrwbRquRoWdhCH2iSiuKgPZc7fsIEpC5Je7g/W2O0n7TKhRTXyVYvtfJoUiCs55UrFV87UgPUzv1O6j98M76qX5DRpYE9Z2Iwd09LmpLaHGtqRO33u8Pju+PGZmQBr7N1eaBHONIgIo5wclGcWYg4l17SlQ7p1BGK8x+LCve7MVA+XevvA9Ig+Yx95urAvVJjAcbMx5+t2Zl0+D19w7/koDdOYHDxF7kLOPEM6e9u7hvcVYg7cecF7K+Rf44BEFXrtFgUVUHnAzcjtcLcCk4hCAJqXqIYrv+R091TRR+x94SEeoKXwUXlnV9uil2vXVDUnUsvcb0U0DFDOG0Xq16KHIj/VYaqquTR2nuEJGzuM95+9dw939hL5L4wdX6YGzp6q+OuwboCveouizP1NwyWvrw9gzp0Ryi/0U3+9SKci542nppAKrZkupdq10h2juwuVFPejcHXr/AYXfvF3Prh6oP7Xsq/fS4814w05aw8JxtVSN1Np4SGMHVWuXsXXmXG1rb9OcFS367OCEpo0OGPlrwWg263mxb7u/1RUHxLGnCxHgol96CfmIoMpFnFZsbir1+41hjv/yl7/orrvuErwxtI2FsBQCVMhUMbwUKsnpJSyFVwnHF4brza1zBjYD9M27odsz0NMA3XYQqyWKMcIZJZnw0Z70Wtj35qu/jhcbqx+eFAQM4amV6g/PxFvnp6Qc84/yxvV4xgBYxXJRsxe9WBkfrzfdja1TaM1cBRrnyXWCSgycqAufjevlRa1qiaF4mci+TpCeSXnUkJqIIiD29W1ZU7sB6GD2AmmN2j/wls4IPq6JzlLjTefJ5DZCLETzqnS5shszueaZrPVF7nCNH1ovd9yeSk3YT+E3bpWTbJcbrhXjijkRLdr2dN2+did9tLJFW4+oVSgYUBte/9aEBlWH9LWp47XV0JqyvptslnTmFYuxBeX82+aUo4D0Bij3DoTQ695i1AWU23FjSccjaHPKCymleFUASMXCTctaoCI30U+82Hyfm6JFvyfmtKefSbRDLvKhct4L2Cdnlr0LFwoGLbw+la6u4e0bRli8osh0uArKOmNcV5HrdpRSMYnQdjznseZOz3ogLAXDmj9wquZs8x0zHlLJss8RZ/FMff/mR/WwW5gQE6lcp1YjcwHhF4fu0hnBJ3Vv6gDdkjxCyzVYESVEitMKDfGZk56ZlfqqoG47bXftNLbe/Ns3izsXE1Uw7x8KLn7VnF3p8XspueUBXdjsuSyw4n1FXrle1C3HCB0P1Wlu/d7a8ogLc+aod9lLqYTWzviNfvlmlZakB2txeogGOm3moFrnVikcCKg9Mli1VVHtPXGwiSxjDHNWtujE3UZryuhwR6g3XmTWwtZd95PS1N/qinMmEg1VisEzu5QbzgVvubtSjRqlygKqNxC12Jel4UilIqydXHSzteNxYygsJcTdjrtShKXf/e53TWlQjAakXhVr6Kpf//rXddlll3W5lH3PWUt6DSH88HyQKraptc0AfVNb8R4Yb28BdOp7Ys0HuBAmyqFGmHlPkPJ4p6kYWCaUB4BLf8ilqoT3EWso4YWlgBAEGp5whBvzAoDwC/AQhOT2Fiobku1N95LIPfHecl35+Gw1t3eGrnvnEBAdCToaUpvxorcmUlrTgjd8g+DLDXboTs7HOif0sAarQVs7S1TrxIxiF1wfRol33AJzntdJJpeB7+Q7EnAZUUqrnUEaeOD5Smz7JVXNuFRO6yq59eM73uk0LTIlcT7e7hzd/fYavdEyTIFgWNXhoAZWh7Tv5KH6/PbDRQ5kdmN+Vq6Lmx8MFENrwxpZH1UAdrz1jYMLxR7AYMEp348XlJelgFfou+7pmrQoVjZsn/2KUkWUAD9+lSq+N75/yPZ6u7FuGBKI5sEot7lt/DNQKYCOBw8jMdEshOQCXiF9BFyUG7lUaPaQJxhd8Z4TRo8hz4/BNt8zQw+eqcDcZ6TwAKltteSmOi91gsajnXICmnvgLdpqSmceepfnUdLs4e9p2usHGXb2Qg3OkFqnXW1uSN8N/k3nhf/WcfmK9EBdnzxaf0l/zgPOC3vPMeR+aadRuvLI7VQV6aQSLQmg+9iuTusaVT1xvpymxUrXDpeTaJHbuFhurFXBmoFykq3mwCGXPbHTKUp+5tiM0cPT/j3jIf1xZpPWpcNa4g413CdhEyGWVtBJq82pVW00pAOGNEiBoBa5w1RdN0QXHTpRo+o7CUdxAtgwb1LtOHO9QDRXGb/+BtCJKoN8r9woFGQy34adJ84V0sC8pdwqfabCEYL872nds9B2hbuEHO/p06d3XNZXhKV8g4Dzv/3tbyaVy0/5uWzCUvRWCGA5W5944glDHMdZT6TcT3/6UxMKv6m1zQB9U1vxHhhvbwF0vAiAcyxzCGXIjvCk9VSYuZ0q8l0RHIXAMv0hT5D+ALYIFS8lDDF7WXgnP0VZY7NuRFAC7slPZ64A6n4OPgQ9YfpEJhRrljzOEslx+AGQT7x5pj5Yti4729E8LrQeOYcCGWCbdl0lCYVfHwtfqEoanhZKse0TeEfjnFWqdtpNeDthgzwtA9AduY4UoETcesAeV9h4b3g2YJ6r3qk7SDuefasCK99X9OmL5daO6OI9cdYtVWD1bMWqR2pNIqyF6WF6LHqE5lXvoB3H1OuE3UZrRF10gyliTt5Z0qx3lzSrPZF5VzgY0DYja7XHhEEmSoDoD5QngChzZ8uCAfgqrUAUW8N8v6d/hKVV0jtsQTnAnEMYxYlvBOXGLyj39hdljP2aj5+h3LH7uY91xoMOsVFvk1X66V+lr/k0hLt3F6AzB5w1REuheBK5ZQ2feP/gHKiEUda7doT0ssfZ6xj0bLRWd/a8s/wdhe/9imEYN+HteVpiy4PknnJPwa305i3f1UkLjyqy3RxFA2kF0gndGblKuwc+NNe/lNpOZycuzAPwc4P0ncbW6edHfUbbjdqQ+6PSAD0082ZFX7rOGDCcZFsmBSDnqSa54ToldjxB8QN/kuFkWd8efvQBXf9WwIDyZlWbWAFbSq7OaRcF6GoDMdWZFHxXw0LtOn6HgfrsISfm9dBbBm+ADT/ZQNRyk+DdJZKukjK80nLF+zyMXBieKpUmyLeCMcP+sD84Y23aQHf0MttvuFrQ8TjL+qrBJcKZevvtt3d0oa8IS7/zne+YCiQPPfRQlzRJDJesayHCUjz+GNxxLpHuQHQSDiOqjFCdpLcqtPTVOhZ672aAvjGuSj/rE8oEIL2nG+ABUENokW0IYwiSyNkmNJcw80qzq+PJxovuJ+eV8HvAMWGJgF36Wg74KuWdueadcCcUSvJ8/ITf2+iEUryCXqBOH3766Gzd/+bSnNsAtWtkXUS7jKtXVTikeCqt1xas1cp1iY7rC4H04VqjE4L/0pcDz2u8s1JBJ2WC1uG7NfdZL7WLz9w1cLxV0YzHQq7xuFOH96kx/6WDv/J9OWs+VtVTF8qFaZecTFp8nYJLX5eSMS0d8BklQzWqT6xSe6heM7e/RB+nR2qPCQO18/rQSu9AlzXF9PcPV2lANKjaSFD8e8HqNq1qbtMOg6Xx4WZVOUkDzJljQHA5+6Knv7FK5dYBxK2nnL+jHPHtlgvKvePmsKeftnxLT8+J9/nseb5rFJHueDN7s8+lvgtgCMEUSq1XlvbXkmvdOZ+4l+golH3I0rIVcvIsUdQrSRbK/sYYDZDgnXhJAWT0o7t73oHk8/lrFJj1SP7YJSek5LG3KL3NF/NvnUSb9v/537RchUt9koq0X/A93RL6lRHRzyR301nJCwuEtFMHPSPTiViasuVgfXOfLbTLuPwcHBUD6Km4wjNvUvTFqw2zu20YkTlLIkqqzY3okfTe+jg9WiO1VnuGZmun2iYljv6jUmM7OTHue+Rh3fiONESZNIKV7iDFFFJMYQ1Tk06LPKvDqmfrk+H7mxB3StENdhsUO/AnSg//jK9P1gJR6zm23nWMOuhB/YHFnbWDmBCW7p4AYjwfw7idIyqB4LDwlnIrR1/cGKqdXHLJJWafZLOb9wVhab5IzT/96U+CvT0fYSm6EMZu7kdXhvD0mGOOMbno6AzXX3+9r2/h03rRZoD+aV3ZXhxXdxSgUrpJfiyhfvxkNxv2Sl8gbatkbpBl0C2FNdqStyH8Yfj1lkbyM2Y8NpBhlfLOXM+14ff8jn7kY/4lr5L8NowcpTRv2Pt1//xItzy/sIOdPfs59dGgrj5hR02bOERvLWo0IfEfLl9nrs+Vh05g+vbOJ0Z5IZ9xCeqOW6OvVr+kqenXVC9KrXV6zG3JnlYXfveQBqjNk5/uaq07QM+ED9aXvvNrQ5IUfeYSBZe9qfSgrYz3I7Bmrpw1HylRPVzzo9upJhpSONmigY0fqLV6tBbV76aVI6Zpz/0O3wBcv724STMXNGiLodV6b1GD5i5rUDrRrqZYWoNrotpjq6E6fJfx+vCtmRttnXHWqzs1dC0oB5hzIHs95aVyIhTag3jkkQV9EfLGfsdzfsstt5i8uE9jwxOHV/jII480ChPg08v6bvO5N0YDU671KPd8AijzPQCQAcq5AASeNCK6YM2uRMPQjIGE+aaMmlV8kePI6Ert+dB9pykwN5O7mrOFqpX80vVKb5/bS768sV1HXvcfNSYLmVVdfSP4lC4NT1dEbUq7jnaO3awWVReYqowHfZsRtbrrW3tqACU0ijQL0AsakFw3k84EEVxVboLP8Gu3KPr8r6VEi3kjwPz19NZ6LD1VC9xRanWj+pg/VWW84XjHx2i1Tgn9U8ccuJeSU7/T0dMHn3hKf36rSU3pKgXchAH33Md5dWjoLf285h450QFKjp9mQtxpwdWzFd/5VCV3PLHYkDf4va0pDhBFd+Df3tz1nqz8UUOBpzUAACAASURBVHJnPTfg2CEKhciQXOH63Xl2rnuJpuFbsoCdf6ObWe864N1PWqBNVetJ7oliYz/vvPOMbKZ6SH9tX/3qV00Zteuuu05HHXVUR9TpIYccYqJHf/nLX/bXoVWk35sBekWmcdN+SKVZcvPNZjFliIMazzM5LKWEdxdbPVuDFqbRUhqHJPdC4IZHCoXL7yEEQzAHCeGN3W2WBZ9cxnz9sLmVfog9cvWHuZ/+2iJdPeMjU0INDzbN5oMHHTwiYX3vkImmLnoq7erG5+bpvteXmpxt21B6RjmrTQj7Nlqo7YOfKO6G1OZGNdBpUTpSqyOP+7qSqxdowqxbFV71vuQm5ToBpQNRxYIDNL8ZNcjNeNqNBz2tFe5gPeV+Vls6q6QpZ2ragV8yYDz6n18p0PiJ0cacdctMOGPzsN20NBY2ZXHqm2YrnGhWPFyvlsgwuYGwBuz1DaV2PKHLNLwwe5lenLNMirVo9uqEBtdVa1D9ALWmgiYPvSYS0o5j6hRf9N5GVWc8ey0BASgrhdhXvfcAYqynHFDu9ZRXEpR730mUClZ3Sob5Uaa6+/1k30+qC8Q1lDD7NDbk7COPPKIHHnjAROEcccQRhggNwwQysL+VaCsHoBMxBVDGq0NYez5jRCGjcSl7w5CEzZlj0qTIb88G/HhFCV1Gca1EWz3nVY2afqSJQura1ktsJyhFahX/2qPS8M7Sm1yLIfK8e97W6hbkdn6Avq0W6MmqH3bwg/w+eZR+kzypSPcdDa0J696zpmjc4EJAvvMxxQA6hHDh129TYM0cyQkqNW6q4nucLbfeY+hvb1DNPScosGqWGt0aPZHaU4+m99as9HhVKaYxWqMPtIXxpFPic5BaTLURZmsLLdMV2y/S+GOvNJ1qjaf0P4+9rf/MWmrmN5Z2DKnptqFlCrkJfTn6qo4MvqT00MlKD+ucW1KrEjt9RcmdTu7WEsMjghMD2WiBKA+0XmNktF89pFsd8XEz3yZgl8iQnjov8nWDfYOx11vKjXmx84QzI1+fClVj8THsilxyxhlnGJn84x//uCLP64uHELmJsRvZRsQHZ/rpp59u+GVOPfVUff/73++Lbm0079wM0Deapei/HYEwA08xALQnwpTszBBOyDuKecdteDe52ABOvMLlhDHZ9wJs8Q6Wm3PKIUCODSRD9B/wUwxYlGsUKLSLvP0gygCBaPtBSCUHEqFx5bYPljbp8kdnac6KdWqNd1X8BlYHNbKuSkfuPEpnTMukKCxuaNMdLy3UQ28tU1MspcFq0t6B9zTGWaMqtWtX52Ot1EC9kd7aZJwTInniFq3aZefdMsQ8jiNn2TsKffJvOe0NcuvHKTVmN/3k1r/p7NAjqnPatM6t1iq3Xu+6W6ldUW2rT/Rweh89G95Hd5++m7aoTSi06CU5LSsVWPaWCXGPDd5WS5tjGtY0S9XxNRn23doJWh4ep1GBtaqrrlL7F36ndartIHpbuLZNc9tqVTugVsvaHEP0Q4796nVx7TC6zpDKkZM+tHluWREV5a5JqfcBSjBu5QNhKDVeTzlgGYWPb5I/e0PJ4vtGFsBo3Z3vutS5sdejRHzzm980oO3+++/X1772NfPzaWt4l5ARJ510kskPxABjPbuEpB522GG9st7dnddSOFIwZmKUgHMEoFwsjzdX2lWp/bXM8HxLGGRzsVljCMNwwpx3p/H9cp5xvhy08nbVLHxWSts0oxyUnaGoUjudrBRlyGoGqSWe1km3vGpKgmVaYQ/6TvpI06NXmFSj3WJ/UKMGFOh+xnt+06m76ICtC4fOex9SCKAHVrynqqcuktPeqHT1IDnplJxYk9JDt1HbEb+Xopn0Jq6ruftYtcaT+mXiFL2S3lYrVK+YIgZcw2RCLjmNXg5Rk8kjhygPwH7+5JX6/EnnGpl/6/Of6J+zVyvW0qB0W5Oi6VY1pSPm+j0j8/Ttuuc1Pr1YyXFTJVKseGZ7g+lX7IAfKz1yp+4ssYku8lbiYH4I7ba564R84/m1QBTvel9FwyBjyDvuK1nunWjkBDoa6SzMFfKOubHedcugb8PyAZGVyGcvd7FPPvlkY7C78EJSRvp3Q1cnHJ58es4c9sRVV11l6p/3JKbY2GdtM0Df2FeoH/QP6z4A1OZd91T5MyythGn7JeShXyiW5GoBSEthNfdOO54NnkHYZ3cankb6g8BhHAj/fK27RoF8z+VwIYyS9YIRH9Z5FEJyejmwuwPQefbtL36iO19eqFXN7YIQDk92XVVYO42p0/LmuL6x93gd/pkRuvLxDzX9jWVGoTEKktI6LDBTE5wV+sQdoeFq0H6Btw0wX+CM0Tx3jLYeXqOv7jRAYTeu+K5fNx6eXO1L1z6nI9vu116BDzTXHatmwbBL9fS0tnUW6S+pw/Sv9C7m1ru+sYt2HJsJeXTWfqyqv19mlKWmwEBVrXxbgXRSyVCVyUkPV9VpSJWj4Jq5mjXuZC2Obm3mzNQpHzxEbyxq1ovzGjR/dYup+c7YRtVHTS31VS1xDauNqHbtHEPcly/VoDv7qxL3Qs6CIsI3bBvr6vWUAyTIJSe8rrdAebYiRVjkfvvtVxbJXLnzBGnjHXfcob/+9a8GwOFVPu6443TiiSf6jjgo9919cR+KKvuA3Gc8yUQn4TlCiUKBQpb5IZXsi75n7xc8dcUa+5oIEq4lUsBb/zvfvd2NPAI4YRzNV9bMvhfjKnNOykG5zRLP8U7qm9fHVyh813FS8xIfj3TkDhilv0/8gc57Y7TaDBFmh4Qoev+FuktX65QC12XA+e4TBur/vtmZy130wevLrAGmbIg7YNMCzsizVyg053G5AyeY6CiH0nL8mWhRbP/LlNwuE8Ifmv2oqh77ru6L763/Tp5izgxgOQYIIrCIycoU9sz0k79BXhpVXFEl9f0pUW296/66+flP9NK8BkOCikE2mYgrkUyZUm0hJ61vbbVGZ+wcUeiT501IuwuxXDplUqySkw5TYo+zMjVJu9GKMYxjELIglD+R715m+N4EROhV5HOTutFXRoJ8U82e8pLN0T/mibMbnqHeCsvP1z9SkMjVPuuss7qxW/r2Vstj5DW0E7l18803myg55DDnK151P2THfTuayr99M0Cv/Jxuck+0LLlYHQF+HJSVzgNnUiET4YMtBdwgAFCs8YqgBAGOSqm3yXshocO6CpjtbuM5gG9C2PGkEz6Zi8kaNngOCLw4PdFQ1jA8oHQTZYAxA8DlBWblvJfQ9YffWmrC11tiScN4PqQmZMLeCVm85LCJuvOVxbr1xUVdHj9ca3VE8GWtcAcJ9vVhatA+gXfVpojGDa7VoK331pYjBirQutKEKSYA6KEN2dSprX7CH1/RhMRH+lboMSXckJZpiGF8n+As1yp3kK5JHq816jSO/OfCvU34vVG8Fs9U5M3b5TTMNyHwyUBUDQMmmzq4gVRcbjKmganVapxykWp3OLyLBzGRSmv2ihY9/u4KUw9+8ogajayLKpZMm3DQfSYN0cq5b/c5+2uhdQV0cRCyDywox7CEUuf1lPeF57oDDvQwsZB3fgBGcEHwQ3QB4BT5g3Hg8ssvL+cT6Tf3ECnBPoBlmeghjDfMAbW/AQGw9pbKWdEXg/fjQUcRB2xjeMJ46nd/l1L9InvsnEt8b8whP4WiqgAynH+f+9znygIyrCWGAMCXJZ4z/WlcrMjth0lta9Y7w7ND3rv2+sHkPvp/ybMN4WbGl9zxVXZjaTPP+da0LXThoZMUDPoHqOga/HCe2aoinPk05rPub6cq0LLc5HlTQtNJrTfUuGkltzpQ7UffZiKxIi9eo+Srt+vY5ov0oTpJaLuOsXOImdQA+p3WyECLfnPS7vrT62v1yZo2NbUnDfFbQ1tC8ZSrwdUhU1KtsS2h7UfV6fuHTtTk+pRCC55TYPm7pooIBHOE3nuZ4MudUL5NDGd+HBnWu24BO8Yb9CNbd72nvevoOESG9FW6kt85zsWgb+cJ0A5beW8bGA4++GBdcMEFBqR/Glo2hwRYAhZ38tM31bKmmwH6p2Fn9/EYvGVs+MhsLUOEFkC9VECcbziQScCkXsjznO9e+gjoBZCidMIe6TccF0HBYc9YKtUAPzyXcDOeS264V0HDoAAo6q7Xvlh/AeZ4yOgPBoNKGCF457tLmjT99SXGm4was82IATp6xyEaN6hK+//uVbUluyqCY7RKRwRf0nx3lPFSwLo+LfCuRjlrTJj6IYd+UdGQo0DDQqW23F+pLfbrGBrAGK/Fjc/N1ztL163/f1cHBt7U5wKvaajTZLwfi9NDNT19gD50vQqYNG2rQfrjV3bunKpUQollH8h95WaFl72hpuhIRaI1qopGVR1bLtUOV/sXru0Ij8ye46WN7Zq5oFFrWhMmgqAqFDRgfddxAzXzlZeNUcaP8lRs7Sr9e1Mq7p13xLfC3uPH6yn3C1oq3a9czwOskAveE1b1bAZ6W6uduSBHEVZaQpEvu+yy3hhqn70DYEtEzbnnnmvGjfxFXmCcIKQfY2exVJ0+67znxZxJ7OlcjT2PsZSzATnsJ/3I+xwANt8FTO5+G/3BA0eFDQwffhjg6T/zDzGh33PL9gfjEpEBjI0Uq2wgEZjztEKPnCvFm42nt1D7MDVORyV+boyomWZBeqFQ90JPzNz/113f1d4r75NSCbnI9ynnyB06Ke+NlpzUVo9hH/JjAbv9s+bJ7ym0+GUFYo2ZcPxApt9Ook3tVcO08IBrFN5iqka/eZ1efusdndtwktapZn1uvqUgzblzOv5z0uCIzj1osm5+YZE53+aubFFjW1LtiZQIEKuJBEz0FBFVwwZEdMDWQ3Xa1HF+t4u5DsP3gjVt5hlbDqk2KVP5Gga0cs+YXN51W6KMM6vS3nUMoBgUAOj9pVmvP7qSNWyw37yl3CpVMq7QnHD+QaJ29NFH95ep89VP+21bOQUXU18YQHx1tocv2gzQe3iCN4XH5yKJ4/9QegDrhEcCiMupeeydv6efftqU4/ATephv3gHEKFUcDChV2cA4130AWPqOclPJhiCCjAhlDaCB58YaMyrptS/WZ/pB+CQhnoRro6hW4iBOp12tXBdTwHGMF53nL2yI6cs3vb5B5mKt2vSl4ItyXafDuz1IzQZkE07YUDVOx+2xhdJDt1Vy0qGGhZ0GC/z/vbJYj7+/Uu1ZoJ/f16tF450VSimoj93RHsWyc1YgsHvzh/ub/uEt5od9MrI6qe0W3aPatsUmxJHmVg9VfMp3lJqwT8FpbUuktLI5bhQqygUNqQkbBbIU70axdavE720ZGjtulA/2Ih49gOnGBMq94yXdhTDdShj/coXwWwZ6lNJsuUXNV8DOp5VhFtnN/sdz/sMf/tDkZRLJc/zxxxtg3pfMxeXs+XwAHdCLQQqvIQYXlMBSW6lyGhmDpx5gyTv9GpgYA+cfQMavbGZfk2tOxBZnS6GyW86iVxR6/CI5q2cXzCvHOf2l2C/0vrxcJaWC9K61zjHGfjjwfAVCIRPe7STb5Q4YqcQJf5EGdaba2LWxYbG2moAF59lrx++Dsx9X9Kn/JyexTm6w2tgT3GRcL7g76on0XlpePVmBUTto+/ByNX30ku5s3csYc/O3rn2PBB1NHFZt0rae+mClthxaozUtCXMuEUEWCDgGpA+sCmnisBoD0CnD+ZMv+tclPli2Tne8vEjwnPAsSEdP3G209t5qcM5uAtDRVbpbo9tboozoSGQCupe3RFl3vcYYQyEIIwe9v7Rso4I9PyzZHEZMADrzZEPiK32O8k5kMnnbeNI3t0/nDGwG6J/Ode3VURVicUeo4ynGQ8uhYdlFS+0gAumpp54yuUrdJebgWXgvUK5QkLCEFlI6uxPG6GechOdhzKA8ivXuE9JDw0vVGw3mVzyEHC5wCbBWEEJVykNmleTFje064n9fNYpGdtvB+VhTA7OMggRre53TqqQC+tgdo7Vunc44aEcduMcuWtSUMKGEze1JPT1rpd5bsk4rPEzwpc4XJd5u+3ytUdQhSIPwDK+WYbptb1Ro4fNymhabmump8Xtn8hnLbNkEPmU+plu32bBGPGsAc4AKa8+48QigdOGB2ZgbJHF4H8uJpmFcdg4sAz1zYEP4UaoKeSlhlkUGXXvttV2miPqzv/71r41sARD97ne/M97mfI36tTfccIPx4GLE/NGPfqTTTjut43L6hBHgz3/+s0nTwaD4q1/9SocffnjHNYTZ/+xnP+vyCoxsGP5sY6xcc9NNN5nKEHheeDd9zNVg1f3FL35hZMEXvvAFffvb395gHADN7srh3tpfuQA65xIh38h/9lG5rNalpCIx94Bz9lkpYfR2v3L+AWT8eOc4U0hJwLji2/jQuFC69XOKxBskN5V3eT5IjdPRiZ8r0eFF59JyQbqrSc5SPTPqf6Xgeq+8mzaknanPnqbUgV2jVNjLGDdYU86mogDRTSt617EKLHnNpEbRXne30R/SxxivNIbjtjF7a+HaVi1euUYNSSjhcgH0zPiyafT4d00kqN3H12tFc1yjB0WNo37hmlZ9vKbdnHNcEw0HDEgfNbBKe20xSP91oD8y1hXNMV3+2GzzbIA5ZxVnXU04qIsPm6TtRm1IukfIOLKiuwA9ewMgj6zHGDDKGnhz18uRB/Y7hMW9vzR0WTh78vWZb49v3c4V+jHGP0s2h5Gju3oV3wFOr0cffdTI883t0zkDmwH6p3Nde3VUxcqsWWIyADHeqHJIsjiUn3nmGWMtLFeZyp4UBCneBUrbAEbJ28rl5S+3Rnipi2C9++Rl4Rnkp5Jh9YX6g2cXryAGFIAbUQbMczEyO79j9CrJx9w0Ux+tatvgVvL6tnKWabKzSHVq0yoN1Jz0OC3RsI5rUYQWrW1XMOgY73RDa1zx/Lqkj+652rI+oBuP2coA1ErtrXwvBqCT8+8nrNVH531f4gWkgFK+JwvKUbKsogvgYK0qHS3iu6M+L4RAplQ5wrgAnTan3DsHhCf69XL89Kc/NcYcgKxt99xzj/EuA9KJ8iF3jt/zHQG+s9uNN96oiy++2JDhkLbDvjjzzDNNXjfkPzR+/5e//MVcgxwAoMHYy9ht+UUA+vTp0zVjxoyOVzAO7/4C1AO4yZ9nXX/+85+b0kbI41yGScaHgomxIdtbiyykBFgxdnOfy9grl7H3OaNss7nffIcoud1Rlv1U2+D9GF9JW2L+2Q/lvBMPOszRxaJG8PBhfMDIBGeLX4878/P+P+7Wdh/doqpVbxdcm/Nj5+hhd1/PNaXmo3cC+guij+i7w9/o+r7W1dKQSUp87ZHM/7c1yFn4ktKppJKjd5czYLjvOQy9eYdCL1xjUpLSTkiXNx6pmbExGuGuVt3gEaoft4PmrGzR+8taFHYTakx6AXpXj3muSRldHzGEcLFkSs3tKRNrBe+I1wjNU8JBR1XhgH76xW30ue39sdQ/+NYyw9my5dBqE4lGM5ERq9t02HbD9O39NowwKEc2lvoher3rAFHkIcYum7vuNySZ+yi1BeFaf2mcITht/PSZeUKfs9515Co6preUWzmRpTwXOYxjJZ+htb/M5+Z+5p+BzQB98+7o9gwQFuunoRDjLaIuOGAIK69fqysKFiGXlJnxq0j76RPXePPBc3n5K1GCzG9fELyUnCCsHsVqjz326FZIv9/3AtAxUtgwSC+ZHQolxotScx+97wYcsYYAwfeWNOtb//e21sXyI2vAupvDkzEgIlVHwmpqSyqWKjfvkfs6Fa8Hz9pdk4bnZoT3O39+r+NAJUqiWKlAv88rdB17yQLSQqDc+wwMVnhKesswVO44/eZZsu9Qihg/kSE0wCs/gPKiHrgcHQTwMk933XVXx2/xYhByD/C2DQMCtdJzhcIDtADygGDbvve975lwT6IDaHyLeNXJAbeN5wHQAO40APqDDz5oPLO5GnuA5/BsAD8NeQ3AZhxnn3123iUAfDJ3AD5CUWmAexRDGOwZw957713uEvbafRagsxeI5iK6wG/ud7FOArwx+Oy+++45L0WOIstR0EslOM1+oB8OFt7DXiB1i2+41P1ty8Zt2fAfhf75CwlSzhytMV2jveI3mNKVGXY5L6j1I5ctG3paL9b/SMProoaszTY86Omxeyh5/J0KPv9bBV69WUq0SaGIVDVYib2+q9QuXy22PJnft6xS9OGzpLWf6NHUXvpl46GKpwOKBF0FagZr9KABSrtpfbiiRVsNqdb8NW2mnJzfNm5QVO2JtCGBc9yU4i7jsGPpCvBrwgH96PDJOmrnUb4eDyv8U++v1FbDqETS2ZY0tmvi0BpdeeSG3Ae9AdCzO+/1rgPY2ffe3PV8eh4RHnwfyML+0pCJyBHkX6kNGeQt5YZsxUhqvetEhPkx3uFcAuSjT3eX2LfUMWy+vvdmYDNA7725/tS+yS9AtxNAeCTeG5Rmcl0hIioGurFCUlqJMjN+BFipk229/OSDA4wJe7c5iZWodVtqf/BUoRRjPKhELfdi7wfwIOhR7LyN93OAMv8AjnI9Z16AzvNhW7//jWX6x+xVJld77KCoFqxuVXPcj3JXbDS5ft8VlNsrjtt5uC4/snfSCHgnAAyDR08BdAvKbeg2ihJglPf5AaSAMr7P3kqtKGcluQdDB3IjVyQCY0ZJBDgByq1HmWtJYeiu/Lj++usNk/nDDz9suo/hCe/Rfffdp2OOOaZjSNRwBSwht7IbgO6LX/yirrzyyo5fXXrppfrtb39rvnu8Kiht//M//2Nqrtt2yimnGNZjFDMaAB2Qj6xCbmEo+O///u8OdnWMoXiJCcm0Xnfug1iIuSB8PrsBYAHijzzyiAF47CnmkOgSvM+EaHLvJZdcoq9//evlLmGv3Uf/UYpZC9YeoOwnTNxPB5kPfqZMmbLB5SjfvJM55J1+jdH53gsxImvI3Gc3xkgkGKlRfLtEQ5XTuhijE20KPX6BArMeNqXJstu1iWN0Q+rLHkZ3D8AWnuRibOyOTtoupKtWfTdTvmx9TXAlWuUk25Q86CdyPnxcwblPdX11sFpu9UDFv/R7pSf4A0lU5Jj3r7/oN3NG6ePEEMWcatXX1ynuRAVfCBwhC9a0a7uRtWqOpbRgTavctKukK0EqTyW04oFajAJa0K6gHM83P3CyBALSaVPG6YJDJvpannI86JQtw6uaa5/4emk3L/LmZGMw8nrXAZX0y0sAhs7VHwx9dloYE5FmlQgtR3e23nXOLJrNXS9EyofhHYcK91Y6laGby1/R2zlbOQu7e2ZXtFO9+LDNAL0XJ/vT+Krs8MFSxmgtkVhf8aYXqlMOUMQyTJmZnmwo9yi1ABU8T3jUAcsA13KVnnL6yzvxlBGGDEBmjjAa9FRoNEo/hgC8Y7mUP5RQjCqAPBTAUhXcbIBu37GyOaZfPfORYb0lH/Dx9zNezso0VyG8Mq6Uqwry1AkDdOPxWyscrTFleHqjYexhH5Vr6MjVRwtAbE45/7bh635AufeZAD8A4sYeNsc8ko5h9yseBZQV5oA/OdQtl4Bfr4Tf9b/11lsNeMWjSSPihb6gGHu9KgBlADDfTXaDfA2CH3II8bwzniOOOMIYLXke8obyOcgBPOSAbN4HsEZGWaPoE088YTzcyKnly5cbYI3Ci8xAwUNm4p3i+/WShFE7F0BH2Hx2IwrgmmuuEQYGImdQpplP9hQGAZSlP/zhD6a0VSX3sd/5L/U65of67ewVZFepXuVC78OYwVmRDTAwDNlzoxxPdq53Yuihqkd2BQivlz4fgPc7Z9l13Z3Fryl89wlSnAiKrsbTtOvoO4nz9Ex6j/VFx4KeK1wNVoMaVa90HqC+W3SJ/vKDryoy8w8KzrxZSq6PxHOCSo/ZTW4qoeC8f+TsuhuuVWqH45X43FV+h6aH316m6TM/UV3E0YerEwo6mZBzzh5yu9nXLfGkCU2PJVKCczQUdDSyNqilTUmfAH3DyC/geigQMClZAPSTdhujSz8/OW+/qaH+/tJ1Juw+nkzr6Q9WqrE9aUp20s+V6+KqDgf1g8MmafscOejIIfZJOaSHviezhAvRXdD1kMv8eL3rfIvIISJy+ktDRtNnUpMq2WwamrfkHcZQ6133GjY4I5ArnAPetLy+4EFhDoq9t1QeFJ7JnjnhhBOMAeLee++t5FT3m2dtBuj9Zqk2zo52B6AzIu63dcoBpChQufIiscLiNTvkkEN6ZSIAKSi6eF4I7UZRRQnvreZVlJgjysORw4iwRjCXCpCL9RtFHhBQSOHGmgnYQCllPvC4+1V28wF0+vXmoibd89oSLW+Oaf6qVq1syV0SqdgYMr+3SqSjYTUhfW77EYZJl7a0sU3vLGnW9iOr9d1t12mHwDw5iVa50YFKj9xZ6eHbdwmz9Pe+0q7KBpal3d15tQXl1lPOv62n3HuQl/p8Qnb51nq6vF+p/cq+Hk8fgJFvE1COUoOH0oJyZElPWd3vvvtu3XbbbcaTTbMAnW/IC9TI+77zzjuNHMluRKQQus7vWTu+u1NPPdV4zAHajINxkZeOMYCx8H1SZgtgD+jM1ZBbXPeDH/ygI18dgG5Bv72H5yJTnnzyyQ0eg1cfJT/X7wiJB5CSD92fGjILY1WlG2uETLT5qKwlBl5+MKhW8swg9QFDttdIyz7iW6iUl558YEABeygjTl0FZ/xYwTfvlJIb8ob8KnGSpqcOUJsiahGVNbrmog9Tg64J3KBfpr+queL8dBRVQicGntX/C9+ryJAJSpz+jKlRHpj3T6m9SYEPHpSz5uOCbPJyQkpNPFjx4zaMAMm3xpT9fPidZdp6eK0Wrm3XorVtJle8PZnSqLqoSXN6cd5aORRZg4yOtKxkWkHMD4GgCXvPH9/V+Zt8XnQY8Cm59l8HbKVTp+TWJejPdc/OM2VDKR/KU6PBgOqrQ6avPGNEXUQn7jZG0ybm3s/sExi+NxaA7l0Pr3cdmY1+hWzDaI1u052zq9Lf/HYSjQAAIABJREFUdr7nIUvQWfOltVSqHzZtwAJ2DKLIMPRAdGTOC75/q4P1FQ+Kn/eWwoPCHmFPANB/8pOfmD3hjTSr1Pz2h+dsBuj9YZU24j52F6DboSGMyOsEICCsAYBeyyAfKx4JWNx7swGAUIBQ/slb7AklL9d4cuW9Yy1FOHNAoEAR4usXIBebMxRymLv9hF5zYOCh491+Q+kKAXT6RkkacgBRiM69+x2VkAKYd2h7TqjXtiPruvx+9sp1On3MEh0ceU94YdxIrZxYk5ROKDXxUKVH7lRsqrr1e0KN8Y5mpxL4eajNX7Oecu6xgLRSig1lEVlf9vrG2DASMX4AEDIDIG4NE90pv1jKWAHMV111lSE3opUT4u6VewBy9gMs6+SJo7R6v2tSDvA84QEnrByvO99fvgZPB/ITT3g5Ie70A8/6/fffv8ErHnvsMQP2Afjsx0rJn1Lmv5xrixGZlvNM7mFdYEuHXR0F2lu2rdwKA/n6km1E5TvFkIsMwBhQibVgLKRKeKs4tLas08IZN2nyvDtV3ba4S/d+kzhB96YOMISemZD2DcnidteHml51hZrcGi1zB2uUs0b1TlewHz/jX9LI7RW65xQFPs5EphRuASX3OleJ/X9Y7MKO389c0KD/fW6eRg+sMh7oeGuzmlratLgtrKN3HaPn5q5ROOCoKhI0RrNke4tWNbaqMR1VUo4WN2RY2XO34gC9riqokXVVuuJL22iH0V3PJfvMB95cqttfWmRC7gdEQ8ZQsKwpZkqzfffALTWwOmxKuUUL1EHvboUL3xNagQvRZZBR6FXsZ2S6N3e90o6ICnTZyD/0QtJWequxHzG+MkcXXXSRiaZCP6bqB1U9IE/GeNsXPCjF+Fe6w4PSW/O7sb5nM0DfWFemn/QrX43ZcrtPKDseJ3JsUBIA6ygeKEIopX1RjoMa4ShbCGU8XXgxSmHGLWcuCuW9Y6xgLhB8KGbZIY/lvC+Xd6bQc1h3PGkcrjYVoBAbaTGA7n3XE+8u0w8eoh5v+Q01ce+Jgw2Rjm0mEmH5Sl0w5EVtP7pebk0nO7zTvESK1Ckx+fNy3KQpqabIhiVsyu9R5k4ML+yhQjWJve+woNx6yrEsez3llfYS4xkgPHeXXXbp7lArdj+GKWuUQC7gPbAM7B2evoq9rfiDUI4otUa+r51/lBQ8KoT62ca3SUi633rpgDw8rjC552oor0QYnXjiiSbPPFdjrpgTQtjxPljl6IILLjBedRpgFVBXjCSOZ3E/e9DKeeSe39rdxWey967oKYDOfkRWk4NuDbl8Oz1RDQLeA/grMOYQ/YDnnrMoV5WAcmeWqgPwDfBcGoCAcRnSuW0mK/qbiXLcTkb851I76cLEOVotbw35DUuuHe38W78N/1HBQH4fdPzkBxS553jJ9UHQFqpS+9efljvUfzlIjL83/nu+Xpu3SjXN8xRqX6PmdESTImv11V0H6aqlu6s6ElJdNKSGhrXmOxk4aIgWNcWVTksfrlinhCEm3XAM2V7zzn+7qgpIg2qjqq8O6/OfGaEzp+UvX/rde9/VksaYRtdDvpdpgPSFDe06a58JOnKnkUWXFn0F8FiodGzRh/TSBRgn2cuQ4VoQakPhbT1xb4h3Ma6i3ug2/UUH60sjNhWNiMA6/vjjTTQTzi1ab/Og+DFO+zUSY5gmeg89knMGGWp/emNdN8Z3bAboG+Oq9KM+VRqgM3QENQo5QB2BTEg3CjkKcV+wfVKSCGUbDx194gDBQ4ViVAmvRa7lJpyfnEmIQHI15p1oA+akVEb8XM/jUEf5J2y4lEaoLUodgpV1QpHLBRpLAei8f6dfPJejG7mJ3vL1d59JgzUwGtLgmrBRqZY2xjTCXaGLhr+qupETpUCo89b2RgWXvaF0/fhMPnpkgPGmp8bs3vW6UiYnx7XW41UIoGczjzOfXk95pUG5t5t4B1CavIRi3RxyWbcTumdBOeUHCde07OtEs7Dn8K7Am9DbjdB2yNGYK7sWNsyP3GzC3PFCUx4NQxqpIBDAYfy44447THeJhKG0GsAeZe/qq682ZSQBe0TG0KiswD0o2/xJ/jdGMaIwLAEUhgLKsiGLMOKQg06uMp5cy+4LEMdIQGg8Rk/APTnZ+cqskV9JfXbmF4Mp3zh/IoNZC8AbZHb8e2NQmP2sf08BdOaD/cA+YL6Z3576PjkTMO4ha1lrvtFKR3TBTk3jLACIcN55jQCh63ZWoGVZx5SnXEcXxb+th7qUXMu9Ip/VbE2PXqGAkwekB8Imkql4cxQ/6CdK7fnt4peuvwKdYk1rQm3xpD546Ld6dbmrRLBau0aX6eDQ2xrqrtVPai/Tu4mxGrjeuz9kyFCtWJcweeoHbTtMVz8zW03tKZNv3zVSgH93UsMBzgNyNdxZp12HxFQ3fgfVRCPaY3y9pmwxUKFgwOgNueq4f+POt9SeSGlYbaTL2D5Z26ZT9xyrE3cfU3TM6Cvsjf4A0JcuXSp+8PxmN1tP3AJ2r3edvOS+MhQiH/nu+zINjAinH//4x4ZNnoZswEjY2zwofvhXivGgEBEGlwqGadaadeb7AKQTNUCEAJwtm2LbDNA3xVWv4Jh7AqDb7vFshCGMmYSy8u++AOgos3hFrDKEhxHByAGLIlMJD3b2kqC8400rlsNI+CtKFH3qjtGAQ51w9XLGgvJDqBr9YJ2sMcM7plIAOor/Bfe+qX9+bImJiteizbWlL//i1iZssWV9oXSA+onbhjRt3dNyI/WmLm6muQp88oICTYuU3OpgqXqwnFijnPYGJbc8yBAWVaqRpoERJHtdvaAcYMoBBSjnB3DaU0p/9rhYRw7dXApTpeYg33M4jG2NcgAh35sF5dkeSfYalva+8KADflEa8PB41wXvOTnkKJwobxCt2Yifb3zjG4Z5HVlCQ35AAgdIZhwHHXSQ8WhbzyXXALTPOeccE6XCdwXrO6H1XuPOySefbGqa8/0zV5Atka/HN2ibJeihNjvGAIwCv//97/MqmBgASCXC+EHfUIT5k72BLMYzT/k3myvY0/uiEs9H6eMbq2Rj/BiKALIYUXIRbFbyfZwJGK5YC77P7rLC5+ob+9EqyOxjgJ6XJdp58QaFn72iy62JdEDT4jeYMPdi7crgrTo1nJv4rdi9md87Suz9PSX3y0SD+GlzV7bo3tcWa/byFinerM80/EunDHxHk2s6eRwo6/aGttMVyW9oXSqoYYMGKJZ0TSj5yXuMNXXL77/557pm2c5qVo3S5KmvJ7/L/A2A7igUDGpMtE1T69fqzFEfaXxguVQzXMmJhyo+ZooxFLMP2Tu2Ievtz29mfKRn56zRFoOrOmRLazyltW0JU55tjwkbMvhnzwHygP2BzNjYm1+DsDfEGxCHt5X9b+uuY7DsLWMh8pjoor6sdPLAAw+IaiJUhaH1FQ+Kn/dagJ7Ng/Ktb31LpNRhZObvGKY5B/k+0Gsx0GCIJLKM32+KbTNA3xRXvYJj7kmAbruJMCTMDqGMQo7i2J2a3KUOn/rrhK96SVe8xgMUY7zHlVSYbDij31BoFHQbnohyXqpnBTAAsUx3Snag2OHRR+hSPo8fG2FQDKDze8IpbSg3Suiv35LmrsnFv+5vBZ/73l5aF09p3qpWBQOOth05QMNqwwrOeULBFe8qXT9OCteI8Pbg/GeVHrZ9FzDuNCyQE1+n1Midpap6pUbsZMB7dxoAHaWC1A3vmG05MAvKK8087rfPzD+RGYQc9nSzhEEWlAM+2H98TxgxCqVMsM9o3lzZnu6vfT4KGvIAudRbSmFvja3QewDov/vd74yxknJvmzJAxyPPt8yeJcKAvP+e3AucfQB0QBcGlp56F4YvgLk1AmzgpUwnFfnVhl7c91PjdEQCRvVsYyr/7gSj22u+Hq/6UXnbORBRYs+zlDzA//2U87zy8dkmempANKCmxjVqbmrSuEiLfjviCY0NN5u+pFvWKt7WrOd2/70+Ck7QgjVtGjYgon0nDdWu4+oVevceRZ6+WHOSI3VvYj+97m6tZrdGrU6ViCIYEExpSy3Rl8IztZ/zpoa4a03klVs7UgpF5YaiSu52hlK7nGrkfkNrXM/NWWVI+PacMFBDasOmH3NXtemqZz7WmpaE6qtCSqRdU2N9ypaDDPN7aD3haaEJ5CxHPvUHgF5uSlU+7zrnB+drT3rXMVLiRPAaU8vb0OXfBbkozObW4Osn1Dzf29DbyuVB8fPefCHuRx11lNFTqUBCCckZM2aUPyGf0js3A/RP6cL21rAQVAjLnm6ABixwKIUoxghHPBa94VlEcKAU5QoZ89Z0ryRxW6GyZ/nm2psXTqg5gs9vLmR2lEB31hNlktBe9ob1yucC6NleY5ROQjgBqcw1a9vQGtMlD87Sa580KpbKlf2Xv6fnHbCFztx3iw0viDUbQB5YO09OKm7YiQPNS4y3HGWK5rQ3KjD/OQUa5mVy1Z2A0nVjlNz5K0oT9l5mQ6lnTdjHGwso9w4FsIynt9IlZOw7GDfhgdYQw7cMGAeUo1j5NbyRc4fc6QslCaDKt4XHvycVwTK3WI/ehpw46aSTjELXn0jiKulBJ3ICgzEGW7xozAkkTX5lbakLhMGTiAtSrJCNREr1ROO7xAiAHIaVPt+3GLz3awp+lKM8X+yL+h/3q1ld65qPXqcWvV11VoHudwX0HRc6YaV2PE7xw67qkNF+5gDP+V0zF6smEtSC1W1KJBNy4q2KOyFNrVqk/x1yr6LNCxSIrxM10NLbfEGJvS+QO3y7zsen4orefZwCS98U7PFKk4NP+c4Mq/pSDRdV0kdrtajq2bUFpHCVXCcgVQ1S7IS79ccPgrrthYXCM04bEA3qtKlj9Y0pGcPHrGXr9PC7K/TB8lZDZnfA5ME6ZtfRqo16UrIKDB6AjoG1t0gz/axDvmsqkc/NmYKRzIbCe73rAHZAYCUNWqQn4Xjoqe/Qz3ySSgWoffzxxzsu7yselGLvLcaDQioVefREBJTqWPIzV/35ms0AvT+v3kbQ994C6OReohjhvQGoIyRRjlGQKs2Wmz2tCA9C6wsdeHh/UaKYD/rU3Xrlfsqe5Vt+Dis8IYSzAiTw1hYzZCDsCdOslID05sgDur25mRygKPiAQQ7OUrzGLbGE9vpNprxVsTYoGtS/v79P7svctJx1y6REm5z19XZdQt4hh3NdBRa+qODCF40XJD14SylYJadtlfG6J/a7VG4tSpm/xp6w0QEAU8ZMZITXEOHvST1/FUoOHgIO3Uo1Dmi+XQvKmQ/AOONHgSpHeUIe4L30hnJXqr/FnkMOMN8U4+nud17sXX31e+aWdQLY8mP38LXXXmtqq8P2vSl60PH4EalkI7lYH/JBCcOsNOM0MtR6tJHNeLYxAiDTK93YyxgPCRUGmBfkoHDTivx6SynVvkE39mq/Tss1NOv/vag1rX+EL9JWweX+hxCMKPblPyk98aCSy2D++pm5euGjNaZ2OGRrVSFHgdZVakqFFHVS+lHo//Tl4H9kSN2qBmW83nWjFD/mNrlEWAHFV89R9IHTpZaVcpLtUhpgnQHXGzQnKLk5fkd+vZvWY4NO1UUrD1cylQmfJ7YgnnIVCTr6zbGf0QFbDzXfFd9bPJnKhM6vR/3eUPhCk4fBCANrfwDoPVHWE8MtOoYF7Hh52dc2HJ7vtJg+VGh+0fPIjyZCsK8aHCDIIW998L7iQSn2XuaoEA8K63HFFVcYx9sZZ5zRkVaFfs889zQhc1+toZ/3bgbofmZp8zV5Z6C3ADoeMzxWljkTIUzoDN4+yyLeEx4MDku/ChgKFRZhwm8BHoS9l+thK5VVPdcCoXRxmDAvAJlCdVEB6OStVbp2KhEG9IHDkgMSoGpJz8rJr35/abNOuu0N31/kWz/cT4EN3Rob3B/8aIaCS9+QWzuMJEEFP3hAgaYlSteOkKrqMszCwaiUTiq+x5lKT/pcwT54QTmeckJGGS9eKvaGJfDyPZBeupD1wfhFLnN3mpd9nvHzHeEpZw4wAnWXXLEviXpYW8aA/OkLkrrurIufe1GUYAjmO+X7RcHF6Ed+MsCdXHuI6fpT47xg3cptXrCMkdhLpokBd9q0aRUNKWYNIJSk34BlzhHLe8K5UqnmrdtOihPvRVYX5aBYt0KRP06V4vCEdLZZqXE6KvFzJZQJ2d4w5F06vHa2bkxdXmQIjhQMS05QiWnfU3Kv88oa8q0vLND/vbJI62Ip1UYcBdrXSsmE1rlR1apVUwOzdE3kDxliUCcoN5yp+pGc+h0lp/5XZgSNCxWdfqrcVFyBxkWSWyhiMJgHvGeMFCcmLtfr6a1VHc7kndOMBzie0l5bDdbNp3Yt3cWebWyNKxpyFMRpvz533RLM5SKaA6BDGFau7lHWRJd5E/obeh2Rdj3RcnnXAXwWrJfjXSc6EOOHJfPsiX4Xe+bPfvYzo0vddtttXS7tCx4UOlDovXaP0+dcPCgY7wHoeNCJnMShg7xlnYhWwygM18qm2DYD9E1x1Ss45u4qPn67AmjAk5MtyK23GGHVHZK0fP1AUKCAQV7h15KHkgNIx+OBAs9PqV5CyxxfKqt69jg44AESeBzx+uHJzpXbS+kowuIqDdBtf6zHHI8j1uxyLdh/e3OZfvKY/xJsT567h8YO6iy1lne/JdoUXPKqAqs+lNOySsHZhI6l5Q7ZRiLvD8WodY0pwRbf9RtK7XjCBo9irlFuMYzwJ4YRy75uQ/bxPPL3jRWg43nAMg/gKLVl59SjgFqSt0rVabd96st67Sh9pNcQDtyXTL6lro/f65HphGwTmYTMw8OBVxUZy5rCRN/TUUt+++r3uu6cUxgpAMvsbzzZ2cCn0rLThtADHthf9uzAQIKMqVTUCM+C8JBvHkDOmuLRRH754qBItCn8h6mZSCRPuy3xeV2ZOi1raTq96IMj0j+2e0iDZk/P74nmbiegFORqR94oRXzI8Byb4YNlzbpw+nta3hzT4ECbMSi0KWo85qO1SmOdVbopcnUmdB1DrgHArlJb7q/4CevLHbquIo+dp8DHM+RgkCjINL9hiblMtzL/v2/777RMQ1Vb1ZWlvSWWNPXNH/lOJnIJGTNj1ir97c2lWtoUU30koMMnpHTMNhEFhk1W2gnlJZrD2E4EVH8A6OglfF+9RbjGniey0OtdR+exgJ05K6ab8M1wnuWrsONXJnXnuosvvtjIYqpt9Ndmq4BQ/vPuu+/WqaeeahxwGIG9JHEnnHBCn5a068v53QzQ+3L2PwXv7o7iU8rwveVfct1nmdX5XTnlwvL1hfGRg37IIYcUJK3Kdb8FO7Z+MWClmPC3z6kEaZu3T1ipAV54cHOVQ0PJJCyupxTvYiRxfvfCGwsbddodb/m9XJOG1ejBs0sgPIuvU2DNx4r8+5dyWlbIHbhFpxOoZZWUiim+36VKb3Wg6UM+UA4wh6Qne703But7ockDHKCAkIPqp9nxk65gIwUsKO9J9nm/7L9+xlDqNSjPhDc+9thjFU0FKLUffXE9oYoAxWzPTV/0pZR3lntOocwDzlHgMQ7nMrTisUSx7A7Bph2LDaHH2IyHzis//j97ZwEmV3W+8feOrMVDFEkIlH/wYm2RBivu7q7FC22RAgVSKC1QoBQIVtwpUry4FgkUKDRIkIQEQtxWR//P72zO5u5k5N6ZO7OzyZ7nWUKy997j53zvJ++H0hfBFUt3qYXvwNaPkI+F3iqfUXyxt7DAeimht69V5OXOrO6N6Tr9uO3mDpbzxd9pB6nRkPRWr99qufh3uavAWylco5aTP/YVc57tgze9MVm3vDlZamtSyEmpRgmtFJqj5nREuzlv6MTo4xIu6KTdBKAn25QaNFptR73c8TlnzteqvWcXOa0LFuVpz53Pvf3CyPg9ru/hWu3VdJb+m15VDbVRM7e43ccTKePmvtbyfbTbOkP1nykLNKuxzRDVYTnvG2pTW+M8xVJp7dowQWeM/FqxDY9VcoWfGqWRZYXnXOKHVFsAdBRrpXoqeVkDpTyDZyTyUZBeIV7bw1gBBt2x6yjVbd51FGTZeBg4D7jjCmXY8dqOYp47+eSTTUpbsnp018KZzPgSPrrzzjsvs6nU8s1fD0Dvrqu7StpdrODjt/lYHgvF39m4Z2JoOVw59EuNw8K9Exb3Yll6uQQQevAAsKRCXtoUJGmbHWt3OjQ0xVhiLPFdPiI8v3OV7fmgADrfzp4jPXcrj9l0JZ26ZWdhN++huOB7Rf/zd4WmvS+nrVHEpjuphNKJmNJ9h6tli4s0M15nYugBpbD3W0t5NlDurqsr83d7mUcUOAggY8aMyfk4e55+03+EG/pvQbn1FPBSVynP4PoGmIGtuNKFfYRl8/bbbzeeNf/973+NW16502xVup/Z6kOBg2DIvAcdc13O/vkNxWKOsSZzbkNEiLUsl3L19ddfN3dNKXwEnI8oPgDHmS70dlzwhGJ/8vtSCopjwDlnFneAG8RRP2Fanjkommaq5oaNTfoyd9m59WJN0KgszXQUUkqHhJ/XRdE7cncj0qDU0HXUdvBjpXTVvBtPpnTps1/o3x98rEg6rj7huLGi908v0H7O81o/9KVGhWfJwaU+lWgnf+u7glqPf6dT3bV37iRn4XeGQNRY0QHzxuqeL30frvq1UqTGxK4/0vYT/S5xtJIKm9CreDLdAeWJQ4+GHfWri2pWU0zJtDSo3tGo5GTjuTVPfUx6txsH3quVekux7a9QemB7HDTrh3MZJQ5nM+eiVSZ5jV0veaCL+ACyGm0vB6+C3+ZwRrhj11FiuWPXrXWdvcMZyE9XlSOPPNJ485x33nld1YSS67UcJqQjZaxPO+20buH1UXLHfXygB6D7GKyeR5ccAYAzFwHa/nJqaxGCAbZe8h4DqrmoEOBxJeYdr+zQmT3k4AAsb7/99p6t39nWCW3iMgKsM1ZY4PK1KVtqt6DWHxc5bUEAteODW9wmm2wSaBylu71BAvRNr3hdC9vyWTA6jxQe6rce8mNtOKJwnl7zZqJVkS+elGZNVHj+t3KaZykdqlFbKqQf+q2nz2o3UF19fQe5HevSq2cEniAA2mqNXSb/OAKIzd9tRxIrh02HhqszwopVSvjpf1BrGDdc4tDLxTafr53sH7T+CJV4G6AsIO3NXnvtFVT3uvw7AEFCdZh3/uQchByPfj777LNmjWTLatHlDc/RAD8AnWfxdEEJ4YU4E9I8zvNiFTTcDSjF+BNX81yuycTrYtHPS+BWYAKshZ61O2LEiCXOLdYybsfcBV5L6ItnFHnsOGN5tuWN5No6Mn6mEiImu3PBvTyqhC6P3KjdItkJP+OhOn29zq8VXntPE+/vNbwsV5snTZ2m1x8Zp/829dG80EDNS9WrNQm4Tqpebdo49KlOiz6m3uGESYmWxoJ+8OOdPhd94TyFJz4thevlzJ+8iAzOurQ77WDdcZQO1yndZ3h7vDrw23KgAMijfXVqn6v0/LdpA8Bt6VUTUmsiZUD70D61BqDzajqV0KrhH9SvhlGTpsZ668Ll39VWideVXO9QJTZszw/NmkUW4/xmzXLH8G+AIO5eW/LFrnud7yCf68p0mYX6YZnhue/Yd4QGYl3n/5HhuhKg77fffkYm/dWvflWoG1X7ewvQ7733XqNoIHXnnnvuae4VZAp+UAIHmb64agcjR8N6AHp3m7Eqay8XOlZBm/IIS5JXsOKnK6S1wSruh5gDgRJAxEGLQAKZnN+28S4WEg7DIAptYrwQeLG65Bqvcruc0xeEcNqCmxdCOKAjiNypqVRa81vjaoiGVRttF9CCBOiXPP257v/ABwswsX+rDNC4A727huLS6Ex6Xc1zp6tx4QK1Nc1TovcKclbbVgNHrJ59nGCGn/uNQrMnmvzp6d7DlBr0f0pDNLeoVAMDbL51zHontnrLLbc0a8KCciwLrA2shADzro5vLAfbfL5xsSkBcWs///zzzdigHDjjjDOMd013siZ7OceOPvpoo+BE6WpZ3DknsM4hTB166KFlVch6aaOfZ7wCdNY/dw1KZwt0CtXzzjvvGAs794vfwn1AfYQW4bqeT2nrKz48oyEIw1jomdNcFnpeQfEFaOIu8FNCH96jyDOnd7yCcfn4+Ol6IbUhPOQd/x4RzOQp89/VNFUP1v5BfZyWJapauNm5mrL8zpo5Y7qa5s1SQ79BGjR4sDl//Hjp0G8UeXgfbLB8VEPfOFe/m7Wd3k2spiGarfp0ixrVoNnqq93Cb+m0Pq8YwrjEz05SYqPO6eCcGRNU8/RpclrmyGmBbG5Ru00KtQFK9VtJTtN0JUdtpcRPjlfNM7+WM/9bE0uPtT0Zimps6AT9c96qWtDmAs2LItTDYceMVF00bKz+lGQirqGhBVqxvtUoiOck6vXnld7SRrHxSq28ueJbXWD2Jwoe+sqazSTL5exyg3U30ZwF7OU0ruRbR6xJ9lpXpizzss7d1nX2EMUdu15pJTUu4ZzBxx57rJfmV+UzNk3nPvvsYzJIoPhALuYM5Hf8aVP2VoqjoNoGqgegV9uMdLP2WOGNQwt3QA4qgGfQZGPvv/++uZzR+vsp1q2biwAtPBsdtyWvBY00OckRwoMqtAlXQtoE6MHNMBMYP//882W1aNu+2LZgCbSkRF5c8HONxQufztDd70zR1HmtJo3MdmsM0dGbjVRDTchYiIIQBF78bKZ+9fCnvqZjQENEr51emPjMum8jqDZN/0r9NV+Deteo77BVVDt8dZPLNlcJTftA4e/ebRe3IrVyFv6gdLRWyVFbKzVkbSOokTYJTbwXTxBfHQzoYev+yv4FPAAcrKW8mjTZXOYoO4ohs/M6VKwFrCc2lAGBgfXLv1133XU6/PDDdcwx7Raspa2cc845Bowz/8w7CgiUiVhWuyMxHgIfd1W+wjzjqYVljDvM61n13nvvmT3i926CRBSrJ9Z3fgopj7lj+fEaH277Sr8RgFGwYKEamv6yAAAgAElEQVTPd75bLpd8IS5ZxzAZU83f1pVa5nT8+q3kmjolformqrdJFwY4xywcV0Q1iqtBbboqcr3GRD7p9EnSm7Ue+29F3rtRkY/uMd+M1w/R1BV302d1GyoUDhtZAMs6Fs1cBKyWeZ8+AVxZyxM/+6/OfvIb9YnNUu/UfGPdRqE8Lb2cHKV0e+3lGrTK+ortcbO0iNHd3ThyoYc/uN2k4HQWTjOs77jDq6aPcfOHRC6+9YVKrraDnNlfKPLxAwp9/77S0V56om53/fqDwcrlEA84j4SkmkhYvWsjmtPUZtznh4fmakhtQtPivbRa3TyNG/mKauZ+qeR6h6txrYOMgoc9Cg9CITJaG68O4LT/b/vHemcNZmOGL9f5Vu0K62z9Rh7EUGT5V9zWddZjrtj1IMcQBfpvf/vbpYLd/JFHHjF7E7kcGRHlNz/8PyFVeKaVIpMGOe6V/lYPQK/0iC9l9XH5Wxcqd+ozhBwYw4MS6rHqQcpRLDGHZTPHTRBBE4u6l7ZxQCCAQRIXdGHscDUn5g/hDi2ytaCQ2g2irkodTLitYgHCIwL3awTGQpd95ngAzi955gvFkin1rsEKkDZue5usMkB/3nNNJeJxz0JvvrFuiSf108ve9DUdkO28d1b2uGrmwR1TzZhbS7Hn8W+dr8hn/5RDHt2aXgrN/ExO80w5jTOU7jNUiVW2U3LEpvr8m6lVZzHAamhzlKOQQmnD/mAMSnUt9TVJPh72S2bn9dMIBXYtIHjliq9H648V49RTi0v/5LU9Pc8FMwL5ALo71RjKUr93jF/vLupDmc25D6gC3HspAHruLz/u5zZkhXOMurJl8HDXjWcKSgPyuvsuC6er5lri4xdZf9OOjoudoVfS6xmrOSA9pohRYPJ3XLY30ye6rvZv6us0t1fnhNR60GOKfPqYIh/f3/FvSsZMGrTYmLM1c9V9Ojx7EOQBRBawW08WznSULexnQgLsXf/upLka+/QXWjH2lSIts7TA6afJif5qStUqIUc/Dk/SiWu06md7nZa/+7EmRd4dp8hnj0nx1nZeuHCtkqvtqPjmZ7cTzmWUzf/yhmY15VcSkUptuV41Wr5/nb6a2aiWeEoDnUbVKaYVa5t13tB/a3T8c6lhoOaOGav3v55tyAkxPHhVKLmbZa3rbqI5Mw2OY75XbrBe7Zws2RYBJHwoKa0RylrXUdyyf1CEldu6jvfW5Zdf3u3SXbrH07q4I+fCRo8Srad0HoEegN6zIkoaAS5A6zJlP4TAjwCCRaLYNGOZjUJrybeKjfOz3+PwxHJN27BiFoqdtyy+kEGVq+BqjiYZpnXriu8193oQbbK53q1bM5cm84qw6pX4CCvE0Xf9R1/MaDIxdB3jHUsKQH3lvmtrzSHBscre+MZkXfvqZM/dHzmwTk+esJiZ2IJygCkXKwIsgnKx7tvOvEmKfvG0Uv1HKTTrM/H3dP1y7WRC8Ral+62o1PD19WlsuBF8utKlj/lmrbEH6D/7FSGPuUawwGUXAd2vgsbzZATwYK5Y+WI+jVuddeUH+ONCi3WO8ciloDnssMOMi/vvfve7TlWSDxbBCTAF6/fVV1+dl3APSzzCCcALJd25554rvm0L6/TSSy/VHXfcYaynEJbBpL7DDjt0PMPvsULgnQFAwauAZ3jWliOOOMJ8w10gAnv77bezDhnrg3XBesBl1iphrYWtmHHuyndyAXSUygA5zmCAXDFZLPzwo7it2dTnJ6TIr/s5axrLOe733CuFLPTMTyn3nZEDJr+l2vv26Jjq+akG7Rkbq8kaorSB6DLgHLgOqiWyerS+1WM1v1dN3yFq2/M2qb6/6u7YlriodgI240q+KGA7XKPWw55RevCapg7WqVWo4f3DfsUSx78RhoM7vztsYOrcFp3x8P9UM2ei6uJz9UlyJUMYh8s9pZdaNbQuqT8ftrXWGNYn/5JNp+VM/6/CU9hDaaVW+Ik5441Le0aZMrdZ2/+tM+lcto8D0PtEpXgckjhHwyILtfXgBdo0+qU21keqDyWU7j9Sc0cfqPHTZFKnIsd4mdtC+6+Qdd2SzRX6jp/fw/XAHqjWtKPZ+kIKXPZuLg4OywyPXMEPSjHuV8sOXywfkm2LJSkl3SUyW3cvGPJuvvnmpaIvQc9FD0APekSXse9lA+h2CKwbKs8gIGBVL/YieeONN8w3vFobCk0DByegGO2nZeDN1raSLAqFGuH6vZthHSEbYYPD14uV30c1WR/NzPVOW7DuoGThUmF8CsXYzmuOa5+b3jUpw3HPc18mMxbGdNrWq2i3tQcVpeXP1b9rX5mkG9/81lP3L9l1tHZcY2AnorMgY6qdBVMV+fwJpesHCjdIk7aH3L1tC9rz+Q5ZW04qpi+i6ygZ7W28SypZmFNAiAWigFLm1lqerNAAYEEAgSSuVEGinP1zx8r7rYex4H07FoB9hHo7Fl723C9/+Uuj3Lvkkks6qn/ggQdMXCAgnRjeG2+8Ubfccovhecjm/jxu3DiRzxbhBLCPlxAxhZDm7Lrrrua7/P7uu+82z7APUdwR9w4xmSULA6wfcMAB5hvMHyCfkBXqtQoGAPr06dN12223dbQX4J0rNdhFF11kuDfoC+eutXb4HetqeZ72cw+5C/OO9ZuzDetyZuyu17YDMhDCCzFRs/+oz6s1O7N+r+7n9BWFD95ZKIn8xMajoCKcbOutt/baffMcdVq36ciUf6v2n8fKaZtvfvdBalWdHjtRUzRkkW19SQA7JNKmp36zrXrVRBT+/EmTd9wA3XhTRw5xC9IBqK1HvbpE+jWUH9xbkNxRUDCiaLOu8NZ74OqXvtLz7/5XrbGYftBAozigRBU3lHYpJ6rd1lteY3ddw9cY5Hv4nnen6JJnv8z7vT61EW0+PKFXJrcqkQ6pwYkratLCxXX0StN0wE6/MPHxMxJ99PH/PjV3SLlycVuFnNsV3h27HpR1nXMKJaDf8JDAJqaID0EazFnrxbuOcXQzw1vrugXshbK+ZGse84Cx6plnnvEd7lJEd8v+Cnco6fZQdnMHcw6zd+1P2RtQxRX0APQqnpzu0LR8AN1e3MRbA/YQfP3GgNsxADQgbAAqgioWiEKKwyWBAJxp0UCIx+LuNS90qW2zDOsIWLha0qZCboml1sklTMw7QplbSMV9kHnD7b2Qt0EskdJeN76jprakBjREO5qUSKU0uymu3+80Wlus2i9QgE4lyVRKY654QwvzeA5usXJvHbNWyFiHmF9rKS+kdPA1rsmYIp8/KWfeNwot+E7pugHmddzcU8uNVqr/SIWapuuL6NqK1w4oKMz7qjvHw6xv4sit+zoCrLUM54rdZC28+uqrZr0XC1iCaHuhb6BgAKTi2eJF6WcVFIwFQIf3rdcAY+J3jwGSWUukiLEFizQxvgBvWzjv9thjD2MFzyxYugHyWNxtgZWXkBoUkhTAFYD7pJNO6niG71E3wD1b4cxijTOPlo0fgI6g+Nhj3tJWXXHFFWZ8scRXWplUaO6L+X0mQOdMAxxguaN/XtZQrnrxXOD7+YiMUI5gaUepg/dMMfVxfmERz2c1czPQo8Dxw7dC/1Ai4EGzzTbbeB5mm4vbkj7ZvkVeukDR924y37k/saX+kDhUzcK7ajFp3OJKHO241mD9Ze+1FZr8hmofPqydFR4L+iKStQ4reiiq2C7XKrn6bp3ayN7GPZ+7CuDK2Wf5I7C0WyVcr94NeuLWS3V9yy/UpPpF32i37+NKHnPqjYv5s6d4Z7IvNFhPfjxNZz76Wc7HCAnba71hevS9b9SUipi2hJ20BoebzRjUOnHdtv+P1Fo7xNzJyEKE6lWquOc4W+x6sdZ19gTnMJ4A3aGwz8l4w9ntRZGb2ScAunWFZz8DQrmLbey6l3uINnC+o+zr7uRprCVizB9//HHTl4022siErFj2drxgiLVfVksPQF9WZz6gfhcC6LYagCeabYAnhwvWBj8AiUOxGIHDSzfdKdC42BGg7EGJYIV2r5xkVJltZKzIS86hjZDBWHGBFSPUeem/rY84+2wXBBcJViIuBtzecylJ/vbyV7pv/FT1rYuqPhpSMp024HxIn1rdefj6xhpQTJxcoT4kU2nd/OZkjXv9W6VcqWuiIWmfUUlt96M+HURnftZcoXozf+8s/F7hb15RZNJrSuPaXttPqd5DlRq8Zrs1KVyjz6NrKaFIJ/djv/Xke95q7K11mL8DQNlzXHyF3NatAAJwrNb4c/rPngXE5nPFd48FoBzwYr0G8pFLeZkPmMwRtm66qR2A0B6EiYceesikirGF3K4wLAOWMwu5infaaSf94Q9/6PgV5Gx/+ctfjOuuTetz2WWXCVZ1W0hHQ8gPZ2m2guUU0AkAtYRuAHTAOUoXgArjhuUiKI8kL2PWlc9YgO6O/4Y1PQiQg4IXZWY28jzqYz6YK6z0pdRXyLqN0gmhneKOu/Yz7qw7Ymy32267gq/RN2s55+Fs4Q/RFy9Q5P2b9FVquA6O/U7TNTDLd9sBe0M0pFfO2Ey9I2nV3bqVnLlfLXqW33dOq5nuN8K4uqt+oGkDDPfc08xBtjVtMxBwLianvK/ohPt0aPxsJU1M/OICUVzYCWlAr1q9+KtNFAktae3veBqX/vaOFxyrxta4xlz5b7UllqSI4+0zt11F943/TlPntRgyPWq1bvcrRBeqORnSaaMXqP+gFT2l/ivYoBIesADdgna7Duwa8GNd52zEauqX96GE5pf0apAKbHs/WcCOVxchNhaw57Kuo2jnGdZ8uTwoShokHy9zbnK3cXdyR6NEZhy4T/kTJQhrZFktPQB9WZ35gPrNBvNTECLQAAN8sSbgquPFlbZSacewhmBFQMAFFGNp8UvM42c8sj3L4UQedKwYuNjTJgTrYr0PCrWHA5/xpb5cc8FlwoWAMIoARHxrpga5sS2hi578TOMnzTUEccgtg/vU6oKdV9e6K/QJjMU915ghfP1v0g/65PsFxoq/5VoradjQIb4UQYXGquDv480KT3pN4e/GG+beVJ/l5SRa5KQShiRu4sK6jpSEBb/l8QGbAsxahxGUEXr4AYz5VYqw9iCiKqcyw2PXcj5mBSXYpt1KJf7dzbxO363rOgoKv2ORqwFYxEnfdM8995hH8BJCyATcuJV5f/zjH03sN144mQWXPlzOn3zySWN5x7UY4jnmke8REnTQQQcZqyngGssg+3T33Xc3yoZsZy/CMr9HqYaLui2439tYTxSlpIpDMUeduRQx1iJa6lxVy/uc64wld5Df+O98fSCFF98m3tldGF/qA/Tmi1n1Oj7UAWdAtowigHdy0yO4Y10tpIjLVSdAFmUSaUXzKYQtMHe7Ped8Ptas6MsX6pT3B+vZxIYZVS8GtyhUH/3lT7XKoF4Kffe+au/dfVGucfcri8B6uEaJtfdX27Z/MnuLPQPJlKfsMR/eqz0fj+lzZc8IQw1rDOurh47dMOsYOHMnKfKfvys86VVDXJf80baKb3CM1Ds/2d8Nr0/Sda9+o0UZ1IzSASB+9JqO+g4fpdvemqqFzS3Gek6oGCWeDql3KKaadExHjZilPffaP2fss9d1FPRzudK4UY+1rOc6d1EowSvUlTnF/YwHshLnajk4WjiXkPf4sdZ1d+y6vef4HZ4/3HPcaZSguU/4JvwpeIMh96HkhxiVe8/Kfcjv3IGZ5cQTTzRZTih+uU/8zMWy8GwPQF8WZrlMfcwW1+e1KjRlAE8EAsAwwm0+geC5554z7qBe4n68tiHbc/SJy96mw+Jgoq1+U9uU0gYEbzwGsGJwsSGMI1Tzw0WGRT1I92OrEEDwKyTYcYkwNmg78TQgdsx9+TJ+H3+3QBNnNqlfXUQbrzLQxKRziQeVZs2OLd9jrvhBQLUpwQDqWKq6TCufTis05ys5sz6V07ZQ6do+Sg9aXamBq+rrbyaZcSB0oZRigSh950Jn3izzPEJqKd4WxNix3rs613m+8XFb+ll/rEd+GAsAp1VQsCZKGYtcbfjrX/9qXNGty7gF6LiFu1m2sVLfddddZs9kFs4+XNf5Pf1hzR5yyCHCYo4CE0UYa5m49CeeeML0A5COIg1gj4Uhs/A9crXjXZDPbRQSO4S8+++/37gYLu2FPceZyt4olG/c71ggpLLuULLY4o5vB7h7cV0tVC/zDTgAPLuLTdfGeYzQXMp653xn/9u7J9cdaWOT/bB8z5zfpK2uebeTl5P7+xCkvXDaJhrat878c+TfVyv65hUukG4t6fzpKB0K6fM1Ttd3AzY2ChCvCsV5Hz6hMY/Xd1ioM/sYVlInrfSdDt9yTdWN3EiOy4qOl1TNo0cpNH+q0hHamTau+OnlRqttz1ulun45p5E9/tTH03THv97WtNawhmmujoo+qz3rPtDF9Wfq8YWjlYi3CqJ3vM0oxKLDij8yPFe3HD1GQ4YtX2iZdOnvCxHNZaZxQ0HIPV0q+W+lOo1shhLWa2hVse1iHJFpLGC31nXuEcjpuCc40zhXysF9guIZy/att95qFM4Y1QDb+++/f0dYl/FGSZI6sb0QXoIMyTlrw3C8cp9wf6LoZH1wZ7MeUDYGpVAvdh66+r0egN7VM9CN6y8FoNNt3sdCjQYcwAloyUZaxHOQI1WKNI22cfBgOcdtjkNw44039iwAlDqluawYHNIc0GhOLUFMEAdYpkLAS/stYRH14/ZuNbm53g0KoNNWyz7uBuWAGWsJxGqFYqXLALodBOInSQ8Urulg9uUSog/FxI5hkePCpv/8yZ6xoJxLuxTB3D1vWNBwv/bDMO1lzQT5jBWUbL52mxrPMq8HNRa52gz5GyQ9KA4pxbi4229jlQGQo3zDZR5iOJSC7r1trSvEpJ999tnG6k7YibuccsopRmEAXweeSYUKZwh53KlvWSiw4CP0Bb02pk6dalj7IY6ioDQjttYPe7qX8bfg2Vq3uRfxaMLChRLAa8aNfHW5vbeyeVO5yeCsK7OXtttnTn7gv3rp89lZX6kNS5fttba2XWNw++9TCdU+eKBC37bzMeQqiRGbK77//Z5czfnGE+O/0NnPTHE5zXd2UQ8rpd9H7tb+dW/rh0GbafZGp2vwkGHmjqt992+KvH+L1DDEELbZdqp5luJjzlbyxwcvbmbTLJOGLfT9f4ySNrnqdnLmT1bNSxe1p6KDSBQ3+XRCdya207Whg9SnV4OmzW1UPAW/PU856qU2jd2in3bYYjM/Q10VzxZK44bXB4rC7hJqgwwGR0M5s/pkmzj2PnLfBRdcYJS1yKf77befCZGCwwSFepDcJyeffLIhUcZjy5Zf//rXhsjU7Znlbiv8KdxLnEn2jPXCfcI3L774YqPwxkuINQMWwMNs7733rop13FWN6AHoXTXyS0G9pQJ0OwQWDANeAFa4T7utdzZGOpPErBJDaPOU04agUsYVancuS4l9D4DG4YnV1As4LlSfF6tJtm9wkGLVZ94KWfZLAeiAMUt0BigHlHGh58rTXTUAPcugMV4oYJg3L8Wdo50LGiuRtQ4XwwDrpU4uYNxFc6WR8fKNcjzjZl7nIuf8wYMDRYxX61lQ7YJpnTQ3WFNsgSQOxQbuhrYwz7icZyOJy9YWXCfpD9/PVlgPKHcQznCfpzAOgPNHH33UWD+9kLqh4KEeFALutG6ZdVoX5sw/bb3FkkMFNQ9+vuOVL8XPN3kWcI4VnfnnLOSHWOig3Xbd4Jl6rfs8lvuglGn57tpSwTltnjS7WXvfNN7k93aXiCP1q49oz/WX1xm/WLXjV6Hvxqv2wQPa/56IGTCbdV9seKwSvxjraeqemzBDv3n4EyVMDPmS8eP91Kjlndm6v+916q0mTVnjOH0dGa1o4xStO/1hNbROk9N7qEKu2HOncZoS5EDfvp3w0VnwnWqePEnO3G8Wk9yRGz0ZN8ShivZarFBIpzQjXquj0xdoWmR59aoJq6mlVY1tSUVCae2y9lD9aPhAjR7aWxuM6NepXk8drpKHMq3rrDUA+iqrrGLuNKuQDMLoUK4uB5nes9g2oiAgawfK1aefftpwjZAx5Ljjjuv4ZKncJ3hWkakEBTTgnzON8KvDDz/cKIgzC2cTymPIU92pRwtxnyAP8U0wwNixY43XF3LO7bffbu4z7idIUZfV0gPQl9WZD6DfQQF02xSAIto3636JOydafD8u2AF0q9MnOEAAhWh5AcUIyCgQcEcN2hJjK+YSgAQqW6yhfYbLzlr4aQtu78WwivI9r3GHucbWWvaJjcpFaOcXoNvc1ABziPIKgXJ326oZoDNnxKQSJ5qrsN4tyZtlnregvNwhHrQJ92jcgD3Fcwa94Vzf43xhL9ixcOdrR5GH5h3irWJyV5fa7H/+85+C6dxNYGNdDW+44Qbj5o5wQXo0LN2cHxDAYcUF2FNwG6QPADvm+corrzTZFHD7xFWZgjDGOyhM+PPCCy80SjEEW8vQTcwfgJ42uXOfM38oLhhD3sMaAWhkDSJEYXnlTCtVEcM8lessLHWe3O+XC6BzRjGX7E3uCgBzOdakzbbB2kIox2OIdRGE+7z7XkEox0JoPZK8kMF5nSeyemx+xZua17ok0K4JOzr25yN10hYu7490WjWPHqnwN69Kydbc1Tghxbb7s5I/PqRgU6YvaNO+N4/X7KZYBvWccZzXEM020eHjaq7XBg3TlK7t184mT8q3tial0yk11g5RKFJr5BN+ws0zlFx7P8W3PN/UH331EoU/eVDp3kNNnLrizVLbQoUaf2hXCtT06tzOeLMm9Ntcf+l7tj6b3mi8rHrVhtWWCosMKagzIiFHG48aoAt3GW3S0XXnghzFGWaNDIByN5+BH6K5So4D+5u9V6msPtn6hocUZz7nOLIyilbubEJAbSmV+4Tv/O1vfxNWc+YFZcoJJ5zQSfnsbtuDDz5o+FK4U9xpHXNxn4wfP97cTXgDUAd3nvseos7TTz/dKAZgeF9WSw9AX1ZnPoB+A7o4aIMuADIER4AM1iDcEjmUChHXBN0OvoeLO+0ACHBoICQjjGGxwJJVqnCbrc1+Ut3YmHBADDGIAAG/GmiAD+NLPuVSCoIq84brNZZDN8DzAtDpi7WUswYAINZS7ifmvpoBOpY25jeT8TnTS4B1ZfteaeswcdTMn98UTaWsHfsuewz3bkIoWNOWsRYFBeeA2+2WdrIHC4VXBNGuzG8ApLEkEJ7jBqdYz4khR3BijknD5k51hlCFlZvCXkGo4RuALEARac3cIJtwAwQjBBXOHFwa//SnP3USgnKBY+LUsWCggMMKASETYwtIpy7Y43OxALMebTgGe5G9yTlBSBKAnzWMFR7rSj4LfDnGvthvspZsfudiv5HtPe4E4i9ZhwBmP2eVn3bYUC/2AEI568TvWV+oPlsHnhycO/zdsnXzbjam9kLfbGpL6O7xU/XipzM1pymm7xd0zkfvfv/S3dfQ7j8e1vmTkMy9daUi77QTT+UsvYao5Zg3pdrehZqkv785WTe+9rUa453Z4e2LxKH/PXKZtohOaI93b1hOijZIrfPktM6D/kypaG/FIr2UTsQMIeqkDc5V/eq/0MABA9T7nh0NIE9H603aTSUWkemS3YMSaVjsIg/4T7QoOXpXTR9zqV54+0PVDRii68cv0KzGmNqSKSXT7bZ+nOq3Xn2wtlljsFYf2lurDs4A+gV73vUPcLYAzllfKIIB6eVK4xZ0b1Gkcm5XMqtPZh8IrbrooouM4rdc3CfcUVjpcT1HgYwnKVZ5+FAgGM0syOacewDufMUa31AoQzpHqlDuM0KCMs8y+kg7iGlfVksPQF9WZz6AfpcLoNM0N1kbQgFCZiY5TgBdKPiJbCl0EPQA7mgLcckBGAcplBVKp5Ot0QjLXByMW75UaNneLeRSX3CQXA9g5UGwx8rH2KBgAXzkAujMq40pR+gvFpS721jNAJ01A+ABvGX23Y+XgJ858fssTNF4QmTjg/D7LS/PszbczOu8Y/O150sNRzutAs9LPUE+g2s7JDrEH3cH67HfvmNFRWFnQ40sIR1eTfSXfYqAyPh3lzQ45QDoKJJsajPSVAYNmO28ca5PmTJFEyZMMGPOPJSrWEJW5p7z3Fo2i+nbJ9/N1xF3fqjmDJf2XG0/4xejdMxm7d4j7r6jFP+/Z/ZTQ2xG9ldDNVCGq22vO5RaefOCQ5NKp/XshBm68OHxalTDouctWG93e19XX+ix2ovkOCGlB4yUYs1yWuZIFmTbt8J1alx9X3096lDNnLtALc3N2nrihapJNSsSmy+H53Fv5/NuLwAHKzh1paRInaZtd5P+M11mft+fHdbYpz5VazLTBb/979GQo/qasLZfc7B+t8Nqqo0siocv2POufYA7D3DOXYeckm1NBZnGLejeImcBVgGtXVX+8Y9/mHhzvK/KxX1CdhR4l4hvtwUwjRs9Clr3vGF0IEzhkUceMeFchQrrm7sTBTeeongD7LLLLkYBwHdRRBOPjscZbUBpvayWHoC+rM58AP0uJ0C3zaMOLEwcAja9VyXcfG391I2Qki1mGMs6pG1YpQDpWKOKEWIyp4LvIfT5JSKxqdC4QGwsvxfrKwcu1kgvuW+9LhvGBmES0A3hB274CMiMD5e0tZTze0AYc0ubg1B0VDNAR7GDQoJxYIzou3VfD6LvXucn33O4VbOec+W7D6KOTMI7lDg2HRrgzwvoxU0OV/AgyLH89glQuttuuxnFgpe2+v1+Vz/PmYArJ8KSze+OuyJjjhUd5Q0C2zXXXGOsx92hBAnQAa14Q3DWIpyikIRdvxyFcx3lK0SC3EUIreXw3LJtxzsEAMK821R7xazxtkRS213zlmY2evOyg6/8wl1Ha5/1FzOVc06wDrkzftpnuvo8c2L2Ia7pY0jl2va5W6kRm3qehlP/eo9emD885/Nr6BtdUXOTZmqg6tSqDZyJijqLmavdL6Z7DVd8zG+0YJXd5Lz0B/Wb+A+Fkz6s8DAAACAASURBVK1KORE5IUchCEOzOdXX9dfs1fbTJ6kfaf3Il+ozb4JunvYj/WlBrvXkdETOh0PSnj8err3WH641h/dRlH+o0sJ9Bzjnnkcm8Lqmqsm6jtyCLGoJIbtiqEnb+fDDD3dYlsvBfQKXCueZGxzfd999OuqoowxAd2f7IXyKGHgUiIVSJmdynzC3ZEQhXIywPxSPeCm88MIL2mijjUy2kqC5PLpizoqtswegFztyPe8ZYYELtNyFDYtADIjDnRAgDIAIMvYuVx8QjLhI8qXFsqRtgC5cbksFNgj9CCW4GRZTcCFDsYBAh/AIuV0+xYGXmPdi2oEQSxsYQ4Q9gBcWJ+qzoBxwFfQ8VhNAd8dRc7kjaNJfLiIElaD7Xsw8Zb6DZp41EzTwRdtvXddZ46yJUgjvujJFD541xAIzn0Eo5YKYt3J+A6vNeeedZxSSAF3WLa6WWD8Ap92hcFe50wIV22a+gVKCe4n0XgilxICWw8OLPYOylrYT247FCeG5nPwQMDfbPPHFMLXbcX3r6zk65u6PloCkuca9b11EL5++ieqj7fHVhFVw7zO+NlVdaMIjqn3q1MWp14jvJp47EVO69xC1HvN6e9YMj+WUO9/SS5Oa5Yg47+xW6JBSalCbokpohDNDF0Tv1PqhL3PWkFx+IyU2/ZVqnj/HkMXhDq80oD6ttADXnd3qU06tYuEG1TgxObS9to+un72hrkjsk6OOzlZ1/ta3PqKhfWp15nY/0qarDPTY+8o9hiIecE58MrKbV3Ce2UI30ZzlRqhk7DrKSWRQ9mBXFcKoIHKFMZ1SDu4TQDecKPCoWBd3Qq3oN/XZwnwgKxx44IHGVd1d8nGfYLzh/reAnhBLUrsxtnApkfoTzhTLg9FVY93V9fYA9K6egW5cf6UAOkI9G5pYTg56hETchIO0WueaBgQxLJu4/OYrHFRoVrGQAtAzmej9TDNaRtxHbeyqn3fdzyJAMm7ME4qDXIDLT8x7MW1BuMTSRD0WkJUTmHY1QEdgYH1a1313HDXKE+YFgbNaC+5lsKMHkfrGuvEzFowJlj+7BkrNsw5wwTPDTUpTqTFFkGBPMZ/V4vlQjr5bAjhc+gGgnMWWjJL4RNwaEby7QwkCoFsXXQRL4s0RIIvNglFozDgvUUKh2CQkhjrhJCButxzhJxbwMNfcZ5YDA2VAMUqo5z+dqdMe8uZdAQHaJbutrl3XbY8/p++Ac/rJPnPXH5r8pmofP66deI34bSckRerVtsu1Sq2ydaFh7fT7O9+Zosufm6hkDkb3xQ9DG5dSSGkNUKMui96kzUP/VcjJFsPuKN1vhOLr7K+ad8cZ13vc47HwL2lBb68hFapTKEVKzqhSfYbrqTkr6tTYCTn6siTz/NA+NWqKJdW7NqJbD11PKy9n3fZ9DUdZHrYegXg7eUn/6KcRNo2b5UqwYJ1v2AwTxazdXG0g5htFO3u/qwpu54R8wLRuS9DcJ5yVl1xyie666y4Dmrmzd911V/Nvbm4awmG4FzAIZcrIubhPYGtHvugphUegB6AXHqOeJ3KMQKUAOgciFivLUskhjMDPoUDBuh20tc92GfIKgATKAC8FgZ3DEzIMm5atkNtP5ndt34JgCrWxi4wfByvCTiYwAjjhukoMZbmKF5K4oOruCoBuyc0s4zh7w8ZRI2RalzAuO0BONQP0UizTjAOujHYc+H/WnQXlQWrEmWeUYXAdVLqgZIGQEWVaOcBSpftTqD76iSWFsxbyILxiYKj/zW9+ozPPPLPQ61Xx+1IBOmMAaMTlknGwgj8KOKzOnJ9BKR6x1OFFhQcUP9biiOWMM5yzJcjiJoPj7MLDxXq7UI89yzKJGvO14etZTdrt+ncNA3m+Eg1JZ2+/mrYaPUjD+taZeuk7+4s7NJu11Zk7SZFPHpAzb5LS/VZSYu39lR7oPy5/YWtC+9w8XlPmtvgazl5q0cbOBP0+epdWdGZlBerxtfZVeO7XcuZ/K6d1YUf8eVs6ooTC6uUsIo5bVDMWfPqarumjSfG+2q3592pSbZZUcEta0FccUGdSqs9vieuQn62kU7Z0MeH76lmwD7NnOKeJO85FSBlUjZlp3DLBuiU4LAWw48bN2Q9pcFcVyNNQetxyyy1d1YSS60WxiyGAu5swKhT37h/uVEJZy5VKtuQOVOgDPQC9QgO9NFZTKYBu88wSe+cuNuYaqzVafoSmoPLB2noQyEiZg5Dkp0D0hhCLdQXNIkKdV7euTIWEn3pzPYurJIoDNMAIPfTHgsZiSOn8tqnSAL0SwI0+cVlbMMqYWCCKC382QaAaNPCF5g7L9LBhwzzHfrk9BhgLlFSMv2VeDwq0ZLYb7xb2Zldo461XBF4zXVF/oTkM6vfWgg64Jd3NWWedZSxIWNGJwYexvitY9IvpX7EAnTFgnlFycsdkAg3OASxJW265ZdGpLm1/qIv7jLABQAAeIu4CVwjhMZn/Xsx4uOvMRQZHe7gfsqU6ZH8XSu15yG3v6z9TFmRtXr+6sCGPq42EjOW3LhLShsvXapPec7T+Omt6Pn9K6Tvv/rCgVTtf+5ZaEtkZ3bN9H3d3QDZW9VrFNVyztZJmaHBovjYJT9AOofGqd2JqHHOepk/6TL1+eFdObIFuSOyql1PrK5kOad3QVzo+8qTWDdkQEUdpJ6KkE1EqFNHY5n31UGpzxRUxrvGLS2eAHnKkFfrXK5ZMaW5TTKsN7a0/77GGVhnUtQzvnBOc0SiUuiKO2FrXLXAPwhU+VxaWUtegn/d/+9vfGs8dYre7a/n9739vFAzI67jCY21HbkBm4pxGXqWQD727ZAkpx1z0APRyjOoy8s1iBR6/wwNTMsCGlD7ZCpsZoQbtZtCs6rhvos2zeYn9tJ0LAeUCln7I2riovMQOYj1BQCPGNeiCsIXbO4ch7cGNkX8rhpTOT9uWFoCOIAsoR/jA0sOF4scVlHVMXD4xntVavHggWOWEtbLx92weA+XsI+uYfRW026SXNrO3mXf2DftoWSkoHFE84hXBWVsu5Us5xrMYhTLvEG6EJRC31lzKiH/9618mN3IpBKaWEA2PJuLNsxHBkbkAy3JQgId1TB+9ksHhEWP3PFY8BGyrlKS9mUpocngfdfeH+s+38ztN6bA+NZrfmjA5vomd7t8Q0bymVjW1JbXbOkN0zs5rdTwP43qIPGZlLHe/+YX++OLUImsA2NO+djd4wPtG+kwHRF7Tw6nNNb3XGlKkVrPnzjWR7A1qNa7yzapVf6dR46JXa3RoUd1OWOm6/kr2Gqb4nMm6I7a17kturR/S/RVTNIs1HUZ3qS4aVmNb0kS3Ey7QUBPW4RuvpF+OGenZMFBk57O+htzDOUFoRhChUqW2rZB13SrTC1nXberJrjzz4f1ASUiu8+5aAOVkBrFgHC9TziE8DOHZgAgP5c5TTz2lHXfcsbt2s+R29wD0kodw2f1ApQA6qanQ4Bci5mDTA4YBULikY9kqdOAWmj3ccLhgSrGSMU4c7LD+Ei+Lu1c+V99cHgOF2ur19whlNp87QhU5dbGuYwEqV+nOAJ2LAwEdUM6flnGcdYEF16tnBGPL3PKDAF6thbAOlFKZruN2HNiLjAMeGBaUA9hK3Wt+xwMuCku45/fdUp9nD6G0A5ihIICwB84Iv542pbajnO+j8ESIApTxQ1wwhXlmDR955JHlrD7wb/sF6CgjbAo1FGr5rMWwDqNA5jwoprhj26krF68BBI7cIUGEdbCGLclrMfnN8SKxYJ0/EbItWOf8cJ8HC1piuvWtKXpl4mw1tyUVx9rbHNeAhqgB6M0tzUomU0qEalQTCeuOw9fXVzObdN973+u/3803AH3dFfrqpC1W1qgClmFn9heKfPygIWhLLbeakuseoHSfxczwueZnzbEvZgXAxcwn7+DIvoJmalikUdN6raWp81tVpzat6Mw2RHG4pM9Uf+0eflMXRO9aVE1I6fr+BtATV58ctZWa+62mZ3/oqz980l/xZFopKO3SMrnRKXgetCVSBpyjJhjQEFFs0S+v2mdtbbZqZUnjODfwNiGMq1TC3GLHvtB7xVrX4dLhHIFjqKvK4YcfbhjOf/e733VVEwKvF6UkykeA+dNPP22MUyggujLWP/BOFvHBHoBexKD1vNI+AgjpCBLlJknCmoyV1+tmRVhAeOcQ5iAFSPkBUe75DVIgQtilXcT35VMgAJ7xGih3rk0ELC5S6kKYAqD7jZf3uhe6G0BHcGUdAcqZL4RzhE/WUilxUdXAAltoztyu424h3I4DoJxxyGYxK/TtIH+PUol9jcKr0gXr/UEHHWTGgPFC4XLVVVd18GRUuj3lqA/FA95JKGIs2OLMB6xDVIbycmm1oKPkBZyz50n/U0j59PLLL+e1sOebH1sXbuuZhGiZ78EPQZtKURhbMjibvqoUpnbbvsxwHzcxpjt9JsD8sx8a9eHU+fr7m99qUK+oWltbDLBs6NWgWFJqbE3omM1G6JZ/T9acxrhx3QaQUrAMn7bVKB380xWz3unhz59UzTO/kpKJxQRyNb3VtvedSi2fXyl68DVP64N5dYFuJUD6aE3V1PAKWpjEJT6toZqr3k57zPvcdG8Nd+bosdrfm7+nlt9QqZU2UzrkKDXgR0qP2ETpviuYXPRXvPCVHv1wmhKMRyqthBmUtJJpx4B1LOeECtRE2r0NiK/fYa0humS3ynn4IKthiEDJ5CYTC3RQA/6YnzRu3DnsF6+cRAE31Xxu33331U477aRTTz21HJ+v6DeRdV966SWRJQR+DYA5RHReZf2KNrYLKusB6F0w6EtLlcRns8GwGuF2586NGGQf0VoCbv0Qc3DoAjwBoAjRCD7F5I5Fq4cwFCRTtFUgcOlmI7ij3VioKpVrE4037mgI27SH+ONiFRq55r07APTMNGC4q1pQXorrqntMcG9nfgt5gwS5f/x+Cws6BUtBphsrBINBrw2/7bPPE9aCIqUS1gz2KhZkvAdIB3P11VcbN0Pc77Bk4IWytJVHH33UdAnlFEpYxoC1i5Vjq6220jnnnNOtADpnEMCxUMFjC08s1hVz7GW9kyZozTXX9E3extmL0pa6vIBulAa42RcTckW/3WRw/L0Yy3mh8XOnlmS/sG8I7bLWdc7Sb2Y16/h7PlQi3mZAZUNDvbH/QnIWdmTI4j7+foFa4tiL+U1ahIiDSXvXhPWnPdfQ1qMHd25KrFl1N/1MTtsCKVxL5+iwlGxTatBotR32r/Z/y1F+mDFDO93wgVqNK3mQpT2BW5J0a4v6Qtw6ru7Elq8X+lK31lyhdLSXnEidUg2D5LTMbmenjzYoufIWim9+jlK1ffXON3P12sTZxkK+wUp9td6wOh1//yeaMj+uhnC6Pd96qF2hBqv7z0YN0PUHlJ/QjDlHTrMpyIqRtYIc8WK/ZV3hLWi3yiy7V5An8X6EB6KrCuD8iCOO0NFHH91VTSi5Xs49MlLgeYaCG+8j7pOuULaX3JkyfqAHoJdxcJf2TyPsWMZxwI1fMjSv44PWkrqwZPgtvIcgj9Dlxb088/vlIOWhDi4A6wqGwAUwtiDQq0u/37HI9bxNY4dWGMEUEIawGeQlS3+Jey+XEsfdt1wu2tn6T5ssARJgNMg0YNnqwyLP/OKiVk3FMq+zFnA3A5QBTrwQQXVVP7DW4BrMWi1HyRZnj8smAhouvLvvvrsB5/vtt185qq/ab2LFPfbYY/Xss89WRXyp14EqBND5PZ4RNo2SH3Z+UpMhXHqNt6UugDmKWKxFXl2B4YfAg6cYgGDjzS1ZViGvAK/jWug5QgWsK7z1wqEP17w9W5/ND6t/r1oTQ90cS6olntROaw3Ri5/P0pymmFoTKYUdx7h2uync+tdHNO7AdfXjFft1VB/+8jnV/PPY9jzogFus6MmYlCa9mZQcubkSAN3huVNkTfziM533z481saWPYoqYxGpBlPYI9SULMevHhZ/UmX2elyI1chqnL3rSkUIRpcnxHooqueo2iu94ZdamXPnCV7rrHWLYsaanjYIj6kjxlLTXGr103M9HmvVVLu841hNyA/sGxXNQyuwgxr3Ub7jTuCFLsv/Y4yhky5HGzUt7CaUCzHbne4cc5yiAMe6RBYQUbowvawn50yqFg8z64mVsq+2ZHoBebTPSjdrDhrIaRwQNgDSWFqwBXgUOL93FuoumHxBbbAGA0D4EBYQbrxb/N954w/SnXGncUGyglUXzTJtoG/+PK2ml4pTdad1sznJAJAAN0B6EG2s1AfTM3Nxu604hVuJi1599DyEGJthKeUfkaq/bIsz8I0SjKGKd43IL+Vq1x1OXg1GXM429xznBnwi1ueLsd9hhB2PFOOqoo0pdFlX7vmVxdzcQpRbkT1g/KuG9ENTg5APorH88wngG91zWv5/ix9OKMx9Bn3HkjM9Me5mvXqxNCK1+LU3Wco5XTBAu7X7Gxv0s9XMP44nRlg7ricmOvlwQVtJx1Ks2ql+MHqyjNl1Jh9z2gb6f32pi1TGCZ0vVVhOWtl19iI7ffKR+NLi3jHv7Eye2x2+TczzRmhUSx7Y8V8mfnpizC4zVD5++qa+fuFx/bNtX32roIut3KUR1uSB6SqM0XeP63qbR8c+kdLK9XSHSq7W7sBuLejiqtn3vzZpK7sbXJ+mvL1sm+MXdGtQQ1mXbDlaqaa7xQEThxFnGj581l2+urVIL5Tbg3O++KXYdVfo91i2EwRTOPvZQpnXd7qtyK74YZ8Kpdt5550oPQ2D1YcxjTXLW4mHD/3PX8oMRBxzB+fjEE08EaigKrAMV+lAPQK/QQC+N1ViAbvvGIYbQDCEawj7CWxBpzxBK2LBs6lILQjeWC+saS9xfPhdGXBex3AepcMjWB6yWKCI4qBg7Dv9KMX0DGhGa3HnXIdyjPRyejDsaYy+unrnmp6sBOuNKPwGj9I34OMu+Xm4OBfeYUD+W31wZCUpd34WEKXfKJPaAm3ndWliqIc7Oyzgg5GOR8xP6ku27bgsfgiYWIDsu+fgG9tprL2NFP/nkkzt99vrrr9fll19urKOcHbjDjxkzJmeXrrvuOl177bUmdhM353PPPbdTahnO2UsvvdS4lqO841z985//LBQE7lKoXs4UcujedNNNRgkDxwV1F/JMskolxgnhifMAYRUlU3dJscY40X+bvsc9bsw5ruOc8YxFMV4+48ePN8zqhcjbOHsYO9YV69avVRMLP+3zoxjxy9TuZe8V84x1g2bfQiDG2uE8+t+kafp62mw1pFs1athAoyS893/NevCDH9Qabyc/y1dIM4bV/ewxA7XCPVtIiTYp1Z6mKXtxFNvxKiXX2T/vd8MTHtE1j/9bt8e2MqnO2tOdUfwA9cxnl+wNcerHhJ7SWTUPLm5PuK7dHT8VN3nR8QqI7fxXpVbeolOb5zXHteVVb3aQwrl/2a8+on//5ufm3ub+s14M7H0AOuPMOYeCuhhQyZ1Ovnq+jaJpabV2ck9yPjCOyGT2fHBb162hyo5/uazr7CE8xgiz2mKLzmuhmD3ZVe9Aqsne5wcgzrnM/WJ/+DuyJ/diJeWzrhqPnCdV2vo7VVvLetpT9SPAJsq2fPh34pG4iAF2WGFLObxxWUZoLsatL9sgulnM+S6W+Vzpz1555ZUOYaLcE0K7iFFGIcFlgKa0EkQr1EkYwKabbtqpi7Y9KDSwLHtNE5dtnCoN0BH++LGg3FoQAOUIJUF4BRSzHhCSUGBVCqDbtHAoBqibdWXBZ65c7Xh0UPxa6YoZj1LeKTZlHeva7dIPaLJeFIyNVyvQIYccYkhtzj777I5uPPDAAzr00EMFWN5ss8104403mnyvAKtsMcbjxo0zucVvvvlmA3ghpcR9/N577zVufxR+f/fdd5tnOKtgjj/jjDNE+I1V4nmpF1APAQ+5ZQHZF198sVBA4p6aK5wFyzDKBiy+EBwCDAnPOP/88wuC0VLmthzvZgPo3FEoIlnreDAVq4T0ko6Ts4i7jHVAfcXUZclPvYZ1uAmwutpyTro6FNGs2Wxu0JzRNtzou5nzdOdXEX0+B9bywiUadrTJqAG6cdRrqnvjsg639pxv1g9Uyy/HmxjvXAXr/VG3v6fZs2doSNsUfZJe2aRGy10sGLd86vna3RmoA/+viVyrHSPvtSsAIovI6gDoxNM3DFTbPncrvVxnI8X9732nsU9/kbOie45cX+uv1L/T7wGcGCrsncC+sJZ1r3cj9woeJ3wLcN5V92nhlVHaExacs3cIRcmlvCuUxs1yPRSjCHH3gLninHr++eerLkyutJHueTvbCPRY0HvWRdEjkAug2w8i+GKN4zLAXRZim2KsE6US4+TqoDv9GcRoCK2ZLs4wTAKUveQvL3ogM15kzLg8EViw8GdrV1B18R0Eb0BjJkC3dXAZ83ssvyhcEC79ajX5hmWEDrLtmZcXWlcEQZsCEKuYtRT4tVaVo53sBZRX5WTotwz0rCEszDYtHOPAOi4EDCpJvlbKGPthxEeAst4DKCqwSrtdPosRMI8//nijfBw7dmxHN5hXBFaAty0otvbYYw9jBc8s7DmAPCDYll/96leGIZ3wGgrcGVjVTzrppI5n+B5WWIA7pVC9CHZ8h28D+ClYLjhfAO70JbPgDbXddtuZ91AaWMs+YBSA9Ze//KVbkeO5AXqxMeC51isKDJQc2cJCqJezk32Fe2wpOcxRntkQg3x7x7rfcu5SykEG53Xvcu4D5iiAHC93B/tz2vSZun/8t7rnk2Yl0tDE5S5A44G9orpirzX18y8uVeSj9n2RszgRA3hTo3JbISGsO+z2DxSON6rfwi80OTVYP6QHeOi2Fwt79t6M1rcaV3O1RkXntgN1AHqkXqnVdlRs52uWILm76sWvdPOb3+Zs077rD9dFu+YODWSdcC5a6zqKS7eykn2eeV8wN8hkFrRWw73qYVJ8P8I9ylmHzJoPnGf7cLFp3Ao10ipT8Fzw40VT6Ls9v6/OEegB6NU5L92iVYUAuu0EIAErDYIF4A6BrxBIcA9AELnI8w0oQNiCYlIL8WMVCbjiIPwGSZhWaHKtEIbHAOMG0OL/UXCUqoHNVjdABwESa2C+wuWNBQeXUObRK8Mx3ywXQLcCBmOEhYpLFYEB8IVioxiFUKH5KeX3AHTmd+ONNy7lM0u8y95yuzB6ddPO1giUMXwPYFnNpVA8P2uBs4dx4Ye9Yy1FeA+UujYAu3i4AFQpnIe4jj700EPac889O4butNNOMwAF1trMgvIPVt4//OEPHb+CAIhvst9QHKBkuuyyyzqx9h544IF66623jFu8l3qZU84QBE536Awu+vQB9/nMgpUeUIkSgPOP97DA015clLGi77PPPiZuuxznUjnWHuuaH2v9o09BxOPmig3n3ON3uBVTV6mKXuYDxTfjn6t0FRlctvawhgFz5IcvNnzgnx9N019e+FJzmhKLrOmLLdQ2shs39371UZ261SgdMKpVdbf9wrC35yvpXkMU3+7PSq7WOVTEvpNMpXX8vR9pypxmLd/8mck1/nFqpJKGkz1fyQfQCznsS0M0Vy/Wn63eapacsGlfbNtLpYbllqj03UlzdcSd7cqPbGVonxq9fPpmnrcSLsbWss7Zieej2+PKglb+nRCNUs9Qzw2r8INWCYEswV4rpZ+FrOvWHd5LFzm7ULQT6hRkZiEvdfc8U/kR6AHolR/zpaZGDguvBaGhWCI53D6x3JY7lRFCFACUfllGelyJsHBVkpkUUI5gZ90YuShxw+TfbFo2PwqOQnOEqzCkcF5BI8DIpmUDxHmJQw0SoHPhoSSw7pBWq8zFBTBHIObPQvGghcalHL+3yqpCyhAvdWeS3SEEW2+BUkBHudnRvfTNyzPZvBGsogJAzn7GI8YKmIxPkPsGizLg+IYbbjDNZR9xRsHo7fZG+eMf/2gAMPs6s8ACf9tttxnCNSzvMKRD/sMe43tYW8m3joX2scceMyD7xRdfNLHv7Cn666Ve3OE5xzIFu+OOO87whuA2n1mog/39pz/9yfyK9w8++GCdeOKJhq+CdvH/3QmgM64ARpQSWLODsv5lIzIF7KAQQQAHnJcS5mXnBoWMBfvZ9ki1kMHRNs461i3nMJ4mpew9Uotd8swXmjIvk/itHazXhqX6mojG7rq6tll9sKIvnqfIf25fTLqWbbBCEcOQ3nbgw0oNXz/rkfPUJ9P1t5e/kZNoUb+WbzUzUadpykjvtsSbaUVDYbXzzjsmDJA85dl53Du/7Ii0co4u7vOI9huxUIkNjlJq1JZ5j8O1xr6c07sAxcV/ztlCNRH/bPScL8yhBeyAcwqyEKC13ISqXu6AcjwTJDjP1r5SrOvMB4Ya5J9SlX3lGLuebwY7Aj0APdjxXKa+5geg24Hh0AcMYgnwSiSHpQirNm7o5S5WkYAwzQWE+xdkHF7jUoNoH0oC2uG2YPJ34iWx9AM0+F0QBHwWWJDyzY/bNfOIsIhlzoYH5BNASwXoXGpuYQFhz+bVRdh2W/D8pFkLYr78fAPhGmE+VzhBvm+xBrCeWYEJ65RlXgeEBgEAqB/ARj2FyMP89Lscz9qxRFi03gOEOLA/rKW8nIo1rMkA3rvuust0zwJlwLBbAcNzPMO+ziwoWXBd5/fMLy7nxLZjMYcbAs4E5hsXcxhtWfeA9G222cYAe7x/vNRrAboF/bYdfJe9T8q0zHLYYYcZIHvNNdeYX+Hujms9ud+xnGPp704WdAAGClcEXO6TUgBj5lhlpgK1xHOsQ/ZRUB4G+VJwVgsZnN0LnHMolINSrM9ubNNeN43XzMYlc9k3RKTlatM66yc1WnHYEA0etJyW+/YZ1bx9tZwF3y15/JgY75CJVU+O3lWxXa7NekSl0mk99tEPeviDaZrb2KJovFEtzU2akWpQTDWd3qlRTD/VZ6p1EhpZ16ydDzpZM5rTgmX9w6nzF6dPy3MYhpQUWdP3WW+or+crrAAAIABJREFUxu66hkLfvKzwVy/KSbQqucJGpq2q7dPpCztd97YmzW7J+lVs+e+ds7nqo4Ws/vlPaO4aPBm5Y9g37nOWezgfmWY5zv5yfRNwjlKNMAzulaD2ba72ujkivBDNsf9RLNLOoJSL5RrLnu+WPgI9AL30MVwmv5CLEdfrYGB5AqQjHBYikiMWE4u21xyzXtuQ7zmEORs3SL0A4kqBdAiluBiypZXjYCaGmXELKg0aIIOfYojLABgAD6yZWEkgQMp2qRUD0HnHTWbDhWSZ1/PFUlczQEdwZ369AnS3Cz9AzcZOlzOunrVFO9dZZ50gtlLg32BMIJtCYQWIZb25FRVeYlyDaBSpbnCVfuSRR8znvLia56qXeaUvWMxhWSdOnDlw7yUssuwHXBshpsPqDt+Cl3qLcXG/4oorBEkcygJiq7Goc/bQRkAusevlzm4RxDy5vwGwKMf64C4DxOD2y1nKHi+VeC5b31nzKFkyz+pqIYNjb7LWABKMRdDrY/qCVl367ES9MnG2yY0eDpGeLayRA+v1+x1X06BIW4dnFeOHgmT5yHwNf/k0hRZ+b3KLmx/ypFMSbUrXD1By9d2VGraOkmvsKYWjSwx922fPa8b4h9VvwedynLT+NndTvZZcR3PUV+Qy/4nzuS6J3KKhYYD4ok//5ATFtzzfxI3ffcc4jZu8guaqlwcWeEfrrdhHD6z8jKKf/sOkjGt35XeUHrKm2nYd18nd/aT7/6uXv5idc6tctc9a2vL/llNtpDiQzp4BtLLvLbmh21MpMx0l3muluIQHvee9fo8zGA8mlBCVAOeZ7bIA3e5lyyPBcyhFuAswHP3iF78w91+5lQdex63nufKNQA9AL9/YLtVfLhWg28Fx5yfPRSRXqVRnmRMGSCcGHQsx4MhaXsqtuUToJvY0X1o5mwaNg5rncCMs1iKE0Ef4QSm5ubFgWjdPXPO5pN2FseQCLHRxuwnOuPi5LC0oJw7WSx8B6AA2FBjVVvDIwAUfd+FchQsa63Amyy6gPIjY6UJjElT6skL1+Pl9Jhs95w9KGsYJd+ty78lsbQVIP/fcc53cw/FCIa4cFndb2A+A22wkcdm+i8cOSkuY3LMV9hEKw/3220+4z1MK1WtJ4k4//XSdeeaZ5h2APXsrF0kc59B9991nLOebb765yb0LAzzp3bD6Z2Ol9zOnXfGsV94Uv23DmwgPHzw2UHBBKgU4DLpwTuPhYsORqokMjnOLNYNiCZf+oDy8co3h17Oa9MWMJvWri2jDkf1VE24H3V/ObNKjH07TxB/mq380qfX6x7TNd9drxJw3DDh3QovAeTJmSNg6lUi9Yjv+pR2oLyrhj+5RzUsXtLvLA+yTcfPe9HR/LUj30grOLDU4WcL9nLAS6x6o+DaXKPL+3zXrzTv0SGoLXde0lVoyrO+L27A4fn210Pd6fLlrVVtX3/7rZFxOyxzFNzpWiU1+1fHKmY/+T09+PCPnUkOJMahXja7Ye01tOKIzo3uh9cl9RUgIez2X10nmfQV4twSc3FndwRXegnPaimKpGsBvtjRuhDcdeeSRBqAXkqUKzW3P76t/BHoAevXPUVW2MCiAbjtn47+xEgE43URyL7/8srnwK5FyzD3YXDTUve222xqXLizFWIzRIpeaFzzfpMLQyUVRKM0Vc0BMJe0CoCC0ZwJjL4sHgRLrHemTSilcKAiPWNkQ/GEZtZdzPoDO5QjApy+sAzwVuNj5RjYW2UJtrHaAzvy6c87TH0toZkE582ld+IvNU1tonHL9HgsdcwHI6Mpi1wVrA2UNlk937l7OCvgpttwyf4xmufoAeRog+vXXX++owqY7Iy4dN3dAPOnRAC6kx8EtHAvrnXfead7BNZo+ALBZ+1deeaVxw8aSg0KQ8s4775h3mA/+vPDCC413D1YteyYWqpfvAMRREuAaz9kCuCeNZL40a+Uau676LmuKcyroAkDHis4ahUugXKEV7pSYmWRwXc3UTrw5Y8s6DSrcxu88/fvrOTrv8c/U1JY0acQZo9poWGdtIO39/sHGGp1SRMKRPL2kq3x7fY7adrtJqdV3keLNqrv553Ja50qRemMNR1kYii3Anu2heY5JjRbb9hLV/OtMOc2ztSA0UBvOvVAJ0w53cZPLtX/7x5GpurfPX9WQWCBFakx/0gNGqe3QpzpevOXNybryxa9ztiWKPsJx1Kc2oqdO+pkh0/NSUDjhIWQ947y8Q/swutiQIwA+ippy8YB4aVOhZ1DacZYid+A1Vg3gPLPN7CvuZGRR2sle6wHohWa2+/++B6B3/znskh5wYCDsBFk43GEUR2gFoODijYtcVzCp0y/AOMzL22+/vbHc2vYh0CKI0b5iAHGhMfOb9525sAIiAAZg7McdP19cY6G2Zvs9wIkx4kKx7PN2vdhLhUvRMq9jceESt6C8FIIz2lPNAB3NN5frmDFjjAXTzbyOMsOCcq/eAsXMT6F3sNLxA9CodGHP5RLuMpU1rDNiq7faaitPnhVB9+XRRx/V1VdfbYQ7d8F6jls4Y0i8IJZnLNCUI444wuxVgDEFrxPI1tgveM3QF4C0O4UOZ9AJJ5xgXIfZJ7CoQ9yWyeKbr17q4vy66KKLTG52lAEoBa677jrTxmwFZQBu9Lh7YrHlHZQNeDRhVT/mmGMCYUAPel7yfa8cAB1AgpIF4Ib3QzEp+7yOAWcmClkUfNRnCfq8eBZ5rcPvc/AgYGVlbbKWugo4wLp+wN/f17dzWtS/IWJc4Bvb4mqJpVQbCenPG8zXLp+fJcUapRTp5/IA7Ei9Wk78j0KzJ6r2gf2lcI0UCiuRTCqdSikScuTEm/KT0BnHdEnhWqWGrKn41hcp+spYPTJtkM5sPti4rFvX9SXHvL1tYaV0fuQuHR55btEjjtL9VlLr8e90vPL0Jz/oN498mnPaIIqrCTuGqO7cHf5P+224fMEpZp2hSEbGKYUt3K1kzcykgWzXFZ5P7s5zB6MMReaoVnBOexk7yEMxwqAU7upxK7iAeh4IZAR6AHogw7jsfaQcAN2OorXEWiI5DicEknJZJXLNHoIX8e8AdHdxE6ShGUaYLhVUur+P1hqiq2w5dfOtNAALyg2sLJnp4vK9h9UbC2XQgAwNPLGYzCdjhFLDEr2hWbes44BSPwqFQrutmgE6axkXd/rOGADErXWh0us71ziyfnBzx1W73AXQiHeKBeUI+1iFraU8n3skwhX7E1DUFaAA5vPzzjsvK/lbucetEt9/5plnjLUeEI93DeD8N7/5jSGoI1XgL3/5y27H4h40QGfdcl6jqEW5lC90JYg54/wEOFFPNYBzlDYoHAFxNj45iH4W843PpzfqqLs+NGA8kUprTlNMqQwMvlK/Gl2/aaPW/OY2hSa/LqVze1O0bXiC0qN3UN1DBypNujNysadlwJGTThiX89gGR6nmXcJZMsH+Ymt4uravYGdv2/c+pYatpxv/9b7+Nr5pUcq4fD1NK6ykfuZ8pntq20NZTAnVKL7p6UqtvLlhn7///e809umJBYeMFv1yzEidstUqeZ/FSILHD8oWSCuDKqxX7jzrJcZZ7+YOCVKG8tJmC865d+lrNVrO6Qd7bJdddjEeVXhKlYNDw8t49TxT+RHoAeiVH/OlosZyAnQ7QJZIDgAJQQna3Eq6zgEcIEnCrShbwQUeQRWXYNxXsRYHodnEGsHFZV1c/S4YLNJY5mgfY8Ylm8/CUih1j9/63c+zTnChZ5xQbFjwBQAr11xWE0AHgCKI2JRwcAdQEGYB5tUYn1cov3gp64F33WnyADi492NNYU34saqwnrAu441QTqtlrv7i2n788ccbQqyutGCWOh+53sdD4IILLjAeKXh+QEwHwAWoX3zxxcazCU8A5i+Ic69c/XB/l7aybkot7GvuJc41OAYQmrFssxbLWRDWYdMmDKycZ6iXPuAhggKWtlQD38dnPzTq6Ls/NNbi2U1xA9KzlbqIo+t/Nldb/efkvAAdV/dkuM6kYnMSzUqG6xWJRuWA0hMtSvceptajX5Uzd5Jq791DTuu8JZnaSbdW209OrNFY0BPrHy5ylx97z0fC4p+jiR2AP6yE+qlZ6zhfa4PQFzos/IL6h5qUDkWlmj5Krr6LPln7bO15S+5c6O4xOGLjFXXmdqvlnF6bKYY47HJwKLgr5l50e5AB0G32jczMLF7Wo59nkI2wnKMgDzLLgp82eHkWhcauu+5qZDjISMslM3lpS88zlR+BHoBe+TFfKmqsBEBnoCxRG8II1gMswwDXSgiEHI4c4ltvvXXeOUN4RTgDfAG8SiFsoyLq5KIC9BdbECBxUcWijushrlFcRtkK8az0tZwxx1iXEO4rAUixMHHBd5XQaK3CAF0EEDwbsLBZwhwAD1bfai20GXfqYlj9c/WJfWwZ+d2sv6xzlFHFWC8YZzgisCZ2heCCa/tee+1l5nhpBOjEwmMlx5MJJeRRRx1liOn4kzj2a6+91pxVAN6u8GAoZv8EAdAtGRrzbrlRrCW5XHwIlgyOM9SGJKFAtgpP+DqC9ELKN7a0hTsDBQVuweUGcl7nOZFKGRf3b2Y1qzWeyhshvmL/Wv2r7jw1zMvtGm5ymDthYkOMO7pCoXandILbaxoUI059ZDvZZ2jaB6p57Bg5C6d1BukwxlsiOgD1yluodfvLdOA9X2nCtIXm3fYc6Zklt/t9HzXpgvAd2jw6QYlwgwZsc4bW+OdwT8O07gp9dP/R2blmUNQzr8gBnMmVLPZ+YE/xwx6zSlvWV5AK2O4CztnfpLVEbnv88ccrIjtVcs576io8Aj0AvfAY9TyRZQQQyjhUy12wor/00kvGim2BMICn3ERt9MuP0GUJ24glBWwAiItNMYOFBI1pEACTOULARpiC2I5xy3SRAoxxGRBrWq7ilcU9iPq7AqBnWoXZH26rsAUwhE2MHz++y4jNvIwvABqiP2KUSynsUytwsZewkFjX9aBi7AHotLPS7pGMC3sdiylzWoyCoZSxrcS79O/ggw/WgQceaITDyy+/XG+99ZYBZMSys45x87eu1pVoU6l1lArQEe7xcKLPhARZhSMKTs5tUiAFXbhbbOolvm3J4Nhf1jPHMsj7zXjht620A6s59aGcyKX09fvdoJ5/86s5+u2jEzSvOT8/Tn00pCt3XUnbv32knDlfZK8+XGPc2p10UiGl1bzCZmpO12qOM0DfLbe5eq8wurPXT+N01TxzusKT31DaCcnBfd6C80id0pE6OcmYkqtup6lbXaWzHv1U/5u2ULFkShFHiiXlsvoXJqCrU4uWU5NWqW9UfOj6ensSFvz8ZVDvGr12RucMIqwvZASs56xpwq+6stg0mtYVHsMHRKn27iiGONb2x4Jz+ojlvFoVq9wpe++9tzFEPfHEExUP7+zK+e+pe/EI9AD0ntVQ1AhUCqDnImqzRHLENpdLgw9QIRbLEjx5GajM+HlczP3GFiP44tKPJT6owoGPlR/3d1hZAf8WVLhz+AZVX+Z3lkaAzh6wMfWWAMcKEbmswrj1dSXzuJf5tRkVYCH3UyyDb6ZgZWPsy2Hhw8WdWPlyp3TKNg6EbiDkoUQM0sLjZ8zL+SzrmzRrpFQDiOLOP3bsWBOy8eyzzxqlCCnXulMp5d6yKafY25lkaIAICAshzwuyWKZ27hWbCznb9y0Zl917rEdLuhmUuzB1EG/OWY6VtRLeUMWM5YdT5uuYez5SM4g3T1ljWG+dv/0o/fSdUxQ28eiA4kUx6cScOxGF0ik5lu2dFGt1/dS2zaWaM3yLDuUI+8F6SA0aOEB9JtyjyHs3y1n4XXvO85peSkfJf05uwyYD3Fv3f0ip5dfXV7OaNbsxphUH1KtffUQPvv+97nn3W01bEPPU9WGapZhTr4Z+gwxL+2fTG/N6Dqw5rLf+cdxPOr7N+rKEroDzrjhHC3XUKnpZ29y3eEvZO8WP9xXfweOH/UBYSrWCc2TeffbZx3gmPf3001U5J4XmrOf3wYxAD0APZhyXua+UIuj4GSwEH6w2mXHgbiDMgQtQD1qbz4XA5ZWZEstL+xHaiU/EzRwwDCj2KsSTVol3SmFPzdVGywLM5WSt/FhLETKIOytXWVoAembqL4QFy7yOVr7Qpc/lC68BbN3VWlDioJjyQnhliX9sLCHrHs8BBCj+LDehDSRxrNuusPogLBJug0Kj0ikgK7l2iDVGwccZ1t1LsfcWYwC5Izwj2fJBZyqSgxgnNzhHmVrobLF1sidZm4TYcN7zd3tGsS+LCUfgfoAMDyUb+62YbwQxJl6/8dhH03Thk58rlt1/3LCn19eE1ac2rOv2XVPrNL2p8OdPmh8D0wHnOLanXEDZuLsD4B0lNjhKTuM0hb8br2RNP80esYO+GLCV5i5oMoBqVOJLjXz391JNb8P+rmRMTgsW7sWkdOmGIWrb526lh63TqVsnjHtKr85clPu8QIcb1KIB4ZhmaoAGNkTVUBvRpNnNOWPb/7zHGtp13WHmq6wLuGo4v1BylkOB6nW+vD5nleL2vrH8JTZ2PVeoE+AcDxcAfTWDc9p5wAEHGI9GvJO64l7zOhc9z5V/BHoAevnHeKmsoVRXQa+Dgls7FuVcroOWSA5rFoAWITIozb4756zX9mY+Z/On049My3Wub6KQQPDHil6OwsVMHCPAHBDFpca/5Uq3FEQbujNAxy3OupICXvGIsAKvX3c7BHnmtxCvQRBjXuw3WKvEyedSTLk9B/AyobgtGpUU3rFaomiqdMwkfWZdsA5wDSV8ZGkrnAkvvviiAYYwt/N3SOEefPBBc9YSn45LdXcqfgE6IJlzkhAhgGmu/tpQLMYpCH6UYsF55lzwHcucDWC3fBjWFd6LAo0zD3DOfQQhnFdFQVevi9cmztLvn/xcMxZ2tkYTQt67NqK+tWHNb01o29UH65Ld1zDNjd93qHpPfUUOCpEkIXy5Wd7NC5C1UciPvuo2atppnGbNnq0Fkz/SWm+e3P67cFTRWG7385YjXpaGrN4xXOdffb0eXjC63fruoaBISIlk5/nLz1cdqBsOWlchxzF7mVAwFG/uUI1C36im37O2MeBYrxHuLXdWFBtGZcE5Xg7cFdW6frlPDjnkEKNYe+6557rkTqum+e1piyCkNH49PaVnBHyNQKUAutc4cDT8uL1zWAdFJIfVBMGMHMClFLaYtcbzHdzeEexzFUAHlpogU5xkq8uy0GPlR+tPLG8QwmW2urobQHczr6NkCSolXFfn7vayjukvcbbu0A6bsx3LhXUztO78xAd2ldCDNwK8CsXyPXgZj1zPsK+pF8sqHi+A2Z/85CcaNqzdQtXdC+cDXhSQwp144olGcIQUjPMLhSgxksSld6fih9yUMwtFFSCgkPsvwB9SPTxjSiEstGRwtJP/t/HmQY0xgMxa1gE0NrYXwJ6NxwElNd40KJdHjBgRVDMq+p1/TZihS/81UTMXxlQTCamhJqxeNWHThvmtcdVGwjpqk5U0ML1QAxq/0pbTblDNzE8Wx48Xam20d3s+9HRKbfvdp9RK7aFB0cdPUPjLf0nJxGI3+Rzfajn8BWnoWua379/xWx0zeWu1qbZQzYt+b0V4AP2SoH6FfrU6cYtR2nXdoYqEQsZ1mlAFvMHgEfCipPHYkC59zN5RyFqWiBTFLfcVCuRqtpwzF4cddpiRN7lHuuI+69LJ66k86wj0APSehVHUCFQKoAMIIKXxGgcOoMctHUtlqURyAFd+gmKztinHsMggGOVyy8dtF0tFpaxTCKGWiZo2YSkJGnBVO0B3a+O54DvFFQ4aFJgQA+h58803DUlctRKLIcTjDgjYzGWdQJgPeo0UcxDhXYO3ST6FVzHf9fIOwhQxx4AXxot9c8cddxQVEuOlvko/w55AsIUzYZVVVtFjjz2mX//614bhnxRzMAxbD4pKt63Y+rwCdPY/LP2AbcgzC4EYxgqXVO6pYgkLc5HBFdvXQu9ZzyAAOyCGdrtJ5ljfsHqjlOmK/VWo/X5+f92r3+iOt6eoX12k49xqiSc1pyluSNnrwgDbtDYeNUCX772Wek17V9GXL1BoBizvhazodVK4na09vvEpSmz26/amtS1UzQvnKjzhkXYAn684IbUc9Yq03GoKffKwrvvnK7oxuYtiWmSh99TZ7Bb3UcvV66mT2o0MAEG8ISiA83Ip5D01t4wPsc9RLuHCT2FvYUG3rvDV5M6PbHT00UebtkJ62t33WhmndZn7dA9AX+amPJgOI6RxuHOhl1NQR3gglttLPKztGYcxhzNAHXdbQGcxhx5u4NS/0UbZ05IUO5Jut3yI4BDw3QIgwi+uWOUiv8tsN+RxaNXRNjNmXF7UjxIhqFKNAJ11gvumjWdDeCkmH7efMWLuUcBUI0C37Ll4jpBWi33tFmpKsQz6GSM/z0L6g3t5JazWVonDevn73/9u0ozh5UKe2tNOO62q3Sf9jKn7WTxrsLbh0XPyye0uu3/9619NRg0sqgD0QuC12LrL8Z4XgE6frEs33gJeFWlY0PG2KoYLxbq0W4dGr3UGNUacz/YcRClnQQ2KYvZXpdsTVL/sdybOaNSRd32oRDKt3rVhpVJpTW+MGV44GNRtqHrIkXZZZ6gu3WNNhaa+o9r795Pccei5GhaqNcbr+M/PVOJnJ3Z6qubRoxSe+EzBLmEHT/Qaofj2l6pu/LX6etJk3ZfcWpNTg/WqfqxEQbCeHaCPGFCnZ0/ZxJBZPvvGe5rcGNLao1fVZj9aTjXhwq7xBRtehQ9goEFpitzHGubvVtnsDlNDxupKDzDkLkKFUAYCzitxj1XhdPU0KccI9AD0nqVR1AgA0AFzaN0BwOWKAS3FzdzGWsNSjosywpYf4Ym8oFgWcG8sR8F1kjHE6o8ATN5zBKHXXnvNsENXys3JapkB5QhqWE34Ic4UL4QggBkXEQC4EoJevjRrlkDJXtbMayXjpxkDFDDkQa9krHau9cu8sP7c+WfZy4wPKcS8EhuWY394+Sau+IDkchAqUj/rxT0+7A+UFigvIIbbbbfddM0112jHHXf00txu9wwWYdj8YRWmryglcG1/4YUXjEsm55efM7WrB6AQQEcpS5+4K/ymuSQdKHeFX8JANzgP2qXd73hbpnYAjXUP5ozgjEQZz53UXa2uj3w4TX996WthOY8nU2pLmOzmWVnPz93+Rzr4Zysp/OnjqnnieI/D6Cg+5mwlNjzG5Em3JTTpNdU+dFBhK7qrFmz2TSO2U+/ZH8lpmq6TYqfq6VS+tJf549UH1IdV5yT1Q/OiShxpuYYaXbnPWtpoZH+P/esej+H9guKW9ZqNM4E1jhLOygDsOSsDcLZX6s5jX5166qlGYQ+3x9LIY9I9Vkz1trIHoFfv3FR1yzjk0MgC5ACyXNwchkGn6YCACZCOu22xhbYC0hG+cEMFdHohkqNvEOyQUqacBXCEFRvhEWUHLv3UWS6lR2ZfAOhcUgiltnDJIahykbmVB8WOQ1cCdECVvZD506Yg4lJGmC6nB0jmeNEWFDAAn64SdN0CCgooxsO6/jEe/L7a3fDtuGLd5ewJMiUh5xrrxMbbM0/u8XErViDSO//88w2AXRoL8ZDHHXecicPm3MTNnbEAjHJOHXPMMZ7O0moZG8Aw85tZLKM1nlecvQjqfgsp/yDa9KNYrSZwDihH4YUHFW7trHt3Tmq8ybgXrJcR1skglLd+x7mU539Y0KrXv5yj5ydM1zuT5imRyg7Qo2HpH8f+VKsN6aXIc+co+uHt3quN1KttnzuVGvHzjncib1+r6GuXeP/GoiffGXmaVo5N0EPf99df2vbI8b43Mjn7Mk/jKUCByf65UzZR/wY/rvS+u1GxF1ifWM5R2nohNMzMQsL7yF0WsBcbrlKow9R7xhlnGEUnlnOMMz2lZwQyR6AHoPesiaJGACHH5mYllo24auK1g2ZSB1Sj6SQNSKnFTSRH3CpkcvlAUiXSj9k+MZYoI3DnB8Qh6FVKo4qgbUMBMscYoAKAR1ArRG6Xb34qDdBhV0cJY3OnctFa5nWUSJUE5e5xsQC90tZphG/rworSyTLRI4hkjke1WfnzrSsI2vCOKYXAirXN2WC9CHDfZkwsKM+3XohBx0Xx8MMP79TM66+/3hCooVzEG+bqq682Hgm5ynXXXWes0yg76cu5555rLNTuwjfGjRtnFI20DaXApZde2gGQOdOIG84sELzxfcoRRxxh4uTdBXJIyPZyFc51CDirOTWg17shG0DnLgOYsjexgBcbn+qHO8RNBsfZ7yeNmte++nmOMwG3fqyOKIlzeTrBT2EzWvAOe8/GrXOmdIcCCeadz7+vmyakFc8TXr7ein219/rL6xer1Gv4rRtI8RYf3XPURq7zkZstfmfOJNX/fYyUhh3eW0nWLaeJuzymb99/Rr/8dK08Oc4zQToO87mBOwDdeg+cs/1qOvinK3prUBU/xdrEco6bOMrEYu549z2A8pqzwJKhorwOwgOQ/X722Wfr8ccfN5Zz+D16Ss8IZBuBHoDesy58j4AVcgBd1iWPPzkgEeQQdNEIAoBLdRcqhxWbGCQs1oAW4r+xvmU7zOkLwls5049lDj7gCI0qJUgX83yTDEsv84TGOVtxk9txSQHU/QpjlQDoNs4MkMI4upnXy6UJ97t5GAcsbVheyxm7m5mChr3J3HkhyekqJYLfseR5lEsIUZw1forbcsJ5hZLRWk4YI6+WQYjScPkGBNvywAMP6NBDDxUgHe6MG2+8UbfccotpazZFAqD7rLPO0s0332w8hSBlO/bYY3Xvvfea+HbKPffcY4iEbr31Vm266abmnAVs77///rrqqqvMMwAn1pctKC+23XZbc57AeUDhHazEt912W8dzrMN8FmO+iaIOoA64IROB3WsoCfy6dPuZp6CfzQTo9If4T84KazUutk7SJ7IOC8WRVpoMrlB/sIzbHO+sT68ArSerAAAgAElEQVTAxp1+0g1mAOyMp9fvFGpfkL+nnXjdrDRipE57doa+mZ0bdANiYXzvWxfVxWPq9f/snQeUVMXSx2sTu6CI4aGCOSIiYgDMgooJEBUMiCAPDJhzeIhKMAcUMWAWMWHWh5g/s4KCPjEHzCioGJC0Cxu+8+u1lssw4c7MnZk7s9Xn7CHsDd3VfbvrX+FfXd85Worm/+y7O3VNV5fKE2fUE8hpW/SnNL21k8jShb6fs/joN0TW2FTaj3pFauJA9GWAXMG5l919xdeVFBc5T/qAzuvK2Xtv6rs/YbxQiU3VQRTE2uMcZL1oKDxnhpejJpXzm2cQcUWpSsA5hgRrJoFYEjCAbmsjJQloaB6bmHrSFawDgAmPJiyS8GgO/VQtj5nyYiuRHIoufcNrgGLu3dg17JzyHNlshD1RcxeFGPCgIeaZyllGOQOQJDosMFYwH3j6mVOMG37DtDMB0JlDr0dHQSjgnLkMo2Vaa0lnAqDzbC/pHd8mCgWy4E+/xrJM9jHo74hvlHHxjSRqrAsULr4pACffvcoHgJrK99WvXz+XrnDOOec0vB6PNJ5YgLc2+B0A83i8IxuAGyDvLVl2+umnu1BNvLI0CNqIZCHkXBuM6oB5OA2iNZ7x9NNPu6gc3dcA6KwRQtX9NL55SOEmT57swLmW/kLmKK6TJk1y5Yv0//08M9fXACxpGCqoYAGoZv2kq9QzFwCEeOkWuSaD88qevhCNQfoXRuh0qoZ404hYF3xb6lnn20r1/A9yrcyZM6e+ZNzmW8jC0lXk698WyvlPfdZAEBf5roqyYlmtaZn8XVktqzUrk8eO7SirTRstZVOv992t2tYdpeqwiSJNPNEFSxdJxV17StG8FaNdYj34h+rVpUv1DQneqx7zyMrJ0T3p7n+LRC7ttYUc1KGV7zGF7UL0TCULDeI7jjY+TfXQKDTeCbmchsLjsEi0f/CMiy++WMaPH+/AuTelMGwytf6EQwIG0MMxD3nbCzYdDmd+3H5PzZJ/GpsZQB1wBvhLpXwX9/NswkQz0SKJ5ADqWP9peLxQLLK9kb7wwgtOYWfTB0ggA8AFfSO3KtFBkKycIFXDCwng9tNQ1AELHFLK8puoT0EBdG9OJIogyrbXqg1wYDwcnumEPfuRQyrX0H88msyvXy9tvPeoYuwFnV7Su1QUY+0jwNEPV0MqcgjqHgxsrL1YxiUN7Uc+AFMiKTSKIAgvHznYANThw4e7IQFoeccjjzwiBx98cMMwYXknhJjoichG+k737t2d8qZt6NChMnr0aGeAYk1PnDjRhdKzN1D2EZLOHj16uNB6wiUjG/0ALJLneP755zf8GoAOOMf7g+cbssJLL700Jji78sor5dprr3XGBcjiWA+sW/YL1gnrWPfLoOY0089hzwCUIkO85ok83n77A0ggHDbWvqPgPAwh7fSBcwXvOTn3QVbsUGJFDYVXg6mSzPk1FPqVu5/rMERgXG6+7uby1Ofz5Yc/Fkt1bZ3MW7w0qhcdNQZwTo52TW2dLKiqlpE928j+7daSJs+cJiUfP+znte6auorVperIJ6VujWXe0uLv35Tyhw719YyauiJpUzVeasTjiY9JbxfrkdFB+qpNy+T/Tt9JmpJ0n4ctG+A8mliIItKUKK1koaHwRGJFGnv59tlLMdrC38G+Y80kkEgCBtATSch+70sCHMpaG53NSHPq+DueYA5HlEIAXTLlw6IRmPnqUJIXRSOS8/Y5yceldTn1dMlX1bBslSGeMP4Pg0GQChVeJIwBfryQOjCNQMCDybziIYxHaqdGnFQAo5dJG6WPf3tZVyMPw3wA6OmAXwCGWvJhGFfQiYIAo3YiY4mfxYkRgZJRqebj+nlHENcAtFhbGK+cMlxX50qAqfJETiFAFNlgyAl6PLDwshbV+015OrgjINljjrVddtllLvcbUBTZANCEnOPtxvMO0AN8A554HoZN2g033ODqkKtR9IQTTnBh9NEaIZR49wEmXoZ7wu/JqScFifQhwi2RH++MZjDaa6+9XDrGyJEjg5iunD8DQyGRCRhrkHWQxgUMMOzL0dItwgTOmW/2fEAGtbCD/ia8k8y4Meiyltm7MTjhUVc+kEwbAHm/8uNstuXWct0bs+WnvyplnVUrpKykWH5fuES+/X2h/L5wqdTU1LqcdKAspdhWaVpPnMb//7W42pVfO2OvjR1wL/1ggpS+c6MU/T3L95qubnOgLO11s0hRMRuVVFzfRoqWzE94/wNVXWRY3XFRrovFQR/tkdEB+spNSuSaPu1k983WSNiPsF0AOOdbptpCMrpL0OPQSigaCo8uyRpXfQiDHfwhGDqJjuSbs2YS8CMBA+h+pGTX+JZALKDOJqbhdCgxXk91vIf7Db/23cEEF3qJ5FBc8FgnCv0O6t0KMADo5IxGKi8oVnh9IJLC64OxIwgFh5w8gF0qIeHMq5ZlU+bUaH1KFqBr3iuHHhZqQLiC8kRkLUEQhwU5p5HPwoKON9KvYqyh/Ao6UXg1vx6ZZCK/Hk9vx44dk+YayKTcoj2btYfSj3dOowiQl3rJUZT8pmGk0nc83ex5CpQVoL/99ttujrXhpb733nsd90Vkw8t/0kknud/Td76j/v37y1VXXeXCsBkbIZF9+/aVSy65RAihB3TglSdXHZAd2fbdd19nOCMEPV6DxA6wjoe+d+/eK1wKgzt7zahRo1IRT+juQb5ERqHUBxHB4h0ghkH2vsjzgvWhP7kmgwOUQ4jH2iCNKtvebM5X9nQAOyRznDtekrkgjIs6J8icucaI6QxfP1fKuDe+kw1WbyalSmMuIt//sUjWbVEhnTZaVca9/r38uXCJYzV3vDpV1TJvcbXL/CbkvUVFmZzUZUM5ZLvWInW1UjTnI6l44CCRmkpfa7167e1lab9HRUorpPyefaX4lw/j3rewrlw6Vd0ki6Wpr+dHvyg6OHdO87oiWWfVcrnswC2lw7rh5A2INibOQIyKuQbnkX1T7hfOIoy0GFEh72SfveWWW9y+noqTIo3Jt1vzWAIG0PN48sLadSXB0fx0+qn56VgXAZkQeSn4jQcwUvHuBiEXPCwcABzyeKzximVjY+V9hLHCmBxLgUShxxOHooO3hp9U8mdVTvE8P35lSZ8IN0bxAuhzKHn75AegszbUM0yuMMqu1t9NxjMcdoCOdxqQFW/d8w2hwKpVHq85YFOBZyoENX7nkusoBYdSG3TZxGT6EO9aDTH88ccfHWFZZOh6Nr5V+kdYOmuevEJaKiHuOk7WP4Acj/ltt93miOPYhxgLETVENHjz1O+7776GEmje8bK38g0+/vjjcuCBByYUOYCSUH3eF9nIb8dQANkcPyjGrEV+MJxhxMml9yrh4KJcwBzxfQXdIqth6DnInu49A4N+r9/nEVnCXs8ewpmWrW8kVv+YBw2DZy2x3+NZB7BjxE8HrGuUAO/AY8lZOumjOfLgtJ9k438tzzb/2/wqKS8rlusP3Upe+nyuDH/6C1cnvbi4SBZU/gPOS4ukorRYFi2tFcjVRvVsIz3ar10/tMp5UnFfTyn6Y6bfqZCa9n2l+IcpCfPQ+1QNl/fropO31r8sHS/68kzvm7RsJhMHby8rlXtD6X0PKWsXKjjHM52KUyFbHeX7h79jwoQJTn+cNm2aO0/hE/GmHWWrP/ae/JOAAfT8m7O86bEqKCieqhApUEepxguEZVGtoNFAB9Z+QoUAfNluAHT6hAU+FpFc0H1CsSAMitDSRN4N+oVHDmUZb3oqOf70P0gZ0yfSEphzlECULeY8FkBXoIWiBhjhANPwR0BXKkpa2AE63lDyiCMBOlEDSmKGoYLm9QSnY4RJdp0CzDp06BBoCHCyffBer6GyGkWA1xwlHu84cstV2CBhi6w3cs61YXwhr9wbfk6eOmA5GklcNLmQG45SB5M7jed169bN5TFqe/DBB2Xw4MGOC8K7NkaMGOGY4zFeJIoeABjxHgwCkWXdeA9h+eS48w486XybyJu9ib0bz/qRRx7pDJm5Bnx+1xd7k4Jmv/f4uU75VpQ0DznpuZdr2bC/4uEH0BAxkcq+6kcGqV6jEVMYu/jG6Z+eA8kSOGrZPNY+e5h+A+9+96dc/8o30rpFUykvLW7o6jdzF8r2668qZ3WrJ5r8vy9+k/FTfpSPf54vS6prZaUmJVJVXbNcSTZg8eCd15WzutXnlRf98Y1U3NdDpPKvJEQQH1x/VdNK9ll6TYLnJQPQ9VHRPOr1YL3NGmUyvn/70LLwY2Si6oJWCUpC2Fm9lO9e905SlyASZV1C+om+tv/++2e1P/ay/JSAAfT8nLe86rXm30VjfMcaiucVYIcnmI3Xq1SSYwTIywXhF+/Gy48Ci7KLQYHQYr/h+alMEsojTM0o44mUa56PbFGUUQ7xQACK4+WCR+sTBx5KUFBGEPoE0zvzirzIT6dvzD+Kqpd5XdlQg8xJDDtA94aPc2gr6ASc4+nRUP50vUiprD+9h/A8mJ2D5DpItj+a26fy4d+RrPSElON1zhVAJ2yRiIhnnnmmYXhaZo3fEeYO+KWEGgzS7G+ExcPLgWeFxncCAzjAnn0Q0P/iiy+6CB79JgHd/D/P0hB3wicB7rxPG8CTffSII46QK664YjmR863xHMrCYcwjVQZPDqlHGNWIUolsGAufffZZx0zOt8uexLdMegbvwtAUqzxjsvOdreszBdA5HzA8890gG9ZrrkPakakSpEG0ynkW9qbVKDQUnvniu0cPYG+MZ7gmhF7L5jEPXsMIIPvy52fKZ7PnyxorN5Em5KAvWOK856d03Ug6rNuiQTScYUff+4F8/PPfUidFsnDJsvKFXvn169RaLti/nv+i6PeZUv7EoKQ86fHmYujSo2VizZ4pTletbCsz5QPZzPV/+Ra7PjrXXb2jSPOKZSllyRpIUuxwwtsA5+yJ7G9B6SoJX5rCBawdymLCF0Ktc6IhrZkEUpGAAfRUpGb3pCSBeEAdTw6KKh5V2MQ1pPydd95xyiH/znZDaea9+m5veH6QOeDecWFdReHfZ599kvJIAX5RuMnJBexiRPCb45yIfThVuSMvlFaMG8gQrwhgiznGiKCsp0GHa4cdoONBRx4oHPwAjNRT7qdcS6rzkcx95FAnIv5L5nl+r400WLA2VDbRuAconQTYBajmogGyIWSLZGfHe05oOMYzgAK1yvGi0GBS51tlHdAAxxC6YWQDfKDQ4SlX4juu4fvWPHbGy7dDjXT+z1uHnPQY8s95ViRwBjzCxk7EDNEqgHTeRZg+UUypNoBoNqM7Uu2n3pcpgM7ei1xhaA4DUzvnLeuAbwSm9nyqV69zpTm9SjKHkUnPDgC7l++EvZS1rWdztCiB3xZUySPv/Swf/vS3LK2pk1YtyqVn+7Vlx41WW2FZXfPiTHnovZ9k4ZL6FIVYbco5u0qLfwjlpLZamkzYT0p+/STdZSpnLjlenqjdLYXn1Mn/Kk6QFuUlMq9kdTl/3oHyYk0HqZZ60rv6Fhuk773Fv+SivVo3pB+ogSRT57WfAZLuheFFI0D83JOLa1ivRFMRxv7YY4+5vdiaSSBVCRhAT1Vydl/KElAW4mil2fCGaekkFExYmtmUgyqFk0ynp06d6jxeyqKs92Klh1EdpYHf0z8/3m4/7wa8orizsacShsj9yA+lDCuzn75lOkoBpQpFkfGgVGWauCtsAF3DszWfXMOzWdOAz6DJqvyss0TXsPb5/pirTLZoBHjJGCz4Bsm57tSpUya7GfPZKGE33nijYxMuxMa3i5ENJZ1oJ9Yu+x8AlOgF8td79eqVVyHuWm0k6PnC6ELkEIYtIodyGdbOGAlpxygDOM8EkWTQ8vPzPMajnnVNieJMwZDHuec3hP/PRUukcmmttGzeREqLl4W7A9xf/+p3+XV+lbRoWipPzZgjcxcujdu1zVo2kzsHbCv/WrlJ/XW1NdL0mnX9DCfuNc/VbC8nLD0z6ee0kt/k7YrTpa64zMHwVyrbyODaoRHPie9Fn3RCZ9mk5UouQo89QLkCvASlAPZsGJTzBZwjYEpYQqwJ6WbPnj2Tnju7wSTglYABdFsPOZNALMZ3/h9FB8UQRQMlMFrpmkx3HC8iBEixwgI5OMgBR2nF669hoOn0C+WX/N90La/0Dc8cCg3yUw92tL5BXgJYTMeLlmjMKPga8pno2nR/HwaA7q0FTNQA/9bwbIwVhGRHCylOd+xB3U/0CMpuMiUR/b5bQ1g1dB2veaoEeETesE8Q9p2LRvg3YeMQhBVie+qpp1xIPIZI5g1DJFE+ePFR3gmjP/fccxs1QEcO/GgFENYkkRAAR86ObKeqYKSFDE5zsBNxmeTrutVoGyK08J4D0jnHkDvRAskauF/47FeXe75oSY00KS2WqupaKS8pls/nLJDqBKSCbdZaSR49tqOU/AP2iz96RMqfPTUt0dbWFckmVfcl/YwnSy+QDqXfuvvm1jSXTkvHxfCYxwbpp++5sRy36wYrvJtvX8G6pmRpelqi6ipJD0TERaQQFYEelos0x2T6TK75oEGDBPLOgw8+OJlb7VqTQFQJGEC3hZFTCahyE62GOv9H6CjADqCARy+brNIQehBmygEUq9F/vHjqIdbrk1UO9PmAffJ/CXFPt9E3POn0DeWF/PRoHlEAOlECGBgy1RoDQGeMKOda7gsFWcMCvcoL64oSR0HWYA563lgTRGDEW/vJvDNSNoREa21yQlZTDZFWUkJvzfFk+pXutbDdn3jiiS5kPdVvPt0+ZPJ+9g/2YNYvQI8fgBH/hxGHUm8QHrHX5Mv4g/Sga9qWlwQVQwbgRcOyNXII4Mhaz6RnHQ8ngAZjIJ78TL4rk+vO77OJniHKjjQSmoJH/q6M8H5yqOctXipnPfaJLKiqllYtKtyzmNPvfl8sTUuLZMbPCxJ26fDtW8vwHvX56LSKse2kqPKPhPfFuuDmyv3kahmQ1P3t5Sv5b8WIhnsOrxwm78qWMZ4RG6B3b9dSrulTL9NYzVsKVY3QCtZZf+lGFSo4x/mRSedBUgKOcTGlcQcMGCB33XWXHHbYYUE8Mm+fkU+EoWEXsgH0sM9QI+mfMr5HlmYj3BtyGw4AvOqtW7d23uog6n8nEi3KN+/msEnU2JSUSA7PKEA9FYItFCzy7iGJC6pxkAIgKG+HoYO+eUMes5HnX6gAHW+Vhq6jUBDypyRvGJOigRYAOnmqqayPoNZEoufAS4DBJh1SKYxNGCyQDx4u5MG3hBIXSzaJ+hX5eyJFCOXdddddk701kOsJbUchA4zlC0ANZOAiznvOOiHnMp/y0OmrplelIws9s+KRwWm0COuDH65V4Mi3kKphKlq/OSP5FpThupDXI7InzYw0C6KRvHspv2MvVgMJXl/dk/kzGufJtO/+dARy66xWsVyN9PmV1c6j/svfVTL776qoy6WkSKSmTmSl8hL57/GdGwC+LFkkTcfUM8Mn2x6u3l3Oqx6S1G1FUi1fNhkkpcXLcua3rLxTFku9wWHFFhugd9viXzL2sPa+34/MtSwo+z3RJMoVwHr3y4ejL8TwShRIPoDzl19+Wfr27evqnFPRopC/O98LQsSVmMM4huHCWmoSMICemtzsrgxJQJUeLc3G5gdTMN5GFH4OZQ4AVUIyGb4HWRv5e8mwokcSyRFenszhBJDBe0mZtaAbiooqNZo7j/wA6FioMX5kqmUboGMkYYxBN83J03rtmk+unnI/c02EBIafMJM2QchDVEUk/0I8eQJGUNI0gkDJAFGKASOZMKqpx1AJ2IKe70TPI40EojX6Uajeyljeccjxxo4d60oH5ZPXJAiArp7zZMjgFMQoWFfg6IedPNE6xDhMDjYl3pL5ZhM9N4y/R+aklADCAecYRWM15bhQmZNPzb6LzL3A8b0f/pLLnvvKkcaVlSzLScezvqS6Tq7uvaWMnPyFvD5zeY94SVGR1Emd1NaJA/b7tWspp+2xsayzatP6Lv3xvTS9cxeRuugs8LH63bbyLqmUct/iL5VqubXsWtmzZMZy97SvvF0WSLMYz4kN0LtutobcfMTWvt8feSEAXaMZ1HCt3nV0uXggFnBOFAgRk5mM6kt5cJ4bceIceuihbh+E/NPAeb1wmHMiq7bbbju56aabghB1o3yGAfRGOe3hH7QqQFjINbxSa6gDAgjbRinW3KRMKMeUO+vYsWNKnk5yvwHDkN4lQySnYV2ZLM2BEYDceZQVLNTkkxLOnEnFLp8BemTONGPBMqwh2skaiXLFkJ7MV4+ChPc8kdEGT6Q3rJ9vVFnXMWylG+aYqM8ogoRad+3aNdGlGfk9IbZEQ7AmMj3WjAzA50MVFBHyzt7M+iffl6ic3XZLhWna54szcFm6AB1Z6E+qZdS87OSARwx9yBTgqKRnfobu9SRT8zsZY7Kf54ftGvabDz/80KVZAM6TJdhUkjnAI0CQSB728earrSHDn/teflu4RNZdtcIBrZraOvn+98XSaYNVZdj+m7n/u+zZL+W+aT858rXiIhGqhwPOtQHSqZt+3r6bykEdWtX/d12dlHz4oJR8+7IU//w/KVrwc1yx/lrTQnZYenNC0R9U9IZUFjWRTYt+lkGlz8vqRfNXuOesquPl8bp432d0kL5P25Yy5tD4Ie4JO/jPBeyNatDmrCBqRCMaItMPSA3Bc06UXy4q9/gdE9dNmTLF5ZpTrWPIkCEGzv8Rnhp0n3vuOTn88MNl8uTJOYtwS2Y+w3itAfQwzor1qUECyviOUoVSpCCdC9j0Aer8Dk81ADNICya1iHfccce0yLySJZJDaZgxY0bGAYc3dx5PJyR8yDBTLd8AOoogygKKHEoFirgXeKYTmsrBnkq9+kzNTbTnsgYZbzQlCSVXCd4wKJEuobJJ5B0JegysXQweGLSC/Pb99hM5QKbHdx5mTgG/44l2HUYQ6q/fe++9LmyYOe/SpYsj5rvwwgvTeXRO7uUcYT9KtilfCucNzXsWJfusyOuRsXp5MaASso2BLF54MP2AEBNDK2C1UJjaY8kSUI7hUMnv0jWIKXBE7o5scn6JvPhzqSyuLZaykhIHvluvWiFnd9tENv5XvZe+cmmN9LjpnZjh7tp3gPo9A7eVbddbVlvd/a62Ripu2V6KFvwSc8l8V91S9qgeE3dJFUuNzCw/Sorik7HLH7Ury85LxkpVTG989Accv9sGcuoeG6e7rFe430ueytnKnGraE/P5ySef5AU4J8rxwAMPdCUqKamWi7Mn8MlJ8YGREVYaVcQ3NXjwYBeFOnLkyLyKskpRFIHfZgA9cJHaAzMhAS/juypH+h687Hir8WRieQ2KeRriD3Jb44XQ+RkrGxiHEcYEWjwiOTY1DqlshewiVwifAKRYs+lbJoj48gGgE3KqwBNwTri6hq4HCTyzVcLMz9qMdQ25rESukPrA+gU0qGwAE/xOQbmfsP50+hLvXhQ8QqwBjOkYTVLtH7LAk8wehIGQdY6yli54SLU/mbjv7rvvluOPP14uuOACBwKpvU6dd5QuPEenn356Jl6bsWemAtCjkcFlSilXXguAo3p5Aet41vUsYq/C04jhEM95tLzqjAkwBw/mOyPtBsMF6UFBR8xh7GDP//Cb2fLWN3/I/Ko62aBlc+nWrrVstt5ay+0tX/6yQE6c+JH8sXCJVFYvy/cG6gKY1aO+zqrl8vzJO67Q15KPHpImz56Baz2qJP+qbSbbLrk9rpTvlkuka8VnvmZiYOU58rpsE+Pa6AD9oaO3k/brRBgXfL3N/0UaRYJuNHv2bJe3zlmCUThbJdz893bZlRiJKKHGfnjmmWcaOP/HSkS5USJ49t57b7dX0UaPHu3OCwyJiaLxUpmLQr/HAHqhz3CBjS9WaTYO2B9++MGFXAKmyF9Kh4SLwwOAjuIfFADR8nEYE2IRyQGCyGvNZtgoOdGEuOMZI9wdUEboe7Kh2/GWWhgBOnOsuXLIXWu8avhdpjxSAHSiFfyQD+bq8wWgA3gBIRiNvGXigmDoDWpcfPcYmPheglyvfvuHXDBsXX755Y4wjT3jwQcfdPl3hdCIliCKCKZ6wDh7E0ZL1gShiwB38p/zqSUL0L355nwPQYPDRAYo9ib18nIWYRwD1KAMw16ezf7kYp4xDgKKMIRxrmfKMKJj83IFIGcMJurlBThiDIE07skZs+Xm176Tpd4Y9wgBHbJtKxnZs80KfS55d5w0eXVUVHEurSuR9lW3x/R6l8gS+ap8UELvuT68Z+Ul8olsFGfqVgTphPWPP2qbjMuaTrGXELGFzsFaRubZKOGWylrmXOzevbucffbZ8p///Ccr8kmln5m8JxonCcaze+65x0VaEc2GowdgzrXk5lMi78orrywow3UmZazPNoCeDSnbOwKVgIYaRivN5iVpw+sAEEoFaKH4E+K+5557Bu6doI/ffvutY1ZH6fASyaGIAeB32WWXQGUW72HecnKAVPLTUYqUQTUIBTCbAJ0IBKIAopHEqfKlOXF4olKtwZ3qBEHKB3dCUJEeqfYj8j5vBIHWcmZ90k+MXUGsg6D66lWmIXPke0k2HzXVvqjn59FHH5Vbb73VgVZKWkEWhGcFYpwwyiqV8bIPYvCElAvDHXsDgJ39AYMo3kz2jHxqzB+RF35aKmRwfp6byjWcd5wZ/NBY75qznkrd71T6kO17FLyRRoIROdtNSeYAjZzNrHX2QiWZO2biZ/LBrL9dTno0f3iTkiK58uAtZd8t6z2K3lYxrqMUzf8p6pD6lN8h78+D3C3aU+vknSYnyprFf/sSx7CqQfJAXaKqMMuDdEL07z5qG9l+/VV9vSPViziH4RRg//Ry4EQr4eZl4s9FhBJ7IOD8pJNOkosuuqhRgnOdZy1/jH58ySWXuKhPDNN4yuFuGjdunNufSL3hu2G+IBWFKDYW6Wiqa6iQ7zOAXsizW+Bj40NXjzp/0jQvEM/PzJkzXegUivY99SsAACAASURBVCWAKJkwQAAlGw3lzjJ1GCiRHMRLSiSH5Zi6rtms6/zGG2+4A1IBI3Ll4EQZp5EvnW497FwCdA57QkW1HBpjUo+Inxq5QX9GkJqhcOYaoCvQ1NB1clkBY/QLAEY4Lf0MewOgkw+diiHO79h0DSlDPWAJgjjWFbVvyc/OplHNb7+DuI5vBLZivLWMmZxClC48Is8884xLMcgng4RfgB4mcM48Et3EnsxejfE5sta6t+53Ps1HrDXK2Q0oChMzvaYfqJf3q4UVcuOMpTGC1etH1ry8RO7ov420X2eV5YZaNHemVEzYR6R68XL/v2CDfWT0ahfI+Kmz/vn/FUH69SVjpVfZO74+7y9q15WDloxKwAq/PEAvKy6Ss7ptIkftuJ6vd6RykYJz5hdDcKymRnVlhU+3hFsqfSU9kagocqrxDGc6iiOVPmbrHnQ5IqruvPNOJw9SoDgfIs8/2NvZr5TFHSB//vnnZ6ubBfEeA+gFMY2NexAK1LU0mxeoY/Gm/AzKDGADIOwHcONNRPHfZ599Mq58elnpsczT52wC9Fj13r213fEaELZEaH4qDUDDTzYUR6y5hILyo4oUxhnNJ8+1NxiCGbxB6Ro9UpkHgCapDArKlXtAS6GpEQuFhO8Ew1bYG9b87bffPnDuBAxoePD4AYgjG827x2PJWmbvAbBSA5e9ohAbXpJevXrJqaee6qpSkCNK3V9yoFFWYTLOp5YIoGeSDC4VOdEfjM2zZs1y+eYYTLwtWq111qmWb8sFN0Mq4/TeQ5QA6WqMN6ypQOgb7A0XPfO1vDmLGunLg1yXmlsnUlJcJFu1bi73DdpOiiNZ3RbNldLpt0nNV6/IXzXlUrL9QFl5u0NkwPj35b0f1UO+IkC/oGSCHF32vG8xv12zpRy5FHAUi1Vu2f+Xwk4PY/2BW8gB7WMDZ98vj3Ih5zLh4kTgYGxKpnnT0tiXMST7LeGWzHv0Whwm++23n9vzrr766qzoMKn0M9P33HHHHa7Ou6Z8EilG9ANnwHnnnef0O3QGdAzvnoO+g0edOX/ggQdS1iEzPb4wPt8AehhnxfqUkgTU48FG4WV8x9oJQAd0YAEHdFBfMx5YRDlH8d93332zYi1VIjnAJQc/igkKVjYstYyTUlGRip9OAuGgHFLkmqKcE5KfTDQCz8kGQOfgBnji5UOGGBM0LI5DPBuy9LNwObAwFCmRip970rmG+VPPL98BudoKyMljjfYdYNRCXplk9k9nTN578eBuvfXWabOoq6dGy8axnjDmqKzw0OsaUhDHPkP++6hRo6R3794rDAkyNZQ6vIEoo2PGjInLL4G3AbIdAAp5e8OGDZOjjjpquefyDBQeQszp2yGHHOJy4LXO/IgRIxyBm7ehBBOpo43+cw05gyi5RCDwbvoY2QjlZ19SJWzQoEGOuwFlrV+/fkFNY9aeEw+gq7E3MiIra52LeBHKLrInoiVRzW9upf9cq4zwnHdesJ4LnoZkZEf/2XtYq4w3HyojVNfWyjH3zpBp3//V4En3wuCmZcVSVlosdw/YVrZYe+UVxMF3jAEGQx9n8M/zKqXb9VMirlsepI8puUEOLJvqW7SzateQ3ZaMjXN9fY8B5yS3r1xeKi+etqOs1KTU9zv8XsjaBJwTkZMsOI98RzIl3Pz2z3sd+zCecwyU119/faMF53jCMVIQ/Yfewr5y2GGHOb2OVNCJEydKnz593P5Di9S10Hk4J4m4wuBrzZ8EDKD7k5NdlUcSYJPQ/PTI0mx4gBR8QDgTCwRTlxbytGx7xQDBHAocPIBKwsvTIbvzM22vvvqqMwgkqqFLCDRGDhR6jByATL8e8UwAdOaZaAMNXdfQN61THlbv7/Tp013aRbrKSay51bxJ9ZIjIzVWoKz7MVagMPLt8I2EvaVTV169YOopR7HAY8cPyrIX0ESCN9Y+ngK+HwwEkZwHDz30kAwYMMAxnhP+R746XgjCdgHfkQ3QDQi+/fbbpVOnTk4ZOvbYY53X4YADDnCX33///XL00Ue7sHqibNjLIOGh3ux1113nrgGgA6pfeumlhlfQT2/EBuHpeD7Gjx/v5pjwQyJp+L4TRcmopyTs6yJe/4iQimxq4FUl0+/elik5YFgjSoEGeEvWKKr7gIJ19m+ttc5ayBZng1/5sN9gjCCiDO9cJlNW/PZJr/vhj8Uy+eNf5PM5C2SNlcukW5uWstPGqzUAEUD1gLvfd+XXFJwvg9R1UiRFcniH1eS8/do2yJ35UU4Bxqvn/KXPfin3T4vMTV8eoI8vvVK6lH7oexhPVu8iZ1SfGPf6kqIiNx7quFMDfYeNVvP9fL8XKjjHIRC0gTpeCTcl9/PbT64jpQT9DycNe3iu94Nk+h7ktVo2DacV3nMM4hjPNJ/8nHPOkbFjx7qziSgDbXjXORc15xwjMKSiGHit+ZOAAXR/crKr8lAC0RjfGQYbBuGCgBCUAEK3I8EpoAYyL3LQs9kA6BgRUMgI8cMbzEGGEh0Um3zkeAjlZ8MlbNdP0/x05Ivs/Hj6g8pB10PYG6Kt+eTKLh6PJM7P+DJ9DWzfRCLEy7tLtg8a5qpyQblX8jvkkqwyTsQEQIz5DXtLhhVfQYt6yTXXHhlhvPCW0/N6yVXJQEkjjA/gDujl37EiM1BIULwB3trIHz7ooIOcxzuyAbgB8njctVHCDIMOShGNmruQ0sGPoe2ss85yYB4uCRoA/cknn2wAd5HvYSyUvOHZGARoAFYMRgB32Nqj3eMdZyxPSdjXivaP70PHoGcC3mpVRnMdbYOBGOZy1iNRDUGEqWutdQyagGAlO2P/ztTZ4nc9sNfA5M05wTeTrDHC73tSuQ5QftGkz+W3BVVSVlIs1bV10qSkWPp3Xkf677AsR/vzX+bLgLv/J4uX1jSUWvO+r1mpyMDNamTHDVs4Y5mGajNer1Fs4D3vy7Tv58XoKkC9Vj5pMliaFS/1PZzXqtvLv6v/E3H98qaE3dYpk32331T23qKlNK8I3nOOXgOJWCbAebT9CoOUGu/Z5/mWNBQ+kZGaiCc8xnh9MZgG8f35nizPhRgJ2KOfffZZASCjB5L7TUpXNhrRWhieIT/lzOPcJPoUpnZI4dhD0JWpB3/ttdc6WeFZB4jzLWNc5owkTx3DMjp3PvDaZEO2ft5hAN2PlOyavJZArNJsyooLo3pkDXAUGIBUtsNxAOSALN2ACSXSkD8lkgs6TPHll19270vGU68l49hwYUzH0x8vHDEdgM48KfDkgAAgaS5wtBBtADoHcC5Yf/18KJQkASClC9DV+6vh6ygRXrmko1TwTbD2AJRhb4DTjTbaKGZOv+bdKygHnLFuNHRdQ8MVqKmnXEE5cuSbY93x4we88Q6Mf4888shyOdqnnXaaA86klUQ2vkFYglF2tA0dOtTVkgWw0QdCCVF+XnjhBencubMz4vXo0UMGDhzoyv7QAOiAfL5nDDMYCi677LIGxYh7iC5hHWKY03bggQc6Ix3lcgq9eQG6es7DAs5JQwGsKrmpn/WW7HxhkFHPutZaV0b4ROAl2Xclup6+YIwAlONx88MRk+iZQf2etTH0yc/kne/+lFYtKhpyyKmBXlJSJLf16+D+X9uD036Sq1+cuVx9dG9fOq23ilzSbU1XxYVxYxjhHAA4qnHQ5Z//EA+g18knpYOkWal/gF5VVyrbVN0WQRTnDcSvkxIR+eiizIQfA845lwHnueBeQdZKMpeohBt9ZR9mP2YvTOccTWcd8l2yPwOGTzjhBOcIwXCOXpOt6ED0X/RgvONEE/CN4riCdwT5TJgwwZ2lnE+cU5w9fMPw3LCHqU6JQwwgnw/6RDpzFvS9BtCDlqg9L5QSUG8YoCYyv5DNm42PTYRSH+TdYq1kg+natWtWx0PIm27M3hdjASYPiE0uyPJnvANvXMeOHZMC6No3b1k7QCeyi+atTRagAw61FBqbPWBHSd7wNsRTWvMBoLPOvGVl/C4yzbNHNhiRUKYVlCeSi993cB3rkEM3Wk5yMs/JxrUoEFj1vSkDWjIOUB4t716VLvWisid4vcJeL7nWg09mLD///LOLkiBNxkv4CFBG6SOUPLLBcIun4emnn3ZeRMYF+AZI8TxdLzfccIPgNddUHpQ3QjC14W1hneBtQdkkfJ29g++CSAFSAvDU453hm9V23HHHuYgdarkXegOg65zzZ1jAOfNMhAQGT9ZPNhp7s5YR43vBYKVg3RtRkom+sE4xFKHIs9eELYz4r8VLHWEbIeqreLzKtXV1MufvKjln701l/3bLSqjxTfa76z2Z8VPs0oN391hNqisXOiCjUQ3s55qG8tIPIuPeWcYXsbzc60PdO8mn8nDFpUlNyX3Ve8mF1YPjEMXVyaRjt5ZNWv0rqecmuhg+AdJ6cgXOI/sXrYQb80A/d999d8etwVoElObSWITBlfNDI6MSyTnI36txmmceccQRzoNPWhVRBehiGJn5OwaE++67r4HIkbOFMwSPO/oIzhV0tVwZOYKUSS6eZQA9F1K3d+ZMAuodUyI5OqKl2dik8VajEKOgAIC6dOmS1b7i3QKME+Ie2eg7BznKfTLh5YkGAEDHG5co9zTec5Ad/aJ/WrPWuyknAugaeqwhaYSnKUEXwDyZEMywA3S8RYBJLziKJVvmmfWgEQQYjtT7CzD3en8TzXMyv4e4iPdC5BP2hjz5XonkUC85xgXAhYaue72CsULXvV7ydIGCAnQUlp122qlBhOR9U5JNSxh6ZcvcUmOX39NH1kj//v3lqquuckCbMZLvTp4foBvPOBEseOXJVb/wwgujThWywONy7rnnyplnntkA0L2gnxt5Bik2zz33XNinPO3+AdCjnQFpPzjFBzDf7P18dwC3XDGXA17YazgD+ROAouXb2HeC9OZzvvLtqmE3yGenOA0r3Da/slqOvPt99z22aFrW8Pva2jr5ZX6VnLfvprJP2+VrnB//wAx5feYfMbuw8SpF8tgJOy1nyNbULeQ+e84vcvIb0eqf88j6/y+VavmqYmBSw1y61jay+fdni0hxzPtO2mKBnHRYPd9FEI1QcQxOrOlclxWNNh7mlXU4adIkueKKK9z+RzQl5JykIkXyigQhE7/PoPwc+e84joi4wmBHeTP26Ww0jNzqbMFQzDxyFvF39DHSFQDpGBMJZY/kFIhkc89GnwvtHQbQC21GbTy+JKBhjShpXs8ZSgIbNpsP3mpycJMhQ/P18jgX4clHoeZAi9U4zPF+ffXVV86DSh/95o9HeyYsnDvuuGNaAF2fi7cS8AEgp1+ADGQaDaAzDmStnnKUZgVUHOaphvKHHaBjfUbpjeUhY00iRw1dR7YqF5SHbFj1UVSI5Ii3DtNd6+ner3LiO1BLvTfv3g/Bm4LyVLzk8fqfSoi7Po9vBUCOxxyWdXIQiSLBaEBOJN+qN08dDwbeb4xasQwLe++9t4u8IR++sYe4Iye8UhhO+JbSNcaku46VHI05xiOFoSkMjX5prXWAI00963xn6chNa2Ar2WgYxhurDyOe/kJe/WqutFqlwpVMc4byhUuktLhIjuy8rqzZvFw6b7iqrFJRD+BvevUbuen172MOicDyZ07eQTZYvVnUa3h+u4tf9fwOUL58ODq//LTJv6VpEnno3DOo6hx5tW5ZWktkB/6z3idy1KCTA5kOBedhLpWnA0UPgYgTIxSgE7ZxSDNJMaJCRy6aGt8xqh566KGOZwTeEMhGIyt7BN0/9aBjuKC6B3sSMqFP/BvjBSAdXQ/Ajh4KKWnQxH9BjyvfnmcAPd9mzPobqAS8QN3L+E64E4o/ijsKM6HbWPozbeXnnVgu/XguASXkshGODOAjpDUV5lsAOspqUIohMlUDAv3BwsqfSsKk4BMlDXlqKTQOxyBCocIO0EmdYMxegK41t5EJwBh5KSgnkiDT6y7yo2L+6AvKVZiahvjjKdfUB9Yba4dvVEFDugRvQYwZDzd5et7wc7wi5HpHI4mL9k4ieFgneChoPA/iSsjctEHWM3jwYAfQo30/7CcAIUD8RRdd5AAGe9kZZ5zhvOo0DAooV7FI4oKQR1ieQeoApZNoKJfkUxLamgtiMuTOfsDZQ9RUsmSO2ZIpa4bvTfPWORPVs67knH77omH8fAuppPn4fU9Q1333+yK5cNLn8tNfle6ReM+rqmsdWKesOeHvq69UJmfstYl02WwN+WbuQul587txX9+vY2u5oHtsAs4tR70S4/5lnvWji5+RC5rcn9Qwl9aVSJuqewRe+RVrotfJG52myBr7D0vqmdEuZo4Bb/kAznHEsCcTcfXf//63ISqNcxhnCelTuWjsR6QeEoWl7dRTTxVKlk2ZElmGL/gekpaAXkjUFyHr6CAXXHCBPP74487QS1k1gDmRkzCzP/XUUznhFwh+5OF5ogH08MyF9SSHEtB8TgWRAHS8iCjZHDYAZ7xtgGDAVaYAEyH2KD/J5P6Sr03/sFhTvgllPBnvM4RT5KSy2QbZMCDgrVMDAv/m0MMKq6A8EzmOYQfolB8BUAK8CekHbKII8G+UXhTeVAwtQc4dax4vrpdELMjn+32WstNr6Dpr3UvwhhUfRYI/Ic8JguDNb98SXadl1m655Ran6OANh+WW9UlUDt4ZDCEQ7dD49vGSsOfwncCKi/EMQKmEh5Dw8P88S0PcyUEHuPM+2tlnn+28QewFACrC4QmRpPawhmwCxDESkPOOYYPceMLn/ZRZSzTufPg9exEeMsrRwXjPugKs4xmClClTqSNe2fDNE02DYRSDbBDGyWzInm8sstY6exYGHvavWGcP95GfilE5l2H8qcho7oIl8tLnv8lXvy6UX+dXyfs/znO1wpuXlzjG9t8XLnH/vr1/B2ndokIOvvVd+eKXhTFfxTUvnbYs9SXywtgAnSsB6XXyL5knr5SfJSsX1RsO/Lbx1fvIyOp/r3D5wLKXZOjA3lLbOj2GcAXnWtfdb79ycR3fYO/evd2axVsctA6UzpjYq4l8ojSnNoAx+znnRpCNXHeMZV6G9cmTJzuPPfuk15CGMfixxx5znnTOGfQWDWdXLo8g+9aYn2UAvTHPvo19BQko4ztgF+UWYhPAOBsQ+YEATnK1Cd9OhvXcr6ixOtMHvAvJNpQmFGz+BKSjoPsJQ4QUitDZTIFCvMOE7mMRRoHL9CEYVoDOGiJ6ACCmIdlac5s/kzGqJLs2kr0eAxWKFmRl2W54FRWQIy+Ai0YTAM41xF9TU/hm+D+Y3PlW0yV4C3K8eM/J22M/AYRRqxxvLY365RivAMY0cvz69evnvmHWAkARIO0tdce60Tx2lDS+J5Qk/k/TXMhRR6kiAoLfExIPM7x3T0F2I0eOdOGSGAMA+yhcfiJ3gpRPGJ7Fd4mCitL5xBNPuLQbPOx41VCQM7EvInM852HOv/Y7N0RueGut841qKLxGBLDetBoJRr94FT/8vjdX1x13/wz58tcF0nLl8oYuQBr324IlcvxuG0i/TuvKL38vlj3GTI3ZxbKSInl/aBfnhY/W9rruTZk9Px5Le50US61cVDJBBpa9mLQoXqnZRkYvPUS+ltbSSv6QIaVPS+/NyqX60PFJP8t7A3sS+1c+gHOisQgd5/snfDuoCMK0BOi5mbMAJ5GXJI6oJ1jUvV71dN7Hd4lzB7Z2iODQd7U9/PDDrjQafeBs0TroGNi4DvmNHz9eBgwYkDGHVTpjK4R7DaAXwizaGAKXQKzSbF7Wcq1PHqQCh5IOyCAsPJUWSSSHx1/zwGM9D1IoQmmTIWJLtm/KmuzHYJDssyOvDxNAVzZxDV1XhRUllrnJhjxSkSfecw5mQuwy3VizKPmac0/IIUYwBeUoThqxEi10HdCK8Yf1G4ac4kzLy56fOQmw7xPFgGcdsM53QHkhPOvkpgahxGOsIeoDw0uuwmczJUGUeAXrGDq09jQGCRR6DH5BnpeZGke85/a5bZr8vbhaVm1Wn3O+oKpa5i1eKguX1MhWrZvLqJ5byNrNRA659V35fkH0JwHQnz9lRwfyr3/la3n+099c0HmvrdeWATusJx/+NE+Ovf/DuMMrkWppLb/L+CZXysbFy7O+VxWvLOW1ePBjkc1Ff3T1hnvI0kPvEymKTSQXq1OQmWGEwQDD+RbmRsTM4Ycf7s4dHBRhNBgRyk4FEAyp1BZnX4IgjugpmOaDbES24LFnb0ImRFVxrpKiAHcJUUZ6BjPPw4cPd2f0kCFDrHRakBMR8SwD6BkUrj06vyUQj/GdDR4GZbyMKFl4rIPIHwRcAjK8XrNUpOglktM88GhEcoyRA4pycpkK6+QdWt4uG4AU5Zcx56IOuhdsAjg57Dj8tRQa/WKO+T8iHMLaCL3Hu9upU6eMdFHTHRSU48XwErx584H1O9TyiKwhb23yoAneMjJge2jeSYD1Bss4YJ28SwxWe+21lwPr1ElONj2HdYz3CWUYD1QYWa2DnCSMkxg4iDpj/ydyCmMxP9mutR7kuIb99zN5/avfZa3m5fLnoqXOc44HHSjcrKxEVm9WKodvuEQWlrWQcdP+ivrqZmXF8uTxnaTPbdNlflXNcte0XKlMHjmuk7z73V9yxfNfyh+Lqld4BmC+ZbMiqV30lwwpnSTHlD7TcE11u0Pkj+3PkDUe7C4lS2PVU48tkaUdh0j1niOSEhnfBp7YfADnrEuqY2BIIr0vzMYESm6SCoVsiRCDMC5IFnclg2OvI9wfgwDRD7yTiCv2PUjy0HFJh8IABx8KgJ3IL3RGY2tP6lNJ6mID6EmJyy5ujBKIB9Tx9rF5EpLLBgooTIdlG/Z4gD4WzCBaIiI5NmYOKcJpgzAwROtzoQN0LZGjpdCQeSywiXwA6HjiclnCJdHaYiykJRD6HFSLJMJTLgI85RiPwkTwFtSY7TmFIQH2MPZmBet4CgkLJQweAqVEJcjYIzAc4klGAU6npGW+SBQghIEDY1vbtm0bSObYW7JZaz1oeX3w4zz5z1OfyfzKpTK/ssaBc8jiykuKZa3mZfLr31XSpmWFXHf4ttJ1zJSoPuzSYpFNVq+QL+ZGzx/fe4t/yfWHtXecGpM//lUu+O9nsqSm3htOVHzz8lJZa5Vyl/t+2KZ1cvpqb0tdy7ZSu0k3kSb17PDFs/8n5fd2T2H4xbL4zK9FSit83Qs4x1kBOE+nmoyvl6V5EYYiGNAxklFeNlflDNMcRsZupyIIRKLsbdRhJycd8jdAOqlkEJayhwHQSYu0llkJGEDPrHzt6QUkAS/ju+a/ag11zS0mjI+QICyOqXiLIRDDy8ozgmyxiOSwfkJExYacKRbjQgTokXnSGGXUS46yHm/uc+nh97umdD2Tv5xq0zJ6GK/44dtAgdPQdW+oa7TQda+XHPlmipgx1fHZfY1TAqxV8mzJWecH4A6vAJ51wDp5/961Cigg3xzDHeA8U5FKYZoNvHGAc753PHHe/ZAzh/0ADyaROnznmrPuNdSFaTyRfZn67Z9y9Ysz5YtfFjjADEFci4oSqa6qlNriMpHiEjl9z43lvCc/S3kYDx+zvWzVehV3/6jJX8hTM2Y7kF5XVx+BXlxUJOWlxTLqgC1kvy2Xr8WuLy17/lwpnXFv0n2oPOJJqVsvsXEWXh4MufkAzvn+IDgDaL7yyiuNnnFcPd/qRddFgtcccjiihfCkk4pHA6gTNQRI5/+MEC7pzyrpGwygJy0yu6GxS8AL1L2l2ZALSgfeFa7xk/8dKUsUObwrXjbNIOWNx58DSonkICl6+eWX3WacKZKyQgDojEFLfOEBQn54wRWUe/OkE82Xso4TcRHWhpUcPgRC3pJpgBEvwRtAJVYNdy/jOu8IG8FbMuO2axunBFjDABSAOort+++/774ZPOu9evVy3iZySClnB4jJF6b2dGaT3HPAOYo8huZ4hjVvrXXAOvLU8m1EIYVZXv/3+W+u/NpqzZpIsdRI5eJKKa+okKV1RVJdWye9t2klN732XcqibFpaJDf23Vo2adlMTnzwI/l0zooJ7U1Ki+W2fltL5w1j5HzXLJWKm7aWosroofaxOreo5zgp2vKguH1XcA6vQCYIc1MWXJQbAaPkS1M1AX1n7bXXDvLxefcsBeeQwV5zzTUuehKPuDpqKDd3yimnOG4iwuoxLHpbJKjPOwHkSYcNoOfJRFk3wycBNimssmx2Xo86f4dIA8UNbwn55Cgbfpp6HTIN3lCG8ALRd0KPDaCvODvqAdbQdUI2tcQXwDzVlACAL/dmygjjZ50lugYlG88g5ffiNdY63jLNJcdwQX4p8gGYe/N0o3nJ8axhGMJDbgRviWbFfh9mCbC+AS0K1qdOneqICwHso0ePbqgyEOYxpNs39gGiwADmyXJsIL/IWuvsI3jX+TOd1LF0xxXtfojh+t31vsxbVCXlUi3NmlVIcUmJzJ2/VLZZbxVpu9ZKcvMbP6T16qZlxc5TDgFdZCsrLpKmTYrloA6t5D/7xk6JK/p7ljR59Cgpnuvfm//TqjvIjzuMbJB9pPGeEHG4BfIFnAM2qdRA3jSGI2vijIdU7YCQmLnEaEEkEARwRLfB6k7ddfgyhg0b5kp5WsuuBAygZ1fe9rYClEAsxnfAux5kAHQ86olyD/HAAGyykZ+MQkT/8KhjAWejzgRhSj550NUDrGHZAEivBzgIjw7yRuGBWDCsDaBNNEe0PDOMOhzu6ilHZqxvlZPXcGEEb2GdYetXJiVATXrCaamtjjH0zTffdF4owuDxrmtJwEz2IdvPhjAV42O7du3S9lCybxDtpYzwGJHZYyCYA6xnKh0rWZlNeO1TuW3qHKkpKm0I4//XSk3kuN3Wl/FTfpRPZsegcU/2RRHXE1YPcF+pSYnssNFqMubQreI/sa5OKm7Y0r8nvaRcPu3+hPwyr8oRnaIXENnAD8R/kB0C2MLIfu4VBLoZpcnINyesPRt6VZpTm9HbvWHpVO+BU4M66xjk8aS/Pf6fngAAIABJREFU9NJL0rlzZ1drHV0VAmFI6SCr23rrrTPaN3v4ihIwgG6rwiQQkARiAXXylfGmQ6bSqlUrRwAXKw/xvffec4dgst6HVIeAV5iDC7AIazfvxpAQZCmcXAB0PFd+oxAIXQdsokhzUOEBBmwiCw6poHOfiVzAGxRmgI5ShrFIa3bDYaCAHHCOgqxecvJG1XARy0vurU2O0SNomaa6/u0+k0DQEoAEcqeddpIHH3zQAXS+CUANxEqEwePFIy9bwTr7bT5/D4yPs4MfyjL5jRZLRu5E6ShYB7hrrXX26Fzl9ANSGXOzddrIWz8ult/mV8mmLVeS9uusIqOe+VJ+m18p86tqow4Tz3htbZ1U/UP8lkgWsLZDEad/cj0gvWlZiQzccV05uevGcR9RPPNFKX/8qESvWfb74lKp7P+01K3dwUXYcTbyAzcJDY4dfpJJ7fL/8mCuRB8777zzZNKkSe6bC3PEWjAjjv8UHEacw5zlGNOor07E5pgxY9yN6EGAdDzngPQRI0a474w5z8Q3nY0x5/s7DKDn+wxa/0MlAfUYshlqWSglkmMDhPEdZQ1LLgdGZOgYtS8B8dmqj8tmzeG17777CmBdS8ett956DkAG4akIG0CnPwBxDV1HAdHQdYB5JuvBs1jhKACkBk0EGOSHAEBnLWIoApijIBNl4SV4i1eb3AjegpwNe1a+SYA9Hm9vZGPvQeGFcIlQeDxWGGzxqh988MGO7TyfwLoS5jFewp0TRYgFMY8KGAHshMTjxVWSuSANy7H6ypg5J3/66SfnRY4c8/UvfyMPTJ8llUtrpLpmxUrkKzUplpuOaC//eeIzmfP3El8iAYxDDuetag5Yb7VKudwxYBvZcI165vZYrcl/j5eSz5/y9S53UUm5LB7yrsjKy8jn1CCB7oKRhPOTaCnlDMBQG5a1i+51wQUXyCOPPOL0m6Cq4vgXYDiv5BwnKg6Ajo6KofD1119v6CzOJNJxiADq2LGjjBs3zukqQUQOhlMi4e6VAfRwz4/1Lk8lEA+oAw4BaYQRA9IBQboBvvPOOw6cZytPCmXntddecwBdD1cOX7y8KD+AdA7kVBjpderCANA5jFCMNVeavsUiL8v0kmPukXXYlAZC1ZGResqRGSADOfHjNSYZwVumV4k9v9AloIZCPHyAdcpdYhgFrONdJ6Q0nX030/IDBMFTwXkBOM+0YTPaeAAU6lln7yL6ScF6Jry7apDgnYBz3hfZhtw/Q6Z8+6fU1NbXRve2smKRN87aRVZp2kSOGv+eTP/h77jTRPk2HsKf/NXrcF+1aanceHh72W79VRNOddnz50jpjPsSXucuKC6TmjY9ZckBNzdcT44y/ApegwSpTshc2fi5WMPgOS9yBeqYI8qC3XPPPQ6ck7rXmJu3Tjm5+BiWYGnHU/7www87jgxqnCvHA3rAFVdc4f4fbiJruZOAAfTcyd7e3AgkwGHBBqkedfWm8ydgESDMhghYg1EdgA4gxouejYZXn1AnAHpk0/7Rd8IwIRFJxUKeK4CODNVLTlg2CqSyruN1yZXySxQFMtHyJdmY52jvoA8a3o+cMBzhgUJGyOejjz6SPfbYw825EbzlapbsvY1FAgDdyZMnO7CO8gzQhAkezzrAKFf7VTT5a+k4zjbY6YOItEp3nukT+xiAUb27CtaJ/knl7PL2SWvZY7hmPmIZJE6a+KG88uXvMYezb9uWct2hW8mdb30no//v25jXUSudPpf8k29eVV0rtf+A9S1bNZexh20la6/ir1Z50R9fS8UduyYWcVGp1G64m1T1ukWkvL7Em6bn4VHF6BHrLEEuhMEjf6LxAOlK8Jet9cE5Bbi85ZZbXOoeJGjWiL6oc0RvGLQOP/xw6dSpkwt1x4hx2223Of2Tv6dKfGsyzowEDKBnRq72VJPAchKIBdS5aPbs2S70HYszCg/ALVsAnZAnCIyiAXT6Rr+xuNI/cv1SIZLLFkBXYiGIilDW+EExU1CejfBHP8ue8EiUvVwAdN7rJXjjwCa8X73kqnTy/6wLL0mchrop6zrrNV2l14+87BqTQGOTAPsyJE6AdUA7+xhgHc/6DjvskDPvJPMA+IKfAmWenPNceUrjrYnIWuvsXQrW2e+SNXawb2KwxKBJtEA8IHPHW9/JtXGAN/1+8riO8q/m5bLb6LdW8LLruEqLi6Rfp3UEP/xrX/4hldU1smbzcjlsu9bSZ9tWSe+95eO7SfGvnzSILTKvva55a6nqc6/Urbmlu4bzFHCuofyxwHnkPHCfcgYA2DE8Ef6uofCZirTgvdddd537gRQusjRYY9tDvOMlkuCQQw5xc/Huu++675ZGBOX9998vd955p4uW4+/RokIas+xyOXYD6LmUvr270UmAQwSPND80bx4vIWR41NkgKW2Rjdqi5Bq//fbbss8++8SdC/oLIQ55aABewKXfjTyTAF0ZxdVTzr8BkOQFYkzIVG33dBYuSg/ypPxeNhoKtbLSE5JIKJuG96OsRiN4o38YEugjyihy5DojeMvGjNk7TALLJIAS/eKLLzqCOeoTYyg94IADnGedMNRslh8DeAHOIY0iXz5ZoJuLeVWjpIbCcx4lU2udM4X62eyJgPNEZ8qXv8yXg26dHneou2+6upy258bS57bY1+FBH7TT+nLGXpu4fPYlNbXSvLw0aWCuHSn++v+k/LH+9XpHRO9cKH5xE1l89vdpgfNog8ZTq551jMOZSENgTm+88Ua58sorHfM4HuLG3Lxs7ciB7xa5UNMc7hu4L7ShH9x7771y7bXXOsK4RLpgY5ZrtsduAD3bErf3mQREnAfVW0NdgQ/h5oBLDjSUCEKP/ALhVAQLQJ8yZYrsvffevm5nM8ebDsmIXyK5oAG6Ak5AOYBTGcUxHGCpp4xZMizuvgYe4EUAdLz7mcqN00gCzbdnjllTyrruzc2MFrpuBG8BTrY9yiQQoASIbHn55ZedZx1WeAy8PXv2dGCdaJdMhhKTAgPrM/woKPn5GD2jef8K1pFnvFrr7NOMmfMZj6wfY8jflUtl99FvyZI4DO3FItK3Y2t5YPrPcVdH5w1ayF1HbetKqqXd6uqk6dWtVwDn+lxA+uKTP5a6pqs3kMUS1h6k/qFpCOg3nE8YO7wkc6kYfJhTwrRHjhzpUkOooGCtXgK9e/eWI488Uvr06eNC2hWko1cSoaON7wCegUzpJDYfqUnAAHpqcrO7TAKBSCASqL/11luujiyHojKqQxoHWVsm8oMIeSLvvVu3bkmNJxkiuXQBuobMcajjCebdCjhRrpCVV1n89NNPQw3QOQgxMuCBCqopCZ56yllXWpscb7lXcTeCt6Ckbs8xCeROAnzzMDDDVA1YZ08BrEMyt+eeewZ6XrD3EuINMM9WCdBMS5Z9EOOlgnW8jJo3DWikaSg/hH3JhPKf8chH8vxnc9MeAkB+983WkKt7bykrlZem/bzyO7tI8e9fxgTpiwa9Il/+WSxz5syJSYKXdif+eYCSzGkJN+ZDjSV+Sea4Z/z48TJ06FBXq1vLggbVx1Sfc/nll8v5558vp512WkMZs1Sflep9GEMGDhzo9gZKPrIvsEdARnnOOee47xjAHmloQ6b5aHxLVU5hvs8Aephnx/rWKCSggAmFa8aMGbLhhhs6ki42SRQIWL8BXtT15nd+rPh+BQeTPOW0UmXr9EMkpwA9mY3fmyvNAc5hA+DUfPJ4nqKwA3TSBAhbpcRJOs1L8AZBD1EDGrpOeoR6I4zgLR0p270mgfBLALCDcffRRx+VJ554whkx999/f6eUY3xNh3+DHGSikiDcilY6LvzS8ddDb611zkX2T4y/gPNk5ffr/Crpet3b/l4c5yoAekVZiQzZbQM5dtcN0n5eybRbpPyVkVGfU1dUIjN6PCtz5v6ZcXAe2QGNbNBQeLy9nPfwBmAsiXbecw8502eddZZL/YDQNAwNfeqwww5zOhx90jrjme6bl61d3wUgpxb8zTff7GR16KGHOqI4cvRhcsfpg85pgDzTs5Pa8w2gpyY3u8skELgEFKgDRvk7TVnfyd0iPx1QhhcDr3oq4WCRnSZs8b333nMel1RbIiI5vwBdw980dB1DhIZle3OlE/UTkjjyNDFohLGRy888JgvQMVpo/XYMNoB8QvpVRl4lUtcS99CM4C2MK8H6ZBIIXgJ880RFKVgH9JBXCsEcZKDJkH2xV/EDqRSAqTE09ubp06e7M4R9E+MnEVtKMuc35Pvsxz6WZz75LS2RNfmHyX2LVs3lnoHbSpMSIHsarbZGmo5tK7Jk/gpe9Lkb9JD3Wg904DxZg0QaPVrhVm/EHGsXYwkGZ2WER/5cQ+TIySef7NI9YpHcBtkvP8/CoQJPAYD4kksucWkR2QLo2j/SD6kIpA4RADne/Ouvv14mTJggRxxxhHN4EOLOtUOGDPEzNLsmBxIwgJ4DodsrTQLxJMDGGq00G/cQjodHnWsgasOjkY71E+WDHLsgrM/0GWWOEG4vkVwsgM7/owwpwRsHMcqjeslTrWObDwAdbw2pDIkah6uGrfMnYZZegjeNpojlJef3RvCWSMr2e5NAYUoAsE6YNmAdkrlZs2Y5jzpgHQ+7RmpFjp79BIPwL7/84gAHALUxNCIPkBdVVMjT5WxlD1bPLnsw4BWwyNkb74z69e9K2e/GqVJZHVkNPTlJNisrkvVXX0km/HtbWTmAMHdZ8ItUPNxXiuZ+7kA6nvO563eX91v1l+07dswpOI8mGSWZYw6eeuopt5ZZk88884yr4w1hYlgaIeUYsmCS79q1a1YAOrqEGo14LxEFkBoS+aEgHRniMYcMjprnePi9RHKRpHJhkWdj74cB9Ma+Amz8oZVALKCuHmty1LHyA9QBbak0PPOEOHGYBNUIq6JvhEZq/ry3lrZ6gQHmXIt3XL3AjCfdFnaA/v3337sQ1Gg1WjUvEkUQ+XCdGi2YYxRlL/O/N5/c1cwtKXGAHGDOTzrGm3Tnwe43CZgEwiMBlPCPP/64AayzRxM5RRh8jx493D7MfoHCj4JPAwhlqixWeCRT3xMlwSM3l+iraHsnaWjeWuuEXsertf7213/IcffPkPo4ptQaXvQ9t2gpo/u0C3Y/r5ovsvhP+eLnefLr738JhHBhn2tkDwjF2ARg50xk/eIV3mWXXVITcEB3TZw4US699FKXMogekw2AftNNN7lwdbzjkPbiET/ppJMcNwX55cypgnSMGX379nWjnTp1qnTu3DmgkdtjMiUBA+iZkqw91yQQkATYYKOVZsNjDdgjpxnlCqCerKcDFnTIf7p06RJQb5c9hnAvvDAYAWD+xYoL8ETxUS851uZkyHf8dDLsAJ1yeiiDlNKjeUvFIR/m2kvw5iUHNII3PyvArjEJmATiSYB9hLxy9ax/8skn7gwgVBgvG2lU1EbOJCt8mGaIcxCjRDIkeEpyRlQbYJFzLVqt9Sf+97NcOOmLlEF6cZHIuCO2lt02Tc0IH0vOugY4cwhrDzs4ZxyEZR911FFy1113OWBOfW9I0AjpvvDCC3O2pH788UcHhiFg0xrj2QDoAHHe069fP7niiiucQ4S1CEhHVoB0ZbWn+sObb77poiWp+GAt/BIwgB7+ObIemgScBGKVZiMEj9JdHBKE5qFk+D1sOZxRzjLJforVm9B3QgMhfIkVVhnUNIcdoDNPyB0jBX9iwIgsFWcEb0GtBnuOScAkkAisc34AyMeOHeuMg5whvXr1cj9rr712sJ7bkE2HMtRTYgqgl0rjbCZdTBnhAe9aPgwv78zfFknfu96XqurkfelknU85dzdpXpE+i7uODXDOOYlhIl/AOQATD/Ctt97qAGmYosMwElDq0OtsYA3QR85yIgWDdkQoKZyWycW4dtVVV7mKP+gVp5xyiksJGDVqlNMLL774Yldujdx4dQwE3adUvh27J7YEDKDb6jAJ5JEENNc4Wg118rmpUU7e4AYbbCAbb7yxC3eO1wDPHNSZtKj6JYkLahrCCNBR4Mix5+CcPXu2y2uMJHjzhq5zvRG8BbUi7DkmAZNAPAmwZ+63334u5B1CKVixCSN+9913ZYcddnBAHY8lHrowAaN0Z5VyYhiog2SoV0ZyBeuAM4yx7//ZRO6aPlf+WFTtuu03M728ROT987sGJnf6R6UTDMN4fYNIK0t3HhLdj6cYBnIMSP/+978Dk0Wi9/r9PaloRDN626BBg1xdcVjUo6Wz+X12tOvQ/0hhIyqR+SN1hZzzAQMGyIgRI1yKBiSygHEMGgB05pr8c2v5IwED6PkzV9ZTk0CDBDTUmY1agZwyvgMECS0njBprKjl1sSyleA+4dtddd82YdHMB0AkLx0CRywZTqpfgjfnBm8KfHKzbbrttvaJWV+d+mEfNF8PqbgRvuZw9e7dJoPFI4KGHHnKpTnjZvIZCeEQA6vxQxo09C4I5wDolP/MZrEOYB+EqwAYAnYmmnCIK1t/6fqE88HWJVNb4f9tWrVeWh4/p5P+GOFcqOMfbj+c8H8D522+/Lb1793beYRjH82XNZSLE/bXXXmtIR1RwznRjQNOKCzhbyEknCoZGSh0RekTC0KKVYwtkcdlDApeAAfTARWoPNAlkTwIK7KKVZsM7jgKCt5bNmjzwyMMNxQGveyYJVhoLQGecECwpwRuGEthVlXVdQ/tRejGMoBh6QbkRvGXvu7E3mQRMAv4lwN5GZBY11gHrAAWqUChYV8Zz/0/M7ZWAGbhbKIMFf0u2GlFu1734hTz04V9S7UqpwqMevz0xpKO0WSt9Fn3mkGgBDPf5As6J4GCNYTiipFq+gHNmNGiAzvdHPjk8B3x/yk1DNQZSFSitSAWdTp06OQI4yPTw4HubsbUn+trC9XsD6OGaD+uNSSAlCcRifOdhhFQDwgGAEMmRG6cHHZs++Yc777xzSu/1c1MhA3Ss0XgjMIYAzDGGeAnevB4KNaYAzrkHpdbrJWd+8kkB8TP3do1JwCRQWBJgH2OvI7+VGtSwSHOu4FUHTLVt2za0+xh957zDew5DPUbTbLcl1bUy+qWZcu+7P3leHR2s77n56nJj3w5pd1HBOUZjwLmXeDTth2foAZS7o4TaBRdcIGeeeWZo11SGhr/CY3HCQPoGGR7rFpBO2D+gfPLkyQ0ecgxPAHnKAFKKDseMtfyUgAH0/Jw367VJIKoElPEd4Ii1VMPe+X9CnVBOKNvVpk0blwNNDp5u6JkSaaEBdHIKFZBjuSbPX8vE4Y3RdIJYoeveMmhKBpcp2dtzTQImAZNApiSg+dbkrAPWYbGG/0TBOpUqwrLH0VfSuYgaA5xzDuaydb78FVmwNFoPloH1N07ZVlZftUVa4BQ9AM85edL5As5Jt+jevbucffbZ8p///Cet8edyjoN+NymNr7zyipMLThdSGIkyUFJgzU0nH37kyJGO7d5a/krAAHr+zp313CQQUwIKigHqNPXMsoEDyNnAAZUASjzsWoojEyLNd4BO/5XgDWBOGDsWbA1dJ4zdCN4ysXLsmSYBk0A+SYB9Em8eYJ0yT3jxyI+F4RpQnCuwDkiFCA9itLCwlk///i856p7/xZze3m0qZM+Wi50BWMu3YVRPJsqKcUMgRslTSMLyoWweBHaEbRPSftFFFyU13nz6VlLtKzodjPaXXXaZS5V77733XESEgnP9U59vOeepSjr39xlAz/0cWA9MAhmTQKzSbBCMzJw5U8iHRgEgBz1TYW/5CNA55PCOq6ecMQDI9UfZ8Y3gLWNL1x5sEjAJ5LEEMGQ+++yzLmcd0A64BKwTBk+ObLZKPHEG4pGlPxgJwkSM9sQHP8tF//1CIjnj+m7fSi7s3sZFwXEOeWuta/k2UqniGTwUnDNujBL5AM6JcACcDx48WC699FID5zG+f0A3zPYwxJNWB2BnPUSC8zzePqzrONbq0DCtmQRMAgUrAQWR0Uqzkb+ER53DHFZeynOQFx1kyxeADoGPAnJyxKnb7iV4i1eb3Ajeglwx9iyTgEmgkCRAySfC3wHrkyZNciG55BcD1uE/CfrM8XoPZ8yYIeTvwkAfRpC6pKZWXvrsN/lg1jzZcPWm0qP92tKi6YrlURPVWvcaPNQowZmWL+AchwHg/IgjjnCM7bmKtsiX7445prICKQAYYYhYUab2fBmD9TO+BAyg2woxCTQSCShJmbc0288//yz8QFiG9ZoDnbym9dZbL7ADMqwAXRUeZV0nt9xbm1zzulgeKjtvXr8RvDWSD8eGaRKIIgGYwGGXxnsFl0fr1q2lf//+MmzYsFACwbBMIh6/l156yYF1iOYI2Vawvvvuu7uIriAaoPyDDz5wjwKcZ8oIEERfk32Gpl3hWYfoVWutEwqPJ5VwfqLkiBgIo1Eicrx8S/vtt5/jLqBEWGMG58xttDSGaAzsXDtlyhQ5+uij3Vzff//9yS4luz7EEjCAHuLJsa6ZBDIhAW9pth9//NEd8OSn0bQuOtcA2rHIJpPzFq2/2Qbon3/+uVNKotVBRzlUQE7oIF4HJXhDsTGCt0ysOHumSaDwJIDHivrhePwoY0mu77HHHisDBgyQa665pvAGnIERAaIJ1X3kkUccWGd/7tmzpwNqe+yxR8ppVzwHFnDOgQ4dOmQtnD4DIkr4SM5XPKic4wB28s05xzC0c35nKnUtYcd8XoAOAjjfd9995eabbzZwXlRfeg8SONYx87nDDjvElKYy9FNSrZCMUD6XT0FfZgC9oKfXBmcSiC0BNnYOAKzvbOxexne86hwQHO4wvhPqnWrLJUDn3RxwGroOk23z5s0bQtdh8jWCt1Rn1u4zCZgEvBK4+uqrZdy4ca70kbXkJEBe7ZtvvimPPvqoPPnkk451nJBnwuC7devWwFSd6Kl4jgHn7O1bbbVVowF8eFgJ52f8kPNx5kHa16JFiwaSOW9UWCI5ZuP3ENQCzImcuP322wvakJKMPO+77z4X5o9uxneBkQn54ETwtkhvu9U5T0bK4b/WAHr458h6aBLIqARilWZjs4ftHWWT0G+AOuA22aaGAO5L1xvv592wwHKoYXTAW87fOdjUU64hf/EI3riXH0LtstFnP+Oya0wCJoFwS4CazXjWp0+fHu6Ohrx3nD1Tp05tAOtEdgHkAOv8SeWMaI0ULcA51Um23HLLRrN3c8Z9+OGHzuBOqLOmCQDw8KrzA4M9RgtlhM91mTk8/hhgiN675557DJz/s6AhVuzTp48D5D169HAEi0TlUMqQ6BJarDD4kH/W1r0kJWAAPUmB2eUmgUKVgJfx3QumCUOkfjqhaITMEfqejCU+GwAdEiJvbXLAdatWrZynHOOCEbwV6qq1cZkEwiEB9kjA0ejRo+WYY44JR6cKoBecS5SSonQbeetUHsGjDlgH4GE0xoiKYRYugHXWWUc233zzRgXO8ZzDLUOufawcfs5xDB2AdQzXsNkrWKdsaDYN0ZzV1Dlv166dy5u20OxlH+r555/vSHuvuOIKt6b33ntvGThwoCurZh7yAtjQkhiCAfQkhGWXmgQagwRilWbDOwHTKkrQ+uuv73K8/RDQZAKg08d58+Y15JMD0JXgjf+HgV1z0I3grTGsWhujSSAYCYwYMUJGjhwZ92HTpk1r4O3gQlKCunTp4n7uuOOOYDpiT1lBAuz7eIoVrGMU2WuvvRxTOeRikPZBmJVNsJnLacJzDhEef2Ic8gt0uR6QDFjnT+5LtdZ6suOH+wXPMOfzww8/HBgpYLL9CMv1Xm8487LPPvvIbrvt5urAk6LRr18/ufbaa113b7jhBqfbsMatFb4EDKAX/hzbCE0CSUtAw7+xuqMU0TRHnby2L7/8UihFxiG7wQYbxA1PCwqg0xcvwRv90TJohLCrcgJJHF4E+qWHHx50/s8bup60UOwGk4BJoOAlAGDhJ16jJKXW0wacQ2gGkdP48eMbTc5zrhcCezts5RDyTZgwwXnN1113XUcwRygwKU2FDNQVnHM+p8NS7621DmCnKVhPVGs92TWAzgBjP9FtGFnCTmCX7PhSvZ60DOaBcP+xY8e6KgfvvPOOHHbYYQ6U8zv0nyFDhjjZYYjya4xJtU92X+4lYAA993NgPTAJhFYC0UqzKVAHLFOajTw3wt4JLYymEKUK0JWdVkPXMQyQN6egXEMbEZ7XS05uG4cX4fgKymG1LWRlLbQLyDpmEihgCRBuDTjHgwuxk7cWdQEPOzRDe+aZZxyIue6666Rr164uZ50weLzKu+66qwPrvXr1cqRphbT/A87/97//uXMvHXAeOZE8j1x1DYUnbB5DB4CdP9NZ35zfpCVAWgdjvxq4QrOYctQRyBCpBIFD4aabbpJXX33VVYNAd7nzzjtlp512ctGCw4cPd3Kj9jklHa0VvgQMoBf+HNsITQJpS4CDG6WAA5u/01ThIeQdjzqHN16Mli1bLqcMJQPQeQcKgnrKeR+EP0rwphZ3I3hLe0rtASYBk0AaEtCwdtJ98OB6wQvGQWuZlwDpCG3btpXDDz+84WWcDdTVxkP7xBNPyLvvvuuiGwDr/MQyJGe+t8G8QcE5T9tmm20y5klFjoBqJZmDHR7jOGCdMz6ZevVUUundu7dLiXv66addmHau2uWXX+6MOETawaWz8847y5VXXulIcHPVRo0aJbfddpt89NFHTt+BEG7o0KFOXhopiDMEzzo6FmsgHWNJrsZp701OAgbQk5OXXW0SaNQS8AJ1wq68pdkgkSNHHS83hx054bREAJ2DXwE54JxDSQE5z4hXm5zfeb3kSgbXqCfJBm8SMAlkXAKEsw8aNCjqe9SImfFO2AviSoB5IMoBQAZgf/vtt12uNp5cwDpey3zyrGOwxnNOn/GcZxOkAbIVrPN3gKSGwscLVYe75pBDDnFh2kQ85Jo9nprrffv2lU6dOjmHw7BhwxwwhpAtVnWAdD8zTbXTdEHVU3i/hqp37tzZGVwo08i8EvZOCgfRy9ABAAAgAElEQVSh7vQVfguMgQbO052N/LnfAHr+zJX11CQQGgnEKs3G4fHtt986DwYgm9B3Dj3Kv9BQLNQyr6HrCxcudGFvGrqOdT1abXI1BnCgAco5xCx0PTRLwjpiEjAJmARCKwHOHaK9qLEOWH/ttdekffv2DWB90003DTVYV3AOuAPIZROcR04qpKwK1gm/hgVewbrXO47xnegGAP3zzz/vrgtbI5yfvrMeqMeeiUapWiWt5fk4M8glV3CO3jRmzBhnSJo4caKst956UbthLO6ZmJ3wPtMAenjnxnpmEgi9BGIxvnMww7CL94KQQnKmyLWCwRVvOcqSl+BNw+Viha4bwVvol4J10CRgEjAJ5IUEOGc4h8jpJW/95ZdfdlFfGgZP2HyYPOuAczyqgPJcg/PICYaDRnPWOd+R7QsvvCAHH3ywKzmIIZ5/43EPYyPqD0cCXnRY04NuVHygjjn1zAHpGDcwBMyePVtOOOEER2jInBI9SFQENdCRmzWTgAF0WwMmAZNA2hKIBdSxnJOfjrUdTzpedX68dVe9BG90BA9BZOh6mJSltIVlDzAJmARMAiaBUEiA8wd2cfJ+8ay/+OKLAks/5HKATEBbLlOnYO8mrB1va4cOHXLqOU80YfQVQwJlCqdMmeKMHDCPUyqMMO1cyjFa35l7jDKA4zfeeCPR8FL6PWHqRBnsuOOOLpKAEH/WG3nv1K/nvaeddprL0cfTTkk1yOIA69YatwQMoDfu+bfRmwQCk0AsxndegNVYw9GN4C0wkduDTAImAZOASSBACUCMBpEZYP25555zocgK1gFN2QSZCnjhZdl6661DDc51CvD2Dx482FV4Oeuss+T//u//ZNKkSS7fH3b9MBnbTzrpJOfZfvPNN12Jvkw2jAB4y+E/OOecc9yrIJrEIHTjjTe6yAMlyL366qsdk7u1xi0BA+iNe/5t9CaBwCUQCdT5N4qGAnMlTDGCt8BFbw80CZgETAImgYAkgMfz2WefdbnBADnqglPHG5AFqVcm88C94BzPeTYNA6mKD3COxxzPMGkDWs0ADhoi6TIRQp5qX0855RTHR/D666/LRhttlOpjVrjPmyeuug5zyc+pp54qU6dOdeSSGC+0zZo1y8nnqquuckzt5MPvsssugfXJHpSfEjCAnp/zZr02CYReAl7GdzzoWM6N4C3002YdNAmYBApYApdeeqkDm3gz8cwSbmstsQQ4w8ilxrOOh50SXXjWAevUqlbCr8RPSnwFYO69995ztcLxnOcDOIfo7OSTT3ah7a+88orjngljQy8BnFOCj5rj5J9nohE1gDGH9L699trLrR3W0C233OLSKY455hg5++yzV3g1YB1vvhHCZWJW8uuZBtDza76styaBvJOAetQB6PmgaOSdgK3DJgGTgEnApwSGDx/uSmACBO68804D6D7l5r0MYjRCtwHrEM3hSVfP+m677ZZUjfDI1+NtJo87n8A5YPL00093XnNAL+XAwtpOPPFEeeCBB9y8eWufU0kGo0sQ7d5775WBAwcKNdevu+46obQbZRlpVLgBpOO95xrqndNYU/HK1QXRL3tGfknAAHp+zZf11iRgEjAJmARMAiYBk0BaEgAwAKrMg56WGF3oMiHJsMEDuvg3YB3ysa5duyYFugDneM4pVUYJuHwwaAPOzz33XBdVADj3lhNLT7KZuTtWDvzdd98t//73vwN76fnnny/XXHONI/Yjx90LvimzduuttzojAevkkksuCey99qDCkYAB9MKZSxuJScAkYBIwCZgETAImgYQSMICeUERJX0AONmBMwTo57N27d3dh8IQ5x/PQKjin2kmumeP9DhxwPmzYMDfeTIaL++1PGK5jDZDuQBg9of6ffvqpQPp29NFHu8gVbYB02NoffPBBR0ZIaT9rJgGvBAyg23owCZgETAImAZOAScAk0IgkYAA9s5NNTjaEYITBk+9M3fV9993XgfV99tnHlR3VVllZ6Uqp5RM4J3WNcmoTJkxw4HyLLbbIrEBD/vRYOePUNIe1HS85rPGE0tMWLVrkOCBgu2/Xrl3IR2fdy4UEDKDnQur2TpOAScAkYBIwCZgETAIBSGDEiBEOLMVr06ZNk44dOzZcYgA9AMH7fATgbfr06Q1g/aeffpK9997bhTcTAt23b1+56KKLHHjPh7B2wPkVV1zhcqnxEoeJnd3nlAR6mXrNkQtz+8cffzhyP23XX3+9nHHGGXLxxRe7EnQzZ86Ugw8+2K2JDTfc0F2mjO+BdsweltcSMICe19NnnTcJmARMAiYBk4BJoDFLYO7cucJPvAYQgHhMmwH03KwYwPqHH37owsIffvhhVwt78803d3WvYYUnDDpMtcIjpQSQvPbaa2XMmDGOKG+bbbbJjSBD8lYiJbTc3v777y+zZ8+Wzz//XA455BCBkG7nnXd2PcWYQZm1HXfc0fEMYFTTeughGYp1I2QSMIAesgmx7pgETAImAZOAScAkEC4JUJ4Mz9eFF14YeiIsP5IzgO5HSpm7BiC3xx57OHC+7bbbOsIw8pX5PzzrPXv2lDXWWCNUYB1wfsMNN7h63c8//7x06tQpcwIK+ZMjQ9ohBCwrK3PM7SuvvLJst912sueee7pcdFIaMLogM1jcKaPWo0cPN0LznId8onPYPQPoORS+vdokYBIwCZgETAImgXBL4JdffpH+/fs7jyHM53gQ87X98MMPLgSXWsyQV73xxhtuKJtuuqkDFtayIwG85QDwO+64w3lgAWpfffWV86yTs06dekq2AdZhhV9rrbVyCtbpH8zjo0aNkmeffdbVfm+MDeI/mPpXW221hlrl48aNk4kTJzom++bNm8t5550nlFrje+IH0E5KA+kLXkBu4LwxriD/YzaA7l9WdqVJwCRgEjAJmARMAo1MAo8//rjcdddd8tZbbzmvGMRfNHJPx44dKx9//LH7fT40Skndc889K3SVXGK8gNayIwFI4wB50XLOAW7ffvttQ846/AGERgPW+WndunVWwTr9oQwZpcMAobvvvnt2hBSyt/C9Dxo0yBG7TZo0yRlNAOtTpkxxRi84BOASuO+++5zhCzZ3DF+dO3d24e59+vQJ2YisO2GWgAH0MM+O9c0kYBIwCZgETAImgZxKAGDy/vvvu9B2vJx33nmnrL/++o6Zm1DVVq1aOa+nkkXltLP28oKSAOB41qxZgpGIH4xEkP0pWN9ggw0yCtZ5P4Dz7LPPdlEXhOA31oYsYK3HwEXUA2ki66yzjsyfP98ZWtgPAOHsF5DAMW/sD/BDkBZw5JFHNlbR2bhTkIAB9BSEZreYBEwCJgGTgEnAJFD4Evj1118dgdfqq68uQ4cOlfbt2ws1jNdcc03Bs0kY8gsvvOC8il7CKCQDYEeRDzPpV+HPYOGMEIA4Z84cZwwiiuP11193bOEK1vHWBrnWeB9EduRR8z7KxDXW5g1HRxY33nijEwWRBcrE/tlnnzmiP1jbu3fv7iJrSIchLYC8c2smgWQkYAA9GWnZtSYBk4BJwCRgEjAJNBoJPPPMM07BPv74453yvcMOOziirP32288p37Ax//333w3ywFsGqN9yyy0bjYxsoNmXAICRtfbkk0868EyKQps2bRxYJ9SauuTpgnU89kOGDJGHHnrIkdY19uYlhkM2N910kzPCkd6yySabuBJrXbp0cWCc759rDj/8cAfYabFqpTd2udr4o0vAALqtDJOAScAkYBIwCZgETAJRJEBO6csvvyw333yz81ZS85k8bkJ+8ZqvvfbazstYVVXlFPLrrrvOlc5auHChHHPMMTJs2DBXOsvbUNQBT+kCKJswkwASAKz/+eefLgSdNfjiiy/KRhtt5AxKhFq3a9cu6frq5FhTs5vwdp7RmFtkZIzKYvLkyc5Ix7d/++23S9u2bR0T/1lnnSUrrbSS2ysw4Okc2ffemFdR8mM3gJ68zOwOk4BJwCRgEjAJmAQKXAIQPx133HFSXl4u999/vxstHkq8ZeST7rrrrg4QUf8YsjhqHeNhB5RDJMWfeN75oVVWVi5Xi9wU9wJfQDka3rx58xyZG2vzueeecxwJrFuANnXLoxHTebsKS/tRRx3lwrcPO+ywHI0iHK/1gnOY2anogEEEzzg8FC+99JKrhkAUDd9/hw4dZPHixa7kGiRxtFgAPxwjtF6EVQIG0MM6M9Yvk4BJwCRgEjAJmARyJgG8iIS3w9wMCzONsFa85Oeee66cdtppThmnUXYKDzu5qSjntH79+slff/3lvJDksONNe/PNN2X48OHOew7Aj2ymzNdLhIgFgA/1wvEAjxkzxuX7W0tOApQFI00DsM6frEM864TBU8ccjgRvo5TgEUcc4UqqsX7N61svHfaA1157zTGyf/TRR87IcfHFFzs5YtBgX/jtt99c+gvXaLNSasmtV7t6mQQMoNtqMAmYBEwCJgGTgEnAJBAhAQA6ivdll10m2223nfstXkX+3bRpU+dJh7AL0I2nHeCNZ00bbO8A/A8//FBatGjh8ngBSVyDkl9RUSFXXHGF+x1gydsas2JPzvOAAQMcSN9ll10cWKReOOHDeC2tpSaBRYsWOUJDctbxsBOGDVjHu46B6e2335ZDDz3URYOQxmHgvF7OrMPRo0e7VBdY8wlr51t//vnnZeedd3bXYNi48MIL3TcOi7s1k0C6EjCAnq4E7X6TgEnAJGASMAmYBBqFBGBw32yzzWTJkiUuvJW66OT+opwroEQQhLPjZYe8CzBOXWvC36lnDcDnGbA+AzoBowBQwmfJae/fv39DeCzPwqtOi/R2FqrAkRMGkXHjxjUMkfxevJWXX355oQ47q+NifQIqAeusX3gR4E0AnGNsCgM4z3UUhRrJzjvvPJdnThQH8oGXAkMdKQN8sxDFUW6Nb9nIIbO6jAv6ZQbQC3p6bXAmAZOAScAkYBIwCaQigWjh5gDzM844w4Gbzz//3D2W0FY8uwAKQmFp7733nsvfPfroo51HjXBtlHo88lpLGpB05ZVXyiWXXOI8byj35LpTyg3vPeHJhMuTA99YGvJt1qyZPPLII8uRk5FO8MEHH7gwY2vBSmDp0qWOqf2NN95wedRhAOe5iqKIZFrn34cccojLLSeao3fv3kJkDJEG7A941jGcnXnmmQ1yM7b2YNdnY32aAfTGOvM2bpOAScAkYBIwCZgE0pYAivoFF1zgQmBR1FHYIYiDnAug2bJlS+nataurlwxQ59+0H374wSn8m2++uWOBJuT4qaeecuzZgPpvvvnGhdCT2w4QIBe70BsM+Hgj33rrrYbwYcZM1ME999zjyPesFb4EshlFoZ5yvl/yyWfNmuXC/qn7vtpqq7koA0jz4JuAuR1SSNrvv/8uBxxwgPs3ETTWTAJBSsAAepDStGeZBEwCJgGTgEnAJFCwEsA7BiBXIjgdKF50wq/x/K255pounJ1SbJtuuqlT+PG+AbqpK62Nclh45wAFmsvK/wHae/To4fKAqW1NyPu2227rCOgKPTddATr50ORFa7v00ksFFm2NWijYBWYDc+kj2Yqi0O/p0UcflWOPPdZ9d9OmTXNRMUTK8M3yd9YfQB1iuO7du8t3333nfr/yyiu7VBdaoX+btjSzKwED6NmVt73NJGASMAmYBEwCJoEClsDcuXOd501zxgljJ3T4gQceaACd5LQC6O+66y7nSdcGQzw/U6ZMcZ5k2sCBA12u64MPPuieG69hPCAEF9BAeTdy5POpZROc5ZNcGlNfsxVFoYCadBL4Dai2AIM9jZB22vTp050xDqJH0k8wkhHp0rp1a0cSCWinWfWFxrRCszNWA+jZkbO9xSRgEjAJmARMAiaBApUAyj5KutY+9g4TYI2Cf/rpp8taa63lfgXZHMCb8HbAO+3XX3+Vs846S6i/TiitNvLaYeAGzAMOEjXIqgi5hV2e+/r27etKZmnue6L7c/17og+23357l9Ovjfx8wo6NJC7Xs5P592czigJeAwgJhw4d6rzk2khJIZ2C6BaiWKjaQMOYRv9WWWWVBkI4SOKiffeZl5S9oZAlYAC9kGfXxmYSMAmYBEwCJgGTQOgkQLmr/fbbr4EJng5C0gVAB0wD5mkwwPN3yrBRki3ZMNpTTz3VAd0mTZo4EitY5AHvAJCwNiUIw3BBmPttt93mcvQ/+eQTV+bKWmFLIJtRFOSVY/whLYX1RirJMccc46JVtthiC8cXQVoFqSp41/Gae78dI4Qr7LWYy9EZQM+l9O3dJgGTgEnAJGASMAkUtASihb9S0mrixImO5V0bBHKEp1OjGkBAo/waP4Bq8mP9htIqcADsE6J71VVXyZw5c9yfeATJj6cOO624uDh08seoQF9nz54tW221lQv733333UPXT+tQZiSQjSgK9XyTbtKxY0fnBd94441lxowZrsY50S7kmD/++OPy7rvvOgCPUY3v1ppJINMSMICeaQnb800CJgGTgEnAJGASMAlEkYB6xAEJMLVDEkftdG0nnXSSq6FOfuzqq6/uS4b6TFjgKfXWq1cvV7tZ2/z586V58+a+nmUXmQRyIYFsRVEoSMfwtdtuu8nUqVNlwoQJjpgxskEMRzlFDFrJRrLkQob2zvyWgAH0/J4/671JwCRgEjAJmARMAnkmgVgKPkBd656TS44HHG/irbfeKtRNJ8y7bdu2juU6VlMvO6HheOQpAUc5qO+//97VVye/HfBOGSmYqDfaaKPlHmU5tXm2mAq0u9mKovCud1IqKJ9G1AqVFyB6jPxWLay9QBdcyIZlAD1kE2LdMQmYBEwCJgGTgEmgcUkAEMCPN9wc4ECueosWLRxY4O/klBMWf8455yQUEDmz5PPCPE1N9m7durlc9J49e7qcW0A/rPDkd/OOaEYD+gBIIX/dmkmgUCXgBel8axDBjR8/XnbdddeGagyFOnYbVzglYAA9nPNivTIJmARMAiYBk4BJwCTQIAHIqm644QYXtt6lSxeJ5slTkA3bdJ8+fRwYHz58uMujpeb69ddfLyeffHID4F577bUd+Rw112GtPu+881x4L4zva6yxxnLSj2ZEsOkxCRSKBLwgfa+99nJVEF577TVHrGjNJJBtCRhAz7bE7X0mAZOAScAkYBIwCZgEfEgg2VxXBe2UZBs3bpyMGDHCkcsNHjzYhbevu+668vXXX7syZpRvw0tI+Duh7s8995wrybbz/7d337Ga1eXagF+JDVswarBF0YNYIBZsaNRgBzQqlliwBBF7IaBErGCNxlhQo0bBhhWJJQYlKhqkBAVFMfYaRREbYo//fHnWl3e+kQ8MemBm7ntfb0I4Z5iZ/TzXvf3j3mut37rrXZcr6nNY3cEHH7zcIj8HzfkQaBfYvKTPD63mcMK5y8SHwJYWUNC3tLivR4AAAQIECBD4DwWmfM+t5pfkdvO5vf2f//zncvDcvJpsXh81Rf0Vr3jF6mtf+9rq4x//+OoTn/jE6pxzzlle7fbCF75w9YIXvGC5wj6l/nGPe9xyS/2hhx66Ov7445db5ef3z5X7vffe+18md2X9PwzSb9+mBS58BsMlfXPCNr2U4eIEFPS4yAxMgAABAgQIELhogfPPP3/1kIc8ZCnlcwVwPg9+8IOX59vf//73L6+OWn9+8IMfLKfDT8me4n3b2952eSZ9PvPfppDvtNNOy/uf5/cde+yxqyc84QmrV7/61Rf5xafMzGeeW9+In5NOOmk1r8s788wzl1fEzQ9CJgsfAgQI/CcCCvp/ouX3EiBAgAABAgQCBH7729+urn3tay+TnnbaaasnP/nJy63s++233/Jrf/3rXzfdvjuvd5tn2+eE97nFfT7z/O1cSZ/b4+eq+nxe9rKXLa98m9dRTWH/xS9+sXrNa16zOuyww1bXv/71A1Qu2xE/85nPrE455ZTV7rvvvpwBoKBftt7/7m+/uNPW179+4f/+nz5OsvU285U3goCCvhFStiMBAgQIECCwIQQuqpjMbbtvf/vbVy9/+cuXK+lz8NUtbnGL5UC4+fcrX/nK5dVSc7jc+vPGN75xNa9qm2fRb3rTmy6/PFeH51n2eQXWbrvttlxtf8pTnrJ60pOetPrhD3+4HFw3z6zPifEX/my0AjSPIijoW+d/cuv/DcxrCT/wgQ8sbzGY7/k73vGOy0Cb37Y+hyPe/OY33zqD+qoELkZAQfetQYAAAQIECBDYIAJzG/aXv/zl5X3qD3rQg1a//vWvV/vuu+9yS/y8em0+55133uq5z33u6je/+c1qrgrPZ0rPlPFzzz139b73vW+5+j4HzM3hcs961rNW++yzz+pDH/rQ8oz7lKJb3epWF/nqtvl75rP5K+Ua6RX0rZvqt7/97dWd73znpZSfffbZq9vc5jbLmwye+tSnbhrs3e9+93IHyPwwaq+99tq6A/vqBDYTUNB9OxAgQIAAAQIEigXm6vUU44t6NnxOdZ/npOd59fWV7ynw88q1Rz7ykavnPOc5i8y3vvWt5f+e59Tn8Ll5V/SUnzmQ7nWve93ye/74xz8uz6vPlfqnPe1pm0SntM/V+Xvd616ra1zjGsXS/281BX3Lx7z5XRrzSsL54dMcjDhlfb5n59/zpoL19/T88Omoo45aXjW48847b/mBfUUCFyOgoPvWIECAAAECBAhsEIFLchr83Mo+B8HNs+a77LLLIjNFZm5pn+I+V9znNvcjjzxyuXV+zz33XH7PP/7xj+W2+R122GH5vX/+85+Xkj9FaU6C//GPf7wcWDflaZ5h3/wz5Wr9uSQn1W/rcSnoWzah9W3rf/jDH5Zb2Oc1g3MGw/oHRT/5yU+WIv6Nb3xj+Z6c8xjmM9+jc3DixT2zvmW38NUI/F8BBd13AgECBAgQIEBggwpc1Guk5jb2ec3a4x//+E0q817o+fU5Cf4617nOUrR//vOfL8+k3+AGN1h+35z8Ps+j3+1ud1uea59ydMYZZywHzl3lKldZnXXWWUsxmj970EEHLX9mnhOe97PPu9ebPgr6lk9z7gZ54AMfuLrggguWHwhNEX/LW96yaZD5fn3ta1+7OvHEE5e3EcxrBDfa2QhbPhVf8b8RUND/GzV/hgABAgQIECCwQQR+97vfLVfOr3CFKyxXJueE+F133XUpN3OVfX2I3JT3eaf6vDN9CtKU+p/97GdLKZ/b5+cW97e+9a2r97znPauvfvWry7Pu8/z6FPu5Bfmb3/zm8lz7+u8b3vka6x8iJF1ZV9C3zP841t8b//znP1f3ve99l+/LeT3gnI0w/zz96U9fDi5cf+bNA1PSDzjggOW5dB8C26KAgr4tpmImAgQIECBAgMBWEri4q4p/+tOfVle/+tWX29rnNvU5WO7GN77x6sADD1ydfPLJy2vY5rn0OSTu6KOPXr3gBS9Ybis+4YQTlquW22+//ervf//7UqI++clPruaK5xSlufV9Xun2hS98YbkaP79/rqzPQXRJr2+b26XnNPv5jM3rX//61T3vec/ldv4b3ehGWynN/i/7+9//fnnkYv49P0jacccdl/fQzw+Tjj322OUVgkccccQmiCnz88MmV8/7vzdSN1TQU5MzNwECBAgQIEBgCwisn1ufLzVXhuf09ynq8470uRr5+c9/fnWzm91sdY973GMp6de97nWXW4vnv61f3Ta3Hc8J8nMw1xSoF7/4xcuV9DkZ/iMf+chq7733Xl35yldeCvxc5XzTm960lPj5tSnxc7v85gfMzRX4+Rrzg4Jt5eC5L33pS0shv/BnbqeeXX0uPYF5deDlL3/55Rny+T7Zf//9lx+EzA9I5pny+UxJn1cFHnfcccvV9blLw4dAgoCCnpCSGQkQIECAAAEC24DAHMI1r1A77LDDVs9+9rOXiebZ9Hkl2/z6+qT4eYZ9iumc8L7ffvv9f5PP1fhDDjlkOR3+1FNP/Zf/Pre+T/GaK5xzovxchZ+vNbcrz2f+26te9arltW5z+Nd8HPK1DXxzbKER1le+560Bc07CPGs+j2E873nPW75P5gdD688cUDiPVcz3ypyXcItb3GILTenLEPjvBRT0/97OnyRAgAABAgQIbCiBU045ZSlBc8r7/e53v9X6SuZFIcxV8k996lPLLcYPeMADlqvpN7/5zZd/5gr4+tn0ww8/fHlm/YpXvOLy18xV9Ln9fW6Fn8+Uq+c///lLmZ8r93e5y11Wp59++vKe6ylfu++++4bKYCMvu37mfP49d3DM7erve9/7VvMDn/n3fC/NQYXzJoL1Z37gM2V+7vLwIZAgoKAnpGRGAgQIECBAgMA2JLAu1P/uOd557doxxxyzesc73rEcCLe+8n73u999ueV7StR73/ve1R3ucIdNBX1uSf7whz+8XJGfq/V77LHHaqeddlp+39yyPLc1n3baactJ8XMY2BT1eTf73F4+X2N9ovzmVK6ub0PfOJfCKFPK55yDOatg3mk+3yPzOf/881cf/OAHVy95yUuWuzfc0n4pYPsrtoqAgr5V2H1RAgQIECBAgMDGEfjb3/62+ulPf7q65S1vuVztfOpTn7q8k3quim/+mXdXzxX3uS1+fggwxfvlL3/58v/PQWBzBX1ur5/blefPzg8B5vVZ8+q3O93pTsvJ8fPfL+7jYLD877k5+G1ua1+flD9nIqw/c6X8ox/96Orggw9evo+OOuqo/IVtsOEEFPQNF7mFCRAgQIAAAQJbRmBuRZ7P+tn09Vedd6PPYXBTvNenas/r1ub29zl0bl7JNp+5+j2lfG6pn2fQ5wr6Lrvssnr4wx++PIe+vuV5DpqbK6ZTzB71qEctf/acc85ZTvHed999l9PmN/9MUZ9/tttuuy0D4av81wIX9UOVuYI+j1rc//73X730pS9dvm/WnzmQcP77DjvssHr0ox/9X39df5DA1hJQ0LeWvK9LgAABAgQIECCwSWBuUZ4r4HOFfW5/n3eoH3nkkcv70ed25rkV/uyzz15uaXA86w8AACAASURBVJ/b3OeK+bq8PfnJT1798pe/XE5//5//+Z/l75z3ss+p8vPqrXkuea7YzzPzt7/97amHCGx+xsHchTE/6FmfVfCud71rKefzw5pnPvOZ//KM+b87GyFkdWNuYAEFfQOHb3UCBAgQIECAwNYQuLjnwr/+9a8vpWsK+NyqPu9Sn8Po5t3r8070V77ylcthYFPap6jNbc7z/usHPvCBy3Ppm5/gPaVtbqt/wxvesJS3xzzmMauzzjpreWXbvI5r/t55vdv8HfPPfOaK/Pzfrqxvje+Kf/2a67sj5lcns29/+9ur7bfffrlaPj+Imc+73/3u5ZV9D3vYw5bHJuYRCh8C6QIKenqC5idAgAABAgQIFArMre8HHXTQUroOPPDA1ZWudKWliP3lL39Zffazn9208TyPPs+pz6nw61ua52r6Yx/72NVuu+22XIWfz21uc5vVd7/73eXd63OFdV7R9ra3vW05hG4K+1x5Xxf1Qs7YleZxh8n8EY94xPImgHmF31WucpXVV77yleWRhznlf/1GgMl63ofuQyBZQEFPTs/sBAgQIECAAIEigYt7Zn294nHHHbeU9iltc4L3vGLt6KOPXq6ozpX1KeHzmdI2vzZXXudE73mufcr7/JkXvehFy++ZW+rn/dlT7ubfc+jcnnvuuVzB9+q2LftNtX5UYf49n/UPSuaHL/OIwpTyG97whst/O+OMM1b777//cjDg5Dp3O0zek+fk7UMgXUBBT0/Q/AQIECBAgACBQoGLO8jty1/+8vL+86td7WqreQ55boefW9WnzM0r3OaW93kuea6Sv/Od71xuiZ6r5t///veXPzeve5vPqaeeujzrPn/PEUccsbzW7c1vfvPy5z/2sY9teta5kHabXWmulF/1qlddspur43MK+zzmMFfO5w6K+czjEZP1/Pr8UObCZwo4qX+bjddgl1BAQb+EUH4bAQIECBAgQIDAticwV06f8YxnrD796U+vHvKQhyzvUD/llFNWT3ziE5fnz+czt8k/9KEPXZ5XvvKVr7z82jyjPu9jf/zjH79cVZ9n3Ofvuve9771cVZ9b5H22nMC8Hm3OCfjRj3606dT9OWtgftjyile8Ynlt2vozv+eOd7zj8t7zvfbaa8sN6SsR2AICCvoWQPYlCBAgQIAAAQIELh2BuYI6V0nnRO/Nr5aefvrpq3lV25TsubI+J7dPCZ9Xuk1Zn9ewTYGfz1yhndPg57nzOTV+Dqe75jWvubxf+y1vectyJX1OlHc19tLJ7JL8LXMI3Pyg5Tvf+c7q5JNPXu28887LyfyT3RWucIUly3mt2nymuM95BHNI3BwO6EOgSUBBb0rTLgQIECBAgACBDSZwUSfCzzvWp9TtuOOOS8Gb8j63ra9P+Z5XuM370qeQzzPt55577vI881zFPfPMM1fHH3/8pufZNxjnVl13HkOYPOaxhbkLYh5HmLMB5jV688OSucthfrDyjne8Yynrcwu8D4E2AQW9LVH7ECBAgAABAgQ2oMCFDxhbE8zzy3NK+13vetfl9vZ1ob/Pfe6z/P9za/zmJf+CCy5YnoOeK/Q+l63Aeeedt5y6Ps+brz+T1ZT0ufNh/pkD++aW9jkg8IQTTlhd61rXWor74YcfvvyRi3tl32U7ub+dwGUnoKBfdrb+ZgIECBAgQIAAgW1UYK7QHnLIIat99tlndcABByzv2J5bqnfddVevW9sCmc0Bb3M43y677LKctD+nss/z//OZcwSe/vSnLz88+eIXv7jaY489NhXxef3e3B0xn83flb4FRvYlCGwRAQV9izD7IgQIECBAgAABAltD4OKeI58rr8ccc8zqNa95zWpuib/DHe6wut71rre8tu2KV7zi1hh1w3zNOSdgXnv3uc99bnmn+VwlP+usszadCzC3tP/5z39eXp/2kY98ZHXSSSetbne72/2Lj/MBNsy3y4ZbVEHfcJFbmAABAgQIECBAYHOBeQZ9nnuegj5Xa5W/y/77Yw7me/3rX7+aV6vNQW8PetCDlnMC5qr5XEGf197d+ta3Xp144onLMHOb+01ucpPLfjBfgcBWFlDQt3IAvjwBAgQIECBAgMDWEZhbpC93ucuttttuu60zwAb/qmecccZS0n/yk5+sDjvssKWkz+fss89efm2eO58T2+eE/XVR3+Bk1t8AAgr6BgjZigQIECBAgAABAv9ewFXzrfMdMmV8HjP48Y9/vLzabm5vX38ufPDfvB5v8wPlts7EviqBy1ZAQb9sff3tBAgQIECAAAECBAj8G4F5B/qU9HmP/eYlffNC7rR230IbRUBB3yhJ25MAAQIECBAgQIDANiow70Cfkv69731vudX90EMP3UYnNRaBy1ZAQb9sff3tBAgQIECAAAECBAhcAoE5CG6K+fWvf/3Vm9/85kvwJ/wWAn0CCnpfpjYiQIAAAQIECBAgECnwq1/9ajlNfz7OBYiM0ND/SwEF/X8J6I8TIECAAAECBAgQIHDpCnjm/NL19LflCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECA6SZ76QAAH51JREFUAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAECBAgQIECgWEBBLw7XagQIECBAgAABAgQIECCQI6Cg52RlUgIECBAgQIAAAQIECBAoFlDQi8O1GgECBAgQIECAAAECBAjkCCjoOVmZlAABAgQIECBAgAABAgSKBRT04nCtRoAAAQIECBAgQIAAAQI5Agp6TlYmJUCAAAECBAgQIECAAIFiAQW9OFyrESBAgAABAgQIECBAgECOgIKek5VJCRAgQIAAAQIECBAgQKBYQEEvDtdqBAgQIECAAAECBAgQIJAjoKDnZGVSAgQIECBAgAABAgQIECgWUNCLw7UaAQIECBAgQIAAAQIECOQIKOg5WZmUAAECBAgQIECAAAECBIoFFPTicK1GgAABAgQIECBAgAABAjkCCnpOViYlQIAAAQIECBAgQIAAgWIBBb04XKsRIECAAAECBAgQIECAQI6Agp6TlUkJECBAgAABAgQIECBAoFhAQS8O12oECBAgQIAAAQIECBAgkCOgoOdkZVICBAgQIECAAAECBAgQKBZQ0IvDtRoBAgQIECBAgAABAgQI5Ago6DlZmZQAAQIECBAgQIAAAQIEigUU9OJwrUaAAAECBAgQIECAAAECOQIKek5WJiVAgAABAgQIECBAgACBYgEFvThcqxEgQIAAAQIECBAgQIBAjoCCnpOVSQkQIECAAAEC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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,18))\n",
    "\n",
    "ax1 = fig.add_subplot(1, 2, 1, projection='3d')\n",
    "ax1.scatter(X_training[:,0], X_training[:,1], Y_training, label=\"Input Data\")\n",
    "ax1.scatter(X_training[:,0], X_training[:,1], prediction_training, label=\"Prediction Data\")\n",
    "ax1.set_xlabel(\"Norm. Temperature\")\n",
    "ax1.set_ylabel(\"Norm. Relative Humidity\")\n",
    "ax1.set_zlabel(\"Norm. Atm. Pressure\")\n",
    "ax1.set_title(\"Training Set\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2 = fig.add_subplot(1, 2, 2, projection='3d')\n",
    "ax2.scatter(X_test[:,0], X_test[:,1], Y_test, label=\"Input Data\")\n",
    "ax2.scatter(X_test[:,0], X_test[:,1], prediction_test, label=\"Prediction Data\")\n",
    "ax2.set_xlabel(\"Norm. Temperature\")\n",
    "ax2.set_ylabel(\"Norm. Relative Humidity\")\n",
    "ax2.set_zlabel(\"Norm. Atm. Pressure\")\n",
    "ax2.set_title(\"Test Set\")\n",
    "ax2.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinary Least-Squares using scikit-learn\n",
    "Another fast way to compute the LS estimate is through sklearn.linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 0.9931630416692415\n",
      "Least-Squares Coefficients: [-0.00322181 -0.00009138]\n",
      "Measure on training data: 0.3582229490168114\n"
     ]
    }
   ],
   "source": [
    "LinReg = linear_model.LinearRegression()  # build the object LinearRegression\n",
    "LinReg.fit(X_training, Y_training)  # estimate the LS coefficients\n",
    "print(\"Intercept:\", LinReg.intercept_)\n",
    "print(\"Least-Squares Coefficients:\", LinReg.coef_)\n",
    "prediction_training = LinReg.predict(X_training)  # predict output values on training set\n",
    "prediction_test = LinReg.predict(X_test)  # predict output values on test set\n",
    "print(\"Measure on training data:\", 1-LinReg.score(X_training, Y_training))\n",
    " "
   ]
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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