{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "39749f1a",
   "metadata": {},
   "source": [
    "# Example Notebook for Harris & Speagle (2023): Photometric Completeness Modelled With Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c8ba11",
   "metadata": {},
   "source": [
    "This notebook provides examples for how to use the neural networks trained and tested as part of Harris & Speagle (2023) to reproduce some of the results, test different aspects of the performance, and train similar neural networks on your own.\n",
    "\n",
    "If you have questions about the contents included below, please contact Josh Speagle (j.speagle@utoronto.ca) and Bill Harris (harris@physics.mcmaster.ca)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87b6b874",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2dfe3c3",
   "metadata": {},
   "source": [
    "## Loading in Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab8a0a4",
   "metadata": {},
   "source": [
    "The Zeonodo link includes the artificial star test (AST) data used to train each model. The format in which these are stored are slightly different and enumerated in the notebook below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c269224e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NGC 1275 with a flat/steep LF\n",
    "# 0: x position (pix)\n",
    "# 1: y position (pix)\n",
    "# 2: radius from cluster center (arcsec)\n",
    "# 3: F475W_input (input mag)\n",
    "# 4: F814W_input (input mag)\n",
    "# 5: F475W_meas (measured mag)\n",
    "# 6: F814W_meas (measured mag)\n",
    "# 7: Sky noise (see paper)\n",
    "# 8: Crowding (see paper)\n",
    "# 9: Recovered? (1/0 = yes/no)\n",
    "data_ngc1275_flat = np.loadtxt('data/ngc1275_flat_ast.dat')\n",
    "data_ngc1275_steep = np.loadtxt('data/ngc1275_steep_ast.dat')\n",
    "\n",
    "# NGC 3377\n",
    "# 0: x position (pix)\n",
    "# 1: y position (pix)\n",
    "# 2: radius from cluster center (arcmin)\n",
    "# 3: F606W_input (input mag)\n",
    "# 4: F814W_input (input mag)\n",
    "# 5: F606W_meas (measured mag)\n",
    "# 6: F814W_meas (measured mag)\n",
    "# 7: Combined background/crowding (see paper)\n",
    "# 8: Recovered? (1/0 = yes/no)\n",
    "data_ngc3377 = np.loadtxt('data/ngc3377_ast.dat')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9471c8",
   "metadata": {},
   "source": [
    "Transforming these into the inputs expected for each of the NN models is done below. Note that the NNs are specifically trained on the *measured* quantities so that they implicitly account for potential biases/scattering as part of the detection and measurement process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aac28033",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NGC 1275 (flat LF)\n",
    "n_ngc1275_flat = len(data_ngc1275_flat)  # number\n",
    "mag_meas = data_ngc1275_flat[:, 5]  # F475W\n",
    "color_meas = data_ngc1275_flat[:, 5] - data_ngc1275_flat[:, 6]  # F475W - F814W\n",
    "skynoise, crowd = data_ngc1275_flat[:, 7:9].T  # sky noise and crowding\n",
    "recovered = np.array(data_ngc1275_flat[:, 9], dtype='int')  # recovery class\n",
    "X_ngc1275_flat = np.c_[mag_meas, color_meas, skynoise, crowd]  # NN inputs (all)\n",
    "Y_ngc1275_flat = np.array(recovered)  # NN targets\n",
    "\n",
    "# NGC 1275 (steep LF)\n",
    "n_ngc1275_steep = len(data_ngc1275_steep)  # number\n",
    "mag_meas = data_ngc1275_steep[:, 5]  # F475W\n",
    "color_meas = data_ngc1275_steep[:, 5] - data_ngc1275_steep[:, 6]  # F475W - F814W\n",
    "skynoise, crowd = data_ngc1275_steep[:, 7:9].T  # sky noise and crowding\n",
    "recovered = np.array(data_ngc1275_steep[:, 9], dtype='int')  # recovery class\n",
    "X_ngc1275_steep = np.c_[mag_meas, color_meas, skynoise, crowd]  # NN inputs (all)\n",
    "Y_ngc1275_steep = np.array(recovered)  # NN targets\n",
    "\n",
    "# NGC 3377\n",
    "n_ngc3377 = len(data_ngc3377)\n",
    "mag_meas = data_ngc3377[:, 6]  # F814W\n",
    "color_meas = data_ngc3377[:, 5] - data_ngc3377[:, 6]  # F606W - F814W\n",
    "bkgcrowd = np.exp(data_ngc3377[:, 7].T)  # bkgcrowd (note the exp!)\n",
    "recovered = np.array(data_ngc3377[:, 8], dtype='int')  # recovery class\n",
    "X_ngc3377 = np.c_[mag_meas, color_meas, bkgcrowd]  # NN inputs (all)\n",
    "Y_ngc3377 = np.array(recovered)  # NN targets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9849cfa2",
   "metadata": {},
   "source": [
    "## Loading in Neural Network Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb6fbe7",
   "metadata": {},
   "source": [
    "The neural network (NN) models are trained using [scikit-learn's MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) and stored as `joblib` files. The naming structure follows the following convention: `nn_clf_[cluster name]_[optional descriptor]_[variable flags]_[optional training size]`. A few examples are listed below:\n",
    "- `nn_clf_ngc1275_flat_1111_-1.5.joblib`: \n",
    "  - The `nn_clf` prefix indicates this is a NN classifier model. (All the models provided are NN classifier models.)\n",
    "  - `ngc1275_flat` highlights that this model was trained on artificial star test (AST) data for NGC 1275, and that it used a \"flat\" luminosity function (as opposed to a \"steep\" luminosity function).\n",
    "  - `1111` means that this model included all input parameters as part of the training, i.e. it included the F475W magnitude (1), F475W - F814W colour (1), the sky noise (1), and the crowding (1) parameters.\n",
    "  - `-1.5` indicates that the training set (originally 80% of the input data) was reduced to $10^{-1.5} \\approx 0.032$ of its original size.\n",
    "- `nn_clf_ngc3377_110.joblib`:\n",
    "  - As above, the `nn_clf` prefix indicates this is a NN classifier model.\n",
    "  - `ngc3377` indicates the model was trained on AST data for NGC 3377 (there was only one luminosity function explored in the paper).\n",
    "  - `110` means that the model included the first two parameters but not the third as part of the training, i.e. it included the F814W magnitude (1) and the F606W - F814W colour (1) but not the local crowding/background (0).\n",
    "  \n",
    "**For reference, the default, full-complexity, and fully-trained neural networks are:**\n",
    "- **`nn_clf_ngc1275_flat_1111.joblib`**\n",
    "- **`nn_clf_ngc1275_steep_1111.joblib`**\n",
    "- **`nn_clf_ngc3377_111.joblib`**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b791328",
   "metadata": {},
   "source": [
    "Note that this example notebook assumes that, after downloading all files from the Zenodo link, you have organized the corresponding NN and AST files into a slightly more organized file structure. As a result, you might need to slightly modify the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3e78b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# load in default models\n",
    "\n",
    "# ordered potential inputs to NGC 1275 models are: [F475W, F475W - F814W, skynoise, crowd]\n",
    "clf_ngc1275_flat = load('NNs/ngc_1275_flat/nn_clf_ngc1275_flat_1111.joblib')\n",
    "clf_ngc1275_steep = load('NNs/ngc_1275_steep/nn_clf_ngc1275_steep_1111.joblib')\n",
    "\n",
    "# order potential inputs to NGC 337 model are: [F814W, F606W - F814W, bkgcrowd]\n",
    "clf_ngc3377 = load('NNs/ngc_3377/nn_clf_ngc3377_111.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47113a56",
   "metadata": {},
   "source": [
    "As always, we can get more info on these models using the `help()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ffeb20cd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on MLPClassifier in module sklearn.neural_network._multilayer_perceptron object:\n",
      "\n",
      "class MLPClassifier(sklearn.base.ClassifierMixin, BaseMultilayerPerceptron)\n",
      " |  MLPClassifier(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)\n",
      " |  \n",
      " |  Multi-layer Perceptron classifier.\n",
      " |  \n",
      " |  This model optimizes the log-loss function using LBFGS or stochastic\n",
      " |  gradient descent.\n",
      " |  \n",
      " |  .. versionadded:: 0.18\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  hidden_layer_sizes : array-like of shape(n_layers - 2,), default=(100,)\n",
      " |      The ith element represents the number of neurons in the ith\n",
      " |      hidden layer.\n",
      " |  \n",
      " |  activation : {'identity', 'logistic', 'tanh', 'relu'}, default='relu'\n",
      " |      Activation function for the hidden layer.\n",
      " |  \n",
      " |      - 'identity', no-op activation, useful to implement linear bottleneck,\n",
      " |        returns f(x) = x\n",
      " |  \n",
      " |      - 'logistic', the logistic sigmoid function,\n",
      " |        returns f(x) = 1 / (1 + exp(-x)).\n",
      " |  \n",
      " |      - 'tanh', the hyperbolic tan function,\n",
      " |        returns f(x) = tanh(x).\n",
      " |  \n",
      " |      - 'relu', the rectified linear unit function,\n",
      " |        returns f(x) = max(0, x)\n",
      " |  \n",
      " |  solver : {'lbfgs', 'sgd', 'adam'}, default='adam'\n",
      " |      The solver for weight optimization.\n",
      " |  \n",
      " |      - 'lbfgs' is an optimizer in the family of quasi-Newton methods.\n",
      " |  \n",
      " |      - 'sgd' refers to stochastic gradient descent.\n",
      " |  \n",
      " |      - 'adam' refers to a stochastic gradient-based optimizer proposed\n",
      " |        by Kingma, Diederik, and Jimmy Ba\n",
      " |  \n",
      " |      Note: The default solver 'adam' works pretty well on relatively\n",
      " |      large datasets (with thousands of training samples or more) in terms of\n",
      " |      both training time and validation score.\n",
      " |      For small datasets, however, 'lbfgs' can converge faster and perform\n",
      " |      better.\n",
      " |  \n",
      " |  alpha : float, default=0.0001\n",
      " |      Strength of the L2 regularization term. The L2 regularization term\n",
      " |      is divided by the sample size when added to the loss.\n",
      " |  \n",
      " |  batch_size : int, default='auto'\n",
      " |      Size of minibatches for stochastic optimizers.\n",
      " |      If the solver is 'lbfgs', the classifier will not use minibatch.\n",
      " |      When set to \"auto\", `batch_size=min(200, n_samples)`.\n",
      " |  \n",
      " |  learning_rate : {'constant', 'invscaling', 'adaptive'}, default='constant'\n",
      " |      Learning rate schedule for weight updates.\n",
      " |  \n",
      " |      - 'constant' is a constant learning rate given by\n",
      " |        'learning_rate_init'.\n",
      " |  \n",
      " |      - 'invscaling' gradually decreases the learning rate at each\n",
      " |        time step 't' using an inverse scaling exponent of 'power_t'.\n",
      " |        effective_learning_rate = learning_rate_init / pow(t, power_t)\n",
      " |  \n",
      " |      - 'adaptive' keeps the learning rate constant to\n",
      " |        'learning_rate_init' as long as training loss keeps decreasing.\n",
      " |        Each time two consecutive epochs fail to decrease training loss by at\n",
      " |        least tol, or fail to increase validation score by at least tol if\n",
      " |        'early_stopping' is on, the current learning rate is divided by 5.\n",
      " |  \n",
      " |      Only used when ``solver='sgd'``.\n",
      " |  \n",
      " |  learning_rate_init : float, default=0.001\n",
      " |      The initial learning rate used. It controls the step-size\n",
      " |      in updating the weights. Only used when solver='sgd' or 'adam'.\n",
      " |  \n",
      " |  power_t : float, default=0.5\n",
      " |      The exponent for inverse scaling learning rate.\n",
      " |      It is used in updating effective learning rate when the learning_rate\n",
      " |      is set to 'invscaling'. Only used when solver='sgd'.\n",
      " |  \n",
      " |  max_iter : int, default=200\n",
      " |      Maximum number of iterations. The solver iterates until convergence\n",
      " |      (determined by 'tol') or this number of iterations. For stochastic\n",
      " |      solvers ('sgd', 'adam'), note that this determines the number of epochs\n",
      " |      (how many times each data point will be used), not the number of\n",
      " |      gradient steps.\n",
      " |  \n",
      " |  shuffle : bool, default=True\n",
      " |      Whether to shuffle samples in each iteration. Only used when\n",
      " |      solver='sgd' or 'adam'.\n",
      " |  \n",
      " |  random_state : int, RandomState instance, default=None\n",
      " |      Determines random number generation for weights and bias\n",
      " |      initialization, train-test split if early stopping is used, and batch\n",
      " |      sampling when solver='sgd' or 'adam'.\n",
      " |      Pass an int for reproducible results across multiple function calls.\n",
      " |      See :term:`Glossary <random_state>`.\n",
      " |  \n",
      " |  tol : float, default=1e-4\n",
      " |      Tolerance for the optimization. When the loss or score is not improving\n",
      " |      by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,\n",
      " |      unless ``learning_rate`` is set to 'adaptive', convergence is\n",
      " |      considered to be reached and training stops.\n",
      " |  \n",
      " |  verbose : bool, default=False\n",
      " |      Whether to print progress messages to stdout.\n",
      " |  \n",
      " |  warm_start : bool, default=False\n",
      " |      When set to True, reuse the solution of the previous\n",
      " |      call to fit as initialization, otherwise, just erase the\n",
      " |      previous solution. See :term:`the Glossary <warm_start>`.\n",
      " |  \n",
      " |  momentum : float, default=0.9\n",
      " |      Momentum for gradient descent update. Should be between 0 and 1. Only\n",
      " |      used when solver='sgd'.\n",
      " |  \n",
      " |  nesterovs_momentum : bool, default=True\n",
      " |      Whether to use Nesterov's momentum. Only used when solver='sgd' and\n",
      " |      momentum > 0.\n",
      " |  \n",
      " |  early_stopping : bool, default=False\n",
      " |      Whether to use early stopping to terminate training when validation\n",
      " |      score is not improving. If set to true, it will automatically set\n",
      " |      aside 10% of training data as validation and terminate training when\n",
      " |      validation score is not improving by at least tol for\n",
      " |      ``n_iter_no_change`` consecutive epochs. The split is stratified,\n",
      " |      except in a multilabel setting.\n",
      " |      If early stopping is False, then the training stops when the training\n",
      " |      loss does not improve by more than tol for n_iter_no_change consecutive\n",
      " |      passes over the training set.\n",
      " |      Only effective when solver='sgd' or 'adam'.\n",
      " |  \n",
      " |  validation_fraction : float, default=0.1\n",
      " |      The proportion of training data to set aside as validation set for\n",
      " |      early stopping. Must be between 0 and 1.\n",
      " |      Only used if early_stopping is True.\n",
      " |  \n",
      " |  beta_1 : float, default=0.9\n",
      " |      Exponential decay rate for estimates of first moment vector in adam,\n",
      " |      should be in [0, 1). Only used when solver='adam'.\n",
      " |  \n",
      " |  beta_2 : float, default=0.999\n",
      " |      Exponential decay rate for estimates of second moment vector in adam,\n",
      " |      should be in [0, 1). Only used when solver='adam'.\n",
      " |  \n",
      " |  epsilon : float, default=1e-8\n",
      " |      Value for numerical stability in adam. Only used when solver='adam'.\n",
      " |  \n",
      " |  n_iter_no_change : int, default=10\n",
      " |      Maximum number of epochs to not meet ``tol`` improvement.\n",
      " |      Only effective when solver='sgd' or 'adam'.\n",
      " |  \n",
      " |      .. versionadded:: 0.20\n",
      " |  \n",
      " |  max_fun : int, default=15000\n",
      " |      Only used when solver='lbfgs'. Maximum number of loss function calls.\n",
      " |      The solver iterates until convergence (determined by 'tol'), number\n",
      " |      of iterations reaches max_iter, or this number of loss function calls.\n",
      " |      Note that number of loss function calls will be greater than or equal\n",
      " |      to the number of iterations for the `MLPClassifier`.\n",
      " |  \n",
      " |      .. versionadded:: 0.22\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  classes_ : ndarray or list of ndarray of shape (n_classes,)\n",
      " |      Class labels for each output.\n",
      " |  \n",
      " |  loss_ : float\n",
      " |      The current loss computed with the loss function.\n",
      " |  \n",
      " |  best_loss_ : float or None\n",
      " |      The minimum loss reached by the solver throughout fitting.\n",
      " |      If `early_stopping=True`, this attribute is set ot `None`. Refer to\n",
      " |      the `best_validation_score_` fitted attribute instead.\n",
      " |  \n",
      " |  loss_curve_ : list of shape (`n_iter_`,)\n",
      " |      The ith element in the list represents the loss at the ith iteration.\n",
      " |  \n",
      " |  validation_scores_ : list of shape (`n_iter_`,) or None\n",
      " |      The score at each iteration on a held-out validation set. The score\n",
      " |      reported is the accuracy score. Only available if `early_stopping=True`,\n",
      " |      otherwise the attribute is set to `None`.\n",
      " |  \n",
      " |  best_validation_score_ : float or None\n",
      " |      The best validation score (i.e. accuracy score) that triggered the\n",
      " |      early stopping. Only available if `early_stopping=True`, otherwise the\n",
      " |      attribute is set to `None`.\n",
      " |  \n",
      " |  t_ : int\n",
      " |      The number of training samples seen by the solver during fitting.\n",
      " |  \n",
      " |  coefs_ : list of shape (n_layers - 1,)\n",
      " |      The ith element in the list represents the weight matrix corresponding\n",
      " |      to layer i.\n",
      " |  \n",
      " |  intercepts_ : list of shape (n_layers - 1,)\n",
      " |      The ith element in the list represents the bias vector corresponding to\n",
      " |      layer i + 1.\n",
      " |  \n",
      " |  n_features_in_ : int\n",
      " |      Number of features seen during :term:`fit`.\n",
      " |  \n",
      " |      .. versionadded:: 0.24\n",
      " |  \n",
      " |  feature_names_in_ : ndarray of shape (`n_features_in_`,)\n",
      " |      Names of features seen during :term:`fit`. Defined only when `X`\n",
      " |      has feature names that are all strings.\n",
      " |  \n",
      " |      .. versionadded:: 1.0\n",
      " |  \n",
      " |  n_iter_ : int\n",
      " |      The number of iterations the solver has run.\n",
      " |  \n",
      " |  n_layers_ : int\n",
      " |      Number of layers.\n",
      " |  \n",
      " |  n_outputs_ : int\n",
      " |      Number of outputs.\n",
      " |  \n",
      " |  out_activation_ : str\n",
      " |      Name of the output activation function.\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  MLPRegressor : Multi-layer Perceptron regressor.\n",
      " |  BernoulliRBM : Bernoulli Restricted Boltzmann Machine (RBM).\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  MLPClassifier trains iteratively since at each time step\n",
      " |  the partial derivatives of the loss function with respect to the model\n",
      " |  parameters are computed to update the parameters.\n",
      " |  \n",
      " |  It can also have a regularization term added to the loss function\n",
      " |  that shrinks model parameters to prevent overfitting.\n",
      " |  \n",
      " |  This implementation works with data represented as dense numpy arrays or\n",
      " |  sparse scipy arrays of floating point values.\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  Hinton, Geoffrey E. \"Connectionist learning procedures.\"\n",
      " |  Artificial intelligence 40.1 (1989): 185-234.\n",
      " |  \n",
      " |  Glorot, Xavier, and Yoshua Bengio.\n",
      " |  \"Understanding the difficulty of training deep feedforward neural networks.\"\n",
      " |  International Conference on Artificial Intelligence and Statistics. 2010.\n",
      " |  \n",
      " |  :arxiv:`He, Kaiming, et al (2015). \"Delving deep into rectifiers:\n",
      " |  Surpassing human-level performance on imagenet classification.\" <1502.01852>`\n",
      " |  \n",
      " |  :arxiv:`Kingma, Diederik, and Jimmy Ba (2014)\n",
      " |  \"Adam: A method for stochastic optimization.\" <1412.6980>`\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn.neural_network import MLPClassifier\n",
      " |  >>> from sklearn.datasets import make_classification\n",
      " |  >>> from sklearn.model_selection import train_test_split\n",
      " |  >>> X, y = make_classification(n_samples=100, random_state=1)\n",
      " |  >>> X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,\n",
      " |  ...                                                     random_state=1)\n",
      " |  >>> clf = MLPClassifier(random_state=1, max_iter=300).fit(X_train, y_train)\n",
      " |  >>> clf.predict_proba(X_test[:1])\n",
      " |  array([[0.038..., 0.961...]])\n",
      " |  >>> clf.predict(X_test[:5, :])\n",
      " |  array([1, 0, 1, 0, 1])\n",
      " |  >>> clf.score(X_test, y_test)\n",
      " |  0.8...\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      MLPClassifier\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      BaseMultilayerPerceptron\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  partial_fit(self, X, y, classes=None)\n",
      " |      Update the model with a single iteration over the given data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The input data.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,)\n",
      " |          The target values.\n",
      " |      \n",
      " |      classes : array of shape (n_classes,), default=None\n",
      " |          Classes across all calls to partial_fit.\n",
      " |          Can be obtained via `np.unique(y_all)`, where y_all is the\n",
      " |          target vector of the entire dataset.\n",
      " |          This argument is required for the first call to partial_fit\n",
      " |          and can be omitted in the subsequent calls.\n",
      " |          Note that y doesn't need to contain all labels in `classes`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Trained MLP model.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict using the multi-layer perceptron classifier.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The input data.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : ndarray, shape (n_samples,) or (n_samples, n_classes)\n",
      " |          The predicted classes.\n",
      " |  \n",
      " |  predict_log_proba(self, X)\n",
      " |      Return the log of probability estimates.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : ndarray of shape (n_samples, n_features)\n",
      " |          The input data.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      log_y_prob : ndarray of shape (n_samples, n_classes)\n",
      " |          The predicted log-probability of the sample for each class\n",
      " |          in the model, where classes are ordered as they are in\n",
      " |          `self.classes_`. Equivalent to `log(predict_proba(X))`.\n",
      " |  \n",
      " |  predict_proba(self, X)\n",
      " |      Probability estimates.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          The input data.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_prob : ndarray of shape (n_samples, n_classes)\n",
      " |          The predicted probability of the sample for each class in the\n",
      " |          model, where classes are ordered as they are in `self.classes_`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from BaseMultilayerPerceptron:\n",
      " |  \n",
      " |  fit(self, X, y)\n",
      " |      Fit the model to data matrix X and target(s) y.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : ndarray or sparse matrix of shape (n_samples, n_features)\n",
      " |          The input data.\n",
      " |      \n",
      " |      y : ndarray of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          The target values (class labels in classification, real numbers in\n",
      " |          regression).\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Returns a trained MLP model.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes inherited from BaseMultilayerPerceptron:\n",
      " |  \n",
      " |  __annotations__ = {'_parameter_constraints': <class 'dict'>}\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# See also:\n",
    "# https://scikit-learn.org/stable/modules/neural_networks_supervised.html\n",
    "help(clf_ngc3377)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f035d97",
   "metadata": {},
   "source": [
    "## Using the Neural Network Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db66c68f",
   "metadata": {},
   "source": [
    "The neural network models should generally be used to predict the recovery probability for an input object (i.e. how likely an object with the measured photometry would be recovered on average). Some examples are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eeba0311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: [[27.665  2.279  3.244  6.802]\n",
      " [26.284  1.542  3.098  6.802]\n",
      " [25.459  0.847  3.418  6.397]\n",
      " [29.21   3.358  3.459  6.109]\n",
      " [27.93  -0.42   2.92   7.09 ]]\n",
      "NN Output: [[0.21710557 0.78289443]\n",
      " [0.02224194 0.97775806]\n",
      " [0.01294968 0.98705032]\n",
      " [0.99517294 0.00482706]\n",
      " [0.10327993 0.89672007]]\n",
      "P(recover): [0.78289443 0.97775806 0.98705032 0.00482706 0.89672007]\n",
      "Input: [[27.665  2.279  3.244  6.802]\n",
      " [26.284  1.542  3.098  6.802]\n",
      " [25.459  0.847  3.418  6.397]\n",
      " [29.21   3.358  3.459  6.109]\n",
      " [27.93  -0.42   2.92   7.09 ]]\n",
      "NN Output: [[-1.52737156e+00 -2.44757417e-01]\n",
      " [-3.80577570e+00 -2.24930193e-02]\n",
      " [-4.34668394e+00 -1.30342615e-02]\n",
      " [-4.83874410e-03 -5.33351847e+00]\n",
      " [-2.27031220e+00 -1.09011540e-01]]\n",
      "Math Check: [[-1.52737156e+00 -2.44757417e-01]\n",
      " [-3.80577570e+00 -2.24930193e-02]\n",
      " [-4.34668394e+00 -1.30342615e-02]\n",
      " [-4.83874410e-03 -5.33351847e+00]\n",
      " [-2.27031220e+00 -1.09011540e-01]]\n"
     ]
    }
   ],
   "source": [
    "# generating predictions for a multiple objects\n",
    "# the output format is 2 columns: the first is P(Class=0), the second is P(Class=1)\n",
    "# P(Class=1) is probability of recovery given the inputs\n",
    "\n",
    "# predict probability\n",
    "print('Input:', X_ngc1275_flat[5:10])\n",
    "print('NN Output:', clf_ngc1275_flat.predict_proba(X_ngc1275_flat[5:10]))  # return both\n",
    "print('P(recover):', clf_ngc1275_flat.predict_proba(X_ngc1275_flat[5:10])[:, 1])  # return just P(recover)\n",
    "\n",
    "# predict log-probability\n",
    "print('Input:', X_ngc1275_flat[5:10])\n",
    "print('NN Output:', clf_ngc1275_flat.predict_log_proba(X_ngc1275_flat[5:10]))  # direct call\n",
    "print('Math Check:', np.log(clf_ngc1275_flat.predict_proba(X_ngc1275_flat[5:10])))  # check"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c673d7",
   "metadata": {},
   "source": [
    "In addition to the probability, we can also get the predicted class directly (i.e. 1/0 = recovered/lost) under the assumption that $P({\\rm recovered}) \\geq 0.5$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6248b28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recovered? [1 1 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "# we can also get the predicted class directly (if we want to use P > 0.5 as the cutoff)\n",
    "print('Recovered?', clf_ngc1275_flat.predict(X_ngc1275_flat[5:10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554d7d9a",
   "metadata": {},
   "source": [
    "Finally, note that generating predictions for a single object requires promoting the data to a 2-D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89e8f5f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: [27.886       0.897       0.09995852]\n",
      "Recovery Probability: 0.7960216893650431\n"
     ]
    }
   ],
   "source": [
    "# generating predictions for a single object requires\n",
    "# promoting the data to a 2-D array\n",
    "print('Input:', X_ngc3377[7])\n",
    "print('Recovery Probability:', clf_ngc3377.predict_proba(np.atleast_2d(X_ngc3377[7]))[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace51979",
   "metadata": {},
   "source": [
    "## An Example Figure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b18e4b",
   "metadata": {},
   "source": [
    "We will now use the NN models to make an few example figure to highlight the properties of the AST data and the NN models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d05b35af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rliTddsJCN1YUMKXAfvTumW6sucmUNAsFbHnklmRpsQNGbnBjZdsGua+vO/gZSdKSKltm+T+rxkuSZh+40h4v2b6jcoRtC/L02qmSpP+a+KIb+/OGgyVJY0dUpOz7h8+OdGMnjVglSQr67Fy/Ov59SdKa+mI3tr7CzDU3YD+PQLJFyeAcW6ruP+/tZ2Kz7XE/mmDOae7gi93Yoop7BAAAgO7R1Vzdu31n7f+8eXnGcNNCL9FkSwA3jjclgrO32NjWqaYcb3OpLbfrtNgoq7OtL0rfNPnxoCNtad1xB2yVJL33V9tiLn+tyVO31duywa3Jasd14+0xClbay+ymY0z+X1NlW/xll5n3o4W2fG88bOaQCNmY017EO//tz8Psa64nIuvs+3VjTJHKZtuZT18cY0oIF3xiSw1X7W8+o0LP2A3DfCnzK1nmjGdvpMyd+jMzz1Ufu7GeahUJAACAruss3/aHbCsOX61pfxEvsK09EkGTc/pbbC6ZSLa8iA6yuWmw3uTMmZsb3VjjCDNO5lbb4rqpxOTHoRqb6/qS7S18njYXrfmp7TRaI3auLXnmdTxs89ScdSavb8u37TliwWR7jlzPj8ScthuenxXGsz09/pzNYsn57KT9htO2xN9mt8uoMZ9DIGyP63xugVr7GcUKk/u2ej6PePLzyPScR62nHQgAoFewuAIAAKCLqFwBAAAA7DmergMAAAC6B7l1z+jVxRW33HKL/va3v+mjjz5SVlaWjjjiCN12222aMGFCu+0+/PBDXXXVVVq2bJni8bgmT56sxx9/XKNGjeqlmaM/2Vn1iY6221XOeB3tG8jLc18n8kx1h7ZBnifWKswq321j/W6stdGstG1qs6tv95u6PmXsjxvN03qfNpe4sX83m+fNDir5wo2t2DhCkvRhxVB7jDazyvinUxa5sbpiO68nt0yTJG1ujLixfcZuliQNC29zYw++e5gkKRi2q2mnjTDVMP69daI9Xtyc3yHFZW7sozozn8aoXQlc2Wo+o6oWWwljQ31BuzEkaZ+h5inCf2+x3yeyg+YJxWbP5zZ7uql68XbFCDfmyzVz3XSWnd/srG9JkhJRWwmDJ+4AAP0NeTV6ypzsc93Xixsf6taxO8vBvO85c2g6yFafCNeaJ+Ra8r1PzZmvzSH7dJ1TBSKy2m636XCTa259bh83lphsKk5EPRUpGkuSFSJsWquWQ812U4faamnvh2z+mf22yamDEfv0WqAlOecqe1Ni9NM1kqSPr852Y60Rc7yho6rcWE29ydtb6+xThL5CM2DVYbYiXqDS5MUnzFrhxp7/+3RJUixkj9uSTPnXv2E/y7ZS81lmbbLjOdUsnPOVpA1hUxZjcMnBbqyzpyEBAOgvyK2xt+osR/Pm9vNG/kiSFC+w92lj2SZ/bsu2OWJGo8kbo3n23q0/WeWhqdTuG2g22zkV1yQpFnIqo9n7uU7Vi1B1ixuLFtmqDfK1P64ktRSY/YN1NhbLNrHoIDt2KHm9ENrmGXuQGTsQtdcLsZxkJYwMe06ZFaaSR2te2LOv+TxyvrCV84L15trBF7PjtRWaz8FbbSOz3FR1TgTsMeKZ5n1fqz2GL2rG9kXsfXp/slp23HMv26nAAQDoGf6db5I+y5Yt06WXXqrXXntNS5cuVVtbm2bPnq2GhgZ3mzVr1uioo47SxIkT9cILL+jdd9/Vz3/+c2VmZnYyMgAAQPo4q4DT9QvoKvJqAADQH6U7rya3xu4gtwYAAP0RuXXP6NXKFYsWLWr3+/vvv19DhgzRW2+9pWOOOUaSdO211+rEE0/UL3/5S3e7ffbZRwAAAAAM8moAAACge5BbAwAAYEd6dXHF9rZtMy0HCgtNe4N4PK5nn31WV155pebMmaO3335bY8eO1dVXX62vfe1rvThTdJddbdmxJ+Pt6rjdVbq2o1Yh/mRp4ECTLRPWONSU9G0ZZMuE+YLm9eZaW+orO2xKlR02dJ0bqxpmygX/YcVRbmxQkSnRW11p971o+suS2rfQ+KxsiCRpSdUUN9aWsEVsBoVMmbO3V492Y5P3Na1GNkbz3dhx+30iSWqI2dYel5X+S5J0y+cnubGg35zTP9dNcmPhZBuP/OwmN7ahsUCStGpTqRsbnyyvfMPop9zYj1efLknaWm9Ly00ZuklS+/Ynz1fuJ0nKCNqScMH1pqza0NNsLectMVMeefBdrwoAdlU6V+qyAhjdgbwa3aGj3Lq7W4HsDn+R+Xud/fFWN7b2HJNDOq1AJKl+tMkDM8tsvupvaf+eJBW/lSxJXGz3jSXbeWi0bQviaDrGtsZoKzPtOT4o87TpCMdT9glE7ff2uslmEr56Wxb5w8tMnl3wmr1Er5lijl31zmA3lllpxhm00R6j/FhTQtjb6sQ5xrK/TXdjweS5142xcyn4xIxTO8ZeDwTrzLxC9jQVrDPlnTOftecZ2WBKEVccZEsXD5ZtEQIAu4K+0OgPyK0xEMwKnO6+9s0w9439jbaFRluyXUZom82P40GTQ2Zus7mp057DF0t49jX5pc+TJmc0mt+Eq2x7i1iW03rE5rWt2TZPDW8127bm2/w+q9zcX24YYdvrBTLNPv5WOwenHUhzsa0u47QXyWi09+xbkmN75x8tMvs4bT8kKbLGHLelyLbbDlUm73XH7Ym2Fpp5BevsMXwtZpxErt030GDm56+z98sTWck8e4u97nH4wzYHd67RvH+GS2OPp+wDYO9Hbt0zerUtiFcikdAVV1yho446SlOmmP+8t2zZovr6et16662aO3eulixZolNPPVWnnXaali1b1uE40WhUtbW17X4BAAAAA0V35dUSuTUAAAAGNnJrAAAAePWZyhU/+MEP9N577+nll192Y/HkCr9TTjlF//3f/y1JOuigg/TKK6/o7rvv1rHHHpsyzi233KIbbrihZyYNAAAGpISkuNKzUjex802ATnVXXi2RWwMAgPRKZ17tjA/sCXJrAADQX5Bb94w+sbjisssu09NPP60XX3xRI0aMcOPFxcXKyMjQpEmT2m2///77t0tova6++mpdccUV7u9ra2s1cuTI9Ewce6y7WnF4Sxbv7r47m0tH27nH9Qc6jfk3V0mS6r40yo0VrDblvxpm2JJgiUbzT7IhasuYNbSa9hfPrB/kxgaPrDbbt9riM20xc7zSkmo3tqbRlhB2xc031k2NeW5o7Ue2FccPZi6VJA2dblfP18dMmbEtUdtypCV5vI+22FYcP6w6U5IUCduScYcUmxYcz9ZOTplr5dtF9hgHmGOMHVzpxpraTLm5b/zzB27sG4ctTxmvJmpKqE0dssmNlWaako25AVta7sPSEknSZzX2uHVjzNeio6cJAID+rjvzaonceqDbnVy9o7y8q+PsLLf3Z5qyvIkp49zY2EdMHrj1yBI3FguZnLOp1LYAyf/EydFtrt4wzHzNW2fL91ZMNzlzwUpPq43R5jaCb5XNif1hE8vZaG9e+Ftsjr7/+R9Ikv7z3n5uLJybLGccsmMfsc9aSdJrdfu7sWCVmaPTCkSSGksSyXOzx8guM68LP/K0ICwxeXSjp62JM553rltOMnMJrbYliVtznXOSjUXMPg3DEp6Yyd+bi2yscYg57tz9rnRjiz6xvegBAOhPyK0xkPhzPW3u6pslSY37FLgxpyWGk2NLUu0ok89mb/W0vU6200gEbM4ZzUu2nfPZWHOhiYWrbawhmcOGa+14oXqby0eLTf4ZqvHkvcNNHutv87TxyA8kY/b8fMn7276E50eDyUO3FNgWG0q+31BqW4/kfW4+j9Zcm78nMszOTcVBdcb5PDJqbLuPtvzknFvsBANbzb34RK79uYCvytzfTsTtnH055v14fYMbc9uBJFJ/7Nnu2ir5+dMyBAD2TK8urkgkErrsssv0xBNP6IUXXtDYsWPbvR8KhXTIIYfo448/bhf/5JNPNHr06A7HDIfDCnv6TQEAAHS3dPavo3cddkc68mqJ3BoAAKQXfaHRF5FbAwCA/ojcumf06uKKSy+9VI8++qieeuopRSIRlZeXS5Ly8/OVlWVW7/3P//yPzjjjDB1zzDE67rjjtGjRIj3zzDN64YUXenHm6Ih3FeSePOW2O/t2tM+ujrerx3O2m5VxZuqbcbuCNjA4tVpE61hT3SHoWWnbmmtW0Mbq7OrW0CCzCrY4vz5ljMwMu5J1coF5Mu+TrCF2vLgZ77MyG5tYsEWSNCa3yo2dcdSbkqT/ffoUN7bfIZ+7r58tN/0jt9bnuLHDhq2TJL3x9ng3VjzWjHn0qDVu7KXP95UkNUXtyt6/lM2QJPmCdsVxW9icy5SZn7qxEdk1kqRVNbaKxv+MWSxJui76VTdW1WLmNX3YBjf2yooJkqRPcj1P5mWZFcyZYbuSeVC2WSFcUZ7vxgZP2WrG/azYjc0bdbkk6Z+f3+7G9uTvJwAA6UZeje31Vu7S1by8o2sI73ZzB18sSUo0NLox377mhxZNQ221hUC++UFF3Rh7oR9MptRHzLA/+Hi10PxwJHOV3TdytMmZa0JDUvYN1tmnvxKlJlc/OFllQpLe32Jy19w3bVW4bWPt03yv/cdUorARKRoyc42ssjnzqtfMk6+FdalPm2092F5DDFppqlSE6lKrbFRNtNcVsYjnMb0k/7j65Fzt03A1Lcmn+mzhOZW+asaummBnnbPRzCuyzm4XT07fW1mjZj/zOntLoRubk32uJGlx40MpcwIAoC8it8ZAFK+396MDfpNzBppsztk41OSwwQabmzoVK0LbbO4ZHWRyUm8lCSd3TXh+GpWRHLthmF1w5FSsaMu0+aUvbl9nNJr3m4ttHt2W5Wt3DEnKrDJzTHiT8KSGIXYSOVvaksezVeEahpqd8tbbc2pKHi9cbWPu59FojxtPVrPwnnugOXmMvEwbazTJdyJoJ+hUrPDV2+seZSY/myZb9cK5LlrS9LAb66zyH/eyAaD79eriirvuukuSNHPmzHbx+++/X+eff74k6dRTT9Xdd9+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bW751dEpsRZVpEVIQtqWhn3juUEn2j0OSllebfZs8n1Vmk1lp5y0h7fx5yfPn7882paYX1z/oxpy/J952Mx3+vQOw10kk2ldq7O6xgf6sJ3PrPdHTLQj6UruUjlqBeO3qHJ3tOmozsrMx5u53Zcp2zmfkfQIg0WByb9++o91Y9scmt60+ZKgbC9aZb56lz1e5sU3HFaUct2W8KcvbGM10Y75kq41gvX0CIzHZlNvNftsmif8pNDlxVsj25IjWm3zxofvn2PGOrnZfhytNMrrggyPcWM5Gc5ya6bbdh6pMfpq5yV5XtIw3Oek+99tPZONRZt/MSrtrg5LXKZ5Kzp9908Qiq+2+Of/KlSQNP2etG1uzyuTbJV/eYI9Rb9qexOvtgINfTY63wc65bmWBOcc6W2o4EDVXSLFw6nMc3r8n8WZTPrmjP/++8G8EQM9JZ17tjA8AMNxrtcDpbizRlmwV4ffkwsn2ErGQjUUHmdetOfYba6jOxBpG2TYTGY0mb8zcarer3dd8bfXcf83ZYPZtLvYcN5lCxoN23+zNnlZ6BeZrpMwz9ljzfqjWbteavAWfWWm387ea9+OecwrVJlsBZtt5BZNtSpoH2Xy2LTN5jDrvuZvXDcPs/eNgk4nFM1I/y7pR9vojq8JcTySCnhvXzvbZuXbO1cl79tU1bsxpAdLupndrMkePe9q4NCXvb/s8eXnc008lqaO/E3JaxXj/E03GvNstjT2eMt6u2pP7Bd19r6Gzn0F11zGAnkJu3TOoXAEAAAAAAAAAAAAAANAJKld49MQKtJ44xp48Rd9R9YZ2Y3fz00QdrQDcWVWJzo7d0Xizs76VuqE/dVVoR+ebaG1Lee1UMJAkX0byn9AgW+XBqVgRz7aVJloKzcrUUFWz3Tdm3m+J2JWszYXmdbDWDteSb5aCZTTZ7WJbzIrYRJZdFazW1F5H22rNMt23G+3K2B9OfV6SVN6Ql7K9JL1ZbqpA1NXa6g4nf8lUicgNRN3Y69vMU25DcmzVi011ZvlxKGw/t8E5DZKkwP/P3pvHyVXVef+fW3tV752kl+yBhEAISxBkiQoiBBBHcXR0xAUeN36CPIOoOI4LGR9FYRT3cZjRQRRRn3kkDqMQg8oissgqECAb2ZPO1nt3da3398f3nPv9Vuqmu6q7qrvT/X2/Xnl15dxzzzn3VnX155577ucT4CVtz++kp+GOn9PhlVkHjOOaDnhloTCtpm2P9BSN8/89+xrv9XtO+wsA4KF9i7lCe8r0y+eoNUZPIK4X7Qy10Vjj+/gz4eRpHyfEZXb0fr9fssx+BqXriZuisegqV0WZOriuA7dKGXPValdRxhv9u1fIRJ2PkfT2cOPyc6Tww7oQjNROcEazV7Z24y1FfYTmzAYAuLI9o7NTbfwEV3CINGLdDq6Xi5Fu2/7WYreK+m2sBy858WkAwM/7z/LK6jaRps9wF8hvpv84LOk9x4p9HY1eWWx7pGjfJU3sXPFCO+lj6YCRfANp0vBm3sm6TuSirJlra+n4dr6J+6vZQz/7FnC9XF3xE2jRZnpSbWAFX/LPuD9WVC9vji/9r+1ceBL9CHeyFq57/24AwJ4/zfXKGs4kx7vdm/mcN2ykA4kd4nPuxkkXy89JuZ9FfXJMUaYu1dTVtn1FURTlyDgRIwgDxc/hBjPCqaG78CcAJNtMWSfva792e47nfaMHaftQK+vWvoVUFl/M877uE400lCx/dw+2cTuxg8btYgb3l5pBujOY5rJol9lH/AkIWSkqnrq2c/FpMU2eizpmDFwWP2jc2YTrRTBNDWVj3G94wMwpi0e70/XCXs6O7wC587kRn9tz8rFw45btNPAA3R5z48Ap/vvmFjhNWBsQca3g40jhOVH4uFRISnWpKFW3l+uWOFLZWK57xzI+RZlsTDZt/fDDD+Nf/uVf8PTTT2Pv3r1Ys2YNLrvsMgBAJpPB5z//edx777149dVX0dDQgAsuuABf+9rXMHv2bK+N8847Dw899FBBu+9+97vxi19MnIu8OlcoiqIoiqIoiqIoiqIoiqIoiqIoiqIoilIRBgYGcMopp+B73/te0bbBwUE888wz+MIXvoBnnnkGd999NzZu3Ii3vvWtRXU/8pGPYO/evd6/2267bTyGf0TUuUJRFEVRFKVM1LlCURRFURRFUcbOZHu6TlEURVEURVGOViabtr7kkktwySWX+G5raGjA/fffX1D23e9+F6997WuxY8cOzJ8/3ytPJBJoa2srf8BVQhdXoPJRFxNNuVEgEr9IjpHsk0o9f150grCiKtXWadj2ZJs+cR/rkncW7yuOycZ85Ic48sIJH/lXw4sCAYAWssDN79zD7dVSFEdgRpNXZt2EnSzb48Z66LXbx81F+uiL6eCpXNb6BP3cd46IALGEuSzeQN5myf0cW3LusZsAAI9sP8Yr+8ZTF1Jf20RshThtM1+zDwDQe4Btil/tnwkAeGkbW/HUNZJ9WXs9Z5jEwuSXdtXiR7wyG/eRyvB5a26kKJGXdrD9cHtLNwDgmd8u88qOu3ArAOAHv73IK7vkTU8BAFa1vcx9PEd9xBLpoj76k3ycT+xZQPV2sx2bYz46OeGSnG00kSiNHI0SeJrsneVnY6QIm+HqKYqiKIoyMYyH9h+uj0rFG9g4jeHiOo7UR6njkm37HdNwZX79XXzcDUXjL4gUOdQJAHDmioiKKCnp6PbOorJ8nDXdgRWkgWUEiFe9l68/nnvfCQCAuotZ0/UvMDEj81nXptK0faifteTb2zcCAH6+gyNF0ksofiPfz2PZspa1d52RpzI25PS5OwEAT69n3Rs7RD+d13OkyAIbL7KSy1758yI6pk6e0MhkSMwHUyJuEKRjl/5w0Ct79W9J8A6I8Q0toQHuWsDjc0zqn9PM2rrjDxQHIq0vM7+eBQBogoTshKVlsiV44lLvtX3/S/0sTpVrdUVRFEVRlMmGmzNaOc86Ot1I2rZ/Lqu/vJHPMhbEvg6IuOosTY0jeiAgykgj1m3hSehkC5UNbuXIi5hpxhFJFpFup2ifml1cVruN+pF6222g7a4Qr/GDtG94gOMvhpqpQqKDy/Ih2jfWLc5HLZUlZ3G/Nc8UX3fYSBGZR2KjQmSkYXoGXbtEOpM85iDtE+zkCG4br4d0xitz4qTz3STvC3O/It/bzfUCZgwBvu6xETB5ua+ND3H5pDvBoCkqjh/0YkTgHxVS6rXhaCj3Wr7k+0yTPIJwso9PmZ709vYW/D8ajSIajR6hdun09PTAcRw0NjYWlP/sZz/DnXfeidbWVlxyySW48cYbUVdX59/IOKCLKxRFURRFUcok7zpwqrQKOK9P1ymKoiiKoijThGrqatu+oiiKoiiKokwHxktbz5s3r6D8xhtvxOrVq8fU9tDQEP7xH/8Rl19+OerreeHde9/7XixatAhtbW148cUX8dnPfhZ//etfi1wvxhNdXKEoiqIoiqIoiqIoiqIoiqIoiqIoiqIoyrDs3LmzYAHEWF0rMpkM/v7v/x75fB7/+q//WrDtIx/5iPd6+fLlWLJkCU4//XQ888wzOO2008bU72jRxRUYXyuiajKWsZRqXexXz74eyebJxpWUE5FQat3hjrmgDZ/YELY+kzZX9KthLbAAwKmpKa7X1UPNii8Oa6+VmcleZLk4tWftvQAgVUeWW8EM2471LqKyeAd3Mdhq2s2w1VjtIuq3bxd/eQ2Fqd9wE8ebPLDxOOo3xcdx4pJdAICd9Y1e2Z9Pv917fdOB1wIAfr672SvbtI8sfxP1bF/W1032ZUMptkDOpOg4v9V/vleWy9ExNdSy3dhJM/YCAJ7L8/lY3kxlL77eK0I2T/ue/8bnvLL/+espAICaJm5v9WvvoZ9/eatX1t5ItkQ50UdfP9mmZeaxxXFsJ523vmP5fW16hcpqntvplbnmPc71D6AIn8+VjK0Z9vMp640h0kdRlPHFdelftdpWlKnKWOwsbYwAMHIUxnCMh24fro+RYjVKxZ6Dcs7pcP2NtG8lzpu7a6/32saByHbteyzrBWaQJnUbi+0eh1o5wi1tNof7WPsNzKbX+Qj7AUc+Qq+HXir+su3t4D6cNO0bENEYP3/irKJ9amvpOHrTrAfb3rTLe/3qjhZqr5+3v/xjE02S4jF0m8SM8BMcsvFiO2n9QIrHnzhE4+pbki0aS3AvX96fdMIOAMCmS48RNYz18gLeN9wRxuFka+m6o+ZZPr+DbSbuYwZbEtfsoX1zYg5l1jOUeSjfGydJ1yfZzVu9suGuOwOxWNG2UlHLXEU5+qimrrbtK4qiTEVGo3u8fXzmMvMNNd7rdC3pz9gB/hLNxkmHpprFPhHaHhxiDZ6LUVl8P5eFzPb+hTy/7QZMvQ4ei40PCSbFHHor72Nj8PzGICOfHSN3QwMiNq/ZjEE8bJ2LmqgQcQ0RNYl8h5aLMhNNYqNFACBvIkACQpbb6JREB19DODkaf2oGi+ZwH+3khljnOxmam3aDoszoaDdTHAtifwJA7sBBGksNx4Xn+yleJChs8/Mpak/e87C4efnaHKfPH1G/KJARcfhc2lgR2U6p18WlXmdXmom+B6jXNUo5jJe2rq+vL1hcMRYymQze9a53YevWrfjjH/84YrunnXYawuEwNm3apIsrFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEWZ2tiFFZs2bcIDDzyAGTNmjLjP+vXrkclk0N7ePg4j9EcXV0whxrKCzc99wpaN+AScePJ+2HolulCM9MS/Xzur4u8DALipVNE2v5W4ssxvH1sWECuk1u4jK5qLaq/wypw4LYl1xKrQ3JyZ9DPOv16RQ+SyMLCA3Szih2g1aijJjglDzbSCNS8eIEu20lKw+i28arU/3QgAiPWL1be1tOI1v5tXrR53Gjkv7O3l43hp2+yi4z31F9fx+M2Tak6Cl922N5ELRCzEq2R3gcaQiPJK3AO9VJbM8FLhYBedh9Ri3ndLL52jWJj7eLGTvggX1Xd6ZY8/ejy1u5RXTjthGl9rfZ9X9pW/vhkA4A7yOd/2Ah1nPs7LbpcetxsAsPGFwjwoAJj9IL8O5OicuzV8Lt0eOgfSXcJ+7tYl7/TK/FayDvuUqLpVKMpRCa0Crk5+nT5dp0xlKuWwNtFPjpTKSE+TlfuE2WiOVzp+lNKH7MevTLY33HhGch/wa88itbWlb2mj97r+CdK4sX0smmc9Tz9zUdbMAyDNP9TOmjP2e+MMMZu/bK0zRKit3ys7fS718fifT+B9+4u/9/tr6ThPOpbdKl786wI+FvtTOGA0vusQAGDrSzwRYMdQv421a9Jsbn6R+xsiGY1gX/E1zpLzX/Veb7uHHCsybXycx/yarklevYw1bt02+tl5Fmv1xmfIxW3Ws4NeWcdZ9J40PMb97vlbcu1YeDuf81f/lp6MO+Zu1uqZheTeERpiFzw/KvH7PNm/ExRFKaaautq2ryiKMhUZle4xDgKBCOto68KMHH9hxrpovvrAKVzPzlfHhZuFdYNIzRTuEsZ1oncxlwWMK1y+hufB49uowcFFrENDnTS3a90oAMAVr2O7SXc6wi0i3WiOKS2+8M3LfJTL4gfMJuEsLd2jvWMiA2ckOriedaQIsTxGqp50cVC40eUi5nzM4PMW20f3GUIDfOyOOdeBAb4vkU/Q++CI98bNSqdtQ9bHwa6V9Hb+EM+r2/c173fvI89jdgLmOIW7hLWxcEI81+57b0fedzH7+Dpb+PwxLsfdvBJUwsXxaHfJO9rHr5TGZNPW/f392Lx5s/f/rVu34rnnnkNzczNmz56Nd77znXjmmWfwm9/8BrlcDh0d9MXc3NyMSCSCLVu24Gc/+xne/OY3Y+bMmXjppZfwyU9+EitWrMDKlSsreWhloYsrFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEWpCE899RTe+MY3ev+//vrrAQBXXHEFVq9ejXvuuQcAcOqppxbs98ADD+C8885DJBLBH/7wB3z7299Gf38/5s2bh0svvRQ33ngjgj4RR+NFWYsrenp6sGbNGvzpT3/Ctm3bMDg4iFmzZmHFihW46KKLcM4551RrnIqiKIqiKJMG13Wq6FxRvdXFyuRCtbWiKIqiKNOdaupq2345fPWrX8Xdd9+NV155BfF4HOeccw5uvvlmLF261Ktz5ZVX4o477ijY78wzz8Tjjz9ekTEro0O1taIoiqIo053Jpq3PO+88uMPYXQy3DQDmzZuHhx56qKw+x4OSFlfs3bsXX/ziF/Gzn/0MbW1teO1rX4tTTz0V8XgcnZ2deOCBB/D1r38dCxYswI033oh3v/vd1R53RRmLzW65lkCTwXpnLBZIfpQaazBivMgw45LnzYlSdIZfFEMgwRbC+UHy6QrEolw2lCoqs7g5ttkK1FIMhZtk61obB2KjQADAqeOYD0tw90FqoztetC3aKcYSIRuzXIRtdKPdZJ+VnMVlNbuMnVgTtxM7RGUDy3l8oT3UdqaRj+PVfeQbHImyXVjIvK6rTXplnZkG7/X5J70MAHhiD9sZ7zlE23MdfEyhQRrDwVr+8jvz9I04nE2dNIau7XwAmTZaUTa/ucsr27irldrr4XPaunw/AOBAV51X1tREds3dSX4fahN0HrJRtpFrStDx7eho9sq2PjYfABARjmqpmfSfzhN4lduM9fTTWcD7RnuoXxkL42boXI4UZeP7mTb7+P3+TIbvCUVRFKV6THVtXWkqpVkngrHEePxu8KcltXOkbaXarNr+ZDu2LDSnOE5ORnbYenmfuAdZz9PqIirEb9/g4kUAAHfXXm6oiXRo4u4nvKKhN70GABB9bqtX1nMWxcnNXrPNK6vZQVpuw9Xc75CJ75z32t1e2c6/zAEARJ5gzfny7ykOJL+c7YzDHaQXZ13CESCvbm4DAMxNdHtlL9TOFfuQrW/zYo7J2N1Jx7T4l2zRa2M3ut7O0STR9TSewLv3e2V9HY3U3uNsF5w4SGN8JbPIK7NHHGB57MWBNGzisu430PkP7uVzNGAiU3rP4kmTmNHHqTq+Tjn2B1TvwAp5bUVlPUtY0zc9T5q/79wlXpnfZ8J+Fo6W329FUaYmDz30EK655hqcccYZyGaz+NznPodVq1bhpZdeQk0NR5ZefPHFuP32273/R6ytvjLuqLZWlOHxuy5wRbSE1e35WPGtohBPH8MxcrbnOBGvl0ERkV7SkO6xnKGRPWg0X4D3Tc6hMYQPcr9ZE1cdPcDztDY+DwD6lpN+dgZ4n/qNVNcVsRbJdhP5zFIeeRPZkRdf1wNzqZ4j5ooD2eKYEddEZ2T4zwDCA/QzlOJOhppMVEiax5xupg4j3XyynBztk23mBgNpGoSTFDEe9qZjWGSIB6htV1xH2XsYBfGKyeJrNDdNUYUBUS8/YN4nh8dso0LWpX/ulXmfI5/4EIDjQFZF3lPUr9zH1hvpevXC4LsK6st9CiJDfer5UYl7diPdtxqP65hKR6TqfQhFGR0lLa445ZRT8IEPfAB/+ctfsHz5ct86yWQSv/71r3Hrrbdi586d+NSnPlXRgSqKoiiKokwWXHgRnlVpW5naqLZWFEVRFEUhqqmrbfvlsHbt2oL/33777WhpacHTTz+NN7zhDV55NBpFW1tbBUaojBXV1oqiKIqiKMRk09ZTlZIWV6xfvx6zZs0atk48Hsd73vMevOc978GBAwcqMrjxohIrskpt42hc/SWfykeeVlAWrA602/O8xHM4R4+Cp/xHeGofgeLMHOtYMdKqOtuPdbAACp0tLE6Ifg2sqwUA5Lp6AADBGc1F9d2BAf6PcS5wmhuL2x1Ke69Ti1uLtlvHCrtqFgAifbTa0xGragdbqd7QsTy+mpfM02G9vGrVOlx01/O+2Qydv3icx5IZpH1OWLDdK3tykJ8YOzZhfn/Fg4qvdLcAAMIzeryy7TvJkaJ+Bp+Pp7aR20VzIz9x19VFT60FmngMyf30PgzVc72wcdQ4sZ2fVBzM0sregwF+8q2vn564a6zn97XzZfMIYjufo5NaqJ3t/S1eWSBjXEAWcr3oNjqXbU/w+KJ7aFxOmlcU58z3WsFnQny2LL4raH1W4o7mCVRFURRlajDVtfVUY0TNOYYnR/z29XOBGAsjjcuvP+macThrD9zmvb541lXUh6hvy9yBYp0kCRhN5baztso99SKAQseMXCM9zSU1WNA4VmSPn++VxQ6RgO58A5dZx4TEJhbI9km1g/38lFjdNvrZ/QZ+NG/G/aSPWxYf8sr2gzTnyYler2x3M7lQ/Hk3u0YEhE1abj693reDx++kSZNuFg/OhvppiqLxt6x7D77G7GvcKgDA6Sd933kW69TEb6gs08z95sNUJp8mDC0gjTvYzw4dbiedEGnsGUzR/8QDfKh7PblndD4nv7tIl+eEcYU9lw2bWOejg77Darfw9cfvzOfSfl6Aws+RoijKZKGnh+ZBmpsL54cefPBBtLS0oLGxEeeeey6+8pWvoKWlxa8JpcqotlaUMjBz7XY+nP5jHB2iXOZkSVtL94ZkC9WL7xWuDI3WWYGbs07LdY+z3rZOEpkQtxdMUjuZBladTo76GJorXB6GuL857aTvO17gufahmdRmqoXbSWynY3HFrYXBVqoXHmDlmzW3CmIHuSxoLo/cEJfZufraDu6j+xjqI5Dj8cUPFc/tO1nqNxeV9znodXQvX1ekW41r3QCLa6ev+JrKNfPVjnCzsM4VrpjLdozbknS4KHjfbZlxqXCEA1M+SddF1hWCBmbGL+4BSUeK4Vwl4Gf7X+CA4Ra0cXg7w5X5tu3DcHP2Y5mLH0+3irH2dzS7girKZKOkxRUjCdSx1lcURVEURTmaqGZ+XTVz8ZTJgWprRVEURVEUYrxyoXt7ewvKo9EootHiyNjCfV1cf/31eN3rXlfgiHDJJZfg7/7u77BgwQJs3boVX/jCF3D++efj6aefHrFNpfKotlYURVEURSHGS1tPd0paXHHPPfeU3OBb3/rWUQ9GURRFURRFUaY6qq0VRVEURVHGl3nz5hX8/8Ybb8Tq1auH3efjH/84nn/+eTzyyCMF5e9+N1sPLV++HKeffjoWLFiA3/72t/jbv/3bio1ZKQ3V1oqiKIqiKMp4UtLiissuu6zg/47jwBV2O46w8MnlclCIkeIvvHojRAaUak9Uar3hIjTkOP3G5Yu1gvKJ8JB9rYq/b9g+hh2X3zH59FfQt+lH9iGjPxgqc8L86xCoJ+tgJyT6iJI1Vk7YB4baKV/T7ez2ypx6svDKzmbLSDdIvyPR7Z1eWfpEWinviF+ZSA9Zd/UsjHM9chpG02NszdV5OtWb8QSPeWCuGVMfjzmXp377unjfj7zxAQDAj9efxWMO8O/zfzxBGaJOii3NXLP9nWc9icPZ28U2yw31A0Xbl82neI7aMJ/7v2xZCADYsZEt3JoWkK3bsbUHvbL7ti0DAMyd0e2Vbd9Hdsw9/XyO8jEaX7uILXl047H0gg8DuaU0vtiLbEsXP0j79s1hK7VAmjzhwt1smxZaSBbTub37io5RfhaH+10fDZWy/VIUpcJUM8BOw+umPKqtR8dE/U0clbYuUZcHYrGislKPbaQ+hovp8+OixPu9136xIHa73JY71Fm0r1NDOiogYjysHJOWtPft/HbRvl5USNznqd+mBn5tdHl4236vKBFrBwAMtrCm6zq5CQCQbOffozkP0s/Utkav7MC5pK2bH+b3Y2A2/R4OikiOQDPFyD361FKv7GPn/x4A8G/PvaF4zADy/TSeoNDoNqojL+yTP/X2/wYAfO+Hl3E9o8frNvK+PccVfyfsPZvqzXqMy+z4I31cNpSi65Ql57/qlW275xgAQI4vF7zolPrHWW/HttJ1R80i/n7qXmqiTDbw91fzeuow08DvYcx+zsV7aONAZMyMoijTlHEKht65cyfq63n+YiSHiWuvvRb33HMPHn74YcydO3fYuu3t7ViwYAE2bdo05uEq5aPaWlGGx+8+Qz7FuRWhWRT9LL+LU02k/cIilSJtEpXzQjdma2mnfH2WCwdo395lIp8uInIyDMFmujaQM/zZNP3PTXNpqJ8nd3dvp3nhQJAH60WTiPnt5PHUdmgPf9dHu+i7IF0nvh/MsLIiSdxG3rliTjlsEu8ycS6M9FA7oaRoz3z3uAHxZLf5DspFRcyI2cUNi+Pso7lzNyJu2dWbOez9fE/B3ouRfzq9uA9xf8O79srw++Bm6X1yUzxPH4iT5rdRIABHhMjvT3sdWHDPyCeSY1XkPcXbZXyIvWcmv6fN+N0Sv6MLxuCU9hT9cJEYlZprKDdmpNR+9Z6AUhbjpK2nO4GRqwD5fN77t27dOpx66qm477770N3djZ6eHtx777047bTTsHbt2mqPV1EURVEURTmM3bt3433vex9mzJiBRCKBU089FU8//bS33XVdrF69GrNnz0Y8Hsd5552H9evXT+CIpzeqrRVFURRFUcaX+vr6gn9HWlzhui4+/vGP4+6778Yf//hHLFq0aMS2Dx06hJ07d6K9vb3Sw1ZKQLW1oiiKoiiKMp6U5Fwhue666/Bv//ZveN3rXueVXXTRRUgkEvjoRz+Kl19+uaIDVBRFURRFmXRUM7+uzHa7urqwcuVKvPGNb8R9992HlpYWbNmyBY2NjV6dW265Bbfeeit+/OMf47jjjsOXv/xlXHjhhdiwYQPq6uoqfABKOai2VhRFURRlWlPlXOhytfU111yDu+66C//93/+Nuro6dHR0AAAaGhoQj8fR39+P1atX4x3veAfa29uxbds2/NM//RNmzpyJt7/97dU4AqUMVFsriqIoijKtmWTaeqpS9uKKLVu2oKGhoai8oaEB27Ztq8SYpgwjxQOUaxM0Yn9ltjNS7EfZtkh5tm3yixlZl7yzqD1HPCngt48X7TFCxIrfsQSt3aOIbLDRH26GrcoCseKnFdbu+9eifu2+wVmziuo7zY3ea9dEiQS72TctkKZzk2/kOIpsjIxjQkNsi9a1lCxzG7fw+PrnmPbYRRnxbeQhPOjzUERAuLBljR1a7TY+B//xyLn0Isr9Ov3iq6CWGkjM7veKBrrIHuz/PX06j6GJrMLy2/iYsIwGeVbrNq9o12AjAODJvxznlcXmUdvherYbC5qxPrqfnwpJb6T3cNciEXUyZOzhevl9+/vzHgUA/PyZ1/JYMsaYR9jEZffTcbj1wrvI2LTV7OKyVDOdX/u+AYAzaCydm5u8snxPL/30iZsZKe7Hj0p/JyiKMj24+eabMW/ePNx+++1e2cKFC73XruviW9/6Fj73uc95GdB33HEHWltbcdddd+Gqq64a7yErAtXWpVNpa02/fSphATpSOyPFb/iNZTTjKzcyUI6/5HhAQ17EfYRMtEd2956iejIGxcZCyPgQr71de3kfkOB1kkJvmddDx7MYzhlt3buQzRlnPU8WuDOfZq0bTJHWHRL6cuXxmwEAT29fxv0a2+PYdvY9zhjb41gnTyb8xz2rAADhxaydJakI9RNawLo31UmaNNrMZd/fcK4ZH+/rttN57YnyGOrmk/5MP8ua9DUXvQQAeLaTx9/2pl0AgD1/Yiv7mS+Qtn1hAZcteIXO0eBMPkfdJvXEngMA6JtD29NiPV7rU3Q9kRUWxwPz6dqgbkO3V2Y/C+7KU70y25v8fZCfI8tYYndUWyuKMhp+8IMfAADOO++8gvLbb78dV155JYLBIF544QX85Cc/QXd3N9rb2/HGN74Rv/zlL3XR8iRAtbWiML5ayMyTy3hsv0i+TII0dU5sSraQ9kvsEdEYcyiCObe51ivLzSNNF9nIEXNDi0jkHjOP47a37qE4kkCI56jntHQDAHbumsFjaWdRGoyaOfa4iI/oNfO4g6zv3TSNMdMmBK1jIr+jPAcc7iMdm49wWSBNZfVbeVyZWirLxlj32jn4VD2fj2DGRIUMiciLrGknJEzkfeIycrUm+nAPx147fSaCu5ZzS9xek/uX4WOz0Yz5fo7sdsw1RD4tzkGQzpGNAgEAV8SBeGXm3okT4RxD7z6JGLMTFOfcRo6IaA9fLR98V3F/Zp/7c/+3qJ4s82vXfs7HEu0x1mvvSvRbaTRqXFGqS0mxIJIzzjgD1113Hfbu5Qmvjo4OfPKTn8RrX/vaYfZUFEVRFEWZGrhudf+Vwz333IPTTz8df/d3f4eWlhasWLEC//Ef/+Ft37p1Kzo6OrBq1SqvLBqN4txzz8Wjjz5aqVOijBLV1oqiKIqiTGeqravL1dau6/r+u/LKKwEA8Xgcv/vd77B//36k02ls374dP/7xjzFv3rzKnxylbFRbK4qiKIoynZls2nqqUrZzxX/+53/i7W9/OxYsWID58+cDAHbs2IHjjjsOv/71rys9vinNcCvGCp4WMytKR1pt5uf8UGr/0qFhOPxW7o3mKUKLm+JHs4Z7Kkm6T9gyz5kCvDIyP8huEbleeqorKJ6Gy3XRyk/pVuGEzK+BWLF7cevVtE2UOScca/riVav2eyTQ3ceD7uym+vX8xEKmlcYa6uPjbVhP9fIJXgEa6aHXvQt4fPkwrQZNzuQuwuYwAxkuS9mF+SeIFaopGn9qJp8/u6QqfID7DS3m8edfonEPpHjtVajPuGeIJ+mytVRWv+yQV9a3nlYV/z7FT9flctTOGa/d6JUdSNJq5oP97HpRF6XGu5P8ROM7L/ozAODXW072ymqsY0YDr5L9+WNnAQAiXXyceZ9vt1CS9nHEebPnI9rH72vtRvqcyPfGkt3HK6zt58j3d058ZkfzOzlcmaIo04Ne83fMEo1GfbOhX331VfzgBz/A9ddfj3/6p3/CX/7yF/zv//2/EY1G8YEPfMCzMm5tbS3Yr7W1Fdu3b6/eASglodp6bPjp49E8deLn3nD4tpEYVsuK7X5uFSO1U2qZ3/j9xuBXJl0lLHKs0mHAYnW2O8Aa3DXuA7I9Z65xmOgST2GZ7fJJua4z6HuqbkeLVxbeth8AkFnIZYGUsGozJLbRd+Ys8DVCqp70WN9C1o1DM40DXBvPCPz5lcUAAHkGUs3FMwa5GSQiY5tYI9a9nrTh/s38dF3LYqGPnyVtPbiE22l+hsbVs4Sf8EuZtoMLuN/gjuL3pLeD2qtf0eWVvbCfzm+mjvd9dXMbACAsnsw7eBL1W7eedWqPMY3rPYufWItsoifZGrew+LfOIHW7i4aE+D4W192Lad9IGx9bFMYKo5v7cI07SaluFeVsL7eeoiiKMnVQba0ojK8Wcmn+MxAXWn2ANFpubqNXFu8kJ4GBNuEIbWRlpI/1Ze6vpE0DJ/PccjZJWtldLubLu6i/rbt5gju8g64DsotYD4aDxvUgy/q9toWvNfo7yaGhtpnLBqwJxH4xnx6h43SEo3HQzAsHB4WlvXnpinqumStONfHcuHXwiHYJ5+Mm2jmxj+eUbXuBNNfL1NEkdbyDjzOQLr6eCe83TnjCGcJtpgl/p4+P16ml+XR3kNtzrTuFcI2wd0Ad2Z69K5oRk+PWfSIv3DbMPRF5PWidJJwIz/vLezveuH3uvBZcp4rxHD5WP1eLUl0VR6P9fecL/MZX4r6VcqGsBHotpCjVpezFFYsXL8bzzz+P+++/H6+88gpc18WyZctwwQUXFHxRK4qiKIqiTFXcKubX2XYPf/rtxhtvxOrVq4vq5/N5nH766bjpppsAACtWrMD69evxgx/8AB/4wAe8eofrNNd1VbtNAlRbK4qiKIoynammrrbtK9MH1daKoiiKokxnVFuPD2UvrgBocn7VqlV4wxvegGg0quJUURRFURSlwuzcuRP1wqXJz7UCANrb27Fs2bKCshNOOAG/+tWvAABtbfT0ckdHB9rb2706+/fvL3KzUCYG1daKoiiKoiiKUhlUWyuKoiiKoijVpOzFFfl8Hl/5ylfwb//2b9i3bx82btyIY445Bl/4whewcOFCfOhDH6rGOCcVI8VzVIKR2vWN5xgmemDEMedzRUXDWRb52jGNEIMw4jHZaBKfsch4DjdF23P9HH8RNHZYjs+Np5y0HzbtyPgQb/iJBL9uoJtZweYm7nc3Wf7mhU17YN5sehESFmnNjUVtB5NktZVs5xiMcL+JMomwxVjkENmwuYv4OELG2yzEh4tsDV0Y1u7hc2Xtjpvv5T7651PZUKs4p2GyKstFhbXZTrbtdY4lS7HgXh5Drob2mbNin1e25xDZknV2NPC+bemi9hoXdwIAulNxr0xGf1j2dtM5tzEiAPDzp84EAJy4ZJdX1jVE7fQMcntOLZ3LTC1bqiVqhS2ZIf8sjTW9jC2J43+ldqJdvG9yHlnaRXrS3IexWiuImTnUWdTHxbOuAlD4O1BuZM9YmSi7MUWZVrgO/atW2wDq6+sLFlcciZUrV2LDhg0FZRs3bsSCBQsAAIsWLUJbWxvuv/9+rFixAgCQTqfx0EMP4eabb67w4JVyUW3tT6l6u1LxAeXWH834ytXWMlaj1OgEWWZjPAqsXE0/fhEgsg+7XY7Llsl4ENu21T8AR4Q4Naytc5u3AgBCc2ZzPdOf/CZtWkcxctnj5/O+7c04nHyUNL2NqgCAlIihsNTtoD4OncS6cfYj6aJ996wk6+K6bWxhW/d+yr/o+MNcryyToXp9C7mP2B8oriQqut8X4THHjGtuVGjTwXYqzM1grWm3p/v5uiI3gzR8uINjSMKdZnsb9xdd0wgAmPl+zuzY+hItpqvZw2e45zhqL1PLx96wiX7KCJKwcSTe8zq+HmjcSNcDXW/ni5JZv6D3uOMsfq/DxiY6+txWr2ztgdsAABe8/itemTNUrK1tPclkstZVFKVKVFNX2/aVaYNqa0Upxi+SwU1zLIQTI40YHOK50b55pFezcf4Oje+j173HsGbONpt2DrHeDiRJa6Y5NQ/zFh4EAOze3+iV1a+gKL2smAu20XahetbJySTHUITiNMb+bu4vGKXx1yzp9sqGXqB+siL2Ot2UN8fJx+SYQ3a5GmrM9HdO3GZINdIxW50MADV7qb2UiK6O9lA9ed5qdpnrrACXBcy9AjfEHbth0vkBEfeBZHHEBlJ0bhwRe27bdsJ83ZAf8Ln/YeJgcj18f8OL+XDzRfVXRd7D/zHRHW5GRJqI+0He/mJBmxfzIevxCL1X9+f+b2F9Webz+bXb5PaCeqa/kebi/aNzjhxrUnAvbpjIzfG4fzgaSh3XZB2/UiKqrceFwMhVCvnyl7+MH//4x7jlllsQEflKJ510En74wx9WdHCKoiiKoijK8HziE5/A448/jptuugmbN2/GXXfdhX//93/HNddcA4Ce3Lruuutw0003Yc2aNXjxxRdx5ZVXIpFI4PLLL5/g0SuqrRVFURRFURSlMqi2VhRFURRFUapN2c4VP/nJT/Dv//7veNOb3oT/7//7/7zyk08+Ga+88kpFBzdZORqfmhnN+IbbR7o8/K7/jlGNqQjjWCHbtg4Tboqf9PJWAFqnCwAwjhSucJXwc7FwgmYFqHgSOGf2cXPC3cHUk44Ubg+tsPXcKgDAuGK4YvWl20rLcnP1wn2im5wSgmnhBhGnfSKdvBo100irR0MpXiHpmGFlErwizD4R1j+b+43SYl/0HMf7ukHzWjg6IEftROfxUttkDz+pZh0rYsf08bgy1M+OPbzkOBSlNiP7+Wsk3UarbkNt7AzR+xLt0xXjp9KOO2knAGDDVranXzCPzu+u5/kxvAUnd9BxivO216xwdjO8NiyxmS6YnTPYpST4J3Kp6F3GK7HtmubcEJ+32AE6R50ncB9ZczrmruVz4NYbR5ADvHLafmYLPos+zit+q2T9nlCt1PfIZP8+UpSpgOv6LmavWNvlcMYZZ2DNmjX47Gc/iy996UtYtGgRvvWtb+G9732vV+eGG25AMpnE1Vdfja6uLpx55plYt24d6urqKjx6pVymsrYu9WkHPyeEyf63rNLautR6vu5xR8CeS3l+S8W6WPg5Yfi5aIyEdb1wffZ1G/l7yOmmn6FXdnhlg2cdCwCI7WN9aZ0rehbxk1nW0a1uQzfvu5A0f+wQ62ir+Zpf5uuLxkLzHwDAzr/MobGvYD0YfpbGOriE9eDxK8lh7YUt7HAx6yEeV+dy+lJPPMHHmXjTfmrPuF4AQM7cAHJF24lNVBYU8jN2kNob6GOHvfi79xeMGQAii0nr57Zzv00vkn4ebOfz0f0G45zXyTegal6g7QOzi5/FqP8tW3RYl7f0mfxUXNu3SWf3nbvEKzvzvd+g/sX7ihOXUr9bthf1MdJ3x2T/flAUpTyqqatt+8r0YSpra0UpF09TCScBOzdegN3uE6EjHR0GZ5PTQGIXFw5Gqb18gudDl64gzffKDp7j7Ryg+f45Ld1e2c7dNGe8avl6r+zV2pkAgIP97MwsSaZIZ4fC3F8gQOPq7WDdaw0c8rVcL3yQriFC/XycuRj9kajZJVwlclTWJxw62h4t/mOSnEnnoW4Hz7sPNdP5CInLHsf+IcoJx48GmqUOHeL5ece6icj3IVj83rhxM4fd2S06obEUuFlkqL28cCmx71xA3Dex13fyXkqBO4XXh8/T6j5uFwWuEn5O5T7t+F3nei4Wsr45l9Lhwms2xPco1qV/XlIfFbnW8DmeyXq9Uok5CWXyo9p6fCh7ccXu3buxePHiovJ8Po9MJuOzh6IoiqIoilJN3vKWt+Atb3nLEbc7joPVq1dj9erV4zcopSRUWyuKoiiKoihKZVBtrSiKoiiKolSbsmNBTjzxRPzpT38qKv+v//ovL8e7VL761a/ijDPOQF1dHVpaWnDZZZcVZYb39/fj4x//OObOnYt4PI4TTjgBP/jBD8odtqIoiqIoSuVwq/xPmTZUSlurrlYURVEU5aik2rpatfW0QrW1oiiKoijTGtXW40LZzhU33ngj3v/+92P37t3I5/O4++67sWHDBvzkJz/Bb37zm7Laeuihh3DNNdfgjDPOQDabxec+9zmsWrUKL730EmpqyPrpE5/4BB544AHceeedWLhwIdatW4err74as2fPxtve9rZyh192jEeplqSl2h6PBr8x+/VRiZgBGW9gowz8jq3UKBBf62IRoVFgCWXK80OporJArDjiI9jU4L1ee+A2AMBFtVfwrg1kA5zr7PLKCqI/bDszKK7CTbJfl5ske1ynjm1vHfOZzL26o2jfAkK0ZslGgQCAGzBlfezpO9RC9l/OENts5ZvJMrlho4jsaCPbtJ5j+Nc1YBbbxw/wN1msk153H8drpiIHTbudfP5SzVQv3SV+/Zt4DIEsWVmlN4rolCjtI+M+3B00rkyTsH1btBcAkAjxcW6Jk51bXzfHvezoJBvjWW0c4zGYIb+24Dy2Fd7zDMWGRI7juBd0mQiQJu5jcB6N39kj3q/5ZEsWPsTHmWqmsqA4dhvB4oqPZbTbnKNWtqAL99DnUq5IC5p4GTcr3kP5+R0Gv++OsVDN7yBFURSlelRKW0+ErgYqE5Fn4yvGSqV1fiXHNFIfpcYOjiamQ+pVq2Ocue3D1rPaeiTsGOR7aMcvNZMdY0j0kd29h+oNsPZLrTwRABDtYC1s40BsFIjERoEAQKqOeqyJcyTH4Ezap2Er14v0kG7b+jbZHmnE2u0sCMMmDSTZwRp25h7SiME0R2gMzKfX0VrWgL2X8msbB9K3hPViup+uAzIrWFvXPBvH4dS9nuI+9m/maL7+BfQzeojHeqCT+mjcxvtml1F/WR4qghHS+VbbA0BkE/WbmsHnKGfqZVhae2UDs9n2dmgmXWPU/J47CaToxNVt4GNLtZmG2mZ5ZXnzPsmomEvm/QMA1bKKoijK6DnatbWiVBsbneBEWDPbmL58mBV8tI/0cbKVNWdwyOjBk8QcesrEYNSyM8zO7kbqI8Ca84zZNJ/+1/0ctz2rleaF8yJ7JJWl8WVy3G9dnPuLhknjprO8PZkiLVrbytcQ/S7pz6ZWnlPuGaD56GwdR1kEUmYevEFEp7j0OnaAy5Iz6VhinbyvqYZUE48lYCR/UER+Z2vMnPcgXw+EO7rpONsbuWwvlbm9HEvo2HjFBEd62zgQJyzew4HiaHVvTOK+in3fc739YrvpQ8xv+8Vee/EXMgrEEVd9Nj5bRHbYiJCC+W+fTAHfe2x2X9neGK4TSr2f5ld/uHuABTEow1zL++3rd66qyVS8f1CJOSlFGQ1lO1f8zd/8DX75y1/i3nvvheM4+OIXv4iXX34Z//M//4MLL7ywrLbWrl2LK6+8EieeeCJOOeUU3H777dixYweefvppr85jjz2GK664Aueddx4WLlyIj370ozjllFPw1FNPlTt0RVEURVGUiuC6TlX/KdOHSmlr1dWKoiiKohyNVFtXq7aeXqi2VhRFURRlOqPaenwoa3FFNpvFP//zP2PZsmV46KGH0N/fj8HBQTzyyCNYtWrVmAfT00OrFZub+Ymm173udbjnnnuwe/duuK6LBx54ABs3bsRFF1005v4URVEURVEUZaKoprZWXa0oiqIoiqJMJ1RbK4qiKIqiKONBWbEgoVAI//Iv/4Irrrhi5Mpl4rourr/+erzuda/D8uXLvfLvfOc7+MhHPoK5c+ciFAohEAjghz/8IV73utf5tpNKpZAS9ke9vb2+9UplNBa940G5ESAj1S/ZPtnEhtjIkCP2YaM/ZASI7UvsK2NI/Lavir8PQGHUgu3HRjIcqSy77wC98LGxWieO1/bhBHmsuS66aArFi+15/XCiwm+331jgdnHkhWMiTNx6tuGyMRTJeXXcb5zWO/XN4bK63WSr1ryBj2OogepFBtiGa3AWlWXj0lqLVpHlw1wW7aSyQIZXmGUP8fgzJw+YMrYbC5gIjkw/17NfHoEUr9Ha+th8AEC6ja3gYLZHZrCFW3obWbMdaBKWZhmqF5/J1tDpebTPwH6O57CjnjmDLdIO5ul9D4T5fMSNtXK6iY89NEh7R7r42AeNO3EwLSJWdtO5juwb8Mpy5r2TK9KsXVpBFIi1QJOfbVNW6vfJkSy6JtN3kKIo0Iw5ZcxUS1tXSlcDY9PWlfrbVKrNY7n9lWzVOQbrzJHqexEaMaG7xOvDsTEcgH8ciN++rojd8Mp27R22nqet/SLwRhiXF7kn2vOsZhtZ4wbjiwAAazfe4pXZWIgC4tReaF9P0aaaFGvE7BLSl/vOktF29DNTJ3T+ImNd3M9f4rP/TJquZxHXq9lDujLVzOrPRmIMLmDL3Myf5tIwD/G4hjjFw4sDcdKsP9MmaqRuPv8+hfroHIU72N63b1MLtc3NIVNH4x5q5zE4naTRu5azFp65phEA0HuuiNJL07FEmzmyI/YwvScxETMyYNyaY+KY7Lmc9Tzr/J5FNNZIH5/LHvM+hIQVso1iQYrHEuwmLZ8Xn9n7dn4bwMixecP97qomVpSjFNXVSgWY6tpaUcrF73rGNZEMch4cWZq3zMW4zMmbODxxyWEfVnaTfEvJiZPOi8ZYI6aGSCPOaOboiQGTVbewsdMrmxWj7c8emOuV/f0Ccod5oX+OV7ZQCO1HDh4LANjazYK7rZl+j/Ye5AjxYAPpzp5ejvjLN9AYozt5fjtbQ8eZE/Pp0UN0oK64c2aPPRsXcdxGA0f6REyGaSaQ4bJcxMyNH2QtnGlrBACEDvD8ths3gjssOs4YHd3N9bz6GTH/HvB5kjxQ/Fy1a+awHdGHFwciPhP28+E4xe0WXofmi7bL2A+/ez88vuL7RvDpr+Czatst895Ywb4iisNv31KjQsrd7juf7xMp4rdPpeI8JlOEaqWY7OObMFRbV52yY0EuuOACPPjggxUfyMc//nE8//zz+PnPf15Q/p3vfAePP/447rnnHjz99NP4xje+gauvvhq///3vfdv56le/ioaGBu/fvHnzKj5WRVEURVEURakE1dDWldLVgGprRVEURVEU5ehBtbWiKIqiKIpSbRzXdctaw3Lbbbdh9erVeO9734vXvOY1qKmpKdj+1re+texBXHvttfj1r3+Nhx9+GIsWLfLKk8kkGhoasGbNGlx66aVe+Yc//GHs2rULa9euLWrLbwXwvHnz0NPTg/r6et8nWib6KZdSV4LJlX7W3WGkfceyis93DCWuoBsNvu0M43xhHScAwDXvuXSuWNv9IwDARbW8Yj0/WPzknrdPVLhKDJBjgZtjt4iArSc+X2ihVbJOttgdIzeTx+Ka1aMD83m1bN1mWnGareN+M7W0ajQf4VWabpBeZ2NclknQa0d0a50wZFn38fQz0cH75s3DcAPLeZVp/GV+Ysyuyg2LxfO9y2klbLhWrLDtoXE7WW471kbnbahDfC/U077uIK+IDdZRWTAkBruZ9omd2O0VhYK0Pfn4TK9saAmd/ybhXNHTS/vmhvjzUruBViGnmvkrLtxDYw2Jj4E9Hy3P8vvq5GifcDefI8c4ksj3Ord3Hw7HlZ+Pwxiv75iJ/k5TlGrQ29uLhoYG7+/5RI9j3m03IhA/8tPlYyGfHMLOq/55wo9VGR8qra0rqauBkbX1ZKJUVyjLWJ7aKFWDy23W3eF3gz8tqV/pBmH3kf36uVTIp4j8xmXb9HO98EM6V+QOdRaVOWYM2d17ivYtcOMo0QHDIh0uso3k2yCfpItup7H0ndzilVl3BLtNbg+m+Imq4BC97ngta/C8eXgtzA/XIWg+9gOziy+XgynWvwEjj6WbRX0b69TsE01F+w8uSReVrTx+MwBgczfr3oPrZxXVs7o+LNwiMnXFT3hZ542hGfw8Rfdp1G/b/azLu4+j7ZlaoZn7qb26bVxWt4v23fkmftKvbltRt5ixnjRzIJUt2hboZlc461ji58aiGlZRqs9k0NbjoasB1dbTDdXWisLYJ/QLnvw3r4PNrFEzx7TRz1rWiNZdufsYLvPmlI+Rjgn0I1TDZa3NxY4s9VHSeac27vLKBo0IP5iq9cqW1ZLD3nO97Gbx5KaF3usTF9F1R3OUdeXj2+h3c/kcviZ57vljAADhWezYlttB3wf5KF8bJHYbhwYh+bPGNs4RcjZkmqnbxftm4qSZo8K5IjSYN/tyWT5KJ6nmpf1eWXouXR8FB/m6wMnkCn4CAA52m0ZEmXGzsE7ZAOD20Dl3hAt3vt/c30hzHwFz/0OWWecKJ+TjZiFwjZtJwVjEvRvH3P+Q91M8FwvpSOFTZh0cpKuEVyav5c0+0vFhOHzvM/m4nI/kXDGWeQDfcZnj9HOuqMa1kF9/SuWYDLpajkO19fhQViwIAHzsYx8DANx6661F2xzHQS5X/OV0JFzXxbXXXos1a9bgwQcfLBCpAJDJZJDJZBA4zMIoGAwin/exHAIQjUYRFTfJFUVRFEVRFGWyUiltXQ1dDai2VhRFURRFUY4eVFsriqIoiqIo1absWJB8Pn/Ef+UsrACAa665BnfeeSfuuusu1NXVoaOjAx0dHUgmaSlgfX09zj33XHz605/Ggw8+iK1bt+LHP/4xfvKTn+Dtb397uUNXFEVRFEWpDG6V/00i7rjjDvz2t7/1/n/DDTegsbER55xzDrZv3z6BI5saVEpbq65WFEVRFOWopNq6WrX1tEK1taIoiqIo05pppq1LoRr6u+xYkEriOMX2pQBw++2348orrwQAdHR04LOf/SzWrVuHzs5OLFiwAB/96EfxiU984oj7S0ZryTJcxEWlrHkmyrp/uGgUSblxH6OxVpY4Piu31yXvpH1EJIrXn4kHOdJ2D2H1FEhQLIe0uUKYXjsRtrh1fWI+vHaE5ZYzQBdVbp2wGQwZO7FBtlt2TZkbE/bDCfJSy0d5LKE+sgbsP4atkGMHyKYr2crjC2RMbEU/j7PzBGo7y8kjXvzF0ExRZuyOc+wShiA7pKF/qbFxy/DvV/0GGmPfCrYujG2i/pLHsI1YpIOOKd0kzp+xWou/Ksb/mh4AQP5pPpfZOjqmbJy/kpqOIVvnuij3sfvZdqrXzPZk8SY6gNgf+Lz1L6Cf+Xki/uSv4qBtv+Z81ezifhMHaPyhJD9tEOmkdgK9fLLy28nKLlDL77+1zC7Ax3ZsLL8v1fzO0EgRZbIy6SzW/q3KsSD/3+SxV1u6dCl+8IMf4Pzzz8djjz2GN73pTfjWt76F3/zmNwiFQrj77rsneogKxkdXA/w7cB7ehpATLvvv2WSK4RspjqBaY/aL9pBRIeXGGPrFh4y0z1jOh8SOX8aMBBfTk52DS1mAJh7fAoBjRADAjZuIuWSqqCzbyhoxtI90I6KsJS2pNrYQDg6RzpLxIbasbz7327CJxHDPEt43ZyL5ZLxG2sjKoEjw6DuR/lPfzDbE6WeLYz/SS4ReTJvxpPm5hsYXSVv3nsX16h8nnTo0g9tp3Eha9MBpPK7QAhq/s15cLxyin35RIbK9yIouAMDApkavrG471cuJS7G+JaSz2x4SkSImPsSOSZITkYbWFtkvisW+HwAQ3ka2yDJSxkbOuAOc4VdqjM5omOjvJUWZSCaDth4PXQ2otlZGx3hr68ny+VSODkrV+Xb+24tzABCI0NxtoJXj53KzSHtnGlgQHjyJXgdEOsTAXGrHFR/v/Gy6DpjT0s19mIyNpY0cp9wYJt17XKzDK/tD5wkAgIWJQ16ZjQp5ubvNK3t1H19XzGyi+L1cnnVqWy2V7erha4juLjNX2xv2ypwcDTw4VBzxF0hzWWKfz+0yI22DIhEl2kva1hW/77GD1GC6kfsN99FJjBzgawg3ROMviACx9yOCfGxOt4kbjHB7ron7kPcybNSGK67L3JwZtFxYZmJh8v2ch2hjY+TnxDfiw4dALV9TuWbxWUGkiJ0Td8U1hGOOT8Zz+Hxn+kVYlHqtXG4Mhowjseey1Oto3/ZGUc+vfrnRpyP1N1GUej6OVibL3/Lpqq1LoRr6u+xYkC996UvDbv/iF79YclulrOtoa2vD7bffXnKbiqIoiqIo1ccx/6rV9uRh586dWLx4MQDg17/+Nd75znfiox/9KFauXInzzjtvYgc3BaiUtlZdrSiKoijK0Uk1dbVtf/Kg2rq6qLZWFEVRFGV6M720dSlUQ3+XvbhizZo1Bf/PZDLYunUrQqEQjj322LIWV0xmylkNdvi2Uldf+TlDlLtySzo2SCeHkvqV+5Y5Zt+xyPPj86S+daaw7hEAkB8ST6qlUoX7Ario9goqko4PZh+7DQAc4z4REO4XedNeICpcJSyyXm8v9Z/pKWovOItXxtrVl+jhlZ32Cbp8PTsiWGcDV6wotdvdAH/xOGY1aCDFqzn7FtMTaJFePm9Ds0wfQbmv6SvNqz5jnfTa7ebhJWfQStDEXr4oTLZQO7GDXDY0k9ue+Sgd+6FTiy8k3SF+b/JR2i4dKYbaadzxvfzVcvLFrwAANrXwuex/nh6hy7Tw+MNt5gm1JJ+3zt2NAIC+Bv6c5CPUb1T0ke6hVbKZhTzW7ExaStz0CK/Skw4eltrt1F4ww8drHSvk+Q10m9XF+3k1tf28uUleFWyp1JOvI+1T6SfupuLqUUVRxkZtbS0OHTqE+fPnY926dfjEJz4BAIjFYp4trjJ6jlZt/d89PxlxhfpkejrBr/9KPxF/8ayrvNd+TlZ2DGPRA/Kc2qf8JdJ9wrpKlOpCJ7Ft+/Unjy1gypxGdlFwt5ClYnzXXm6whvS/daYAgHwjafSgcK7oOqMVALtLAPB1rMjHw0Vl1p0iKnR0x2upv/ptrOnkE3mWxs30XXboRNb0s54njb5nJWvOsHVp285uFUPtVC/cyTpZ4vSbJ8Eixdq65llxDZE2T5udyMe+bwadt6YXWaufsHInAODx7Sd4ZbFDPu4TC6zTHe+bfYLGLc+AdbsICIeO5mdozAdO47KGTVQvlOLj6FlE9Rq28jmv2cFP5FnsOQ91898M+VmwWGeTtQdu88qq6S4x0d9LiqJMT1RbV5ejVVsrSjmUqmGsE0FAzFEjbF4Lp4Cc0da5GDsm1G8nfddzDGvcnNWz0rligPbNCCeJv5v3LADglDhbrT/Sv5Tay/F9gQ+1PQwAeGJgsVfWbwTt29uf9cp+lFzpvV7SeAAAsLGrxSvbtJ9cOOLC+Ti0xzji1fF1QD5q53v5mJyscWjg4SPVRGVyPt1q4ExcuF6YueS8OL2umb/Ph7iefW3dKgDAGaRrILeG560DXeYeRQ1fI9j7EY58D81756b5eB37vkr3CeOAUeA+YV5btwqAnSYCwmUQgUBB/3If6XCRF45znjuFdKHI+zhfuMVlw7lU+OE31+BbJh0pvP6Lr8tKdakodb6/EvfaRto+kdcy5V6jHY3XXZNpPksZO9XQ32Uvrnj22WeLynp7e3HllVdqppyiKIqiKNODambMTbLsugsvvBAf/vCHsWLFCmzcuBGXXnopAGD9+vVYuHDhxA5uCqDaWlEURVGUaU21s5tVW08rVFsriqIoijKtmWbauhSqob8DI1cZmfr6enzpS1/CF77whUo0pyiKoiiKokwSvv/97+Pss8/GgQMH8Ktf/QozZpD70NNPP433vOc9Ezy6qYlqa0VRFEVRlKmJauvxR7W1oiiKoijK9KUa+rts54oj0d3djZ6enpErHiX4WdsMZzE0FmuYsdj++0WBlGqLJPf1s1mqxLHJiA8b+7HuCO2tir+P6mU4JsNGgNiYDom0tFqXvBNAYdRJsNbEgYh9nQjZCuc6u7jMWFoFbH1Z78BBPhS7vYktsF1j1xXYvo8H1kARFW5tcVTI0EK2EI7uIctfR1iC1b1Cv0OHXsP1IgNkqSVjKyI9dI7SDWwJZu3LHHY7Q/wQ/advHvdh40BkvXQD/yeYDJjt3F/GHHLtq3wuM7W0Pbkww8dpbI/zYd73L08dV9Sfa/aNdvG4Ug3F9s7BOmq7sZ4txg51xIvrzaPtmQ62lqvdQO9h9/HcsT2mxlfYnixdb19zmbVWzjTzmMLd1K8TmMUdH6LPkRdpA469sZ9nKvS3iQbGbjGltlSKMkFMI+eK3t5efOc730EgULgmd/Xq1di5c+cEjWrqMxW0dal/o0b6WzicTvXbV0Zj2OiPUvsoVfv7lckoAz/8+rBRIgVRG9KO9bB9Jba/cmI/bN8ywsTuL/v1izVxjeWrjCPxoh06DnhljokAcUR7Xj0R8WEj19whjldrfnhHYX2xT6qtlsdnLItzUf5eSuwn3Rh9bqtXNm/fbABAzxLeNzhE1xDpOh5LspX6Sxxk3diziHTgjBeEiDXIuIy560g37lrF1ybBvXzs777gzwCANb9+nVeWNikqmTr+wq/ZQ6+706y3Q/10fH0Lub9n/2cZAKCuj8u6TyNLYBtBAgAzn7bniPsYbDcat5bLYibxLtLHZV1vp/dm1hq+PrLnV9pF5yLUn4wC2XcWXTg0v8z6OPYKRcQMnDqHj/e53QCA4IlLvbL8FraOtqjWVZRpwDR7uk619cQwFbS1ogyHnRP3iy1wImLOu6kBQGH0RDBJOi/Zwvq451jSeel6ETPRRro94HBZfYI0XzTIWnh3qhEA8MeDrPPe3kquMo/0LPHK9pkJ5/0pjhh82wyqN5Dn64HBFM/P7h2k8ddFWWtaUhkxbz3T3F/I8nxvzTbanhNTy+lm0vqhPq5X/wodX99c/p6Om1sEsR4+zqy9JomIOeX9JjJ7v5hr76ex5mr5mEJJ0u+BLhb1rrnn4PSJqI2mRtrWL8pMjAfCxfGJ8j6IvX6z9zkAjgCRBE3cp4wZ+V3/HQCOcG0tojYK4kXMqSnoT7TJhebzI+bLvTZlZIfd7sqbCjaahs+533V2qbEVtt+Ca2rT9liuQ0qd769m9IR8n/xiV0Yaj7dvibEn1YxznAiO6uOYZtq6FKqhv8teXPGd73yn4P+u62Lv3r346U9/iosvvnhUg1AURVEURVEmJ4sWLcLevXvR0tJSUN7Z2YlFixYhl/PJ0FRKRrW1oiiKoijK9EG1dXVRba0oiqIoiqJIqqG/y15c8c1vfrPg/4FAALNmzcIVV1yBz372s2UPQFEURVEU5ajDdehftdqeRLiu/5Lk/v5+xHyesFfKQ7W1oiiKoijTmmrqatv+JEK1dXVRba0oiqIoyrRmmmnrUqiG/i57ccXWrVtHrjQFGM72ZTJYwpQd4zFMLMER9xnLWEzkSEE0gvm4XVR7hVeSHxSWVmaMfhEgfsgYD2ttHDTWZgCQ7yd72mANR0VYK6pAlG248ibSwRHnKN/TW1TPs7QaZOti10SAOFle2ZRpISutUHeS9+2jsUQ62XcsM5PGFRwUsRpZsrma+eQhryzdQn2kRERFqonOUXwfW2v1LqS2B+byF8XMZ038RjeXuUFjB8ynDy1P8vbOE+l1Yjdb5AzNorJ0g4gmMZEewR4+b9FDVJZuFFbD+wutdgBgaCmdw1yLWBG2n8bvRtnqK5ehfQ/tn+GVBdI0/kCGv8Tdl+kc1fSLPsyY617l/pPtVNZ7DNdrfZLGkA9ye6kmOqbwQLENtCPffzsmn8+TjAoZjtH87pVq0aUoijIWrr/+egCA4zj44he/iESC/57mcjk88cQTOPXUUydodFOH6aKth2Okv2Hl6nJrVwqUF5kxmv5HQ6X+jvvFeeRFxIZfhInfPn772ugPGXViLWFl3Ed2M31+g4sXcUMmxsMv4sE5dkFRvcGzjvWKEhvIb7fvZH6aIL7PWA1v56gSJ2n0ViNbCNvYkNSpPBYbAWLjRgBg6Ph22iak2r7TSS+2PlWs/XoXspbMm8uBBWt55z2vM5Ei4nCbN7DGXdNPcSBD7WzBO+sx0poDs1l/dlxorgnS4rptPr0nkU18DTG4gNqJvMjXTHXrI0XHdOhCuhZpfFjEvZiIkABffmDGeqpno1EAIPIEnddoL1fseC1tj4g4EouNAgE4DiTdIK7pzDmPdGdwOE63sEI2P0cT7aMoijLZUW09Pqi2VqYTqyIiJz5P+lNGAVjNL28quZ3dAArn39060nmZBOvecK/RjWnWq4Nh0qT5eTzn3f8KRVs3rNjrlZ1RS7+HS+Ico50Dtf2W5r96ZTUB0o3bojO9sr/006RtrRC2r5v3qvc6maP58WyeNXMyQ2WRGO/Tk6J7BE4Dz52nmk2cdYTPR9MLdHxDM/k4O0+g102v8LWBnSPO1IhrA3MKAyJpI9lG5zzcy4VJc68gvp/H5ybonDtpoY+zPk9O28j0hIhc7KFjkvGKXvyifPra2N8XvP/m3ogT5+sL7/6MiPi4uPFDRUOx93kKokBErLidHy+4HrcR7jLaw0Z6iDIbW1FwrWy3i/E7ITrp69I/5z7MPn7X2fL34fC+ZNt+kSIjxYPa4/Cdc3CKbz77tVeN65qxxHNU6zpLr+WUiaKa+rvsxRWH09vbiz/+8Y9YunQpTjjhhLE2pyiKoiiKMulx3cIoyEq3PRl49lnKOnVdFy+88AIiIjczEonglFNOwac+9amJGt6URbW1oiiKoijTiWrqatv+ZEC19cSg2lpRFEVRlOnEdNHWpVBN/V324op3vetdeMMb3oCPf/zjSCaTOP3007Ft2za4rotf/OIXeMc73jGqgUxnRnp6zW8113BPovm2k88V1fNbkScdLqz7hO/qO+mEYV7b+gCvSpRlh287HLti1s3wys6gcafI+7gAWGcKgFdM2tWLAK+WzO7t8MpC7W20b3KoaF95jgINtKI0u+8Aj8W050T5F9Dihvh8hDbspHriFzWzqJW2bdrjlQ2dSytx615h9450Kx1v92Let2Fr8RNekd6cqcerVu1TYg1beAVtz7G0clcs5vVcLByxkPXQybyasn4z/czFXLEP/RyYy23HOml7PipWMJsn6RrFk3Tdp5jxC6eJ+CYad7aG+6jppO251wr7ib/S+5Cp5XrhXqqXPIZXHsd20vlKzeR6uRpa3dp7gjiO/XQiAikey0AbldXu4ROSitFxOjE+3lyCzqWT4n7defR5CuzjJykD5nOcE59j+9ku+Oybz9tIqzUrvapVV4oqilIqDzzwAADgf/2v/4Vvf/vbqK+vH2EPZTSoti6dkf6GDfeExljcMUaqP5y29tP0Iz4BY/BzlyhHN/i5VDhzyUHA3cVPltk2rRMcwO4U0kHA27erh8tOX05lL27mMuMaFzDuFwCQWUhOFM6fn/PKrDtGpJGfmrKOFDU7WOfno6Sj8o1suza0lJ5uSzy+xStLL20EwE4XANA3n45j4KyFOJzYweIZAetgAQCtT5FWS9VxWd9C0pCDLewol15CT+7VP87HsestrCvb7qd9BvpZH0f7aHumj0X6YJr6kTp6YLZ9YpDH6JinB3uO4z5C/bTvjBf4mIY8twsuq9tmj4Pb23Yp1cvW8pNjrY/R6745PJZUM7VTw5czyEWLn8iyjhV+72EgxfrYPmnnDvC1kH3KTX4WLaP5nVMURZlMqLYeH1RbK9MJN5sdvkKYNGtAujrXkabOzajlMvOUfSDHujEfIX0ZSnJZttE4qAVZN550ziaqL2zi92TIzWJzkt3oPjDjzwCA/+o6wytbGCPn5kSA9fvJCZpX/13Xcq/sxYPt3uuV7eRikc6zTn22by4AIJMRjgpBGrc7wLo9MkhjDB3isWbMaQhKE4gu2jfVwPWSM6i/aA8fu2Nexg/w3H0uTuct3M9lsR3dAIB8HV+XBbrIvS3fxE58gUPkpA1xn8GNmvnoJJ8j6zrhBISLRi+1F5jJ12Doozn2nHErAYBAjN3qPMy9EenC7M1r+7hLFDimCOz9G7ndu7/kMycu7+N4DhM+jg8S38+854TBn1XvOls6V/jdEfbpr9TriQIHjHFkpOueiXKfqFYSgN7LUMZCNfV3sV//CDz88MN4/etfDwBYs2YNXNdFd3c3vvOd7+DLX/5yxQamKIqiKIoyaXGr/G8Scfvtt+vkbxVRba0oiqIoyrSm2rpatfW0QrW1oiiKoijTmmmmrUuhGvq7bOeKnp4eNDebDN61a/GOd7wDiUQCl156KT796U9XdHCKoiiKoijKxDIwMICvfe1r+MMf/oD9+/cjn88XbH/11VePsKdSCqqtFUVRFEVRpg+qrauLamtFURRFURRFUg39Xfbiinnz5uGxxx5Dc3Mz1q5di1/8gqx9urq6EPOxrj3aGYvtzGj2rbglsY3n8Kk3kiXxcBZD0srJLz7EiRZbTF3c+KGC+ofvY62n5L7D2ZvJ+JCAWXWUOyTiGYzNVSDB1me5zi4q8+lDxowE6skOKyjsjJ0aY60rYiEQMmURthhDUwP9zAqr3v1k6+W2zvDKYgfJHixXz2OJbaEYkjZ2OEZuJh1bcibbtYVNckYww8vEgkk6joF2tiQODtH2eB/X61tg7MlEWd1WcUgpKs/WsLGNY9oRLm1wg2SbVb+F2+kFnbdUE9dreoa+ZrpO4vMRNO0k27ksvo/qpTewHVokVdgXAGTqqb/obj7nQ2107Db2AwDcENWrfVVExRhHNnteAKBxCxUONXE918fTJ5A2Y53FBxfopjfCreFzDvM6IKJn8kN0IEf67B9eNlIs0Fi+l9Q+S1EqhOvQv2q1PYn48Ic/jIceegjvf//70d7eDmcEm0alPKabth4LlY72kJEXNo5gJH08XB++UXpjGGd+iLWEjfaQfViduvbAbUX1AD4miV/cgj0Psr/g4kXFAxJxIBb3qRepX6GZvW1x1rjhbfupD/mZNprZbgOAXDu1k2ngfW3MRDDF2i+2j6I4ssfP98rqNnQDAPpMPAgAJPaTzkvXceRe4xbS8p0niPGRiy6CQub3zyZdmROXNXXbih/RyPfTuIZY5iOxifsbfDddfwxu4nGF+0hs9p6V9MqaH7bnRtgxm0OWmrl2u7lOEbo8kKZ9Os7lCYLmZ0yESTt/Zw+1k2ae9Rhr5k7juDzzaRbA3cfRPgt+28v1TqNrqqGZrJlnPUeDsO8RwLEsPUv42qVux1BxWZTeu9ArO7yyS+b9Aw7n4pM+Ty86OK6xVBtajQpRlKOEaupq2/4kQrV1dVFtrUwr5PeHX+SBwUZGAIAzSPozEGe9mjNxFQExz2z1ZedrM2Jf0pDpft43Fiyeuw+bLOq3Nj3rlW3LUKzf+5sf88oeT1Jk9vFRzp3bkqYokYYw6+TGOL9+oWs2jTnP2nVGHc3pd3TyU8mBOhp3LM4CP3uQtqfFHbHMXNLPDRtZH0f66dgzNXx+Y91UT54jG5mSi/JYIl3U39As/r6JBkw78v1qaaSiFJ9fGwGCbtbgaKZ67oCIRzdx5jJeL9BI11buoS5uz3wmbOw6AMBEV+d7uI+AjWXPiQxx21eE57wvqr2C2s3zOXACfExu1kSxiPs5XiyHT2SHxO+adLgIzYIye15FjL3fPqVe33vbxftlI0DGcl1RcH/OnJfRRIuUOoZKx2lU+h7FSOi13BiYZtq6FKqhv8teXHHdddfhve99L2pra7FgwQKcd955AMh27aSTThrzgBRFURRFUZTJw3333Yff/va3WLly5UQPZUqi2lpRFEVRFGX6oNq6uqi2VhRFURRFUSTV0N9lL664+uqrceaZZ2LHjh248MILEQjQyrxjjjnmqM+u81tpVanVaJWoN9yKO/m61BVjfm4WftulS4W3TTyBvyr+vqL2bJkk19tbVCZXFHoOGD79WVeLw/fx2j5woKgsPzhYNC67wtKu0gTYKcMRfTgh00dQOCEM8CpZHouPxUGPcTMQDgf5BK3oDXbw6tFwiPbNR7iP1EJaxevkxCrYPhpX/CCv+kwbZ42QcGAYnE0rYmt38YrcAyuoLN7JT7GFBgt/AkCWzT0wOCtQVGYdH/Liyb28WUianMWrvKLm8JKtPC63yzHHUfy+wef0hfqLV43lw3JVsFM0/vot1HbvsVyW2G7eY+Hw0/QKHciOi8X7GqT3Ji+GFx6g/iK9vGLXGTLnP80rinN79wEAgrNmcnvG2cSp4VXB9jALHF/8Vt/6fLb9GM33kq72VJTK4rj0r1ptTyaampo8a12l8kxlbT0ejKR7h/u75+fsMFIbw7lMjeRc4esy51Nmn9qR47OOE/eLsuHc6CTSocOpIYEnnwiySNcLd9feonpWz/zOZwyO2DdvnOQCPmXO3PbiPpYv5npR0m/R7exGF2ylp7DSjexcYZ0tYq/s9cp6z5xH9VOF9o6Hk4vRkcQOcb1wH+nLoZni6bRDxe1YZwgnzfUaXyy+pO5bIDR6p9GEdawrh2bQPvWP85NgnWeRhgx38HHmZpDunPsb1oj9s4v7qNtO4wnUsk7tW2j34XrWsSKYFu4YUfew+kDrU9TOzgv56b/mZ8wTfGwy553LzqW8bzPovWnY1I/DsQ4WABDqpmsr6TwY9HFAKXANLBPVvYpydFBNXW3bn0yotq4uqq2VqcZw1xAFTgg+T+Faje4KTe9dD4RZvw22FTtQW10cOiS0aSvNUTc28qTs5m6aE71yITtSnJMgS+Y92Qav7O/raNL4ngGeL58dprIXhuZ5ZbvS9P24J8n7ntDQwdsHGwEAB5PsiNaTpONc1HqI6/2J2qw5gzXpoQT9QYgd4AnpfMTMz4sp2VyYjj3cL/6AmNNr3SoAIJugfQNZUa+XKkY7WcMGB0lb52PifkSa5pmdjHCLsPcrHDFh3tlNRY18PqwLRIH7t3FNtq4WAIAk6W1XuHUjTeMKiHrWAcOJ87VJ3pTZdgF2wAgIE6D80PD3bOCa66cQH7vfvR/kc0XH5FHwOfe5mWD68HWMHsHNYtjre+u6cYR6Y5prH8ZpplKMx7VQNfvQa7nRM920dSlUQ3+XvbgCAF7zmtfgNa95TUHZpZdeWpEBKYqiKIqiKJOH//N//g+++MUv4o477kBCRG0plUO1taIoiqIoyvRAtXX1UW2tKIqiKIqiWKqhv0e1uGLXrl245557sGPHDqTThU+S3HrrrRUZmKIoiqIoyqTFhXwYuPJtTyK+8Y1vYMuWLWhtbcXChQsRDocLtj/zzDMTNLKpg2prRVEURVGmLdXU1bb9SYRq6+qj2lpRFEVRlGnLNNPWpVAN/V324oo//OEPeOtb34pFixZhw4YNWL58ObZt2wbXdXHaaaeVPYDJxHhY7Y9k2ztceyP1MZIV8XDtDNt2PldUNNJx2BgPv3pH2tdv/G4qd8Q+ZPRIoNZYY2U4OmNt94+K2zVWUE6OjyngYzeV76EIE1fWM9ZXua4erywUpUgJN+kTGbLvoPcyGKF6MPUBAFmyrMrXcf/hbrJpy4oyGxuSjbPtVWjgyHbHtg0AyNSST9dQA+9b00HHNNTIZbV7+DjTdaacndTQN4/KInzoyJvvn9gB/jZNN5JNV/1mrpdqNOMScR85s28gyWOwUSLRTq7Xv5QugmM7+bwNtdB7XL+Jv75yxo6seT2PxfabOMDnqnch7dP4Co8vZiJT8uL7NDxI7SS2sE1xdibZzUmDs8CCuQAANy/eD/tZ8LFCc0Qcjf1cBsRKORtlMxKj+R4p9/tmNGj0iKJMTS677LKJHsKUZipr66lIRSL3RtDHfqw9cFtJ45MRIDa+wy/Go9T4EAgLYRsR4jfW+3Z+23ttI0xy7Wy5GLDtCB3tRYS8yMIxbGIh3Dhr4VyMNFX8Dy94Zf1vPgUA0Pn2hV5Z+wMkXlNttWJf0pqJg6zVehaR6JOxGs0v0s/BNi4LpmjfcB+XhTvNWJZ1e2UD/Y0AgEwz6+nmZ1gHDiyg66J0h9B8RtoGRDyHjQMJplgLZ/vNdUCUy+w+iY5i6+fIJrbynfkCjUdGdgyYSBEZ7VG7PWD65bK+OaRZmzfwMdm4lcQGvsbZezG9h7Oe5zgSy1ArjyWxja6tAt3Ckth8FnyjQJrY9njtxlsAABcfd4NXNtzvl/wc28+salNFUSYTqq2ri2prZapRro4J1NYWlTliPtqtJU2abOc4Y8dI5eRMnqd1jYTMzhA6L0kaMTyTNeIJzRSZPOTyxOoxZvrzhRSP5SM7TwcAHFfDER89JpN6X4ojKuJBmgs+p2mLV7Y/zdtf3E2CNtPF1wuRGaT5Nm/iCMLQcTTHenAra82QifZzxDSunYeWNvdWqwfFoduokIFW1tZRc50QTHKD6Xo6D5FuXtjlmmjwYCdr4XwdTWY7A3wvw4sFaeLjdcy9jvxBnqN2bEyGjD2388tZbs9GbOT7OBrFMRHo9t4HADjmvoUT5Pc/EKN95b0RL/J9hJgOOf9dEPXuFeYLxgIA67I+URw+8SG2bVfcA7o/938BFN4rGjbu3ue6XfZr27M/j4RvHzaipNR9R5jj90Pn3xWlfKqhv8teXPHZz34Wn/zkJ/GlL30JdXV1+NWvfoWWlha8973vxcUXX1zxASqKoiiKokw6XIf+VavtScSNN9440UOY0qi2VhRFURRlWlNNXW3bn0Sotq4uqq0VRVEURZnWTDNtXQrV0N/FS8tG4OWXX8YVV1wBAAiFQkgmk6itrcWXvvQl3HzzzRUfoKIoiqIoiqJMVVRbK4qiKIqiKEplUG2tKIqiKIqiVJuynStqamqQMnY+s2fPxpYtW3DiiScCAA4ePDjcrpOKUi13RqpXrv3OSPXHYt1f7lhKjRYYqa9S40j8jkPaJlmrKmkXZSMTpK2TtX26P/uLYcdwUe0VphG2mPKzjvIL2JDbPUyZtLbKd3ZRWUjYXRn7LaeG7dXcOnrt9Akr3P30+xKe3cpdtDdSvRyPylqHSdvgYJq2u8HiVWJdy9k6zNr7BjPFQUg2HgQA0vW8zipdR22mGrltawncP5vPpbVIHmwrHkPNHu7Pxm3kw1yv4VUaf0cL71O7nX5Km+LGZ8mWrOd4HuvMJ2kMvYu5j5CJHHF9lot1H8NjbthG7SRn+FiWiQ+CbSc1r9ErC/ekTD1RMWTaTnNZvp/eY/kZsp8ZaXdmRy+jQMYjmkhSafswtSNTphXVzK+bZNl1gUCAbSd9yOWK48OU0pkq2nosjMYKc7T1ZX9+enYsf8vGoq0llToHpUaA+MWH2O1yXy/u4xBb0g4XayID0uw+BREQJhbCXb7YK3K37qGfoo9Ykn4/cqJe3YZu85Oby8dN3Mdctj1OHCQ9lqpj7Td7zTYAwNDxbBt88FrSY9EnmoqOI9rHOq/5VBM9ci+L2Mxy6qPtIe5jYDZ/Z865LWL6YOvdk07fCwB4/lfLvLLGDa7Zt2gI6DiXxxDba2LuNnLZ0Ax7vcD71D2/HwCwa9UsryyxnfaNHeLx1eyhduw1AAAMttPrhPgKStXTO9p3fptX1r6WjmPX2/hcznqO3q/gEP9tcLr7AAB58b7a99196kWvLBAje+TfiRgcv8+WrZcXsTUW+TkeDWqzqygTwDTLhVZtXV1UWyuTkUrrC9vOqsh7ijeK7xB3gOYoA/U82eraOUqXvxzdgPlOEt+XuRj9J3SI4z7aT6FIj3iI8zJ60qTLZoX6vLKbDrwWANASYf07N0Zz6F0Zni/fm6IouIuaWA/e/MpFAIBXG2Z6ZXnxlHRbM7W5L8DHFAiYqOlmju3ObqdIEree52ezJqUk08gH2von0tFDYq54aAb1FxfRgpkaKqvbwcceMPPt+Qjv65gY8EBa3FsI0HYbBSJx5D2KBN0bCXTzubT3FAIzxHVKyoh+cT8if+DI328yEt27byH3HTJz3uKz40WFyIbMPm6ez5+8TwKfv1+2H1fEldhYEdmO72c5XxwX710biL+jXpm4B+RXbzhGivEY9nc4X9rfbb/okVLHJxnv+weVZjzmXqY900xbl0I19HfZiyvOOuss/PnPf8ayZctw6aWX4pOf/CReeOEF3H333TjrrLPKHoCiKIqiKIoyeVmzZk3B/zOZDJ599lnccccd+Od//ucJGtXUQbW1oiiKoijK9EG1dXVRba0oiqIoiqJIqqG/Hdd1y1pn8uqrr6K/vx8nn3wyBgcH8alPfQqPPPIIFi9ejG9+85tYsGDBqAZSLXp7e9HQ0ICenh7U19dP+Mqo0TzRNhZnjbFQqnOFV1+4UFhXCVlmV/E5crWkcKmw5euSdxaNIVjPbgxru39UPL6AfDbOtGddKkQfth3bBsCuGH6rOO3KTXlMF7dezfXsCuAGHh+i5mm5bImrncSqqNwcWpXrrRgGEOw3q0d72fUiM5/q5eLCMcO4WAw1cVmsi44jGxerbxuDpg+Iffl1rJtW2PbNKz6n8QO8YjdgFupmYzzWXKy4LJuw+/JXTc8S+jnzr1xmn5aLH+I+OpfSGIK8KBipZrNyuo/7iHW6BT8Bdt6IdnNZeMA8mSecOjIJqpfnhdho2kgrgHNi5XGkh8pCB/u5onGxcGP82XH20lOC1sECYMcKV64eF59Li/f0p8/v0mjQVZzKVOLwv+cTPY553/g/CMSLnzqoBPnkEHZ+8gsTfqwjcdddd+GXv/wl/vu//3uih3JUo9p6fBgPzTxSu9YNQj5tXwmHC+lC4fck/0juE35YZwCJU5Mo2nc4VwznWPHZtU9XGbcKAHAHyC0iIN0s7LZGfhLNOlJI8tHiZwQCKdK9gSQLx8GF9B1qXRcAIBch7ZcRTg3tD5AjxStXNXpl8b3FWti6s1lHDADYs5LGkq1lDSv3jR2ifaz+BYBEB/VtneAAoG43tdk3h49tznu3AgB2/tcxXlnanJqgcKkYWEFP6dU8G+d+D1Lbdbu44s43RcyYhKPcVtKn2SiXhVK0bzBV7PGX2MZPIB48nd67Wb/fzmM5dQ7tO8T7RjtIP9v3AwASj28BwJ8DQHxmOg54ZXa7/fwBwFrhbDGeqLZWphKTQVuPh64GVFtPN452ba0ohzOcE7Qj5o+duNGBYu4xMGsGAMCtZR2Vq6M5zGyCNfbQDNKfA+2sYQfbSA9m5/D85ZK5NOe5v7/WK3v9HNJ0C2OHvLLX1ZC93BOD7Dx3Spz04jPJhV5ZzCHdfs++U7yy1ji5NuwaaPTKZsV4Lvb5fWTzls3ynG2q38zLZvh8hDvpmGp2clnPCXRu6jaLawPz5ycszKaDyeLbZXbuPCQut4JGM8cOZUQZaeDgEF8vBAZJj7th4a7dT/o938TnMniQdLabEH8Tza07Z4BdOdxBM4gG3hcH6RrNFW7Y3mdBOCnbJ7fdtLiYGAbpOOG5Xoh7KAVuFbYfOf9t3SlcH+9wn9uS8jo0b+fOZT375LnD7793H0o4Q3j7+DypPiaXihHqHS3zIyPh67JxFDJe12+T5W+5auvyGYv+Ltu54phjeGInkUjgX//1X8vuVFEURVEURTm6OfPMM/GRj3xkoodx1KPaWlEURVEURVFtXRlUWyuKoiiKoiilMBb9HRi5SjHd3d344Q9/iM9+9rPo7KRVcc888wx27949qkEoiqIoiqIcVbhV/jfJSSaT+O53v4u5c+dO9FCmBKqtFUVRFEWZtlRbV6u2nnaotlYURVEUZdqi2rokxqq/y3aueP7553HBBRegoaEB27Ztw0c+8hE0NzdjzZo12L59O37yk5+MaiDjRSXsX8bShp9N0EhtD1evmnY2ZbedZ8un0VggFUSIGKzNVK63t2hbqW3L82fb8esrl8kWlclIBs9yLcy/NjZKJNfZVbSvjBmBfG1wTHyIK+JDnJdeBQAEW2YWH0iILbzCOw5SUX2NV5ZPUHvZOFu9OTljHSYcuiImGiPaxcfbsyjC23upcv98ts2a9bRpTzh4dZ5I22t3cJmN4IgLC7KeY2jcoSH+1k3soXVdWXFa0g3GlixQvOYrvo/3DaSK7bxsJEqqkctqOug4MjXcXnIGjaV+O1va9c+lYw+J+JBsnOpFetjWzcaBOGmZUUIWao54D20rgVrx3piIkIIoEJ8oG8tIUSClWlpVMyroaLc3UxSldJqamjzrSABwXRd9fX1IJBK48847h9lTKQXV1pVjOP05Gt1Y6j6l1vvd4E+L+ii1vVItR0cav430GNX5MOOX2AiQ34ltXjTJ+g1FY7GxJJLs7j3ea2sDmxdlwcWLigfWSDor0M3+vakFFFERFWU2wiLYxpa5NqJCkjLbZz7NujEXJVWXj/D3X+dys20TXw80brDbeN/BBayzmzfQ/gMXcPxFw2Ok120EHgAMzSBr5mQ768rEt8m6PFzHOjVn4jtyLN9R/zjZQAfSXC/aR3q88wQW3DNeoLKm5znaxUZ7SA6cRn3MfbDYvlfGtARNf26c+7BxILkYn49sI41PRor4xcJkzWcm6BMVI+Nohvtdr6ZenUzfd4qiHL2otq4uR7u2VpTDGVZ/iLgHG8UQaGrkMjvHKmIV3CCVJWexprOxeT3HiyiLdPH8bDBAOm9OA0f97Uk2AACOiXOs252HzgEAvK3pGa/sqUFyldkw2OqV7TX7pnKsrXMujeXQAM9vb9k9i8c/YOpGhU61cSB5EV0dp2PuX8jVYvvofMkI6eQs2sfJcpmN33PyxXcQHVEUP0hzxHb+HQDyJmJaRotYLRzs40wRN1F8r8CNGYGf4sgOxy923ERsOEkxz2yuo+Tfl3xXN1UXMR2Ona8Wn4nhokJccQ6cEJ17R37uxOv8oInzk/dB8sX3W7z9xb52zjwv585tJEpIxKLbY5H3oWQciMVn3t1vvr3U+3N+9Yebdyj1mmQ01y6jmQcoF78okGoe03DtVOperDK9qYb+LntxxfXXX48rr7wSt9xyC+rqOAv3kksuweWXXz6qQSiKoiiKohxVuA79q1bbk4hvfetbBf8PBAKYNWsWzjzzTDQ1NU3MoKYQrobAcgABAABJREFUqq0VRVEURZnWVFNX2/YnEaqtq4tqa0VRFEVRpjXTTFuXQjX0d9mLK5588kncdtttReVz5sxBR0fHqAZxtFGp1VdjeRquEiu3Ks1Yx2JXDxasGDSrDEt19ChYlZgvXtnprR70ca6Q9W29VfH3FVVbl+SVTHYMgYRwiwgV/1rZJ62CTQ3cnXG7cOKxon1ze/d5ZYH6eqq/gFf2BnqTAApdHmxZLMLnoPcYWhlbs5fdFpyccY2Ic71YN6/2zdRSeevjPP6wcbtI13N/jRtp9WjsEJ+3dD3tm43xF2zDq7Q9J576syuEg5liRwrrQgEAYfOAX0CsAI4Um5j4kq6jsUrHjFRDcR+ZGqdozI2bxSpZQ66R3uNgP2/Lz6T3Jrj7oFfmtrfQi1fZ0sNzMRErd+1KXNfHMWUsq1bL2adcJtP3jaIo48cVV1wx0UOY0qi2rhwT7VA3EqU+be83Fr8nYPwYjUuexbpQAOxEYZ0kRsLPkUL25edwYcukS8HaA7cVjd3dtZfGIuoFk8VaLbq9s6A+ADjHkvND6A9Pc8UTlwIABhfWe0WxfaSjm3u43c4T6YZMhu/LoG476cWhGVw2uISe8FrwK9aSfXP4emDf6fQz01/8dFrzBtbRkR7ShIMt/BThvtOpzXCfeALNPNAWEA+WDcwmvTvjheKn62r3cB89i0irdx/HBxA7ZH/y9UCig3R0qp6vFxL76Xpiz7l83mY9R+cr28rXOGFzDgdb2C3Ent+Cp/DmtgMA3C5+8tG6lOQ2b+Uy+74P8ZN+9nNZ6mfc77OtKIoyUai2ri6qrZWpxqrIewAUOgjYOWwnIuajjSNFfibrMidN+jLbxPUsdo4UANKN9DpyiLVf7ljSb6Ega0TrMNEQSXplsSD1kXd5zrg+RLrtsYElXlltkMpWCAvkA0PLAABzarq9slc6af47k+Ox1NQJHVhP5yGbE45z5nVmN7sIwxxeuJeP0zo7d54s3N4O0vaAMCrmQxFz2X25gjYAIJugMUqHC8/FTbi9hffRZHaumcfnZIzTSM+gKDNzxMKtItdC72dgUIh/q599nLLdJL83To1535N8/vI9PhPrEeuuzXPU9h5FIMLHkbduztIJQzwJbveRDhiBON2byA/xdda69M8BHKblbTtu8fWMHFdR/SPsA7f4vpCfw4V3nS22+V5z2/6c4W8ql3rPzvY3lnn/I90L8Nr2cZ8YDyo1p6L3IZRKUg39XezvNAKxWAy9PhENGzZswKxZs3z2UBRFURRFmVo4bnX/TTa6u7vxjW98Ax/+8IfxkY98BN/85jfR09Mz8o7KiKi2VhRFURRlOlNtXa3aenqh2lpRFEVRlOnMZNPWDz/8MP7mb/4Gs2fPhuM4+PWvf12w3XVdrF69GrNnz0Y8Hsd5552H9evXF9RJpVK49tprMXPmTNTU1OCtb30rdu3aVdY4Kq2/y15c8ba3vQ1f+tKXkMmYTCnHwY4dO/CP//iPeMc73jHqgSiKoiiKoiiTj6eeegrHHnssvvnNb6KzsxMHDx7ErbfeimOPPRbPPPPMyA0ow6LaWlEURVEUZfqg2rq6qLZWFEVRFEWZPAwMDOCUU07B9773Pd/tt9xyC2699VZ873vfw5NPPom2tjZceOGF6Ovr8+pcd911WLNmDX7xi1/gkUceQX9/P97ylrcglyt2qfGjGvrbcV0/35wj09vbize/+c1Yv349+vr6MHv2bHR0dODss8/Gvffei5qampEbGUd6e3vR0NCAnp4e1NfXT8o4jaORSkUQlNpOOdZHpfTna8csokJsRMlIY7E4fjZcIu7Btndx44e8slz/AIBCK2QbHxKIcXtebIiMj2ij1fb5+rhXZGNBEGLbtGwjbc/WsoWXje6I72OLrv553F/tTrLpOngKt50yjnLSujhlYjdckcQS6aOvk/hBGUNCZf1zI15ZeJDKehfwznU7qe1IL/fRO5/H7Y1vd7bgOACO9IgMsFVd3kR/DDWzXVfLk/SFbG2eAbZ6Sxzgfmte7aY2EjxmS/AgPwGRNdZsoT2dXMF+oQeFld4Big2Rn4lgLX1X2s9BAT4RNdVkPCJFFGWsHP73fKLHMf/mLyMQL80uv1zyySHs+MznJ/xYLa9//euxePFi/Md//AdCxuYxm83iwx/+MF599VU8/PDDEzzCo5ujXVtPNSbqb2KltG6p/fi1J6M9rCYd0c50DNdWfpraxj3kRQREqdh93eWLvbLg3s6ieq5tW8T1OSZmZOj4dt53iPRYupH1aKSbNO7md7N2DvWTmIwdEtF2fXyJnamj8po9rFP3nU3ba7ezXsyYFI3GjVyv41x63fwM15uxnjT/7jewvXPEzDmkRYRJ0Eh92W8oRf0ePInbW/Bb0rZSHzduNjbQ3WwrnGqjAUY7+r0yp5s63vP2hV5Z88t0LnMxfo4j8fgWAIA7IGyPTWSLbQMA3DidV99YEIH9fPoho2w0AkRRjsxk+Hs+HroaUG093VBtrRzN+MUWeJEHIo4gWEe6rSB+oamRXogYBzdhtHUNa9f+BfQ7kGpgrTbYaiMPuNvB+TSH2TSbn+ptSpA23P7sHK/s6kt+BwB4e93zXtm7X/xfAIATZ3AUTyRA7e0c4Gz7OQlq++UujsLe10m/B7G4mLfey3FzoUYqz3eIvxvOYT8B5BOk5Rv/yucj1kW6uH82H3vIylOxb9MGo2dFpHbKRGXLuedchMrCAzyPG0zS9kBOaHATm+cMcjSGG6c550Av62MvDiQgBmOjOCLFc+TeNvBnwRVlGO6mYz5fVOTFiIDjQ1zRhv3c5Qd5zAWRHSauPeAz1rwYl2OOr2Bf8/mWURbe9aKM4nDMe+eK8dvfERkX73U8/I1Xv1iQonbhf43uxdmL+0h+kRyVuB8px2fbnkrz+Uf7PdvJ8rf8aNDWjuNgzZo1uOyyywCQa8Xs2bNx3XXX4TOf+QwAcqlobW3FzTffjKuuugo9PT2YNWsWfvrTn+Ld7343AGDPnj2YN28e7r33Xlx00UUj9lsN/R0auUoh9fX1eOSRR/DHP/4RzzzzDPL5PE477TRccMEFZXeuKIqiKIqiTG6eeuqpAvEJAKFQCDfccANOP/30CRzZ1EC1taIoiqIoyvRBtXV1UW2tKIqiKIpydLB161Z0dHRg1apVXlk0GsW5556LRx99FFdddRWefvppZDKZgjqzZ8/G8uXL8eijj5a0uKIa+rvsxRWW888/H+eff/5od1cURVEURVGOAurr67Fjxw4cf/zxBeU7d+5EXV3dEfZSykW1taIoiqIoytRHtfX4oNpaURRFURSlevT29hb8PxqNIurj8D8cHR3kcNTa2lpQ3traiu3bt3t1IpEImpqaiurY/UeiGvp7VIsr/vKXv+DBBx/E/v37kT/MQujWW28d1UDGi6PVWuZwSrXKKddSx8+e12/fSp1H2Y6N5SiwUvIZv2+ZjfQYIU6hVJtlv/Pgay1lWJe803t9Ue0VRdtXxd8H4LBYCGNFnDtwwCuz8SKOWEFl40CkVVZgkOyMAyICBIe6aN84x3mEjJ1YIM1lwSRZc0lrs/hBbjtvLM1infy7XbunOD3IxmgMzio+L4OtbP9l23bEV4W1SIt0sw2bjfZI7GGbsEgfnYdUI9t/5eLFcSSJ/dRHqokLh5qpXtNGtpFLtpG9mbRwszEk2TiPJdVOX6jhHrZry0eKrc2C3WSD5ibZMtmpI6u6fC1bqTmdXTgcvzgQ+9mXn6FSfycVRVGqxbvf/W586EMfwte//nWcc845cBwHjzzyCD796U/jPe95z0QPb0pwNGvrqcZoNPNwWnIkDTtcG6Vq1CONudzrABm1MJxmvmTePxSVyUiRtQduK2rbbh8uzgHgOBA55osS7y/YBnBUhF97zoubvddZs09BtITR4O6uvdyviRKRkRdIkYYM7eOibCvtK+M8bARIvjhNDgDHfXQfx1qzluYI0L+Ar13ciNHbG7le3SbSwn0LWYsPtidMu1wW6bPRI1zWvZR+zl3LOrRvaSMAYOYLxfa40b5ia+Cuk3nyom6HOf8p1ta5djqvsUO8r9XPwSG+nnFsVIeJNpTtuDICxsSChObM9ory5j2WNsWWkq/jFEVRJhGqrauPamvlaMWz+/eLKHBYI+ZMDr0TEQLURBi4YdZg+dpYwTaA52fzIrkh2kXbu5fx74sTF5ENhpDZOb6Yo0LufPUMAMB3d7M7TNvCQwX1JS9v4xi+l9MmXiQkxhc20R3dYn67j7V3Pk3HFO3k85Fuon1qdoi53RnFUSdWj0fENG14gPrOifuBPcfSeXXFvokD1Eeon3X04CIaV81O1sfBJM1rywgQL4Ijyu9XYD8Nws2IuAyjd2WUHmaR3nb6ee7Z7Sm8oUltmwPIiBg+8/mQ8TGu+SzIz46d13bEfQs3b+rJ2Gszl+2I2BJ5D8NGiBREPJpID9mObVviF3Vh2y6ID0HeHkhxGz4x7wW/Sz77DBshL8bi3bcaod5Y7qOVeq0vo+3Hk9HEkJQ6J6H3Oo5O5s2bV/D/G2+8EatXrx5VW46M/wF9Vx1edjil1LFUQ3+Xvbjipptuwuc//3ksXboUra2tBYMv9UAURVEURVGUo4Ovf/3rcBwHH/jAB5A1F7XhcBgf+9jH8LWvfW2CR3f0o9paURRFURRl+qDaurqotlYURVEURak+O3fuRH19vff/cl0rAKCtrQ0AuVO0t/Piu/3793tuFm1tbUin0+jq6ipwr9i/fz/OOeeckvqphv52XNdnydYwtLa24uabb8aVV145qg7Hm97eXjQ0NOA8vA0hJzztVkEN99ScZKxPxo12LOVsL6cP2c6oniwcxkXDF+Fq4ZgVwtKlwnflpOnDESuK7T7B2pqi+tLpwO4jV306NbSPOyDqmRWeTnMj95GIFbXtRngMuYR1tuCy5EwqCw3xiuNsLGB+8sWpfeJNlg3Oonp1u3llb3CIvnYiPbw6N9RHK3oPrmjwyuKd1nmDv6asO0UmwX3U76B2wt28Kjg1g44zWyNWdkdon1yY97UuFrGDPJbwQVrlm22Sjh+03RWrcwNpGp9cPYwe8cSjxTiqyFXB2X3kWCLfa+89zhc/TQiIz7RYoWo/W9X6fa1Ue4oyVuzf856engLhNlHjWHDzlxGIFX+fVoL80BC2f+bzE36shzM4OIgtW7bAdV0sXrwYiUTxU8RK+Ryt2nqyfT7Hi0ro1clGudcLI+lt+90on1gq93yN1MdwFLhUGOTTX/aJMEd8h2cWttC+T7/C+xg3C8lQK2nDXJT1Zc0O0m/5KGvndCM/CrjrPCpvfpHbaV5PTxtufjfbT+ZmkNasW896MWikbVq4VCbbSSfG9/J1QPOGXMFYACBgtKt1qwCAbNRo4YhwhYsW9iXLWv/MziDZRnPsMe73wKlUMXaQtXrjZtLFgZRw7NtL7Qwdz5Ml0e1UllrA71fsFXYTGY68j2OJ/bxJ14v7dn67pPYUZToyGf6ej4euBlRbTzdUWyuTkXIdqCEWAtkn+i9q+KBX5gRJi0r3Ys8BoWWGV2b1W7qJ9WXezIl2H8PadaiFtFw2IZwm7BAaeL505gzSsLEQ67y+IdKD2Tzr41iYth/axjfBGud3AwC69vLn/MSluwAA618RTz9HSddGEtxvNsP6M7LJaNI46898mF6H+3gMAaNtg2zagJw5DVL3xg4Zxw/hkBzrpvMQ7uXjDA3Q62yNmEM3rtCRXjG/3WVcILLFrh1Icz10G/eJJp4Hd/qNQ7JwmnDM3wbXuJXQdtOOvC9gHSKE21vezDPn+8VctbkVKP/u5k170q3ZukUExN+mtd0/AlCCc4Kc1x7GCdzPgbzgms/+HgjXFt85c1tP3Ob0bc+MpeB+j3G2sL9nRfsM08fhfUmkY4ZsezyoxPX9dGY0cz6T5W/50aCtHcfBmjVrcNlllwEg94nZs2fjE5/4BG644QYAQDqdRktLC26++WZcddVV6OnpwaxZs3DnnXfiXe+i3629e/di7ty5uPfee3HRRReV3H8l9XfZzhWBQAArV64cdYeKoiiKoijK5CeXy2H9+vVYsmQJ4vE4EokETjrpJABAMpnE888/j+XLlyMQCIzQkjIcqq0VRVEURVGmPqqtxwfV1oqiKIqiKJOH/v5+bN7M8a1bt27Fc889h+bmZsyfPx/XXXcdbrrpJixZsgRLlizBTTfdhEQigcsvvxwA0NDQgA996EP45Cc/iRkzZqC5uRmf+tSncNJJJ+GCCy44UrcAqqu/y97jE5/4BL7//e+X3ZGiKIqiKMqUwXWq+28S8NOf/hQf/OAHEZE5roZIJIIPfvCDuOuuuyZgZFML1daKoiiKokxrqq2rVVtPK1RbK4qiKIoyrZlk2vqpp57CihUrsGLFCgDA9ddfjxUrVuCLX/wiAOCGG27Addddh6uvvhqnn346du/ejXXr1qGuji08v/nNb+Kyyy7Du971LqxcuRKJRAL/8z//g2BwGHccVFd/lx0Lks/ncemll2Ljxo1YtmwZwuFwwfa77757VAOpFpPFkmWyMx4Wx55l1AiRByW3N4I1cLlRJ6OxQCr1vK2Kvw8AsC555/BjMvZUBVEhKfIqkzZc+SHhX2YItc4CAOQ6u7wyx+fLxYmTJZCMqHCb+HfDNau0MjO5PydHXxOZ2mKzGxm7YYn08nucrqcxJPawNbSNHEk3cHvh/uLPRXImbQ8NuUX1ehbxd0+tiRyx46Q+AkVlNsqkbitbJg/OpeOM9LDVW+yl3QCAoWVzvDLbjhsUMSjGztgZ4FgQey6dQT5et7PbDJ6P11qzBcT3Us7H4tjPIk0y3O/BRFl5VfN3SZm+TJa/557F2te+Ut1YkH/83IQf6+tf/3pcc801+Pu/97d8/L//9//ie9/7Hh5++OFxHtnUQrW1UkkuSrzfe/27wZ8WbR/u722l7EBLje8Yrg95HDJeZLh9/Y7tknn/AADI7t7jldnv7oCID3FtH8KW19u2i6Mq/CJFBk4lvZjY1uuVpdpqvdfhHtLtnSfyxMDAbNKT0qa4+RWy4+08nr8DMtyMR902o8vrWJOG+9yCdgGg7S/Ub99c1vw2wk/qXr/xWWzEhyTZyjmqNmak+eEdXpnbSO24W7ZzmYlYkX3U7UoX9A8Aga30PklNLGM+LDYWRL6HNgLE77NTYHs8irga1anKVGQy/D0fD10NqLaebqi2Vo4WfOMI7Ny5K+M5aC4zWM/C0GpRLwoEQH4hxa/JedChNooizoi52/45NE+baiweU6aB+w3MJh2YGRJzwVlqZ/ExHV7RjoMU/RGJsL5sq6cIi74U68b9r5q4kjoRjWGTFpIi3iJFfQQyrGvlfbzgEP3HFcNyzLAd0bTdP5AVZeZ1fB+fo6CJog6KSOp8iPat3cW6cWAOnfPwgIi9TlLHoUERC7KX5uXzDRwDbd8TOX8MGyeeF++1uU2X7+UIkMAsOm9uV484EHMOxP0BG+NRMN9v4kXcvM/tP79IDhFlYe8p2MhxGp5b0C5VCIiXTsFYCpDxIObzLe9b2H1KvU71LRPj97bJ369hIkBGo/PH49paOTqYLH/Lp5u2LoVq6u+ynSuuvfZaPPDAAzjuuOMwY8YMNDQ0FPxTFEVRFEWZ8rhV/jcJ2LBhA84666wjbj/jjDPw8ssvj+OIpiaqrRVFURRFmdZUW1eXqa2/+tWv4owzzkBdXR1aWlpw2WWXYcOGDYVDdl2sXr0as2fPRjwex3nnnYf169cP265q6/FBtbWiKIqiKNOaSaatJ5Jq6u/ix9BH4Cc/+Ql+9atf4dJLLx1Vh8rkpNSnv8bUR/YXFWnHa28U4xqLO8awKyNH4aJh3SzkNm6n+Ffzd/13FO0rnTBsmZ9bRaBlpvfaNatk3SyvUHV62ckBKVp9GpErZw3hQ/x6aC6tSpOOFIOzaUVcqJ9X7AaTuYJtABAcKv4GliurLU3P0VNpA8c0emVZ40gRFA8xBtM01qEmPm+xrmxB/wDgNNATC9k6XsVbs4OO3Q2IFdHGfSKY5JW2gTS1E+xgZ5DMfDqvof3iOLroqUV3YJDL6ukpvdyBg9xelFZv53v5KUdv1bBYzetEeZW3/XzIMj/G4sZSLn59jMfvpqIo1WdgYAC98jvqMPr6+jA4OHjE7UppqLZWKomfW4VkPP7eVqIPeRx+1yTWnUDWCxoXg4tnXeWVWQcEv6c2pJuF3RfiiTDPnaImUVwmsI4V+Tg/GRv5M99gS688EQA7NdBr+hnt6PfKdl1MT6U1bGXtuvds0r3RTtap0rHCkjPSUDphHDi1WC82ryfdO9Qa531j8aJ6s35PrhOZhS2iHunTYIqvEYLmYTlZL5Ai/RyY2+6VOclMQbsAu4UUaGbzPki3Cvs+yffQOojI93A4h0JJqc4qI7VTLfQpN0WZeB566CFcc801OOOMM5DNZvG5z30Oq1atwksvvYSaGnoS+ZZbbsGtt96KH//4xzjuuOPw5S9/GRdeeCE2bNhQYGEsUW09Pqi2Vo4W7NPz8m+/dQlw8/w8bCBmNJ2P+4BTy+4IgR76/sg3scNFLmbcG8Sca95Mnebioj0jLyOd3G+qhV6/ZjHrt119jQCAzVvaeN8gtZPOs/bcfIDGFermedqQkZDRbULTmbKMz9dmagZr4vhenidNzaSdwn081oiZqnXFlLjVx7Isesgt6BcAAlkqCw8Uz4Nn47xzfL+ZLz/Ac+huuHgOPt9EBxPYx05sbg3pbVc4VzgRunaQLhDWdcJuo33M34XA8Db83mdHtmfK5J6ew0WMr3HsZ7DA7S1V7Jp9+DgBdtwGABfD2/Mfjq/DhcD73XCcojKpk1dF3kNlPi4VErm9pH7lvj66vBLXGpNV+0/WcSlKuVRTf5ftXNHc3Ixjjz12VJ0piqIoiqJMCabBCuAlS5bg0UcfPeL2Rx55BEuWLBnHEU1NVFsriqIoijKtmWRP161duxZXXnklTjzxRJxyyim4/fbbsWPHDjz99NM0XNfFt771LXzuc5/D3/7t32L58uW44447MDg4OGxms2rr8UG1taIoiqIo05pJpq0nkmrq77IXV6xevRo33nijrqZWFEVRFEWZwlx++eX4/Oc/j+eff75o21//+ld88YtfxOWXXz4BI5taqLZWFEVRFEWpPr29vQX/UsM8lSvp6SFXo+ZmctfZunUrOjo6sGrVKq9ONBrFueeeO+zkrWrr8UG1taIoiqIoigJUV387ruuWtc5kxYoV2LJlC1zXxcKFCxEOhwu2P/PMM6MaSLXo7e1FQ0MDenp6UF9fP9HDKaDSsRtThfG2HfJ7Hy4M/T0AwAmzfZm1ufKL8SjVBmqk4/H2EbEQfnEqNgJEWm/ZqAg3U2yp5VnIAXDiZPHlJoeKygDAqTP2cSm2EcvOm0XtDHLchxujc5NsYxuxaCeNJx/h8ffPJauwxH4el40NSTewjViqifap3cnH5NkKp9kKLtNIYx2ayd89NTsGzTY+znA3tZNLFH5HUf98bHaswV7uN1dP7QTF8SJL9nDOkDjnQ6adEB+va63buthuyInScbrinNoIEPk+eG3k+Hhl9Ivf5+Pw9oCx/d7o95IyWZksf8/tOBZ+5Su+NvOVID80hG2f+9yEH2smk8GqVavwyCOP4IILLsDxxx8Px3Hw8ssv4/e//z1WrlyJ+++/v0gLKuWh2rpyTOW/YUfjsY1kiWq/Q/2iTEa6Hig1KmS4sRTY3g4V6zG7XW7z4kN8kJEhbpw1ab6xpqhuwMRk7FvJ7c1YTzbB3Ys5piNxkLRw3xy+JrFRHDmR+hHpo0v6XMQp2ndwptg3TfWivawbbazJ4EL+fU7Vk9ZsenKfV3ZwZVtBG7Id2cfMp8gC2enu88py7XScwb2dRWWBbrZWtucqsJXjPvywcS/y/bBlEr+/06V+3ip1TXo0/u4q04PJ8Pd8PHQ1wNr6cG688UasXr162H1d18Xb3vY2dHV14U9/+hMA4NFHH8XKlSuxe/duzJ7NMUYf/ehHsX37dvzud7/zbUu19fig2lqZaHznmU1EQUFsgY8+9WKAxbxgoKmJtsm5x4yZr2xq8MryDTQ/K+dkUzOovd4FrNVyZip2aBZrukyzmbMNc1mwy8RMiEdz4wtI3zkO1+s/aPRbn4g4zpEmDfezNh2aS2MOH+CxhPtoe7qR2wslzb4sJZEVKXYBM9SCaA+jj/Pi1z2xv/iWVyhJOwUzvM0xpzrewZEdqRn0NynSLeaoB2lON1fHf6/C2ygnWmp/x8Rwuwkx195nIlu6RQShjbqQESCDNAanpvj6wQnyG5Hdb2Kn3bzYTudfzil7Y6/lqBg3mSyuZ28PiviNksv88Lvd6NNOqfp4xGtDGwHi0+9Iv4eTHb2WODqYLH/Lx1tbT/TxlkI19Xdo5CqFXHbZZWV3oiiKoiiKohxdhMNhrFu3Dt/85jdx11134eGHH4brujjuuOPwla98Bdddd51O/lYA1daKoiiKoijVZ+fOnQUTwNFodJjaxMc//nE8//zzeOSRR4q2OYfdXHJdt6hMotp6fFBtrSiKoiiKogDV1d9lL6648cYbR9WRoiiKoijKlKGaGXOTKLsuHA7jhhtuwA033DDRQ5myqLZWFEVRFGVaU+3sZtN2fX19WU/XXXvttbjnnnvw8MMPY+7cuV55Wxs5+XR0dKC9vd0r379/P1pbW4dtU7V19VFtrSiKoijKtGactPXRQrX0d9mLK0phpNXaijIclbKEGm6fker7RXGMZG1cSh8jWc4O27+JKgHgRUA4fk9aiHiIQIIs4ZyQsH/rIguyAltjES+S3bGbtgtrueAr24u6CdTXAQASWeHDZl/X87hq9toIEB5DIE31Ij0ckxE39SDaS80m27Lonn6xLx1f7CB3a6M/nBx/s3edQPvOfGw/12uk8xHoZas31JO3XGamiDfZTBbIqcU8MeJFiYTYhs1avckIFSdVHBVi7fykhV+ukyyYrW0bbadzlD9CNmip8TNjoVwr5PGO8VEURZmOqLYujan8d6jSUQV+VLrtkuPwRigrtW2/aI/h9vU7XhstciRs9ISvxabQzuhiy9+AfS1tm038ReufOcoiHyc9G+1jLRwcotd5EffRt7B4JmPeH4r1dmwf6d1It7gOiJHujHb043BktEfjZtq36wzWwjYOREaFwETfJR4/4BXZeJSh4/mGo+3Pbazzymw0ipMUkXu79tKLGtbl7gDpYkeUBRcvohfiPFtKjXA80j6VZip/LynKVMV1XVx77bVYs2YNHnzwQSxatKhg+6JFi9DW1ob7778fK1asAACk02k89NBDuPnmmydiyEqZqLZWqonf334bQ+DFFwBeTIKMKFgVeQ9tEvO4+a4uABwPAsDTlTYKBAByNebJWzFNmw9RH6FB1o/5oGO2cb3Edto3F+N6qRaay6zZyhWTLmm5fJw7CaTMPKn4lWrYTD97juN6tRvM+ETMSLqO+nND3K+NIQkLuRrIcOMBo0nTDVxmI/LkMdXupvnZwVZ+Ijncnzc/ObraNecjW8P1Ynup80Avz8/amI/wni7uxMSJSz3rxXykRdR0gPpwZASI2R4IyTeC5qjte077mKhprgXHa4/zUmzMh5xn9uJlMjwW256T5/cmb8Yi49Htd6SbFecq79oXcjD82t6T8IsAkZE45veg1Ej1EbW8T8yHbXs0cZPjyUjjq9S4Kn2cE33eFGUyEBi5CnDCCSfgrrvuQjqdHrbepk2b8LGPfUwvJhRFURRFmdq4Vf6nTGlUWyuKoiiKohiqravL1NbXXHMN7rzzTtx1112oq6tDR0cHOjo6kDQ59Y7j4LrrrsNNN92ENWvW4MUXX8SVV16JRCKByy+/fAwnQhktqq0VRVEURVEMk0xbT1VKcq74/ve/j8985jO45pprsGrVKpx++umYPXs2YrEYurq68NJLL+GRRx7BSy+9hI9//OO4+uqrqz3uKcFkXNk1Hk+hV6qPSjlclLpqcTinidH0Wyp+Y7FlboZXj0rHCotd6ZzrH+CxGFeMixs/5JXJVaih1llU5nNRKp8Yc3v76MVMfgov0E1lufpiR43ajcVPlkkXCOs+0b2E+2heX9yeda7IxcWTeVvM03JZ4dqRbgEADC3kld3W2SLTyO3FN5KzRaZhVvHw+ovPgSvGnG2hYw92C6eJrt6ifbz+a3hFcbCZxpXr7CquJxxJfD9vgWBRUaV/X0ttbzJ+jymKokx2VFtPH8bDDWK8tbVf36W6VPhp64tnXVXSGPyO029f6T7xu8GfljQ+P8cKT/cKRww/xwrbhxwL2lhXOkYfu8blAQCCe8mxouPSBV5Z6x87AACRBtaBfXNpn9ghfjqs9fFi14mB+eSEkY3yU2I9S8jFLSdcL6y27lvaWNSGdauQNGwS7nHddD3Rd3ILH0fKuNE1ssYNdRe3Y8+BJNdOLnr5Rm4veeY8AEDtvX/1yqwjiXwqxB7R2gO3eWWXzPsHAIe9D4YjXb8N52KiGldRpi8/+MEPAADnnXdeQfntt9+OK6+8EgBwww03IJlM4uqrr0ZXVxfOPPNMrFu3DnV1dVDGH9XWymTCPpXv96S+H6vi7/Ne23lcJybmQa1WyrEetPe1HOH+mw+SWkrP4PnSoUYqy9SyHgwbF4ua3VyWbDUtCsMB61jh+jyaGxwU87l1NBcb6Oe5ynQdNRTu4QYdM9SsmDIOD9D2xF6ul6m3x8Z373LiqzVkprgbtgrnY9Nf7V45L0zb4/uFg4TdluF6wb1Gqyd4YG6k+JaZ00nz2q6Ye7YOxQVz6NYdWjg6uBnrcizaNW4SuYOHuMy4Tlh3CcDfkUI6m3j1zBgKnCvM67xwrLZOEoUH5xQdh2vHn5fHS/2uSxc7RQDimk9+9n0cJPzwve9iHTDkmO2cuM99EL9rTb/xHWmfw+tV89p7LG2UehyltjOafcvdR52vlalISYsrzj//fDz55JN49NFH8ctf/hJ33XUXtm3bhmQyiZkzZ2LFihX4wAc+gPe9731obGys8pAVRVEURVEmFself9VqW5naqLZWFEVRFEUhqqmrbfvl4PrdeDq8TcfB6tWrsXr16tENSqkoqq0VRVEURVGIyaatpyolLa6wnHPOOTjnnHOqNRZFURRFURRlknDMMcfgySefxIwZMyZ6KFMW1daKoiiKoijTA9XW1Ue1taIoiqIoimKppv4ua3GFMvmYDFZE1eyjXJujSlm6DrfPWOISRiob7nh9t5mIDwC4MPT39ELYYuV6iyMqbDsyesIV9mD5QYq4CCREBIixIHNFvEjAWNQ5r+7ixo19cnCQLdfCW/cBALLzhD1ynpa39RxX65XV7qQx1O7lffOR4vgLZ4giTEIBYRk3myI2bGQI7Uv2ZaH+4va8GBEAvae1AwDqXuF4DjdG9mvSXs8ZNOcoL8qMFZybEBbRs0wMyQFuzzE20G6fsHE2kS6B2hruN8m2037Y90zGwQxnZ1zwmSnRNk1RlBJxHfpXrbYnAdu2bUMuV/ydoShTmZG0dbnWmaOJyyiX0WhrP/05GtvTSpwHGe1Qbht++9qYDqD08yv3ORx5DmxEhWzPRorI+Lzc+g1F7QQOCb04l/Rn6587i+qF/vC093rWnNkACuNDLDsvrPdeL7iH7IR3XcwTBg1b6ft7YDZbEmdM5Eikh7VkcIjq9c3n8SWMfXLw6Ve4w2NpDDU7+HrAa2MvH0evifaoe2gT93v8fDq2V3YUjSW6nfet32b09oxmr8yO3hXxLO4AXa/4XR/J6Bb7Oj/kr7FLjXtUFKXKVFNX2/YnAaqtFWV6YCMRfOMNJE5x3oaNbXaCYk7WzsW2iBtDxmEnPSMuyswP0ZV9HT/Ic5np2uKxOEYaumIaNt1kopXr+XurfoOJLRFPLTs5Oo7BNi4MmPbyEVlG/cYPcFnexNdF+rgscdAtOo5YN48/kwgUbY935c1xiLbDVC+2Teht8z7kG3kuFmbO1hFRIYG+4pg7rx5EdEbS1MuLfs28uivKAhGKws6neV442ERR026PmLu3ESBirtgxc8C2XYDn6Qswn6eCbXYM0pHJfhbl589nrhhurrD+Efr1nXsuGBftL6NCfPc1jBjnYcY6lsiOka6FKzEPUKl9/ajUva7xuB9Q6WOf7P1OGqaJti6Faupvn9QsRVEURVEURVEURVEURVEURVEURVEURVEUxaLOFUc54/E03FgYq7NGufuUWr9Sjh+lnt9S65W7qrJgu3CxGK6eXUUq3Q8KVpaaFaC/67/DK1oVf19RM/kherIsOEM4LxgnB+lm4Rq3hWA/u2PkaqmsfnPxk2/J1oj3Opik9jK1/FUVqKXVvk6OV926QVotF93PLhWRQ7R62A3xGjLrXCGdJqxjxdBcfvovto3KsnXs7hHup/Yy7Y1cto9WFzuD4mm4FK1gzh7ildGh+XOoXoSPDXXk2pHdsdsrcsJ0nAXvjcCv/KLaKwCU4IpinU2OsF1RlDJx4T0ZUpW2JwkvvfQSOjo6hq1z8sknj9NoFKX6jEWrVaO/w6mUth7NUz3DYd0bgOFdIEZDuW4bkjFpa599pSvC4eSE9pP1rKOFdNmw/cl61l3BOX25V+Z2k1aesZ6fnrMuEcE0a9e+pY0AgJo9/FRf3YZuAMDBk/gpx84TSNs2v8y63Bt/hJ8+6ZtLmnXmseyYkY+TBreOEwAQ7aCn73Lt7DThOVuYp/EA4VghyjzHiqjQx0kaV/5QsaOHdJ8ILl4EAAjs2ltUz8+5ZCTG8gTaWNtRFAXV1dW2/UmCamtFmVpcGHwXgMKn8j2NIOda3TyK8CkLzjS6LS40Z9rMdSZZv7lBmuvMxrkP10ydBsR8qXV86FrK9WKHir8U4wdIBzri4d6BuVSv4RWek7Xys2YPt2H3iR1kLZk4QP0Gh7gsF7M/uaxuJ+2cFWXefG9QOBYneG7XthPr4vMXMvPHoQHhXhwOmva4nudUnOb5VaefHSE8zNyudE5zGujg813dXlnAuK25ae7XMS4R8rlu63BhXaABce0gnCECcXIiyXUKN2Qzl+yEim/jrUv/3Hvt97mz9woK5oTt5058/vw07KrIe0w1fq+99o7gyuK33bv+NL8rAP++lHp9Nxa3xFKdGKup7fUaYfyZ9ud8GmnrUqiW/tbFFYqiKIqiKIovb3rTm+C6xarZcRy4rgvHcdTeWFEURVEURVFKQLW1oiiKoiiKoowf1dLfurhCURRFURSlTBy3MGe00m1PFp544gnMmjVrooehKIqiKIqiTFGqqatt+5MF1daKoiiKoihKNZlO2roUqqW/S15c8brXvQ7nn38+zjvvPJxzzjmIDWOJqkweJtoCp5z+xzPCpKoxHtL27bCyUi2YR7KiKtuySozJRk+sS97J/Ul7MDtWWWaiQoL1bD+c66VIjEILZLI3yw+ypVrQWKQ5vRwBEjxA9mZOSNjX1VG8SMP+Hu62lmyUw3vZ+s7GcoQPiUgR0176xHncxyBZssk4knCWLM/SLbVc1k0Wb7FdveJ486bfbhxOZOehovE5wv4NQTomGwUCgO3kmvj8Yd9Bql4rY1XIlk7Gf9jzB/C5dqJsI2fP9Yg2Zz6xMdWiUrE7iqJMPPPnz0dLS8tED2NKotpaKYdK6+RK/32uVBSI33GOJTql1DZsPIdfpMRI9rJ+yAiLQKn1zBicrXu8Mqv9Ql2sB4dOpUiMeb/Y5pVlFtL3dCDFGtLGeMx+RETzxY48mub1fdyeif4YXChi8/aRnbGNApEExJgtKTNOAIhZG2thZ22jRAJJ1tFunPp15rZzvc1bAQChObO5XhddL6RXnuiVhf7wdNEYhrtmktsvnnWVVybjW4ZDNa6iKKNBtXX1UG2tVIqy5rTMk7C+MQl5fgrWi3QIFs8Zywhh184zZsUTtGYeN9fMc5mBQZpnjPSwjupeTJ/5eBfvO9RM2q9hC0dApBpofKFBvjsWMgl0QzM44iF6yNQb4Ho20kPGeMQPUduumFa1/Yb75L6m/gEei42lCw1xvWCatgdSXC8iIp+DQzkz1rBXFjto5oD7WGuG7Dx0UOhf894EekUUiDnXMgLEew/F+5Xv7Mbh5Hv7isq8CJBGjsNz0+mCnwDP7ToiVsM1T047ol83Rcfkinpw6Jjk585+xmSMhy9O8fWAF9khn+w2/RVE3tgYnCrc7yn1Pslw12ajiZEsd3w6510e4x2Dqih+VEt/DzfXU8DSpUtx11134YILLkBTUxPOO+88fOlLX8Kf/vQnZDKZkRvw4Qc/+AFOPvlk1NfXo76+HmeffTbuu+8+b7vruli9ejVmz56NeDyO8847D+vXrx9VX4qiKIqiKBXDrfI/ZcpTaW2tulpRFEVRlKOSautq1dbTAtXWiqIoiqIoUG09TpS8uOJHP/oRNm/ejB07duC2227DscceizvuuAPnnnsuGhsbceGFF+KrX/1qWZ3PnTsXX/va1/DUU0/hqaeewvnnn4+3ve1tnhi95ZZbcOutt+J73/sennzySbS1teHCCy9EX1/xqkBFURRFURSlcpx77rmIiKdolMpSaW2tulpRFEVRFGXyotq6uqi2VhRFURRFUSTV1N+O67pjWmeyc+dO3Hbbbfjud7+L/v5+5HK5kXcahubmZvzLv/wLPvjBD2L27Nm47rrr8JnPfAYAkEql0NraiptvvhlXXXXVCC0Rvb29aGhoQE9PD+pFpIGijCc2YkNGM4xkJ2wZ1opYxH3Ytv3sk0ayVLqo9grvtYz08PCJOvHrL+jzO7a2+0cACu12nXicXojvC7eGymzsB8DRH9nGONczdm5Oju3hQgfJnthNcFxGoJvK8o1sm5dLkGVceMdBbq+eYjmcLvavy89oNG30FdVzI5ym5KRNjEeA16nZfdwYf2k7A2QJ52aLvx9lrIqNAMn38lis/Rs17mMfaOwBZT3vfRfRLn6xIKOxzVaUiWKy/D234zjmCzchWCWr2dzQEF79P/804cfqx9DQEH75y19iYGAAF154IZYsWTLRQ5pyVFJbV1pXA5Pnd1GZXIwm+m4smqPUyI5S+yg3dqWcPi5KvB/AyNEpl8z7h6KyvNGJMj4kuHhRUT1Eje7sOMD7LqIYjaHWeFH1VD1rysR+Y6M8xN81oX0Uu9F1RqtX1vQ8xfDJqJDENtKsjtTMdqxNbIVsx5dbv6FoLL7HI7CxIM7py70yG0MiIzzstYbU1hY/i+DDy/22H15vvD/nilJNJsPf8/HQ1YBq6+mOamulUgw7jysjG3wINtB7m0+yprPRD06E4y1gox1mczZ8PkE6yhXxFtk47ZuLs6YLZOgWT2iAY+L65pvoCXH3xzVDHZrB7SX20RzrUFNx3Ecwwzv3zaHxxbp4Ttb3CWancEwAR3/ISJFwP7UTO5DkPhbR/GvNLhnTIdo2TYa6ef46X0t/QwL94vz2mzbzYqzeDnL81KCM+LDviSP+NrmDdm6Xz68vpu28jJB2qUzGfdgYD1kvYPotKDMR3L/rv8MrG/a+gI34QGGkx+EUfJ5tOyLKxtO/JbZ3eF0PG0Pi1/YI90aqdb3oW7+M46wElY79PBoZTdzLZGWy/C1XbV0aldLfoZGrFLNlyxY8+OCD3r/u7m6cffbZOPfcc0c1CADI5XL4r//6LwwMDODss8/G1q1b0dHRgVWrVnl1otEozj33XDz66KNlCVVFURRFURSlPD796U8jnU7j29/+NgAgnU7j7LPPxvr165FIJHDDDTfg/vvvx9lnnz3BIz36qbS2Vl2tKIqiKIoyuVBtPX6otlYURVEURVGqqb9LXlxx++2344EHHsCDDz6Inp4erFy5Eueeey6uueYanH766QiFRrVOAy+88ALOPvtsDA0Noba2FmvWrMGyZcvw6KOPAgBaW1sL6re2tmL79u1HbC+VSiElnuDuFU+AK2PnaFoxNpnwcw0Y0zk0K0pHatdvJatfmVw96kRpRWyBY4LZHkgkhh1Wrn+gaFyr4u+j9jK8sjdkrHjcdNorc3vod1Ws1wbMit3QfvF7bFwipEuFa1d5Z3nlsXWscAb5OJwIHXN6UYtXZt0xvCf+ADhmhbCb4BV+dqV4sKOL+zXuFE5XD5fNaCpoAwBynbRPsLmpqCzUyqvRYVety5XM4pxbVxH5PuSH6PiO9ETe4YzkbKK/44pSItXMmJsk2XX33XcfbrrpJu//P/vZz7B9+3Zs2rQJ8+fPxwc/+EF8+ctfxm9/+9sJHOXRSzW0daV1NaDaWikNPx3ipyUqpS8q8WSL1D8B81RHqY4Dpeou2bZ1sAAKnSgOxzqZAex2Ife1uLv28uvli2lfn6dTclF+KrHu+f1UdnJLUb1Qd7K4LMV/kKw7RWIbb0+1kd6Oin2yjdS28+fnvLLgiUtp25te45VFO8hlTrptuAOkdQucOsz5cPayI8V9xrHC7/26+KTPe2VrX/gylc3yv8k13HtXDRcTRVGOQLWzm1VbTwtUWyvVplTHKw9hFm4dKxxhTR6oobk96YRg3Qwg5jIDxhVDOum6dTQPmouwpYNrphLTddyH3R7rEfOlpl7dTp4L7ptHhTUdXG+oiTRk/Q4eX7Sbtkf6uJ51p8jGWXPGDtJ8b+9C1qY1u82cZh3P/OYjZh/h/GEdKwIZHl9gQMxRm/nl7Cx+WjrUY3SscDl2TUSPU1PD+5bqXJMvdrPgbdyHY1wl3CEen30/rdMwcNgcu23GljmB4jKBdKzgjul8FThkC/fiwxnJ2djP9cK3nZGut3wM8u/PFffnbRtBO1fCXbDUshFdOSo8X67XDSOfAz1HY2CaaOtSqKb+LllZfuhDH8L8+fPxuc99Dh/84AcRDodH3qkEli5diueeew7d3d341a9+hSuuuAIPPfSQt905zFbLdd2iMslXv/pV/PM//3NFxqYoiqIoijJd2bFjB5YtW+b9f926dXjnO9+JBQsWAAD+4R/+AW9+85snanhHPdXQ1pXW1YBqa0VRFEVRlEqg2rq6qLZWFEVRFEVRJNXU34GRqxDf//73cdZZZ2H16tVoaWnB3/zN3+Ab3/gGnnrqKbg+q9JKJRKJYPHixTj99NPx1a9+Faeccgq+/e1vo62tDQDQ0dFRUH///v1FK4Mln/3sZ9HT0+P927lz56jHpiiKoiiK4otb5X+TgEAgUKDxHn/8cZx11lne/xsbG9HV1eW3q1IC1dDWldbVgGprRVEURVGqTLV1tWrraYFqa0VRFEVRFEwbbV0K1dTfJTtXfOxjH8PHPvYxAMBLL72Ehx56CA8++CD+5V/+BUNDQ1i5ciXe+MY34lOf+tSoBmJxXRepVAqLFi1CW1sb7r//fqxYsQIA5aE89NBDuPnmm4+4fzQaRTQaPeJ2ZWyoHY8/Y7GDHWlfP9spPwsv33rDtefTxpHGZSNEbCwFwHEfEmt5Ju3Ggk0NAICciM7I7juAwwnNpotTd/8hbs+zyBO2bbMoWiNwULQ3m+yCg/1sn5api9PPdrZ/q3l5Hx1HrYjVMHEfbi1/bwQ7yabYGWB75KCNCklymRMyXnrxOI+vrzhmJFBLY3DreCwBY3ts7Y/pOI11nLCrlJZwwfr6gnoAEIgVf9/5xn2Y96RU224/1PZYUaYXxx9/PP7nf/4H119/PdavX48dO3bgjW98o7d9+/btI04eKkdmPLT1WHU1ML219dEelTVR4690f37HUaruLbU9P2yUxFoTQTHSvrKs1AiQkeyl/eIsbByIjQyRvPHCr3mvbezGn/8ff4esfOfXAQA1Owa8skCSNG7f0kavrP4JutETTLHt8YEL6MmOqLCBtvUkgTg9KZxfeSoXmsiR6HNbvaLs8fMBACFwRJ591jcookJgriWQZE1sz0tozuyi/t0tbMd+ybx/AOD/Hsr9s7v3FJWVSjXjbxRFmVqotq4uqq2VCcHHqcRG/bp5vpnjzQuKOUBvX3HTx22ieb9cA88z5qMmQjjNGizcR/otF+O5x3yQ2nO4GsKDR74DFhTt1XTQvtkoH0+s2zV9cFnUxItk4+K4zXYZFZJN0JijfTyfm4uHCsYOAIE0bc9HOB45dNBEx4n4DRkNHTCaMNTN59LpongdV8SCOHauVsZz2AjnlE+MxxHmYr32bDyLmJN1k3SOZBS2R46jsO28uusXS5L3KROfK28+V87jm8+MX4yHb7yF6MPbR/ZRYryNHyNFiZSLbG84Te0b7VGiBtd58EKmy3Eq05Nq6u/yA+cALFu2DMuWLcPHPvYx7NmzB//6r/+K7373u1i7dm1ZIvWf/umfcMkll2DevHno6+vDL37xCzz44INYu3YtHMfBddddh5tuuglLlizBkiVLcNNNNyGRSODyyy8fzbAVRVEURVEqguPSv2q1PRn49Kc/jfe85z347W9/i/Xr1+PNb34zFi1a5G2/99578drXvnYCRzh1qIS2Vl2tKIqiKMrRSDV1tW1/MqDaevxQba0oiqIoynRlumjrUqim/i57ccW+ffvw4IMPev82btyISCSCM888s2DFR6ltvf/978fevXvR0NCAk08+GWvXrsWFF14IALjhhhuQTCZx9dVXo6urC2eeeSbWrVuHurq6coetVIij/Qk+i9+KvLGs0qvmisdy6w272lSORbhLyBWspa72vKj2CgDAOvkUoW1TtJc71EkvArwK2TpcBORqfZ/Vu26NWWXc08+F1kGinl0gQvvJxcKNcKZm+JVdAIDgAl55lptJK8ADg7x62NlObhYwq8Nlf25DLZcFKEXJES4VrnG2cAfE038Nxl0iEeM+bP/bd3E9u2o9yU8z5odoZbQT5nMR8HmiwdajusVuId6+CeHQIVxHLKU6pQxXfySmyneGokxH3vGOd+Dee+/Fb3/7W6xatQrXXnttwfZEIoFrrrlmgkY3daiUtlZdXXmO9r9d1Rp/qXp2NLq3VEeKsRxbqftKt4Nyx+LnKlEqgRhryIKnGw3WCUO6Y1i3t4isfyw5TUj3i7o24xKRYi2cbyRNKt0sMgtbivqd9fvtRdvceLFODWwlF4igOA6vLzG+0D6j340TBwA4c9upTNSzOtq1DhYAYM6BdJyQ58Mb3zCuIYfvb7lv57ePWP9o/05QFGViUW09Pqi2VqqFn4OAEzHOET5zmgExR+nVEw4H+a5uqtfKLl7WqSsoXAXsbKp13gWAXA21HcjwHa7YAdJ3qWbhpJuj7dkoJ8Mn9lG9oRliDtU4TMQO5sW+9Dpdz/UCGSqL9wsXja6kGRP36wapP+mCFhyiY8/F+HZUqIs0X6BHnD/rlCAdgfvYRdjtNk7GfTzPDHN+PYdhAG6ajtM15xkAHOv+G+Dz64j3ydvXvE/yPbS28tKRxAGN25HuGMb1wnfO1c9pYoR5dRSbo3hz7PKzaPctcH6wLhbSYcWeX1nm46LiNz6vrzKu84abF/Z1n/Bz3hhLe+Nw3Xi0M12OU5meVFN/l7y44pprrsEDDzyADRs2IBQK4YwzzsA73/lOvPGNb8Q555yDmM/kyUj86Ec/Gna74zhYvXo1Vq9eXXbbiqIoiqIoyti44IILcMEFF/huu/HGG/Hcc8+N74CmEJXW1qqrFUVRFEVRJjeqrauHamtFURRFURTlcKqlv0teXPHMM8/gsssuwxvf+EasXLkSCfFEtKIoiqIoijI96Onpwc9+9jP86Ec/wnPPPYecX26oMiKqrRVFURRFURTV1pVBtbWiKIqiKIpSCpXQ3yUvrnjsscfKblyZekwmm6CxxHj4MVGRB5U+pyXbcUnrsBKjRCS/67+jtH1FHIhlXfJOAMDFjR/yyrL7DtALYX3mGCs1GY3h7iHL4kBzEzdov/w6u3nf5kaqt5Xtfp06E/MhrJCt4ZkzyLbB7ixq243wV2TgYE/RcTjCos7b10SZ5Lds98qCZqx+ER9OjYg3MfVcMb4CKzh7Lnt7ub8UbQ+1t/EuPbTdvkeAf2xIuVZqo2EyfWcoSkVxwV8g1Wh7EvLHP/4R//mf/4m7774bCxYswDve8Q788Ic/nOhhHbWotlaOViodbee3T6V1/ngzlvHLSBE/rWbLZD0biZEXMRjBDtLWMt5ERoRYAkbPptpqi7bF9gkL5kayObdxHgCQbaWojtArO7wyGwkYXMw5ptbW2RExIjaOxOHUPA8bcyLbg/0Jjk6RESr22ENzZvOYTZk87uCMZt5u4kf8YlwumfcP3LbpeyxxL4qiDEM1dbVtfxKi2rqyqLZWRoPfnOYRtZtLERer4u/jIhM9AYdjN7yIiDBHSuQOHKRtERF1YTVJgPe1EcNuiMsC3RRdnJvB84fBQYqt6FnIWihgIjgyCd43F6W4h7odHC/cP4fG0LCFY9hcEwuRSxTfKop3sB5MNZOWC6RFHHRt8Vxn+KCJWw7znHCuhurZGBGA510D/VwGEymC/m6ul+N4EdfERQMZr8wx59odFG1nOYLFkjfRzE6wOD5E4pjYkHya+7Dvf8G+tg9xU84J+cQ327ldEcVh4y9GqmeR9eycvl8EiCwb7vPtGx/ig1+9cq5vyo2kHnEMY4ibHEu9o5Gj/ZpaGQPTVFuXQiX1d8mLK/x47rnnsGnTJrS3t2PlypVwfL74FUVRFEVRlKOTXbt24cc//jH+8z//EwMDA3jXu96FTCaDX/3qV1i2bNlED2/KodpaURRFURRl6qLaevw5//zzcfvtt2PBggUTPRRFURRFURRlnKmW/i55ccXll1+O2267DXV1dejv78c73vEO3H///QiHw8hkMnjNa16D+++/H42NjaMejKIoiqIoytGA49K/arU9GXjzm9+MRx55BG95y1vw3e9+FxdffDGCwSD+7d/+baKHNiVQba0oiqIoilJdXW3bnwyotq4u99xzj2/5ww8/jN/85jeYN28eAOCtb33reA5LURRFURRlXJku2roUqqm/Hdd1SzoVwWAQe/fuRUtLCz796U/jV7/6Ff7f//t/OO200/Diiy/iXe96Fy6++GLceuutYx5UJent7UVDQwN6enpQX18/0cM5KpkMFkLjPYaybOGOsO9kt1sqOEYR3SHjQkbbZqmWWwWRIcb6TO4rrfaGw4vbkLEb1kptBseH5LeT73Bw1kwuMxbHgX5hh9dH9nX5/gEuy5DVW2j+nKJ6TjxeNCa3t897bSNKXBFbkreRJ7U1GA45BnucOVFm7Qbt+ArKUmz7N9x74mctNxJHy+dcmTpMlr/ndhyL//EmBIUdeSXJDQ1h89f+acKPNRQK4X//7/+Nj33sY1iyZIlXHg6H8de//lWfrhsjqq2nL5NBW0sm09/08RyLjdIAKhP3cKSovJHi10qpXyoXH3cD/8dEcax94cu83ScWxIvx8InLcI7lp3ydbtK2Nh4EAPJxsmDONLAGj27vLOhf1nNe3FzUvzO3nceyeSuN5cSlRf3mRSyIu3wx/XzqRa/Mjr8gBsWcD3fXXq9Mvtf2fORE27Ydew7kPpPtd1dRxsJk+Hs+HroaUG09XQgEAnAcB8NNczuOM6pM7WoyGX4Xj3Yq/fd5xPhjH3dBGRURaKD30RWREo6JunDEd52NunASosxEDWdEBEioj+b2cgmOGbHxHJE+EVthkjPyYY4FCZoo4UwtP18bNvtkRQRIMEM7Z+OiXi/FZWTqwkX7Skv4QJrmI/MxrmfHEDnA85cwv5vOgIgAMefSFecAe/bRz1kzuJqcszUxHlKrDUdezI168S1uvqheQdxH3hygiGr2i9MYdl/Zhz32EJ9f13wXyUgO30hA059fdMdoPvvDxQ5KKhHnMRrGQ29P1H0mvX6YukyWv+XTTVuXQjX1d2DkKoQUp/fddx++9rWv4bTTTgMALF++HF//+tfxm9/8ZtQDURRFURRFOapwq/RvkvCnP/0JfX19OP3003HmmWfie9/7Hg4cODDRw5oyqLZWFEVRFEUxVEtXq7aeNlx00UW45JJL0NHRgXw+7/0LBoN48cUXkc/nJ93CCkVRFEVRlKowDbR1KVRTf5ccCwLAy33et28fli9fXrDtxBNPxM6dOysyKGVyMRlW1Y33GLzVqCOtVh5m31KZqBWZR1qhOuwKVuk0YdsTTge+q1qNK4JfPemYYNuWY3GMU4N0d8h19QAodJDI7z9I9WoSomdqz+3gL0tnySIqk6vH7eusuMhuaqD2WtnhwhkcoheptFfmJqnMifCTeX4rp63DhZtldwl7TAXOFHalX97/gj/X20v9CYcOuzpaOldIxwrLRbVXUNNDYpuPW8hYXFsqgT4RqCiTh7PPPhtnn302vv3tb+MXv/gF/vM//xPXX3898vk87r//fsybNw91dXUjN6QcEdXW05PJ9vdtMo2n0k8bDtdefmho1Pv6MZKe8kO6RUjHheHaCZgnUPzcNqzzg6zn10Zozmweg3GJyDayE5vz5+dom9jHjZP+dLds53rmZ9g4SQBAagEdU/Q5MRajrR1xvJasGLPXbjc7wGV376GxyH2NA0bAx22jAHPdIBnpPR7uybJS3flGcitRba0o0xPV1tXlvvvuwze/+U2cccYZ+P73v4+3vOUtEz0kZZwY779vcg4QZsFOIMHzkfkemruTc5meY0WY3R0c+1I4YVi3L+k+kTNlgQw7IcQ7SPfko3xrZ2C2cbPo5TnFQMbHZcM4K0S6WAs7GbsP68HgQNr0HyraN9jJc5mZVprLDO/t5k6Mo648Njuv6op5UG+7dA42jh7SYTi7j+d2C+d+ibyY5/Wa9sYw/LPF3rxqXtwxtPOyYh7cm8P2cceR872+c+c+9xl858bN+ShwxxjGjadUh4sj7VM2PsdW6Xnksew7Fp1fTca7v+Hu7ej1gDLdqKb+Ltm5AgC+8IUv4Prrr0cgEEBHR0fBtoMHD6K2tnZUg1AURVEURTmqqOYK4Em2CjiRSOCDH/wgHnnkEbzwwgv45Cc/ia997WtoaWnRzOIxotpaURRFUZRpT7V1tWrracMnPvEJ3HPPPfjMZz6Dq666CoODpcUGKIqiKIqiTBmmmbYuhWro75IXV7zhDW/Ahg0b8Oyzz2LZsmXYurXwCZN7770XJ5544qgGoSiKoiiKokx+li5diltuuQW7du3Cz3/+84kezlGNamtFURRFUZTpjWrrynPKKafgqaeeguM4OPXUUwui+BRFURRFUZTpTaX0t+NWSGW++uqriEQimDt3biWaqxi9vb1oaGhAT08P6q3lvqKMM+VaL/nahJVYVu6YRrPvaNr2i/soiK/wsRYbLrZCRmNYZHtBv9/3lhmmL15XZm3p8o1s/+Ps3U/tJYVF3kL6bssLK+Rgeyu9kJEiIWMnFxFWf7aPzi4eq7EOtPZzAODEY+Yn2/DlDhz0XgfMMa/t/pFXdsmca6mebNueB3FObSzLqvj7uJ6IEjm8XoGtm2lHRrv4oRZjSjWZLH/P7TiW3HATgtFYVfrIpYaw6ZZ/mvBjVSYW1daKMv6MRW+Xo61L1UxjuYbwG8tFifcD8I8UsdtGwpnbXlSWbW3wXgeffgUA4IqokEwDadhwD2t196kXi9oJLqYIv3wj22gHuskmOucTHyKjQnKHOgEURp7YSBG/MoCjU2REjC2T2PMlz5HfObT8/+zdeXgUVdr38V9nIWxJUBACw6oiCqggOCgqiwMBXAbFfUFwGx2dUeRxUMARcFhEHxVfdFgcBFxQnHEdFxZFUB9lRFxHHUQEZGRzAcIekq73j+6qPp2u9JL0lvT3c125SKpOnTpVCd13Tk7dt1s7ynMg3aTD+3ky4mqJ2DqTvfLKK3r77bc1evRoNW3aNNXDcZUO/xdRObM8Q1A5kAqy6hmvYf7yEp78QBZCe57OY5S0cOYNjT/NHGreSJKUvS9Qktjptm5gnjGr1DenePDwwNxo3W3+UiF1A2U8snftlySVFwbmGa1s35xozg6j7Ia936wi4h9W7hZjvtE/5+mU/ZDk2bM/ZKxWQ19/nh0lgY3+kheWMYdq7faVgvPUNeZ47XlSs/yxcY+s0lJ/d0Y5FX9ZkCyjH6+ducYsTWL3bczF2v25ldMIKs9RsQ8ZpUSsQMkWu5+guNwTWp7F+b6b+/zbIv6OEGUJkLB9JCk2jfdccTLmntNxfpvfJdJburyXE1snV07kJtE58sgj49UVAAAAkNGIrQEAAICq++1vf0upFQAAAMRd1IsrHnjgAV144YVq06ZNIseDJEr2irdkrixM9HliFetYqvIUW6Qn1cLt659zaWB7hOwE0YzB3GZnSnAbi9t53a7DLeuFuVLYeyA0U4PXvyo8y8hw4TngX/ldbqyS9q+Yztpj1OL0ry4PWmX+362+dm0CTxA766Z37wkZs7mi3burJGR/VsMGIWO2n7gzc3iY12mvDB/QcFigb/+1e8xV43ZmEGOVt3OvvUaWDWcwLllDDNH+TKTT/zkg4RJZY47MtRmB2BrVlcmxdaJEimvD3fNE3B+3PgcecYOkQNxotjMzOSz6cVbIsebTkhX7c2NmZxjU6lbfJ/sD2Se2nu17/Wq2bGvgoKN82zz7Dzmb7Ocss7cExuz1Z4jIMsZs9+357xZnk/2WaGaUsDNNmPfAzk5hGdnosn4OfVrH7Mc+t9fIZuGWkSLWnwU3mfD/B6iyRNduJraGpG3btmnWrFm6++67Uz0UpCm3LAXmnKJV5ptjs4w5RXvuzyoNxD12Vlozu66TfddrZGBo4GtX3iAwf5jtj58OFQbildwSXz85O0MzROTWMeYU/f1k7wmc19vQ18+hhoGsF3k/hfbj8WdMyNmyM7Cx3JeNwSoIZBTz7NwdtE+SlOUJuTb5MwI787WSlOsfwyHjXtmZJsy5T//9dTJPVBxrjm/+02vcX/v75N1vXJs912nMgzqZJsqMcfkV17ks0K4sNNOvnWFiSWkghbzzM+OWGN4lI0UkUceVLtkxYs10F+8Mem7HJmJcyZCOfydLh/uCGoTYOimyIjfx+dOf/qSjjjpK/fv318KFC1VaGpqeCgAAAEBkxNYAAABAcmzdulUTJkxI9TAAAABQC8RUFuRvf/ubXnrpJQ0dOlQFBQW68sordd1116lz586JGh8AAEDa8Vi+j0T1jcxAbA0AADJdIuNqu3/Ufp9//nnY/WvWrEnSSAAAAFKH2Do5YlpccdZZZ2n48OHavn275s2bp7lz52r69Onq1q2brr/+el166aXKz8+P3BHSQrLTCVXnfNGmk6qJKZLidW3h9kc6R7RlH9zO59Z3pPNFm1rXrUSInVYv6DXcpaxFUDkN+9gSf/o6s4SGP4Vf+ZZtge4KCnzt9+4NtGvgS4Pn2XfA2Va22V8qpG4gTaDT3igpYqfByz6sMLBtj6/v7MMPc7Zlu5QKySosCIx/776gsUhSjn9/+S87Au385UCc/iSV7wlcS6yc74Mxrqr8zAAAghFbozqijbvicY5METFmTsLvM27xb7R9RxqfHUtGOofdj1MKRFKZv3SGeQ67pIjdrxQotWHVM0rzrdvo+8QoAWKXKAlq5y8LYpYKsfwlQDxGOQ/5y4GYZU7s8emH0Oswrzf76HbO515/+RGzVEhV7n9FbqVFAACJ1aVLF3k8Hlku6fft7R4zTT9Qkcef4NsKlLzwmuU+7LlEb/i/Kln7/bGLMXfn7MsLlOfw7POX+yg1SlDYpYvrBdpl7fLHWS4/29l7jKTk/p9vz/5ARkT7HHXXB0qgedsU+fYZpYRzNvrKeJjl1axc3xxg1vbAfKNd2sPasTNwDn8cZe0zSnL475XHnC+175sxV2kfY5UH5lrtEh+WcZ9d513Ncsz298lj3A/7+2jMZXr8JUwssyCzv2yIeT6npEekch4u+52yzMY+JyY1ymPb47NLfEjhS35X53e+qsS3bueN9vxV+Z0p2utM9e+nZvkg83sXq1RfB4Dqi7osiKlp06YaNWqUvv76ay1fvlwdO3bUbbfdpubNm8d7fAAAAOnHSvAHMgqxNQAAyFiJjquJrTNC48aN9dhjj2n9+vUhH999951effXVVA8RAAAg8YitkyLqzBWVre4944wzdMYZZ+j//b//p4ULF8ZtYAAAAEBtRWwNAAAAxEe3bt20efNmtWnTxnX/zp07XbNaAAAAALGKenFFpAC0oKBA119/fbUHBLipSqqkcOlxq5LSKlHidd5o0wFXbF/VMYQ7X6TSHtFySwmWVd+fQri83NnmVgbDVl5S4nye7U8xXL5jl7PNTutnp50zBZXkOMz3uXfDf0P6s1PISZL8YzHLjHhyQ19q7XIgZdt+DGnnKQuk5ivfF0iznF0QGI8zLiMNc0VefwmVkDH6uX3viutdWem+SKL9PxePn/lEpkAHopLIlbrV7HfKlCkaM2aMbr31Vk2bNs3XpWVpwoQJmj17tnbs2KEePXro0UcfVadOnao/XlQJsTXihffB+IlUsi6ceKWwjdf53MZvl6sIiq39qZzdSll4/eU3KmPHod4DgVTObiU77P0eswSIvc9fmkOS1NKXraf82/Uh58r5VYvQARgl9+zkzuXGmM2yJjazb/vag8bvwm5nl0GJRaw/R9EeSyyMWiXRT8Dx9/SMcMMNN2ivUdq1otatW2vu3LlJHBHSTdD8pH+he1Baf7uMhPF7Wna+UcLCP1eXVS9QTswq9x1jli/z1Pd/nmWWqPD16TkYKDNi1fWVtbDqBOYMs3btkSTl/hI6j2g1rGe08/2se0oCc4KWPae4PzAXaJ/DY8yXesr8Yy4z5gn9Y/XsCMyhOuWWjbLLVn59/7FloccasZ8dI5oliu2SHFky2vnndi2jRInl/96YpUC8+42SIy7lW5xtLnOfUqDdklLf9zvoZ8EuG2Iea2+zjG3+c0T6OYq6FLZRXqKiSLFdtHPt0ZbgjvZYtz5qY1nKsH/zqEYpECBpiK2TIurFFV6vN3IjAAAApMyqVas0e/ZsnXDCCUHb77vvPj344IOaN2+ejjnmGE2cOFH9+/fXmjVrlJ+fX0lvSCRiawAAACA+zj///LD7DzvsMA0bNixJowEAAEBtFvXiispYllVpWmMgXqqyIjJcu3RYBZkMVckuEe+n6qrTrn/OpSHbvAcOhmxbWvZspce6XWf2EUcEGvqzO3gKAn9c9P6yw/evkR3Ds8u3etvjf6JOkvSzr53MFdb2SnYzi4a94tnIJFHmf6rOzEZhr/Iu959fknKaFwX69q/8toynMTz+FfNZRnYMMzNHxTGYWTTcfj6sg6H317mXLvfZxP85ZBKP5ftIVN9VsWfPHl1xxRV67LHHNHHiRGe7ZVmaNm2axo4dqyFDhkiS5s+fr2bNmmnBggW64YbYn8RF4hBbA1VTnQwBFfuoSj/xzkYXqe94P0U2oP5Q53M7k4PZh1vWBrudmVXCsrNTGPGovf+Nb+4L6c9zVCB9e/mXayQZ2eEUyERhZtGwY2bLyHphZ94wx2mPJct4mtTkxPVGNgv73GZ2OPs6jedPHeZ9c8v+UZ2fC2JrZIpExtV2/wBqtnjEec7cnALzW26xk7nNzIYbyKQbWCTv+nubf5udNUKSPCW+eTzLzOJV6s9iYWTK8B7RyP+JsRC/nq8fO1uFJFk5/jk+Yz7S6S8ncJ3egnr+YwNxTZY/24W1e0/gWP8cpnfHzkC7Ixr72pkZc//rO8Yys/8eDM2oa5WW+vo15iDt7BTlu3cHzmtkxXDamVkxXDjfO7fMD27fD+P+OseY7dyyXfizYsQrg4Tbz1bU7ezsGFX42Q/3O0R1su7Fcl9qYsxaE8cMmIitk8NtfiAmeXl5+vrrr+MxFgAAAPiVlJQEfRx0Wfxkuvnmm3X22WerX79+QdvXr1+vrVu3qri42NmWl5en3r176/3330/I2FF1xNYAAABA7KZPn65hw4bpued8aduffPJJdezYUccee6zGjBmjsgh/tAUAAACiEXXmipEjR7puLy8v17333qvGjX2rGR988MH4jAwAACBdJbJ+nb/fVq1aBW0eN26cxo8f73rIs88+q9WrV+ujjz4K2bd161ZJUrNmzYK2N2vWTBs3bqz+eFElxNYAAACiLjTi4i9/+Yvuv/9+FRcX69Zbb9X69et1//3367bbblNWVpYeeugh5ebmasKECakeKgAAQOIQWydF1Isrpk2bphNPPFGNGjUK2m5Zlr7++ms1aNCAFMZImHin2a0t6Z2qk8IrEdzub3XueaQyFJWd3zyfa2kRM62fvyyH1yjFYfnLgZjp6Tw5vpdLz74DgY7q+FLzWUYaPu8+Xzq87AZGWRD/0+Zmaju7NEn5jz8GttnnMs4blH7Pn07PuyeQClD7QtPvefzp+bKMNH2L9j8lSSqud2WgoT8lots9ingvXdLmJfPnrbb8HwbC2bRpkwqM0kF5ZurNCu1uvfVWLVmyRHUrSX0uhaYspfxEahFbA/EVj9ggHeKLRJWRiNQu0u8VdnkOswyGXULDLNnhWirEv23gMaNC+ssyYt3sTh1CB+tvZ5bcsPsxXyEHHn9X6LF5dYLGJAWXCLHLikT63cW8Zlu4ex1tKuRIqcjT4ecRAGqKefPmad68eRoyZIg+++wzdevWTfPnz9cVV1whSTr22GM1atQoFlckSbznX6tV9s0uBWG5/DXIKBViz3nZ5T8kSeb8nF3qwizZYZYEttv9stPXrvFhgW31ffGHU7rD2CajzIhT+qMsdM4tqKTIft88o/fwQIljs2yIs23df33t8/ONjf4IymMkNPeXYM4ySrNZ/rlHa38gjrLnRnWoNHCsf67SMq7NYZRbzqrrG79VHnptHrlcr5lpxvg+Fde5LKStW5kXtxIhS8ufC23nxv+z4tqf8XMUNs4zypbY562sRHfFbdH+vFcnlkxknEkMCyATRL24YtKkSXrsscf0wAMP6Mwzz3S25+bmat68eerYsWNCBggAAJB2kpC5oqCgIGhxRWVWr16t7du3q1u3bs628vJyvfPOO3rkkUe0Zo2vhv3WrVvVvHlzp8327dtDslkgeYitAQAAxNN1iIstW7aoe/fukqQTTzxRWVlZ6tKli7P/pJNO0ubNm1M0OgAAgCQhtk6KqBdXjB49Wv369dOVV16pc889V1OmTFFubm4ixwbEXTyeMEon6bAC3K2fqtzLaFfnhmtnbnMyNLhkWLD8mSSkQOYIj/FEuJ05wtMgdAV6+Y8/OZ/bWS8qW03t8K/iNs9hP62X07Z1YNuWbUHnl6RyI0uFvco721hJbvfjyQ19OS83MnTYK/DNbCDO98kcs/25y31z3ZZGauL/YSAefvOb3+iLL74I2nb11Vfr2GOP1R133KEjjzxSRUVFWrp0qbp27SpJKi0t1YoVKzR16tRUDBkitgYQPbcn26oTM0dq57bNzvgQlEHiiBskBWeGsJnZLDwtfQv77EwRkmSd1sXX37tjnW3Fv/Y9TZy1PvSPX+Y9sLNimHM61rqNQeeSpEVfTAw51nwi0x6jfR1S4DrNbBWeBr4nWBf9OCtkPNE+gWiyj4lXtj8AgFRUVKSvvvpKrVu31tq1a1VeXq6vvvpKnTp1kiR9+eWXatq0aYpHmTmS+X5mZgiwswpEjH/MY/zsuTgzs4KZ+8Djz17ryTYyPvizPwRt88cN1h4j0+xhLg9N+LNTeMx2Wf5+zOwO/t8RPYfMuUffyLJ+2e1ssnb7Pve4ZbPMMq7EPm+OMRdoZ6Qw5zf95/PUM7J++bNTeIw742S2yDbnFn3XkWVk9rCPNbP/unHmN71Ghgi3uUwjM4Xb99PtZ8F1HjTMXGe0sVqk+Ui3Y8PNa6dD9gkAQHhRL66QpJNPPlmrV6/WzTffrG7duunpp58mXTEAAMg4Hsv3kai+Y5Gfn6/OnTsHbWvQoIEaN27sbB8xYoQmT56s9u3bq3379po8ebLq16+vyy+/PF7DRhUQWwMAgEyXyLja7h+13+WXX66rrrpKgwcP1ltvvaU77rhDt99+u37++Wd5PB5NmjRJF154YaqHCQAAkFDE1skR0+IKSWrYsKHmz5+vZ599Vv3791e5S50sAAAApI9Ro0Zp//79uummm7Rjxw716NFDS5YsUb5ZexUpQWwNAAAAVM+ECRNUr149rVy5UjfccIPuuOMOnXDCCRo1apT27dunc889V3/5y19SPUwAAICM0rZtW23cuDFk+0033aRHH31Uw4cP1/z584P29ejRQytXrkzWEKvEY1lWldeZ/Pe//9Xq1avVr18/NXBJn58OSkpKVFhYqF27dkVVtxzIFHaZCCk4vVrM/SSwFEO4NLoRU65FWQbDrZyGXTYk++h2zjbvJl96YrNkh/eAv91hhYH+/GkCrb2BtH522RAzlZ7XX+7D7M9y+YOamS7PPk+5kWY5q74v3aB3377QbQcC5U+cNHcu126ew75fbvfX/Jmx+zNLndj3LV5pqgFTuryf2+M49o+TlZ3nkm4zDsoPHtB/po9J+bUi+YitgaojvohetPG7azxolthwSTttlw1xi+OzO3UIfLHVV5rPjJnt8iJmDO7Z74svrXp5IdvM0iNupUmc8xqlQNzOF+lnxi4bYsbgNrd00eZ9McuoRIMyd0iWdHg/T0ZcLRFbI72lw//F2iRs6QajdIQ9F7ek9BlnW3GdyyRJWYWB74M9xycFygRnH9HE2WbHFR7jmKDSGnY7f1kQz25jrjDPXxJy155Aw8KGvnbl3kA7f9kQT4N6gXYHS337yowSJmZpEvvYA24lO7KD95ntS0sDzfJ9Y5G/nIdkzD0a/dmZF4P+xBTugQFzHtQ+n3lsuHLFMmJSl3Iwbu2iFakMn32+peXPhRyTyFJvifodh5gTiJ90eS9P19j6xx9/DHqQ7N///rf69++vt99+W3369NHw4cO1bds2zZ0712lTp04dHX744W7dpY2oM1d89913ateuXVCq4pYtW6ply5YJGRgAAABQWxFbAwAAAPHhFlsDAAAgtY444oigr++9914dddRR6t27t7MtLy9PRUVFyR5atYQuaaxE+/bt9eOPPzpfX3LJJdq2bVtCBgUAAJDO7Pp1ifpA7UdsDQAAkPi4mtg6MxBbAwAApHdsXVpaqqeeekrXXHNN0ILY5cuXq2nTpjrmmGN0/fXXa/v27XG4E4kVdeaKitVDXn/9dU2ZMiXuAwKQWPFKJ5aM1MvR9u02FrdSJ27pjJfs95fBMEte+FPQ2aVATGb5DbudmSI4p7lvhZ33YKAkh112w2OkP8pq6Ev3bpcHkQJlNSorW2Lt96dKNtItlZeUhG6zS44Yx2Y19JUUWfTjLGdbcb0rQ9rZXO+9S0q+JfufCm0XAenmAIDYGoi36sQXqSopkqp0vNGeK1I7t1IcA+oPleReiqOsUSCddc7W0D5yftXCt+2/WwIdHtVGklT+5Rpnk1k2xNnWODRlqB2jm/GvW8pnu+yH2da+jopjDMceg1l6JFbEyQBQNcTWtVus5RfsEh+SUao3J3TuK6id1/cz5FaKV5KyGvlLApv9GKVyHXVDt3l+2eX7xCyncdBXbsPylwKRJM/e/f7BGD/P/rk4y94nSf75w6Cfe7ssmTEfqSz/H66McsCq499mzFE6ZY+NeKrcXqxk/PEry3+9llH6xBuuBIiLpUYpFmcu1gr04ZQrrqTsR9B2+5gwZWCCynjYxxr92cdGjMFcSo+4iXcsF215bDfh/o/E43en6vYDoGYr8f9dypaXl6c8t/dFw0svvaSdO3dq+PDhzrZBgwbpoosuUps2bbR+/Xr9+c9/1plnnqnVq1dH7C+Vol5cAQAAAD/L/5GovgEAAIBMkMi42u4fAAAAyARJiq1btWoVtHncuHEaP3582EPnzJmjQYMGqUWLFs62Sy65xPm8c+fO6t69u9q0aaPXXntNQ4YMiduw4y3qxRUejyekbh117ID0Ee0Td7Gubq3smGSsTHV7sizqFcVR9hdu9bt1yDhvWWiGC3vbwEbXOtus0lL/sYFV3Nl2lgozm4U/C4XHWLWeZT/ttj/wdJyZKcNtv8e/eq/cyIDhrGo3xm8f43YPzEwZ4e6H2c4+RyJXKzv32siYwYpoALUFsTWQPlyfdotzNgu3mCmVmTIqjiVakdo75zCyPWT5n6T0/N+nzja3ZxzLfvBljbMzWEhSmZGxwmaZmS0q7jOyRthPX7rGv3LPdmFmsajIvHYzs4XNzJBRVQmNrVOUoQUAkoHYunYLNy8Z9D5vf8+NuTbZmSvq1HE2OXN3RtYFZ34uN/CUrKde3cDnubm+Tw4FJts8/iwVVt1A3/rxZ9+/XuMvW3bGWONYZ16wQYNAu4a+TBnebYESN546/vMaY7UzVmQ1PszZ5t3qS6Ge1SxQ297a4cuYYc5HOlkvzGwWfuU//RQ4r52lwmjnZPMy/m/Z99XMbGtnBFnikqXCNSOF0Z99rDlfambKMDNRVFRZzBdyPvMYlwwXbv2Fi5+Skckh3O8r5v5ot0WSqKwXAGqPTZs2qcDIph4py8TGjRv15ptv6oUXXgjbrnnz5mrTpo3Wrl0bl3EmSkxlQYYPH+7coAMHDujGG29UAzMAkCLeGAAAgBqPzBWoJmJrAAAAkbkCcUFsDQAAoKTF1gUFBUGLKyKZO3eumjZtqrPPPjtsu59//lmbNm1S8+bNqzPKhIt6ccWwYcOCvr7yyivjPhgAAAAgExBbAwAAAPFBbA0AAJCevF6v5s6dq2HDhiknJ7AsYc+ePRo/frwuuOACNW/eXBs2bNCYMWPUpEkTnX/++SkccWQey3LJh1SLlJSUqLCwULt27YppFQ1QmyQjPVml5/anfbNLaCTkHNVIcetW8sLjT9dnmWnzXGTV96XrM9MI2unysuoG0iDZ+82UdnZqQa9RziPL/4SFp0F9Z1uZkQowHHvMkpRllyExS4VUGJ+vgX/cZrmPivtMLmVB3ESbqg6IRbq8n9vj6HjTZGXn1Y18QBWUHzygr/46JuXXCrhJl/+LQDqriaUd4p322CybYaeQdiursXjfk842uySHWV7DKdNxWKGzzbPfF6N7f/4lsO2oNr5jv5gYOPaYUSHjKv92fcj4B7W61dlmlyYxuaVUtkudeI3yJxXbmyKmqQ5zrBtia1RXOryfJyOuloitkd7S4f9iTeJWUiKorK25388pB1LuMo9lztPZpSmyspxtWUYJMWv/ft+/+/YH9hfk+7aZpUL85zNLlXnyG4aMwe7H/NOMPYagbdm+8Xjq1Qsc6+/bLm8SxBi/fX1mO7frdPZnh84PBh1r9+d1+XOSS0nfiPGPPRbzz1P+bWaZDrcSzW7M73+48iFBx8RhTjnasnnEbEDtlC7v5ekcWy9ZskQDBgzQmjVrdMwxxzjb9+/fr/POO0+ffPKJdu7cqebNm6tv3776y1/+olatWiXqEuIi6swVAAAAAAAAAAAAAAAAkRQXF8stz0O9evW0ePHiFIyo+lhcAWSAVK6MTWTGCuccYVZEVyWLgtuKYre+vQd8T82ZWSPMz212BgxP/UBGivIdu3znMO6PvRLbY2TMMDNg2Odzy4oRlGUjL7A/ZAzGvqw8X4aLRTvnhLQvrhdIoen07bIK3WSPP9rvA1CjJbJ+Xa3OKQYAtV8i455E9R1tv1V5Ms/JPmGwM1a4ZYMws17YzCwVrlkjvlwT0p89BrO/bOMJVJvVKD9wzKbQ3yvcfw8IzVhhj8tNvJ+GJLZGrZKkutDReuedd3T//fdr9erV2rJli1588UWdd955zv7hw4dr/vz5Qcf06NFDK1eujMNggcwSjyf/g96n7UwInqyQdiY7C4STxUGBbBEe4/08KP44orFvf47Ln1OMTK9WmX+ezsj4YO3Y6Tu2QYPAMfb83O7dgXa5uSFdW6X+rBjZ5jnK/P0Z84y/7PANxchw4anj78/IrOH8ccscn51Rw8zuYbczr83OWGFms7X3G/fSdV7VziphfG/sOdGg76H/vJGyUETKZpGMbBHxip8rqsq8NQA40iy2rq3CRxoAAAAAAAAAkAH27t2rE088UY888kilbQYOHKgtW7Y4H6+//noSRwgAAAAglchcAQAAECOP5ftIVN8AAABAJkhkXG33H4tBgwZp0KBBYdvk5eWpqKioGqMCAAAA4i/dYuvaisUVqFUipc1C8iUzVVmkMh7RjiXW1LsDGg5zPl+8Z37IfrvEhmWk13NS3xlp7GzWoTLn86yGgZSBS/19myU7PP60gFkFBYHj7fSAZspAf5o+s3yI1/+v271yZaQCdLuX4UrAmGNesv+p6M4HAACAaknG70eRzlHuT6nt1s6tvVnGwy6/YZbcsFNgL/WXFjGPWeyyLaiEh0s5D2vdRudzu4SJWT5k0Y+zQsZlM88Xq0j3Ldz3yyy1Yo8PQHglJSVBX+fl5SnPpaRmNJYvX66mTZuqUaNG6t27tyZNmqSmTZvGY5hARgn3Xuf2PulWAiRSLOGU+zDKeXiy/cm8vcZfiOzSGdnuib6tff4Y4lCgnIZdXiSoTIZ/7s9jzOd5/OU+vLuM1yH/nJ1Z2sPau8+3rU6dwDZ/O/PYLLvscND8YUNfe6Pch/b7xmzPE5rnDWKX6rC8zib7vhbXuczZtqT0GUnBc5meLN/3wTJugc21ZItxr8zSHxWZpUBcY0iXOUjX8iHmOazQvwhWp2SHm3jE21WZtwYAJBdlQQAAAGJlJfgDAAAAyASJjqv9sXWrVq1UWFjofEyZMqVKwx00aJCefvppLVu2TA888IBWrVqlM888UweNBxkAAACAlEhSbJ3pyFwBAAAAAAAAoNbatGmTCoyMj1XNWnHJJZc4n3fu3Fndu3dXmzZt9Nprr2nIkCHVHicAAACA9MbiCtQq6ZQWK1NKlFQnnW2yzxs23aCR0s4p2RFluQy3ciRmKmG7DIZZGsOtHIgn1/eSbJYFKd+xy/ncLj9i7rc/z24YeDn37vOlEcw2Jo6k0Kdo7BIhkcqpOOVAjNR9bikZw91fsxSIW0mRZJaPiSTZY0mna0eMWKkLAEiyWOOGVMUXZjputzG4xZx26Q+z1Ibdzi1ldrgygFVl9+1WXsRtWyTh4t5w7SseUxGlQFDrJCGuLigoCFpcES/NmzdXmzZttHbt2rj3DWSKSO9/dmkHc59drsI81iz94Wzzl+7w7tnjbMv2l93wGhln7HK71t69gYO9gTIZdjkQj1GqzNq/3/eJeV7/5x5jAZflUpbM4y+T4d1jnM8uJ5xlJBt3KeNRvnu3rw+jfIhdGticM3S2meVPyo2yIXY7u5/y0NoeZkkRp8SGUV5jSZlLyRanY4/RkUtJDn8ZD9cSMEY5j0jxbNh4yxhDMkp21GSZ8rcMICMwZ51wlAUBAAAAAAAAgBj9/PPP2rRpk5o3b57qoQAAAABIAjJXAAmSKSs8U3WdkZ7+inVcdraKaM5XkesKa7fMFC6rzc0V5W6y6hor3e0V5EYGCTurxKKdc0LGU15SEmhW3/e0n/eAkcHC34+ZUcNp589+YY7B3GYfE/R9sK/ZJcOFKdy2dFglnezzhnuaM1NeR2oij+X7SFTfAAC4iXdsEI+YoyrZyOz9ZgY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\n",
      "text/plain": [
       "<Figure size 2200x1800 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CMD with coloring based on classification\n",
    "xbins, ybins = np.linspace(-5, 10, 200), np.linspace(21.5, 37, 200)\n",
    "xctrs, yctrs = 0.5 * (xbins[1:] + xbins[:-1]), 0.5 * (ybins[1:] + ybins[:-1])\n",
    "\n",
    "plt.figure(figsize=(22, 18))\n",
    "\n",
    "# counts\n",
    "plt.subplot(3, 3, 1)\n",
    "n1, _, _ = np.histogram2d(X_ngc1275_flat[:, 1], X_ngc1275_flat[:, 0], \n",
    "                          bins=[xbins, ybins])\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow(n1.T, origin='lower', interpolation='nearest',\n",
    "           cmap='viridis', \n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F475W - F814W (measured)')\n",
    "plt.ylabel('F475W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='AST Counts')\n",
    "plt.title('NGC 1275 (Flat LF)')\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "n1, _, _ = np.histogram2d(X_ngc1275_steep[:, 1], X_ngc1275_steep[:, 0], \n",
    "                          bins=[xbins, ybins])\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow(n1.T, origin='lower', interpolation='nearest',\n",
    "           cmap='viridis',\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F475W - F814W (measured)')\n",
    "plt.ylabel('F475W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='AST Counts')\n",
    "plt.title('NGC 1275 (Steep LF)')\n",
    "\n",
    "plt.subplot(3, 3, 3)\n",
    "n1, _, _ = np.histogram2d(X_ngc3377[:, 1], X_ngc3377[:, 0],\n",
    "                          bins=[xbins, ybins])\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow(n1.T, origin='lower', interpolation='nearest',\n",
    "           cmap='viridis',\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F606W - F814W (measured)')\n",
    "plt.ylabel('F814W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='AST Counts')\n",
    "plt.title('NGC 3377')\n",
    "\n",
    "# data\n",
    "plt.subplot(3, 3, 4)\n",
    "n1, _, _ = np.histogram2d(X_ngc1275_flat[:, 1], X_ngc1275_flat[:, 0], \n",
    "                          bins=[xbins, ybins])\n",
    "h1, _, _ = np.histogram2d(X_ngc1275_flat[:, 1], X_ngc1275_flat[:, 0], \n",
    "                          bins=[xbins, ybins], \n",
    "                          weights=Y_ngc1275_flat)\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow((h1/n1).T, origin='lower', interpolation='nearest',\n",
    "           cmap='Spectral', vmin=0, vmax=1,\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F475W - F814W (measured)')\n",
    "plt.ylabel('F475W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='Recovery Fraction (Observed)')\n",
    "plt.title('NGC 1275 (Flat LF)')\n",
    "\n",
    "plt.subplot(3, 3, 5)\n",
    "n1, _, _ = np.histogram2d(X_ngc1275_steep[:, 1], X_ngc1275_steep[:, 0], \n",
    "                          bins=[xbins, ybins])\n",
    "h1, _, _ = np.histogram2d(X_ngc1275_steep[:, 1], X_ngc1275_steep[:, 0], \n",
    "                          bins=[xbins, ybins], \n",
    "                          weights=Y_ngc1275_steep)\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow((h1/n1).T, origin='lower', interpolation='nearest',\n",
    "           cmap='Spectral', vmin=0, vmax=1,\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F475W - F814W (measured)')\n",
    "plt.ylabel('F475W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='Recovery Fraction (Observed)')\n",
    "plt.title('NGC 1275 (Steep LF)')\n",
    "\n",
    "plt.subplot(3, 3, 6)\n",
    "n1, _, _ = np.histogram2d(X_ngc3377[:, 1], X_ngc3377[:, 0],\n",
    "                          bins=[xbins, ybins])\n",
    "h1, _, _ = np.histogram2d(X_ngc3377[:, 1], X_ngc3377[:, 0],\n",
    "                          bins=[xbins, ybins], \n",
    "                          weights=Y_ngc3377)\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow((h1/n1).T, origin='lower', interpolation='nearest',\n",
    "           cmap='Spectral', vmin=0, vmax=1,\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F606W - F814W (measured)')\n",
    "plt.ylabel('F814W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='Recovery Fraction (Observed)')\n",
    "plt.title('NGC 3377')\n",
    "\n",
    "# model\n",
    "plt.subplot(3, 3, 7)\n",
    "n1, _, _ = np.histogram2d(X_ngc1275_flat[:, 1], X_ngc1275_flat[:, 0],\n",
    "                          bins=[xbins, ybins])\n",
    "h1, _, _ = np.histogram2d(X_ngc1275_flat[:, 1], X_ngc1275_flat[:, 0],\n",
    "                          bins=[xbins, ybins], \n",
    "                          weights=clf_ngc1275_flat.predict_proba(X_ngc1275_flat)[:, 1])\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow((h1/n1).T, origin='lower', interpolation='nearest',\n",
    "           cmap='Spectral', vmin=0, vmax=1,\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F475W - F814W (measured)')\n",
    "plt.ylabel('F475W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='Recovery Fraction (Predicted)')\n",
    "\n",
    "plt.subplot(3, 3, 8)\n",
    "n1, _, _ = np.histogram2d(X_ngc1275_steep[:, 1], X_ngc1275_steep[:, 0], \n",
    "                          bins=[xbins, ybins])\n",
    "h1, _, _ = np.histogram2d(X_ngc1275_steep[:, 1], X_ngc1275_steep[:, 0], \n",
    "                          bins=[xbins, ybins], \n",
    "                          weights=clf_ngc1275_steep.predict_proba(X_ngc1275_steep)[:, 1])\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow((h1/n1).T, origin='lower', interpolation='nearest',\n",
    "           cmap='Spectral', vmin=0, vmax=1,\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F475W - F814W (measured)')\n",
    "plt.ylabel('F475W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='Recovery Fraction (Predicted)')\n",
    "\n",
    "plt.subplot(3, 3, 9)\n",
    "n1, _, _ = np.histogram2d(X_ngc3377[:, 1], X_ngc3377[:, 0], bins=[xbins, ybins])\n",
    "h1, _, _ = np.histogram2d(X_ngc3377[:, 1], X_ngc3377[:, 0], bins=[xbins, ybins], \n",
    "                          weights=clf_ngc3377.predict_proba(X_ngc3377)[:, 1])\n",
    "n1[n1 == 0] = np.nan\n",
    "plt.imshow((h1/n1).T, origin='lower', interpolation='nearest',\n",
    "           cmap='Spectral', vmin=0, vmax=1,\n",
    "           extent=[xctrs[0], xctrs[-1], yctrs[0], yctrs[-1]])\n",
    "plt.xlabel('F606W - F814W (measured)')\n",
    "plt.ylabel('F814W (measured)')\n",
    "plt.ylim([yctrs[-1], yctrs[0]])\n",
    "plt.xlim([xctrs[0], xctrs[-1]])\n",
    "plt.colorbar(label='Recovery Fraction (Predicted)')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3e2668",
   "metadata": {},
   "source": [
    "## Training a New Neural Network Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33dbd3a7",
   "metadata": {},
   "source": [
    "If you want to replicate our general training procedure, you can build off of the example code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2ea409f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training/20:\n",
      "Iteration 1, loss = 0.67012197\n",
      "Iteration 2, loss = 0.26604198\n",
      "Iteration 3, loss = 0.25792745\n",
      "Iteration 4, loss = 0.25621488\n",
      "Iteration 5, loss = 0.25427786\n",
      "Iteration 6, loss = 0.24852530\n",
      "Iteration 7, loss = 0.23932950\n",
      "Iteration 8, loss = 0.24173915\n",
      "Iteration 9, loss = 0.22981913\n",
      "Iteration 10, loss = 0.23684920\n",
      "Iteration 11, loss = 0.22647762\n",
      "Iteration 12, loss = 0.22478383\n",
      "Iteration 13, loss = 0.23499775\n",
      "Iteration 14, loss = 0.21777584\n",
      "Iteration 15, loss = 0.22504330\n",
      "Iteration 16, loss = 0.23007637\n",
      "Iteration 17, loss = 0.21112808\n",
      "Iteration 18, loss = 0.21054624\n",
      "Iteration 19, loss = 0.21489312\n",
      "Iteration 20, loss = 0.22474009\n",
      "Iteration 21, loss = 0.21216579\n",
      "Iteration 22, loss = 0.20689812\n",
      "Iteration 23, loss = 0.21875738\n",
      "Iteration 24, loss = 0.21138440\n",
      "Iteration 25, loss = 0.21233560\n",
      "Iteration 26, loss = 0.19872347\n",
      "Iteration 27, loss = 0.20830002\n",
      "Iteration 28, loss = 0.19699000\n",
      "Iteration 29, loss = 0.20508181\n",
      "Iteration 30, loss = 0.19448175\n",
      "Iteration 31, loss = 0.19258067\n",
      "Iteration 32, loss = 0.19674030\n",
      "Iteration 33, loss = 0.21271715\n",
      "Iteration 34, loss = 0.20617257\n",
      "Iteration 35, loss = 0.20209308\n",
      "Iteration 36, loss = 0.19722302\n",
      "Iteration 37, loss = 0.19596826\n",
      "Iteration 38, loss = 0.19194791\n",
      "Iteration 39, loss = 0.20044750\n",
      "Iteration 40, loss = 0.20227401\n",
      "Iteration 41, loss = 0.19138244\n",
      "Iteration 42, loss = 0.19650818\n",
      "Iteration 43, loss = 0.18658669\n",
      "Iteration 44, loss = 0.19510390\n",
      "Iteration 45, loss = 0.17772817\n",
      "Iteration 46, loss = 0.17845877\n",
      "Iteration 47, loss = 0.20500720\n",
      "Iteration 48, loss = 0.20017585\n",
      "Iteration 49, loss = 0.18475225\n",
      "Iteration 50, loss = 0.18652345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joshspeagle\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 51, loss = 0.19940214\n",
      "Iteration 52, loss = 0.17492616\n",
      "Iteration 53, loss = 0.17090540\n",
      "Iteration 54, loss = 0.17088144\n",
      "Iteration 55, loss = 0.18158819\n",
      "Iteration 56, loss = 0.17289792\n",
      "Iteration 57, loss = 0.16999369\n",
      "Iteration 58, loss = 0.18477345\n",
      "Iteration 59, loss = 0.17907855\n",
      "Iteration 60, loss = 0.16648057\n",
      "Iteration 61, loss = 0.16128488\n",
      "Iteration 62, loss = 0.17490689\n",
      "Iteration 63, loss = 0.16977899\n",
      "Iteration 64, loss = 0.16100232\n",
      "Iteration 65, loss = 0.16882809\n",
      "Iteration 66, loss = 0.16721687\n",
      "Iteration 67, loss = 0.15961786\n",
      "Iteration 68, loss = 0.16799357\n",
      "Iteration 69, loss = 0.15798992\n",
      "Iteration 70, loss = 0.16142531\n",
      "Iteration 71, loss = 0.15790109\n",
      "Iteration 72, loss = 0.17719285\n",
      "Iteration 73, loss = 0.17667355\n",
      "Iteration 74, loss = 0.17850372\n",
      "Iteration 75, loss = 0.16830092\n",
      "Iteration 76, loss = 0.16944715\n",
      "Iteration 77, loss = 0.15636325\n",
      "Iteration 78, loss = 0.15783298\n",
      "Iteration 79, loss = 0.15793924\n",
      "Iteration 80, loss = 0.15617518\n",
      "Iteration 81, loss = 0.15828008\n",
      "Iteration 82, loss = 0.15345681\n",
      "Iteration 83, loss = 0.15689129\n",
      "Iteration 84, loss = 0.15332230\n",
      "Iteration 85, loss = 0.15556792\n",
      "Iteration 86, loss = 0.17045923\n",
      "Iteration 87, loss = 0.15444803\n",
      "Iteration 88, loss = 0.15472095\n",
      "Iteration 89, loss = 0.16846783\n",
      "Iteration 90, loss = 0.15739494\n",
      "Iteration 91, loss = 0.15388630\n",
      "Iteration 92, loss = 0.14980484\n",
      "Iteration 93, loss = 0.19107343\n",
      "Iteration 94, loss = 0.15909502\n",
      "Iteration 95, loss = 0.15748896\n",
      "Iteration 96, loss = 0.16600468\n",
      "Iteration 97, loss = 0.16136491\n",
      "Iteration 98, loss = 0.16236664\n",
      "Iteration 99, loss = 0.15440963\n",
      "Iteration 100, loss = 0.14999031\n",
      "Iteration 101, loss = 0.17449241\n",
      "Iteration 102, loss = 0.15165127\n",
      "Iteration 103, loss = 0.14861317\n",
      "Iteration 104, loss = 0.14964467\n",
      "Iteration 105, loss = 0.15537840\n",
      "Iteration 106, loss = 0.15555607\n",
      "Iteration 107, loss = 0.14554122\n",
      "Iteration 108, loss = 0.15913349\n",
      "Iteration 109, loss = 0.15000664\n",
      "Iteration 110, loss = 0.14733933\n",
      "Iteration 111, loss = 0.14927969\n",
      "Iteration 112, loss = 0.14635507\n",
      "Iteration 113, loss = 0.15414117\n",
      "Iteration 114, loss = 0.14727280\n",
      "Iteration 115, loss = 0.14877138\n",
      "Iteration 116, loss = 0.15349755\n",
      "Iteration 117, loss = 0.15357804\n",
      "Iteration 118, loss = 0.15848503\n",
      "Iteration 119, loss = 0.15158548\n",
      "Iteration 120, loss = 0.14581281\n",
      "Iteration 121, loss = 0.15306276\n",
      "Iteration 122, loss = 0.15171050\n",
      "Iteration 123, loss = 0.14826159\n",
      "Iteration 124, loss = 0.15409343\n",
      "Iteration 125, loss = 0.14634114\n",
      "Iteration 126, loss = 0.14435308\n",
      "Iteration 127, loss = 0.16838248\n",
      "Iteration 128, loss = 0.15313191\n",
      "Iteration 129, loss = 0.15257342\n",
      "Iteration 130, loss = 0.14862616\n",
      "Iteration 131, loss = 0.14629821\n",
      "Iteration 132, loss = 0.14569315\n",
      "Iteration 133, loss = 0.14338941\n",
      "Iteration 134, loss = 0.14756098\n",
      "Iteration 135, loss = 0.16644706\n",
      "Iteration 136, loss = 0.16345318\n",
      "Iteration 137, loss = 0.14505169\n",
      "Iteration 138, loss = 0.14873074\n",
      "Iteration 139, loss = 0.15121368\n",
      "Iteration 140, loss = 0.14833938\n",
      "Iteration 141, loss = 0.14902186\n",
      "Iteration 142, loss = 0.14831219\n",
      "Iteration 143, loss = 0.15139001\n",
      "Iteration 144, loss = 0.14883321\n",
      "Iteration 145, loss = 0.14606449\n",
      "Iteration 146, loss = 0.14481076\n",
      "Iteration 147, loss = 0.15778713\n",
      "Iteration 148, loss = 0.14550656\n",
      "Iteration 149, loss = 0.14202878\n",
      "Iteration 150, loss = 0.14445511\n",
      "Iteration 151, loss = 0.15292122\n",
      "Iteration 152, loss = 0.14431698\n",
      "Iteration 153, loss = 0.14530823\n",
      "Iteration 154, loss = 0.14819063\n",
      "Iteration 155, loss = 0.14235979\n",
      "Iteration 156, loss = 0.15457178\n",
      "Iteration 157, loss = 0.14669505\n",
      "Iteration 158, loss = 0.14097971\n",
      "Iteration 159, loss = 0.14122317\n",
      "Iteration 160, loss = 0.14429725\n",
      "Iteration 161, loss = 0.15130296\n",
      "Iteration 162, loss = 0.14236926\n",
      "Iteration 163, loss = 0.14954916\n",
      "Iteration 164, loss = 0.14321234\n",
      "Iteration 165, loss = 0.14147096\n",
      "Iteration 166, loss = 0.14341236\n",
      "Iteration 167, loss = 0.14416469\n",
      "Iteration 168, loss = 0.14576847\n",
      "Iteration 169, loss = 0.14364148\n",
      "Iteration 170, loss = 0.14541857\n",
      "Iteration 171, loss = 0.14138036\n",
      "Iteration 172, loss = 0.14180041\n",
      "Iteration 173, loss = 0.14270359\n",
      "Iteration 174, loss = 0.14245852\n",
      "Iteration 175, loss = 0.14254817\n",
      "Iteration 176, loss = 0.14174912\n",
      "Iteration 177, loss = 0.14361656\n",
      "Iteration 178, loss = 0.14573880\n",
      "Iteration 179, loss = 0.14329447\n",
      "Iteration 180, loss = 0.14507471\n",
      "Iteration 181, loss = 0.14426880\n",
      "Iteration 182, loss = 0.14224647\n",
      "Iteration 183, loss = 0.14236176\n",
      "Iteration 184, loss = 0.14379602\n",
      "Iteration 185, loss = 0.14149660\n",
      "Iteration 186, loss = 0.14661262\n",
      "Iteration 187, loss = 0.14148802\n",
      "Iteration 188, loss = 0.14367315\n",
      "Iteration 189, loss = 0.14246051\n",
      "Iteration 190, loss = 0.14459192\n",
      "Iteration 191, loss = 0.14178897\n",
      "Iteration 192, loss = 0.15419180\n",
      "Iteration 193, loss = 0.15427421\n",
      "Iteration 194, loss = 0.14196381\n",
      "Iteration 195, loss = 0.14464692\n",
      "Iteration 196, loss = 0.14010765\n",
      "Iteration 197, loss = 0.14401789\n",
      "Iteration 198, loss = 0.14260660\n",
      "Iteration 199, loss = 0.14501669\n",
      "Iteration 200, loss = 0.14908287\n",
      "Iteration 201, loss = 0.14815252\n",
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      "Iteration 203, loss = 0.14304206\n",
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      "Iteration 205, loss = 0.13762782\n",
      "Iteration 206, loss = 0.14223284\n",
      "Iteration 207, loss = 0.13779321\n",
      "Iteration 208, loss = 0.14064893\n",
      "Iteration 209, loss = 0.13872846\n",
      "Iteration 210, loss = 0.14006948\n",
      "Iteration 211, loss = 0.13963235\n",
      "Iteration 212, loss = 0.14080273\n",
      "Iteration 213, loss = 0.14633692\n",
      "Iteration 214, loss = 0.14226603\n",
      "Iteration 215, loss = 0.13967915\n",
      "Iteration 216, loss = 0.14542861\n",
      "Iteration 217, loss = 0.14229990\n",
      "Iteration 218, loss = 0.14333247\n",
      "Iteration 219, loss = 0.13937301\n",
      "Iteration 220, loss = 0.13790751\n",
      "Iteration 221, loss = 0.13832883\n",
      "Iteration 222, loss = 0.13757549\n",
      "Iteration 223, loss = 0.14731786\n",
      "Iteration 224, loss = 0.14120657\n",
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      "Iteration 226, loss = 0.13914936\n",
      "Iteration 227, loss = 0.13867097\n",
      "Iteration 228, loss = 0.13812021\n",
      "Iteration 229, loss = 0.13831033\n",
      "Iteration 230, loss = 0.13853115\n",
      "Iteration 231, loss = 0.14010269\n",
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      "Iteration 235, loss = 0.14206514\n",
      "Iteration 236, loss = 0.14075104\n",
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      "Iteration 238, loss = 0.14001262\n",
      "Iteration 239, loss = 0.14203483\n",
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      "Iteration 242, loss = 0.13906129\n",
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      "Iteration 249, loss = 0.13903937\n",
      "Iteration 250, loss = 0.14104599\n",
      "Iteration 251, loss = 0.14225687\n",
      "Iteration 252, loss = 0.13827125\n",
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      "Iteration 255, loss = 0.13823678\n",
      "Iteration 256, loss = 0.14350104\n",
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      "Iteration 271, loss = 0.13752669\n",
      "Iteration 272, loss = 0.14339737\n",
      "Iteration 273, loss = 0.13911010\n",
      "Iteration 274, loss = 0.13558817\n",
      "Iteration 275, loss = 0.13640089\n",
      "Iteration 276, loss = 0.13654510\n",
      "Iteration 277, loss = 0.13748205\n",
      "Iteration 278, loss = 0.13772033\n",
      "Iteration 279, loss = 0.14074794\n",
      "Iteration 280, loss = 0.13867669\n",
      "Iteration 281, loss = 0.13740484\n",
      "Iteration 282, loss = 0.14048015\n",
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    },
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     "output_type": "stream",
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      "Training/5:\n",
      "Iteration 751, loss = 0.14941018\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 798, loss = 0.14756630\n",
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      "Training:\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1045, loss = 0.14240431\n",
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      "Iteration 1133, loss = 0.13827484\n",
      "Iteration 1134, loss = 0.13761919\n",
      "Iteration 1135, loss = 0.13802005\n",
      "Iteration 1136, loss = 0.13846642\n",
      "Iteration 1137, loss = 0.13795958\n",
      "Iteration 1138, loss = 0.13812164\n",
      "Iteration 1139, loss = 0.13839581\n",
      "Iteration 1140, loss = 0.13761520\n",
      "Iteration 1141, loss = 0.13872606\n",
      "Iteration 1142, loss = 0.13771132\n",
      "Iteration 1143, loss = 0.13796214\n",
      "Iteration 1144, loss = 0.13803611\n",
      "Iteration 1145, loss = 0.13835516\n",
      "Iteration 1146, loss = 0.13779452\n",
      "Iteration 1147, loss = 0.13823575\n",
      "Iteration 1148, loss = 0.13863422\n",
      "Iteration 1149, loss = 0.13825465\n",
      "Iteration 1150, loss = 0.13862435\n",
      "Iteration 1151, loss = 0.13709125\n",
      "Iteration 1152, loss = 0.13739009\n",
      "Iteration 1153, loss = 0.13709298\n",
      "Iteration 1154, loss = 0.13734462\n",
      "Iteration 1155, loss = 0.13747024\n",
      "Iteration 1156, loss = 0.13721998\n",
      "Iteration 1157, loss = 0.13719407\n",
      "Iteration 1158, loss = 0.13718470\n",
      "Iteration 1159, loss = 0.13731117\n",
      "Iteration 1160, loss = 0.13724723\n",
      "Iteration 1161, loss = 0.13757696\n",
      "Iteration 1162, loss = 0.13721993\n",
      "Iteration 1163, loss = 0.13699450\n",
      "Iteration 1164, loss = 0.13744913\n",
      "Iteration 1165, loss = 0.13728196\n",
      "Iteration 1166, loss = 0.13705664\n",
      "Iteration 1167, loss = 0.13750934\n",
      "Iteration 1168, loss = 0.13690378\n",
      "Iteration 1169, loss = 0.13696534\n",
      "Iteration 1170, loss = 0.13685973\n",
      "Iteration 1171, loss = 0.13713002\n",
      "Iteration 1172, loss = 0.13707071\n",
      "Iteration 1173, loss = 0.13750422\n",
      "Iteration 1174, loss = 0.13704200\n",
      "Iteration 1175, loss = 0.13730166\n",
      "Iteration 1176, loss = 0.13702941\n",
      "Iteration 1177, loss = 0.13712260\n",
      "Iteration 1178, loss = 0.13668775\n",
      "Iteration 1179, loss = 0.13713528\n",
      "Iteration 1180, loss = 0.13696520\n",
      "Iteration 1181, loss = 0.13729991\n",
      "Iteration 1182, loss = 0.13719984\n",
      "Iteration 1183, loss = 0.13704343\n",
      "Iteration 1184, loss = 0.13693332\n",
      "Iteration 1185, loss = 0.13654529\n",
      "Iteration 1186, loss = 0.13764130\n",
      "Iteration 1187, loss = 0.13694077\n",
      "Iteration 1188, loss = 0.13716470\n",
      "Iteration 1189, loss = 0.13708413\n",
      "Iteration 1190, loss = 0.13687270\n",
      "Iteration 1191, loss = 0.13701863\n",
      "Iteration 1192, loss = 0.13686245\n",
      "Iteration 1193, loss = 0.13692057\n",
      "Iteration 1194, loss = 0.13669510\n",
      "Iteration 1195, loss = 0.13716230\n",
      "Iteration 1196, loss = 0.13718953\n",
      "Iteration 1197, loss = 0.13711018\n",
      "Iteration 1198, loss = 0.13706825\n",
      "Iteration 1199, loss = 0.13681774\n",
      "Iteration 1200, loss = 0.13725642\n",
      "Iteration 1201, loss = 0.13644100\n",
      "Iteration 1202, loss = 0.13674720\n",
      "Iteration 1203, loss = 0.13621368\n",
      "Iteration 1204, loss = 0.13634800\n",
      "Iteration 1205, loss = 0.13636721\n",
      "Iteration 1206, loss = 0.13661220\n",
      "Iteration 1207, loss = 0.13637859\n",
      "Iteration 1208, loss = 0.13619659\n",
      "Iteration 1209, loss = 0.13632678\n",
      "Iteration 1210, loss = 0.13636806\n",
      "Iteration 1211, loss = 0.13618592\n",
      "Iteration 1212, loss = 0.13631910\n",
      "Iteration 1213, loss = 0.13618439\n",
      "Iteration 1214, loss = 0.13662626\n",
      "Iteration 1215, loss = 0.13628411\n",
      "Iteration 1216, loss = 0.13625033\n",
      "Iteration 1217, loss = 0.13617768\n",
      "Iteration 1218, loss = 0.13636649\n",
      "Iteration 1219, loss = 0.13645472\n",
      "Iteration 1220, loss = 0.13609298\n",
      "Iteration 1221, loss = 0.13646299\n",
      "Iteration 1222, loss = 0.13614572\n",
      "Iteration 1223, loss = 0.13617698\n",
      "Iteration 1224, loss = 0.13675464\n",
      "Iteration 1225, loss = 0.13645256\n",
      "Iteration 1226, loss = 0.13624153\n",
      "Iteration 1227, loss = 0.13650820\n",
      "Iteration 1228, loss = 0.13626076\n",
      "Iteration 1229, loss = 0.13616429\n",
      "Iteration 1230, loss = 0.13625686\n",
      "Iteration 1231, loss = 0.13672377\n",
      "Iteration 1232, loss = 0.13618723\n",
      "Iteration 1233, loss = 0.13600261\n",
      "Iteration 1234, loss = 0.13640601\n",
      "Iteration 1235, loss = 0.13640516\n",
      "Iteration 1236, loss = 0.13610970\n",
      "Iteration 1237, loss = 0.13620712\n",
      "Iteration 1238, loss = 0.13633884\n",
      "Iteration 1239, loss = 0.13643043\n",
      "Iteration 1240, loss = 0.13605471\n",
      "Iteration 1241, loss = 0.13608031\n",
      "Iteration 1242, loss = 0.13615444\n",
      "Iteration 1243, loss = 0.13624642\n",
      "Iteration 1244, loss = 0.13609540\n",
      "Iteration 1245, loss = 0.13636357\n",
      "Iteration 1246, loss = 0.13640923\n",
      "Iteration 1247, loss = 0.13611939\n",
      "Iteration 1248, loss = 0.13613480\n",
      "Iteration 1249, loss = 0.13624717\n",
      "Iteration 1250, loss = 0.13607388\n",
      "Final Training:\n",
      "Iteration 1251, loss = 0.13624849\n",
      "Iteration 1252, loss = 0.13606129\n",
      "Iteration 1253, loss = 0.13616089\n",
      "Iteration 1254, loss = 0.13641740\n",
      "Iteration 1255, loss = 0.13626449\n",
      "Iteration 1256, loss = 0.13600309\n",
      "Iteration 1257, loss = 0.13619748\n",
      "Iteration 1258, loss = 0.13605362\n",
      "Iteration 1259, loss = 0.13642615\n",
      "Iteration 1260, loss = 0.13629239\n",
      "Iteration 1261, loss = 0.13611063\n",
      "Iteration 1262, loss = 0.13613659\n",
      "Iteration 1263, loss = 0.13599396\n",
      "Iteration 1264, loss = 0.13600325\n",
      "Iteration 1265, loss = 0.13629853\n",
      "Iteration 1266, loss = 0.13626341\n",
      "Iteration 1267, loss = 0.13618760\n",
      "Iteration 1268, loss = 0.13621196\n",
      "Iteration 1269, loss = 0.13630629\n",
      "Iteration 1270, loss = 0.13594974\n",
      "Iteration 1271, loss = 0.13607853\n",
      "Iteration 1272, loss = 0.13612207\n",
      "Iteration 1273, loss = 0.13606446\n",
      "Iteration 1274, loss = 0.13626736\n",
      "Iteration 1275, loss = 0.13614107\n",
      "Iteration 1276, loss = 0.13595955\n",
      "Iteration 1277, loss = 0.13628772\n",
      "Iteration 1278, loss = 0.13609887\n",
      "Iteration 1279, loss = 0.13613794\n",
      "Iteration 1280, loss = 0.13601348\n",
      "Iteration 1281, loss = 0.13615126\n",
      "Iteration 1282, loss = 0.13609470\n",
      "Iteration 1283, loss = 0.13586710\n",
      "Iteration 1284, loss = 0.13604856\n",
      "Iteration 1285, loss = 0.13614381\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1286, loss = 0.13589265\n",
      "Iteration 1287, loss = 0.13594571\n",
      "Iteration 1288, loss = 0.13620908\n",
      "Iteration 1289, loss = 0.13634676\n",
      "Iteration 1290, loss = 0.13611753\n",
      "Iteration 1291, loss = 0.13607976\n",
      "Iteration 1292, loss = 0.13623347\n",
      "Iteration 1293, loss = 0.13597846\n",
      "Iteration 1294, loss = 0.13648379\n",
      "Iteration 1295, loss = 0.13604913\n",
      "Iteration 1296, loss = 0.13632360\n",
      "Iteration 1297, loss = 0.13572079\n",
      "Iteration 1298, loss = 0.13636174\n",
      "Iteration 1299, loss = 0.13609603\n",
      "Iteration 1300, loss = 0.13607661\n",
      "Iteration 1301, loss = 0.13596915\n",
      "Iteration 1302, loss = 0.13582875\n",
      "Iteration 1303, loss = 0.13601143\n",
      "Iteration 1304, loss = 0.13625163\n",
      "Iteration 1305, loss = 0.13646610\n",
      "Iteration 1306, loss = 0.13601451\n",
      "Iteration 1307, loss = 0.13614621\n",
      "Iteration 1308, loss = 0.13595594\n",
      "Iteration 1309, loss = 0.13609173\n",
      "Iteration 1310, loss = 0.13616696\n",
      "Iteration 1311, loss = 0.13593100\n",
      "Iteration 1312, loss = 0.13603709\n",
      "Iteration 1313, loss = 0.13594736\n",
      "Iteration 1314, loss = 0.13608804\n",
      "Iteration 1315, loss = 0.13619392\n",
      "Iteration 1316, loss = 0.13633356\n",
      "Iteration 1317, loss = 0.13597878\n",
      "Iteration 1318, loss = 0.13599598\n",
      "Iteration 1319, loss = 0.13620419\n",
      "Iteration 1320, loss = 0.13589213\n",
      "Iteration 1321, loss = 0.13583455\n",
      "Iteration 1322, loss = 0.13624298\n",
      "Iteration 1323, loss = 0.13586409\n",
      "Iteration 1324, loss = 0.13589589\n",
      "Iteration 1325, loss = 0.13577827\n",
      "Iteration 1326, loss = 0.13625602\n",
      "Iteration 1327, loss = 0.13580133\n",
      "Iteration 1328, loss = 0.13610396\n",
      "Iteration 1329, loss = 0.13599729\n",
      "Iteration 1330, loss = 0.13596100\n",
      "Iteration 1331, loss = 0.13634331\n",
      "Iteration 1332, loss = 0.13613000\n",
      "Iteration 1333, loss = 0.13560938\n",
      "Iteration 1334, loss = 0.13592250\n",
      "Iteration 1335, loss = 0.13597714\n",
      "Iteration 1336, loss = 0.13580972\n",
      "Iteration 1337, loss = 0.13589464\n",
      "Iteration 1338, loss = 0.13588802\n",
      "Iteration 1339, loss = 0.13594244\n",
      "Iteration 1340, loss = 0.13606159\n",
      "Iteration 1341, loss = 0.13581589\n",
      "Iteration 1342, loss = 0.13603921\n",
      "Iteration 1343, loss = 0.13592495\n",
      "Iteration 1344, loss = 0.13608210\n",
      "Iteration 1345, loss = 0.13572233\n",
      "Iteration 1346, loss = 0.13594076\n",
      "Iteration 1347, loss = 0.13576691\n",
      "Iteration 1348, loss = 0.13643943\n",
      "Iteration 1349, loss = 0.13586824\n",
      "Iteration 1350, loss = 0.13598991\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(batch_size=256, hidden_layer_sizes=(200, 200, 200),\n",
       "              learning_rate_init=0.0001, max_iter=100, n_iter_no_change=inf,\n",
       "              verbose=True, warm_start=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(batch_size=256, hidden_layer_sizes=(200, 200, 200),\n",
       "              learning_rate_init=0.0001, max_iter=100, n_iter_no_change=inf,\n",
       "              verbose=True, warm_start=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(batch_size=256, hidden_layer_sizes=(200, 200, 200),\n",
       "              learning_rate_init=0.0001, max_iter=100, n_iter_no_change=inf,\n",
       "              verbose=True, warm_start=True)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(20230329)  # arbitrary random seed for reproducibility\n",
    "\n",
    "# decleare inputs and targets\n",
    "X, Y = X_ngc1275_flat, Y_ngc1275_flat\n",
    "n = len(Y)\n",
    "\n",
    "# split randomly into training and testing sets\n",
    "idxs = np.random.choice(np.arange(n), size=n, replace=False)\n",
    "idxs_train, idxs_test = idxs[:int(0.8 * n)], idxs[int(0.8 * n):]\n",
    "X_train, Y_train = X[idxs_train], Y[idxs_train]\n",
    "X_test, Y_test = X[idxs_test], Y[idxs_test]\n",
    "\n",
    "# train NN classifier\n",
    "learning_rates = np.logspace(-2.5, -4, 10)\n",
    "clf = MLPClassifier(solver='adam', activation='relu', \n",
    "                    hidden_layer_sizes=(200, 200, 200), \n",
    "                    alpha=1e-4,\n",
    "                    learning_rate='constant', learning_rate_init=1e-3,\n",
    "                    batch_size=256, max_iter=50, n_iter_no_change=np.inf, \n",
    "                    warm_start=True, verbose=True)\n",
    "\n",
    "# go through a learning rate schedule\n",
    "print('Training/20:')\n",
    "for i, lr in enumerate(learning_rates):\n",
    "    clf.learning_rate_init = lr\n",
    "    clf.fit(X_train[::20], Y_train[::20])\n",
    "print('Training/10:')\n",
    "for i, lr in enumerate(learning_rates[5:]):\n",
    "    clf.learning_rate_init = lr\n",
    "    clf.fit(X_train[::10], Y_train[::10])\n",
    "print('Training/5:')\n",
    "for i, lr in enumerate(learning_rates[5:]):\n",
    "    clf.learning_rate_init = lr\n",
    "    clf.fit(X_train[::5], Y_train[::5])\n",
    "print('Training:')\n",
    "for i, lr in enumerate(learning_rates[5:]):\n",
    "    clf.learning_rate_init = lr\n",
    "    clf.fit(X_train, Y_train)\n",
    "print('Final Training:')\n",
    "clf.learning_rate_init = lr\n",
    "clf.max_iter = 100 \n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51fe335b",
   "metadata": {},
   "outputs": [
    {
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UsPwUSMrj/g0yMIo04uNQ0rkXnU2lBgAghRBqICW0adMm7PH3338v28cfAAGkKOfawMw4Q1rx8cCoa08N7l2kEddnYh/du5LkmFDA/Yu04udQ0nIMF7GnBgAghWQe+hSg4jlDjd27d2vt2rXKzc011CIAlRFrAyOt+Xj5Keea/Ny7SCd+n+hjBai0Qjrzcd+byX5WAID4/Pz3v5f7fHD7jpivTaUGUkJubq6qVq0aduz777831BoAlZZzbWBmnCGN+Hpg1Fllxb2LdOIq1PDPoKgkKq2Q1vzd9/K5GQBMy8/PV7t27TRq1CjTTYlJoGatcv+rkpurOueeG9O1qdRASggEAmrdurW++eab0mPLly/XqaeearBVACob57redrDEUEsAA1wDo/6Z20KVFXAQn4Ua7r6X+xdpxM99L1WSAGDczJkzlZeXZ7oZMcsddn+FXds/PSoqvdatW4c9ZrNwAAnnHBhlxhnSmZ8GRp2DouyHg7Ti3+VrJLn7XpafAvyBvhcAkMIINZAyWrVqFfa4oKDAUEsAVFau2aLMOEM68fESGM57V7bt7yU9gGi49sMx04xYOffUsEuokkQa8fN+VnxuBgCkMEINpIwmTZqEPd6wYYOhlgCotJxl9Mw4Qzrx88Co896VJJawQZqyfDQoKolKDaS3ytT3UuEMAEghhBpIGY0aNQp7vH79ekMtAVBZuWd78+UM6SR8YMVPA6Oue1esy4804vMQwMoI38aRexdpxfZx35vpvHepsgIApA5CDaQMZ6hBpQaAhGNtYKQzPy/X5BFqUKmBdOFeas0/g6KSqNRAmvNx3+vc1Dzk458FAFDpEGogZThDjY0bNyrElx4ACcTawEhnroFRP80W9Vh+yuYzAtKVj+5dyWNPDQJJpBFf972ZznuXSg0AQOog1EDKaNy4cdjjkpISbdmyxVBrAFRKrA0M/MpHAytyLF8jSWKzYaQLV6GGj+5dyV2pQaiBdOan+5fPzQCAFEaogZTRsGFD17GbbrqJag0ACeOq1GDGGdKJa9UI/wysWAF3W91L8gCVlX9nektelRp8tkca8XOlBhXOAIAURqiBlJGdna06deqEHXvuuef0zjvvGGoRgErHuS4/awMjnfh4YEWZVGoAfuVcwkYMjCKd+HlCgfNzM3vRAQBSCKEGUopXtcbbb79toCUAKiMqNYCD+GdchT01kN5cgaSZZsSMPTUAf3J+brbpdwEAqYNQAyll/fr1rmOff/65gZYAqJRYGxjpzM+DEc7ZohLr8iN9OEINy09VVhJ7aiC9OQN4H92+rgkFJUGWfgQApAxCDaSUatWquY795je/MdASAJURawMjrfl4YNSyLFcoyWxvpA3XIKJ/7l1JsjLCl49jTw2kMz/1vZ4TCqiSBACkCEINpJS//OUvrmPbtm0z0BIAlRFrAwMH8dPAijzuX0INpAm/z4y2nJUaTChAWvHxflYeSz/S9wIAUgWhBlLKdddd5zo2ffp0jRo1yvdf6ACkAOe63sw2Qxpx9aN+GliR3JUa3L9IF86PwF4DjanMtacG9y7Sh5/7XtdkAtH3AgBSh88+EaOya9mypSZMmOA6fv3112vevHkGWgSgMnHNFmW2GdKJ75ewoVID8CMqNZDWKlmoQd8LAEgVhBpIOU2bNvU8fuKJJ2ro0KEqKChIcosAVBrOPTX4Ygb4h2tPHGaLIk34eFBUkrtSg6UfAX+gUgMAkMIINZBy6tatW+Zzd955p4477jjt2bMneQ0CUGm4ZpzxxQzpJOTvgVHLufxUSYmhlgDJ5rx3zbQiVlams+8l1EAacfa9PrqBnf2uRN8LAEgdhBpIOTk5OeU+v2HDBr388stJag2ASsW1rjcDK0hj/hlX2c81MEooiTTh933l2FMD+JWfJhRkZrqP0fcCAFIEoQZSzqFCDUmaP39+EloCoLJhTw2kNZ8vYWMF2FMDacrv966j77WDzPRGGvFxKGl5/K5hQhAAIFUQaiDl1KpV65DnVK1aNQktAVDpsCY/0lr4wIrXYEVKcw6Mcv8iTVmWz77COQNJ13I8QCXmCDWsgI/6Xio1AAApzGefiJEOAh5rdzoRagCIhWttYGaLIo3Yfp/tTaUG0pWPZ3pLXlWS9L1IYz7qe61AwNVeu4S+FwCQGgg14Etffvmlpk2bpiADGgCikRE+44x1vQEfcVZa8RkAacLvgSR9L9Kbv0NJZ5WkQvS9AIDUQKgBX3r33XfVs2dPXXDBBaabAsBHXLNF+WKGdOLzgVGLUAPpyjkm6rt7l74X6cvvoaSzSpK+FwCQKgg14GtvvPGGVq1aZboZAPzCOShKCT3SiWuyqL8GVtyzRZntjXTh85nezkFR+l6kE5+HkvS9AIBURaiBlHTjjTdGfO63335bcQ0BUKk4Z3rLtt0z6IDKyuf/1pktirTlmultphmxsjId9y6VGkgn9L0AAFQIQg2kpEGDBqlDhw4RnVutWrUKbg2ASsO5UbjEZsNIHz4fGHXNFuXeRbry20xvy3nvMtMbacQVavjr/nWGkvS9AIBUQaiBlNS8eXMtWLBAderUOeS5e/bsSUKLAFQGrkoNSTZl9EhTllfIl8Is12bDDKwgTTgGRS2fhRquSo1giaGWACnAX7eve/k4QkkAQIrw17dZpBXLstS4ceNDnkeoASBiHqGGShhcQbrw9xIYrOuNtOXzmd7OQVGFfP67CIiGz0NJd9/LhAIAQGrIPPQpgDm5ublatmxZuecUFhZq7Nixql69ui6++GJleA1aAoC8Z6av/edd3stSpbDMhg2Vc9GFymrRwnRT4Ceu5af8NbDiXNd7+5R3teODGYZaE5tA1WxVP/lk1T7rLP8NbCF1+OzfjuUYFA1u3aqCm28x1JrYZTVvrpxLL1FmTo7ppsBXKlffu+nZZ2VlVjHUmtgEatZQ7TPOVI2TTzLdFABAAhFqIKWdffbZmjVrVrnn/PWvfy3986xZs/TUU09VcKsA+FaGu9sL7dploCHxKdqxQ5uefkaH3fN/ppsCH7F9Hmo4Z4vaRUWyi4oMNSY2oR07tP1/byu7VStVbdvWdHPgE7bfKxucE45sW6EdO8y0JQ57v/1WWye+ogb9/nrok4FfVLq+d89e2dprqDGxCe3Yoc3PPaeqRx6hjLp1TTcHAJAg/pqairTzl7/8Jarzn376aQVZYxtAGQJVs2VlZ5tuRkKUbNigkM8GdGGY38dFK9FARNHq1aabAD/x+aBo5bp3V5luAvyGvjc12LaKfi4w3QoAQAIRaiCl5eTk6P7774/qNTt8OPMLQHJYmZmq3etM081IHEJcRMPn6/LXPPVUBWrVMt2MxGA/EMTDZ6FGZsOGqn7CCaabkRh+r5pB8vk8lKzds6esKpVkgQ/2AwGASqWS9E6ozIYMGaL169frkUceiej8bdu2qW5lmVECIOFqn3mmqp90kko2bDTdlKiEdu3SprFjw47ZQQZGEQefDaxUadxYuUPvU9Hq1bJL/DUwsf1/b2nf9ytKH9sEkoiKvwfSLctSvb9cpdp/OEvBbdtNNycqRatWatvrk0of28ESg61B5eCvvrdq27bKHT5cRT//7LtfRZvHjVNwy5bSx3777AAAKB+hBlKeZVn6y1/+EnGosX27v74sAUi+zJwc3230Gdy5032QGWeIhmu2qJlmxMPKylL2b35juhlR2zm7TvgBKjUQDce9awX8d/NalqUqTZqoSpMmppsSHyYTIFqVoO8NVK+uqr/9relmRM2q6lhy1ub+BYDKhOWn4As1atSI+Nxt27ZVYEsAwAwr4O6yme2NqDgHRn1WqeFnViB8o2RmiyI+3LvJYmU67l0mEyBa9L3GWBnhc3jpewGgciHUgC9EE2pQqQGgUsrIcB8j1EA8GFhJngzHR24GRhEN1344SBrLce9SqQH4hkXfCwCVGqEGfCGaUKN///4aNWqUSkpY8xZA5WF5hBrsqYFo2Cy7YIyrUoNAElGwfb7RsJ9RqYF4ufpe7t/kcfW9fA4CgMqEUAO+UL169YjPXblypa6//noNGTKkAlsEAEnmVanB4Aqi4ZzszcBK8jhnixJqIBrcu+Y4BkVVEnSHTEA0uH+TxlWpEWTSIwD/yc/PV7t27TRq1CjTTUk5bBQOX8jwGsw7hIceekgPPvhgBbQGAJLPsqz9X4QPGkxhxhniw8BKsrjW9WajcESDQXRjXIOi0v7/HwxMI1KEkuY4+14+NwPwoZkzZyovL890M1ISlRqo1EIMGgCoRJzLYDDjDFFhYNQcKjUQF5afMsZrYhVL3CIa9L3GsKcGAFRuhBrwjfPOOy/q1xQWFlZASwDAENYGRjycAysBBkaTxb2nBvcu4sCtmzSe+1kxSI1ouPbEYQgmaeh7AaBSo0eFbzz22GP685//HNVr1q1bV0GtAYDkY8YZEslitnfSuKqsuHcRDcegKPduEgU8vi5TqQH4Ap+bAaByI9SAbxx22GF66aWX9N5770X8GkINAJWKa8YZX84QBZsZisY4BkbtEu5dRME105tQI1k8KzVY3hbRcIWShtqRjpyfm+l7AaBSIdSA71SvXj3ic9euXVuBLQGA5HINrjCwgii4lkxhZCVp3PcuAyuInHu5I+7dpAl47KnBhAJEyHOpMvrepKFKEgAqN0IN+E61atUiPresSo1FixbpxRdfZM8NAP6SwYwzJBADK8nDut6IB3s4GOMaFBVVkogTfW/y0PcCQKVGqAHfiaZSY8iQIdqwYUPYsalTp+q4447TJZdcorZt22rjxo2JbiIAVAjWBkZcGBg1h3sX8XAVajAomiyWx54ahBqIGP2uUXxuBoDKjVADvhNNqCFJ99xzT9jj++67TyW/bPBXWFio//znPwlrGwBUKGacIR6ugVE+BiaLlZEZ9ph7F3Eh1Egejz01WPoREfMKNbw2n0fFcFY40/cCQKVCjwrfiTbU+Oijj8p9PHLkyHibBABJ4VqXP1hipiEAouKaLcq9i6g498Mx04q05FWpwdKPiBSVGkY5K61s+l4AqFQINeA70YYaX3/9dbnPFxcXx9McAEgeZpwhHq6Nws00Iy1RZYV4uO5dbt5ksSzLXa1hc/8iHty/SeOaDMS9CwCVCaEGfKdGjRpq3rx5xOc3aNCgAlsDAMnjWtubtYERDcfAqMXAaNK4Nhvm3kU0mO1tlGu2dwmzvREhr6XK6HqTxlXhTN8LAJUKoQZ8x7IsPf/886pdu3ZE52/cuNG1WTgA+JKrUoMvZ4gGs72NsZxLYDBbFJGzCSTNcg2Mcv8CvkCVJABUaoQa8KUuXbpo/vz5+utf/6rrrrtOLVu2LPf81q1b64svvijzeeeXRQBIRe4ZZ3w5Q+To68xxVmqwrjfiQ6iRTM6+lwkFiJRXr0somTz0vQBQuRFqwLdat26txx9/XE888YQ6duxY7rk7duzQsGHDynx+06ZNCW4dAFSADOdsbwZWEAXW5TcnwLreiAP3rlmOvlf0vYiU12QC7t/kcS7bSt8LAJUKoQYqhfr16x/ynDfeeEOhMmY1FxQUJLpJAJBwlquMnoEVxIGBlaSxnIEk63ojGs5xUe7dpHL3vQyMIg7cv0njqrKi7wWASoVQA5VCu3btIjpvz549nscJNQD4gmu2KAMriIJrwigDK0mTkRn+mHsX0XBVaphpRtpy9r0MjCJSVGqYRaUGAFRqhBqoFK688krVqFHjkOc9+OCDnsdXr159yNcuX75ct956qx577DEVFxdH3UYAiJd7XW/WBkYUWMLGGGelBoOigH9QJYmYEWoYRaUGAFRumYc+BUh9OTk5mjx5soYNG6ZGjRrp5Zdf9jzv7rvv9jy+dOnScq+/c+dOnXjiidq6dauk/SHI8OHD42kyAESPjcIRDzYKN8c5KFrCwAqiYDt+1zMomlyZ9L2IEf2uWc7PzfS9AFCpUKmBSuO0007TBx98oJdeekktW7aM6rWLFy8u9/k333yzNNCQpH//+9+xNBEA4sJsUcSFJWyMsTKZLYrEsQg1ksrV9zIwikgRahhFpQYAVG6EGgnUp08f5eTk6PzzzzfdlLRXq1atqM4/VKixZMkS17Gy9ucAgArj2lODL2eIHQOjSWSxrjfiwMCoWSwfhxh5rz5F35s07KkBAJUaoUYC3XDDDRo/frzpZkDRhxqrV6/W9u3btW7dOg0ePFj9+vXTDz/8UPp8Xl6e6zU//vhj3O0EgGi4ZouyBAaiwp4aplCpgXjY7IdjlJURvmKzzcAo4AvuvejoewGgMiHUSKD8/PyoB9NRMapXrx71a7777jv9+c9/1ogRI/TEE08oPz9foV8GDLOzs13nr1ixIu52AkBUnOt68+UMUWBg1KCA895lUBRxcFb+oEJZVGogZh6lGs7qAVQYZ6jBfjgAULkY71GHDRumE044QbVq1VKjRo30xz/+8ZCbNkdr9uzZ6t27t3Jzc2VZliZPnux53ujRo9WyZUtVrVpVnTp10pw5cxLaDiRPMIaBvokTJ+rDDz8sfbxq1arSZaeKiopc5xNqAEg29tRAXJxjK4QaSeMaFLVtKq0QOVafMos9NRAr7/Wnkt+OdOWq1Cgx1BAAQEUwHmp8+OGHGjBggD799FNNmzZNJSUl6tmzp3bt2uV5/scff6zi4mLX8SVLlmjdunWer9m1a5c6dOigxx57rMx2TJw4UTfeeKPuuOMOLViwQF26dFGvXr20atWq0nM6deqk9u3bu/5bs2ZNlD81KprXv5FDGTFihOvYli1bJBFqAEgRrj01GBRFFFiX3xznbFGJSitEznb8rmdQNLlcoSR9LyJEv2uUxZ4aAFCpZR76lIr13nvvhT1+9tln1ahRI82fP1+nnnpq2HOhUEgDBgxQmzZt9PLLLyvjly+Iy5YtU35+vgYNGqRbbrnF9R69evVSr169ym3Hww8/rKuvvlrXXHONJGnkyJGaOnWqxowZo2HDhkmS5s+fH/PPieTyCiFisW3bNknSvn37XM9NnTpVjzzyiI477jh16dIlIe8HAOWhUgNxcQ2uMDCaLK4lMLR/Txz+DyAm/MNJKlffS6UGIkWlhlnOSg2WjgOASsV4pYbTgUHkevXquZ4LBAKaMmWKFixYoMsvv1yhUEgrVqxQ9+7ddc4553gGGpEoKirS/Pnz1bNnz7DjPXv21Ny5c2O6JswqKUlMaemBf49eIcny5ct14403qmvXrnrzzTcT8n4AUB7nZsOs6424MK6SPF5rqCfoswrSALO9jaLvRcw8710636Rx9r0EkgBQqaRUqGHbtgYPHqzf//73at++vec5ubm5mjFjhj7++GP17dtX3bt3V48ePfT444/H/L6FhYUKBoNq3Lhx2PHGjRuXuaSVlzPOOEMXXHCBpkyZory8PH3xxRee540aNUrt2rVTt27dYm4zyhfL8lNeygs1DrBtW/3790/I+wFAuVyVGpTRIwoMjBpjZbqLo9lTAxFz3LsWM72Ti74X8CVn30u/CwCVi/Hlpw52/fXX6+uvv9ZHH31U7nnNmzfX+PHj1bVrV7Vq1UpPP/10Qj7cO69h23ZU1506dWpE5w0YMEADBgzQzz//rGbNmkXVRkSmW7du+vrrr+O+TiShhiT2VQGQFK7NhtnwEFFhYNQYy2MeEcvHIVbcu0lF34tY2R6TCbh9k8e9pwb9LgBUJilTqfH3v/9db731lmbOnKm8vLxyz12/fr2uu+469e7dW7t379agQYPieu8GDRooIyPDVZWxYcMGV/UG/OEf//iHqlSpEvd1Fi1aJClxe3QAQFycs0WZcYZ4eC2JhArhWr5G3L+InHtglFHRpMpwzvam6g1xINVIHteeGvS7AFCZGP82a9u2rr/+ek2aNEkzZsxQy5Ytyz2/sLBQPXr0UNu2bUtf88orr+gf//hHzG3IyspSp06dNG3atLDj06ZN0+9+97uYrwtzmjVrps8++yzu67zwwgsaPnw4oQaA1OCaLcqMM0TOa8YoksM1W1SSzZ4aiJQr02BQNJmsgOPvm0oNxIP7N2mo1ACAys14qDFgwAA9//zzevHFF1WrVi2tW7dO69at0549e1znhkIhnXnmmWrRooUmTpyozMxMtW3bVtOnT9e4ceM0YsQIz/fYuXOnvvrqK3311VeSpB9//FFfffWVVq1aVXrO4MGD9dRTT+mZZ57Rd999p0GDBmnVqlXq169fhfzcqHjHHnusnnzyybivM2TIEG3YsCEBLQKA+FjO2aKs641oOGcXM7CSPBnuSg0xYxSRcgaS3LvJRd+LWHn9nuf+TR5npQahBgBUKsb31BgzZowkuTbNfvbZZ3XllVeGHQsEAho2bJi6dOmirKys0uNHH320pk+frvr163u+x7x585Sfn1/6ePDgwZKkK664QuPGjZMkXXTRRdq0aZPuuecerV27Vu3bt9eUKVPUokWLOH9CmHTBBRfomWee0SeffOL5fP/+/TV69OhDXufll19OdNMAIGqudb1DfDlDPBhYSRqvpb4YXEGkqLIyir4XCUWokTSWc0JBKBT1vqkAgNRlPNSIdimE008/3fN4x44dy3xNt27dInqf/v37q3///lG1B6mtTp06mj17trp06aJPP/3U9fzll1+uuXPnllbxAEBKc+6pUcLACqLAwKgxlmXtnzF6UJDB2t6InLNSw0wr0hZ9L2JFv2uWV5VkMChlGh8GAwAkgPHlp4CKlpmZqaOOOsrzuSOOOELHHntsklsEALFxbTbMoCii4VrCxkwz0hVreyNRmGWcXO6+l3sXEfIKNbh/k8ZrPys+OwNA5UGogbTQunVr17GGDRuqbt26atu2rYEWAUAMrPBum7WBEQ8GRpOMtb0RK/bUMMtZqcGeGoiQ12oR3L1J5FGRQd8LAJUHoQbSwh//+EfXscLCQknSkUcemeTWAEBsmC2K+DAwapJzbW8GVhAp98Ao924yOffUsIMlhlqCSoG+N2m8Jm8QSgJA5UGogbTgFVxcfPHFkkSlBgD/YLYo4uAaGGVgJblcmw1z/yJCrMtvlmuzYf5/IEIsP2WW194ZTAgCgEqDUANpY/To0WGPL7vsMknS4YcfbqA1Fe+RRx5RTk6OjjjiCM2bN890cwAkALNFERfXZG8GVpLJYrNhxIp71yjnuvz0vYA/eO2pQZUkAFQeHtE1UDn169dPW7Zs0YwZM/THP/5RZ5xxhqT9G4mfd955mjRpUlzXt207ZdYnX7dunQYNGiTbtrV161bdfPPNmjlzpulmAYhXhqPbplID0WC2t1nOSg2b+xcxSpHPm2nDWalB34tIUalhlvPelaiSBIBKhFADacOyLN1+++26/fbbXc+NGTNGDRs21MaNG/X+++9r586dUV+/pKREVapUSURT4/bUU0+FLTMya9Ysc40BkDDOSg1K6BEVlp8yynKEknYJs70RIde9a6YZ6cq1Hw59LyJFqGGU896VqJIEgMqEUAOQ1KhRIz3++OOSpO3bt6tevXoKRlmaWlRUlDKhRgkDJUDlxPI1SCgGVpKKPTUQM6qsjHIuYUPfi3gQaiSPZ6UG9y8AVBbsqQE41K5dWyNHjoz6dfv27Ut8Y2KUKstgAUgsK9Px5YzlaxANx4xRuorkcu2pwbreiJTz3vVYJx4Vx3JsNkylBiLGso9GWZbl+rBjs3wcAFQaVGoAHurWrRv1a4qKihLfkBgRagCVlBU+kBXas1cb//uYocbErkpenmr3OlOBqlVNNyVt2CyBYZ6jUmPH1Pe1+7PPDTUmNoGaNVXz1C7K/s1vTDcFSB5HiFSydq3/+t6Apew2bVTrtNMIxZLJo+/le1qSZQTCqqu2vPSiAlWrGWxQ9DJyclTr9NNUpXFj000BgJRCqAF4INQAkIrclRq29n77rZnGxGHvt98quHmz6l/9F9NNSW/0FUnl3FOjuKBAxQUFhloTuz1ffqnD7r1HGTF8VkJsbOdSZdy7SeVclz+0e48/+95F30ihkGqfeabppqQNV6bBvZt0VkZm2HKtRSt+MNia2O399lsddv9QvucDwEGYpgF4yM7Ojvo1qbT8VMBjBla0e4QASD2B6tVNNyFh9i1baroJ6YUlMIyrLPevXVysfT/8aLoZaY5BrWSqLPeuJO1dssR0E9IMfa9pleX+DW7ZopING0w3AwBSCqEG4CEzM/oiplSv1CguLjbQEgCJlFG/vrLbtDHdjISw+Z2UXCw/ZVyNziebbkLC2CXcv0nFbG+jstu0UUaD+qabkRD0vUnm7Hu5d5Ouxu86m25CwtjFJaabAAApheWnAA8nnXSSatWqpR07dkT8moNDjW3btmn9+vVq3bq1Z9VERfMKNUpK+BAE+J1lWWpw/QDt/fprlWzZYro5UQlu3qKds2aVPmajxiQj1DCu+vHHK6NefRX9sMK9nFCK2zljpoJbt/56gOrP5KLSyqhAtWpqfOut2vP11wrt2mW6OVEpWrlSe+Z/+esB+l6z6HeTrvbZZyurZSsVr/Hfco/bJr8pHfx5Icj3eQA4GKEG4KF69er673//qyuvvDLi1xxYfurTTz/V2WefrU2bNqlbt26aNm1aTJUf8aBSA6i8AtnZqn7CCaabEbWilSvDQg2FGBRF+slu1VLZrVqabkbU9ny5ICzU8Fso43uu2d5mmpHOMmrVUs1TTjHdjKjt+vTTsFDDpu9NLu5d4yzLUrX2R6la+6NMNyVq29+ZIvugJa6ZEAQA4Vh+CijDFVdcoUsvvTTi8w9Uajz00EPatGmTJGnWrFmaNGlShbSvPFRqAEg5jk2SD960EUlApQbikeH4ykClhlncu4iQc5NzKjWSzNH3sskzomE5+15CSQAIQ6gBlKNatWoRn3ugUuO1114LO/7Pf/4zoW2KBJUaAFKN64uZbctmSZXk4e8acbAC4QOjhJJJZocPRDMwiog5lsG1Wb4muVx9L/cuouDse5lQAABhCDWAcpQVamQ4Zz2p7I3CtxhY955KDQApJ+D+vcls7+TxijQYGEXEMh33r81sb6O4dxEhKjXMYvIG4uG6f1n6EQDCEGoA5Sgr1Fi7dq1at24ddmz37t3atm2b61wToYbX5uRUagAwyVWpIWacJRXLTyEOVGqYxcAoYuYYFGVPjSRzFWrQ7yIKzkor+l4ACMNG4UA5ygo1srKylJ2dHXbswgsv9DzXRIUEy08BSDkeFW5UahjG4AoixbreZrkyDe5dRMg50YlKDbPodxEFy1klSd8LAGGo1ADKUV6okZWVleTWRM5rRh/LTwEwyVVCL8mmjD55mOmNOFgZ4fOgbAZGzWJgFBGyMp33LoOiyUXfizi49tSg7wWAgxFqAOWIplKjPMkOFIIeX1io1ABglNeeGoStyeMVangsVQh4CjgG0ZktmlzO+5dQA5Fy/lthMkFyOe9d5+9SoByupVvpewEgDN9mgXJ4hRqBQEAZGRlRVWqsX78+kc06JK8QhUoNACZ57qlB9UDy8HeNOFCpYZgr1DDTDPgPlRqpxWuJYKBMzr6XPTWAtJSfn6927dpp1KhRppuScthTAyiHV6hxIMyIJtQoKChQ06ZNIz5/69atqlOnTswffKnUAJBqvJafolIjiQg1EAdXKBnk3k2u8PuXgVFEynLtqcGgaFJRGYM4uO5fKjWAtDRz5kzl5eWZbkZKolIDKEd5oUY0y08VFBREdF5JSYl69+6tnJwctW7dWsuWLYv4PQ5GqAEg5XgsdcSeGsnjlWkwMIqIsa63WYSSiJVjQgH9bnK5K1LpdxEF5/1L3wsAYQg1gHIkqlJj69atEZ03btw4vf3225KkH374QQ8++GDE73Ewlp8CkHK8KjWYMQr4gpXpuH+ZLZpUroFRAklEyFUlGQyy9KNJ3LuIguv+pe8FgDCEGkA5vEKNo446SlJ0lRo7d+4s93nbtjV06FBde+21YceffPLJiN/jYFRqAEg1lmW5qjVY2zuZPEs1kt8M+BOVGqmFexeR8ppQQLVG8pAfIR6uz83cuwBwMEINoBzNmjVzHbvlllskRVepsWvXrnKfX7hwoe68887oGlcOKjUApCKvGaNIEu/1p5LfDvgSe2oYxsAoYuRak1+i700qqqwQO+fnZpu+FwDCEGoA5fjNb36jiy++WJKUkZGhhx56SGeddZak6EKNQ1VqfPXVV57HGzVqFPF7HIxKDQApibW9UwuDK4hURmbYQ2aLJplr+Sm+wiFCHpUaVEkaRL+LaLgmFND3AsDBMg99CpDeXnjhBd1yyy2qXbu2WrVqVXo8muWn1q9fr8LCQjVo0MDz+X379nke9wonIkGlBoBUZAUCYXMWbX4vJY9XgMTgCiJkBRz/VljXO7lcoYaZZsB/vCo1CCWTyNn3cu8iCuypAQDlY5oPcAiWZaljx45hgYYUXaXGU089pYYNG+rmm2/2fH7v3r2ex7dv3x7TZn6RVGosXrxYTz31lJYsWRL19QEgJs4vZ2xWmjyef9eMriBCVGoYxu9KxMhzTw0GRk2xmEyAaLj2s+LeBYCDEWoAMYqmUuOABx98UD/88IPreFmVGsXFxWU+Vx6vqoyDQ4158+bpuOOO07XXXqtjjz1WCxYsiPo9ACBazhmjVGoA/uDaU4NB0eRyhJIMjCJSrpneYmA0qVwTCrh3ETl338uEAgA4GKEGEKOylpI6lHHjxrmOlRdcbNu2Ler38KrUODjouOmmm0rfc+/evWVWkABAQmU6y+j5cpYsXlV/jIsiYs7ZoiUMihrFzYtIeVZq0PcmSywV90Ap51509L0AEIZQA4hR69atY3rd5s2bXcfKWn5K2r8EVbQOtfzU7Nmzw5774IMPon4PAIiW5RgYFbNFzWJgFBGyHIGkTaVGUjEwilh57qnBwGjyuAo16HcROefnZvpeAAhHqAHEKJGhRnmVGrGEGmwUDiAlOcroWQID8AnL8ZWBPTWSy7VROAOjiFBmpvsYA6PmcO8iGs7lp+h7ASAMoQYQI+fG4ZFKRqgRyUbhAJBslmOzYZbASCKvmd4eM3gBL65KjSATJcxiYBSRsSzLNZBuMzCaPASSiINzTxz6XgAIx7dZIEbVqlVTXl5e1K9LxvJTVGoASEnOjcKp1Eger1CDwRVEyrl0XIjlkJLK+ffNvYtouDYbpu9NHue9a6YV8CnnnjgEkgAQhlADiEObNm2ifs2mTZtcx2LdKHzv3r0aPny4hgwZojVr1pQep1IDQCpyzzhjYCVpWJMfcbBcS8cxUcIoBkYRBWeVJHtqJBF9L+LAnhoAUD6PRTYBRKp169aaOXNmVK9Zs2aNbNveXw7+i1iXn7ruuus0YcIESdJLL72kH374QRkZGZ5VGYQaAIxzzRZlxlnSUKmBeDBb1CyWsEEcrIxAeL0AA6PJw72LeLj21ODeBYCDUakBxOGMM86I+jV79+51LUFVXqjx97//XXv27PF87kCgIUmrVq3Se++9J8m7UoPlpwCY5ppxxu+lpLE9Qg2GVhApV5UVg6JJxmxvxMHZ9xJKGmMRaiAK7r6XexcADkaoAcThvPPO0+jRo9WjRw917do14tcde+yx+vLLL0sfl7enhhQeXhzgNUD1+eefa8OGDSw/BSA1ZTrX5efLGeALzk3lWb4mqZyf+RgYRTScy8dRqZFEru9r3LuIgrPvpVIDAMIQagBxsCxLf/vb3zR9+nTNmjVLc+fOVY0aNQ75utWrV+vcc8/VO++8o88//7zcSg1JmjVrluuY12vuuece5ebmllZsHCzZlRpr1qxRly5dVK1aNV1xxRWH/BkBVH6uSg2+nCWP10RvBkYRISvTsSY/g6Jmce8iGlRqGOM1CQ2IlGs/HD43A0AYQg0ggTp37qzvv/9eH3zwwSHP/fnnn3X22WfrpJNO8gwtDub1/O7duz3P9arSkJJfqfHoo4/qo48+0t69ezV+/HhNmTIlqe8PIAWxNrBB7KmBOFjOe5dB0aQKsS4/Ymc5qySDLP2YNM6u1znzHiiP63MzfS8AHIxeFUiwJk2aKD8/XxnOTTXjcNRRR7mOlbXPRlmSHWoMHz487PHVV1+d1PcHkHpcM85Yfip52CgccXAOijJb1DTuXUSBSg2DqNRA7Fx7atD3AkAYQg2gAliWFdEyVJHyqsqINtQwvVH4rl27wh5v3bpVt99+uwYPHqyff/7ZUKsAJFXAMRDHl7PkIdRAPJyziwkkk4slbBAH9tQwyHnv0u0iCs5Qg74XAMJlHvoUALGoWbOmtm/fHtNr+/TpozfeeKP0sTMQkKIPNZ588knt2LFDjzzySExtipczVLngggs0ffp0SdK0adO0cOFCBSjJBio199rAfDkzilADEWK2qGEMjCIezkqNEu5fY+h3EQ3Hd2P6XgAIR6gBVJBatWrF/NoGDRqEPU5EpYYkvfzyy6pXr17M7YpH6KCZJfv27SsNNCTpm2++0axZs9S9e3cTTQOQJMwWNYiZ3oiDa7YoAytmMTCKKLjuX5sJBUlD34s40PcCQPmYFg1UkMMOOyzm1zqDh0SFGpI0evTomF6XSF5t//TTTw20BEBSMVvUHI+BFYuBUUTKWanBEhhJFn7/cu8iKs77l743eWzuXcTB+bmZvhcAwhBqABUkNzc35tfm5OSEPfZafsor6PCLffv2uY4tXbrUQEsAJJNzs2EqNZLHlWkwsIIoWM7lIYNB2cxATh7X8lPcv4gcVZIpxGL4BZFz3btBs3tkAkCqoVcFKkg8oUZFVmqkAkINIE05Z5yxp0YSMQCNODiXwJDYsDSJXAESoQaiQd9rjB2i70Uc2IsOAMpFqAFUkKZNm8b8WmelRlFRkWuj7USHGsmccUmoAaQnZosaxKAo4uGs1JBY2zuZGBdFHJx9r81s7+Sh70Uc+NwMAOUj1AAqSCIrNSR3tUaiQw1naFKRvEKNrVu3avPmzUlrAwADmHGWOhhYQRSszEzXMdb2TiL3+nFGmgGfcm02zL1rDH0vokGVFQCUi1ADqCCJDjWc+2okOtQoKipK6PXK4xVqSNLOnTuT1gYAyWcFHF/mmS2aPK7ZomaaAX9y7akhNhs2ioFRRMFyhhrM9k4iyqwQO9dedFRIAkAYQg2ggsSz/JSJSo1UCDXKOg6gkqBSwxw2dUY8vPbUsLl/k4ZQEvFgtrc53LuIh2NCARWSABCOUAOoIIcddljMr61Tp44sxyy8ig41iouLE3q98hBqAOmJtYENcgysOPsYoDyumd6S7CQuW5n2CCURB/re1EHfi2i4+l4qnAEgDKEGUEGqVq0a12urV68edsy5/JQz5IgXlRoAKpxztijL1ySNzZr8iIdXpQYzRpPIEUp6bdwOlIW+1xj3zHr6XkSBKisAKBefiIEKdM0118T0uqysLNWoUSPsGMtPAfA759rANrNFk8eVaTCwgsh57qnB2t5J4w4lgcg5Z3vT9xpE34soUGUFAOXLPPQpAGI1cuRINW3aVGvXrlW1atU0atQolUSwXINlWYes1KioUMO2bU2aNEkFBQW69NJLPff3OJRDffkm1ADSlOX4csaMM3MYWEE0vCo1CDWSh1AS8XAOjNL3Jg/3LuLh3IuOKisACEOlBlCBatSoobvvvltPPPGERo4cqTVr1ui1116L+LUHS1alxr333qvzzz9fAwcOVKdOncrca6OkpESzZ8/W8uXLXc+V9ZrgLwMghBpAenJWajDjLJmY6Y3YWYGAazCOZTBMYmAUkaNSwyCqrBAHV6WGbVO5BwAHIdQAkqhhw4bq06ePDj/88EOe66zUeOqpp9S1a1cdfvjhuuuuuypsT41//etfpcd++uknvfLKK65zQ6GQunbtqq5du6pdu3aaOHGi57WcDgQxhBpAmnIOrDAomjzOL8HMFkW0WAbDHAaxEA9npRWzvZOIvhdxCFAlCQDlIdQAkiwQCGjatGmHPM9ZqTF9+nTNnj1bK1eu1L333qv33nsvoe0qK4iYO3eu69jUqVNLj5eUlOjee+8Ne76sSo1YQ41du3bpoosuUs2aNdWrVy9t3rzZ+4cAkNKc6/LbwUMvx4cKwrgKomSx2bA5hJKIg6vvJZA0h1sXUXBVaoj9rADgYIQagAGtW7fWm2++We45zkoNp2RtFO61B8izzz4b9vjbb7+N6FqxhhqvvvqqXnnlFe3atUvvvfeennzySc/zAKQ452xRKjWSxzEoajEoiii5lo+zuX+N4fZFNOh7zaHKCvFgPysAKBcbhQOG9OzZU3Xr1tXWrVvDjl966aWSpKysrKS2p6ioyHONTq+qi71797qOhUIhBX6ZCVZWpcaB10Ubalx11VVhj4cMGaJbb73V81wAqcu5rndw2zZt+9//DLUmVpayWrVUtaOOMt2QqNgh5yAWo6KIkhU+F2rnnDnau3ixocbEJqN2bVXrdLwyatY49MmpxBEgEUoiGs6+t+jn1b7re63MTFVt21ZZESzhm1KcEwoCzClF5Lz+vWx/911ZSR4niFdmw4aq3qmTrCpVTDcFQCVDqAEYUrVqVX344Yd68MEHNWXKFFWvXl3HHXec/v3vf0vyXvapIhUVFXmGFV6VGl7nbdu2TTk5OZISv/wUgErC8eUstHOntr8zxVBj4pNz8Z9Vs2tX082IHYOiiJKzUmP3p58Zakl8ds6eo8Z33uGrYMA16cRHbUcKcPS9JWvX+bLv3fa/t9Vw4A2qesQRppsSMfeEMe5dRCHDPVy3Y9p0Aw2J395vv1X9q6823QwAlQxTBQCDjjnmGI0fP16FhYVatWqVJk+erMMOO0yS1KdPn4S9z5lnnqkHH3xQJ598cpnnvPLKK+revbvruFdA4RU+HLzPBRuFA/Dit5ll5dn9xRemmxAdVsBAnCrL/VtcUKDigjWmmxEfQg1EobLcuwqFtHv+fNOtiA7LTyEOVpXKMwd595cLZJcx8REAYkWoAaSo0047LWHXGjt2rG666SY1aNCgzHMmTJigTz/91HXcK9TYsmWL69imTZvKfY1EqAGku+yWLRWoXs10MxIitMddsZbamOmN+FRt1850ExLG3uez+5eBUcShatu2rmoNv7L91ve6CjXoexG5QHa2stu0Md2MxAgGCTUAJFzliX6BSuZPf/qTHnvsMb333nvq0KGDhg4dGvO1Mn5ZS/fA8lDROBBQbNiwQZZlqWHDhiooKHCddyDUsG1bO3bs8LzWgVCjrEqOeEKNL7/8Updddpk2bNigu+++WwMGDIj5WvHau3evpk2bprVr1+rEE09Ux44djbUFSCWB6tXVcPBN2vXRHAXL+D2RqoJbtqrohx9KH9shn2/UyMAKolT3/POVkVNPxT+v9tyDK5XtWbhQKvn1nrVLfHb/MjCKOGTl5anhwBu0+7PPFfJZoFe8Zo1K1q4rfUzfi3RT/6/XaecHH6h4wwbTTYlOSYn2LPw67JB7fzcAiA+hBpCiLMvSgAEDSgfnBw4cqEaNGsV0rczM/bd6LKHGnj179OCDD+rWW2+VZVl64IEHwpaaOmDTpk3asmWLzjvvPM2aNcvzWrFuFB6JwYMHa/EvG5YOHDhQf/rTn9SkSZOYrxerzZs36+STT9by5csl7f+7nzJlik4//fSktwVIRVl5TZX15z+bbkbUdn+5QJvGjv31QNBnX8zKGITevn271q5dq1atWqkKGziiHFaVKqp95hmmmxGTgltuUWj7QUGq3wdGWZcfUap6xBG+2ovigG3vvKPt/3v71wN+63tZ+xFxyqhZU3XOPdd0M6IW3LlLexb+w3HQ730vgFRTOepQgTRQ3tJRhxJPpcb69ev1r3/9S6FQSMFgUDfffLPneUuXLtUzzzxTZqAhVezyUx9++GHpn4PBoJ555pmYrxWPN998szTQkPZvtD5+/HgjbQGQOM5Nku1giaGWxMi10bC0aNEiHXnkkTryyCPVuXNnbd++3UzbgApmOTZbtf02MMrsVqQp973r/74XSAdWhnuokUoNAIlGqAH4hBVHufKBSo2mTZtG/dqvv/5au3fvPuR59913n/7xj3+Ue87q1aslJSbU6Nevn0488UQ9/PDDnstgmBqcW7t2retYYWGhgZYASKhAeKjhu9mijt+TlhXQsGHDSn9nzZ8/Xy+//LKJlgEVL+D4DOW3gVEHy/nzAJWVc2DU530vy08hbWRkuI+V+LvvBZB6CDUAHznrrLM8j/fr10/Tpk0L26z7YAcqNZo1a1ZhbYvEyJEjFQwGExJqPPHEE/riiy900003aebMma7ny9q3o6IdqEY5WCShEIDU5pxx5rd1vb3C35deeins8aGCacCvfF+p4VzChoFRpAnLMTDq9743nklqgJ9YASo1AFQ89tQAfOTWW2/VRx99FFaF0KJFC40YMUJVq1Yt83WpEmqsX79eU6ZMSfjyU9dff73r2IENzpPNK8Ag1AAqAeeMM78Nikaw0XDA4wsoUCk4Z3v7fGAUSBvOfslvfa8LoQbShFelBntqAEgwvr0CPnLqqafqhx9+0KJFi/T666/r0Ucf1fz588MCjZo1a7ped2D5KdOhhiQtXLiwzPDi+eef16OPPqp9+/ZpxIgRatOmjc4+++xDXvO7775zHSsqKtKyZct06aWX6uqrr1ZBQUHcbY8EoQZQOfl9tqiLR6jBDFJUVlbAuSeOzwZGWcIGacr3fS/3LtKUZVmuUJJKDQCJRqUG4DP169dX/fr11b59e8/n8/LytGTJkrBjByo1ateuXeHtO5Sffvqp3IqMgQMH6qmnntKiRYskSd9//31M77N371716tVLP/zwg6T9wcfcuXNjulY0CDWASso5W7TE5wMrHgg1UFlZmY4Zo34bGHXiXkWacIYa/ut7TTcAMMfKyAgPMthTA0CCUakBVDJ5eXmuY6m0pMihQg1JpYFGPObMmVMaaEjSJ598kpTNw70CjF27dlX4+wKoWO7Zon6bbcZsUaQxZ6UGA6OAPzjvXb/1vVRqIJ1RqQGggqXOSCeAhPAKNVJJJKFGIvz444+uYx9//HGFvy+VGkAlFXDuqRH01zr3VGogjVk+31PjwP1r65f9NSy+wiE9uO5d363J7ww1zLQCMMFVaeW7+xdAqmP5KaCSSfVQY9WqVWrevLmR9z7rrLM0YsQI3XjjjRX2HmWFGrZtlw4YLliwQMuXL1d+fr4aNmxYYW0BkDiu5WskKRTy3ggxFdm2gsGgvlq4UOvXr5NVN8d1SipV9QEJlRH+lcePe2qsXr1ai775RhmBgBq3bave7Y8y3Sqg4jmrJP02KEqlBtKZ76ucAaQ6vr0Clcyf/vSnsMeHH3542OMJEyYksTVuxcXFnlUUyTJo0KAKvb5XqGHbdml1yquvvqrjjjtOF110kTp37qz169dXaHsAJIjXgL/PBlfWrF2jNWvWKBgMaZ3H7x4qNVBpBRz/tn1WqREsKdE3336jYDCoouJi3T/s/qRUvQKmuWZ6+3xQlH4W6cRyfnb22edmAKmPUAOoZDp27KhLLrlE0v5Zt0OGDAl7/sILL9TAgQPVunVrE82TJIUMfyEpLi6usGuXtdTUgeOPPfZY6bEVK1bojjvuqLC2AEgc18CKfDbjzLa1fPn3vz5MwCXXrFmjnTt3JuBKQMWynJUaPttTY9fuXSo5qM3btm0rd0nNn376SRMmTNCKFSsS3hbbtvXSSy+pf//+evfddxN+fSCMc08Nvw2K+mmZSiDRMn1+/wJIeYQaQCU0YcIEffnll1q2bJn++te/hj2XlZWlkSNHavny5erVq1fM71GjRg01adIk3qYasXnz5gq79qFCjdmzZ4cdf/rpp7V27doKaw+ABPGo1PDTwKht2wqWlJR7TqSBr23buuiii9S0aVMdfvjhmjNnTiKaCFQYv++pEXT8rrElff75557nfvvttzryyCN1+eWX65hjjtHChQtVUlKi0aNHa+DAgVqwYEFcbXnttdfUt29fjRkzRmeddVa54UowGNTrr7+uyZMnG5/QAn/y+54arr23qNRAGrE89qMDgEQi1AAqIcuydOyxx+o3v/lNueddeeWVMb/H6aefripVqsT8epMKCwsr7NrlhRplLRXhDDpisX79evXu3VuHH3647r33Xn9tYAz4gJXpsQ2Z7aNBOluqVr16+AGHnTt3RvS748MPP9Qrr7wiSdq0aZP++c9/JqqVkqR9+/Zp+vTpWr58eUKvizTmmu3to3tXUjDoDiTLChPuvffe0s8bu3fv1rhx4zR8+HANGDBAjz76qE4++eS4JlNce+21YY+feOKJMs+9+OKLdf7556tPnz66+uqrY35PpLFKtyY/oQbSiCOU9P/9CyDVEGoAaeyCCy7QpEmTNHjwYL311ltRvfa8885Tptcgnw9s2rQpIdfZtm2bRowYoaefflolv8yALi/U+PnnnyusPffff7/efvttrVy5UnfddZc+/fTTuK8J4FeudYEl2YeofEg11apVK/2zV3RRUlKioqIiSfvD30GDBunaa6/V999/H3be448/Hvb4ww8/TNgs7OLiYp100kk6/fTT1a5dO02aNCkh10V6szKd6/L7a7aos8rK1v5QIxQK6euvv9a2bdtKn5s4cWLYuSNHjtSdd95Z+rioqEjPPfdczG05+L0k6cUXX/Q8r6CgQK+++mrp43Hjxmn79u0xvy/Sk3tNfn/1u67OlkoNpBEqNQBUNEINII1ZlqU+ffrooYce0llnnRXx6wKBgHr37u0ZalStWtV1LC8vL+Jr9+7dWzt27NAPP/ygWrVqRfy6aCSiUsO2bZ166qkaPHiwrrnmGv3tb3+TVH6osWrVKs/ntm7dWu573X///WrUqJFOPvlkLVu2zPOcRx99NOzxbbfddoifwO3dd9/VVVddpdGjR7NMBODksaeGrzYstUNh80PLqsfYsWOHJOmyyy7TyJEj9dRTT6l79+6lwa0kbdy40fW6pUuXJqSZ//vf/7Rw4UJJ+0OWe++9NyHXRZrze6WGx/JTW7ZsUYsWLdShQwc1atRIH3zwQem9cyjvvPNOTO3YsGGD61izZs08z/X6nVBQUBDT+yKNOffD8dm966roJNRAOnFWWvnt/gWQ8gg1AEiSMrwG7Mpw//33q27dup6hRp8+fVzHTj75ZFkRfog/6aSTVLNmTbVs2VLdunWLuE3R2LRpk8aOHavOnTvr+uuv14oVK8IG7A4oLCzUddddpzPOOMM1APDJJ5/o66+/Ln381FNPad++fZ7XkfaHGitXrvR8bsuWLWW29dtvv9Udd9yhjRs36rPPPtOQIUO0du1a7d27t9yfsaz3KsuCBQt01llnady4cRowYICefPLJqF4PVHaelRo+m3F2cFhZ1ipTO3fu1N69e/Xee++VHlu9erWqVKmia665Ri+//LJnMNyuXTvNnz/f85rOvTp27txZ5t5GziqQr776Snv37tV1112nvLw8XX755dq1a5d344EyONflt30229tVqfHL/XugArSoqEinnXaaOnbsGNH1srOzY2qH134cOTk5nud6hZ/r16+P6X2Rvvy+H87BbNvWjp07TDcDSBrLFWr4q+8FkPoINQCUqXfv3go4BvIWLlyoW2+9VdL+TcedcnNzXcfq16+vY445JqL3bNmyZemfO3ToEE1zI/bmm2/qr3/9qz799FONGjVKrVu3Vvv27fXTTz+FnXfTTTfpySef1Pvvv6+zzz5bDzzwQOnSCd98843ruuXNQIy1UsO5VvUbb7yh3NxctW3bVkuWLCnzdWXt31EW52zogQMHRvV6oNLzfaWGrVAEe4Dceeed+uSTTzyfe/rpp3XxxReHBboHGz58eNjjn3/+WSeccIJq166t/v37y7Ztvf7662rSpIkaNGjguReHV5XY+PHj9eSTT6qgoEATJkxQv379DvlzAGGcS2CE/LXvVDDorNSIr/0H9tTYsGGD7rrrLg0ePFjvvffeIffU+fLLL13HylpC02tyxbp162JoLdKa32d6/3JPrVy5Uu9Pm6ahQ4eqU6dO+uyzzww3DEgCVyjps/sXQMoj1ABQpuOPP14TJkxQzZo1lZWVpbFjx4aFE/fdd1/Y+eeff746d+7suo5lWREvh3TwMgb5+fkxtrx8XssuLF26NGwJp2AwqPHjx4edc9tttyk/P1+7d+/2HHgrbx+LN998s8xQo7xKjbJmM//000/697//Xebrog01Jk+eHNfrgUrPc08NH80YtW2FDhoMKmvo8oUXXlD37t1jeouD18+XpNtvv13z5s3T3r17NWbMGL3zzju6+eabtWvXLtm2rfvvv1+rV692NNPdstdeey3s8fPPP68PPvggpjYiPVW2So14rVixQosWLdJxxx2ne++9VyNGjFCvXr100UUXhVWc2rYddk96BZobN270vG+9PvMQaiBazpneCoUOGb6lFNvW6tWr9fWiRSoqKpKt/eFg165dXZOpgMrGuaeG3yqcAaQ+Qg0Apa6//vrSP1epUkUDBgxQ3759VVhYqF27dunaa68NO//MM8/UGWecIUlq3LixbrnlFv3+9793XXfx4sW66KKLdOKJJx6yDYcffnjpnytq+amyjBgxovTP3377rec5X375pf797397BhGXXHJJmdd+5pln9PTTT3s+9/XXX5e5DnZ5S0Q8++yzZT53qOWpnHz1BREwwLIsyeebDR9cqVFRd/yB3yW2bWvChAlhzw0ZMkQ//vjjr+0JhfT222+HnbNnzx7XNWfPnu069sorrySiuUgXzoFRPwWScldqxGvfvn066aSTXBWmr776qgYOHKji4mJdf/31yszM1JFHHqlFixZJkr777jvXtfbs2eO5JJ1XqDFo0CDde++9rs3GD5g9e7buvfdeffHFF7H8WKiMPCYU+G2z4bAw75fOd9++fXrqqafivvbcuXM1YsQILV++PO5rAQnnrNTw2b0LIPURagAoNXz4cA0ZMkR//vOfNWvWLNWvX1/S/oF1r/0zMjMz9e6772rVqlX6/vvvdcIJJ6hx48au8w7M+uvZs2e579+5c+ewSo1AIKA77rgjnh8pZmUtvyJJ//d//6fbb789Ye+1fPlydezYUZdffrnruaKionJfGwqFXOvVS9GHGl68BhfL89133+mHH36I+32BVGVZztne/vlyZtu2Z4VZohUWFmrLli06//zzXc95hcXOwc01a9a4zvGqHDswyGrSli1b2N/DJ1yzvSNYii2VBB2VJYkIJcvq40ePHq2srCyNGjVKoVBIy5Yt0zHHHKNWrVqVOQGjVatWYcHGihUr9L///c/z3APLXTnNmDFDXbt21V133aUTTzwxIcFGKBTSwoULqT71Mde9K8n20RI2tm17fk6XpKFDh8qyLF122WWaO3duuZXbXt5++239/ve/1+DBg9WhQwfPz+DBYFAzZszQ1KlTy9z3D6goVkb4+IGf7l0A/kCoAaBU9erVNWzYML300kv63e9+F9FrLMtSs2bNVLNmzdJjB1d8SNKVV14pSTrhhBNcr7/lllt0zTXXqF+/fq6lSyTp1ltvjXg/jkQ4MMu4vFCjokyYMEErVqwIO+a10ebBzjnnHM9zohm8LKtKo7w9QpwGDhyodu3aqXXr1ho5cmTEr5P2D04eeeSRysrK0i233BLVa4GkclVq+OjLWSh8+amKMmfOHHXo0EGTJk2K6PyFCxdq1qxZ+uMf/6hmzZp5rsPvZdGiRZ6/52bPnq2jjjpKeXl5uvTSSytseY/77rtPDRs2VMOGDfXyyy97nrN161addtppsixLPXv2LHN2OpLAuQSG3yo1Spx7apStS5cuFdKGg6usnHbu3Fk62WPHjh064ogjyr3WCy+84Lp/nbPWnUucOm3YsKF0o/Sy2tS+fXt17NhRTZs2Lbf9TuvWrdOwYcM0YsSIcvc9QxJ47Wflp8F5Wyo+eEk3j1Oef/55nXLKKapXr54sy1K3bt300ksv6W9/+5vuuusurV27Vq+++qp69+6tAQMGaOrUqdq4caNuueWW0s/we/bs0WOPPea69sCBA9WjRw+deeaZuuqqqyrqpwS8UakBoIIRagBIuNtuu01HHXWUpP1fri+88EJJ+/focDrqqKP05JNPasyYMWratKnr+Vq1amnBggWaNWtWhbb5gAsuuEB33XWXnnvuuaS8n5Mz1NiwYUO557/zzjthe4EcLNIlpcratyPSUGPdunWlbbBtW//85z+jWirjn//8p5YuXari4mL95z//8dyIFEgFfl8b+ODlp0IVtADV5Zdf7tonozwvv/yy8vPz9eabb5Y7QOm0c+dOV2BRXFysiy++WIsXL1ZBQYFeeOEFnXvuuYf8Xeg1e9W2bb3yyisaOHCgTjvtNPXq1UszZsyQJK1evVp33323gsGg9uzZo8GDB3u+x9ixY0v3/pg2bZrq1q2r6667jtmyBliOQNL229JxjkqNHqed5rmHWdu2bTVu3LiIJ6YcEO35Xp588kk1bNhQtWvXPuRngH379umqq64Kqyp96aWXws556623ynz9f/7zHzVu3FgtWrTQtdde61mJMXr06NLlsjZt2qR//OMfntf67LPP9MYbb2j37t368ccftX79evXp00e33367Bg8erKuvvrrcnwUVy/LazyrkoyVTbVslJb9WakTS93744Yfq27evHn/8cd17773Kzc3VhRdeqLffflujR4/WmWeeqUaNGrmWgxsxYoTuvfdeXX311fr000+1fv16jRo1qvT5559/XsuWLdOMGTO0cOFC2bat2267TTk5OTr66KP13//+V8FgkCVpkTB+/9wMIPW515MBgDjl5ubq66+/1tatW5WTk7N/Lfpfjh9xxBFaunSppP3LS0WyGXggENCpp57q+dzXX3+d0EqO119/Xa+//nrCrhetK664QvPnz1dubq6kQ1dqSCpzRvS2bdtUt25dz+dCoZA+/fRTNW3atMxy93nz5unOO+/U559/rgsuuEDPPPOMsrKytH37dg0bNkzr16/XoEGDtGzZsrDX7dy5U5s3b1bDhg0P2XZp/ybqB3vooYf0wgsvRPRaIKl8PePMvfzUXXfdpfvvvz+hg+zJXI5p0aJFatWqVenjadOmuZav+vrrr7V8+XL99re/db3+QAjyv//9T127di0d2Dww6HPXXXeFnT9nzhytWrVKkyZNChu0Xbt2rdasWeMK5m+99VbXez755JPq0aOHLrrooph+ZsTIOTCahKqlRHKGBLVr19H7Eydqzpw5Ouyww9SxY0eVlJQoIyNDlmXpuuuu09y5cz2v1aJFi7CKqLPPPluvvfaaunfvXuZrIuW1t0ZZxo8frypVqpS7r8DKlSu1cOFC7dixQ2vXrtWOHTvUuXNnDRkyRNL+zzJPPfWUtm/frlNPPVUfffSRCgoK1KZNG9dni0mTJmnr1q2qW7euPvroI33zzTd6++239c4775TbzkmTJqmgoMBz4g2SIMNjuCLoo2DYtpMaZB/ot1588UXddNNNrucPrqLKzc0t7TO3bt2qG264QTfccIOys7PVo0cPvfDCC6pbt66mTZumxYsX6/zzz+c+QHScn5v9VOEMwBcINQBUiEAgoHr16rmO//e//9Ull1yiTZs26Z577gnbQ6M8B4IRp6OPPjrmNv7hD3845JfZZFu3bp1OP/10ffPNN5IOXakhqczNAQsLC8NCjc8++0z33XefsrKyNG/evNJNPMsaXDt4VuOBGc8XXHCBrrjiCk2ePFmSNHXqVA0fPtz12o0bN0YcajhF8jMDJrjWBvbTwKhjTw3blk466SQtX75c7du3jzmMOPPMM/Xee+8lqpVR+eyzz3TuueeWPn7xxRc9z1u6dKl++9vf6oMPPtCoUaPUsGFDXXXVVfrqq69KQ+xp06bp3HPP1eeff64dO3Z4XmfXrl169dVXPfcGWbp0acSDPbfddhuhRpI51+W3/TQoKvfyU1WrZqtmzZrq1atX6bGD9z677LLLtGnTJteg5uzZs9W5c2ctXbpUy5YtU7169XTyyScrOztb77//viZOnJjUyoSnn35a3333nZ599lnP5w8//PCIrvPKK6/olVdeKX08Z84cz/PeeOMNZWZmeu5hVp4LL7xQM2fOVFZWVtjxYDCoESNGaObMmTrjjDN0/fXXK+C1sTViZgXcn//9tC6/bYdUUhy+/NTBE7wqyt69ezV06NByz/Haw0raX0k1ZcoU5eTkqGXLlqVLt/3f//2fvv/+e8/vd4AXV6WGz5Z+BJD6+NQFIKlOP/10FRQUaN++fVFvAn5g4/IDDmxKHmuw8fe//12///3vD3neiSeeqNmzZyfti+rixYu1cOFCbd++/ZAbhZfn4BmTu3fv1vnnn6+3335bkyZNKg00JGnixIkRXe/CCy/U8OHDSwMNaf8XIq9gKNJgYvfu3a5jidjkHKgQzsEVvy1hc3CoISkrK0uHH364xo4dqwyvdcsPkpmZ6QowmzdvrmOPPbbc1/373//WsmXL9H//938xt7sskyZNKl0mo6SkpMzlaj777DOtXLlSZ599tt544w2NHTtWnTt31t/+9rew8z744IMyA40Dvv32W88B0wMVaxMnTtRZZ51Vbv8Wzdr+SBBn/+2n5WskhRyVGtlVq5Z7fiAQ0ODBg7V7927deuut6tGjh5566il16dJFmZmZOuqoo9SnTx917dpV2dnZkqQaNWroL3/5i0KhkJYsWaK1a9fqiSee0LHHHqu+ffvq1ltvVfXq1aNq98iRI9WkSZNyz5k7d+4h9+BIlGeffba0yiMac+fO1bXXXus6/tJLL+nmm2/WlClTNHDgwLDPR0iQTK9KDf/0vSUlJQodtJyTrf0bfNu2rWAwqAkTJrjCslRycH+1ZcsWPf/88wZbA79xLv3ot8/NAFIfoQaApKtSpUrYjMJI/etf/wp7/NBDD0mShg8frlq1akV9vc6dO6t58+aHPO/iiy9Wly5dPL/QxiKS6pAJEybEXbGwadOm0j+///77Ua1XXxavwYDPPvvMdSySZbOk8DYecPDa2Js3b9Y999yjBx54QNu3b49qrw4g0VyVGn6aceas1JBKBzP79u2rr7/+WpMnT9b8+fN18sknly6Bd8CQIUM0ePBg9e7dW9L+QdO7775bPXr0KPMtL7zwQt18881q06aNrrnmmoT/SEuXLi2tajuwRI2XoUOH6g9/+ENCAtMnn3xSS5YscR1ftmyZPv30U/Xt21fvvvuu7r///rjfK1oFBQWaNm2aiouLD33yL3744QcNHTpUr732WqVeR933lRpRhhoHVKtWTQ888ICmT58ecQWGZVk64ogj1KRJE1133XX68ssv9cILL+iBBx7QmjVr9Nlnn2n16tU677zzXL8nDujYsaO2b99eOtB/+umnR/TeFW3OnDllzk4/lPHjx+u0004r3Tjctm3XTPi7775bBQUFmj59eoUsxbd27VrNnDnTcw+RyspzTw0ffRbcs3uP61jt2rUl7e9HL730Um3fvl2bN2+WbdtasmSJrr76avXu3Vu33367VqxYoUceeUSnnHJKspvu6dNPP1VBQYGWL18u27ZVWFio9evXS9of+l9//fW69957PSctlaekpETbt2+PqU2hUEg//fQT3xFSkRV+//rp3gXgD4QaAHzjmmuu0fXXX6/27dtryJAh+vOf/yxJ6tWrl1auXFk6uOX0ySeflG7WekB+fr5q165duqH5AdWrV9fgwYNLHzdq1EhXXHGFpP2bTr711lv673//6/k+Rx55ZNhSCWUFLWeddVb5P6ikhx9+WD179jzkeeU5eFkq58+fSF6zjiMNZLzW3z4wMGnbtk4//XT961//0m233aY6deqoQYMGateuncaPH58yA3B79+7Vgw8+qNtvvz3izdXhU861gW0/LYHh3lPjQKghSe3atdO5556r4447Tp988okKCgpk27Z+/vln/fjjj7r33nuVmZmp119/XfPmzdPSpUt11VVXqXv37rrrrrs8l4k5uBIiNzdX/fr1i6itVapU0QsvvKCFCxdqzJgx5Z7bpUsX3XnnnYf8veq1ZFQsygpGli1bpgcffND1d1wWr0D3gG3btmnFihV67LHH9Morr6ioqEgPPPCAWrZsqT/+8Y9av3691q1bp5dffllr166VJM2YMUPNmjVTz5491bFjR+3bt0+2bZcbcGzcuFGdOnXSnXfeqQsuuECPPPJIRG33JWclkp+WjpM71KgaYaiRaHXq1NGJJ56ovLw8vf766yooKHAtHfXCCy/ok08+Kf0MdNJJJ+n999/X2LFjTTQ5oT744IPSveLq1KnjCjgXLVqkvLw8nX766Tr55JNLA5CDFRYWek78mDt3rgYMGKAbb7xR06dPD3tu4sSJOuaYY5Sbm6vu3bvr5JNPLjeknTJlirp166YLL7xQK1euVDAY1KJFi7Rz587YfnCTvKoIfbT81N494YP7tv1rqHFAdna2cnJyJO1fmuqpp57SW2+9paFDh6pVq1a64YYb9NFHH2nZsmUaMWKE5syZo127dmnBggXas2dP1JOuIqlSL8tLL72kvLw8/fa3v1UgEFDDhg3VpEkT5ebmqn379ho1apTuuusuNWzYMOx72bx58zR06FDdeeeduvPOOzVjxgw9//zzuvPOOzVmzBg1adJEOTk5uv766yP+fG/btsaNG6dmzZqpZcuWOuKIIzwnWsEgV6WGf+5dAD5hw5jVq1fbkuzVq1ebbgpQKYRCIfuII46wtX8Ssi3J7ty5sx0KhWzbtu1hw4bZOTk59rHHHmt/++23tm3b9tq1a0tfk5eXZ8+dO9e2/7+9+46v6fzjAP65yc3eZA87RkisGLHFqL3T1kgpvxZFKTVK1SqK1qjVUkVrq1HUipEgpTQESawKEhGRkC3r5j6/P9Spm3szkLhu8nm/Xvf1yjnnOfc85+Y5d5zv83wfIURwcLBYs2aNePTokcZj7dixQ1SoUEE6joeHhzh//rxIS0sTe/fuFREREUIIoVKX5w8hhKhdu7bGbcX56NGjh1Rfd3f3Ej/ei4+vvvpKOrZCoRBXrlwR6enpaq/j0aNH1fa1tLQUQggRHh5e4DEWL16cb1uIiYkRgYGBIi0trcjt51UNHjxYqpObm5vIyckp8WOSdsTOniOiho+QHmnn/tJ2lYos9fQZscrVVax0efaYZGcvLl26VOzHOXLkiJg4caIICAhQ25aVlSVWrlwppk+fLqKjo8X169dVrml9fX0REhIiEhISpH2USqWYN2+eaNSokRg/frwYPXr0G30vK6lHUFCQ2uuTmJgo/P391co2bNhQZblDhw7CzMxMWi7o88TAwEB88803Gv9XM2bMUClbr1694msIb5nUU6dUrt2HCxdqu0ovZWvdetK1u9LFVfw4c6a2qyRRKpXi3LlzYsOGDeLhw4f5lrt//76Qy+Vav/ZefIwcOVLjekNDw2I7xpw5c6TXYPbs2QKAkMvlYvTo0dL3otWrVws9PT2V/bZu3SqEEGLVqlUan3fp0qVi6dKlYsaMGSIuLk46xsWLF4W+vr5UrlWrVqJx48YCePb9KiQkpIRaQsmJGvmJyvWbpUO/nUMXLVK5dj8oX176XVJcsrOzxaxZs0S/fv3E/v37hVKpFHPnzpWut1atWokJEyaIhQsXipSUFCGEEAkJCcLDwyPfdtuuXTvRqlWrYrkG8rbtwh6dO3cWc+fOFRMnThQTJkwQ27dvF+vXrxfNmjUTffr0Effv3xeHDh0SvXv3VtvX1NRUHD58WHTp0kVUqlRJzJw5U4SGhorAwEChUCiK9XWnwj3Ztl3l2n28aZO2q0SkU3jPuHAMamgRGyhR8Tt69KhwdXUVdnZ2Yvjw4SIxMVFle25urto+GRkZIjw8XGRmZhZ7ffL+MHZ0dBRCCLFs2bI38oM9IyNDREZGvpFjvfgYOXKkEEKItLQ00aBBAwFA2NjYiKtXr6q8Plu2bNG4f0pKiti1a1eBx6hSpYrG1zw4OFiYmpoKAMLd3V0kJSUV+//1OYVCoVYvTTdzqXR4OG+ealDj3yCoLkgODFK5sTLRzk4KvmpTt27dpGtnxIgRhZYvLNipK48hQ4ZI5xQSEiK8vLxK7Fh6enrizp07aq/l8/fJFx+aPiN1SU5OjggPD1e7aZh65oxqUGPefC3V8NVsruOpcv1u+GaBtqv0SrZs2SLq1q0rOnXqJC5cuCCqVq2q1gZtbW3F6dOnRevWrYWlpaXKNktLS3Hw4EERHBws4uPjhYmJSaHtXy6XCwsLC7X1rVu3FjExMWrrx40bJ1JTU0VCQoJYsmRJsVyDVatWVQk0vPjQdB0+f3Tu3LnIx/j666/F9u3bRc2aNQss16VLlyL/v27evCk++OADMWjQIBEWFlZg2eK8UR8dHS1Wr14tgoKChFKpFNGjR6sGNTS8n72tzs2erXLtfuTs8saO/fDhQ43v/c+lpKSIP/74Q3rPDAsLEzt27FDZJycnp0jXmS48WrduLbKzs0v+hSfJkx07VIMaGzdqu0pEOoX3jAvH9FNEVKp06NAB9+7dw6NHj/DDDz/A2tpaZbumyb6NjY3h4eGhkoqluHz//fcqy0uWLAEAfPrppwgKCkLPnj2L/Zgv8vb2xu7du0v0GJo8Tz+1ZMkSXLx4EcCzCQbzzouiKf0U8Cw3/D///FPgMSIjI9VSKZw7dw7NmzeXcvneunUL/v7+WLZs2WvPUZJfPTXVi0opvbx5+XVnGH1OdrbKsgBK5D3vZe3Zswe//fYbDhw4gFWrVhVa3sPDAz4+PgWWMTQ0RMWKFYuriiViw4YNcHV1Ra9evdCwYUNcuXKlxI6lVCqxZs0aiGedmaBQKPDXX39pzHmur68PR0dHTJ8+Hbm5uUhPT8f//vc/NGrUCD/88EOBx7l16xYmTJiAefPmFTjh+j///IOtW7fm+578vJ4FnY8m0dHRcHV1Re3ateHl5aUyp4HanBo6lgJDqcybfkr71+6r6N+/P0JDQ3Ho0CF4e3vj6tWr+OOPP+Dl5QWZTIamTZvi9OnTaNGiBQIDA5GUlITk5GRMmjQJfn5+2LdvHzp37oxmzZrB1tYWP/74IxwcHFC5cmX8/vvvEELg8ePH0nxpMpkMe/fuxdmzZ1GvXj2pHmZmZli2bBmcnJxga2urUsfevXvD3Nwc5cuXx7hx44oldd3t27fzzfdf0NwDhw4dKvIxvvzyS7z33nsa5/x50cGDB3Hv3j38/vvv+X4HE0LgwIEDqF69On755Rds2rQJvXr1QlZWFh4+fIhRo0ahQoUKaNmyJW7evImhQ4fCwsIC7dq1KzC1XlEkJCSgWbNmGDlyJFq3bo2NGzeqf/Yq839/eNtkZKimCSvqfDjFwcHBQWNqyOcsLCzQpUsXeHh4QCaToXbt2vDz81PZRy6XFzh3li4JCgrCzp07ATxLnTts2DD0798fX331Fb755hvs2LEDp06dwuDBg1GlShXUrFkTs2fPxnfffYeRI0eiX79+RU5/u3//fnh7e6N9+/a4efNmvuViY2Px+++/a0xJl5SUhJiYGDx9+rTAz9S3mdpcdDr0vZmIdMPLz9RLRPSW0xS40JYhQ4bg1q1bCAwMROfOndGvXz9pW6tWraBQKPD7778X+jzlypXDkydPXvr44eHh+Pzzz196v9f16NEjKJVKTJ8+XWX97t27kZSUJAWb8vtBHR0dXeCPgOd8fHxw8uRJ2NraIigoCG3atFErs3//fuzfvx8LFy7ErVu3YGpq+tLnk5/bt2+rrdPXlP85jz179mD37t3w8fHBiBEj3qo2S/mT5Z1TQ4cmG85RqM6tIPDs5r+2yeVy9O3b96X2WbJkCdq0aYPMzExYWlpKQY7IyEhkZ2dj1qxZ6NGjBwYPHoz9+/er7Ovh4YG+fftiyZIlSEtLg4GBAWbPng1nZ2ecOnUK3bp1Q40aNfD+++8XGmhYs2YNRo0a9VITc78oJibmjc3DM3/+fMyfP79IZePi4vD111/D2toakZGRWLduHYBnOdErVaqE+/fvIy4uDh988AHc3NwAPLv54uvri/v37wMALl26hJ07d0KpVOLRo0ewsbGBkZERTp8+jXfeeQcZGRmwsLBAWFiYdAMaAE6dOoUPP/wQqampmD9/PoYNG4bAwEDMnz8f5cqVQ05ODvbu3YsKFSpg+/btaNSokbTvV199JU1YGxYWhmXLlsHa2hrXr1/H0CZNUP7Fk9SxyUqVeW4EGZmYaKkmxcvExARdunRBly5dkJGRAZM85yWTyWBpaYkFCxZo3N/f3x/+/v4q68qVK4fQ0FAcO3YMderUQa1atQAAISEhOHHiBMLCwtCpUyfUrFkTAPDZZ59h2rRpAJ51BMk734CHhwfi4+PRtGlTjZ/5uijvje6OHTvCy8sLHTp0gIuLC5YvX44ff/xRpcw///yDevXqqQRNoqOjUaNGDWn5xIkTmDVrllqHnsuXL+POnTuoW7cuoqOjYW5ujvr160MmkwGAdJNYJpNh9erViI6Olvb98MMPcWvESKiE8XToszcrU3WicGMT7cyH8zoWLFiAO3fuFNvcVNo0cOBADBw4sMjl83bG2rVrFwYPHozy5ctjxIgRaNu2Lc6dOwelUglbW1tcvXoVV65cQXBwsLRPjRo1EBQUhOvXr+PXX39F7dq14enpiZ9//lnq+FW+fHkEBwfjzz//xK1bt5Ceno61a9ciI+O/9rNo0SKt/KZ7HWrfm5W69dlLRDpAe4NEiEOJiCg0NLTQ4dLu7u7iwYMHYteuXaJbt25i6tSpYtOmTa81BNvZ2VnjegcHh2IZ4l2zZk2N82U8f9y4cUMolUrx8ccfa9y+du1a0aJFiyIda9y4cSI9Pb3A3MDPH1u2bBFnzpwRx44dE1lZWSImJkYlF/VzWVlZ4uzZswXmBxdCiDVr1qgdI7/89c8FBwerlF+9evVrtSF6c+IWL1EZRp9y/IS2q1RkUXt/V0mBMcHOLt85g3RBQkKCCA4OFqmpqfmWUSqVok+fPtK11q5dO/HgwQMhhBBJSUni+PHjGq//59LT08WcOXPE8OHDxbFjx0SzZs2k5+rYsaNQKpUa3+dsbW3FlClTxK+//ioWL178RtNrFOfD2NhYGBgYFFjGzs5OuLq6atw2atQoYW9vL4Bnc1ZdvnxZLTWOh4eHmDRpkqhVq5banFgARK1atfI9drly5cTp06dFenq6UCqVBdazpa2tCOzQUQS900ncHjZMPJgxo7ibZIn6uZq7yvV7iCk8itWJEyfEtm3bREZGRr5lHjx4IObPn6+169HIyEj4+vpq/X2hsIebm5u4e/eu6NixY6FlR40aJVxcXISZmZmoUaOGGDt2rMZyC5ycxEoXV7G7obeIHPY/kR4eXmAaoaysrLdi/oSTJ0+KIf/W/fnjy4YNtV2t16ZUKsVff/0lli5dKn7++Wdx9uxZlRRvcrlczJgxQ3z00UeiTZs2Kv/LOXPmiAMHDoiZM2eqzReV9+Hk5FToNTF//nyRkJCgksqytD709fXF3bt3tfq/f9lUzUn79qt8b47/cU0J1YyodOI948LJhCjC+DkqEffv34ebm5s0XJ6Iyp7o6GiVXqp5NW/eHKdPn5Z6sz2XmpoKW1tbZOdJKQMATZs2xfXr15GUlKS2TV9fHzExMXBwcECzZs1w9uxZle3r1q3Djh07cOTIkVc7oWIybNgwqXdwYYyMjODm5lZouqr8GBgYYPny5XjnnXdgZWUFQ0NDNG7cGBEREbC0tMThw4fzTXczZcoUtR6kn3/+ORYtWpTv8aZNm4Z58+aprNP0UZyQkIBZs2bh0aNH+Pzzz1V6JJN2PFq2DFnX/uulat2vLyzat9dijYou8rffcHjcZ9Ly7awszLj9DywtLbVYq5KnVCoRHBwMGxsb1K5dW+299GVkZGRg9+7dyMzMxLvvvgsLCwtp24YNG7BixQpUrVoVK1euVElpc/PmTfz9999o2bIlAgICMGrUKGRmZmo6BJUQL2NjDC//7H9iYmIMny5dUXPZUo1lQ0NDsXPnTtStWxd+fn6v1WaKy7oqVZH1wue9x+Lv0Obdd7VYo7IrPj4ex48fx9atWxEYGIiUlBSUK1cOn3zyCXbu3In4+Hh8+OGHiI2NxZYtW1T2rV+/Pj777DMMHDgQDx8+xDfffIOffvpJ6o09adIkrFmzRu37m5OTEzZv3oymTZuie/fuOH78uLRNLpfDwsICiYmJJX7u2jLP0QlW/46CtbaywnY9GfaFhaFr166YP38+6tSpg9DQUNy6dQsXLlzAunXrkJiYiKpVq+KTTz5B+/bt4eHhgUOHDmHVqlXIzs5G9+7d0b59e9SpUwdKpRI7d+7EuXPn0L17d/j6+gIAsrOz8eTJEzg6OiI2NhZjxozBgQMHYGBggDVr1qB///4q9RRCYM6cOdiyZQvi4+Px5MkTvGdtjVZm5lKZxEoVMe3MmTf34r0hjx8/xm+//QYLCwv069dPZSRoYmIiAgICUK9ePVSvXl1ar1AosHz5cly/fh29evVCp06dEBkZiZSUFNSrVw8ymQx79uzB1KlTYW5uDktLS5w/fx6ZmZlo3rw51q1bh6pVqwJ4NjqwWbNm0mersbExxowZA3t7e0ycOPHNvhglqGLFivjoo48QFhaGcuXKoU+fPihfvjwuXbqEzZs3486dO2jatCkaNGiAmzdvokKFCoiMjERMTAwGDRqEQYMGAXiWfmvGjBlISkpC27Zt0ahRI/j4+EijzTMzM3HixAmYm5vD29sbenp66Nu3Lw4dOgQfHx/89ttvcHR0VPt8fD46U09PD3Z2dkg9fBgPtm6Fvp4+jI2NYVLXC7YjR77x141IV/GeceEY1NAiNlAievr0KczMzDRua926NTZt2pTv+0PXrl1x8OBBtfUzZ86Er68v2rdvrxb0mD17tpQSqnfv3ti7d6/K9r///hsNGzbE8uXL8emnn77CGek2U1NT+Pr64sCBA9K6fv36STl48/Lz88Nvv/2msm7w4MHYsGGDyrrnOeD19PRQvXp13Lp1S2X7jRs3VH7oAcC7774rHdfKygorV67EF198AZlMhlWrVqFr166vdI5lXUxMDK5fv45GjRq99A39+BUrkRkWJi1b9eoFy07vFHcVS8TNrVtxbOIkafmfrCzMi46C8RvM703PREdH49tvv1VL0dKpUyd069YNDx8+xNdff/1ax7C2ttYY2C6r6hgbY2T5/wJNWUZGcJ43F4sXL4aJiQnmz58PPT097N+/H3PnzlXZ97vvvkNqaiouX74MIQQ8PT3Rp08fVKhQAXFxcahZs6bKjZ3IyEjI5fJ8Oyzk5OTg9u3bqFSpksr1l5aWhtjYWFSuXBkZGRl4+vQprK2tsXHDBijmqLaHBqtWommPHsXx0tBrUCgUSE5OhrW1tXQzUAgBmUyGlJQULFq0CNHR0WjcuDGGDRumcR6jhw8f4ujRo6hUqRJatmyJ+Ph4HDt2DPXr14eNjQ3u3bsHLy8vKTVXVlYWfvjhB9y/fx9DhgxB7dq1ATxLD7Vw4UIYGhpi/PjxOHLkCBYuXAgnJyc0adIES5cufWOvS3H72tERNi/k5l/9OAFhxRQY9vT0xKNHj6TUdQDQt29fZGZm4vjx44UGoJs0aYK+ffsiPT0ds2bNUtv+vrU1Wr4Q1HhasyY+PxZQLHUvi5RKJZRKJeRy9SzqDx48wK5du5CWloYuXbqgbt26AJ7NpeHn56dx3oqyyMHBQaW9F8bIyAhZWVlq662trWFubo6mTZvC1tYWd+7cUekUZ2BggLZGxuhpZQUAqFq1Kur07AGX8eNf/yT+9ejRI+jr66N8+fJq21JTU2FqalqktMBEbyveMy4cgxpaxAZKREIIuLu7q+Vp9vT0RGhoaIFzLZw9exbNmjVTW79t2za89957OHLkCD7++GOkpKSga9eu+Pzzz1UmyuzZsyf27dunsu+TJ09gY2ODzMxMzJw5M9881mWNpo9KpVIJDw8P3LhxQ2V9165dVYIiX3/9NRYtWgRnZ2ds3rwZ06ZNw+HDh1X2mTdvHr744gtpWaFQwMDAIN/6ODo6IioqqsAypO7cuXNo37490tPTUalSJVy4cEFtktiCJKxejYzL/82zYNWzByw7dy6Jqha78I0bETTtS2n5n6wsfBv3kPO5aNHjx48xbdo0xMfHY8KECSrv5+np6WjXrh3++usvAM/eR3Jzc/Hrr7/C2toavXv3xp9//olz587h8ePHUi/KTz75BLdu3UK3bt3w4Ycfahx19/HHH8PPzw/t2rXD0aNH0alTJ2mbra0tkpOTX3mekLdVLSMjjLa1k5aTc3Mx9WFssTx3w4YN0b17d8jlcgQEBCAoKAjAs04EY8aMwYYNG3D+/HlUrFgR1tbW+O677xAfHw8LCwt8++23OH78OHbs2FHgMVa6qP5O8NmwHvV1ZJQYaZ8QAp999hlWrVqlcm17enrC1NRUep95WRUrVsS9e/eKq5r5mungCLsXbmL/+DgBV3RktFt/a2u0eCGooaxfD6PzzPVEJU+hUGDJkiWYO3cukpOTAQB169ZFSkoKqlatinfffRcff/yx2n4WFhaYM2cOBg8ejJEjR2Lbtm1qZRwdHeHi4oK7d+/i8ePHJX4uuqSduTn6WFlLy9cyM5HQoT0cHBxw/PhxVKlSBQMGDIC1tTXS0tLg5uaGlJQUfPvtt7h9+za6deuGyZMnIzIyEsHBwUhJSYGRkRGsra1x8eJFrF+/HnK5HHXr1kXNmjVRs2ZN+Pn5YcKECfjjjz/g7u6OefPmITY2FnK5HO+88w5kMhlOnz6NJ0+eSB0UsrKyIJPJcP78eVy5cgVWVlbo06cP9PX1YWBggLS0NJiYmCA6OhqHDh1ChQoV0KFDBxgZGSE0NBSnT5+Gp6cnWrVqJX2vzszMxPXr12FmZgYbGxs8fvwYhoaGqFy5svR6PHr0CKampjAzM0NUVBTs7OwKnftRqVTi8uXLSE5ORrNmzSCXyxEfH481a9ZAX18frVq1gr29PaKiouDo6Ig6deqo7Hv48GH8/fff6NixI5o2bZrvcXJzc3H16lXo6enBy8sLwH9B+6ysLOjr60Mul0MIIb0+z4ONiYmJCA8Ph5eX1yuNCL9//z727duHGjVqoF27dirzLpU1vGdcOAY1tIgNlIgA4MCBA/D395d61Q4ZMgTTp09HlSpVCt330qVLWL16NdauXQsAaNmyJY4cOSL16Hv+5UMTTSM98n4kREdH4/3338eff/6psr53797Ys2dPkc6vNFAqlWqv448//ogRI0aolW3SpAnOnTsHAGqTl7dt2xYpKSkICQlR2cfX11clncT169elyU3zc+vWLVSrVu1lT6VIsrOzIZfLS90N7y5duuDQoUPS8vLlyzF69Ogi75/w4xpkXLokLVt27Qqr7t2KtY4l5fJPPyF45n+9SG/n5OC7uIdarBEVRqFQ4OTJk3BwcJB+UOYnMzNTbdRNTEwM5s2bh5iYGJibm6NWrVr45JNPYGNjI5URQmD48OFYv349ateujYMHDyI8PBwfffTRS9+stLS0REpKykvt86ZUNzLC2BeCGmnKXEyOLZ6gRkmTAViRJ6jRdutW1GrZQvMORPl4+vQpIiIikJGRAS8vL1j924P6+vXr6N27tzQJuJ+fH5o1a4bAwECkpaVh9OjR8PX1RUREBObPnw87OzssXLgQlpaWCAsLw4MHD9CtWzeNHUDs7e1RtWpVXL16FWlpaWjUqBFyc3ORlpYGDw8PxMTE4MKFCzA2NoajoyPu3r2r9l4yw8EB9vL/OnH89OQxLmVkqB3rbZQ3qGHo44P/7Sw4iEklJycnB0IIlfRYz/38889YtWoV3N3dsXLlSqSmpsLFxUW6UZueno6xY8eqpMft0KEDdu7cKV1LwLPUTnK5HG5ubsjNzcXIkSOxdu1auLm5YdGiRTAwMICpqSm++uorXLhwAQBgZ2eHnj17onfv3jhx4gTu3r2LESNGwNvbGw4ODhrTDeuCNmbm8LO2lpZvZmViWUKC9iqkgbm5OTIyMpCb+/qTmJubm6NixYowMTHB1atXNY5wAQAPDw/ExsZqTBtYsWJF6OvrQyaTwczMDCkpKc9SeaWmwt7eHpGRkbh//z6AZ2nWChtNVq1aNbRp0wampqYICgrC5cuXVbY/HzWqp6cHuVyOevXqITk5WQr8PFeuXDmYmZkhOjoaMpkMNjY2aN++PY4fPy4F86ysrGBvby9lJDA3N0fXrl3RsGFDAICNjQ3Mzc2RlJSE8+fP4/LlyzA2NoaLiwuys7Ohr68Pc3Nz/PLLLyp1rFChAnbu3InGjRsXeK6lEe8ZF45BDS1iAyWi5woaTl0UGRkZiIuLQ4UKFYp8I/qPP/5At27/3ZB98WZ8Xnv27MF7772HnJwc1KtXD3/88QdcXFxUylSrVi3feS2mT5+OuLg4rFmzRuP2GjVqqI14eG7nzp1o3rw5tm/fDisrK3To0AFubm5FOcViM2bMGFy7dg09e/bERx99hOHDh2Pjxo35lk9NTYW5uTkaNmyIixcvqmzT9AXUxMQESUlJ0o+sXbt2oV+/fgXW6dSpU2jZsuUrnU9mZia+//57JCQkYPjw4VJOYuBZj/BZs2bBxsYG27dvR9u2bV/pGG+jvIEpKyurl0rR83jdOjy98Le0bNm5E6x69iyu6pWov1f/gPMvpNW5o1Ri0YMYLdaI3ibZ2dkqN3lycnJw6tQp3L59G23btsWtW7fQp08f6Qe6XC6Hvr4+3n33XSxevBiWlpYQQuDixYsqI04MDQ1hYGCA9PR0aZ2npyeuXr2qsR59+vSBTCbDkCFDsH37dmzatKlI9beyspJ632pSzdAQn9nZS8tPlUpMjH1QpOfWNj0Ay/MENTrv3YPK3t7aqRCVWgV1hCnM4cOHsXfvXhgbG6Nt27bw8vJS6ZGsVCqRnZ1daMpDhUIBfX19pKSk4ODBg/Dw8ID97/sQERiIGzdvAgB+fvIYIToS1BhgbY3mLwQ1XHr2RM+VK7RYIyoOqampePDgAapXr16kayYzM1Pq9f+ixMRElY4GmixduhSfffaZyrrngZHnn3vm5uaQyWRITU19yTMpWa3MzPCe9X/n909WFpYkMA0Yvbw5c+bgyy+/LLxgKcN7xoVjUEOL2ECJSJtycnLg4+ODkJAQGBoaYseOHehZwA3a27dv4969e2jZsiUMDAzw0Ucf4aeffgIAvPPOO/Dy8sp3guyTJ09KQ2Lz8vf3x/fff6/xS32bNm1w/PhxtUCNt7e32miHt02bNm0QGBhY5PLBwcHSzcBZs2Zh5syZBZa3t7fHmDFjMHr0aFi/0AuqKPr27Yvdu3cDAFxcXHDlyhWUK1cOt2/fhru7u9Tbsm7duggNDX2p536b5f3h6eLiIvV2KorH6zfg6QtpOiw6doR1n97FVr+SdPb773Fp4X/XZ7RMhvnRUVqsEemauLg4PHjwAPXq1YNCoYBCoZBGBb5o2bJlWLhwISpUqIBNmzYhJycHR44cQbNmzdCoUSMAQFhYGBYsWIBr167BxsYGnTp1Qt++fVGpUiWV55o2bRrmzZundoyhQ4eic+fOqFmzpkpqBQAIDAxUC8ZWNjTEN7Vq4cnjJ1AKgWyhxGcPdCOooQ/g+xeCGnp6MvQPCoLVCzeMiUqzh3PnISc6GomJiXj85DGy33kHytq1ERoaiqioKOjp6aFatWoYMGAAHBwcoFAo8Pvvv0Mmk6F79+6YOnUqli1bBnNzcwwePBhffPEF+vXrh9OnTwN49l1g/PjxWLNmjdTBxtXVFe+99x4ePXqE3NxcCCFgYWGBMWPGICgoCBs3bsTFixc19u5+PtIlLi4OsT+tQ+N/51KxsbFG6wkT4DxkyBt77ah0iI2NhRAC2dnZMDIygpOTE4BnwcKsrCwYGxtL33HDw8Px999/o3Xr1jAxMcGNGzcQGhqKsLAwpKSkICsrC15eXujatSsMDQ2RnZ2NuLg4TJ06FWH/zhvXpEkTTJgwAW3btsXWrVsxdepUpKWlAXg26rxPnz6Ij49HaGgo0tPTpVFgT58+xdChQzFlyhScOXMGf//0E6r/81+K5bvZWVjEuU3oFbRs2RKnTp3SdjXeON4zLhyDGlrEBkpE2paTk4Nz587B1dVVpUddUWRkZODAgQNIS0tDv379sGLFCkydOjXfsoaGhmqTtb3zzjvS/BJOTk54+PC/dDhNmzZFUFCQxiHimiY513UTJ06En58f6tWrhwEDBqhNQJ6fAQMGYNOmTZg1axbWr1+PevXqYePGjfkGOjT1+Jo0aRIWLFiA2bNnY8aMGSrbMjMzNU5uWhTnzp3D559/DgBYuHAhfHx8IITQSlqr1NRUtbyu1atXz3eEkCZPfvkF6X+elZbN2/nCxs+v2OpYkk5/9x2uLlkqLccYyDH3zh3tVYioiGJjY6Gvr4/Q0FAEBwfD2dkZQ4cOLXBOoZycHMybNw8RERHo3bs3vF1cYLh5M6LuRSEsPAzZuUqMfRCD8uXLo127drh9+zYcHBzQuXNnnDx5Ek+ePEH9+vURGRkJc3NztGnTBj169JDmMnmTLM3MsMLVDan/3lCqVrUq2u7YDgMHhzdaDyJtiZv/DbJfSIdn4z8I5s2bv9RzKBQKldHQQgjcu3cPT58+RdWqVWFkZAQhBG7fvo3U1FR4enoWafR0VlYWfvvtN1y5cgXVqlXDsGHDVL7jPPnlVyQGBUKZq4SRsTEsfNvC5t13X6ruRG9KZmYm9PT01H57JSYmIiAgAA0aNCgw9W3eUZ/pZ88i5scfce/uPdz65x9EZWdjSXISqlevjqioKGRkZCAnJweGhoZQKBRQKpWvVG8DA4NSNxcYqZLL5Xj8+PErzdGhy3jPuHCvlueEiIhKBQMDg1dOYWRiYgK/F27oPu8Nl1f//v2ldAMLFizA5MmTpW0ffvih9PeoUaMwffp0AED9+vVx7NgxjQENAFJvIU0MDQ1hY2ODuLg4lTqEhYXh2rVrqFChAiIjI4t4loUrSj7Toli0aBEWLVoEV1fXlxo9sGXLFtjZ2WHZsmUAgKioKIwePRq//PKL9EPhee+tlStXqgU0gGcBh+cpqfK6d+8eXFxccPLkSTRs2FDqHZafK1euYNiwYYiLi0N0dLS0vnnz5rCwsICdnR3Wr1+vcdROSdI0P0Bhk/GpyROUg1J3+oXk/bGnl/dciN5Sz99zOnbsiI4dOxZpHwMDA5UAbfb9+4iDDBUrVoSTsxMg08OoH39QC7QDKHCencDAQCxYsABXr15Fly5d0KZNG3z++eeIj4/H0KFD0axZMwghMHv2bJw8eRJWVlaYNGkSOnbsiL179yItLQ1NmjRBeno6tm3bhoCAANjZ2aFr167SZPDh4eFQKpVIS0vD48eP0blDB8R/PhFp6emAEDAzN8ezmTaIygh53s/el7/xmTdAIZPJ1EaGyWSyl56rzMjICAMHDsTAgQPzLWNoYAg8j8GWwYluSXfklx7OxsYG7xYhGKf2u01PHybGJtIk3jJHR8ye+oVUTgiBqKgo2NvbQ19fH3fv3oWdnR1SUlLg5uaGnJwc3L9/X5qj5Plk4W5ubkhMTIS7uzucnZ2hUCigp6eHzMxM3Lp1C9bW1nBxccEff/yBkJAQtGzZErVq1UJISAhq1KiBGjVqICAgABEREXBwcICTkxNSU1Ohr68PX19f3LhxA7dv34ahoSHi4+ORkZEBc3NzGBsbo02bNoiIiMDRo0eRmpqKKlWqoFOnTgCejUKNjIzE3bt3YW9vj/feew+VK1dGYmIi7t+/j19//RXZ2dnw9vaGEALlypVDRkYGzpw5AwsLC9SvXx9paWlISEiAiYkJrly5gpCQEFSqVAkymQyJiYlwdHSEv78/3NzccPnyZSQkJOD27dswNzeHq6srvv/+e+l3rr+/P2rXro0TJ04gMjISbm5u+N///gdnZ2ccP34ct27dgr6+PipWrIgHDx7gyZMnMDExgYmJCXx8fFC3bl38/vvviIuLQ1hYGJ48eYL09HSpE6KzszMevDDq1draGo0aNYKDgwPu3r2L4OBgafS/g4MDsrKy4OTkBFNTUyQmJiIxMVGaR+Pu3bsa21O7du3QoUMHdOzYERYWFoW2QSp7OFJDixh1I6LSZujQoVi/fr203L9/fyxduhT29s9ymefm5mLixIk4fvw4evbsiVmzZkk33IUQCAgIwOPHj9GlSxeVSffyWrRoESZNmqSy7sqVKzhz5gxatWqFe/fuoVevXsjJyUGVKlUQGhqq8kXon3/+wYIFCyCEgL+/P7766qtXGtJqaGiIhw8fwt7eHgqF4qX3fxNatWqFXbt2ITk5GXXq1HnpAMzWrVsxadIkREdHQ09PD8HBwZDL5Th9+jQaN26MZs2aSf/D3Nxc1KpVS5ogLj916tTJN6d+STlw4AC6d++uss7d3R03/83RXRSJW7ciLei/dmLWqiXKDRggLQshkJmZqTElj7YdnT0H/7wwp028mRlm3LiuxRoRvTk5Dx7g4ew5KutcV6965fkD3iSRnY37n45VWec4exYM7O3z2YOodHn03WJkvfC9wvr992DRpo32KvQSnmzciPSz/81Xp0sjPIle19O//8bjn/6bWF3u5AinPCPCqfglJCTAyMhI60GAixcvIjw8HO+88450L6AgsbGxsLGxgRACRkZGWhnZ/7bhPePCcaQGEREVm59//hnTpk2Dubk5HDSkxtDX18fixYs17iuTyYrcC7dr164qQY3Zs2fD09MTnp6eAIDatWsjPDwcN2/eRNu2bdV65FerVg1r166Vlvft2yf1pHkZwcHBsLGxQb9+/bBt27aX2vdNOXXqFDp06IC0tLRXGlHSv39/6W+lUomWLVtCCCHlke7QoQN+//13mJiY4MCBA4UGNIBnPZlatGiBbdu2afyClp6ejqioKLi5uWHBggXYunUrkpKSMH78+HxTnBVG00iNhIQE6e/Q0FCMGTMGaWlpmDNnDrp166ZSduvWrbi0cBFaGBnCw6M2jAwNgdz/eos+ePAAPXr0QGhoKPr164fNmzdr7AWuLXmDbhypQWWKpvaemwsUIb2MtmnqfaYLwRiiYqOf58bWK6aoeRvIeJOOyhI93R3hrMtsbW21XQUAQIMGDdCgQYMily8sGwCRJm//N3kiItIpVatWLfFjeHh44ODBg9i8eTPq1KmD8ePHq5Vxd3eHu7t7kZ7PysoKf/31F3766SdUrFgRvXv3RtOmTREVFSVNopfXoUOH4O3tDQBYt24d6tevj4yMDLRo0QLt27d/vRMsZpom+7azs0P8K0zWl/fmeEBAAFasWIEOHToUmH4hr+DgYLi5uSE8PBweHh7S+kuXLqFbt24qw5mfmzZtGvbu3YutW7eiatWqUCqVOHnyJIyNjVVGjCQnJ+PgwYNwd3dHw4YNIYTA2LFj1Z4vMTERCoUC+vr6GDBgAK5duwbg2SSfN2/ehJubGwDg9u3bGDx4MLqbmKKShQUgnqVIg/K/CUJnz54tTV6/fft2+Pr64uOPPy7y61HScnJU2zCDGlSWaLqRKJRK3UjipGlQPYMaVIbI9FVvWQgNk3O/rZgUg8oyWZ7UcSL37RzVTkS6i10FiIhIJ3Xu3BmbNm3ClClT8p1742W4u7tjwYIF+OSTT+Dk5ITLly/j6NGjiIqKwubNm6VyMpkMp06dkvKnAs/mZpg0aRJmzJiBdu3aSUEWPT09LFy4EJs2bULjxo3h7Oyscsw6deoUWq8uXbq8/NwPhXg+QXbefNKvatKkSahfvz7S09Nfet/atWtLk6I/fvwYvXr10hjQeO7ChQto3bo1bt++jbZt26J9+/Zo0aIFPvzwQyiVSqSkpMDT0xMDBgxAo0aN4Ovri//973/SyJK8njx5gr///lsKaADPJkp8cUTRr7/+ipycHOT+22f6fkwMlEolhOK/5/zxxx9VnnfkyJEv/VrkJz4+HidPnnyl1/c5RZ45NeR5c5QTlWaaRmS8pSkDi4RBDSpL9PK0dx0KaqgPteK1S2VI3pEaubo7yoqI3k4cqUFERKSBtbU1OnToAOBZCqaMjAwEBgaiT58+hU6u/t1332H06NEwMjKSAhnPRzGsXbsWW7Zsgbe3N2bPno3ffvsNgYGBqFChAmbPng3lC2kVqlatil27dkEIgSdPnqBKlSoaR428jDp16uDo0aOwsbHBzp078d577xXrxOmvws/PD9OmTcPhw4cRFRVVaPmYmBi1yTw3btyIv/76C9evq84TERgYiMDAwHyfq0+fPggODlZbv27dOnz22WcICAjArFmzAAC5L9yciH0Yi6r/3hTVNHG9UqlEdHS0NNqjIEIIREREwMnJCeXKlVPZdv78efj6+iI9PR2VKlXC2bNn4ejoCACIjIxEUlKSNCmyQqHAqFGjEBYWBktLS1hZWeGHH35AlSpV4JPnmPo6kHaHqLhoHqmhIz2o2dObyjhdHqmhhgFJKkNkaqnjdPjaJSIVyowM3O7aFZbvdILD5EmF71BC+IuWiIioEDKZDMOGDcOwYcOKvE/lypU1rv/oo4/w0UcfScv+/v7w9/cH8GwExYB/J552dXXFoUOHYGxsDABwcXHBzJkzMW3aNBgaGqJPnz7YsWOHNAKhR48e8PHxwRdffJFvnXx9fbFz507pxrm3tzciIiIQEBCAKVOmIDw8vMjnV9zmzp372s+RN6BRFJoCGgCQmpqKihUrqqzLfaHL5cWLl7A9NBSeBvJ8R7zs2LEDEyZMKPD4CoUCXbp0QUBAgLRu0KBBWLZsGaytrTF8+HBphMbdu3cxY8YMrF69GpMmTcKSJUtUgmAAsH//frVjnDx5EtHmFuhpZSWt09fnV0AqQzTOqaEjIzWYforKOl2eUyPv9ctrl8qSPCM1XhzhTES6LeGHH2HiVVfb1WD6KSIiordF//79cenSJezYsQMRERFqc4J88cUXSEhIQGxsLLZs2YJff/0VjRo1gp+fH1avXo1x48ahXbt2AABPT0/Ex8fj7NmzWLFiBU6fPo1jx46pjQQwMjJCt27dEBYWhgcPHuD+/fvo2rVrgfV0dXXNN72Sn58f7t27BxsbG5X1q1atetmX462Tt2N35tOnGDZsmPSa5/XVV1/h4sWLiIiIwKlTp3Do0CEkJiYiNTUVa9euxeLFi+Ht7a0S0ACATZs2oXz58tDX11ebD2XNmjXQ19fHd999pxbQKEje7B1v0yTmRCUtvzk1dAKDGlTGyfLeGNWlkRpCR95niEpA3jk1eD0QlQ7Zd+8iOzIS5q1aabsqHKlBRET0NqlXrx7q1auX7/YXgxL9+/dH//79VbYfO3YMSUlJsLa2BgDY2tqiadOmRTq2k5MTgGe9/UNCQuDs7IyLFy9i165d2LBhAwCgUaNG2LJlC6pVqwY/Pz9cuHAB5ubmkMvl8PHxgaenJwDgyJEj+OSTT5CVlYVFixahQ4cOWLdunTSZ9ssaP348TExMimVEx6tS5EmOba2nD69/R9JopFTiw+bNC33eAp+jmDjkSTfl4OhQ4sckemtoSLeWGRYOfUsLLVTm5SizsrRdBSKtyntjVPEwDhmXL2upNi8nNylJdQXjkVSW5OlQIHJydObapbeLnqUljPLJgkCqnl64gMfrfkZmeDgU8fFwXbEcFu3bq5R5smULnqz7GYr4eBhVqwaHqV/A1Nu7yMeIW7gI9hM/R8al0GKu/ctjUIOIiKiUeR7QeFUymQze/36xcXZ2Rrdu3bB+/XqIf3sMy/7tJdy2bVu0bdtW43M0atQIFy5cUFkXFBSEPXv2wNHREWlpaejTp4/0nC1btkSDBg2ezf/g44MPPvhAJZ1U79690aJFC/j4+GDgwIFITk4G8GzUyIgRI/Dll1+q1cHCwgJPnz6VUnTp6emhbt26uHTpksY6jxs3DsuXL893UvG8/aXdDA0xvLytxrJvMwO5HI0aN9Z2NYjeGE0jNRI3b9ZCTYqHjCM1qCyRqV6/GaGhyMgzilFX8NqlskSWZ1SwyFEgYfUPWqoN6TKTul4wyidLAKlSZmTAqGYNWPXpjZhPx6ptTzl4EHHzv4HjV9Nh2qABErdvR9THw1H1wH4Y/DsX6J0+fSFy1OfxdPtpHTLDrsKwUiUYVa7MoAYRERHpjtf9MW5mZoZBgwZJyykpKQgMDISHhweqVKmiUvbEiRP49NNPcfXqVQwdOhQtWrQAAHTt2hXnzp3DjBkzIJPJMGPGDNSqVQt2dnZYsGCBNOl5q1atsHHjRvzzzz84cOAAzMzMMHDgQBgbG6NPnz64fPkyrKyskJycjKpVq2LixIkYPnw4bG1tpQCJg4MDJkyYgOTkZBw9ehSKiIjXOv+3hUdtD1haWWu7GkRvTmlLt8Ybo1SGyAxK0S0LDQFWotIqb1CDiF5NamoqUlJSpGUjIyMYGRlpLGveqpWUFipGw/bHGzbCum8f2Pj5AQAcp05F+plgJG7dBvsJ4wEAlXfvyrcuiZsuI+XgQaQePgzl06cQCgX0zM1gN2rUK57d65EJoSlRK70J9+/fh5ubG6Kjo+Hq6qrt6hAREZUJQogCAzR37txBZmYmqlevrjL3RFJEBA7085O+VDo6OsDIyAi5ubmwsrSCo6MjTExMcOfOHUTeiURGRmahdZHJ/kuZX61qVTg6OiL24UPcvn37vzIAmjRpAlMzUyQlJSHjaQYUCgUUCgXu3L0LALCxtoadvT3S09IgAMQ/eoQcxX8TIcv19eHg4AC3Cm6ws7WDVc8esOzcuegvGpGOi5s/H9n3orRdjdemb20Np/nz2OObyoy04GAk/rpJ29UoFrYjR8CkrvYnViV6E0RODmImTYIowvdhooKY1PWCbRkcqfH8nnFeM2bMwMyZMwvd/1rNWirpp0R2Nq7XbwCXpUtg2aGDVO7h3HnIunYNFTf9+lL1S9q9B1m3bsFh8qSX2q84laJuD0RERESFK+xmYOV8crZae3ig24rluLt7D6xNTGBkrLmHTHV7O1Rv0hg5OQrI5fpITk5GTo4COTk5CA29BCGAypUroUaNmtDTkyExMQmmpqYw/vf57Kq7o5ZPU9y5cwfJSclwcXWFk/Oz+U6sKlZUrWtyCrKyMmFnZ6/SeTsnOwd37t6Fqakp7O3tIJcbQE9PBujpw7hWLbXcqkSlXbmhQ5G08zco4h5quyqvTL98eVj36sWABpUpZj4+yE1IwNPQUOCFYL0ukRkawbSRN4y9vLRdFaI3RmZgANsRI5Fy4ABykxK1XR3SYXqWltquglZFRETAxcVFWs5vlEZhFIlJQG4u5HnSJ8vLl0d6QsLrVFFrGNQgIiIiKiJrX1/U8/V9qX2cX/i7YSHbX1ShCM/tVMC2ouxPVFYYODjAbrR2hsYT0auT6enBqmdPWPXsqe2qENFLMq5RHcY1xmu7GkQ6zcLCApbFGdhR6xsjXim1qXWf3sVSndfBpI5ERERERERERERERKWQ3MYa0NeHIs+oDMXjJ5CXL6+dSr0mBjWIiIiIiIiIiIiIiEohmaEhjGvXRvqff6qsT//zT5jUr6+lWr0epp8iIiIiIiIiIiIiItJRyvR0ZEdFScvZ9+8j89o16FtZwcDZGeWHDEbM5CkwqVMHJvXqIWnHDuTExsLm/fe0WOtXx6AGEREREREREREREZGOyggLR9TgwdLyo28WAACsevWC8zfzYdmlCxRJSUhYuQqK+HgYubujwo8/wOCFich1CYMaREREREREREREREQ6yqxJY9S6fq3AMuUGDEC5AQPeUI1KFufUICIiIiIiIiIiIiIincCgBhERERERERERERER6QQGNYiIiIiIiIiIiIiISCcwqEFERERERERERERERDqBQQ0iIiIiIiIiIiIiItIJDGoQEREREREREREREZFOYFCDiIiIiIiIiIiIiOgt0rZtW3h4eGDlypXarspbR67tChARERERERERERER0X9OnjwJV1dXbVfjrcSRGkREREREREREREREpBMY1CAiIiIiIiIiIiIiIp3AoAYREREREREREREREekEBjWIiIiIiIiIiIiIiEgnMKhBREREREREREREREQ6gUENIiIiIiIiIiIiIiLSCQxqEBERERERERERERGRTpBruwJlmVKpBADExsZquSZEREREREREREREpG3P7xU/v3dM6hjU0KK4uDgAQOPGjbVcEyIiIiIiIiIiIiJ6W8TFxaFChQrarsZbSSaEENquRFmlUChw6dIlODg4QE+vbGYCS01NhYeHByIiImBhYaHt6hC9MWz7VJax/VNZxbZPZRnbP5VVbPtUlrH9U1n1um1fqVQiLi4O9evXh1zOMQmaMKhBWpWSkgIrKyskJyfD0tJS29UhemPY9qksY/unsoptn8oytn8qq9j2qSxj+6eyim2/5JXN4QFERERERERERERERKRzGNQgIiIiIiIiIiIiIiKdwKAGaZWRkRFmzJgBIyMjbVeF6I1i26eyjO2fyiq2fSrL2P6prGLbp7KM7Z/KKrb9ksc5NYiIiIiIiIiIiIiISCdwpAYREREREREREREREekEBjWIiIiIiIiIiIiIiEgnMKhBREREREREREREREQ6gUEN0ppVq1ahcuXKMDY2RsOGDXH69GltV4notcyfPx+NGjWChYUF7O3t0atXL9y4cUOljBACM2fOhLOzM0xMTNCmTRuEh4erlMnKysKYMWNga2sLMzMz9OjRA/fv33+Tp0L0WubPnw+ZTIZx48ZJ69j2qTSLiYnBoEGDUL58eZiamqJevXoICQmRtrP9U2mlUCjw5ZdfonLlyjAxMUGVKlUwe/ZsKJVKqQzbP5UGp06dQvfu3eHs7AyZTIa9e/eqbC+udp6YmAh/f39YWVnBysoK/v7+SEpKKuGzIypYQe0/JycHkydPhqenJ8zMzODs7IwPPvgADx48UHkOtn/SRYW9979o+PDhkMlkWLp0qcp6tv2Sw6AGacX27dsxbtw4TJs2DZcuXULLli3RuXNnREVFabtqRK8sKCgIo0aNwrlz5xAQEACFQoGOHTsiPT1dKrNw4UIsXrwYK1aswIULF+Do6IgOHTogNTVVKjNu3Djs2bMH27Ztw5kzZ5CWloZu3bohNzdXG6dF9FIuXLiANWvWwMvLS2U92z6VVomJiWjevDkMDAxw6NAhRERE4LvvvoO1tbVUhu2fSqsFCxbghx9+wIoVK3Dt2jUsXLgQixYtwvLly6UybP9UGqSnp6Nu3bpYsWKFxu3F1c4HDBiA0NBQHD58GIcPH0ZoaCj8/f1L/PyIClJQ+3/69CkuXryI6dOn4+LFi9i9ezdu3ryJHj16qJRj+yddVNh7/3N79+7FX3/9BWdnZ7VtbPslSBBpQePGjcWIESNU1tWsWVNMmTJFSzUiKn6PHj0SAERQUJAQQgilUikcHR3FN998I5XJzMwUVlZW4ocffhBCCJGUlCQMDAzEtm3bpDIxMTFCT09PHD58+M2eANFLSk1NFe7u7iIgIEC0bt1ajB07VgjBtk+l2+TJk0WLFi3y3c72T6VZ165dxdChQ1XW9enTRwwaNEgIwfZPpRMAsWfPHmm5uNp5RESEACDOnTsnlTl79qwAIK5fv17CZ0VUNHnbvybnz58XAMS9e/eEEGz/VDrk1/bv378vXFxcRFhYmKhYsaJYsmSJtI1tv2RxpAa9cdnZ2QgJCUHHjh1V1nfs2BF//vmnlmpFVPySk5MBAOXKlQMA3LlzBw8fPlRp+0ZGRmjdurXU9kNCQpCTk6NSxtnZGXXq1OH1QW+9UaNGoWvXrmjfvr3KerZ9Ks327dsHb29v+Pn5wd7eHvXr18fatWul7Wz/VJq1aNECx48fx82bNwEAly9fxpkzZ9ClSxcAbP9UNhRXOz979iysrKzQpEkTqUzTpk1hZWXFa4F0SnJyMmQymTRqle2fSiulUgl/f39MnDgRtWvXVtvOtl+y5NquAJU9CQkJyM3NhYODg8p6BwcHPHz4UEu1IipeQgiMHz8eLVq0QJ06dQBAat+a2v69e/ekMoaGhrCxsVErw+uD3mbbtm1DSEgI/v77b7VtbPtUmkVGRmL16tUYP348pk6divPnz+PTTz+FkZERPvjgA7Z/KtUmT56M5ORk1KxZE/r6+sjNzcXcuXPRv39/AHz/p7KhuNr5w4cPYW9vr/b89vb2vBZIZ2RmZmLKlCkYMGAALC0tAbD9U+m1YMECyOVyfPrppxq3s+2XLAY1SGtkMpnKshBCbR2Rrho9ejSuXLmCM2fOqG17lbbP64PeZtHR0Rg7diyOHj0KY2PjfMux7VNppFQq4e3tjXnz5gEA6tevj/DwcKxevRoffPCBVI7tn0qj7du3Y9OmTdiyZQtq166N0NBQjBs3Ds7Ozhg8eLBUju2fyoLiaOeayvNaIF2Rk5OD999/H0qlEqtWrSq0PNs/6bKQkBAsW7YMFy9efOk2yrZfPJh+it44W1tb6Ovrq0UcHz16pNa7hUgXjRkzBvv27cPJkyfh6uoqrXd0dASAAtu+o6MjsrOzkZiYmG8ZordNSEgIHj16hIYNG0Iul0MulyMoKAjff/895HK51HbZ9qk0cnJygoeHh8q6WrVqISoqCgDf+6l0mzhxIqZMmYL3338fnp6e8Pf3x2effYb58+cDYPunsqG42rmjoyPi4uLUnj8+Pp7XAr31cnJy8O677+LOnTsICAiQRmkAbP9UOp0+fRqPHj1ChQoVpN/A9+7dw4QJE1CpUiUAbPsljUENeuMMDQ3RsGFDBAQEqKwPCAhAs2bNtFQrotcnhMDo0aOxe/dunDhxApUrV1bZXrlyZTg6Oqq0/ezsbAQFBUltv2HDhjAwMFApExsbi7CwMF4f9NZq164drl69itDQUOnh7e2NgQMHIjQ0FFWqVGHbp1KrefPmuHHjhsq6mzdvomLFigD43k+l29OnT6Gnp/qTUl9fH0qlEgDbP5UNxdXOfXx8kJycjPPnz0tl/vrrLyQnJ/NaoLfa84DGrVu3cOzYMZQvX15lO9s/lUb+/v64cuWKym9gZ2dnTJw4EUeOHAHAtl/i3vTM5ERCCLFt2zZhYGAg1q1bJyIiIsS4ceOEmZmZuHv3rrarRvTKRo4cKaysrERgYKCIjY2VHk+fPpXKfPPNN8LKykrs3r1bXL16VfTv3184OTmJlJQUqcyIESOEq6urOHbsmLh48aLw9fUVdevWFQqFQhunRfRKWrduLcaOHSsts+1TaXX+/Hkhl8vF3Llzxa1bt8TmzZuFqamp2LRpk1SG7Z9Kq8GDBwsXFxdx4MABcefOHbF7925ha2srJk2aJJVh+6fSIDU1VVy6dElcunRJABCLFy8Wly5dEvfu3RNCFF8779Spk/Dy8hJnz54VZ8+eFZ6enqJbt25v/HyJXlRQ+8/JyRE9evQQrq6uIjQ0VOV3cFZWlvQcbP+kiwp778+rYsWKYsmSJSrr2PZLDoMapDUrV64UFStWFIaGhqJBgwYiKChI21Uiei0AND7Wr18vlVEqlWLGjBnC0dFRGBkZiVatWomrV6+qPE9GRoYYPXq0KFeunDAxMRHdunUTUVFRb/hsiF5P3qAG2z6VZvv37xd16tQRRkZGombNmmLNmjUq29n+qbRKSUkRY8eOFRUqVBDGxsaiSpUqYtq0aSo3stj+qTQ4efKkxu/5gwcPFkIUXzt//PixGDhwoLCwsBAWFhZi4MCBIjEx8Q2dJZFmBbX/O3fu5Ps7+OTJk9JzsP2TLirsvT8vTUENtv2SIxNCiDcxIoSIiIiIiIiIiIiIiOh1cE4NIiIiIiIiIiIiIiLSCQxqEBERERERERERERGRTmBQg4iIiIiIiIiIiIiIdAKDGkREREREREREREREpBMY1CAiIiIiIiIiIiIiIp3AoAYREREREREREREREekEBjWIiIiIiIiIiIiIiEgnMKhBREREREREREREREQ6gUENIiIiIiLSGTKZDHv37tV2NYiIiIiISEsY1CAiIiIioiIZMmQIZDKZ2qNTp07arhoREREREZURcm1XgIiIiIiIdEenTp2wfv16lXVGRkZaqg0REREREZU1HKlBRERERERFZmRkBEdHR5WHjY0NgGepoVavXo3OnTvDxMQElStXxs6dO1X2v3r1Knx9fWFiYoLy5cvj448/RlpamkqZn3/+GbVr14aRkRGcnJwwevRole0JCQno3bs3TE1N4e7ujn379knbEhMTMXDgQNjZ2cHExATu7u5qQRgiIiIiItJdDGoQEREREVGxmT59Ovr27YvLly9j0KBB6N+/P65duwYAePr0KTp16gQbGxtcuHABO3fuxLFjx1SCFqtXr8aoUaPw8ccf4+rVq9i3bx+qVaumcoxZs2bh3XffxZUrV9ClSxcMHDgQT548kY4fERGBQ4cO4dq1a1i9ejVsbW3f3AtAREREREQlSiaEENquBBERERERvf2GDBmCTZs2wdjYWGX95MmTMX36dMhkMowYMQKrV6+WtjVt2hQNGjTAqlWrsHbtWkyePBnR0dEwMzMDABw8eBDdu3fHgwcP4ODgABcXF3z44Yf4+uuvNdZBJpPhyy+/xJw5cwAA6enpsLCwwMGDB9GpUyf06NEDtra2+Pnnn0voVSAiIiIiIm3inBpERERERFRkbdu2VQlaAEC5cuWkv318fFS2+fj4IDQ0FABw7do11K1bVwpoAEDz5s2hVCpx48YNyGQyPHjwAO3atSuwDl5eXtLfZmZmsLCwwKNHjwAAI0eORN++fXHx4kV07NgRvXr1QrNmzV7pXImIiIiI6O3DoAYRERERERWZmZmZWjqowshkMgCAEEL6W1MZExOTIj2fgYGB2r5KpRIA0LlzZ9y7dw9//PEHjh07hnbt2mHUqFH49ttvX6rORERERET0duKcGkREREREVGzOnTuntlyzZk0AgIeHB0JDQ5Geni5tDw4Ohp6eHqpXrw4LCwtUqlQJx48ff6062NnZSamyli5dijVr1rzW8xERERER0duDIzWIiIiIiKjIsrKy8PDhQ5V1crlcmox7586d8Pb2RosWLbB582acP38e69atAwAMHDgQM2bMwODBgzFz5kzEx8djzJgx8Pf3h4ODAwBg5syZGDFiBOzt7dG5c2ekpqYiODgYY8aMKVL9vvrqKzRs2BC1a9dGVlYWDhw4gFq1ahXjK0BERERERNrEoAYRERERERXZ4cOH4eTkpLKuRo0auH79OgBg1qxZ2LZtGz755BM4Ojpi8+bN8PDwAACYmpriyJEjGDt2LBo1agRTU1P07dsXixcvlp5r8ODByMzMxJIlS/D555/D1tYW/fr1K3L9DA0N8cUXX+Du3bswMTFBy5YtsW3btmI4cyIiIiIiehvIhBBC25UgIiIiIiLdJ5PJsGfPHvTq1UvbVSEiIiIiolKKc2oQEREREREREREREZFOYFCDiIiIiIiIiIiIiIh0AufUICIiIiKiYsHMtkREREREVNI4UoOIiIiIiIiIiIiIiHQCgxpERERERERERERERKQTGNQgIiIiIiIiIiIiIiKdwKAGERERERERERERERHpBAY1iIiIiIiIiIiIiIhIJzCoQUREREREREREREREOoFBDSIiIiIiIiIiIiIi0gkMahARERERERERERERkU5gUIOIiIiIiIiIiIiIiHTC/wFpuykDpFykXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot learning rates and losses as a function of time\n",
    "fig, ax1 = plt.subplots(figsize=(16, 6))\n",
    "ax1.set_xlabel('Epochs')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.semilogy(clf.loss_curve_, lw=3, color='black')\n",
    "ax1.tick_params(axis='y')\n",
    "ax2 = ax1.twinx()\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Learning Rate', color=color)\n",
    "lr_grid = np.concatenate((np.repeat(learning_rates, 50),\n",
    "                          np.repeat(learning_rates[5:], 50),\n",
    "                          np.repeat(learning_rates[5:], 50),\n",
    "                          np.repeat(learning_rates[5:], 50),\n",
    "                          np.repeat(learning_rates[-1], 100)))\n",
    "ax2.semilogy(lr_grid, lw=3, color=color, alpha=0.7)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}