04_[Case_A]_NNs_without_Season_Gaps.ipynb

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      "name": "04_[Case_A]_NNs_without_Season_Gaps.ipynb",
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        "<a href=\"https://colab.research.google.com/github/SoumyaShreeram/Microlensing_with_NeuralNets/blob/master/04_%5BCase_A%5D_NNs_without_Season_Gaps.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## {**Case A**}  Deep learning using NNs with microlensing curves ***without Season Gaps***\n",
        "\n",
        "The following notebook tests NNs on microlensed light curves that do not contain season gaps. The notebook contains the following sections:\n",
        "1. Input parameters\n",
        "2. Loading data <br>\n",
        "  i. Plotting light curves\n",
        "3. Generations of training and testing data sets\n",
        "4. Compiling the 3 networks (can choose one of them for training) <br>\n",
        "  i. CNN <br>\n",
        "  ii. ResNet-7 <br>\n",
        "  iii. ResNet-18\n",
        "5. Training the network on the optimized parameters <br>\n",
        "  i. Plotting loss-accuracy curves <br>\n",
        "  ii. Displaying the confusioni matrix\n",
        "6. Optimazing the network (Run only if required) <br>\n",
        "  i. Optimizing batch size <br>\n",
        "  ii. Optimizing learning rate\n",
        "\n",
        "**Author**: Soumya Shreeram <br>\n",
        "**Script adapted from**: Millon Martin & Kevin Müller <br>\n",
        "**Date**: 23rd February 2020"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deDcrZnEPlzz",
        "colab_type": "text"
      },
      "source": [
        "Import all required python libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIOkLVF5WNgW",
        "colab_type": "code",
        "outputId": "53a98944-3afa-451f-db8b-3bb065d40b1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import pickle\n",
        "import importlib\n",
        "import itertools\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 16})\n",
        "import random\n",
        "import os\n",
        "from IPython.display import Image, SVG\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Activation, InputSpec\n",
        "from tensorflow.python.keras.layers import Conv1D, Conv2D\n",
        "from tensorflow.python.keras.layers import MaxPooling1D, MaxPooling2D, GlobalMaxPooling1D\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten, Add, BatchNormalization, concatenate\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.layers import Layer"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDf-kPS_OvVQ",
        "colab_type": "text"
      },
      "source": [
        "Mount google drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7aoHjFTP2s9",
        "colab_type": "code",
        "outputId": "bba945a5-84ed-4840-c2a2-d1ea1e225d5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFu89FBF3rYM",
        "colab_type": "text"
      },
      "source": [
        "Directory where the `python` files are stored on Google Drive. The `.py` files that are imported must be in the same working directory (nb stands for notebook). To reload the imported files - if you have made any changes to them - use `importlib.reload(short_name_for_the_imported_py_file); eg. dat or nnet` ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQmHoYFN3sX1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.chdir('/content/gdrive/My Drive/Colab Notebooks')\n",
        "\n",
        "import nb_02i_preprocessing_data as dat\n",
        "import nb_03_neural_network_designs as nnet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJbzcl4A8xJQ",
        "colab_type": "text"
      },
      "source": [
        "Setting up the path to the directories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl3kJxIDwwxH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "current_dir = os.getcwd()\n",
        "data_dir = r'/content/gdrive/My Drive/training_set_microlensing/'\n",
        "save_image_dir = r'/content/gdrive/My Drive/Colab Notebooks/Images/'\n",
        "save_model_dir = r'/content/gdrive/My Drive/Colab Notebooks/Models/'\n",
        "save_data_dir = r'/content/gdrive/My Drive/Colab Notebooks/Data/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mF4SshagaAw2",
        "colab_type": "text"
      },
      "source": [
        "### 1. Input parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0jq4sB7oBle",
        "colab_type": "text"
      },
      "source": [
        "The scale radius used here is in pixels, where 1 pixel = 0.67 $R_{\\rm ref}$; $R_{\\rm ref} = 0.048 \\ R_{\\rm E}$ is the reference radius for QSO $J0158$ that is borrowed from [Mosquera & Kochanek (2011)](https://iopscience.iop.org/article/10.1088/0004-637X/738/1/96/pdf)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYQFOUe7Z-98",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "v_t = [500, 300] # transverse velcity\n",
        "v_t_idx = 0 # choose index value for the velocity you would like (0 or 1)\n",
        "r_0 = (2,4,10,15,20,30,40,60,80,100) # scale radius in pixels \n",
        "output_class_names = [str(r) for r in r_0 ]\n",
        "\n",
        "# if with season gaps == True; else False\n",
        "season_gaps = False\n",
        "\n",
        "# if using the new data set generated by Eric (see note below)\n",
        "new_data_set = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uc1nxnGOlgsN",
        "colab_type": "text"
      },
      "source": [
        "If using the `new_data_set`, make sure to have the `numLcurvesPerRadius...npy` file in the `v_x00` folder. This file contains the *number of light curves per radius, total curves, number of samples, etc* such that one does not need to hard-code the parameters when opening the files, whose names contain such variable. See more details on how it is read in the imported file `nb_02i_preprocessing_data.py`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9x31ZGmsWsD",
        "colab_type": "text"
      },
      "source": [
        "### 2. Load the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f58qKFoTr0TS",
        "colab_type": "text"
      },
      "source": [
        "There are 10 categories into which the light-curves are classified. Each category is represented by the scale radius `r_0`. The light-curves corresponding to each `r_0` are collectively stored in the 3D array `l_curves` whose shape corresponds to `= (num_features, num_samples, 1)`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwCb_eMa9AlY",
        "colab_type": "code",
        "outputId": "9035c35e-99c4-4fbf-dd74-35550227c2e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# sets the sampling parameters\n",
        "sample_params = dat.setSamplingParameters(v_t[v_t_idx], data_dir, season_gaps)\n",
        "\n",
        "# initializes data arrays\n",
        "class_cat, l_curves, out_catergories, out_radii = dat.initializer(r_0, \\\n",
        "                                                                  sample_params)\n",
        "\n",
        "for idx, rad in enumerate(r_0):\n",
        "  filename = dat.getFilename(data_dir, idx, rad, v_t[v_t_idx], sample_params, \\\n",
        "                             season_gaps, new_data_set)\n",
        "  \n",
        "  # load data from the file\n",
        "  l_curves, mjhd,_ = dat.loadData(filename, idx, l_curves, sample_params, idx, \\\n",
        "                                  season_gaps, new_data_set)\n",
        "  out_catergories[idx*sample_params[0][idx]:(idx+1)*sample_params[0][idx]] = class_cat[1][idx]\n",
        "  out_radii[idx*sample_params[0][idx]:(idx+1)*sample_params[0][idx]] = r_0[idx]\n",
        "\n",
        "print('Shape of light curves vector:', np.shape(l_curves))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of light curves vector: (50000, 4546, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NMbiX1jAZUQ",
        "colab_type": "code",
        "outputId": "29ded23d-cc29-42d0-f275-545e78268c7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"shape of light curves arrays:\", np.shape(l_curves))\n",
        "dat.saveFile(data_dir, 'Light_Curves/lightcurves_NoSeasonGaps', l_curves)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape of light curves arrays: (50000, 4546, 1)\n",
            "File saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4tKn20MtLo5",
        "colab_type": "text"
      },
      "source": [
        "#### 2.1 Plots of some random light-curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbfCcU01tQAf",
        "colab_type": "code",
        "outputId": "19d5bfc6-49ed-42df-efbb-b17a914265ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(8,4))\n",
        "\n",
        "# random numbers that correspond to the curves shown in the plot\n",
        "n = [23278, 89]\n",
        "\n",
        "# light cureves without interpolation\n",
        "ax.plot(l_curves[n[0], :, 0], 'r.', label='Radius: %i'%(out_radii[n[0]]))\n",
        "ax.plot(l_curves[n[1], :, 0], 'b.', label='Radius: %i'%(out_radii[n[1]]))\n",
        "\n",
        "dat.setLabels(ax, 'HJD (days)', 'Magnitude', ticks=True, legend=True)\n",
        "print('Radii:', out_radii[n[0]], out_radii[n[1]])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Radii: 20.0 2.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_QsEWkm-3xj",
        "colab_type": "text"
      },
      "source": [
        "### 3. Generate training and testing data sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xT004oTHEMmb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_l_curves, test_l_curves, onehot_train, \\\n",
        "onehot_test, _, _ = dat.generateTestTrain(l_curves, out_catergories, out_radii, r_0)\n",
        "\n",
        "trainX, trainy, testX, testy = train_l_curves, onehot_train, test_l_curves, onehot_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Okkl7tfzhu4",
        "colab_type": "text"
      },
      "source": [
        "### 4. Compiling the 3 networks\n",
        "\n",
        "The three networks compiled here are coded in `nb04_Neural_Network_Designs.py`. These `.py` files can be found in the GitHub repository. \n",
        "\n",
        "Note that if you are running this from colab, need to have these `.py` files in your drive folder *Colab Notebooks*. \n",
        "\n",
        "The three networks are the Convolutional Neural Network (**CNN**), Residual Neural Network with 7 layers (**ResNet7**), and the Residual Neural Network with 18 layers (**ResNet18**). Refer to the report for more detailed information about the neural network stuctures."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHoEfwH5C3eE",
        "colab_type": "text"
      },
      "source": [
        "Default constants that can be modified."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqVXaRDVC0uB",
        "colab_type": "code",
        "outputId": "fad7e87a-4437-4424-ff5a-3dcadc5b7ab6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# choose one of these networks to use by setting the boolean to True and rest to False\n",
        "CNN, ResNet7, ResNet18 = False, False, True\n",
        "\n",
        "model_types = [CNN, ResNet7, ResNet18]\n",
        "model_name_list = ['CNN','ResNet7', 'ResNet18']\n",
        "\n",
        "for i in range(len(model_name_list)):\n",
        "  if model_types[i]:\n",
        "    model_name = model_name_list[i]\n",
        "print('Using the Neural Network:', model_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using the Neural Network: ResNet18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGINq-rrVXgU",
        "colab_type": "text"
      },
      "source": [
        "The following code block uses the methods defined in the file `dr04_Neural_Network_Designs.py`: imported as **`nnet`**\n",
        "\n",
        "Model parameters are defined in the methods in **`nnet`** additionally some more details:\n",
        "* `num_hidden_nodes`: number of nodes in hidden fully connected layer\n",
        "*`dropout_ratio`: % of nodes in the hidden layer to dropout during back-propagation update of the network weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PtJ2NtcfaKGH",
        "colab_type": "text"
      },
      "source": [
        "### 4.1 Building model CNN\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSKoa3Fio98p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if CNN:\n",
        "  # reloads the file if any changes are made\n",
        "  importlib.reload(nnet)\n",
        "\n",
        "  # CNN model parameters\n",
        "  num_filter = (32, 32, 44, 44)\n",
        "  kern_size = (21,21,15,15)\n",
        "  maxpoolsize = (None, 15, None, 15)\n",
        "  shortcut_link = (0,1,2,3)\n",
        "  batch_norm = False\n",
        "  dropout_ratio = 0.4  \n",
        "  sampling = 3\n",
        "  num_pieces = 1\n",
        "  num_hidden_nodes = 1500\n",
        "  \n",
        "  # build the model\n",
        "  inputs, outputs = nnet.buildCNN(sample_params[2], num_filter, kern_size, maxpoolsize, \\\n",
        "                                  num_hidden_nodes, dropout_ratio, r_0)\n",
        "  \n",
        "  # optimization, compilation parameters\n",
        "  optimizer_type = Adam(lr=5e-4)  \n",
        "  loss = 'categorical_crossentropy'  \n",
        "  metrics = ['categorical_accuracy'] \n",
        "\n",
        "  # compiles the model\n",
        "  model = nnet.compileDisplayNetwork(inputs, outputs, optimizer_type, loss, \\\n",
        "                                    metrics, save_image_dir+'CNN', True)\n",
        "    \n",
        "  print('Network created')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAoBhclazb3q",
        "colab_type": "text"
      },
      "source": [
        "### 4.2 Compling model ResNet-7\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tu9TLTxAXYeB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if ResNet7:\n",
        "  # reloads the file if any changes are made\n",
        "  importlib.reload(nnet)\n",
        "\n",
        "  # CNN model parameters\n",
        "  num_filter = (32, 32, 44, 44)\n",
        "  kern_size = (21,21,15,15)\n",
        "  maxpoolsize = (None, 15, None, 15)\n",
        "  shortcut_link = (0,1,2,3)\n",
        "  batch_norm = False\n",
        "  dropout_ratio = 0.4  \n",
        "  sampling = 3\n",
        "  num_pieces = 1\n",
        "  num_hidden_nodes = [1500]\n",
        "  \n",
        "  # prepare data with sample cuts with the correct shape\n",
        "  testX_prep = nnet.prepareDataSampleCuts(testX, sampling, num_pieces)\n",
        "  trainX_prep = nnet.prepareDataSampleCuts(trainX, sampling, num_pieces)\n",
        "\n",
        "  # build the model\n",
        "  inputs, outputs = nnet.buildResNet7(num_filter, kern_size, \\\n",
        "                                                  maxpoolsize, num_hidden_nodes[0], \\\n",
        "                                                  dropout_ratio, shortcut_link, \\\n",
        "                                                  batch_norm, trainX_prep.shape[1], r_0)\n",
        "  \n",
        "  # optimization, compilation parameters\n",
        "  optimizer_type = Adam(lr=5e-4)  \n",
        "  loss = 'categorical_crossentropy'  \n",
        "  metrics = ['categorical_accuracy'] \n",
        "\n",
        "  # compiles the model\n",
        "  model = nnet.compileDisplayNetwork(inputs, outputs, optimizer_type, loss, \\\n",
        "                                    metrics, save_image_dir+'ResNet-7', do_print_summary = False)\n",
        "    \n",
        "  print('Network created')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nYK_qyWXcoH",
        "colab_type": "text"
      },
      "source": [
        "### 4.3 Compiling ResNet-18"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNhBFb7Tajxj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if ResNet18:\n",
        "  # reloads the file if any changes are made\n",
        "  importlib.reload(nnet)\n",
        "\n",
        "  # ResNet model parameters\n",
        "  num_filter = (32, 32, 32)\n",
        "  kern_size = (10,20,50)\n",
        "  n_block = 5\n",
        "  maxpoolsize = 3\n",
        "  num_hidden_nodes =[1000.0,]\n",
        "  dropout_ratio = 0.7 \n",
        "  batch_norm = True\n",
        "  sampling = 1\n",
        "  num_pieces = 1\n",
        "\n",
        "  # build network\n",
        "  inputs, outputs = nnet.buildResNet18(sample_params[2], num_filter, kern_size, n_block, \\\n",
        "                                 maxpoolsize, num_hidden_nodes, dropout_ratio, r_0,\\\n",
        "                                 batch_norm)\n",
        "  \n",
        "  # ResNet compilation parameters\n",
        "  learning_rate = 1e-4\n",
        "  max_epochs = 80\n",
        "  decay = learning_rate/max_epochs\n",
        "  optimizer_type = Adam(lr=learning_rate, decay=decay)  \n",
        "  loss = 'categorical_crossentropy'  \n",
        "  metrics = ['categorical_accuracy'] \n",
        "\n",
        "  # compiles network\n",
        "  model = nnet.compileDisplayNetwork(inputs, outputs, optimizer_type, loss, \\\n",
        "                                     metrics,save_image_dir+'ResNet-18', True)\n",
        "  \n",
        "  #model.load_weights('/content/gdrive/My Drive/Colab_Notebooks/Deep_learning_for_optical_IMaging/Resnet_weights.h5')\n",
        "  print('Network created')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMtThgF_QTIY",
        "colab_type": "text"
      },
      "source": [
        "### 5. Training the network on the optimized parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsWpcR3tabcU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "validation_split_ratio = 0.2  \n",
        "max_epochs = 50  \n",
        "batch_size = 60 \n",
        "num_inputs = 1\n",
        "verbose = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCHNBWY9pn_0",
        "colab_type": "text"
      },
      "source": [
        "Note: if you are running ResNet-18 after running ResNet-7, be sure to re-generate the training data sets (i.e. Run the code-block before \"Generate training and testing data sets\"); this is because the input shapes are changes for ResNet-7. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DUitEUIQX8y",
        "colab_type": "code",
        "outputId": "28943cbc-229a-4492-e9ca-02801e3cfd13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "if ResNet7:\n",
        "  # reshaped data is used while training\n",
        "  trainX = trainX_prep\n",
        "  testX = testX_prep\n",
        "\n",
        "# trains the model\n",
        "model, model_history = nnet.trainModel(model, trainX, trainy, batch_size, max_epochs,\\\n",
        "                                  validation_split_ratio, verbose)\n",
        "\n",
        "# evaluates and predicts the results from the trained model\n",
        "results, predictions = nnet.evaluatePredictModel(model, testX, testy, verbose = 1) \n",
        "\n",
        "print('[test loss, test acc] = ', results)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 32000 samples, validate on 8000 samples\n",
            "Epoch 1/50\n",
            "32000/32000 [==============================] - 94s 3ms/sample - loss: 2.2010 - categorical_accuracy: 0.1813 - val_loss: 1.9779 - val_categorical_accuracy: 0.2198\n",
            "Epoch 2/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.8513 - categorical_accuracy: 0.2682 - val_loss: 2.1889 - val_categorical_accuracy: 0.2385\n",
            "Epoch 3/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.7276 - categorical_accuracy: 0.3232 - val_loss: 1.6469 - val_categorical_accuracy: 0.3613\n",
            "Epoch 4/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 1.5605 - categorical_accuracy: 0.3960 - val_loss: 1.5495 - val_categorical_accuracy: 0.4079\n",
            "Epoch 5/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.3862 - categorical_accuracy: 0.4616 - val_loss: 1.3353 - val_categorical_accuracy: 0.4773\n",
            "Epoch 6/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.2653 - categorical_accuracy: 0.5021 - val_loss: 1.1693 - val_categorical_accuracy: 0.5424\n",
            "Epoch 7/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.1769 - categorical_accuracy: 0.5347 - val_loss: 1.1465 - val_categorical_accuracy: 0.5469\n",
            "Epoch 8/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.1005 - categorical_accuracy: 0.5645 - val_loss: 1.1028 - val_categorical_accuracy: 0.5565\n",
            "Epoch 9/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 1.0332 - categorical_accuracy: 0.5888 - val_loss: 0.9787 - val_categorical_accuracy: 0.6003\n",
            "Epoch 10/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.9664 - categorical_accuracy: 0.6120 - val_loss: 1.0478 - val_categorical_accuracy: 0.5975\n",
            "Epoch 11/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.9190 - categorical_accuracy: 0.6283 - val_loss: 0.9476 - val_categorical_accuracy: 0.6127\n",
            "Epoch 12/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.8652 - categorical_accuracy: 0.6495 - val_loss: 0.8135 - val_categorical_accuracy: 0.6691\n",
            "Epoch 13/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.8293 - categorical_accuracy: 0.6632 - val_loss: 0.9037 - val_categorical_accuracy: 0.6330\n",
            "Epoch 14/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.7946 - categorical_accuracy: 0.6743 - val_loss: 0.7543 - val_categorical_accuracy: 0.6917\n",
            "Epoch 15/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.7621 - categorical_accuracy: 0.6896 - val_loss: 0.7535 - val_categorical_accuracy: 0.6888\n",
            "Epoch 16/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.7421 - categorical_accuracy: 0.6944 - val_loss: 0.7785 - val_categorical_accuracy: 0.6745\n",
            "Epoch 17/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.7338 - categorical_accuracy: 0.6979 - val_loss: 0.8352 - val_categorical_accuracy: 0.6494\n",
            "Epoch 18/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.7075 - categorical_accuracy: 0.7108 - val_loss: 0.7856 - val_categorical_accuracy: 0.6743\n",
            "Epoch 19/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6935 - categorical_accuracy: 0.7113 - val_loss: 0.7340 - val_categorical_accuracy: 0.6880\n",
            "Epoch 20/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6791 - categorical_accuracy: 0.7239 - val_loss: 0.7112 - val_categorical_accuracy: 0.7044\n",
            "Epoch 21/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6473 - categorical_accuracy: 0.7337 - val_loss: 0.6886 - val_categorical_accuracy: 0.7095\n",
            "Epoch 22/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6458 - categorical_accuracy: 0.7345 - val_loss: 0.8669 - val_categorical_accuracy: 0.6553\n",
            "Epoch 23/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6337 - categorical_accuracy: 0.7392 - val_loss: 0.6767 - val_categorical_accuracy: 0.7132\n",
            "Epoch 24/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6194 - categorical_accuracy: 0.7467 - val_loss: 0.6923 - val_categorical_accuracy: 0.7149\n",
            "Epoch 25/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.6003 - categorical_accuracy: 0.7534 - val_loss: 0.6431 - val_categorical_accuracy: 0.7284\n",
            "Epoch 26/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.5897 - categorical_accuracy: 0.7576 - val_loss: 0.6821 - val_categorical_accuracy: 0.7188\n",
            "Epoch 27/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5760 - categorical_accuracy: 0.7646 - val_loss: 0.6851 - val_categorical_accuracy: 0.7210\n",
            "Epoch 28/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5740 - categorical_accuracy: 0.7612 - val_loss: 0.6454 - val_categorical_accuracy: 0.7286\n",
            "Epoch 29/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5580 - categorical_accuracy: 0.7690 - val_loss: 0.7690 - val_categorical_accuracy: 0.6933\n",
            "Epoch 30/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5388 - categorical_accuracy: 0.7795 - val_loss: 0.6520 - val_categorical_accuracy: 0.7301\n",
            "Epoch 31/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5342 - categorical_accuracy: 0.7825 - val_loss: 0.6584 - val_categorical_accuracy: 0.7250\n",
            "Epoch 32/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5130 - categorical_accuracy: 0.7903 - val_loss: 0.6778 - val_categorical_accuracy: 0.7106\n",
            "Epoch 33/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5130 - categorical_accuracy: 0.7908 - val_loss: 0.6214 - val_categorical_accuracy: 0.7394\n",
            "Epoch 34/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.5063 - categorical_accuracy: 0.7945 - val_loss: 0.6086 - val_categorical_accuracy: 0.7446\n",
            "Epoch 35/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.4929 - categorical_accuracy: 0.7958 - val_loss: 0.6258 - val_categorical_accuracy: 0.7316\n",
            "Epoch 36/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4856 - categorical_accuracy: 0.8044 - val_loss: 0.6385 - val_categorical_accuracy: 0.7331\n",
            "Epoch 37/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.4700 - categorical_accuracy: 0.8079 - val_loss: 0.6610 - val_categorical_accuracy: 0.7304\n",
            "Epoch 38/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.4623 - categorical_accuracy: 0.8108 - val_loss: 0.6843 - val_categorical_accuracy: 0.7218\n",
            "Epoch 39/50\n",
            "32000/32000 [==============================] - 92s 3ms/sample - loss: 0.4544 - categorical_accuracy: 0.8165 - val_loss: 0.5856 - val_categorical_accuracy: 0.7529\n",
            "Epoch 40/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4459 - categorical_accuracy: 0.8191 - val_loss: 0.5944 - val_categorical_accuracy: 0.7508\n",
            "Epoch 41/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4354 - categorical_accuracy: 0.8250 - val_loss: 0.6343 - val_categorical_accuracy: 0.7341\n",
            "Epoch 42/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4261 - categorical_accuracy: 0.8289 - val_loss: 0.6044 - val_categorical_accuracy: 0.7496\n",
            "Epoch 43/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4252 - categorical_accuracy: 0.8309 - val_loss: 0.8507 - val_categorical_accuracy: 0.6790\n",
            "Epoch 44/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4178 - categorical_accuracy: 0.8324 - val_loss: 0.6489 - val_categorical_accuracy: 0.7395\n",
            "Epoch 45/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4041 - categorical_accuracy: 0.8373 - val_loss: 0.6227 - val_categorical_accuracy: 0.7440\n",
            "Epoch 46/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.4010 - categorical_accuracy: 0.8378 - val_loss: 0.5954 - val_categorical_accuracy: 0.7481\n",
            "Epoch 47/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.3824 - categorical_accuracy: 0.8484 - val_loss: 0.6017 - val_categorical_accuracy: 0.7517\n",
            "Epoch 48/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.3790 - categorical_accuracy: 0.8501 - val_loss: 0.5828 - val_categorical_accuracy: 0.7531\n",
            "Epoch 49/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.3708 - categorical_accuracy: 0.8530 - val_loss: 0.5972 - val_categorical_accuracy: 0.7491\n",
            "Epoch 50/50\n",
            "32000/32000 [==============================] - 91s 3ms/sample - loss: 0.3628 - categorical_accuracy: 0.8572 - val_loss: 0.7050 - val_categorical_accuracy: 0.7211\n",
            "10000/10000 [==============================] - 9s 897us/sample - loss: 0.7191 - categorical_accuracy: 0.7191\n",
            "10000/10000 [==============================] - 8s 819us/sample\n",
            "[test loss, test acc] =  [0.7191428579330444, 0.7191]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1E6Jc5BQbSZ",
        "colab_type": "text"
      },
      "source": [
        "#### 6.1 Plotting loss-accuracy curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mT-IxkOe-QjL",
        "colab_type": "code",
        "outputId": "00b264ee-3894-46dd-abb2-f336f8efa4df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "# plots the history\n",
        "fig, ax = plt.subplots(1,2, figsize=(14,6))\n",
        "labels = ['training', 'validation']\n",
        "loss_Accuracy = ['loss', 'val_loss', 'categorical_accuracy', 'val_categorical_accuracy']\n",
        "\n",
        "model_history = model_history.history\n",
        "dat.plotLoss(ax[0], model_history, loss_Accuracy, model_name, labels)\n",
        "dat.plotAccuracy(ax[1], model_history, loss_Accuracy, model_name, labels)\n",
        " \n",
        "fig.savefig(save_image_dir+'/LandA_%s_epochs_%d_noSeasonGaps.png'%(model_name, max_epochs))    \n",
        "fig.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zi4jXJG8E8F/cACe3hIlXz2LOfUXQ/vbnO/Ev75wKm1M7EJ/HionPmEK2L07g5vq1Em5vjKngxI3yfar37+9BoxMr7jcoAZdvneWFiSYvK89871+4Zs2nq+r5qjpUVR9QN6VRaPPeaOKJVws9Dje4akvcAGKBrmGRzIdcKulSXBcuVQ2bNuoGufof0NwrHIK7N8ig4KCsgTzoh2LyoCsjOI/CRHLPFhwu/zT245huB6pZqvoFrnY/Hnhd9lYVB443rphzHxG0v+Wq2h/XDLwzruImGRgrIqcVE513gDnAtRI04nuIdGBDMfE5tZBti3OBF9cJod0NTOmwgrapLAJTPu1TA+j1P6lOmLmEwfW7UdXxqtrLC3NKuIxMVXeo6qeqOgDXZ/kI9jbjLcxP3vuZEZxDoHaxcZh1HSPYfh9ef54PcP2Vj2Nvf+3QGowfvfcTSnKcCBX6GeH6agWHOVDuAzJx82YmectKfO4l+U6o6mxcf+3TcaOGhvoJdzMRTXzycP3fiyQiTbxjrsTV+hhjDk1X4motZ+AeuoW+JnjhAg/mFuEKZMeLSLiWR8HKM99rAWxR1W+DF3qtmlqEhI3mnAJe9t4H4Wq2lcib3EZzvEChp9TuBzyBvL+fiLTHNRGfoKr5DyHUjZuyEGgblE+WtkBe3zV0hTd2wN9wtawRTZlWEurGnxmH65/c11u8APcZdfZaLESzvzxV/VlV/40bcRxcLXdR2yju4VAsrs92OD8CKRJmCrxCBB76RBJ/azZ+gFlB21QWE3FP/a4OHmjKG3wkUHP4ZtDyboQQkXjcE/Q9eD9UItI19MfWGyyjrvdvNkX7GNdMbKCEnx87OBMN3Jz0944RCHMiIQOaRSmQsfbHDXyzipB+0uqmK/kRuEJEzg8Tz1gJmvu0hN7BpetQEakftO/q7B0MbZ/BbUqT1wftRVzT6cHe4o9waXKHiOxTuBU3R+rxQf/v73cC3BPvPYTJWNUNFDce6CYit4SuFyf0+7sV14+8WjHHHYj73R+tJRtMxhhTwXm/VwNwtab9VPXq0BduQMZ1wHne+BR7cAMx1gaeDM6jvH3W9/JbcL/jO3G/qUeFhBMRaRi0KJDvDQwJdzF7H8BGYyVQW9zUUYF9xeGm8ywwp3GU5xTY5ntcn+GrcK2YvlJvSqziRHM8r/Z8EW5QuiOCwiQRfvDTSM3CzbJxCXvTPPTBO8ALuP7k/w2Xr4jIUcH5eLRUdSXuPqSDiFwWsvouXEvD9/TATzX1IO46uF9EYrzP6L+4hzKPhCtsi8jxXj92RKRtIekQqJEv9n5AVT/DTXPWl5D5vD0veO+viUitMPFJDSmEb/XeDyvquF7//NNxXdimFxdPUzLWR9tUFMeIyOhC1s1S1VdF5HpchvGTiATPo30Urgl1cCFuoohsx01TsQI3DVdPoBluKqdML9wLQAMR+Qb3YyS45nYdcKOMFvm0VVWzRaQvrm/UlyLyKW4Aq9q4AS5W4PrMoqrfe8c5DfhORGbi+pP1xs0HekGxqRTeDNw8mtfhbjQeL6Rp2WW4kWc/FJFZuOke9nhx6Ir78S7xgBuq+qeI3IObR3uOiPwPN9LpBbgpQP6zn03TI/UUri/UP0XkBVVNF5E+wGTgWxH5Ajc6bRUvXt1wU58FmlDu13cCQFUXi8jr7H3qHeoGXFo/LyJX4r6nmbg+cyfgMvHgm5/puCZrk0Xka9yAPzM1aAR5r2ncQLxpU4qLozGm0jod97v+ubpRxvehqnni5tS+E7gCeAbXIqgLcA3QRUQ+xxVSWuGmIEoBtqvqehEZhMuPfxaRj3DzaNfHtWj6DBjiHWoisBT3MPowXL7TBpcPfgacHeW5jcK12vnGuw/YgxtpPBY3anjogJsRnVPINq8Az3t/RzIIWkmP9xSuBv07L7/04e5TfgrdaaRUVUXkHVwt6s24hynhCln/B5yEe8jfVUSm4/pNN8TNu94RNz1W6DzX0bgBV/B/S0QuBBYDx+JaQizDFbgPKFWdLyIf4LoPXoYbCPB+XH56B9DLy1O34qa+C3S/a4h7mHQm8Lh3z7TEC9cKV5O9nci/H3cD3+DuQUPj+JmIPIL7zJZ435nVuEHp2uDuCS5j75gvX+Hy+Ye9B107cNflqJBdD2Tvg/f9bpJvCqEHwRxj9rJXYS8KzhVd2Gt0UPjuuAFQtuOeJM7D/VjHhuz3Blxt83Iv3CbcD/4VgASFuxTXZ3sp7kd1K25Ez1uAuCjO40hcQX8trhC0DlewOyckXB3caKZbvON9h/shH+id68AwaTM6guM/HpReRxcRrg7uafl8XH+2dFxTqtfw5lmN4FjDKWIOR9yUILNwBceduOmzrgkTbp9zjvI7U+i837gBbRQ3DUpg2eG4m7SluBG5t+FqLl6g4ByVEX8nCJpHO0wcGnnb7zMfqrc+EbgHd+OZ5aXXEtwc2xeGhK2OuyFbi7uxVGB4SJhu3vIvy/u6tpe97FV+L+83RHEjLhcVrrUXbk7QsnjcDf8fXh6xA9cMeCT75rPH4boubfJ+U1d5/3cJCZcKfOjlN5nANFxBZ5+8hL3zaA8vIt6XeL+bO3GFw9G4AmwaXmvdkPARn5MXPhnXOmsrQfMaR5H+0aThjbgCaA7uwfwI3EODffKNws4vzPHbsvd+4Kliwl6OK7ht8z7Dlbh7rBuAxAiOlUrR84E3x90brffOcSUuH64fJmzYvDKCOKQRZh7toPVH4x52LMGbMxz3oP0m4Hvve7kL97DoI1zrwMB8522AZ4FfcPdtu3AD8P0XaBZynMD3+eJC4hGYE1sJP698T+BT3LzfObhB+GbgBmQNnSN8AK7vd7a3v+Uh68U7nzygabRpaq/IX+IluDHGGGOMMaYIItIVNy3VC6o6uLjwxphDl/XRNsYYY4wxJjK3e+8vlWssjDEHPeujbYwxxhhjTCG8gaMuwzUzPh83Rea88o2VMeZgZ03HjTHGGGOMKYQ3a8hXuKmfvgCuU9Ut5RopY8xBzwraxhhjjDHGGGNMKbI+2sYYY4wxxhhjTCmygrYxxhhjjDHGGFOKrKBtjDHGGGOMMcaUIitoG2OMMcYYY4wxpcgK2sYYY4wxxhhjTCmygrYxxhhjjDHGGFOKrKBtjDHGGGOMMcaUIitoG2OMMcYYY4wxpcgK2sYYY4wxxhhjTCmygrYxxhhjjDHGGFOKrKBtjDHGGGOMMcaUIitoG2OMMcYYY4wxpcgK2sYYY4wxxhhjTCmygrYxxhhjjDHGGFOKrKBtjDHGGGOMMcaUIitoG2OMMcYYY4wxpcgK2sYYY4wxxhhjTCmygrYxxhhjjDHGGFOKrKBtjDHGGGOMMcaUIitoG2OMMcYYY4wxpcgK2sYYY4wxxhhjTCmygrYxxhhjjDHGGFOKqpR3BA5WdevW1dTU1FLZV1ZWFomJiaWyr0OFpVn0LM2iZ2kWPUuz6EWbZj///PNmVa13AKNU6VieXb4szaJnaRY9S7OSsXSLXjRpVlSeHXVBW0RSVHVDtNtVNKmpqcyePbtU9pWWlkb37t1LZV+HCkuz6FmaRc/SLHqWZtGLNs1EZMWBi03lZHl2+bI0i56lWfQszUrG0i160aRZUXl2SZqOrxSRcSJyWgm2NcYYY4wxxhhjKrWSFLQXA32AqSKyWET+KSJ1SjlexhhjjDHGGGNMhRR1QVtVjwZOBt4CGgNPAKtFZKyInFLK8TPGGGOMMcYYYyqUEo06rqrfqupAoBFwK/An0Bf4SkTmi8itIpJcetE0xhhjjDHGGGMqhv2a3ktVd6jqC0G13G8CTYGncbXco0WkcynE0xhjjDHGGGOMqRBKc3qvzcA2IBuIB6oC/YErROQTYJCqbi3F4xljTKWWnp7Oxo0byc3NLe+o5KtZsyYLFiwo72hUKMFpFhsbS/369alRo0Y5x+rQEs21ZN/x6JVHmtm1ZIw52O1XQVtEYoGLgOuAUwDBDZb2IDAa6ADcCfQCXsQ1LzfGGFOM9PR0NmzYQOPGjYmPj0dEyjtKAGRkZJCUlFTe0ahQAmmmquzatYs1a9YAWAGhjER7Ldl3PHplnWZ2LRljKoISNR0XkSNE5HFgDTAW12z8I+AMVW2tqs+q6nZVTVPVs4EJwFkR7ruViIwUke9FZJOIZIjIbyJyr4hEPNu6iJwtIt+KSJaIbBWR/4lIsxKcrjHGlLmNGzfSuHFjEhISDppCttk/IkJCQgKNGzdm48aN5R2dQ4ZdS5WPXUvGmIog6hptEfkS6I6rvV6Hq71+WVXXFrHZz8AFER5iEHAT8DGuEJ8LnAo8BFwiIieo6q5i4nghMB74HbgDqAkMAb4Rkc7FxNUYY8pdbm4u8fHx5R0NcwDEx8cfVN0BKju7liovu5aMMQezkjQdPxX4CvgP8JGq5kWwzSdApIXb8cAjqrojaNl/RWQJcC9wFTCqsI295uwvAKuArqqa6S2fjCvwDweujTAuxhhTbqz2rXKyz7XsWZpXTva5GmMOZiVpOt5GVU9X1Q8iLGSjqnNVdUyEYWeHFLIDxnnv7YrZRTfctGOvBgrZ3n5/A9KAS73CuDHGGGOMMcaYQ4iqlslxoi5oq+qiAxGRCDTx3jcUE+447/27MOu+B2oArUorUkX5ftOf9Jr+FK9vn10WhzPGGGOMMcYYE8aiHWu58puXGPDNS2VyvBKPOi4i1XH9rjvi+kDvAH4FPgyuSS4NIhID3AfsAd4pJngj731NmHWBZY2BeWGOcy1es/KUlBTS0tJKEt18P2ev4ZONv9KuSr393tehJjMz09IsSpZm0TuY06xmzZpkZGSUdzT2kZeXV2bxOvvss2nTpg1PPfVUxNu0a9eOa6+9lsGDBx/AmEUnXJplZ2cftN89U/l0796ddu3aMWpUoT3v9pGamsrNN9/M0KFDD2DMjDEmetl5OVSLiYs4/K9blvPI3I8Zv+InFCXWF8OTx/alfnzNAxjLEha0RaQP8F+gFm5QtAAFnhWR61R1fCnEL+BZ4ETgnghq1BO8991h1mWHhClAVV8GXgbo3Lmzdu/ePfqYBmm4Yy1DJ05mK7vZ330datLS0izNomRpFr2DOc0WLFhwUE4xVNw0PiW5oS/MxIkTiY2NjSodZs+eTWJiIgkJYX/my0W4NKtWrRodO3YspxiZiqA0r6UJEyYQGxtdr7mffvqJxMSIJ3sxxpgDZlN2Ol+tn8/09fOZvm4+SzLW06pGA3o0bEePhkdxaoO21Irb9/dq1oZFPDz3Yyav+R2AOF8VBrboyp3tzj3ghWwo2ajjZwDvAn7gTVy/5/VAA9xAaZcB74rIdlWdtr8RFJEHgZtxI5s/EsEmO733qmHWVQsJc0A1TqgNwKY9WaiqDdphjDG4UaAjuemvXbt21PuuV69eSaJkTIVk15IxpjLanpPF1xsWuYL1+vnM2bZynzCL09ezOH09/1k0DZ8Ines0p0fDo+jRsB2783J5+I+P+Xqjq59NqFKV61udxu1te+aXz8pCSQZDux9XW3yCql6pqmNU9XPvfSBwEm5Krvv3N3IiMhwYBrwBXB/hZoHRzRuHWRdYFq5ZeamrHluNWnEJ5JLH5t0HXxNQY4wpTQMHDmTGjBm8+OKLiAgiwujRoxERPvvsM/72t78RFxfH559/ztKlS+nduzcNGjQgMTGRTp06MWnSpAL76969OzfffHP+/6mpqTz00ENcd9111KhRgyZNmvDEE08U2CY1NZUnn3wy/38R4eWXX6ZPnz4kJibSvHlz3n777QLb/PDDD3Tq1Cm/lvmzzz5DRKxptyk3di0ZYyqzPL+fpRkbmLr2D15ePJ27f36PS2e8wHGf3kfdcdeT/N519PrqaZ5dMIU521ZSLSaWHg3b8XDHS/i+53B2Xf463/Z8gJEdLuKUlNbEiI8fNy/l4T8+5rQvHqbnl0/w9cZF1IpL4L7257Piwmd5qvPlZVrIhpI1He8IjFPVX8KtVNXZIvI+cPH+RMwrZD8AjAGu1siHh/vJez8RCK1RPwFIBxbvT9yicVhCHbbn7GR11lbqVatRVoc1xlQy8ma/cjmu9n+7+ECe5557jsWLF9O6dWsefvhhAObNc8Nh3HXXXTz11FMcccQRJCUlsXbtWnr27MlDDyPSY4gAACAASURBVD1EfHw848aN48ILL2TOnDm0bt260GM888wzjBgxgjvuuIPJkyczePBgTj75ZE488cRCtxk5ciSPPvoojzzyCK+99hqDBg3ilFNO4fDDDyczM5Nzzz2XM844g7feeou1a9cyZMiQiM/ZVEwH+/VU0a6l5ORku5aMMcXatjuLl5dM54WFX7Bm57ZCw1WLiaVT7VROb3gUpzVoywn1jtinT/aJ9VpyYr2W3Nf+AjJzs/l64yKmrZvLtHXzyMzN5tpWp3JDq9OpEVd+XclKUtDeDawrJsxawveRjoiI3I8rZL8FDFJVfyHhGuIGYlupqoHm4DO8+F0tIs8EzaN9DNAdeENVc0sat2g1SazNH9tXsXrnVjrWSS2rwxpjTJmrWbMmcXFxJCQk0KBBAwAWLlwIwPDhwznzzDPzw9arV49jjjkm//97772XTz75hPHjxzNs2LBCj3HmmWfm18zdcsstPP/883z55ZdFFg6uuOIK+vVzBasHH3yQ5557jpkzZ9KvXz/Gjh1LXl4er732GvHx8Rx11FHce++9XH755SVPCGP2U0W7lnr37m3XkjGmUH+mr+e5BZ/z+tKZ7NzjiogN42vRqkYDmlWvR/Ok+u69untPia+JTyJveF09tho9Gx9Dz8bHFB+4DJWkoP010KWYMF2AmSXYNyJyEzACWImrkb4spG/zBlWd6v39CDAA1zc8DUBVc0XkVty821+LyCu4Kb1uAzbhCvBlponXRGFV1payPKwxppKJpmb5YNS5c+cC/2dlZTFixAgmTZrEunXryM3NJTs7m/bt2xe5n9D1jRo1YuPGjRFvU6VKFerVq5e/zcKFC2nXrh3x8fH5YY4//viIzslUXEVdT8UN+Ffe7FoyxlQEqsrXGxfxzPzJTFz1C4prnHxmw6O5vW1Pzmx0dKUfv6okBe27gO9E5FHgQVXNCqwQkURcQbYdrq92SQTmwT4c12w81Axgapjl+VT1fyKyC9e/+0lc7fqXwF2qWib9swMCBe3VO7eW5WGNMeagEjp68dChQ5kyZQpPPvkkLVu2JCEhgf79+5OTk1PkfkIHfhIR/P6wjZ72axtjDlZ2LRljDkZZudksSl/Hgh1rWbhjLVPWzmH2lmWAG+27X/Mu3NbmLNolH1bOMS07JS1ozwHuAK4VkV+ADUAK0AnXlHsmcFfIUwpV1auK27k3oNrASCJSVFhVnQRMCreuLB2WGChoF94PwRhjKou4uDjy8vKKDTdr1iz69+/PRRddBLh5pZcuXUqrVq0OdBQLaN26NWPGjGHXrl35NXE//vhjmcbBmHDsWjLGlIWtuzNZkr6eJRnrWZK+gcXp61iSsYE/0zewR/OoU7W690qiTtXq1PXea8bFszJrCwu2r2Vh+lpWhmm9W7dqEjce2YMbj+xBShlMp3WwKUlBe2DQ37WA08KE6ea9gilQbEG7srGm48aYQ0lqaio//vgjy5cvp3r16oXWdrVq1YoPP/yQ3r17Exsby4gRI8jOzi7j2MJll13GsGHDuOaaa7jnnntYu3Zt/uBTlb1Jmzm42bVkjCkNe9TPXxkbWZa5kb8yNrn3zE0sy9zE0owNbNmdWeT2WXt2hy1Eh4r1xdCqRkNa12hIm5qNODr5MM5r0on4KnHFbltZlaSg3azUY1GJWdNxY8yhZOjQoQwYMIC2bduya9cu3njjjbDhnn76aa666iq6du1KcnIyQ4YMKZfCQVJSEp988gk33HADHTt2pG3btgwfPpyLL76YatWqlXl8jAmwa8kYE6mM3F0szdjI0owNBd8zN7IyczP+VYVP3pRQpSotk1JoWaMBrWo0oGVSA1rWaEDLpBSqxcSyZXcmW3Znsnl3Rv7fW3Znsj0ni0YJybSp2Yg2NRvTrHo9qvhiyvCsD35RF7RVdcWBiEhl1SRxb0FbVe2prjGmUmvVqhXfffddgWUDBw7cJ1zTpk2ZNq3gDIxDhw4t8H/o3LvLly/fZz/FhQk3M2RomBNOOIFff/01//+JEyciIrRo0WKfbY0pKxXpWsrIyADsWjKmtPnVz8bsdNbt3M66Xe61dte2/P/X7trOssxNbMpOL3QfAjROSM4f0Tswwnfg70bxyUWWT2rEJdAsqf4BOLvKryQ12gWISBKuCfkOVS38Uz5EJcXGkyhxZOXlsGV3JnWrHbwjmRpjzKFozJgxNG/enMMOO4y5c+cyZMgQzjvvPOrWrVveUTOmQrFryZj9l5G7i2nr5vLJql/5bM3vbMjeUew2VX2xNE+qR4ukFFok1adFde89qT4rfpnPmaf2KIOYm1AlKmiLSBVgKHA1QU3JRWQZ8CrwpKruKZUYVgL1qiSSlZvD6p1braBtjDEHmQ0bNvDAAw+wbt06GjRowDnnnMNjjz1W3tEypsKxa8mYklmWsZFJq39j0upfSduwgBz/3mJU7bjqNE5IpmF8LRom1HLv8bVo5C1rmliHRgnJhc47vU4Wl9VpmBBRF7RFJA6YghvsTIFVwDqgIZAK/Bs4S0TOVNWi55Y4RNSPSWR57jZW79xKh9pNyzs6xhhjgtx5553ceeed5R0NYyo8u5bMoWj9ru1sys6gZmw8NeLiSaoST4xv30Lv7rxcVmZtYXnmJpZnbmZZ5kaWZ27mt20rWLBjbX44nwhd6rXi3CYdOLdJR46q1cS6nlZQJanRvh3ojps665+quiSwQkRaAE8B53nhHi2FOFZ49WLcnJc28rgxxhhjjDEVW3ZeDh+t/Jk3/pzJ1HVzUQqOYZBYpSo1YuOpERtPYpWqbMjewdqd2/cJF1AjNp6zGrXn3CYd6dn4mEOuBaz6/bB6PjovDZ0/AxZ/hxx7HnL1i0iYhxYVRUkK2pcBc4HzVbXAXBOqulRELgR+Ay7HCtqAazoONvK4McYYY4wxFZGq8svW5bzx50zGLvuG7Tk7AYjzVeGIpBTSc3eRnruLjNxssvbsJmvPbtbt2p6/vU+EwxPqklq9Ls2q1yO1ej1Sq9fliKQGHFenOXEx+z10VoWhfj+smofOT0PnpcH8mZCxuWCYqf+FuHgY8FSFrdEvySd6BPBCaCE7QFX9IjIZuGW/YlaJBGq0V2dZQdsYY4wxxpiKYHdeLkszNjJ13R+8/udM5mxbmb+uU+1UBh3Rjb7NTqR21er5y/3qJ2vPbnbk7CQ9N5vMPdnUq5pEk8TaxPoOncJ0OLpiDjrzbXTWO7B1TcGVtRsjbbvBUd2haiL64kD002egVgpy/l2lc/ycbPSHDyBzG76eN5fKPotSkk87B6heTJhEILcE+66U8puOW422McYYY4wxB409/jzW7NzGkvT1LE5fx6L0dSxOX8+i9HWsyNqMP2hquzpVq9OveReubHEKxxQy7pJPfCTFxpMUG19Wp3BQ0y2r0Vnvol+/DSvm7F1RuzFy1KlwVHfkqO6Q0rxAzbXfF4M+1xcdezf+GvXwnTao5HFYswid9jKaNhoyt0JCTfS0QUjVhJKfWARKUtCeA1wsIsNVdVPoShGpC1wM/L6/kass8puOW422McYYY4wxZWKn13x77U43KPHqrK3uPej/9dnbCxSmg/lEaJFUn/bJh3N5s5M4t0lHqsbElvFZVDyanYV+9z46822Y9xUE0rd6beSkS5FT+kGrE4tsEu7rcin+9E3o67eg/70GTaqLHNcr8jjk7kZ//BCd+hLMS9u7olkn5IzrcDOMH1glKWiPAt4DfhSRh4CvcKOON8ANkjYMqAcMLqU4Vnj1Y/b20VbVCtvPwBhjjDHGmINJVm42k1b/xq9bl7PWK1Sv3bWNtTu3syN3Z7HbC0KD+Jq0TGpAqxoNOLJGQ1rVaEirGg1okZRySPWd3l+asQWdMgr97HlXcwwQWxXp3Avp2g86nIXExkW8P1/Pm/Hv2Ih+8CD+Zy7Fd98XSJuuRcdh7WL0y1fRr97Y2++7agLSpS9y5vVIi84lPb2oRf3NUdX3RaQDcDfwcpggAjyuqu/vb+QqiwRfHDVi40nP3cW2nKwC/TiMMcbs1b17d9q1a8eoUaPC/h9Ou3btuPjiixk+fHipHtuYisyuJVOZZeflMGXNHN5b/j2frP6VnXt2hw0X56uSP990k4RkmiTUpklibZok1KZxgntvGF+r3ArTmpcHC2bC7p2gfvDnee9+VP2gfqTpMUiTNuUSv0jpltXoJ0+j016G3VluYcvjkdOvQU64GEmsWeJ9y6UjYMcGdNrL+B89D9/ImUjT9gWPn7EV/XYcOuNNWPL93hVN2yNnXIecfPl+xaGkSvStUtV7RORj4CqgI1AT2AH8Cryuqt+VXhQrhyYJtZm/Yw2rsrZYQdsYYyI0YcIEYmNLt5ne6NGjufnmm8nMzDzgxzLmYHEgr6V169Yd8GOZyu23rSt4efF0fOKjcUJy0Ks2jROSSYqNZ4/6mbzmd95b9h0frfqZ9Nxd+dufWK8lZzVqz+GJdWiUkEyj+Fo0jK9F7arVD9qWpJqXh/+x8+DXyUWHEx++f32KdDwruv1nbUdnvEmTBX/g3/ETxFSBmNgC71KnCbQ7rcRppGsXoxMfdwXcPG94rg5n4Tv/bmh7SqmkvYjA1f9BMzbDDxPw//ssfA9+A3WawK+T8c94E37+BPbkuA2qVUdO7IP0uNYV9svx8y/x4xtV/R74vtiABthb0F69c2uhgycYY4wpqHbt2pXyWMaUNbuWzMHoj22rGP77BCas/KnIcNWrVEPz8shatXes5U61U7k09QQuST2e1Or1Ct1Wc3PQj5+AdUvA5wPxgS/Ge/f+j4tHTu6LNOtYaudWHB0/0hWyE2tBq5P2xsWLn/h8aMYWmDsd/zOX4HvoW+TwdpHte2c6/pE94K+faQZoISU2BTjm7/iuewmpF3n5RNcsxP/effDDB67/tfiQEy9BLrj7gKShxMTgGzwW/8M9YV4a/vu7Qu7uvU3DReCYM5Fu/ZG/XXDABzmLVNQzgIvIKV7TcROFwxJdpmNzaRtjKquXX36ZlJQU8vLyCiy/7LLL6NWrF0uXLqV37940aNCAxMREOnXqxKRJk4rcZ/fu3bn55r1TcGzcuJHevXsTHx9P06ZNef311/fZ5umnn6Z9+/YkJibSuHFjrr76arZvd3OZpqWlceWVV5KVlYWIICL5zWRDj7Vt2zYGDBhAcnIy8fHx9OjRg3nz5uWvHz16NNWrV+fLL7+kXbt2JCYmcuqpp7Js2bKo086YYBXtWqpRo4ZdSyYq87ev4dIZL9D+k38xYeVPVIuJ5dY2f+eZzv0Y2vZs+qaeSLeU1hyRlEJ8TByZe7LJ0lza1mzMyA4Xsej8J/j53Ie4s925RReyVdGXrkHfG4bOGIN+9QY6/TU3AvXU/6Kf/8f1Kf74Cfx3dsL/9KXomkUH/Pz1l89cQVt8+G7/HzH3fErM3Z8Qc9dEYu78kJih4/Hd/j6++6YiJ14CuzLwP3ouumNj8fvevQv/o+fBXz9DSnNWdboMOW8ocvatyN9vck2pTx2EnHIFJCbD75/jv+0o/JNHufmti9r3jk34X70J/+3t4PvxEBOLnH4NvucW4rt93AF9UCFx1fDdORFSO7ipwTI2Q5O2SL/H8P3fKmKGfY6v6+UHTSEbSlaj/RXwEnBjKcelUmuS4Araq2zkcWNMCeT1KZ+mTzH/Cz8Sazh9+vRh8ODBTJ06lbPOck3cMjMzmThxIm+88QaZmZn07NmThx56iPj4eMaNG8eFF17InDlzaN26dUTHGDhwICtWrGDatGkkJCRw2223sXz58gJhfD4fzz77LM2bN2fFihXccsst3HLLLbz11lucdNJJPPvss9xzzz0sXboUgOrVw3fnGThwIIsWLWLixIkkJydz7733ctZZZ7F48WLi4920Lbt37+aRRx7h9ddfp1q1agwYMIDrr7+ezz//POJ0M2WvqOspAcgrdO3+ifR6qmjX0u+//0716tXtWjLFWrRjLSPnfMS7y75DUeJ8Vbi+1WncV6U2yZOeQ6onw9E9kPY9kZTmgCssb8/ZybSv0+jT45yojqfjH3TNmqsmIP0eh7j4/D7Q+f2h1Q9rF6NfvuJGyv5+PNJ9INLn/qhqeSOO04a/8D9/OQDyjweR9j0KDSs+H9w0Gt20HP78Ef/j5+N7YDoSVy38vvfk4n+6j+v3ndwI333TWL5gBandu4cPv30D/tduhu/Hu9G9vx2H7/pXkcZHFgy3exf62XPohw/DrgxXg93jWuTi+5E6jUuUDiUhCTXw3T8NnfkW0rorNO900HYNgJIVtDcDu4oNZQpoYjXaxphKLjk5mbPPPpuxY8fmFw4++ugjqlSpQq9evahWrRrHHHNMfvh7772XTz75hPHjxzNs2LBi97948WImT57MrFmz6NKlCwBjxoyhefPmBcINGTIk/+/U1FQef/xxevfuzZgxY4iLi6NmzZqICA0aNCj0WEuWLOHjjz9mxowZnHLKKQC89dZbHH744YwdO5arr74agD179vDiiy9y5JHupmTo0KEMGjTokJphQkR8wK3AdUAqsAl4H7hfVbOK2XY48EARQfao6iHX2beiXUspKSkkJSWFPZZdS5XHvO2reX/5DzRPqke3lDZF1iQHW5m5manr5jJ5ze98uGo2flVifTFc0/JU/tX6LBp9NsoV4FRdU+bv/ufe6zdDju4B7XtQq91p+dPlRso/8230/QdABN+t7xY7NZSef5crmE9/Df3qdfTrt13t7wX3IMmF5xfR0N278D95EWRth2PPQ86/u9htpGo8vjsn4r/neFj8HfqfQXDr2H2uC/X70RcHwi+fQvXaboTulGawYEXh+66VQsw//4f+MAH/KzfCwln47zgGuWQEct4/QXzoN++i79wDm1e6jTr2xHfFE8hhR+1PUpSYJNVBzhlSfMCDQEkK2mnASaUcj0rvsIQ6gM2lbYwpmWhqlstTv379GDBgADt37iQhIYGxY8dy0UUXUa1aNbKyshgxYgSTJk1i3bp15Obmkp2dTfv27YvfMbBgwQJ8Ph9/+9vf8pc1bdqURo0aFQg3ffp0HnnkERYsWMCOHTvIy8sjJyeH9evX7xO2uGOdeOKJ+ctq1qzJ0Ucfzfz58/OXVa1aNb9gANCoUSNycnLYtm3bodRP9RnclJ4fAk8Bbbz/O4pID1Utqi3iBODPMMvbA3cAn5RyXIGir6eMjIxCC41lya6lQ/JaOigtz9zE8N8n8ObSWXhFYQAOT6xDt5Q2dEtpTbeU1rRISkFE2J6TxVfr5zNt3Tymrp3Lkoz1+dtUkRiubnkK9xzdm8Oz0vE/dj665AdXQ3rBv6DuYeicaTB3Omxchn75Cnz5Cgp0qH8kWntUkTXAATpvhiuQAnLlcxHNvyx1miDXvYT2ugP933B01jvo5BfQ6a8hJ18OcdXcgFu5u2FPDuq9s2c3cnh71z85qU7hcVJFX7sJlv8GKS3w3fKmq7GOgCQ3wHf3J/iHdUG/eRcaH4n02fuM0u37ZnTWO1CtOr57p0RVEJbjL8TXtjv65j/RtNHo2LvRb993/caXznaBmrbHd8WTyDFnRLzfQ11JCtrDgB9E5EFgpKrmFreB2VujvWrnlnKOiTHGHDjnnHMOVapUYeLEiZx++ulMmzYtv+nn0KFDmTJlCk8++SQtW7YkISGB/v37k5OTE9UxiqrdWrFiBeeccw7XXHMNI0eOpE6dOvzyyy/07ds36uNEcvwqVaqEXecvpp9bZSEiRwG3ABNU9aKg5cuA54F/AO8Utr2qzgHmhNnvS96fr5VqhCsQu5YOrWvpYLRx1w4envsx/7foS3L8e6giMfRrfhJbd2cxc+NCVmZt4a2/ZvHWX7MAaJyQTIP4Wvy6dTl+3VsgrxEbz6kN2nJGw3ac16Qjh1evi3/m2/hfvdE1Q67TBN/gsUhb1+KBM693014t/xWdMw39YxosnEXSxkX4HzwDOf1q5IonC52uSdcsxP/E+ZCXi5x9K76et0R13tLwCGTw22jvu/CPuw9+mugK/EXQ379Ap7/qmlL//aawc0Xnz+0cF4/vjglIYq3o4tW0Pb4h7+F/rBf6/nD8DVvhO7mv2/e7w9Av/g9iq+K762PkiOOi2jeAJNVGbnoD7fIP/C9dC8t+cSuSGyJ9/42c0h+JiYl6v4eykhS0/wXMBe4BrhKR34H1QOjjYVXVq/YzfpVGoI/26p3brBmUMabSqlq1Kn369GHs2LFs3ryZBg0a0N3rGzZr1iz69+/PRRe58lh2djZLly6lVatWEe27devW+P1+fvzxR046yTWsWrlyJWvXrs0PM3v2bHJycnjmmWeI8W4IQgeJiouL22eQqVBt2rTB7/fz3Xff5Td3TU9P548//uDKK6+MKL6HiL6AAM+GLH8FeBToRxEF7XBEJBFXQF8NTCmFOFZIdi2Z8pKes5On5k/m6fmTydyTjSBc3uwkRna4mOZJ9QHI8/v5Y/sqZmxYwIwNC5m5YSFrdm5jzc5txPpiOLl+S3o0PIozGh5N5zrNqOJz3yHdmY7/+SvQr992BzvhYnzXvez6ZgeRmBho0Rlp0RkuuBvdvYulowbTbPabrsD62xS3XceeBbbTHRvxP3y2a5p9XG+k/1MlTgdpejQxd36E/vkTOn+GmxIrtqp7VYlDqrh3AP+UUTBnKjrmdvSL/8N3xRPQuVf+/b7++RP6mhsgUK59aZ95oCOO07HnIAOeRkcPQf9zJVovFV30jWt674vBd9v7SLtTS3zOANLh7/ienotOfByqJiI9b0GqRdds3zglKWgPDPq7gfcKR3HzbBugZlwCSbHVyMjNZnvOTpKr2hfWGFM59evXj9NPP51ly5bRt29ffF7TuFatWvHhhx/Su3dvYmNjGTFiBNnZ2RHv98gjj+Sss87iuuuu4+WXXyY+Pp7bb789fzAlgJYtW+L3+3n22We58MIL+f7773n22YJlwNTUVLKzs5k6dSodO3YkISGBhISCo5S2bNmS3r175x+rVq1a3HvvvdSoUYPLLrtsP1Kn0jkO8AM/Bi9U1WwR+c1bH60+QA3geVU9UOOSVQgV5VqaPn06Xbp0sWupgtucncHopTN5bO4kNu/OAOCcxh34d8c++0xNG+Pz0aF2UzrUbsqtbc7Cr37mb1/DhuwdHF/3CKrHFhysS/PyYH4a/v9eAxuXucHJrnweOW1QRJVPUjWe1cdeTotLbsP/f4NgyQ/4Hz4b6TYAGfgMUj3Z9X9+rLfbf4vOrpa8FGpg5Yjjiq0h9nU+D375DP+b/4S1i/A/fj4cdSq+AU9DnSb4n7oY9uQgf78RX7cr9i8+Zw+GtYvQL/4P/7/Pgl3pbvlNoyNqIh/RMeKTkH88WCr7OpSVpKDdrNRjcYhoklCbBTvWsmrnFitoG2Mqra5du9K4cWPmz5/Pu+++m7/86aef5qqrrqJr164kJyczZMiQqAoH4KYBuuaaazjttNOoW7cuDzzwABs37p3upH379jz33HM89thjDBs2jJNOOoknn3ySSy+9ND/MSSedxPXXX0/fvn3ZsmULDzzwQP60RMHeeOMNhgwZQq9evcjOzqZLly5MmTKlQGHE0AjYrKq7w6xbA5wkInGqGk1b46twD+v3nW8qiIhcC1wLkJKSQlpaWthwNWvWJCMjI+KD5+XlRRX+QOrQoQONGjVi/vz5vPrqq/nxGjlyJDfffDNdu3alVq1a3HjjjWRmZpKbm5sfJtCfurD/R40axS233MJpp51GnTp1uPvuu1m/fj27d+8mIyODZs2a8dhjj/HUU08xbNgwjj/+eB588EEGDhxIZmYmGRkZHH300Vx11VUMGjSIrVu3cvfdd3PPPffsc6znn3+eu+++O/9aOuGEE/jggw/Ys2cPGRkZ+b8Dwem+c+dOwI22XrVq1ULTKDs7u9DP/mCWmZl5UMQ7T/38mL2aKZmL+XbXSvbgmuq3q5rCNTWPo31MA7bNWUYakU21FgPMXrQZVInfsZpaq36m1upfqLnmV2J3ZwKQWbclC88cxq6Yw2HGjIjjmpmZyYylQI9/07jeeJr+8DoxM8aQ/dMklnYbQv3FU6m79Huyk1L4reu/yP2+6Lm5S18i0msUDed9zOE/jiF23lfk3dmJ3UkpVMtYT3pKG+Y0642WwucuzS/kqMN+InmV6z/9Z9fBrPM3gTD7Pli+axVJaaWZqEY3wI6I9Ad+8/pVFRamHdBJVd/cz/iVm86dO+vs2bNLZV9paWl0796dM6c+ytR1c/n0tKGc3cSmIi9KIM1M5CzNoncwp9mCBQto06ZNeUdjHwfLQFEVSbg0K+rzFZGfVbVzWcRtf4nIUiBWVQ8Ps+5N4AogWVW3R7i/I4GFwJeqWvyIR56i8uxoryX7jkevPNPsYP2tLE555z/zt6/hjT9n8NZf37AhewcAPhH+3qg9Nx3Zg7Mbd4iqm6PuyoBNK9AVc+CPaW5Asy2rCgZqcATSpS9y0b1IbOEPTwoTmma6djH+/wyCRd/sDZRQE99D3yKHtY16/6VJM7eh40eiU0ZB3h5Iqovv8V+QuoeV3jGytqNjbodWJ+LrcU2h4cr7u1YRRZNmReXZJanRHg0MJ8zgJUF6AyOBClvQPhAOS/RGHrcpvowxxlQOO4H6hayrFhQmUoEuZ6+WOEbGHKIycncxZunXfL72D9SbQsu9qrh3iaHFxpWsX7+E5xL2Nqk+skZDrjziFPo170LjhMJHeNe8PfDbFHTtIleo3rTCTfm0aQVkbdt3g6S6yNGnQ/szkKNPR+qnlur5SqNW+EbMQKeMQt/5F+TtwTf0g3IvZANI9WRk4DPomTegX76KdPlHqRayASSxFnJjkQ1/TDkrSUE7EjHsOzhaRETkX0An4FhcM/UVqpoa5T5igWtwGXYLb/FfwBjg/6JswlZqAgOircqykceNMcZUCmuBtiJSNUzz8ca4ZuUR5bkiUgXoD2zBTRVmjInAXxkb5mPbhQAAIABJREFUeWHhF7z+5wzSc3eFDeNTP/cunss/F/+BD2jasj2LzhvMwCO6cXzdFsXWXmt2lutn/Fsh4xPGVoW6h0ODlki7U938103bRzx9VUlJTAxyzq3oSZdCzk4kpXnxG5UhadQKueLx8o6GKScHqqDdCgjzaCsiDwNbgV+A6Ma932s0cBnwAe6peAxwHm5U1JOASwvd8gDaO/K41WgbY4ypFH4CzgT+BnwdWCgi1YAOwMwo9nUekAI8V0ifb2OMR1X5av18nlvwOZ+s/jV/fuuu9Y/kmpanUisugVx/Hrn+PGLSN3L8eyNp9OcfqAh+8TF4yRzkt++Qvw0ovpCdvhn/I+fAnz+6WuqT+0K9pkjdpq5wXa8p1KxfrjPqSHJhYzMbU34iKmiLSGi7hPNFJDVM0BjgcKAr8GkJ49RCVf/yjjsXqB7NxiLSCFfI/khVLw5a/iIuw+8jIterakkfBJTYYYl7p/gyxhhjKoFxuOk+hxBU0Ma1KksAxgYWiEgLXH/uhYXsK9Bs/JCdO9uYwvjVz9qd21iWuYk521bx38VfMnf7agDifFXo2+xEBrc+k051Co5ZrPPS8I+6Dravhxr1iBk8FvJy8T91MTr9NTRrG75b3ym0z7RuWoH/ob/D2kVQLxXfsM+RRpFNI2fMoS7SGu2BQX8r7il1YaN5KfADcFtJIhQoZO+HwGgca4MXqqqKyDrcNCTRDXNbSqxG2xgTDVUt1xoCc2BEOwjpwUxV//AeZN8sIhOAz4A2wGBgBgXn0P4SaIqbd7sA7yH5WcCPqvrHAYinXUuVUGW6lgB27cnhz4wNLElfz9KMDSzL3MRfmRtZlrmJ5ZmbyfHvKRC+QXxNbjyyB9e2PI2U+JoF1qnfj374CDruflA/tDkF35B3kdqNAPAN+wL/o+fCDxPwP3IOvjs+ROILDminK+e66aO2roGm7fHdMzl/e2NM8SItaAcejwmur/OzwHNhwuUB21Q1qxTiVlJLvdcgEfkVmIarae8NXAg8oqrhO7AcYE0S/5+9+46vsjz/OP65TsggYYUAAQKyZclQcctwr7prnbROHNVKq23do1Z/Vuuos+KedbTUUiuCK4goiANU9h5hYyAQAiQ51++P5yQECJAcTnIyvu/X67wO53nu5z7XeXwh5zr3fV/3tjXa+kdfRHYnMTGRgoKCnfakldqvoKCAxMTEeIcRS8OBhQRbbZ0CrAEeB+5w93AF+7iY4N/qmBdB09+luqu2/l3K27qJLwsW8+3095mdt4I5eSuYk7eSJZt2X8OnVUoTOjVqSedGrTilXX/O6XAISQk7f5X39asJPz4Upo4BwM68BTv3bqxMW+t5JKG7xwWj1T98TPjuYwjdOhprHBTu9RmfE/7LqZC/LkjS//gfLC3aFZ0i9VOFEm13X1TyZzO7G/i07LGaxN2LzOw0gsJnz5Y5VQhc5+5PxycyaJqYSlqDZPKLtrC+cBPNkrSXtoiUr1WrVuTk5JCVlUXDhg31w1wd4O4UFBSQk5NDZmZmvMOJGXcvBh6KPHbXruNuzt1HUKMl5vR3qe6prX+XisLFPDvnU+6Y8i/WbNkAq7c/38AS6Ny4Jd0at6Zrk8zSpLpTo5Z0bNSSRokp5XdMpCL4win49HH4e48Eo9CNMwhd9xq2/4nlXmMd+xG653PC9xwH8yYTvn0godvHwoLvCD/8CyjcDAefGUwtT9r1e4tI+SpdDM3d766KQGKsAJhDUKTlE4J1Yr8CnjCz/F3t721mwwh+kSczMzNmm7uX3fQ8gxTy2cK/s8fQKWnXWyjUd7HaKL4+0T2rvJp+zxISEtiwYUONSgw0G6fyyt4zdyc/P5+cnJw4R1V/NGnSBIBly5ZRWFi4x/abN28mJUVJRWXE454lJiaSmZlZ+t+3phudM5Ubvn6dGeuDlY37JrXguE77061JJvs2aUO3xpl0bNSSBqGEPfQU8OIiWPAdPi0bn5YNMz+HgrxtDbofQei3b2IZ7Xbbj7XpSujPE4KR7SU/Er7pIMhbDeFi7Nhh2OVPYQkVi0lEtldVVcfjxsxaEyTYz7n7TWWOvwZMIEi2/1teMTR3HwGMABgwYIDHanP3spue7/vhRBYvX0/r3l0YktUvJv3XRZXZKF4CumeVp3tWebpnlad7Fn9NmjSpcEKWnZ3N/vvvX8UR1S26Z7v2Y+4SbvzmDcYsC0oPdG7UigcOPI/m8/M56pCj9ni9FxcHe1Uvn40vnwPL5+DLZsLsL6Fgw/aNM7tgvQZDn2Oww87BGlRsWr01bxtMI7//Z0G/gJ19ezDdXD+sikQtqkTbzAYDvyfYziMdKG+TPHf3eCTyw4AM4J0dggmb2T+BQwn26f44DrHRLjUdgKX5KogmIiIiUhetLFjPnVP/xbNzPiXsTtPEVG7vewbX9jiO5IREshdkl3udL5uNT3gTn/c1rJgDK+dD0S62om/dFes9BHoPwXoN3uPo9e5Y4+aEbv8Q/+efgsJnAy+Mui8RCVQ6ETazU4B3CYqWLAZmAUW7vah6ZUWey5vn0mCH52rXPi0oMqHK4yIiIiLx44VbscSkve5na3ERM9bnMDV3MVNzFzPlp0VMWjOP/KItJFiIa7sfy539zqJFSuNyr/e1OfgXb+GfvwHzv9m5QfMsaNMNa90teG7TDbochGVk7dx2L1hKGnbRX2Lap0h9Fk3CeRdBYbFT3H1sbMOpHDPbh2D99Tx3L1l4NT3yfDHwVZm2iQT7axcB31VjmNsp2eJrSf7uK0uKiIiISNUI/+9R/KXfQrteWJ9jsb7HQq/BWOqulxi4O6xZQsGcifww9X0+Sm/O22kpzFifQ2G4eKf2J2f1468HXkDPZjsnxA025xH+6Fn883/A9Gwo2aqsYWPskLNh/5Owtt2DUesUFc8VqY2iSbT3A96sqiTbzIYS7LMJ0BJIMrPbIq8XufurZZq/Agwm2H5sYeTYi8D1wNVm1g4YQ5CMXwT0BR5091VVEXtFaC9tERERkfjxTXn4O5Havkun40un46Mfg1ACdDtkW+Kd3jYoODb/G3zBt4Tnf0Now1qSgQFAj4QGPDvkFIpSG9O1cSb9m3egX/o+9Evfh/7NO5TOYtxReNRfOeT1m/GSfbETk+GAUwgdeQHsfzKW3LBa7oOIVK1oEu2NQFVmiZcRJM9l3RN5Hge8ym64e56ZHQrcQbCf54kEI/DTCNZvx3yPzsrQ1HERERGR+PEPnojsDz2Q0Pn34t9/hP/wEcyZBLO+wGd9EaxV3kEIWJuYxLdNm9PWEui9Oodvc1aTdPs/aJRUseTYp43DX/sD5kDf47AjL8AOPhNLaxrbDykicRdNov0xcFisAynh7kP2tm1kxPrayKNG2TZ1/CdtkyMiIiJSjbxgI/7ewwCEfn4H1nMg1nMgnHs3vikPZnxWmngX5q3mx2YZfJCUwNdNm/Nd03T6dTuSP/Y5jT7JzQj/tjdNZ36BffYaHHvFnt87fz3hJ34J7iwZMJROfyx3t1kRqSOiSbT/CHwVmc59r3vJohLZkc//Fh/zFFmbkyCytUuzpFRSGySzsWgzeYUFNE1KjW+QIiIiIvWEf/h32LAWuh0KfY7Z7twqc8a3aMX43gfwWYs0puQuAiAxlMBFnY5gdO9T6FVmvbVd9jj+6Pn4Kzfg/U/EWrTf/Xu/cF2wVVeXg1g84Jd0iv3HE5EaJJpE+06Cadh3A5ea2RRgXTnt3N0v25vgar281fgnz5PRtm/pITOjfWpzZuUtZ+mmn5Roi4iIiFQD31KAj/orAHb2bczfuIrPVs7k81WzGb9yFnM2rNiufePEFK7sdjTDe51IVmRGYll2+Ln4hDdh8n8IPzOM0C3v73Kmon/5Dv7Zq5DUkNB1r+Jzlsf+A4pIjRJNon1xmT93jDzK4wTrreuvrJ4ANMxdvN3hdpFEe0n+Wno3i37PQxERERGpmK0fPk2D9StZnNmRYxZ+yIJp/9jufFqDZA5r2Y2BrbozMLM7h7ToQmqD5F32Z2aErnia8PTPYMoH+LhXsCG/2qmdr80h/MyVwTW/fAjL6g5KtEXqvGgSbc10qaiMdpCcRlLBOnzDWqxxUAitXZoqj4uIiIhUtfkbVvF+zhTGLv6GJ976M1nA8Pb7sCB/DRnJjRiU2YOBrbpzZKt92b95RxqEEirVv6W3wS55FH/iV/hLw/G+x2HN25ae93CY8FOXQH5uUFH8+Kti/AlFpKaqdKLt7ouqIpC6yEIhaNsdFnwLOTOhxxEAtC/Z4itfibaIiIhIrBQUbeWzlTP5YNn3jM6Zyqy8YOR42MLZZG3exNz0VvQ//lpuabc/AzI6kxAK7fV72qCh+IS34Lv3CT97NaE/vFs6hdzHPAnffwiNWxC6+nkVwRWpR6IZ0S5lZmnAvkAjdx8fm5DqFmvXE1/wLZ4zA4sk2hrRFhEREdl77s6cDSv4IOd7Plj2PdkrZlBQvLX0fNPEVE7O7MUD4z8GoNvFj3P3/ufENAYzI3TlM4R/2xu+HoV//g9s4AX4kun4q38AIHTlCCy9dUzfV0RqtqgSbTNrB/wNOBVIIFiP3SBy7khgBHCNu2fHJsxaLLJOm6UzSg+V3eJLREREpD7yDWvx0Y9jR12CtexQqWvXbc3nLz++x9sLJzF/46rtzh3QvCMnZfXjhLZ9OKxlN0LZL+PrVkBWD+yQs2P5EUpZRjvsVw/jf78cf+E6vNcgwo9fBIWbg893yJlV8r4iUnNVOtE2szbAJCATGAW0Yvt9tSdFjp0LZO99iLWbteuJA54zs/RYSaKtEW0RERGpj9yd8OND4bvR+LfvE7r3Syxhz+ujC8NFPDP7E+6aOpK1WzYC0DypESe07cOJWX05oW1fMhs23fY+xUWER94HgJ11a4XeI1p29KX4l2/D1LGE/3AA5K2GVp2wS/5WZe8pIjVXtNt7tQKOc/dPzexOyiTa7l5oZuOBI2IUY+1WMqKds21Eu31aUBRNibaIiIjUR/7Rs/Dd6ODFvMn4B09gp1y/6/buvJ8zhRu/+Qcz1y8DYEhmT+7p/3MOa9ltl2utfcKbsHIeZHbBjjgv5p+jrGAK+QjCv9svSLItROi6V7GGjav0fUWkZoqmAsTJwCh3/3Q3bRYDbXdzvv7I7IJbCFYvxLcUAJCelEbDhCTyCgvI27opzgGKiIiIVB9fMQ9/+XcA2NHBTrD+j1vx1YvLbf9D7hJO+Ogv/OyTh5i5fhldG2fy7yHD+eT4Wzgys/uuk+ziYnzkvcH7nHULlrBXpYkqxFp2KB3BtnPuLK3PIyL1TzT/x8kE5uyhTSGQFkXfdY4lJlHQNIvUdUtg2Szo1B8zo11qc+ZsWMHSTbn0SkqNd5giIiIiVc6Liwk/8UvYko8dcR6hq5+jeNN6mPhPws9dQ+im/5ZW5l5ZsJ47pvyT5+ZmE3anWVIqd/Q9k193P46kCiTNPulfwa4vLTtgg4ZW9UcrFTr6UvzgM7FG6dX2niJS80STaP8EtN9Dm32BFVH0XSdtSu9A6rolQeXxTv0BaJ9Wkmj/RK9mWXGOUERERKTq+agHYNYXkN4Wu+xJAEKXPkb4+w/h2//hX77D6v1P4MFp/+PJWR9RULyVBAtxXY/juLPvmWSkBNOwPW8N4ccuhAXfQdNW0KQV1qRl8OfIw99/DAA74yasQWK1fk4l2SISTaI9ATjNzFq7+07JtJl1A04EXtvb4OqKTen7wAKCX1UjtlUeXxunqERERESqjy+Ygr91JwCha17AGgffhSy9DXbRA/iIK8kbMYz9h/yMZZHZ4Ke3P5D7DziXHk23rUj0dSsJ/+lYWPJjcCBvNTANL+9Nm2dhR11SdR9KRGQXokm0HwROB8aZ2XAgFUr31B4EPAKEgYdiFWRtV9A8smVFmYJo2ktbREREajN3x18cTt/vPsbTH8b6Hb/rtls3B1XGiwuxE67B+p9Qem7t5g083LwxJ2Zkcvjaldzxw0TeO+Vq7up3FgdmdNq+n9zlhO8+Ohi8yOpB6IZ/QbgY8lbh61dBySNvFb4xl9BxV2KJyVV2D0REdqXSiba7TzKzK4GngffKnMqLPBcBl7r7tBjEVydsarYPAF5mL+32qZHK49pLW0RERGoh//AZfPRjNAXCfz4BBpxG6FcPY6277Nz2zduDEeg23bCLHiC/cDNTchfzfs4UHp85lg2FmxnZ5yC++2w0ly+ex7DMAdiOSfbapUGSvXwOtN+P0B0fYc0yS89bVX9gEZFKiKr8oru/ENnC6xrgUCADWA9MBJ5w91mxC7H2K0gPEm2Wz8aLi7CEBqUj2ks2aeq4iIiI1C6+6Hv8peEArOxxApkLJ8DXowhP+QD72e+CKt+Rba02ff8hSe89hIdC3DvoTN4Z8ydm5i0j7Nsme5/Qtg939TubpFYD8LfvIvzMMEJ/nYolpQTvt3pRkGSvnA8d+xO6/UOsSYvq/+AiIhUU9T4H7j4H+G0MY6mzipNSIaMdrF0KqxZCm66la7SXbsqNb3AiIiIileAFGwk/ci4UbsGOvozZPS+iTb/u+Os34+Next+9Hx/3MgtPv4ErC1fzzMgn6OjOvV17c/emHAAaWAJ90rMYkNGJS7oO5ohW+wZ9n3ETPuEtyJmBj7wPO+9P+Mr5QZK9ehF0GUDo1jGl67tFRGqqqt9QUAJZPYNEO2cGtOlK+5JEW1PHRUREpBbx568N1ki364Vd+hh8+VVQ0Ozal/ATrqb4+euweZPp+NKNvJGYREbhVqZntCbnpGt4qlU3DszoRN/09qQkJO3UtyUmE7pyBOE7BgYJe+cDCL9wXfAdqtuhhG79AEtrGodPLSJSOaF4B1BfWFYPADxSEK15ciNSEhJZX7iJDYUF8QxNREREpELC417Fx70MSQ0J/e5tLDl1u/PfNW/FAYcdxSX9D2N5cgoZhVvxxBT2u+0TRgy8mqu7H8vBLbqUm2SXsJ5HYsddCcWFhB88M0iyexxJ6LYxSrJFpNbQiHZ1yeoZPEcKopkZ7VKbM3fDSpbm/0RP7aUtIiIiNZjnzMKfvRoAu/QxrH3v0nNbi4u494f/cO8P/6HYw2zqdSiLL3yMtj+MI9ShH9auZ6Xeyy68H/96FOQuh95HEfrjKKxho5h+HhGRqqREu5pYu54420a0AdqnZQSJ9iYl2iIiIlJz+dbNwbrsLfnYEedhR19Wem7u1rUMf/8OpuYuBuA3PY7nvv1/QVpiCnQcENX7WVozQrd/iE8Zgx1/1U4j5yIiNZ0S7epSMqKdMxN3Lx3RBliiddoiIiJSg/krN8KiqZDZBRv2DGbG1uIi/u/HUdyz4l2KcTo3asULh1/B4NaVG73eFWvfe7tRcxGR2kSJdnVp2grS0iE/F9atgPQ2dGncCoCZecviHJyIiIhI+XzSSHzMk5CQSOi3b2KpTZi4ei6Xf/kc09YtBeDX3Y/j/gPOpVFiSpyjFRGpGZRoVxMzg6weMPvLYJ12ehv6RfbXnvLTojhHJyIiIhJwd8hbAyvn4cvn4C/+BgAb+iD5++zHbZNf5bEZY3Gcro0z+XXDAxl+yAVxjlpEpGaJWaJtZhnAIGAT8JG7F8eq77rCsnris7/Ec2ZgfY6mf/MOAEzJXVw6nVxERESkunhxEf7lO7BwKr5yHqycByvmQUHe9g0HnMZH/Y9i2KibWJS/hgQLcWOvU7iz31lM+vyL+AQvIlKDVTrRNrOrgYuBk9z9p8ixA4EPgOaRZl+b2dHunh+rQOuEdtvWaQN0SGtB08RUVm/OY0XBOtqkpscxOBEREalPfNVCwo9dBLMm7HyyYRNo3QUyu1CQ1Z0bWmYw4uMHANi/eQeeO+xyDsjoVM0Ri4jUHtGMaJ8LeEmSHfEgkA68CGQCpwBXAQ9VtnMzuxk4ADgQ6AQscveOUfSTCtwYibczUADMAh5w939Xtr9YsKztK4+bGf2a78NnK2cyNXexEm0RERGpFuHxr+PPXhOMXKe3xY4bBq27YplBck2TFpgZ7y7+misnvsCqnIWkJCRyV7+z+F2vk0gMafWhiMjuRPN/yW7A/0pemFkLYDDwnLtfGTk2CbiAKBJt4D7gJ+BboFkU12Nm6cDHkVhfBB4G0oCeQIdo+oyJrB7B89JtW3z1S9+WaJ+Y1S9OgYmIiEh94Pnr8eeuwT9/Izhw8JmErnoWa5yxXbuNhZv57eTXeG5uNgCDM3vw7GGX061J62qOWESkdoom0c4AVpV5fUTkuewo8XiC6eXR6OLu8wHM7EegURR9PAZ0BQ5x9+lRxhF7LTtCYjLkLsPz12NpTVUQTURERKqFz/ic8OMXwepFkJyKXfIYdvSlO9WImbxmHheMf4q5G1aSHErkgQPP49oexxGyUJwiFxGpfaJJtH8CWpR5PRgIA2UrYTgQ1f4OJUl2tMysI8Fo+qPuPt3MEoCG7r5xb/qNBUtIgLbdYdH3sGwmdDuktCDa1NzFcY5ORERE6iIvKsT/+Sd85H3gYegygNBvXsfa7rtdu+JwmPt//C93TR1JkRfTp1l73hh4Dfult49T5CIitVc0P03OAE41swwzawacB0x297LlKTsCK2IQXzROJPhc083sVYIq6BvMbKmZ/TZOMZWyrKAgmkcKovVulkWChZiVt5yCoq3xDE1ERETqEN+cT/ijZwn/4QD8X38GHDvzFkJ//mKnJHvRxjUcNfZebpvyDkVezG97nshXp9ytJFtEJErRjGj/DXgXWAoUAanAH3Zocyjw1d6FFrXukef/A9YQFGXbGnl+2Myaufud5V1oZsOAYQCZmZlkZ2fHJKCNGzeW9rVPYUM6AIu/HMPCyHLx9g2asrAwl5c//g89klvG5D1ru7L3TCpG96zydM8qT/es8ur6PTOzEHA9cCXBD+2rgbeBOyq6+4iZNQduAc4A2gEbgB8jfYyvgrDrNF86Ax/7NJ798rZtulrsQ+jaV7Deg3dq/8b8L7h60ovkFRbQpmEzXjriSo5v26eaoxYRqVsqnWi7+ygzu4pIQgq87u6vlZw3syEE66rHxCTCymsceU4CBrr72khcbwPTgT+Y2aPunrvjhe4+AhgBMGDAAB8yZEhMAsrOzqakr3DiSnzyS7QP5dMxcuzw8dNZuOALEjq1YMi+sXnP2q7sPZOK0T2rPN2zytM9q7x6cM8eAX5DUKvlIYLCo78B9jezY909vLuLzawDkE3w3eF5YDbQFOgLZFVd2HWLF27FJ7+Lj3kKpo/bdqL74djxV2OH/hxL2n5V37qt+fx60su8sSBY/XdG+wN59rDLaZHSGBER2TtR7c1QNiEt51w2wVZf8VIQeX6vJMkGcPdCM3sDuINgxH10PIKzdsEWX+RsqzzeP30f3ljwhdZpi4hIrWJmvYHrgJHufnaZ4wsICpOeB7yxh25eI/g+0tfdl1dVrHWZfzea8JOXwPqVwYHkNGzQRUGC3bH8HU0+WzmToZ8/zeL8taQ2SObRARdxebchOxVGExGR6NTFTRCXRp7LWyNe8g94/H4IaLMvWAhWzscLt2CJyfSLFERT5XEREallzgcMeHSH488C9wMXsZtE28wGAUcCv3H35WaWCCS6+6YqirfOcXfCz/06SLLb98ZOuAYbeBGW2qTc9luLi7hz6r/4y4/v4TgHZXTm9YHXaNsuEZEYi1mibWYZwCCC4mMfuXtxrPqupJK14e3KOVdybFU556qFJaVAq06wch6smAvte5du8fX9usWEPaztM0REpLY4iGDnke3qsrj7ZjObEjm/OydHnheb2X+Bk4AEM5sD/Kns0jTZhVlfwKoFkN6W0INTgx1OdtV0/TIu/Pxpvlm7gJAZt+53Onf0O5PEUF0cdxERia9K/5/VzK4m2CP7JHf/KXLsQOADoHmk2ddmdnRFi6BEy8z2ISjGNs/dCyOHPwMWEVRGz3L3nEjbNOCXwDrgy6qMa4/a9QwS7aUzoH1vMhs2pXXDpqwoWM/CjWvo3LhVXMMTERGpoLbAGnffUs65HOBwM0ty911tq1FSwPRZYA7wK4IaKzcAr5pZoru/WN6F1VHAtDbomv0IbYAlHQeycHz5dePcnf9unMlT6yayxYtpndCIWzKG0Gd9SyZ89vlex1Db7llNoHtWebpn0dF9q7xY3bNofsI8F/CSJDviQYLp2C8CmcApBFW+H6ps52Y2FCLluKElkGRmt0VeL3L3V8s0f4VgH+9OwEKCwIrN7BpgFPClmT1FUHX8EqA9cFlV/wCwJ9a2B/7Ne3jODEpWQvVP78AHBd8z5adFSrRFRKS2SAXKS7IBNpdps6tEu6Tq1gbgqJKE3MzeBeYD95nZy+UVVKuOAqY1nRduIfzSWQB0uPBWOnbYuVJ4cTjMBeOf5O3cSQAM7Xwkjx/8S5ompcYsjtp0z2oK3bPK0z2Lju5b5cXqnkUzR7kb8H3JCzNrQZDsPu/ul7v7qcBk4IIoY7oMuCfyaAU0K/P6sop04O7vA8cAc4FbgT8DecBp7v5ClHHFTrtgL22WbiuIVjJ9XAXRRESkFtkEJO/iXEqZNrtSUsD0H2VHvSM7g4wCWrNt1Ft29N1oyM+FDn2xcpJsgPt+/A9vL5pE08RU3hx4La8ceVVMk2wRESlfNCPaGWy/xvmIyPO/yxwbTzC9vNLcfUgs2rr7OODoaGKoapYVVB73ZTNLj/VrHiTaKogmIiK1yDKgl5kllzN9PItgWvmuRrOhphcwreHCnwWT/GzQ0HLPj1sxg7umjsQw3hl8Hcdpb2wRkWoTzYj2T0CLMq8HExRC+aLMMWfbL9myo6wewXPOTDwczIbrnx7MlteItoiI1CKTCb5LHFz2oJmlAP2Br/dwfY0uYFqT+cZc+OY9MMOOOH+n86s353HB+KcIu3Nzn1OVZIuIVLNoEu0ZBIXGMsysGcEemZPdPa9Mm46U/+u0ANYoHZpmwtYCWBMk1t2atCYlIZFF+WvI3RLXJeQiIiJbuqmxAAAgAElEQVQV9RbBj+vDdzh+BcHa7NdLDphZFzPrsUO7dwnWZ19kZo3KtG0DnAHMdve5VRF4bedfvgNFW2G/Y7CMrO3OhT3M0M+fZllBLgNbdefufmfvohcREakq0STafwPaEEz3WkJQ/OypHdocCkzdu9DquJJ12jnBOu0GoQT6NGsPwPca1RYRkVrA3X8AngTOMrORZna5mT0EPAyMY/s9tD8m+LG+7PW5wI0E08wnmtnvzOwmYCJB9fHrquFj1Eo+Ptj5zAZdtNO5B6f9jzHLfiAjuRFvDLyGBqFdb/klIiJVo9KJtruPIqgoPg2YBdxYdp9LMxsCNALGxCjGOsmygkTbc8qs044URJuSq3XaIiJSawwnSJZ7EyTd5wGPAz8rr1r4jiLVw88GNhIUPr2V4PvFUe4+tqqCrs181UKYMR6SGmKHnLXduQmrZnPrd+8A8MoRV9EuLSMOEYqISDTF0LbbUqOcc9mocMmelVN5vH/zyDrtnzSiLSIitYO7FxNs57nbLT3dveNuzo0ERsY2srrLxwcz8u2gM7CGjUuPr928gfM+e4JiD/P73qdwcrv+8QpRRKTei2bq+HbMrLGZtTezJrEIqL7YNqK98xZfGtEWERGR8rg7XlJtfPDQ7Y5f/MUIlm76iUNbdOXe/c+JV4giIkKUibaZNTCzm8xsLrAOWAjkmtncyPGoRsrrldLK49sS7b7pwRrtaetyKAwXxSMqERERqcnmfQ3LZkHTVtD3uNLDj8wYzXtLvyM9KY03B11LYkhfxURE4qnSibaZJQFjgXsJqosvIdieY0nk9b3AR5F2sivNs6BhY9iwFl+/GoAmSal0btSKreEiZq1fvocOREREpL7xzyJF0I44H0sIkulJq+fyx2/eAuDFw4fRoVGLXV4vIiLVI5oR7d8BQ4D/AT3dvaO7HxZZe9Ud+C8wMNJOdsHMoG1kVHvZtoJoJeu0NX1cREREyvKiQnzCP4Bt1cYXbVzDmdmPUuTFXN/zBE7f58B4higiIhHRJNoXAD8CZ7j7nLIn3H0ecBZBRfIL9z68us0iBdF86fTSYyXrtFUQTUREYsnMpprZ1WbWeM+tpUb6/kPIWx0sP+t8ID9t2chJHz/A8oJ1DMnsyQMHnB/vCEVEJCKaRLsrMHpXW3ZEjo8GuuxNYPVCh37B87xvSg/1b15SEE2JtoiIxFQv4AlgmZk9a2YD4h2QVE5pEbRBQ9kSLuSMTx9hxvpl9G7Wjn8fNZykBK3LFhGpKaJJtLcS7JO9O2lAYRR91yvW9SAAfN7k0mP90iNbfOUuxt3jEpeIiNRJ7YDbgdXAZcAkM/vazK4ws7T4hiZ74pvy8K/eDf58xHn88vO/M37VLNo2TGf0Mb+nWZL+E4qI1CTRJNrfAz83s5blnTSzFsDPgal7E1i90OkAsBAs/gHfUgDAPmkZNEtKZfXmPJYXrItzgCIiUle4+0p3v8/dOwMnAe8CfYG/E4xyP2Vm2ni5hvJJI6FwM/QcxB+WfMk7i76icWIKo4/9Pe3TMuIdnoiI7CCaRPsJoCXwlZldZmadzayhmXUys0uASZHzT8Qy0LrIUtKgXS8IF8PCKcExs23rtDV9XEREqoC7j3H3s4H2BKPca4ArgW/MbKKZXWxmKXENUrbj44Nq45907c/D00fTwBL495Dh9I18ZxARkZql0om2u78N3A90AEYAc4CNwFzgOaAT8GCkneyBdQmWyPn8r0uPlSTaU35S5XEREak67r4S+D+CnUKWAQYcDDwPLDGz4XEMTyJ87VL48ROKExL5ReEKAF44/AqOabNfnCMTEZFdiWZEG3e/BTgceAH4DpgfeX4BOMLdb4pZhHVdZJ02c7et0y7Z4ksj2iIiUlXMLMvM7gQWASOB1sAo4AzgHqAYeMjM7olflOLu+Kt/AHdGtWrLusRE7tv/FwztcmS8QxMRkd2odHlKMxsE5Ln7RGBi7EOqX6zLQTg7FkTT1HEREYk9MzOC9dlXRp4bACuB+4AR7r4k0nSUmT0EfExQOO32OIQrgP/nAXzCP9jYIJE7u+/H1fsew037nRrvsEREZA+iGdH+FBgW60DqrQ59ISERls3CN+UB0KtZFg0sgdl5y9lUtCXOAYqISF1gZrcDC4D/AqcCXwDnAe3d/fYySTYA7r4h0jazumOVgH/zP/yNmwEYuv9hdO55FI8d/EuC30tERKQmiybRXgMUxDqQ+soSk6FjP3CH+cF+2ikJSfRo2oawOz+uWxrnCEVEpI64G2gGPAXs5+5D3P1tdy/azTXfAK9US3SyHV86g/DfLgB37uzelw+zOvPMoZfSIJQQ79BERKQCokm0swnWZ0uMWJed99MuWaetgmgiIhIjVwFZ7n6du0+vyAXu/r67X1LFcckOfGMu4QdOh4I8JnTuy73d9uOa7sfSNjU93qGJiEgFRZNo3wZ0N7N7zCwx1gHVS5GCaD5X67RFRKRquPsId8+Pdxyye15cTPjR82H5HDa168FJ3XuSlpjCH3v/LN6hiYhIJVS6GBpwM/AjcAtwmZlNBVYAvkM7d/fL9jK+eqGkIBplR7TTNaItIiKxY2YHAD8Dnols67Xj+dYENVhGufuU6o5PAv76H2HqGGjcgqsGnsamjTnc0vMEWjVsGu/QRESkEqJJtC8u8+fWkUd5nKBSqexJVk9IToPVi/D1q7GmLenXPBjR/j53CWEPE7KodmITEREpcSNwJMHWXeVZSfDvdlfgl9UVlGwTHvcq/t+HIKEBUy99iDfmj6VpYio39jol3qGJiEglRZO9dargo3OMYqzzLCEBOh8QvIiMardMaULbhulsLNrMnLwVcYxORETqiMOAT919xxloQDANDfgEOKJaoxIAfM5X+DNXAGCXPMZvNiwE4IbeJ5GenBbHyEREJBqVTrTdfVFFH1URcF1VXkG0I1vtC8CnK2bEJSYREalTWgN72spiGdCmGmKRMjx/HeEHz4TCLdhxVzF2v8OZsHo2GcmNuL7HCfEOT0REolDj5iOb2c1m9o6ZzTczN7OFe9lfyMy+jPT1XozCjL1yCqId3aYXAJ+smBaXkEREpE7ZBLTcQ5uWwJZqiEXK8P88CLnLoNuhcMmj3DblnwDctN+pNElKjXN0IiISjUon2mZ2jpl9YmZtd3E+y8w+NrOzoozpPuBoYB6QG2UfZV0D7BeDfqpUyYg28yZTMqvv6Na9gWBEO+zheIUmIiJ1wxTgdDNrVN5JM2sCnB5pJ9XEc5fj/3sEgNDFjzBq+Q98s3YBrRs25Zrux8Y5OhERiVY0I9qXA83cfVl5J909B2gaaReNLu6e4e7HEUxhi5qZtSNI3O/cm36qRWZnaNQc1q+CNUsA6No4k/apGazZsoEfcpfEOUAREanlRhCMWH9oZn3LnjCzfsBYoEWknVQT/+c9sLUADjoD73Ywt0dGs2/tczqpDZLjHJ2IiEQrmkS7D/D1HtpMBvruoU253H1+NNftwpPAfOBvMeyzSpgZdBkQvIis0zazMtPHp8crNBERqQPc/S3gFeAQ4DszW2Zmk81sGfAtcDDwqrv/I55x1ie+fC7+8bNgIULn38vbCyfx47qltE/N4IpuR8U7PBER2QvRJNrNgVV7aLOW4FfxuDGznwOnAle5e3E8Y6mo0oJoZddpt1aiLSIiseHuFwNXAdMJiqMdGHmeBgyLnJdq4m/dDsVF2JBfUZzVnTun/guAO/qdQXJCYpyjExGRvRHNPtprgG57aNMNWBdF3zFhZk2Bx4Bn3H1iJa4bBgwDyMzMJDs7OybxbNy4sUJ9ZRSk0AvI/XosP7Q7EYCGRfkAfJLzIx9/+gkJ9WQ/7YreM9lG96zydM8qT/es8mraPXP3EcAIM0sFmgHr3H1TnMOqd3zBd/iENyExGTvnLl6bP4HZeSvo0rgVv+oyMN7hiYjIXoom0Z4AnGZmPdx95o4nzawnQTGV/+5tcHvhAYLR+psrc1HJlw+AAQMG+JAhQ2ISTHZ2NhXpy/t0Izz6dprlzmfwoEFYKEiq9333U2bnrSBtv304tGXXmMRU01X0nsk2umeVp3tWebpnlVdT71kkuVaCHSfh14OvKHbCryls3pa7xwcF0e7qdxaJoWi+nomISE0SzfDoXwkS9M/N7Ddmtq+ZpUWerwfGAwmRdtXOzAYCVwA3uHvcRtWjYRlZkN4GNq2HFXNLj5dUH/94ubb5EhERqe38x09h6hho2AQ76xbeWjiRhRtX06NpW87veHi8wxMRkRiodKLt7pMJtsxqAjwCzADyIs8PR45f7e6TYhhnZTwBTAUmmVnXkkfkXGrkdVzXj+9WyTrtedvWaR/TJki0tZ+2iIjsjcgP4783s4/MbIaZzS/nMS/ecdZl7k749ZsAsNN/D42a89D09wH4fa9TSAjVjyViIiJ1XVRzk9z9WTP7nCDhPoTIGi9gIvC0u8+IXYiV1oFge7E55Zw7KnL8SeDa6gyqoqzrQfjXo2DuZBh4IQBDMnsCMGHVHDYXbyUlISmeIYqISC1kZs2Az4FeBD+QNwHWA0lAw0izZUBhXAKsL756F+Z+BU0zsZOH8/HyaUzNXUxmSlMu7KzRbBGRuiLqRUCRZPq6GMZSaWa2D5AKzHP3ki8GvyT40rCjd4BvgPuBueWcrxGsy0E4249ot0hpTL/0fZiau5gvVs3h6MgIt4iISCXcRpBkXwa8BBQTzEy7h+BH8yeAfOCEOMVX53lxEeF/3AKA/fx2rGEjHpoQjGZf1+N4VRoXEalDaly1DTMbSjAqDdASSDKz2yKvF7n7q2WavwIMBjoBCwHcfdQu+gVY4e7/rIKwY6dkL+0F3+HFRVhC8J/omDa9mZq7mE9WTFeiLSIi0TgN+MzdX4TSfxdxdwcmmtnJwA/ArcDt8QqyLvNxr0DOTMjsjB1zBT/mLuGDZd/TMCGJq/Y9Ot7hiYhIDNXEhUCXEfy6fg/QimBaesnry+IYV7WwxhmQ2Rm2FsCSbWuytZ+2iIjspfYEM7tKhIHkkhfuvgoYDZxXzXHVC76lAH/7TgDs3HuwxCQenj4agEu7DiYjpXE8wxMRkRircSPa7j6kitpaNPHEg3U5CF85H583GevYD4BBmT1IsBBfrZlH3tZNNElKjW+QIiJS22wiSK5LrAda79BmJZBVbRHVIz72KVi7FDr0w444j+WbcnltwQQMY3hPzdYXEalrauKItnQNKo8zd9s67caJDTm4RReKPcz4VbPiFJiIiNRiSwhGtUtMBwaZWdnvAkcCK6o1qnrA16/C//VnAEIX/h8WCvHErA8pDBdz5j4D6Npkx987RESktlOiXQNZOVt8gaaPi4jIXhkHDLaSxdnwFtAFeN/Mfm1m7wCHAu/HK8C6yl/9A+Svg/4nQv8TyS/czNOzPgbghl4nxTk6ERGpCkq0a6JOB4CFYPEP+NbNpYdL9tP+eLn20xYRkUp7GXgXaBd5/ffI6+OBx4GzgS8IqpNLjPj0z/BxL0NiMqFLH8fMeHHeZ+Ruzeewlt04vNW+8Q5RRESqgBLtGsgaNoJ2PaG4CBZOKT1+WMuuJIcSmZq7mDWbN8QxQhERqW3c/Vt3v9rdl0ReF7n7WcBBwPnAYcBgd18XzzjrEi8qJPzcNQDYmTdjbbpSHA7zyPQPAI1mi4jUZUq0a6jypo+nJCRxRKtuAGSvnBGXuEREpHYys0Fm1n/H4+7+jbu/5e6T3D1c3rUSHR/9WLCDSGYX7PQ/AvDukq+Zv3EVnRu14oz2A+IcoYiIVJU9Vh03sxei7Nvdvc5vx1Vluh4E2S9tVxANgunjn6yYzsfLp/HzDgfHJzYREamNPgWeAa6JZaeRYmrXA1cCHYHVwNvAHe6eX4HrfRen8t29UazirG6+din+VrCdV+iyx7GkFAD+Oi1YAv/bXieSENJ4h4hIXVWR7b0ujrJvpx7se11VrMtBOOUVROsNvMMnK7ROW0REKmUNUFAF/T4C/Ab4N/AQ0DPyen8zO7aCo+TjgRE7HCuMaZTVLPzSb2FLPhxyNrZ/MEX8i1WzmbhmLulJaVzSZVCcIxQRkapUkUS7U5VHITvr0BcSEmHZLHzDT1jj5gAMyOhE48QUZuetYGn+WtqlZcQ5UBERqSWygcNj2aGZ9QauA0a6+9llji8AHgPOA96oQFfz3f21WMYWTz5lDEz8JySnEbr4kdLjf50ejGZf0/1Y0hJT4hWeiIhUgz3OWXL3RdE+quMD1FWWmAy9BoM7/q97So83CCUwOLMnoG2+RESkUm4DupvZPWaWGKM+zwcMeHSH488Cm4CLKtqRmSWZWa2dKl7Ct24m/Py1ANg5d2Itgq3L5+at4N3F35AUasC1PY6LZ4giIlINtDioBgsNfQAshI9+HF/0Q+lx7actIiJRuBn4EbgFWGRmo83sRTN7YYfH85Xo8yAgDHxV9qC7bwamRM5XxM8JEvMNZrbKzB43s6aViKPG8FEPwoq50K4Xdsrw0uOPzPgAx7mo8xG0btgsjhGKiEh1qMjU8XKZWRvgGCALSC6nibv7PeUclwqyTvtjx1+Fj3mK8PPXEro7GzPblmgvn467Y2ZxjlRERGqBi8v8uXXkUZ7K1FhpC6xx9y3lnMsBDjezJHffups+vgLeAeYCTYCTgWuBwWZ2uLtvLO8iMxsGDAPIzMwkOzu7giHv3saNG6PuK2V9Dgf+8x5CwPcDrmD95xMAKPYwr+WMB+CIjRkxi7Wm2Jt7Vl/pnlWe7ll0dN8qL1b3LKpE28zuBm7a4Xoj+Me57J+VaO8lO+/P+Bdvw4zP8M/fwAZeSJ/09rRIbsySTWuZu2El3Zrs6ruSiIhIqaqouZIKlJdkA2wu02aXiba7H7LDoVfM7HvgXoJq5vfu4roRRAqoDRgwwIcMGVLxqHcjOzubaPpyd8L/9zMoLsQGDWX/C7eNZk9eM4+8JVvo3KgVlx5/VkzirEmivWf1me5Z5emeRUf3rfJidc8qPXXczC4EbieoEPpzgqT6ZeACgjVZYeBN4Oi9jk6wRunYRX8BwF+5Ed+UR8hCHNW6ZJ22qo+LiMieVVGNlU2UP6sNIKVMm8p6kCA5PyWKa+Pjm/fgu/chtSk29MHtTo1d9iMAx7ftE4/IREQkDqJZo301sBQ40d3/HTm20N3fdPergJ8BvyCY/iUxYEMuhm6HwLoV+Dt3AyXbfAXTx0VEROJkGdDCzMpLtrMIppXvbtp4udy9sKTvvYyv2vgXbwFgZ96MNcvc7tzY5UGdlePb7lftcYmISHxEk2j3Ad5396IyxxJK/uDuY4AxwO/3MjaJsFCI0GVPghn+/t/wJdM4tk2QaL+fM5X1W6MZLBARkfrEzPap6KMS3U4m+C5x8A7vlQL0B76OMtYUoB2wMprr48EXfAeA7bf9hL4NhQV8sWoOITOOitRYERGRui+aRDsRWFvmdQGwY2XQH4F+0QYlO7MuB2LHXgnhYsLPX0uXxpkMyezJxqLNPDcnO97hiYhIzbcQWFCBx/xK9PkWQU2W4Tscv4JgbfbrJQfMrIuZ9SjbyMwydtHvPQR1YP5biVjixrdsgpyZEEqAfbafHj5u5UyKvJhDWnSlWVJanCIUEZHqFk0xtOVAmzKvFwN9d2jTFihCYsrOvxef+A5My8a/eIvf9TqJ7JUzeGzmGK7veQINQgl77kREROqrV9hWtLSsZgSjzx2AbKDCa7Td/QczexK41sxGAu8DPYHfAOOAN8o0/zjyHmW3yrjNzA4FPiX4PtGIoOr4UcAk4PGKxhJXi74HD0O7/bCklO1OjV0WmTbeRtPGRUTqk2gS7e+Asv9afAIMM7OhwEhgCEGRtAl7HZ1sxxo3xy68H//7FfjLN3Dyo9Pp1rg1czasYOTiyfyi46HxDlFERGood794V+fMLERQ6PQq4FeV7Ho4wWj5MILiZWsIEuQ73D28h2uzgV6R98wAioE5wK3Aw5H9uGu80mnjnQ/Y6Vxpoq1CaCIi9Uo0U8ffA/Yzs5JtQu4H1gMvAXnAKIJfq2+LRYCyPTvqUuh6MOQuw/51L8N7ngDAI9M/iHNkIiJSW7l72N3vJkiY76/ktcXu/pC7d3f3ZHfPcvff7bj/tbt3dHfb4dh/3P2EyDUp7p7m7v3d/b7akmQDEEm06bT/docXbVzDrLzlNElsyMEtusQhMBERiZdKJ9ru/pK7p7r7gsjrJcBBwNPAWII9LQ9y94kxjVSASGG0yyOF0f73CBcntyA9KY2Ja+by5eo58Q5PRERqty+A4+MdRG3jCyMj2h23T7Q/jFQbP6ZNby3vEhGpZ6IZ0d6Juy9w92vd/SR3v9rdf4hFv1I+6zIAO+YKKC4i+dU/cOW+QYXTR6aPjnNkIiJSyzUHVLGrEryoMFijDdCx/3bnSqaNH6f12SIi9U5MEm2pfnbBfZDWDH74iN+Fk2lgCfxr8WQWblwd79BERKQWMrNjgXMJdg6RisqZAUVbIbMLlrZtE5bicJiPlk8DtD5bRKQ+qnSibWbnmNknZtZ2F+ezzOxjMztr78OTXbHGGdipNwLQfORfOLfDwYTdeXzm2DhHJiIiNVHk3+7yHp+Z2QJgDJAM/CnOodYqvov12d/+tIDcrfl0btSKLo0z4xCZiIjEUzQj2pcDzdx9WXkn3T2HYF/ty/cmMNkzO/l6aNIS5kzkzuJkAJ6bk03e1k1xjkxERGqgIbt4HAE0Jki0j3P39+IQW+1VUnF8h0R77LJgYoBGs0VE6qdoEu0+wNd7aDOZnffWlhizho2wM28GoPP/nmBwq33JKyzghbmfxTkyERGpadw9tItHgru3cPeT3f2TeMdZ2/iuEu3lJdt6aX22iEh9FE2i3RxYtYc2a4EWUfSNmd1sZu+Y2XwzczNbWMnrsyJ9jDOz5WaWb2bTzOxBM8uIJqaazI6/GjLawaKp/GVrIgB/mzmG4vCeti4VERGRveHhMCwsmTq+bQ/tDYUFfLFqDgkW4qjWveIUnYiIxFM0ifYaoNse2nQD1kXRN8B9wNHAPCA3iutPBe4iSPYfBIYTbFcyHJhiZq2jjKtGsqQU7OzbARjw0Uvsm9aChRtX8+6SPU06EBERkb2ycj4UbID0Nlizbeuws1fMoMiLObhFF5olqYi7iEh9FE2iPQE4zcx6lHfSzHoCpwPjo4ypi7tnuPtxQLnrwPdgPNDB3c9y94fd/Vl3vwK4GmgH3BhlXDWWHXUJZHaB5bN5YnOwT+cj0z+Ic1QiIlKTmNltZla4h2KmW83sj9UdW61VMprdccf12ZFp49rWS0Sk3oom0f4r0AD43Mx+Y2b7mlla5Pl6gkQ3IdKu0tx9fjTXlbl+mruvKOfUW5HnOvevnjVIxH5xFwBDPv8nLUPJTFg9m6/WzItvYCIiUpOcCmTvoZjpp8AZ1RpVLbbn9dkqhCYiUl9VOtF298nANUAT4BFgBpAXeX44cvxqd58UwzhjoV3keWVco6gidsT50K4XoTWL+fumYH32I9NHxzkqERGpQboC0/fQZnqknVSAz/8W2D7RXrRxDbPzVtAksSEHt+gSr9BERCTOGkRzkbs/a2afEyTchwDNCNZkTwSedvcZsQsxZu6OPL+8qwZmNgwYBpCZmUl2dnZM3njjxo0x62t3MvqcR6+ld3D8hP+QNvgE3l44iTO3dqRVg0ZV/t6xVl33rC7RPas83bPK0z2rvBp0zxoCe9r/cTPBVl+yB+5ebiG0DyOj2ce06U2DUEI8QhMRkRogqkQbIJJMXxfDWKqMmd0AnAOM2N3WJe4+AhgBMGDAAB8yZEhM3j87O5tY9bU7Pngw4dmjaDjva/6eV8DQ5o34qlkefx3wsyp/71irrntWl+ieVZ7uWeXpnlVeDbpnS4FD99DmUCCnGmKp/XKXw/pVkNYMWnUsPbxtfbamjYuI1GfRrNGuVczscoLq4/8Dro1zOFXKzAid92cAzpkyjiaFW/n77I9Zu3lDnCMTEZEa4ANgkJmdW95JMzsPGAxo3VFFLCgphNYfMwOgOBzmo+XTADhO+2eLiNRrdTrRNrNLCUaoxwJnu3thnEOqev2Oh54DaZC/jidW55JftIW/zRwT76hERCT+/kKwzOsNMxtpZsPM7JTI87+B14GfgPvjGmUt4Qsi67PLVBz/5qcF5G7Np3OjVnRpnLmLK0VEpD7Y49RxM3sBcOAWd18ZeV0R7u6X7VV0eyGSZD8HfASc4e5b4hVLdTIzQuffS/iOQfxi2pcMb3ESj88cyw29TqZpUmq8wxMRkThx9xwzOwF4h6Cy+OllThuwEDjH3ZfGIbxap6TiOJ23rc8unTauauMiIvVeRdZoX0yQaP+FoGL3xRXs24EqTbTNbB8gFZhXdrTazC4GngU+AU53981VGUdNYz0HQv8TaTDlA/62fCVDOyTz1KyPuLnPafEOTURE4sjdvzazfQm2+jqU7YuZ/rdezPyKlXK29tqWaGvauIhIfVeRRLtT5Dlnh9dVwsyGAh0iL1sCSWZ2W+T1Ind/tUzzVwjWk3Ui+CUeMzsNeJ5gy7G3gLNL1k5FbHT3d6vsA9QQoV/cTXjKB5wz61uua5PJIzM+4PqeJ5DaIDneoYmISBxFkumRkYdEwTfmwuqFkJgCbbsDsKGwgC9XzyXBQhzVuld8AxQRkbjbY6Lt7ot2ONQByHP3KVUTEpcRJM9l3RN5Hge8yu4dQLD2vBmRCuI7WATU+UTbuh0MfY6lwQ8fce+qNfw6KZln53zK9T1PjHdoIiIitdvCyFegDn2x/2fvvqOjqrYHjn/3TW/UQIBQAkgHpT+kKFJEQGzYfoLlWVARe0fsvWDBgqJifYi90CwgRZBeBKSXUEJLQg0t5e7fH3cSAgTIDAkJsD9rzZrk3jN39tz1nmTPOWfvIO9PqQmbFpOpWbQuV4tSoVFFGJwxxpjiIJBiaOPx9ZouDKraXlXlCI/2RxibmOvYU0d5vahqQnrisV8AACAASURBVGHFXtw4l/UH4L/L5hKZmckrC0exP8tWBRpjzOlIRAaISIaIVDrC+XgRSReRh090bCebnEJoufpnj9+0CIBOFW3ZuDHGmMAS7RRgb0EHYgpBg/ZQqxWhu3fweHIKG/Zu47OVfxV1VMYYY4pGD2CCqm7I66SqJuF9mX7JCY3qZJRdCC3X/uz529YB0CK2RlFEZIwxppgJJNGeALQu4DhMIRCRnFntO5bPJzQri5cWjiDTzSriyIwxxhSBM4BFxxizyDfOHIXmUQht4XavWHujUlWKJCZjjDHFSyCJ9gCgjog8KyIhBR2QKWBNu0PVRkTuSOa+1K2sTkvmq9VTizoqY4wxJ14EsOcYY/YBMScglpOW7t8DSUvACYKqXhuv5H072bxvB9HB4VSNKlvEERpjjCkOAkm0HwUWAv2BNSIyRkQ+EZGhhzw+LthQTSDEcZBLHwXgoRX/4qjLiwt/wVW3iCMzxhhzgq3Ha+l1NK040GXE5GXNfFAX4ushoeHAgdnshqUqc0inE2OMMaep/LT3OtQNuX6u4HvkpdD7aJv8kbOvQIcPoMTmVdy6dRuDxeHHtbPoWa1lUYdmjDHmxPkVuENErlLVrw89KSJX43X9eO+ER3YSyVk2XuNAIbSF2w4k2sYYYwwENqNdPZ8PqwZSTEhQMHKxV0T2qZXLQJXnF/yMqhZxZMYYY06gl4HtwDAR+UFE+ohId9/zj8D/gK3AS0UaZXGXRyG0hdu9QmiNStv+bGOMMR6/Z7Tz6KttTgLS/nr026cou3kV12yvyzBZw5ikf+hWuXFRh2aMMeYEUNUkEekCfItXWfziXKcFSASuUNX1RRDeSUMTfTPaCYcXQrMZbWOMMdkCmdE+iIjEiEgVESlREAGZwiEhYUiPBwB4KXEVqPKczWobY8xpRVVnAbWBy4GBwMe+58uBOsBcEbn4yFc4vWlmhrdHGyDB+6JaVS3RNsYYc5iAEm0RCRaRR0RkBd4ytERgm4is8B0PZO+3KWTSqQ9El6HS+iV037mLqcnLGbpiYlGHZYwx5gRS1QxV/UFVH1LVPqr6EDAbeAJYC/xQtBEWY0mLITMd4moiUSUBWLc7lZ0ZeykfXoLyESWLOEBjjDHFhd+JtoiEAr8DzwMJwDpghu85wXd8rG+cKUYkIhrpdjcA727YCECfaR/z89rZRRmWMcaYIiAiQSJymYj8CqwEHgMqAmOLNrLiS/Pcn22z2cYYYw4XyIz2fUB7YBRQT1UTVPVsVU3AW3Y2AmjnG2eKGbmgH4RHU3nFLAaXOxNXlasmvcPETYuLOjRjjDEngIjUEJEX8dp9fQt0BlKB54AaqtqlKOMr1rIrjudKtBf4CqFZom2MMSa3QBLta/D6aF+iqstzn1DVlcBlwL9Ar+MPzxQ0iSmDnH87ALf8O4O+dTqx383govGvMzc1sWiDM8YYUyh8W76uEJE/gGXAw0BpvGXiAvysqk9YwdOj0zwS7QMz2lZx3BhjzAGBJNpnAGNU1c3rpO/4GKDm8QRmCo9ceC+EhMGMHxhUqSVXJbRiZ8ZeLhj3Cst3birq8IwxxhQQEaklIq8AScBwoCMwF7gTqKiqVxRlfCcTdV1IzF46fngPbWvtZYwxJrdAEu10IPoYY6KAjACubU4AKV0ROec6UEX++IDP29xG54oN2bJvJ+ePfZkNe7YVdYjGGGMKxlLgfiALeB1opKotVPVdVbX/2Ptj8yrYuwtKV0RKxQGQ6WaxeMcGAOqXrFSU0RljjClmAkm05wOXi0i5vE6KSCxem5B/jicwU7ikS18AdOJnhGSm80P7e2gZW5PEtGS6jH2Zbft3F3GExhhjCojirTT7XlX/LepgTlrrF3nPVc/MObRi12b2uxlUi4qlRGhkEQVmjDGmOAok0X4HKAfMEJGbfEVVIkSkuoj8F5juO/9OQQZqCpZUbwy1WsGeHeiU4USHhDOqwwPUK1mJhdvX0+PPgezJ3F/UYRpjjDk+j+O17PovMEVEFonIQyJSsYjjOulociIAUr56zjGrOG6MMeZI/E60VfUb4CWgGjAEWA6kASuAj4DqwKu+caYYk/NvA0B/HwxAbHgMv3V6mCqRZZmSvIyrJr1DlpvnVnxjjDEnAVV9XlVrAF2BH/Hqp7wErBWRUSJyZZEGeDJJ9tWJK1ct59DCbV7FcdufbYwx5lCBzGijqv2B1sBQvKIqq3zPQ4E2qvpIgUVoCo2cfSVElYaVs9CVswCoElWW3zs/TNmwaEaun8sz838s4iiNMcYcL1X9TVUvB6oA/YE1eMn3V3hLyxuLSLMiDLHY07wSbZvRNsYYcwQBJdoAqjpNVW9R1eaqWsv3fIuqTi3IAE3hkbAI5Lz/AqC/v59zvG7JSnx9Tj8cEZ6Z/yOj188rqhCNMcYUIFXdoqovqeoZeP2zv8MrXtocb0vYXBG5o0iDLK5SvERbciXa1kPbGGPMkQScaJtTg3S+FQCdPAzdvT3neMeKDXm28eUA9J48mNW7thRJfMYYYwqHqo5T1auAysBDeFvBzgIG+XMdEXFE5F4RWSIi+0RknYgMFJEof2MSkUgRWSUiKiLFq9ZL9ox2rJdo781MZ8WuzQSJQ12rOG6MMeYQlmif5qRSbWjUEdL3ohM/P+jcIw170KNyE7al7+byiYPYl5VeRFEaY4wpLKqaoqqvqWpdoAPecnJ/vIHXOmwRXn/ub4G7gBEi4u/fGc/gFVQtVnT/HtiZDEEhUNqrI7dkxwZcVWqXqEBYUEgRR2iMMaa48TvR9n3TfKzHChGZIyL/E5GehRG4KTjO+bcD3vJxVT1wXBw+b3sbNaLLM2drIndO//xIlzDGGHMKUNUJqto7v+NFpAFecv2Dql6mqh+q6n3AfcB5wNV+XKspcA/wpJ9hF76Utd5zbFXE8f50smXjxhhjjiaQGW0HCAUSfI/KQITvOftYOHAG8H/ANyIyQkSCjjtaUziaX+R9Q5+0GBZNOuhUqdAovm9/N+FBIXy0YgJDl08soiCNMcYUQ/8HCPDmIcc/BPYA+UrafX8jfAj8CvxQkAEWiKMWQrOK48YYYw4XSKJ9JpAE/AW0BcJVtSJect3Od3w9EA/UwftHsxtwd0EEbAqeBIcgHW4GDrT6yq1xmWq838ormtZ3+qfMSV19QuMzxhhTbLUAXGBG7oOqug+Y5zufH/cCdYF+BRpdAcmuOC55JNqNStuMtjHGmMMFB/Ca54GSeG28MrMPqqoLTBGRzsB84HlVvUtErgCWAL3w9nCZYkg63YL+8Dw6/Qd0+2akVNxB56+veQ5/b1nOkOXj6TlxELO7P0uZsOgiitYYY0wxUQlIUdX9eZxLAlqLSKiqHrHIh4hUB54GnlHVRBFJyM8bi0gfoA9AXFwcEyZM8DP0vKWlpR12rWqzJ1EVSExT1vrOzd60AoDdSzcwYVXBvPfJKq97Zo7O7pn/7J4Fxu6b/wrqngWSaF8KDMudZOemqukiMgJvOdldqrpHRMYBl+fn4iLyKNAUaAZUB9aoaoK/QYrIdRz4hnwnMAJ4VFWT/b3W6UBiq0DzHjDzZ3T8UOTSRw8b81bLa5mzNZFZqau5dvJgRnS4H8fvOjfGGGNOIZFAXkk2wL5cY45WTfN9YBV+fhmvqkOAIQDNmzfX9u3b+/PyI5owYQKHXsv950MUSGhxLjXat2d7+m62DP+I8KAQ/q9jD4Kc0/vfwrzumTk6u2f+s3sWGLtv/iuoexbIvwxl8fZoH02Ib1y2TeQ/qX8Br+rpSmCb39EBInIv8BmwA2/J+gd4BVkmBNJu5HThdL4NAP3jAzQr67Dz4UGhfHfu3ZQJjWZ00j88MGsYrronOkxjjDHFxx4g7AjnwnONyZOI9Mbr5327qmYUcGwF5tCl4/9uTwKgfsn40z7JNsYYk7dA/nVYBfQUkZi8TopICaAnkHsjb0Vgaz6vX1NVy6pqZ2CDv8GJSCzwHDAT6KiqQ1T1CbwZ9vrYXvEjO+t8iKvhFX3557c8h1SLjmVYu74EicMbi3/liolvsztjX55jjTHGnPI2ALEikleyHY+3rDzP2Wzfa14HRgObROQMETkDyN4IXdJ3rFRhBO6XlIOLoS30VRxvVNoKoRljjMlbIIn2ELx/PKeLSC8RSRCRCN9zb2A63p6tDwBERID2eEVRjklVVwUQU26X4C1Te1tVc6ZlVXUE3pcE+W5bcroRx0E63wqAm0dRtGxd4s9kTMcHKRkSyQ9rZ9Lut2dZtzv1RIVpjDGm+JiJ97dEy9wHRSQcaAzMOsprI/B6ZncHlud6TPCd7+37/eYCjdhPmpkBWzeACJTxCp8t2JZdcdwKoRljjMmb34m2qr6Ft5+qLvA53hLvNN/zZ3iVxj/0jQMoD3wFDCyIgPMhu8Lp1DzOTQPqiohV8ToCaf9fCA6FOaNylsrlpXOlRkzr9hRnxMQxd+saWo5+ghkpK09gpMYYY4qBrwHF63+d2y14X3r/L/uAiNQUkbq5xuwGrsjj0dd3/lff778USuT5lboe1IXSlZAQb+fcQuuhbYwx5hgCKYaGqvYVkWHADXjfWJfEKzg2F/hcVSflGrsZOLyyVuGp5HtOyuNcEl6/z0rAskNPnsgKpsVZnertKL98HIkfD2BNq5uOOnZgic48lT6OuXs30m7MMzxc5hw6RNU87hhOtntWHNg985/dM//ZPfPfqXzPVHWBiLwL9BORH/CWgdcD7gImAsNyDR+HtyxcfK/NAL479Jq5qo6vVNXDzp9whywbV9Vcrb1s6bgxxpi8BZRoA6jqZGByAcZSUCJ9z3lVQd13yJiDnMgKpsWZxgXjPjGOqgt/oFrleOTCew9r95VbV7cT/aZ/xpDl43k2dTwSX4onz7r0uCqSn2z3rDiwe+Y/u2f+s3vmv9Pgnt0DJOJ9Ud0dSAHeBp7wtf48qR0ohJYAwOZ9O0jdn0ap0EgqRZQuwsiMMcYUZ8dVKlNEokSkiYi0K6iACkB2ddO8CrMcswKqAeq2Qdr1hv170J9fxu2bgPvxnWjy2jyHhzjBvN/qRt5qcS2OCM/M/5GrJr3DnswjdXwxxhhzqlDVLFUdqKp1VDVMVeNV9T5VTTtkXIKqSj6ul6iqoqr9Ci9qPyQfPKO9YNuBZeNeGRpjjDHmcAEl2iJSWUS+x2u/NQsYn+tcWxFZJCLtCyZEv2VXKo/P41w83l4yv6uZn05EBOeuL3BemAYtLoaMfeiv7+DeWRP3vRvRpKV5vuauel0Y1eEBSoRE8N2aGZz/x8ts27+7CD6BMcYYU0CSD604nl0IzZaNG2OMOTK/E20RqYhXWfxiYCRe0bHcX+lOxyuAdlVBBBiAmb7ns/M41wpYeui37CZvUus/BD30E87ABUi7XuC66PhPcO+th/v6Vej6xYe95oL4s5ja9SmqRJZlSvIyzvntWZL25LezmzHGGFO8qG+PtsQenGg3skJoxhhjjiKQGe0n8RLpzqp6GfBH7pO+4iZ/AW2OP7yjE5GqIlJXREJyHf4Z2ItXmCUo19geQA1yVUA1+SNVG+Lc9SXOoGVIpz4QFIJO/Qb3gbNwv3kKzTh4iXj9UvFM6foE9UpWYuH29bQZ8wzLdm4souiNMcaY43CkpeNWCM0YY8xRBJJodwN+UdXxRxmzlgPVv/0iIteKyAARGYDXX7Nk9u8icu0hwz8HFpNrmbiqJgOP4/X0HCsifUTkabwWY0uANwOJy4BUqIlz6wc476xCOt4CWRnot0/jPtgYXXxwXbwqUWX5q8vj/Ce2Jmt2p9BmzDPMSjneFunGGGPMiaOuCym++iSxVXHV5d8dXlOTBiXz2qFmjDHGeAJJtOOA5ccYkwFEBXBtgJuAZ32P8kCpXL8fvdeUj6oOBP4LlAEGAbcD3wDn2rLx4ydl43FuG4Lz9ESoVAeSluA+0Q53yG3o7u0548qGxzCu86NcUOlMUvbv4rzfX2DcxoVFGLkxxhjjh+2bIDMdYmKR8CgS01LYk7mfihGlKBseU9TRGWOMKcYCSbS3AsdaL1Ub2BTAtVHV9r5qo3k92h9hbGIe1/lUVc9S1XBVLa+qN6rqlkBiMnmT+ufgvDoP6fm4t5z8jw9w762PTvseVQUgKiScXzrcR6/qrUnL3Ee3ca/xbeL0Io7cGGOMyYfDCqF5y8YbWSE0Y4wxxxBIoj0FuEhEKuR1UkRqAReQqxK5OXVJaDjO1c/gvDoXap8N2zbiDrwc99VL0eUzUFVCnGA+b3sbd9frQrqbyVWT3uGdJb/nJOPGGGNMcZRdCO3A/mxfxfHSVgjNGGPM0QWSaL+K1496ooh0BSIhp6d2V2AE4AIDCyxKU+xJlQY4z05Gbn4XImJg5s+4/f/jFUwb9SayaytvNO/NC02uRFHunPE5F41/nXW7U4s6dGOMMSZvyYdWHD/QQ9sYY4w5Gr8TbVWdDtwKJOC193rAd2qn7/fqwE2q+m8BxWhOEuI4OF364ryxGLnwPoiJhbUL0E/vxe1TCX3jKh7JDOXL1rdQMiSSkevn0uCXh3lvyR+46hZ1+MYYY8zBjtBD25aOG2OMOZZAZrRR1aFAQ7xCYzOAlcAc4D3gTFW1FlqnMSkbj3P9QJwPknDu/w6adAM3C536Le4LXbn6tWtJDKnMpVWasStjH3fM+Ix2vz7L4u1JRR26McYYk0OzZ7TLJ5CelcmSHRsRhHolA2qsYowx5jQSUKINoKrLVfVeVT1bVWuragtVvVNVlxZkgObkJSGhSKueBPUfhTN4DXL1cxBXA1LXEfP1k3yXEcH3595NhYiS/J28nMYjH+PZ+T+SnpVZ1KEbY4wxkL1HO7Yay3ZuJFOzqBFTjqiQ8KKNyxhjTLHnd6ItIteJyJnHGNNIRK4LPCxzqpGylXF6PoYzaDly20cA6Gf3cWlQNIsueoWbzjiXdDeTJ+Z9T7NRA1i83wrEG2OMKTqqetDS8eW7vGYqdUvYbLYxxphjC2RG+1PgkmOMuQj4JIBrm1OcOA5Ox5uQ9jdA+l7ct66hlBPMR61vYVznR6kRXZ6F29fTb/MIHp/7rc1uG2OMKRpp22BfmlfgM6oU63ZvBaBqVNkiDswYY8zJIOCl48cQBFjvJnNEcuMgbxl54jz0qwEAdKjYgAUXvch99buiKM8t+JlWY55k4bZ1RRytMcaY006uZeMiktMlo4ol2sYYY/KhsBLt2sC2Qrq2OQVIRAzOXf8DJwgd8Ro6fywAkcFhDGzeizfKdychuhxzt66h2ajHeXXhSLLc46tMrqq43z2L+9UAdP+egvgYxhhjTlVbEr1nX8Xx9Xu8Ge0qUWWKKCBjjDEnk+D8DBKRoYccukREEvIYGgRUBdoBo44rMnPKk9qtkCueRL9+Aved63EGzkdivJmCs8IrMr/jC9w/axgfLh/PQ3OG88v6OXza5lZqxsQF9H46ZTj69RPez9O+w7l7GFKjaYF9HmOMMacO9c1oiy/Rzl46XjnSEm1jjDHHlt8Z7RtyPRRofMix7Me1wDl4Lb/uLaggzalLLn0U6rSBbRtw37/FKz7jExMSwZCzb2Jkh/upEFGSyVuWcdaI/nywbJzffbd111b0k7u9X0rGwYaluP1b4f78KnqcM+XGGGNOQYf00F63x5aOG2OMyb/8JtrVfY8agABv5jqW+1EVKKGqrVV1VcGHa041EhSMc9eXEFECZvyI/nno4gnoXrkJC3u8xFUJrdiduZ/bpn1CxW/78d8pH/DdmhnsTD/2MnD98iHYmQz1z8V5dxVyQT/IykC/fAj32c5o6vrC+HjGGGNOUtk9tImtRpbrsmHPdgDiI0sXYVTGGGNOFvlKtFV1je+RCDwN/JTrWO7HelXdXagRm1OOlE9AbnkPAP3kLnTDssPGlA2PYfg5/fiq3R0kRJdjy76dfLryL66YOIjYb26n4+8v8MaiMSzbufGw1+q/E9E/P4bgUJw+HyBhkTg3vY3z6CgoWR4W/ol7/5no1O8K/bMaY4w5SSQfWDq+ed8OMjWLcuElCA8KLeLAjDHGnAz8Loamqk+r6qTCCMacvpx2vZC218D+PbiDeiFZGQDo7u3omvnorBG4Y97hysk/siJlL8saX8uLTa6kbfnaZKnLn5sWcd+s/1Hnpwep9/NDfLZyEpluFpq+D3fIrQDIZY8h8XVy3lOadsN5bT406Qa7t+G+fgXuezeie3YWyT0wxhhTjGRXHS+XcKDiuO3PNsYYk0/5KoaWFxGpCHQE4oGwPIaoqj4b6PXN6UdufhddOgVWzqL55mvJ+mwv7M076a0xeRgPP/wLj1zwBFv3p/Fr0nxGJc3j16T5LNmxgRumDOHFBSP4ZvNWGmxYCvF1kUsePvw9S8XhPDoS/e099PMH0PGfoLN+QS68D7mgHxJZorA/tjHGmGJG9+2GXakQEgYly7Nu3VrA9mcbY4zJv4ASbRF5GnjkkNcLB3pnZ/9sibbJN4kqhXPnl7hPtSc8bbN3MCzKK0QTW9Wr/BpbFV01B6Z/j/v8BTj3fkOZFhdxTY3WXFOjNZluFl+tnspT//yAJC2h1sTRAEy+5F7OCc57uZ+IIBfcgTY4D/eDPrB0CvrVY+gvryEX3ot0vROJKnWiboMxxpiilrM/uyriOKz3VRy3GW1jjDH55ffScRHpBTwO/AVcjpdUfwZcA3wIuMBwoEPBhWlOF1KvLc6bi5lz5RCcoSk4X+wi6I1/CXpsDE6f93Eu649z3zdIl76QsR/3tctwJ36e8/pgJ4hra7ZlyUUvMXb1GkLV5cOqZ9B+7SSaj3qc0evnHVTZ/KD3rlIf59m/cJ4YC/Xawe5tXuuxvgm4Xz+J7tp6om6DMcaYopRyoBAaHKg4Xtl6aBtjjMknvxNt4HZgPXCBqv7oO5aoqsNV9TbgQuBKwNbcmoBIxVrsLlcLiSmLiBx+3nGQm95Beg4ANwt953rcUW8eNCZowmdUWLMALRmH0/slKkaUYs7WRLr/+RpnjniUAXO/ZWrycrIOae0lIkijjgQ9MwnnqfHQ4DzYswP97hncOxJwhz+OZqQX6uc3xhhTtDT50B7a2Xu0bem4McaY/Alk6Xgj4CtVzcx1LCj7B1X9TUR+Ax4ERhxnfMbkSUSQq5/FjSmLfnov+um9uLtSkauege2bvXZegPPft+jTpCfXNurB4GVjeXHBCBZuX8/C7et5fsHPxIbF0DX+LLrHN6ZLfCNKhUYdeI8G7Qlq0B5d/Bfut8/AgrHo989BZgbS+6Wi+ujGGGMK25ZE7zkn0fYtHbcZbWOMMfkUSKIdAqTm+n0vUPKQMQuB2wINypj8crrfgxtVGh18k5cEp22FnSmwezs06Ya0vhKAiOBQ7qvfjb51OjFx0xJGrp/LqKR5rE5L5otVk/li1WSCxKFt+drcUus8rk44myDHW/Ah9doR9MQf6NxfcV/oio58He1wI1KpdlF+dGOMMYUlp+K4l2iv35OdaNuMtjHGmPwJZOn4RqBirt/XAmceMqYSkIkxJ4DT/nqcB76HkDCvevjUbyAsEufmdw9beh4eFEqX+DN5+z/Xs/LS11l00cu80vRqzo2rC8DEzUvoPXkwZ43sz09rZx20n1uaXICcdyNkZeB+es8R93obY4w5ueUsHY+tRqabxYa92xCEShGlizgyY4wxJ4tAEu25QMNcv/8JtBORa0UkSkS64xVJm1sQARqTH9LiYpz+YyA82vv9qmeQ8glHf40I9UrF82DDC5nQZQApVw1mSKubqBpVln+3r+fSCW/SasxTjN2wMCepll4vQmRJmDsGZo8s7I9ljDGmKCQfmNHeuHc7ripxESUIDQq4K6oxxpjTTCCJ9kigoYhU9/3+ErAD+BTYCfyCV4l8QEEEaEx+ScPzcF6ahdw9DOl2j9+vLxUaxS21z2PZJa8xqMV1lA8vwYyUlXQe+xId/3iRqcnLkZLlkSufBvBmtdP3FfTHMMYYU4QkKwO2bwRxoEy8FUIzxhgTEL8TbVX9VFUjVXW17/d1QAtgMPA7MARooarTCjRSY/JB4uvgtP0/JCjo2IOPICwohDvrnc+qS1/nxSZXUio0kvGbFtF6zNNcOO41vq/bjKzK9WDzKnTEwAKM/sh043Lcv4ZZxXNjjClkYWlbQBXKxCPBIbY/2xhjTEACmdE+jKquVtV+qtpVVW9X1QUFcV1jilJUSDiPNLqI1Ze9wWONLiYqOIxRSfO4csr7dKtaBYD0759lzZrC3SWhs0fiPtQUHdQL98Gz0IXjC/X9jDHmdBa2a7P3g2/7UXbF8cqRtj/bGGNM/hVIol3QRMQRkXtFZImI7BORdSIyUESijv1qEJFoEekvIgtEZJeIpIjI3yJyg+TVmNmYoygVGsVzTa5g5aWv82qz/+OcuLqMjy3PtxWrEpKxn79fv4QGPz/Mw7OHM2nzEtKzCqYOoKrijhiI+/JFsC8NokpD0hLcpzvgvtUL3baxQN7HGGPMAeG+RFtifa299viWjtuMtjHGGD/kK9EWkVARmSEi40Qk5BjjxonItKONy4c3gNeBRcCdwLfAXcAIETlqzL7zY4BngZnA/cBzeL2+P8HbU26M3+IiSvJAg+5M7DKA5CsHE3b96+wPDuHKDWspt3IOr/w7knN/e45Sw/vQ+Y+XeGHBz0xNXk6G63/irRnp6OCb0c8fAFXk6udwPtyI/N/zEBKOTh6Ge3dd3NGD0AJK7I0xxuSa0c5u7ZXdQ9v2aBtjjPFDfstn9gaaAT1UNeNIg1Q1XUReBUYDvfAKpPlFRBrgJdc/qGrPXMdXA4OAq4FhR7nEf4C2wJuqem+u178HLAFuBR72Ny5jcisTFs2lzXriXv4EOvxxfl6zlmfb9Oa3zYtZuH09YzcuZOzGhQBUcuHZ5FQuXjaX8JJxRHTqg7S9BonOexmi7kzBfa0nLJ4EoRE4/T5Hzr4cALmsP9rm/3A/uRtmj0A/uRsd/wnOze+dKi02rQAAIABJREFUsM9ujDGnskMT7XU5e7TLFFVIxhhjTkL5XTp+GbBKVUcfa6Cq/gosB64IMKb/w6ta/uYhxz8E9uAl/UdTwve84ZC40oEUYHeAcRlzGOnxAJSvTvSGZbyydScLLnqJzVe8y9fn9OPB+Oa8v3I5C377hutn/Eqp7ZsJXzMf/bgfGX0qkvVWL3TheNR1c66n6xbhPtrSS7JLV8J55q+cJDvnPeOqE/TILzgP/ez9IZg4D3dAa2pOegvNPOL3YMYYY/IhZ+l4dqLtqzpeOdISbWOMMfmX3xntJniz1Pk1CejmfziAV8HcBWbkPqiq+0Rknu/80cwAtgMPiUgiMB2IBK7Hm5W/LcC4jDmMhIbj3PAG7iuXoF8/gba+mnL7dnH575/Rc8InkLEfgM01m/Flo9YsWD+f3olL6ZSyCSYPw508jKzy1QnucBPE1UCH3AZ7d0LN5jgP/YyUqXTk925xEc6ZndDvn0d/eZVKC37CfeVinHu/QSKiT9QtMMaYU0rYrk3eD7HVSM/KZNPeHTgiVLJiaMYYY/yQ30Q7Ftjsx3U3A4FuZqoEpKjq/jzOJQGtRSTUN0N9GFXdJiIXAR8B3+Q6tQvoqao/HemNRaQP0AcgLi6OCRMmBPgRDpaWllZg1zpdnFT3TEvQoGoLyqydSdr9TYnYuQFRb5Y6pUY71je5ml0V6tMSqFuhGz/VW8bjm6bRPXEB169bRZUtq9HhB9rOJ9c8l2UdH8GdvwxYduz3r9SZ6Esr02DEI4TOHcOOB5rzb/cXybA/Co/ppPrfWTFh98x/ds9OHpqV5bX3Aoitysa921GUShGlCXYCbxtpjDHm9JPfRHsv4M8UWTSwz/9wAG/2Oa8km1zXjASO1lA4DVgI/AL8DZQB7gCGicjFqvpHXi9S1SF4fcBp3ry5tm/f3u/g8zJhwgQK6lqni5PtnmntL3Dvb0TkjvUQFIy0vRa55GHiKtcj7pCxF3EBWW4/RifN4/ZFv6EL/uDGtSvolLyJt2rU5e2GtSiT9jtlw2IoGxad86gZHceNZ5xDVEh4HhG0Z3pYNM3/eJKYLUtpNfpBnAG/IRVqnoiPf9I62f53VhzYPfOf3bOTyPaNOG4WlCyPhEWwbvtawAqhGWOM8V9+E+11QHM/rtscWOt/OIC3D7v8Ec6F5xqTJxFphJdc36uq7+c6/hVe8v2hiNRU1awA4zPmMBJfB+eer9DEf5CONyPlqh51fJDj0KNKU3pUacqSVtfz3tKx3Jk4jeR9u9CMvWzL2MvKXVsOe90r/47ktWbXcGXCfzi0U93eUlVwnp+K+0I3WD0H97GzcfqPRmoe/f+6um83bNsAFc447JrGGHMsvm4fd+MVG00AkvFWlD2hqketiyIidYAngKZ4K9pC8P5+GA28qqonto9h8hrv+ZDWXpWtEJoxxhg/5TfRngD0FZHmqjrraANFpBnQGng7wJg2APVFJCyP5ePxeMvKjzabfS9eQv5t7oOqukdERgH98P4QWBlgfMbkSVr1RFr1PPbAQ9QtWYlBLa9jUMvryHJdtqXvJnX/LlL3p5G6P42t6Wmk7Evjq8SpzE5dzdV/vcPgZWMZ1PI6zix9cEIvpeJwnp7gVS2f/wfuk+1x7v8OaXLBQeN02yZ09gh01i8wfyxk7IO6bXGufx0541hlEIwx5iBv4LXg/BEYCNTz/d5ERDqpqnuU11YGKvpeux7IBBrhbeO6WkQaq+rh3zoWEs1OtHMKoWW39rJE2xhjjH/ym2i/A9wOfCsi3VR1cV6DRKQuXoKbBQTab2gmcD7QEvgr17XDgcZ4hdaOJt73nNdmquBDno0pVoIch9jwGGLDYw47d0+9Cxi6YiL9537DxM1LaDLyMfrW7sQzjS+ndFhUzjiJiMF5ZCQ6+Cb0ry9xX+6B3PYRUrMFOutndOYvsGI6qB64eHg0LJmM+2hLpF1v5JoXkNgqJ+IjG2NOYsfbklNVxwHj8rjuJLxZ8RuAVwo26qNI8RJtKZcAwPqc1l62dNwYY4x/8tXeS1WXAs8A1YC5IvKliNwoIuf7Hv8VkS+BuXizxU/7XhOIrwEF7jnk+C14e7P/l31ARGr6kvvcFvmeb8h9UERKARcD24AVAcZmTJEJchxuqX0eyy55jTvrng/AO0v/oPZPD/DhsvFk5Zo0kpBQpN9nyMUPQ1Ym+u4NuPc1QIf1h+XTIDgUml2I3DoEZ8hGnPfXIxc/BMGhXnJ+V23c4Y+je9OK6uMaY04Ox9uS80h8U8uc2KqOh81oe0vHLdE2xhjjr3zP7KrqMyKSCTwJXIP3j2tuAmQAj6nqi4EGpKoLRORdoJ+I/IC3Tyt7GdpEDv5mfBxe8p97Y+mbwHXAS7792lPwiqHdgrc87Q7bn21OZqXDohjU8jpuqXUed874jImbl9Bn2seUcMKo9ssfVIgoSYWIUt5z03Noq/tpOmIQblQpnGbdCWpxKZx1PhIeddB1pffLaOfb0P89gk79Bv3+OXTcR8jVzyHtb0CCrOKuMeYwx9uSE8hZtRaNt/WrPvCy75Q/rUWPW/bS8Zwe2r4ZbeuhbYwxxl9+LaFW1RdE5H/AjUAbvMQVYCMwGfhEVdcc6fV+uAdIxNuj1R1Iwdvz/cQx9nqhqmtEpCVecZWOeMvW9gLzgPtV9YcCiM+YIteodBXGn/8Y366ZzoOzv2Lt7lQWbF/Hgu3rDhtb+vzL2BESAo5SNWkSNXYspmZMHDWiy1Ejpjx1S1aiUakqSFx15L6v0aV34352Hyyfjr5/MzrqDaT7PUjbXkhYRL7i08wMmD0SXTAW6XwbUq1RQd8CY0zRO66WnLnczMG1XRKB3qr6V97DC8kRZ7Qt0TbGGOMfv/cq+xLpJwshltzvkYVXUGXgMcYlHOH4SuD6go/MmOJFRLgyoRWXV2vJT3/+So0mDdi0dzub9u7wnvftYNPeHWzYs43EtBTW79lKYloyiWnJ/Llp0UHXalSqCv3qdqZX9dZE1WmN8/xU9O+v0S8fhnX/ou/fgn75MNKpD9Kl7xH3cOvm1ei4j9DxQ2H7Ju/YpC9w7v8eOatzod8TY8wJVRAtOQF+ApbgzWo3AS4CYo/2AhHpg/eFPHFxccffq1yV1ptXEwRMXrKWvauS2bJvJw7CkunzWC752m13WrJe8f6ze+Y/u2eBsfvmv4K6Z1YUzJhTgCMOZYIiaVymGt5uirztz8pgze4UVu3awspdW1iVtoVVu7bwd/JyFmxfx63ThvLQ7OHceMY59K3TiTPaXI22vBSd+i06+i1YOQv96SX0l1eh5aU43e6Cum0hMwNm/YI7dgjMz9WmPr4elK3sVUB/sRty+8c4515X+DfEGHOiHFdLzmyquh6v6jjATyLyPTBTRCKPtB1NVYcAQwCaN2+ux9urXPfvwf27Pbs2JNKuy4Ws2rUF1kF8ZBk6ntfhuK59qrNe8f6ze+Y/u2eBsfvmv4K6Z5ZoG3MaCQsKoXaJitQuUfGg4+lZmXy3ZgbvLP2DqcnLeWPxr7y5+De6xp9Jv7rn06XdNTjtenlLyUcPQqd9C9O+w532HVRt5M1c70z2LhYSjrS+Eul4C9RtA6rolw+jI15D37keN2Utctlj1rPbmFPD8bbkzJOqzheRuUBfIOC6L/6QsEiCHhvDvAkTaI8tGzfGGHN8LNE2xhAaFMw1NVpzTY3WzE5dzbtL/mDY6qmMTvqH0Un/UDo0ikqRpYkLL0Fc0zbUOvNsOi2cTJM5vxOxdoF3kSoNkc59vPZg0bkKBYsg172KW64q+snd6PDHIXkt3PIeEmT/CTLmJHe8LTmPJgKvmGmRsNZexhhjjof9lWuMOUizstUZ2qYPrzT7P4aumMh7S8eyZncK29J382+ucc+UCiHs3AvonLyRLWHhpFSux3klw2m/5V/aSz0qH/LHqdP1TrRMZdy3rkHHfYhuTcK592skIvqwGFQVtm2E1XPQ7ZshMx0y93vPGfu9R2Y6qCJtrkZqNivku2KMOYKvgf54RUxzFy7LsyUnEKKqS3Idq6Cqmw69qIicBzQEJhRO2MeWM6NtFceNMcYEwBJtY0yeYsNjeKjhhTzQoBtb9u1k894dbM553pHz+4b4bSxNXc2O3cmsWjGRj1dMBKBmTHnOi6tP+wr16FG5CSVCI5H/XIrz5J+4L/eAuaNxn2qP88hIL4leNRtdNQddPQdWzYEdm/MVp45+C7l+IHJBP1uObswJVgAtOQeLSEXgT7ze2eFAM7yOIbuA+wv9QxxBTmsvWzpujDEmAJZoG2OOyhHH15e71BHHZLku87atYcKmxYzftIhJW5aw0ldw7aMVE4gODqdXjdbcXrsjZ9U5G+e5v3Ff6AqrZuPeGg95de2LKgXVm3r9bIPDIDgUQnI9h4TBhmXonx+jQ++CJVPgtg+RiJjj+ryasR8d/wk65m2k9tnITe8goeHHfqExp6+AW3ICXwHXAdcC5QDFS7g/AF5V1bWFFPMxHZjRtqXjxhhj/GeJtjHmuAU5Ds3KVqdZ2erc36AbmW4W87auYfymRYxYP5e/tizlg2V/8sGyPzm7XC1ur92RK56ZSMhrV8DyaRATCzWaITWaItWbQo1mUD4hXzPU7lnno4Nv8lqRJc7DeeB7pEoDvz+Dpu9Dxw9Ff3wRUr3ix7p+EZq0GOfBn5CS5fy+ZmFQVXBdJCio4K+duh6d9IW3z/4I7dsCvvauVHTsEKTphdZT/RRzPC05VfUb4JvCiez4HNijbTPaxhhj/GeJtjGmwAU7QTSPrUHz2Bo82PBC/t2+ng+W/clnK/9iavJypiYv596waP57aT+uKPsmqaFhOT2/N+3dzqYlI9k41+sHXjGyFJdVbc5lVVvkWZTIaX0lWu0s3IGXw7qFuI+2RPp8gHNO73zFqun7vD3jP70MW5O8g1UaIl1uR398CZb+jdv/PziPjkIq1yvI2+QXzUhH/x6OjnwD1vwDTbvjdL0TzuxcIEvmdeNy3Kc7Quo69Nd3cQb8jlSpXwCRgy6divvGVd61v38O584vkP9cViDXNqawrNttxdCMMcYEzhJtY0yha1CqMoNaXseLTa7kq8SpDF46jjlbE3lt8RheO8Zrl+/axKTNS7hn5pe0ij2Dy6u1pGe1FiREH5hhlvg6OC9ORz+8HZ34Ofr2tbhLJiM3vJnnsm/NSIc926n0z3e4w67xCq8BVDsT5/InoOWliOOgLS/FffkiWDkL97Gzce7/DjmzUwHemWPTnSnoHx+gv77jtVHLNnsk7uyRUKmOtz+9/fUBL5vXtQtxn+3sXT80ArYm4T7RDqf/aKTWfwKPXRUd/Rb6xYOQlQmlK8K2jbiv9USueQG55BHbV2+Kpb2Z6aTs30WIE0T58BJFHY4xxpiTkCXaxpgTJioknJtrncdNZ7RnVuoqBi8dx9ytaygfXoIKESV9e8FL5vxcPrwEC7at47s1Mxid9A/TUlYwLWUFD8weRvOy1elZtQWdKjbkrDJVCQmLhDs+hbpt0aF3esnpoolQpjLs2XHwI2MfADWzA0to7CXYLS5GHCcnXildEefpibhvXwvTf8B9/gLklsE4nW4J+B5oZgbM/AndmYyUrgSlK0GZSlAyDgkOOTBu/WJ01JvoxM9z4qVKQ6THfciZnb0vFH4fDBuWep/3q/5I+xu8pLtS7fzHs3I27vNdYFcqNOqIc89w3Pf+6yXyT3fEefBH5KzO/n/O3TtwB98I038AQLrfg/R6GR39Jvq/R9Bh/WH9Ym9ffUiY39c3pjBlLxuPjyyDI84xRhtjjDGHs0TbGHPCiQgtYmvSIrbmMcc2KFWZq6ufze6MfYzZMJ/v1sxg5Pq5zEpdzazU1Tw69xvCg0JoXrY6rWLP4OxajWk74FfKvnsjJC3xHodygiCyJDuiK1L6uhegeY8jzqxKWCTOfd+iw/qjP7+MftAHd8MypNdLfu2T1n270XEfoSMGQuo679jBNwVKlPeS7uAwb+96tibdcC68Fxp1zIlTLuuPXvwQzPgJd8zbsHgSOuZtdMzb0PgCnEsehvrnHnXGWJdMwX2hG+zd6S1Fv/87JDQc54EfvH3vk77AfbE7zt3/Q86+Iv+fdfU8byn/5pUQUQLnjk9ylorLxQ+hFWvjDuqFTvoC3bzSS+ZLls/39Y0pbDn7s621lzHGmABZom2MOSlEhYRzebWWXF6tJXsz0/ltw3x+Xjebv5OXs2znJiZvWcbkLctyxtdt24Xe+7OIjSpDyZIVKFM6nnJlKlOxbDXKlYgjyAli/oQJtG/R/pjvLY6D9H4Jt2It9MPb0BGvoZuWe7PgVRocdUZWd6Z4CfCv70Ca98c78XWRuu3Q7Ru9ZevbNsD2zV5Ls+y2ZqERyLnXeTPB8XXzjisoGM6+nKCzL0cT/0F/fQf9638w71fceb/CGS1xLnnksJl6AF0wzlsWv38PcvYVyJ1fIiGh3nWDQ7zVATFl0VFv4r5xFZK2FafzrUe9T6rqfZkw9E6v13lCY5z7vkUqnnFw3C0vwXl2itfmbenfuI+2xHl4hBVJM8VGTsVx259tjDEmQH4n2iLyElBRVa/3/f45kKSqj4rIy0CF7HPGGFMYIoJDuaRqcy6p2hyA1H27mJG6iqnJy5mWvILpKStZkrGLAQ6wdw/sXQ+bZuW8PtQJpnJkGcpkhnD+3C00L1ud5mVrUDmyzFFngJ2ON6Hlq+MO7Akzf8ad+TMEhUDVhkhCE8iuml7tTNiVgo4YiI77CNL3eheo1Qrn0kegWY/DE9+sTNixxUu6d6ZAzeZIidh83xNJOAu57UO010vob++howfBihm4r13m7eO+6EHknN5ISBg6e6Q345yx31tufttHh83Oi+PA9a9DTCw6fAA65DbcXalQ+uwDMWekw6YVkLQYTVqMLpkC8371Xt+pj7dHPiwi73irN8Z5cSbuKxd7cQ5ojXPPcKRZ93x/5oKi2zZC4j9QtSGUibd94yanEFplm9E2xhgToEBmtC8GagPZyXRvYDHwKHDRIeeMMabQlQ2PoWv8WXSNPwsAV12W7NjI3K2JrN2dyrrdqazbs9V73r2VrelprErbwipg1oKknOvEhZf0Jd3VaR7rJd4RQSFEBocRGRRKRHAo4Q3b4zw/Df3+OXTlTNi4DFbPRVfPhfFDveXg4nhLwd0s78JNunozy/XaHXmJelCwt2y8TKXjuhcSUxa5/HG0x/3on0PREa95+7jfvxn9+gmkVU9vb3dWJtKlL3Lj24cl/TnXEkF6PoYbUxb9qC/61WPUq9GOrJmDIGmxtzQ8K/PgF4VF5rvqu5SugPPUBHTwjeiU4bgvXQiNOiEdbkRaXnpC+pfrxhW4T55zoCBeTCwkNEaqN4HqTbznCrUKpZ2aKb6stZcxxpjjVVBLx+3rf2NMseGIQ/1S8dQvFZ/n+T2Z+1m3O5Vvp/zO3oqRzEpZzczUVWzet4NRSfMYlTTvqNcPDwqhRIUY2p91B1fF1eeCDAhf9y+smoMmzoV1/wIgba9BLn4ISTiroD/iMUlYJNK1H9r5VnTqN177srULvD3c4M1w9345X7O3zvm34UaXQQf1JnbVX7Aq+00E4mpAfD0kvh5Uroc06oiUq+ZHnBFw9zCo0hD9/jlYMBZdMBaNKo2064V0uAmp3jiQW3BMmrwG95mOXpJdrhrs2Qm7UnJiAN8++rBIaNihQNupmeItZ+l4pC0dN8YYExjbo22MOe1EBodRp2Ql2kYm0L5Je8DbX7w6LZmZKauYlbqK2VsTSd2fxt7MdPZk7WdvZgZ7svazLysj5/HNmul8s2Y6YU4IXSo1omfXm+hRuSmlJMhblh1Vsmg/KN5+a2nXC217jbd3+48PkPrnenu//UgYndZXonE1WPXj+1Rv1RGpXB8q1j7i0nC/YvTNnGuXvuiUr9BxH8PqOd6e81/f8WaWO9yEtPk/JKZgZhg1NcnrG56yFuq0xnnsNwiPgpR1kOitUNDVcyFxnjcmu51afD2k653e/vnwqAKJxRQ/62xG2xhjzHGyRNsYY/CSvRox5akRU56rqrc64jhXXfZlZbBhzzZ+WT+H79fM5O/k5fyyfg6/rJ9DsATRoUJ9ulduTO0SFageXY5q0bGEB4WewE9zOBGBJl0JatI18GvUbM665r2p2bZ9wQWW+/rRpZEufaFLX3T1PPTPj73ibqvnoh/3Q4feBTWaIQ07II06QJ02ASW7umML7rOdvKXvNZrhPDoaiYj2TparCuWqIi0uPjB+2yZ0/FD0t/e8/egf9fXaqXW4GbngDqR8QgHdAVNcZM9o2x5tY4wxgbJE2xhj/OCIQ2RwGGeUqMB99btxX/1ubNizjZ/WzuL7tTOZuHkJv29cwO8bF+S8RhAqRZaienQ5qkeXp3p0Oc4sXYX2cfUoGx5ThJ+m+JLqjZGb3kavfRWd+RP651D4dzysnImunIn+/LJXiK52K2+5esMOUKvVQb3I86K7tuI+08lr+1a1Ec6A34658kBKV/DaqV30IDr9B3T0W7Bsqld9fuTr0OJinO73IPXPKchbYIrIXjeDbem7CXNCKBdeoqjDMcYYc5KyRNsYY45TpcjS9K3bmb51O5Oybxe/rJvD38nLWJ2WzOq0ZNbuTiVpzzaS9mw7qAWZIDQuU5UOFRrQsWID2pWvQ3RI4RcAO5lIaDjS5mpoczW6Nw2WTkEX/oku/BNWzYbFf6GL/0K/eQpKlEPaXI2cc61Xtf2QpfG6ewfuc+fD2gUQXxfnibFITP734EpwCNLmKmhzFbpiJjp6EPr31zDjR9ysDIIs0T4lJGftBqByVGnbj2+MMSZglmgbY0wBig2P4cZa53JjrXNzjmW6WazbnZqTeK9K28LU5BX8vWU5c7euYe7WNQxcNJpgCeI/sTU5r0I9yoeXxBHBESFIHO9nvOfI4FDOLF2V2iUq4EjeFcNPRRIRDY27II27AKBp22DRRC/x/ud3r7r6mLe9gm+V6iDnXOsVVCufgO5Nw32xm5ecx9XAeXwsUrJ84LGc0QK56wv02lfQ399HGnUsqI9pilh2om2F0IwxxhwPS7SNMaaQBTtBVI8pT/WYgxO7vZnp/J28jD83LWLcxn+ZmbqKKcnLmJK87AhXOlhMSDhNyyTQvGyNnLZkNWPiTptZOIkuDS0vQVpegqp6e7knfYFOHuYl3cMHoMMHQL1zIDMdlk+DslVwnvwTKZt3RXq/YyhdEbnq6QK5liketmRmz2jb/mxjjDGBs0TbGGOKSERwKB0rNqRjxYY83wR2pO9h0uYlTNmyjN2Z+3FRXFWy1MVV1/esbE/fw5ytiazfs5WJm5cwcfOSnGuWCo2kSZkEmpSpRpMy1Whcuhp1S1Yi2Dm1+0CLCNRoitRoil77Ksz/A534BTrzJ1g8yRtUuqKXZPvRfsycfg7MaFuibYwxJnCWaBtjTDFRMjSSHlWa0qNK03yN37R3O7NTVzPL95iZ4vUCH79pEeM3LcoZF+aE0Kh05ZwEPCG6HGXDookNi6FsWDQlQiJOqVlwCQqGJl2RJl3RPTvR6T/A0ilIjweQimcUdXimmNuSmQZAlShbOv7/7d17eFXVmcfx70vIhSSQAEHAoCCgRUFFK1otrVCpo6X10tFW+6C2tVbbqrWt03acjtdenM5jtVXnabXTi4hWbdWq09Hx0likKmilXuoFhCAEkHALCbkC7/yxd+BwOCc5J9k5l+T3eZ797Jy11t5Z+81J3qyz915bRER6TgNtEZE8NWZIJXPHHcXccUcBwbPA17Zs4W+balm6ZdXu+79rm+p3D8YTGWwFVJWUM7J4KPuVDOPwynEcWzWJGVUTmTx0dF7fB26lw7DZn4fZn892VyRP7D6jrYG2iIj0Qk8G2muB2Glx3wPqktSJiEiGmBnVpSOoLh2x11nxre3bWbp5FUs3v8fSLatY17yVjW2NbGprYmNbI9t3tLG+pYH1LQ28AXudDa8oLGVG1URmjDyIGVUT2dK+maIN79DQ0UJDezNb25tp6AjX7c2UF5Ywaeh+TCofzeRhozmgdCQFg/J3oC4Dz4bOWcd16biIiPRC2gNtdz8p7vWEZHU9ZWaDgK8DFwMTgHrgfuBqd9+e4j5GAFcBZwDjgEbg9XAfC6Pop4hIPqgsKmPWmMOYNeawhPWtO9vZ1NbEprYm6pq38HJ4GfqSTStY17KVp9a9zlPrXt+zweMPpvy9CwcVMKFsFJOHjWZi+X5UFZczrGgIwwr3LBVFpbu/HllcTung4t4eskiP1e/QPdoiItJ7uXrp+M3A5cBDwE3AoeHro8xsjrvv6mpjMxsP1ADlwH8D7wAVwBFANFPNioj0EyUFRbvPhB8x/EBOrT5yd11d82aWbFzB4o3vsmTTSpbVr2FM5UgqCkupLCqloihcF5ZSUTSEhvYWljeu593GDbzbuIG1LVtY1rieZY3r0+hPISOLg0vZq4rLd389pWIsnxl/HGNLh/dFGERo7Ghhu7czpKCIEcXl2e6OiIjksZwbaJvZVOAy4EF3/+eY8pXAz4BzgHu62c3dBMd2hLuv66u+ioj0d9WlI6g+cARnHHgMADU1NcyaNSvl7Zt3tLEiHHSvbKpnS/t2tnW07Fna93y9tb2ZTW1NtO7soK55C3XNW/bZ3zdfWsBJY6Zy3sSZnHngMZQX6m4lic7q7ZsBOKBsRL+aIFBERDIv5wbawLmAAbfEld8J3AjMo4uBtpl9FJgJXO7u68ysECh09+Y+6q+IiCRROriYacMPYNrwA1Jq7+4072hjY1sTm8L7yDvvJX9m/T94bM0rPLnudZ5c9zqlLxZz5gEf5LyJMzlp7NR+/wgz6XtrmoOBtu7PFhGR3srFgfYMYBewOLbQ3VvNbGlY35VPhOv3zOxR4FSgwMyWAde7+91Rd1hERKJhZpQVllBWWML48qq96i6dcjKb25p4YNWLzH93EYvq32HByr8/htv+AAAR2klEQVSyYOVfGTOkgjljp3F0+Aiz6SPGU1lUlqWjkHy1evsmQDOOi4hI7+XiQHt/YKO7tyWoqwNOMLMid29Psv0HwvWdwDLgAqAI+BYw38wK3f3XUXdaRET63ojici4+5CQuPuQkVjRuYMHKRcx/dxHLGtdz94pF3L1i0e62B5WP2v3s8COHH0h16XBGlQxjVMlQSgqKkn6PhvZmapvqqW3aSO32elY1bWTS0NGcP2kmQwuHZOIwJUt2D7R1RltERHopFwfapUCiQTZAa0ybZAPtoeG6EZjdOSA3s4eBFcAPzey3iSZUM7MvA18GGD16NDU1NT06gHhNTU2R7WugUMzSp5ilTzFLX67F7CMMZ2blXJaXbeLNtnqWdWxiefsmVnRsZmVTPSub6nnwvSX7bFdqhVQWlFAxqIThBUMYhPH+jibW72yicVfiFPSdJffyqfIpfHroVEYNTv1sea7FTJJb3XnpeJkG2iIi0ju5ONBuBvZLUlcS0yaZlnB9b+xZb3ffYmaPAOcTnPV+M35Dd78DuAPgmGOO8XQm/OlKupMHiWLWE4pZ+hSz9OVqzGbHvd6xaydvNazllc2reGVzLW9sreP91gbqWxvZ0LqNZu+geUcHa2ncZ1+lg4uZUFbFhPIqJpSPorp0OI/XvcrCDW/zu8ZX+X3TG5x70PF867BTOXLE+G77lqsxk32t6ZwMrVSXjouISO/k4kB7LXCYmRUnuHy8muCy8mRnswHWhOtEz5LpnIFcz4YREenHBg8q2D0J23mTZu5V5+40dDRT39pIfes2NrRuo2PXTsaXVzGhrIpRJcP2mXH6qsNPZ/HGd7npjT/x+/cWM3/Fc8xf8RwnjZnKlVM/wT/tf4Rmqe4HVjfrHm0REYlGLg60lwAnA8cCCzsLzawEmA78pZvtFwOXAOMS1HWWbeh9N0VEJB+ZGZVFZVQWlXHwsDEpb3ds1STuO/Eyapvq+embT/DLZTU8vf4Nnl7/BnOrp/PYSVf2Ya+lr7n7Xo/3EhER6Y1B2e5AAvcBDlwRV34Rwb3ZCzoLzGySmU2Ja/cwwf3Z88ysPKbtWOAM4B13X94XHRcRkf5vQvkobp4xj9Vn/ZT/OPocqkuHM2fstGx3S3qpdWcHB5WPYlRBGRWFpdnujoiI5LmcO6Pt7q+Z2e3ApWb2IPAn4FDgcuBZ9n6G9tPAeILnbnduv8XMrgR+AbxgZr8imHX8K+H6sowciIiI9GuVRWV8e9onueLQU9jFPvNrSp4ZMriIV0/7ETU1NboNQEREei3nBtqhK4BaghnA5wIbgVuBqxPNFh7P3e8ws43At4EbCJ7L/TzwOXdf1OXGIiIiaSgqyNVUKiIiItkS+X8HZnYDcLu7J5qMLCXuvhO4KVy6ajehi7oHgQd72gcRERERERGRnkjpHm0zG2VmPzOzR8zsk900vxG4pfddExEREREREck/qU6GdjvwBeCTxExGloi7bweeN7MLe9k3ERERERERkbyT6kB7PHAwwQRjT6fQ/mXg4p52SkRERERERCRfpXqP9t8Bd/evpNh+IvBBMyt297aedU1EREREREQk/6R6RvtO4Idp7PfKcD0yve6IiIiIiIiI5LeUBtruvgTYZWavmNlFZjY+WVszmwpMA3YCPZ55XERERERERCQfpXpGG+CrwEqC+7RXmNlaM/uDmV0WDq47lYbrVak881pERERERESkP0l5oO3uHe7+aeBCoBYYA5xJ8CivV8OB94+AdmAX8Gb03RURERERERHJbemc0QbA3X/t7pOAGcB3gf8BthIMvL8DPAu0AA0R9lNEREREREQkL6Q66/g+3P1lgsd4/RjAzKYBHwFOBE4BxkXRQREREREREZF8Yu4e/U7NKoG73P20yHeeIWZWD6yKaHdVwMaI9jVQKGbpU8zSp5ilTzFLX7oxG+/uo/qqM/2RcnbWKWbpU8zSp5j1jOKWvnRiljRn98lAG8DMJrv78j7ZeZ4xs5fc/Zhs9yOfKGbpU8zSp5ilTzFLn2KWX/TzSp9ilj7FLH2KWc8obumLKmZp36OdKg2yRUREREREZCDqs4G2iIiIiIiIyECkgXZm3JHtDuQhxSx9iln6FLP0KWbpU8zyi35e6VPM0qeYpU8x6xnFLX2RxKzP7tEWERERERERGYh0RltEREREREQkQhpoi4iIiIiIiERIA20RERERERGRCGmg3QfMbJCZfcPM3jKzVjNbbWY3mVlZtvuWC8zsX83sATNbYWZuZrXdtD/OzJ4ys0Yz22Zmj5vZ9Ax1N+vM7BAzu97MXjCz+jAOS83s3xK9p8zsA2b2sJltMbPtZrbQzD6Wjb5nSxiDBWb2ppk1mFlz+Pv4EzMbm6T9gI5ZImZWGvN7eluC+gEftzA2iZamBG0HfLxylfJ2csrZ6VHOTp9ydjSUs1OTybw9ONquS+hm4HLgIeAm4NDw9VFmNsfdd2Wzczngh8Bm4G9AZVcNzexDQA1QB1wdFl8KLDSzE9z9tT7sZ674IvA14BFgAdABzAa+D3zGzD7k7i0AZjYJ+CuwA/gx0ABcBDxhZqe6+1NZ6H82jAPGEvwOriGIx+HAl4FzzGy6u28Axawb1wOjElUobntZyL4zlHbEvlC8cp7ydnLK2elRzk6fcnY0lLNTl5m87e5aIlyAqcAu4A9x5ZcBDnwu233M9gJMjPn6daC2i7aLgW1AdUxZdVj2f9k+lgzF6xigIkH598P31KUxZfcDO4HpMWXlwCrgbcInDQzUBTg7jNm3FbNuY3V0mFy+Gcbstrh6xS04Zgd+k0I7xStHF+XtbuOjnJ1evJSzo4ulcnbqsVLOTj1WGcvbunQ8eucCBtwSV34n0AzMy3iPcoy7r0ilnZlNBmYAD7h7Xcz2dcADwBwzG9M3vcwd7v6SuzckqLovXE8DCC9JOw2ocfelMds3Ab8EDiGI50C2KlwPB8UsGTMrIPib9TjwYIJ6xS2OmRWZWXmSOsUrtylvd0E5Oz3K2ZFSzk6BcnbPZCJva6AdvRkEn4wvji1091ZgKQPwjdwLnbF6PkHdCwT/GH0wc93JOePC9fvh+gigmOTxggH2/jOzEjOrMrNxZnYy8Iuw6k/hWjFL7BvAFIJLPhNR3PZ2FsGArNHMNpjZrWZWEVOveOU25e1oKGd3TTm7G8rZPaacnb6M5G3dox29/YGN7t6WoK4OOMHMity9PcP9ykf7h+u6BHWdZdUZ6ktOCT+9/HeCy4TuCYsVr319Cbg15nUtMM/dF4avFbM4ZnYQcB1wvbvXmtmEBM0Utz0WE5ytWw4MAz5B8M/OieE9qU0oXrlOeTsaep8noZydMuXsNCln90jG8rYG2tErBRIla4DWmDZK2N0rDdeJ4tka12aguQU4HrjK3d8OyxSvfT0MvEVwT81RBJcBVcXUK2b7+jmwAvhJF20Ut5C7HxdXdJeZvQr8APh6uFa8cpvydjT0Pk9OOTs1ytnpU85OUybztgba0WsG9ktSVxLTRrrXGafiBHUDNpZmdgPBJ293uPuPYqoUrzjuvoZgBlOAh83sD8ASMysNY6eYxTCzecDHgY+6e0cXTRW3rv0ncA0wlyBhK165TXk7GnqfJ6CcnTrl7PQoZ0eqT/K27tGO3lqgyswS/WCqCS5P06fiqVkbrhNdmtFZluiSjn7LzK4Fvgf8Grgkrlrx6oa7vwq8Anw1LFLMQuHfrJ8Q3Au33swmh5MbjQ+bVIRllShuXQr/4VnLnjMxilduU96Oht7ncZSze0c5Oznl7Gj1Vd7WQDt6SwjiemxsoZmVANOBl7LRqTy1JFwfn6DuQwTT87+cue5kV5iwrwF+C3zJw+cMxHiN4BKXZPECvf8AhgAjwq8Vsz2GEDx/cy6wLGapCevnha+/hOLWpfDv/Tj2THqkeOU25e1oKGfHUM6OjHJ2YsrZEeqrvK2BdvTuI0gmV8SVX0RwLf+CjPcoT7n7coI38dlm1jkpAeHXZwPPuPv6bPUvk8zsaoKEPR/4orvvim8TTt7wKDDLzI6M2bac4A/tMuJm1e2vkj1CxsxmEzxa5QVQzOJsJ/i9il86zyQ8Hr5+RHELmNnIJFU3ENya9SjofZYHlLcjoJy9h3J2epSze0Q5uwcynbdt3w/YpLfM7FaC+3EeIrik41DgcmAR8LFEf3AHEjM7jz2XtlwGFAE3ha9Xufv8mLYnAH8muGfn1phtRgMfdve/Z6TTWWRmXwNuA94jmLU0/v3zvrs/GbadTPCL3wHcDGwj+GfxcGCuuz+RqX5nk5k9BIwFniF4DmcJwWNlziG4p2ZW53MRFbOuhTOYrgRud/dLY8oHfNzM7GaCT7b/TPD7WU4we+ls4EVgtru3hG0HfLxymfJ2csrZ6VHOTp9ydnSUs7uW8bzt7loiXoAC4FvA2wSXHdQR3EdRnu2+5cJCcFmLJ1lqErQ/HngaaAIagSeAo7N9HBmM12+6iNc+MSP4B/GPwFaCBPUcMCfbx5HhmH0GeAxYTTA7ZAvBTKa3AgcmaD/gY9ZFLCeE77PbFLd9jv/08O9RXfg+207w3OWrgBLFK38W5e0uY6OcnV68lLPTj5lydnSxVM7uOj4Zzds6oy0iIiIiIiISId2jLSIiIiIiIhIhDbRFREREREREIqSBtoiIiIiIiEiENNAWERERERERiZAG2iIiIiIiIiIR0kBbREREREREJEIaaIuIiIiIiIhESANtEclpZnatmbmZzcp2X0RERCQ55WyRPTTQFunnwoTX3TIr2/0UEREZ6JSzRfqPwdnugIhkzHVd1NVmqhMiIiLSLeVskTyngbbIAOHu12a7DyIiItI95WyR/KdLx0VkL7H3V5nZBWb2ipm1mNkGM/uVmY1Jst3BZnaXmdWZWbuZrQ1fH5ykfYGZXWJmi8ysIfwey83sl11sc5aZLTazZjPbbGa/M7PqKI9fREQkXyhni+QundEWkWS+AZwM3Ac8DswEvgDMMrPj3L2+s6GZzQCeAoYCjwD/AKYA84DTzWyOuy+JaV8EPAZ8HFgN3ANsAyYAZwLPAcvi+vNV4LRw/88CxwGfBY40s+nu3hblwYuIiOQR5WyRHKOBtsgAYWbXJqlqdfcbE5SfChzn7q/E7ONm4ArgRuDCsMyAu4BhwDx3XxDT/rPA74D5ZnaYu+8Kq64lSNiPAmfHJlwzKw73Fe8UYIa7vxbT9h7gXOB04P6kBy8iIpJHlLNF8p+5e7b7ICJ9yMy6+yVvcPfKmPbXAtcAv3L3C+P2VQGsAoqBSndvM7MPE3ya/by7n5Dg+y8k+GT9RHf/i5kVAJuAImCyu6/tpv+d/fmBu38vrm428Axwk7tf2c1xioiI5DTlbJH+Q/doiwwQ7m5JlsokmzybYB8NwFKgBDg0LD46XD+TZD+d5UeF6ylABfBqdwk7zksJylaH6+Fp7EdERCSnKWeL5D8NtEUkmfeTlK8P1xVx63VJ2neWV8at69Lsz9YEZTvCdUGa+xIREelPlLNFcowG2iKSzOgk5Z0zmDbErRPObAqMjWvXmXw186iIiEg0lLNFcowG2iKSzInxBeH9XtOBVuDNsLhz4pVZSfYzO1z/LVy/RZC4jzCz/SPpqYiIyMCmnC2SYzTQFpFkzjOzo+LKriW47OzemFlHFwFvAzPN7KzYxuHrjwDvEEy+grvvBP4LGAL8PJyxNHabIjMbFfGxiIiI9GfK2SI5Ro/3EhkgunhUCMDD7r40rux/gUVmdj/BPVszw6UW+G5nI3d3M7sAeBK4z8z+SPAJ+AeAM4BG4PyYx4QAXEfwTM1PAe+Y2WNhuwMIngP6L8BvenSgIiIieU45WyT/aaAtMnBc00VdLcHMpLFuBh4ieAbnZ4EmgkR6lbtviG3o7i+a2Qzge8AcgmS8EbgXuMHd345r325mpwCXAOcDFwAGrA2/53PpH56IiEi/oZwtkuf0HG0R2UvMMzBnu3tNdnsjIiIiyShni+Qu3aMtIiIiIiIiEiENtEVEREREREQipIG2iIiIiIiISIR0j7aIiIiIiIhIhHRGW0RERERERCRCGmiLiIiIiIiIREgDbREREREREZEIaaAtIiIiIiIiEiENtEVEREREREQi9P+OGZ80hh2msAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Toe5_pLsstuo",
        "colab_type": "text"
      },
      "source": [
        "Saves the model history if one needs to do a cumulative plot of the loss-accuracy curves for all the models in the end."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaTW7dNCQeDs",
        "colab_type": "code",
        "cellView": "code",
        "colab": {}
      },
      "source": [
        "data = [model_history[loss_Accuracy[0]], model_history[loss_Accuracy[1]], model_history[loss_Accuracy[2]], model_history[loss_Accuracy[3]]]\n",
        "\n",
        "# saves the loss and accuracy curves\n",
        "filename = save_data_dir+'noSeasonGaps_%s.npy'%model_name\n",
        "with open(filename, 'wb') as f:\n",
        "  np.save(f, data, allow_pickle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr-jbZX8u_bV",
        "colab_type": "text"
      },
      "source": [
        "#### 6.2 Displaying the classification report, confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Z-OxLmpv_KC",
        "colab_type": "code",
        "outputId": "905f063e-a56d-4384-ea5e-8e464cc22874",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "y_predict = np.argmax(predictions, axis=1)\n",
        "y_test = np.argmax(testy, axis=1)\n",
        "\n",
        "print('Test set classification report:\\n')\n",
        "print(classification_report(y_test, y_predict,  \\\n",
        "                            target_names=output_class_names))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test set classification report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           2       0.92      0.93      0.93      1043\n",
            "           4       0.91      0.90      0.90       973\n",
            "          10       0.88      0.88      0.88       991\n",
            "          15       0.80      0.85      0.83      1028\n",
            "          20       0.90      0.78      0.84      1020\n",
            "          30       0.67      0.71      0.69      1016\n",
            "          40       0.67      0.64      0.66       975\n",
            "          60       0.59      0.74      0.66      1005\n",
            "          80       0.46      0.24      0.32       966\n",
            "         100       0.50      0.62      0.55       983\n",
            "\n",
            "    accuracy                           0.73     10000\n",
            "   macro avg       0.73      0.73      0.72     10000\n",
            "weighted avg       0.73      0.73      0.73     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYbr9Vpsv0Zu",
        "colab_type": "code",
        "outputId": "f90ff268-2a11-4b85-9e54-086106bc0ad9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        }
      },
      "source": [
        "# need this to avoid callback errors\n",
        "from sklearn.metrics import confusion_matrix\n",
        "importlib.reload(dat)\n",
        "\n",
        "# generate confusion matrix\n",
        "cfm = confusion_matrix(y_test, y_predict)  \n",
        "\n",
        "# row normalisation of confusion matrix\n",
        "cfm_probability = cfm.astype('float')/cfm.sum(axis=1)[:, np.newaxis]  \n",
        "# confusion probability matrix\n",
        "cfm_probability = cfm_probability * 100.0  \n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
        "dat.plotConfusionMatrix(ax, cfm_probability, output_class_names, 0)\n",
        "\n",
        "# save figure\n",
        "fig.savefig(save_image_dir+'/cfm_%s_epochs_%d_noSeasonGaps.png'%(model_name, max_epochs))    \n",
        "fig.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-x_2o3VOUtE2",
        "colab_type": "text"
      },
      "source": [
        "### 6. Optimizing the network\n",
        "\n",
        "* `validation_split_ratio`: Percentage of the seen dataset to be put aside for validation, rest is for training\n",
        "* `max_epochs`: maxmimum number of epochs to be iterated\n",
        "* `batch_size`: batch size for the training data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4im8AyzDU_YC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "validation_split_ratio = 0.2  \n",
        "max_epochs = 50  \n",
        "batch_size = 60 \n",
        "num_inputs = 1\n",
        "verbose = 1\n",
        "\n",
        "# series of values to be optimized\n",
        "batch_size_arr = [20, 40, 60, 80, 100, 120]\n",
        "learning_rate_arr = [1e-4, 1e-3, 1e-2, 1e-1]\n",
        "dropout_ratio_arr = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-VX60_KAee0",
        "colab_type": "text"
      },
      "source": [
        "#### 5.1 Optimizing batch size"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXnVZJPHksoi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "results_arr = []\n",
        "for batch_size in batch_size_arr:  \n",
        "  # trains the model\n",
        "  model, model_history = nnet.trainModel(model, trainX, trainy, batch_size, max_epochs,\\\n",
        "                                    validation_split_ratio, verbose)\n",
        "\n",
        "  # evaluates and predicts the results from the trained model\n",
        "  results, predictions = nnet.evaluatePredictModel(model, testX, testy, verbose = 1)  \n",
        "  results_arr.append(results)\n",
        "\n",
        "  # prints ongoing progress\n",
        "  print('Training completed for batch_size %d'%batch_size)\n",
        "  print('batch_size %d:'%batch_size + ' [test loss, test acc] = ', results)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-S3TWW5QN53",
        "colab_type": "text"
      },
      "source": [
        "#### 5.2 Optimizing learning rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ml4lPcvcQQrD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "results_lr_arr = []\n",
        "importlib.reload(nnet)\n",
        "\n",
        "for learning_rate in learning_rate_arr:  \n",
        "  # compile model\n",
        "  model = nnet.compileResNet(sample_params, r_0, save_image_dir, learning_rate, momentum)\n",
        "\n",
        "\n",
        "  # trains the model\n",
        "  model, model_history = nnet.trainModel(model, trainX, trainy, batch_size, max_epochs,\\\n",
        "                                    validation_split_ratio, verbose)\n",
        "\n",
        "  # evaluates and predicts the results from the trained model\n",
        "  results, predictions = nnet.evaluatePredictModel(model, testX, testy, verbose = 1)  \n",
        "  results_lr_arr.append(results)\n",
        "\n",
        "  # prints ongoing progress\n",
        "  print('Training completed for learning rate %d'%learning_rate)\n",
        "  print('learning rate %d:'%learning_rate + ' [test loss, test acc] = ', results)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}