BCI_Workshop_2024.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuNa18uDidWi"
      },
      "source": [
        "## **Tools to develop EEG event detection pipelines**\n",
        "## See more detail about the workshop here: https://thinknew.github.io/BCIWorkshop2024/\n",
        "## Download code to use offline or on Google Collab via this repo: https://github.com/rchia16/bciworkshop2024\n",
        "\n",
        "### Purpose\n",
        "**This notebook aims to demonstrate usage of pre-developed code for time-series classification, specifically:**\n",
        "\n",
        "* Demonstrate a preprocessing pipeline\n",
        "* Develop and tune your own deep learning classification pipeline\n",
        "* Evaluate the performance of applied methods"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hjw6kvLkQjH"
      },
      "source": [
        "# Setting Up the Environment\n",
        "The prerequisite packages to this tutorial are:\n",
        "\n",
        "\n",
        "*   [**MNE**](https://mne.tools/stable/install/mne_python.html#installing-mne-python-and-its-dependencies): EEG Data Package\n",
        "*   [**NumPy**](https://www.scipy.org/install.html): Numerical Computing (SciPy Download Page)\n",
        "*   [**SciPy**](https://www.scipy.org/install.html): Scientific Computing\n",
        "*   [**MatPlotLib**](https://matplotlib.org/users/installing.html): General Purpose Machine Learning Library\n",
        "*   [**Scikit-Learn**](https://scikit-learn.org/stable/install.html): General Purpose Machine Learning Library\n",
        "*   [**Tensorflow 2.0**](https://www.tensorflow.org/guide/effective_tf2/): Deep Learning Library from Google Brain\n",
        "*   [**PyWavelets**](https://pywavelets.readthedocs.io/): Open source wavelet transform software\n",
        "*   [**EntropyHub**](https://www.entropyhub.xyz/index.html): Open source toolkit for entropic data analysis\n",
        "\n",
        "We will use pip to setup the environment. In your own local machine, consider using conda and the environment.yml file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33UZvUsPiYiG",
        "outputId": "cad37b26-7f20-48bc-9d87-261a9e235cfe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ],
      "source": [
        "!pip install EntropyHub\n",
        "!pip install mne\n",
        "!pip install seaborn\n",
        "!pip install PyWavelets\n",
        "!pip install emd\n",
        "!pip install pyxdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9FhBXm-l1Aw"
      },
      "source": [
        "Import all of our required modules. Several functions have already been developed for use and are defined in:\n",
        "\n",
        "* augmentations.py\n",
        "* dsp.py\n",
        "* features.py\n",
        "* models.py\n",
        "* utils.py\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Augmentations**\n",
        "\n",
        "Contains the numpy implementation of time series data augmentations including transformations, pattern mixing, generative models, and decomposition ([Iwana B.K. & Uchida S., 2021](https://doi.org/10.1371/journal.pone.0254841) )\n",
        "\n",
        "**Digital Signal Processing (DSP)**\n",
        "\n",
        "Contains several signal processing functions for filtering, averaging, and transformations typically used in EEG analysis.\n",
        "\n",
        "**Features**\n",
        "\n",
        "Contains several calculations covering statisticaly, temporal, and frequency based features. Not all functions support multi-dimensional (> 1-D) calculation. Specifically, sample entropy, approximate entropy, PSD, zero-crossings, and peak functions take 1-D inputs only.\n",
        "\n",
        "**Models**\n",
        "\n",
        "Contains basic descriptions of linear and non-linear classification models. All models can be reached via the `'get_model'` function. Scikit-learn models may also be wrapped in a GridSearch hyperparameter optimisation function, see `'set_gridsearch'` and `'get_best_gridsearch'`. Confident users may adapt neural network architectures as desired.\n",
        "\n",
        "**Utils**\n",
        "\n",
        "Provides several functions for ease of use including sliding window process, model persistence, and model evaluation among others."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Environment Setup"
      ],
      "metadata": {
        "id": "bVwLxA-Q-W6u"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ONgqMrIGltrp"
      },
      "outputs": [],
      "source": [
        "import emd\n",
        "import mne\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from multiprocessing import Pool, cpu_count\n",
        "from functools import partial\n",
        "from itertools import repeat\n",
        "\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "from utils import *\n",
        "from dsp import run_hht, std_scale, notch_filter, butter_bandpass_filter\n",
        "from augmentations import *\n",
        "from features import *\n",
        "from models import *\n",
        "from configs import FS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "MLFDhrABo1_O"
      },
      "outputs": [],
      "source": [
        "# Set random seeds for reproducibility\n",
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Z0M5ARlckY1h"
      },
      "outputs": [],
      "source": [
        "# Set window size and shift\n",
        "window_size = 1 # seconds\n",
        "window_shift = 0.25 # seconds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SSW_TbENiqwB"
      },
      "source": [
        "# Pipeline Overview\n",
        "Steps\n",
        "\n",
        "1.   Load data\n",
        "2.   Preprocess\n",
        "3.   Split dataset\n",
        "4.   Initialise models\n",
        "5.   Train\n",
        "6.   Model persistence\n",
        "7.   See evaluation\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "TY1u9s-9-xRu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "piJPoVBVoeZs",
        "outputId": "bd7dee99-4a18-49d0-ed06-1d1827d7f536"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "124.99491110488259\n"
          ]
        }
      ],
      "source": [
        "# Load data\n",
        "eeg_data = \"./sample_data/14_06_Allen_open_close_eyes_25Hz.xdf\"\n",
        "\n",
        "(data_dict, marker_dict), header = load_xdf(eeg_data)\n",
        "\n",
        "fs = get_xdf_fs(data_dict)\n",
        "print(fs)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Measured frequency closely matches the EEG headset datasheet spec\n",
        "# Set to the datasheet spec.\n",
        "fs = FS"
      ],
      "metadata": {
        "id": "cf-oCPyXbAVl"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(marker_dict['time_series'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjiKmoZb85Q8",
        "outputId": "53db4830-9050-47c1-9443-f0596568d244"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = marker_dict['time_series']"
      ],
      "metadata": {
        "id": "nRzSM2nb-F_H"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('eyes' in labels[1][0])\n",
        "print('open' in labels[1][0])\n",
        "print('eyes' not in labels[1][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POy60Wrd-I0D",
        "outputId": "186eda31-a780-4f7c-e7cc-485820483f48"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Format the LabStreamingLayer output for simpler processing\n",
        "marker_labels, marker_timestamps = get_marker_data(marker_dict, re_str='open')\n",
        "\n",
        "data, data_timestamps = get_eeg_data(data_dict)"
      ],
      "metadata": {
        "id": "TNu9lzT6bFN0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1acafe4f-e19c-4322-cd7c-9c7de747e1c0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes'], ['Freq_25.0Hz'], ['close_eyes'], ['Freq_25.0Hz'], ['open_eyes']]\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n",
            "close_eyes\n",
            "open_eyes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up the preprocessing steps for our data\n",
        "def preprocess(data, fs, do_plot=False):\n",
        "    # 5 - 40 bandpass filtering\n",
        "    filt = butter_bandpass_filter(data, 5, 40, fs=fs, axis=0)\n",
        "\n",
        "    if do_plot:\n",
        "        plt.figure(figsize=(10, 7))\n",
        "        plt.subplot(211)\n",
        "        plt.plot(data[:, 0])\n",
        "        plt.plot(filt[:, 0])\n",
        "        plt.legend(['raw', 'processed'])\n",
        "\n",
        "        plt.subplot(212)\n",
        "        plt.title(\"FFT\")\n",
        "        plt.plot(*run_fft(data[:,0], fs=fs))\n",
        "        plt.plot(*run_fft(filt[:,0], fs=fs))\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "    return filt\n"
      ],
      "metadata": {
        "id": "D8bGC7In6WKH"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = preprocess(data[:120, :2], fs, do_plot=True)\n",
        "print(tmp.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "M52xBjB8Ahrj",
        "outputId": "a95a8b82-ba37-4bc8-8974-c78a7b70ffc3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(120, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we run the preprocessing pipeline on the data before segmenting into windows."
      ],
      "metadata": {
        "id": "icalmMGB6kzk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pre-process"
      ],
      "metadata": {
        "id": "Xpq2XOht-zvV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# segment data\n",
        "t0_array = marker_timestamps[:-1]\n",
        "t1_array = marker_timestamps[1:]\n",
        "\n",
        "keys = np.unique(marker_labels)\n",
        "data_segments = {int(k): [] for k in keys if not np.isnan(k) and int(k) in\n",
        "                [0, 1]}\n",
        "\n",
        "for lbl, t0, t1 in zip(marker_labels[:-1], t0_array, t1_array):\n",
        "    mask = (data_timestamps >= t0) & (data_timestamps < t1)\n",
        "\n",
        "    # get timestamps in period of interest\n",
        "    data_segment = data[mask]\n",
        "\n",
        "    # preprocess\n",
        "    data_segment = preprocess(data_segment, fs)\n",
        "\n",
        "    # create windows from period of interest\n",
        "    data_wins = get_windows(data_segment, int(window_size*fs),\n",
        "                            int(window_shift*fs))\n",
        "\n",
        "    data_segments[int(lbl)].append(data_wins)\n",
        "\n",
        "n_classes = len(data_segments)\n",
        "\n",
        "# Create arrays of labels and input data\n",
        "x, y = [], []\n",
        "for k, v in data_segments.items():\n",
        "    x_in = np.vstack(v)\n",
        "    x.append(x_in)\n",
        "    y.append(np.repeat(k, len(x_in)))\n",
        "\n",
        "x = np.concatenate(x, axis=0)\n",
        "y = np.concatenate(y, axis=0).reshape(-1, 1)"
      ],
      "metadata": {
        "id": "eD760Q4E6U9J"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0l7SXcJ-BoPo",
        "outputId": "6f8b3611-7853-48e2-ce66-7f23e45fbbe8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(799, 120, 11)\n",
            "(799, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We only only the EEG channels which can be found in 'get_channel_names'\n",
        "ch_names = get_channel_names(data_dict)\n",
        "print(ch_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZbDf7CTRAlp",
        "outputId": "7d4e8083-764f-482b-b713-c2ee6f98b65f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'F3', 1: 'F4', 2: 'C3', 3: 'Cz', 4: 'C4', 5: 'P3', 6: 'P4', 7: 'BIP', 8: 'accX', 9: 'accY', 10: 'accZ'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = x[..., :7]\n",
        "print(x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pySg-gzxRO1Y",
        "outputId": "37f55fc7-2822-4c34-8333-60067d8907bc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(799, 120, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# For Hilbert-Huang Transform\n",
        "hht = []\n",
        "f = []\n",
        "freq_edges, freq_centres = emd.spectra.define_hist_bins(5, 40, 5*50, 'linear')"
      ],
      "metadata": {
        "id": "OLjJWSH-ttbz"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Dataset\n",
        "\n",
        "We split the dataset before completing any feature calculations and transforms. This is to introduce augmentations to the training dataset only."
      ],
      "metadata": {
        "id": "jNIh1jLd-_Ex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random shuffle split to train and test sets.\n",
        "x_train, x_test, y_train, y_test = split_dataset(x, y)"
      ],
      "metadata": {
        "id": "BiEbeaU1_C_l"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_augs(data):\n",
        "    ''' A function to apply augmentations\n",
        "    :param data: numpy-like array of shape (N, T, C)'''\n",
        "    data_augs = jitter(data)\n",
        "    data_augs = scaling(data_augs)\n",
        "    return data_augs"
      ],
      "metadata": {
        "id": "SModly5Cv9OT"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_my_features(data, fname, m_func):\n",
        "  '''Returns presaved features from fname suffix, otherwise, calculates features\n",
        "  and HHT. Features are saved to pkl format and HHT frequency and magnitude is\n",
        "  saved as .npy\n",
        "  :param data: 1-D numpy-like array to calculate features and transforms\n",
        "  :type fname: Filename string suffix for calculations. Typically set as train\n",
        "  or test.'''\n",
        "  feat_fname = 'features_'+fname+'.pkl'\n",
        "  freq_fname = 'freqs_'+fname+'.npy'\n",
        "  hht_fname = 'hht_' + fname+'.npy'\n",
        "  try:\n",
        "    return pd.read_pickle(feat_fname), np.load(freq_fname), np.load(hht_fname)\n",
        "  except:\n",
        "    with Pool(cpu_count()) as p:\n",
        "        tmp_dict = p.map(m_func, data)\n",
        "        hht = p.starmap(run_hht, zip((data), repeat(freq_edges)))\n",
        "  # Create train and test dataframes from dictionary inputs\n",
        "  df = pd.DataFrame(tmp_dict)\n",
        "  df.replace([np.inf, -np.inf], 0, inplace=True)\n",
        "  df.replace(np.nan, 0, inplace=True)\n",
        "  df.to_pickle(feat_fname)\n",
        "\n",
        "  # Create array of augmented train HHT and non-augmented test HHT\n",
        "  f, hht_ = zip(*hht)\n",
        "  f = f[0]\n",
        "  hht = np.array(hht_)\n",
        "  np.save('freqs_'+fname, f)\n",
        "  np.save('hht_'+fname, hht)\n",
        "  return df, f, hht"
      ],
      "metadata": {
        "id": "CJ7Wgnx2weTu"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "m_ch = 6"
      ],
      "metadata": {
        "id": "JTStwjByTyxe"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply augmentations\n",
        "x_train_aug = apply_augs(x_train)\n",
        "tmp_dict = []\n",
        "m_func = partial(return_all_features, ch=m_ch)\n",
        "\n",
        "df_train, f, hht_train = get_my_features(x_train_aug[..., m_ch], 'train', m_func)\n",
        "df_test, _, hht_test = get_my_features(x_test[..., m_ch], 'test', m_func)"
      ],
      "metadata": {
        "id": "zsYwbBNTv5zr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99893b7f-8045-44de-a974-877e49239d00"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What does our feature data look like?\n",
        "print(df_train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L22iFSiiwGZW",
        "outputId": "cebe901f-8293-452f-d656-6cad112b829d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   ch6_mean    ch6_std  ch6_median    ch6_max    ch6_min  ch6_pk2pk  ch6_skew  \\\n",
            "0 -0.032301   6.477987   -0.104059  15.175903 -17.438462  32.614364 -0.087371   \n",
            "1  0.499506  10.961955    0.733298  32.289224 -26.754143  59.043366  0.144003   \n",
            "2  0.172817   4.944423    0.186346  14.048770 -11.619976  25.668746  0.276517   \n",
            "3  0.167072   4.363309    0.197776  11.044938 -10.942986  21.987924  0.055893   \n",
            "4  0.101932   4.576230   -0.509800  16.430418  -8.849767  25.280185  0.808259   \n",
            "\n",
            "   ch6_kurtosis  ch6_sample_entropy  ch6_approx_entropy  ...    ch6_rms  \\\n",
            "0     -0.591940                 0.0            1.190543  ...   6.478067   \n",
            "1      0.399903                 0.0            1.190543  ...  10.973330   \n",
            "2     -0.198114                 0.0            1.190543  ...   4.947442   \n",
            "3     -0.274230                 0.0            1.190543  ...   4.366506   \n",
            "4      0.757445                 0.0            1.190543  ...   4.577365   \n",
            "\n",
            "   ch6_max_freq  ch6_mean_power_band_0  ch6_mean_power_band_1  \\\n",
            "0          12.0               1.123425               1.462206   \n",
            "1          12.0               3.822700               3.751637   \n",
            "2          23.0               0.501164               0.768882   \n",
            "3          11.0               0.370994               0.532926   \n",
            "4           9.0               0.372442               0.582726   \n",
            "\n",
            "   ch6_mean_power_band_2  ch6_se_power_band_0  ch6_se_power_band_1  \\\n",
            "0               0.083329             0.431137             0.647887   \n",
            "1               0.049293             0.557363             0.540761   \n",
            "2               0.064835             0.454613             0.689695   \n",
            "3               0.047955             0.372737             0.668495   \n",
            "4               0.069127             0.292238             0.678823   \n",
            "\n",
            "   ch6_se_power_band_2  ch6_n_zero_crossings  ch6_npks  \n",
            "0             0.316630                    29         1  \n",
            "1             0.356649                    25         2  \n",
            "2             0.313514                    31         1  \n",
            "3             0.265331                    32         2  \n",
            "4             0.329784                    37         1  \n",
            "\n",
            "[5 rows x 21 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "3dax5y6Uj8If",
        "outputId": "0a084534-071f-480d-994b-864d4d8473b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 1000x600 with 1 Axes>,\n",
              " <Axes: title={'center': 'Transform Plot'}>)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# What does the HHT look like?\n",
        "plot_channel_transform(data_timestamps[:int(window_size*fs)], f, hht_test[10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "_o69jjrnBQvb"
      },
      "outputs": [],
      "source": [
        "# Setup features for training\n",
        "x_feats_train = df_train.values.astype('float32')\n",
        "x_feats_test = df_test.values.astype('float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "p7Touy5fw8iw"
      },
      "outputs": [],
      "source": [
        "# Perform One Hot Encoding on labels\n",
        "y_ohe, ohe = do_one_hot(y.reshape(-1, 1))\n",
        "n_classes = len(np.unique(y))\n",
        "\n",
        "y_train_ohe = ohe.transform(y_train.reshape(-1,1)).toarray().astype(int)\n",
        "y_test_ohe = ohe.transform(y_test.reshape(-1,1)).toarray().astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Initialisation\n",
        "\n",
        "We have supplied several models for you to use. These can be initialised by specifying any of the following strings into the first argument of `get_model`:\n",
        "\n",
        "*   svm : Support Vector Machine\n",
        "*   cca : Canonical Correlation Analysis\n",
        "*   lda : Linear Dicriminant Analysis\n",
        "*   tree: Decision Tree\n",
        "*   nb : Categorical Naive Bayes\n",
        "*   knn : K Nearest Neighours\n",
        "*   fnn : Fully connected Neural Network\n",
        "*   cnn1d : 1-D Convolutional Neural Network\n",
        "*   cnn2d : 2-D Convolutional Neural Network\n",
        "*   lstm : Long Short-Term Memory Neural Network\n",
        "*   attn : Patch input to Attention Encoder Neural Network\n",
        "\n",
        "Note CNN2D will only work for 2D transformations on data, such as STFT, HHT, or CWT. See dsp.py for functions."
      ],
      "metadata": {
        "id": "SUID3ApPAttv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this example we use FNN and LDA. The FNN model was detailed in `model.py` as:\n",
        "```\n",
        "self.model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Dense(32),\n",
        "  tf.keras.layers.Dense(64, activation='relu'),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dense(128),\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(self.n_classes, activation='softmax'),\n",
        "])\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "QblKPkkcOEfD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "tvHMLdjiw1rg"
      },
      "outputs": [],
      "source": [
        "fnn = get_model('fnn', n_classes=n_classes)\n",
        "lda = get_model('lda', n_classes=n_classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Training and Evaluation\n",
        "\n",
        "The fully connected neural network (FNN) trains on the preprocessed and augmented windowed data while the linear discriminant analysis (LDA) model trains on extracted features from augmented training sets."
      ],
      "metadata": {
        "id": "2rhXpa2QAw-I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Network"
      ],
      "metadata": {
        "id": "fS1ImDaJBJp2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1QMloh_w_UM",
        "outputId": "513e1a12-6bbf-419d-eaa7-4226b4dd9a4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "15/15 [==============================] - 2s 40ms/step - loss: 2.2979 - categorical_accuracy: 0.6096 - val_loss: 1.4951 - val_categorical_accuracy: 0.6938\n",
            "Epoch 2/10\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.4625 - categorical_accuracy: 0.8392 - val_loss: 0.5625 - val_categorical_accuracy: 0.8125\n",
            "Epoch 3/10\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.1852 - categorical_accuracy: 0.9248 - val_loss: 0.5488 - val_categorical_accuracy: 0.7812\n",
            "Epoch 4/10\n",
            "15/15 [==============================] - 0s 22ms/step - loss: 0.0777 - categorical_accuracy: 0.9749 - val_loss: 0.4052 - val_categorical_accuracy: 0.8500\n",
            "Epoch 5/10\n",
            "15/15 [==============================] - 0s 24ms/step - loss: 0.0455 - categorical_accuracy: 0.9937 - val_loss: 0.4041 - val_categorical_accuracy: 0.8687\n",
            "Epoch 6/10\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.0234 - categorical_accuracy: 1.0000 - val_loss: 0.4087 - val_categorical_accuracy: 0.8500\n",
            "Epoch 7/10\n",
            "15/15 [==============================] - 0s 25ms/step - loss: 0.0167 - categorical_accuracy: 1.0000 - val_loss: 0.4298 - val_categorical_accuracy: 0.8875\n",
            "Epoch 8/10\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.0123 - categorical_accuracy: 1.0000 - val_loss: 0.4157 - val_categorical_accuracy: 0.8750\n",
            "Epoch 9/10\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.0090 - categorical_accuracy: 1.0000 - val_loss: 0.4409 - val_categorical_accuracy: 0.8875\n",
            "Epoch 10/10\n",
            "15/15 [==============================] - 0s 23ms/step - loss: 0.0076 - categorical_accuracy: 1.0000 - val_loss: 0.4293 - val_categorical_accuracy: 0.8938\n",
            "5/5 [==============================] - 0s 7ms/step\n"
          ]
        }
      ],
      "source": [
        "fnn.compile_model()\n",
        "fnn.fit_model(x_train_aug, y_train_ohe, validation_split=0.25)\n",
        "y_hat_fnn = fnn.model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plot_roc_curve(y_test_ohe, y_hat_fnn)\n",
        "\n",
        "pred = np.argmax(y_hat_fnn, axis=1)\n",
        "plt.figure()\n",
        "plot_confusion_matrix(y_test, pred, labels=np.unique(y))\n",
        "\n",
        "show_classifier_performance(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1cq3vyKkwPsI",
        "outputId": "d543d4c8-26a0-4b73-c0e1-60bc2166ce3c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1GoFe1rjX/XQuCu/05uQzdppWuuLM7fDy/Jhjwbv5DYqa497Tt57LScG/TJwq4eCVamHO2P2r85MjAmh6kzI+ccXW9ks5CPzTzJkiDl5+7n7s7t8DyXtbQ+zLt6nkF1c8777iS96NPpwroAipc9kZu31DIIHXNNxtFE6NXHEFwoHr2fFbn52LAYqGL9yP3bI5kKDLGm42iKB23rjPnb23smFa68eV3Ir6L92P3fxXPgqf03KlXiT1A+8AcCjcjG5NDm76Qu0ml5yxG3flo+gpLZfqRNGgwG0A/ov0xrYqF3dNUKPF/dgNewMoflzHuZoxhBS+CcD5GAMRK/ISa4BKrz8Q+48/xQ/rOVM9lpeL3wDgukXHtZ8DACjT7oHYw3kp/o+BU1UsvFLqOgARdi1Xl+YDIKh1GEc1xSkzdAdmo2fKjAxV3ISFhREQEOCyLyAggPDwcKKjozGZTAmOGT9+PCNHjnxSKYoMLNoWzf5r+5l1aBaV8lZyac0QTx+lFNFWOxabAwC7QxFlsd2/HwVAjNXu3B8f63go1qESxt6NuX9/ZqDDgSL+Odmd+/Xcf57ae6+ZFscD+20PxcbFaHA4/9UneV6b83zx54+PHa6f54yNVXpi4k6L415vCQXEKEOC52FROud++yNirUp/P1YlFRvXsmBzOa/OeW9i57VxP9aqHhGrtPdj0WFKJFYpjfP5PBirSyRflch5LUrn7GPiGnvvvErzQOz9+xOLdTyQQ2wSz80Z+0AOlyIVN2w2cuv1xCo9Vo1nywuNUko9Oiz9aTQali9fTsuWLZOMKVWqFN26dWPIkCHOfWvWrOGVV14hKioq0eImsZaboKAg7ty5g6+vb4J4kXXF2GLouKYjdmVn0SuL8NZ7y2Wpp/SylJfOizbf7WTP2VuAHTR2QAMPfrhoLHH/Kj33uxemLvbvj1/GbIx7o89Il6U0dy+jmdcEx53zGJQi/hk7gJh7vxfzAx8BFsCm0bjEKiD6XqxJxX+sgRWwuhmrV4r7v3m4a9dheXUGxjIv378sFROFzRpz77LU/ctHUXfjWh68zL73L0vFRmG1xF9qeuAyz91bKBRGbx/0BqP7sZaYuEtNGi0mnxz3YyNuo5TDrdi4S01x/ydsVguWmAg0aDBlz5mq2Jio8LhLTUZvDPcuCcVdaoq/fJQrVbGx0RFxl5oM3hi9H7zUdPuRsVu3bKFrp46ULFmc0KVLyOabG61Wi8mgS9PLUuHh4fj5+aXo8ztDtdzky5ePK1euuOy7cuUKvr6+iRY2AF5eXnh5eSV6n8h6HtUPY1nzZS7xeq3e+aH0oAc/wOLptDrM2oStPe7Eeum8eOBLIwBajTbRViR3Yo06o0tBAXFfKJKLVcq1dYN7H0vRVvsD+zSAEZsdbPbHi42xOuDeN/z4WLsdohKJvRVlu1fYAOhAPfRCAChjwn2piK1WOCe5sxmdb9Jmo08isXog4fuMW7GJvh27Ews+D5YODgdqdW/U9fNo9fcLGIcdUBpMGoXm3kuhFCi7Bj1gTGGsDtDrFPGfXcoe1wLhTmz2BgOhyquuz83oCyT84DLn9k/4hJOMzfuYsT7gk/B3Z86V5zFj9ZAt4d+cW7HGXAlj0ZPdlPB9xr3YHInEgo93wtc9PtbhcDB+/HiGDx+Ow+HAz88Xhx18vBP7e3qyMlRxU7NmTdasWeOyb926ddSsWdNDGYmMptXKVpy7e465L8+lsn9lADac28DAzQOpFlCNWY1neTjDp4NSilbTtz9QQDy9/h7a0Nmqgt2KcXk3dOd3JX+QO+6CZkLane5JUXY7Z3/VE3sngNL/C4UchQEIGz6aO6vW4N+/N7m7dATAdvUqJ0JagF5Hmd1bnee4On4Ct5b+TJ6eb5H3nbcBcNy9y/G6jQB4ZtcWMMR9jFyd9A035y4kV+cOBLzXN+4EVhvHatQBoNSf/0OXPTsA176eyo1Z88hptOFf24LW6PkPQ+GeK1eu0KlTJ9atWwdA586dmTJlCj6JFHqe4NHiJiIighMnTjhvnz59mv3795MrVy4KFSrEkCFDuHjxInPnzgXgnXfe4dtvv2XQoEG8+eab/PHHH4SGhvLrr7966imIp1isPZb3Nr4HwKQXJrlc3hD3+6wkJspizxCFzcOtKqwfC8d/82xSTwll0xB9PRAAh19JtPEjj+Jb8AwmMOWI2/aOv3Svub8PQH/vb0bvfX+/9YEZREx+YDDcj3k4Vm+9H+vtB6a4lhPNvctCMUePoTEk7Kcinm5//PEHb7zxBmFhYZjNZqZOnUqXLl08nZYLj/a52bRpEy+88EKC/V26dGH27Nl07dqVM2fOsGnTJpdj3nvvPQ4fPkzBggUZNmwYXbt2TfFjunPNTmRsUdaoBCN83BkenJm50zLj0jLiIdozf2LYOBKNzeKyX6MBjbNnh4KrR+L+bf4NFKzxxPN8miiHA+vVcBwGP7xKl0ajjStKHBYL2Gxo9Ho091pMlMOBionrq6U1378UkmisUqjouP5XGtP9PlXKYkG5G/vAPpEx2Gw2nn32WY4cOUK5cuUIDQ2lbNmyT+Sx3fn8fmo6FD8pUtxkTKmZ30Sv1bPmdNxlzFeKveLsdPukJNcy4mlRFjvVxqx/ZFy1wjlZ+k5Nz34A3Q2DabUg6sYjQ5UCVaEjmte+cX6YK6sVZbWCTof2gf53juhoUAqNlxcaXVzxpmw2lMUCWi1ab+/UxcbEgMOBxmhEo49rHFd2Oyo21r1YjQbtA30JHbGxYLejMRicrR3uxAqRVg4cOMD06dP58ssvMZuf3KhSKW6SIcVNxlRrYS3uWu+y+tXVFPaN6zuw6Ogixu0cx0uFX2Ji/YnO2AahDbgafZWlzZbyTK5nPJJvevRZaavbSBPtzjQ7X7yaxXOj0yZevGg199tFPObWGbh5EgLKQ6PRkERGSinODppE9OETFFu1Eq+SJeMODw0lbPgIfBo0IGjKt874Ey82wHrpEkWWhmKqUAGAO6tWcemDQWSrVZNCM2c6Y082bYrlxEkKzZlDtuC4FqG769dzoU9fTJUrU2TRQmfs6VatiTl0iILTp5G9fn0AIrZt4/xbb+P1zDMUW7HcGXu2U2eidu+mwORJ+DZuDEDU3r2c7fAGhsKFKPHA7OvnevYkcvOfBI4bR47X4jrhxhw5wulXX0Pv70/JPzc7Yy/068/dtWsJGDaUXG+84e4rLoSL//3vf5w9e5bu3bt7NI9MO1pKiIwi2pq2fVZ8iWCUfhZemnSYe+VM2p8yzelN8PoM8E+6WFVRUVgux7XuOO5dYhFCpJ7NZmPEiBGMHz8evV5P1apVqVKliqfTShFpuRFPJYvdwsjtcZMvjqg5Apsj7kM9tdPuP2lRFhtlh8d9606LPiu6fXPx+u09HLmKY639flqkCIBRp80YfR7yPQsBKbuu74iKQuPtLZel5LKUeAwXLlygffv2bN0aN3runXfeYdKkSXh7e65forTciAzP5rCx8uRKAD4O/jjR+VgMWkOi/WgenBTvaWA26h5/CvLDcfPvaKt0xquqXGZIjvahPgBJfcBrE5kbS6PXO4uMVMcm8uav0enQJNI3wa3YRObrcidWiJRas2YNnTt35saNG2TPnp0ff/yRNm3aeDott0hxI54q8R2H9Vo9A6oOAHiyHYH/WQrXjqT6cKXA5nCAXTFQfxoAw6bdoNM+4shk2K1wdhuggQqtUn+eDEophbJaXeZCcUTF/T9JrIXm4eJGCJFyH3/8MePGxa03VqVKFUJDQylevLiHs3KfFDfiqVJ7UW1sysb6VuvpVr7bk33wg8vg57cf6xQawHDvp0/8X9dfj5lXvCLPg1/BNDpZxqCU4myHN/AqUZzA0aOd+4/Xfh4VHU3x9esxFoxbbPPG7Nlc+3Iivk2bEjh2jLReCJEKuXLFzWrct29fJkyYkGFn+JfiRmR+EVfh6GqU3YbV7kg8Rtkx/PkpGsBWsjHq3myu7rDZFYt2nXPZ5+/rxSsVAnnsMUc6PVTp+njnyIBUdDTR+/YRvW8fAUOGJNsqE9+yY710yTnPihDi0SIjI8mWLRsAAwYMIDg4mOeff97DWT0e6VAsniqJzWfzWGyx8GNDCPsnReF7HCVpYxnusipwasR3Ik7rheOyGmWxcGPmTJTFQp533nEWLcldlpKJ4YRIGYvFwqBBg1i7di27d+9+apZOSIp0KBZPrcRWl7bYLYzbOQ69Vs+g6oMSLPDI+d2ou5ew2JJodUmG7uQ69GH/4PDOwZrI0snGRisvJtpaP3Zhk2BJgDSilIobAZOaUTVJzUDrTuwjZqt93Jlt0esT7VeTu2fPBK9lYi04MjJIiJQ7deoUbdu25e+//wZg1apVtG/f3sNZpR0pbsQTo5Si82+d2X9tPwDb2m/D1+iLzWHj11O/EmOPYUDVAa7FzcFl8NNbaEhsbeSU6xn+Jusc1YDkh2a/8hiPES89Wmvi+57k7NAev2bNAIg5fJgzrdtgyJ+fEn9scMZeHPA+ERs2kG/USHLeG+FgOXmSU82ao8uZk1Lb73cCuvzxUMJXryZgyIfkurc2jPXSZU42bIjGZOKZfXudsVdGj+b20mXk7d+PPO+8A4D91i3+q1UbgDJH73fEvvrFF9yaO4/cPXvi/17/uOcQHc2xKlUBKL13j3OUz7UpU7nx3Xfk7NyJfB995DxHfGzJv7ahz5XY6sZCiNT46aefePPNNwkPDydnzpzMmTOHZvfeVzILKW5EuotfhkApxZQXv2fekTlM/2cK0RY7emyAkY5lurD7wmY05/cTE18YWCLwWt0fDXDEEUQEqRvivc5e1VnYpFerSnqL73viiIoie4MGMiJICOG2mJgYBg4cyJQpUwCoVasWixYtolChQh7OLO1JnxuRrhJdhkBzb/FDZSB+Kv0Xtbv5wTAZnSbhf8cH+8E87oR4GbUPjCMmhgvvvouy2Qn6/ju03t5Z5rKU9KERIm307duXb7+NW4Jk8ODBjB49GkMGupQra0slQ4qb9PVgK02MPQaH3UD1sfGXTGygceCrLGQn2nmMryaKBcax5NJEcEXlIEbd/4C7iS99rX25oPI+HYs4CiFEBhUWFsZLL73EhAkTaHxvLbOMRDoUC4+430pzE3Ph6ejMZ7l7ZCzc66Dbpekhfj65mM53Iuh38yYPf19wBDxL9q5ryf5An5u8wP/ubWfUVhchhPCE6Oholi9fTocOHQDIly8fBw4cQKtNg5GoTzkpbkSacS4WqbGiM58FQK+NAoeJqoVykl1rB+AfLz16rQEeXPfJxx9t65mYTdKXRAghHtfRo0dp06YNBw8eRK/XO5dPyAqFDUhxI9KDMvBrrWkYf+5AgLFnXK+aq2D9H7yr0WDKUwrNhwfAKIVMSjmiozndujUARZcuTXStIyGEAJg7dy69evUiKioKf39/56zDWYkUNyJVHux0qjGZiLbaibLYMdhtaJWdfP8swRh9Jy5WgbJr0AF6sy+a12eC0ezsSJomnU4fnMzNnVh3VoxOi9Wl41eBdnN1aZTCcuIkzhdUCCEeEhkZSd++fZk1axYAL774IvPnzycwMNDDmT15WaN9SqQ527VrHKtSlWM1gmk1fTtlh6+l2pj1dD+0khWrP+Z26G9xge2X4Oh9iGPLAjm2LBDeOwL5ygNwdfJXHKtSlauTv3rgxLa481apiuPuXefu6999Hxf76acueRyrEcyxKlWxXbvm3Hdz3jyOValK2PARLrH/1avPsSpVsZ67v0TCrdBQjlWpyqVBg11iT4Y05liVqsQeP+7cd2fVao5VqcqF//s/l9hTLVtyrEpVog8ccO67u349x6pU5Xz3Hi6xZ9q151iVqkTu2OHcF7F1K8eqVOVsx04usee6vcmxKlWJ2LQJjZcXhebModCcOWgy6FovQoj08++//1KjRg1mzZqFVqtl5MiR/O9//8uShQ1Iy41IIWWxcHPePABydXL9EHYZ5n3P33rFmYCCdC9WH2205f4delnzJzU0Oh3Zgmt4Og0hxFPq5MmTHD58mMDAQBYuXEj9+vU9nZJHyVBwkSKOqKj7s8v2yoFGD8oGDqX472YEACX9fcAB0XevUifQjF2nYWeHnZj0pmQvNcllqUdflopfckEIIeIppVxGkM6YMYNmzZrh7+/vwazSjwwFF2lPr8e3eVM4uAzr9cPY9RoMSmEAymjBAcTciPsj81KKVpEKyr+OXqtHo9E4p9p/kMZoTLB6szuxkMQaQ+7EJrEeUWIddt2K1esTLUgeLF5SEyuEEAAHDhzg3XffZfHixQQFBQHw1ltveTirp4cUNyJFNAYD3xUozijzTd7Pk4//ZTeS72pl/G+VBcCqj+JI8eXoNVr21Z7MUL+CkKekh7MWQojMRSnF999/T79+/YiNjeX9998nNDTU02k9daS4ESkSbbXjd3U3GOCaygFEcUbl4z9HBQAq5gdYDmih+AueS1QIITKp8PBwevTowZIlSwB45ZVXmDp1qoezejpJcSNSrIb2KACTK3ZCVeuCXqPHoIu7TOOl1xBrl6JGCCHSw969e2nbti0nTpxAr9czfvx4BgwYkGUm5XOXFDciRaLuXMW88iZ7yE+R9hXJbc6eIMaslUn5hBAirW3cuJHGjRtjsVgoVKgQS5Ys4bnnnvN0Wk81KW6yulObsZ34gxiLFY0Gst3rxKoURMTEYLc7MBj0aMKvY46NO0Tllr40QgjxpDz33HOULl2aYsWKMXPmzCw547C7pLjJyuxWWNSe/27ZYXUubvtAzaaXANAAf/6dj2IntFyoFc2LQbcIb+7FHkcJQrLl8GjaQgiR2f37778888wz6HQ6TCYTGzduJFeuXLJ4cArJxbqs7Pp/YI0EXfycLRqs1XsSXaUHM22NiVRx+0+pQObYG7PW+BKbCg/Ax1tmyBVCiPSglGLSpElUrlyZ8ePHO/fnzp1bChs3SMtNVnblXwBKlihN9I5FjJi7l11brM67DeWi2PDj87TzyYbBK64/jcmgkz8wIYRIBzdv3qRr166sWrUKgEOHDiWYqE+kjBQ3WZjl4n62XsqBIxbKOQzsumR1ub9i0QIE5csnf1hCCJHO/vrrL9q1a8f58+cxGo1MmjSJXr16yftvKklxk4XFXj5M4J9m4AL1bb+DzgeAv4c2xGzUSSuNEEKkM4fDwRdffMFHH32E3W6nRIkShIaGUrlyZU+nlqFJcZOFaa8d5UwRB3dUNhyauO5X1QrnJHc2oxQ1QgjxBJw8eZLhw4djt9tp37493333HdmzJ5xqQ7hHipusKuom2aIv8fJzUD7mR7YPbS6tNUII8YSVLFmSb7/9FqUUb7/9trz/phEpbrKAGFsMNrsNuxUM1gg0tmgc53ej7FouOnITgRmzUYfZKP8dhBAiPTkcDj799FMaNmxIjRo1AHj77bc9nFXmI59mWUCvde/Q/IudHCxeguH5/wRg/x0zXr/l42oODdT3bH5CCJEVXLlyhU6dOrFu3Tp++OEHDh06RLZs2TydVqYkxU0mE2OLoeOajgDMbzIfb703ulg7z1yE/LEncfhrsOm02JQOL8CBhmqFc2Iy6DybuBBCZGJ//PEHb7zxBmFhYZhMJkaMGCGFTTqS4iaTcSgHx24dc24DfFZ3ElvytKMip1AaDbZ+BynllRPLsAj80bA0Vx65ziuEEOnAbrczevRoRo0ahVKKcuXKERoaStmyZT2dWqYmxU0m46Xz4ruG3xFrc2C364hSNpQxO6fqFaalYTf2Yi9izlUgLjibLHQphBDpJTw8nBYtWrBp0yYA3nzzTb755hvMZnnvTW9S3GQyWo2WX5cdoueNz7FoYoC4daL66KMAsJVvjVyAEkKI9Ofj40O2bNnIli0b06dPp2PHjp5OKcuQ4iYDU0oRbbW77Iuy2ClybSOF9NcSxN/S5iRHuWZPKj0hhMhybDYbVqsVk8mEVqtlzpw5XL9+ndKlS3s6tSxFipsMSilFq+nb2XP21kP32Onme5M/dd5ULt0effW3ccTGcrnvx2h1RvyUAeldI4QQae/ChQt06NCBokWLMmfOHCBuwcvcuXN7OLOsR1YFz6CirXb2nL2F1mHHyxaL0X5vXSiNnWUFLtE/d17wyYd3wDOY8pXBevIsscf/A4fDs4kLIUQmtGbNGipVqsSWLVtYvnw5Z86c8XRKWZq03GRwwWFHGL5rNl6VKpFv7jxibDH0Wjae7vPCuXBlDgWnP4dPnToEzfgRAI2Xl4czFkKIzMNqtfLxxx8zYcIEAKpUqcKSJUsoUqSIZxPL4qS4yWDi+9lEWVz72mg1GsxGPWajD0s0BThtvUEMRgA0Oh0+tWt7Il0hhMi0zp07R7t27di+fTsAffv2ZcKECXjJl0iPk+ImA0msn83OfGUI2rETs7fxfqAlisIvXofXfkTz7PMeyFQIITI3h8NB48aNOXLkCH5+fsycOZPXXnvN02mJe6TPTQYS388GQKscVLh2gtcM1zH7mNF6e98PtEah1YM2e040eqlfhRAirWm1Wr766iuee+459u3bJ4XNU0Y++TIIZbPhiIrCaLdi0RnYObAO1+sMirvz43Zwr4iJscXQ3XAHAgP4QafHO5lzCiGESLlTp05x8uRJXnrpJQBeeuklGjRogFYr7QRPG/mNZBDhv/3G+eeCGbFjJgAmow5jieIYSxSHB5ZOcCgH+/WK/d5eOAxS2gghRFr46aefqFy5Mq1ateLkyZPO/VLYPJ2k5SaD0ppMFF+9OsF+o87I5JuRYI3CaPTzQGZCCJF5xMTEMHDgQKZMmQJAzZo1MRgMHs5KPIoUNxmE78svo6tTjxZj/0g2Tq/V0+BuODis4J39CWUnhBCZz3///Ufbtm3Zt28fAIMGDWLMmDFS3GQAUtw8ZZRSKIsF7b2hhMpuJ2rPHrQmE5oSpbDoHvFHZbfGFTYABlmcTQghUmPx4sX06NGDu3fvkjt3bubOnUuTJk08nZZIISluniJKKc52eANdrlwETfk2bl9sLOc6dwEgaMfOR57DHhPOXu+4wqiK3lsWyRRCiFTYuXMnd+/epU6dOixcuJCCBQt6OiXhBiluniIqOproe82fjqgotGYzWrMZQ/786PPlQ2MyPfIcsbF3eDMwAICdKKTtRgghUkYphebeAI3PPvuMEiVK0LNnT/QypUaGI7+xp4jGYCDfqJGoWAuaB67pFvt1NRpv7wQrgCd6DlsMxS0W0OjQSC9+IYRIkfnz57Nw4UJWrlyJXq/HaDTSu3dvT6clUkmKm6eEIyoKgBytWiUoSrQpaLGJZ7LbWHExDHwCQJ/y44QQIiuKjIykb9++zJo1C4BZs2bRvXt3D2clHpd8tX9KnGjQkGNVqmJ5YP6EVLHGFUnSmVgIIZL377//UqNGDWbNmoVGo+GTTz7hzTff9HRaIg14vLiZMmUKRYoUwdvbm+DgYHbt2pVs/OTJkyldujQmk4mgoCDee+89YmJinlC2acMRG8vFgR9wceAHOGJjHxmvlCLKYkuwWGaiLJFx/xqzPWaWQgiROSmlmDVrFtWrV+fw4cPky5ePDRs2MGLECHQ6GYaRGXj0stSSJUsYMGAA06dPJzg4mMmTJxMSEsKxY8fw9/dPEL9w4UI+/PBDZs6cSa1atTh+/Dhdu3ZFo9EwceJEDzyDVLLbCb83AV/gqJEAlNiwHgCNt+uswoktlpmcmNg79M2XF7xi+MYWg7deZikWQogHjRw5kpEj4957X3rpJebPn5/oZ47IuDzacjNx4kS6d+9Ot27dKFu2LNOnT8dsNjNz5sxE4//66y9q165Nhw4dKFKkCI0aNaJ9+/aPbO152mgMBgKGfEjAkA+dHYfjR0Y93N/mwcUy41UrnBOTIfFvFw5LFDtMJnZoLTiUI32egBBCZGBt27bF19eXsWPH8vvvv0thkwl5rOXGYrGwZ88ehgwZ4tyn1Wpp2LAh27dvT/SYWrVqMX/+fHbt2kWNGjU4deoUa9asoVOnTkk+TmxsLLEPXPoJDw9PuyeRShqDgVxdurh93N9DG2I26jAZdM7hig8z2mIZf/U6BFbCqDM+bqpCCJHhKaU4cOAAlSpVAqBMmTKcPn2aXLlyeTYxkW481nJz/fp17HY7AQEBLvsDAgIICwtL9JgOHTowatQonn/+eQwGA8WLF6d+/fp89NFHST7O+PHj8fPzc/4EBQWl6fN4ksxGHWajPsnCBkBvi6FpZBRNvfOj18pgOCFE1hYeHk6HDh2oWrUqW7Zsce6XwiZz83iHYnds2rSJcePGMXXqVPbu3cvPP//Mr7/+yujRo5M8ZsiQIdy5c8f5c/78+SeYceKUw4HlwkUsFy6iHGl86chyb7SUUUZLCSGytn379lG1alUWL16MRqPhyJEjnk5JPCEe+2qfJ08edDodV65ccdl/5coV8uXLl+gxw4YNo1OnTrz99tsAVKhQgcjISHr06MHHH3+c6NLzXl5eeN1bp+lpoWJiONmwIQCl9+5BY067QsRuieCI0QhYKOOwo9NKz38hRNailGLq1KkMGDAAi8VCoUKFWLx4MTVr1vR0auIJ8VjLjdFopGrVqmzYsMG5z+FwsGHDhiT/A0ZFRSUoYOKH7Sml0i/ZdKAxmVK0nIK7Yi13aV8gH+1v/UWs/dHDzIUQIjO5ffs2rVu3pk+fPlgsFpo3b86+ffuksMliPNopY8CAAXTp0oVq1apRo0YNJk+eTGRkJN26dQOgc+fOFChQgPHjxwPQrFkzJk6cSOXKlQkODubEiRMMGzaMZs2aZai5CbRmM8/s25su59ZYo8lvtYHJL9m+OUIIkRmtWLGCn376CYPBwOeff06/fv3kvTAL8mhx07ZtW65du8bw4cMJCwujUqVK/P77785OxufOnXNpqRk6dCgajYahQ4dy8eJF8ubNS7NmzRg7dqynnsJTx2SLZe2FSxDSV5ZfEEJkOV26dOGff/6hffv2VK9e3dPpCA/RqIx2PecxhYeH4+fnx507d/D19fV0Oo8UZbFRdvhaAA6PCsFsfEQ9urAdHP8Nmn0FVbumf4JCCOFBN2/eZOjQoc6RsSLzcufzW8YKpzOlFCo6GrjXz0ajwWGxEDZsOBqjgYBhw9Aa03A+Guu95RdkbSkhRCa3fft22rVrx7lz57hz5w4LFizwdEriKZGhhoJnNEopznZ4g2NVqnKsSlXst+7NNGyzcXfDBm4vXQY2W5o+Zqwlkv/zz8P/nf5JOhQLITIlh8PBhAkTqFu3LufOnaN48eK8//77nk5LPEWkuElHGo2GIosW4j/Q9Y9OazaT++23MFWpkuyIqbgFM1OwWOYD7NYoNmYzs/H2YewO944VQoin3fXr12nWrBmDBg3CZrPRtm1b9u7dS5UqVTydmniKyGWpJyBnhw7k7NDBpZDJ/eab5O7ZM8le/O4umBnPYI1ixPUbUPs9DDrDY+UthBBPk/3799O0aVMuXryIl5cXX3/9Nd27d5fRUCIBKW6eAG0ik/RpHtHP5uEFM5NbLPNBBks0rSIjoUhj0EpxI4TIPAoWLAhA6dKlCQ0N5dlnn/VwRuJpJcVNOnJYLFz94gsA/AcOTHXH4b+HNiR3NmPKvp1Y7y2/IB2KhRCZQHh4uHNkTJ48eVi7di2FCxfGx8fHw5mJp5n0uUlPNhu35s7j1tx5j9Vx2GxMYhVwhwMu7Yfzu50/DmsUJwwGTkRfxaHSeN0qIYR4gjZu3Ejp0qWZM2eOc1+5cuWksBGPJC036Uij15O7Z0/ndpr7/UPY9Z3LrhiNhlcLBsKGHuzssBOztOAIITIYu93OmDFjGDVqFA6HgylTptCpU6dE1w8UIjFS3KSDB+e2yds/jab+jrgGtuj7t8/vul/Y5CgMzsdQ5NTowSjfbIQQGc/ly5fp2LEjf/zxBwDdunXjm2++kcJGuEWKm3SgoqM5VqUqkDarfuv3zobfk5jDoWYfCLm//IQZ+POxHk0IITxj3bp1dOzYkatXr5ItWzamTZtGp06dPJ2WyICkuMkAtOe339vQx/3EK/QcNBjumaSEECINnTp1ipdffhm73U6FChUIDQ3lmWee8XRaIoOS4iaNKIuFa1OmApCn97uU3rsHINlJ+lJKE3s3buOVL2W9KCFEplSsWDEGDx7MjRs3mDRpEqY0eO8UWZcUN2lE2Wzc+C6uD0yenj0Sndsm1WLD4/71fvSicLH2WIZvi2vNGVV7FF46r7TLQwgh0tBvv/1G6dKlKVasGABjxoyRCflEmpAeWmlFrydn507k7NwJ0nhklCbmTtyG16NXMbc77Kw5vYY1p9fI8gtCiKeS1Wpl0KBBNGnShHbt2mGxWACksBFpRlpu0ojWaCTfRx+lz8ndaLkx6AwMqj7IuS2EEE+Tc+fO0a5dO7Zvj+tLWKNGDZRSHs5KZDZS3GQAGneKG62BTmVldIEQ4umzcuVKunbtyq1bt/Dz82PGjBm8/vrrnk5LZEJyWeopp8EB8R2KU3BZSgghnjYWi4UBAwbQokULbt26RfXq1dm7d68UNiLdSHGTRhxRURx5pgxHnimDIyoqzc7rQwwa7jXZej+6uHEoBxcjLnIx4qIsvyCEeCoopfjzz7gZuPr378/WrVudnYiFSA9yWeopl517hZLOCHrvR8bH2GJo/FNjAFl+QQjhUUopNBoNXl5ehIaGcvDgQVq0aOHptEQWIMVNGtGYTJT8a5tzO61k19wrbrx8H1hiIXkmvcwPIYTwnNjYWAYOHEiOHDkYPXo0EDePjbTWiCdFips0otFo0OfKlebn9Y1vuUnBJSkAs8HMrjd2pXkeQgiREidOnKBt27bs3bsXrVZLly5dKFGihKfTElmM9Ll5yiiliLLYiLLEzVHjbLlJwUgpIYTwpNDQUKpUqcLevXvJnTs3K1eulMJGeIS03KQRZbFwY+ZMAHK/+SYao9H9cyhFq+nb2XP2lnOfs8+NjJQSQjyloqOjee+99/ju3iztzz//PIsWLaJgwYIezkxkVVLcpBFls3Ft8lcA5Orc+ZHFjVKKaKvrDMJRFrtLYQNQITdwlxRflrLYLYzbOQ6Aj4I/wqhzv8gSQoiUUkrRsGFD/vrrLzQaDUOGDGHkyJHo03imdiHcIf/70opeT47WrZzbyUmsheZhfw9tiNmow7TjCPxBii9L2Rw2fvrvJwAGVR8kxY0QIl1pNBq6d+/Of//9x/z582nUqJGnUxJCipu0ojUaCbw3KuBRoq0JW2geVK1wTnJnM8atsxI/O7FXyoobg9ZA38p9ndtCCJHWoqKiOHv2LGXKlAGga9eutGjRgpw5c3o4MyHiSHHjYfEtNA8yGXT3F5CLXzQzhZelDDoDPZ7tkZYpCiGE0+HDh2nTpg137txh//795M6dG0AKG/FUkdFSHmY26jAb9S4/LivjxsS33EiHYiGEZ82ePZtq1arx77//YrPZOHPmjKdTEiJRUtykEUdUFEcrV+Fo5SppuvyCOyuCQ1x/npsxN7kZc1NW2hVCpImIiAi6dOlCt27diI6OpmHDhuzfv5+qVat6OjUhEiXFTRpS0dGo6Oi0Pambl6WibdHUW1KPekvqEW1L41yEEFnOwYMHqV69OnPnzkWr1TJmzBjWrl1LQECAp1MTIknS5yaNaLy9Kb5+vXM7zchlKSGEB3322WccPXqU/Pnzs2jRIurWrevplIR4JClu0ohGq8VYsEDan9jNy1Jmg5mDXQ6mfR5CiCxpypQpmEwmxo0bR968eT2djhApIpelnnZuXpYSQojHsW/fPj744ANnnz0/Pz9++OEHKWxEhvJYLTcxMTF4p+UlmAxMWa3cWrgQgJwdOqAxpMEcM3YrWOOXX5C1pYQQ6UcpxbRp03jvvfewWCyULVuWbt26eTotIVLF7ZYbh8PB6NGjKVCgAD4+Ppw6dQqAYcOGMWPGjDRPMKNQVitXxn/KlfGfoqzW1J/IZoG5LeDzYvBFqfv73Vh+4bNdn/HZrs+w2C2pz0MIkWXcuXOHNm3a0Lt3bywWC82aNaNFixaeTkuIVHO7uBkzZgyzZ8/m888/x/jA+knly5fnxx9/TNPkMhSdDt+mTfFt2hR0ukfHJ+XCLji1CaJuQPTNuH35KoAuZS1BNoeN+UfmM//IfGwOW+rzEEJkCbt376Zy5cosW7YMg8HAxIkT+eWXX8iVK5enUxMi1dy+LDV37ly+//57GjRowDvvvOPcX7FiRY4ePZqmyWUkWi8vCnwx4fFPdHZ73L8lQ+ClUXHbuYqm+HCD1kD3Ct2d20IIkZSZM2fyzjvvYLVaKVKkCEuWLKFGjRqeTkuIx+Z2cXPx4kVKlCiRYL/D4cD6OJdjRJxzf8X9W6Ih+D+TaIjFbsHmsGHQGjDca9FRSjnntelbua/rLMdCCJGIEiVKYLfbee2115gxYwY5cuTwdEpCpAm3L0uVLVuWLVu2JNi/bNkyKleunCZJZVl2G5zfFbdduGaSYZP2TCJ4YTDTDkxz7ou2RRO8MJjghcEyeZ8QIkm3b992btetW5edO3eybNkyKWxEpuJ2y83w4cPp0qULFy9exOFw8PPPP3Ps2DHmzp3L6tWr0yPHDMERFcWJBg0BKLFhPVqz2f2ThP0Dloi4OW38ywIQZY0ieGEwADs77MRsSMV5hRBZnsPhYOLEiYwdO5bt27fzzDNxLcPVqlXzcGZCpD23i5sWLVqwatUqRo0aRbZs2Rg+fDhVqlRh1apVvPTSS+mRY4Zhv3XLrfi3dGswrl4F2nsNaDfjRp4R9Bxok+6U/F7V9+hbua9LnxqT3sTODjud20IIEe/69et07dqVX3/9FYB58+YxduxYD2clRPpJ1Tw3derUYd26dWmdS4am8fam2KqVzu1HCdJcYZhhPvyTyJ1F709vbtKb2Nx2s3MbwKgzYtQZXQ7RaDTSqiOESGDr1q20b9+eCxcu4OXlxVdffUWPHj08nZYQ6crt4qZYsWLs3r2b3Llzu+y/ffs2VapUcc57k9VotFq8SpZMcXxOIgBQ3jnQPN///h1evlCpw/3zajTk8pYhmUII9zgcDj777DOGDRuG3W6nVKlShIaGUrFiRU+nJkS6c7u4OXPmDHa7PcH+2NhYLl68mCZJZQXZNDEAKJ98aJ5/z8PZCCEym9mzZ/PRRx8B0LFjR6ZNm4aPj4+HsxLiyUhxcbNy5Urn9tq1a/Hzu78cgN1uZ8OGDRQpUiRNk8tIlNXK7eXLAcjx6quPXH4hG/eKG2O2ZOOsdiuz/p0FQLdy3ZxDv4UQIjmdO3dm8eLFtGvXjm7dusn0ECJLSXFx07JlSyDuMkmXLl1c7jMYDBQpUoQvv/wyTZPLSJTVStjwEQD4NW36yOLGfK+44VHFjcPKN/u+AaBjmY5S3AghEmW325kxYwZdu3bFaDSi1+tZu3atFDUiS0pxceNwOAAoWrQou3fvJk+ePOmWVIak0+HToIFz+1F84i9LGZNvJtZr9bxe8nXnthBCPCwsLIw33niDP/74g6NHjzJx4kQAKWxEluX2p+Xp06fTI48MT+vlRdCUb1Mcn9KWG6POyCe1PnmMzIQQmdn69evp2LEjV65cwWw2y2SqQpDKoeCRkZFs3ryZc+fOYbG4rjz9f//3f2mSWGbn7HNjSL64EUKIxNhsNkaOHMnYsWNRSlGhQgVCQ0Odk/MJkZW5Xdzs27ePJk2aEBUVRWRkJLly5eL69euYzWb8/f2luEkhsya+5UZGLwgh3HPx4kU6dOjAn3/+CUD37t356quvMJlkAk8hIBVrS7333ns0a9aMW7duYTKZ2LFjB2fPnqVq1ap88cUX6ZFjhuCIjubEiw048WIDHNGPXtvJh5T1uYmyRlFjQQ1qLKhBlDUqTXIVQmRs0dHR7Nu3Dx8fHxYuXMj3338vhY0QD3C75Wb//v189913aLVadDodsbGxFCtWjM8//5wuXbrw2muvpUeeTz+lsF665Nx+lPstN4++LCULYQohlFLODsIlSpQgNDSU4sWLU9KNyUOFyCrcbrkxGAxo762F5O/vz7lz5wDw8/Pj/PnzaZtdBqLx8qLI0lCKLA1F4+WVaIxSiiiLjSiLPcXz3Hjrvfn99d/5/fXf8dY/elkHIUTmc/78eerVq8f69eud+xo3biyFjRBJcLvlpnLlyuzevZuSJUtSr149hg8fzvXr15k3bx7ly5dPjxwzBI1Oh6lChSTvV0rRavp29pyNW1xzviFlLTdajZYCPgXSLE8hRMayatUqunbtys2bN+nduzeHDx9Gl4LpJoTIytxuuRk3bhyBgYEAjB07lpw5c9KrVy+uXbvGd999l+YJZhbRVruzsIH789wYTb6eSkkI8RSzWCy8//77NG/enJs3b1KtWjV+++03KWyESAG3W26qVavm3Pb39+f3339P04QyKmWzEf7bbwD4vvwyGn3SL+3fQxuSa/ZIuA4ar+Q7FFsdVhYfXQxAu2faYdDKDMVCZHZnzpyhbdu27Nq1C4B+/frx2Wef4ZXEJW8hhCu3W26SsnfvXpo2ber2cVOmTKFIkSJ4e3sTHBzs/GNOyu3bt+nduzeBgYF4eXlRqlQp1qxZk9q004yyWLj0wSAufTAI9dDcPw8zG3Vo40c+pWBtqc93f87nuz/HaremVbpCiKfU+fPnqVy5Mrt27SJHjhwsX76cyZMnS2EjhBvcarlZu3Yt69atw2g08vbbb1OsWDGOHj3Khx9+yKpVqwgJCXHrwZcsWcKAAQOYPn06wcHBTJ48mZCQEI4dO4a/v3+CeIvFwksvvYS/vz/Lli2jQIECnD17lhw5crj1uOlCqyVbrZrO7UeyRMT9+4ih4DqtjiZFmzi3hRCZW8GCBWnWrBn//fcfixcvpnDhwp5OSYgMR6NUCsYtAzNmzKB79+7kypWLW7dukTt3biZOnEjfvn1p27Yt/fr1o0yZMm49eHBwMNWrV+fbb+OWLXA4HAQFBdG3b18+/PDDBPHTp09nwoQJHD16FMMjFqZMSnh4OH5+fty5cwdf3/Tv76KUItpqJ8pip9qYuJEOh0eFYP40Hzis8N6/4Fcw3fMQQjy9Tp48SY4cOcidOzcAUVFRGAyGVL/PCZEZufP5neLLUl999RWfffYZ169fJzQ0lOvXrzN16lQOHjzI9OnT3S5sLBYLe/bsoWHDhveT0Wpp2LAh27dvT/SYlStXUrNmTXr37k1AQADly5dn3Lhx2O32JB8nNjaW8PBwl58nJX6EVNnha52FDQB2S1xhAzJDsRBZXGhoKJUrV6Zbt27Ef9c0m81S2AjxGFJc3Jw8eZLWrVsD8Nprr6HX65kwYQIFC6au1eH69evY7XYCAgJc9gcEBBAWFpboMadOnWLZsmXY7XbWrFnDsGHD+PLLLxkzZkySjzN+/Hj8/PycP0FBQanKNzUeHiEFUK1wTkwq5v6OFEziJ4TIfGJiYujVqxdt27bl7t273Lx584l++RIiM0txn5vo6GjMZjMAGo0GLy8v55DwJ8XhcODv78/333+PTqejatWqXLx4kQkTJjBixIhEjxkyZAgDBgxw3g4PD0+XAscRHc3pe8Vf0aVL0T40FfrfQxtiNuowGXRo7lyI26nzAl3y386irFE0/qkxAL+//jtmgznNcxdCPFnHjx+nTZs2HDhwAIh7nxo1ahT6ZEZZCiFSzq2/pB9//BEfn7jLKDabjdmzZ5MnTx6XmJQunJknTx50Oh1Xrlxx2X/lyhXy5cuX6DGBgYEYDAaXeR7KlClDWFgYFosFo9GY4BgvL68nM8pAKSwnTjq3H2Y26jAb773czs7EKWu1uRV769FBQogMYcGCBfTs2ZPIyEjy5s3LvHnz3B6MIYRIXoqLm0KFCvHDDz84b+fLl4958+a5xGg0mhQXN0ajkapVq7JhwwZatmwJxLXMbNiwgT59+iR6TO3atVm4cCEOh8O5BMTx48cJDAxMtLB5kjReXhSaM8e5nSxLZNy/j5jjBuKWX1jefLlzWwiRcUVFRTF06FAiIyOpX78+CxYsIH/+/J5OS4hMJ8XFzZkzZ9L8wQcMGECXLl2oVq0aNWrUYPLkyURGRtKtWzcAOnfuTIECBRg/fjwAvXr14ttvv6Vfv3707duX//77j3HjxqW4oEpPGp2ObME1UhacwmHgELf8QomcJR4jMyHE08JsNrNkyRJnn0GZbViI9OHRC7xt27bl2rVrDB8+nLCwMCpVqsTvv//u7GR87tw5ZwsNQFBQEGvXruW9997j2WefpUCBAvTr14/Bgwd76imkTnzLjXQmFiLTmzNnDna7nTfffBOAGjVqUKNGCr8ICSFSxeO91/r06ZPkZahNmzYl2FezZk127NiRzlm5T9lsRNzL16d+/WSXXyA25X1urA4rv5z4BYAWJVrI8gtCZBARERH07t2buXPn4uXlxfPPP0+pUqU8nZYQWYLHi5vMQlksXOjTF4DSe/ckX9y4cVnKarcycvtIAJoUbSLFjRAZwMGDB2nTpg1Hjx5Fq9UydOhQihcv7um0hMgypLhJK1otpsqVndvJcl6WenRxo9PqeCHoBee2EOLppZRixowZ9O3bl5iYGPLnz8/ChQupV6+ep1MTIkuR4iaNaL29KbJoYcqC3ehz46Xz4usXv36MzIQQT4JSii5dujhHkTZu3Ji5c+eSN29eD2cmRNaTqlXBT548ydChQ2nfvj1Xr14F4LfffuPff/9N0+QyLTfnuRFCPP00Gg0lS5ZEp9Px6aef8uuvv0phI4SHuF3cbN68mQoVKrBz505+/vlnIiLiPqgPHDiQ5CzBWZ03sWjunIfb5+J+Iu5NXCjrSgmRoSmluHXr/iSbH330EXv27GHw4MEuIz2FEE+W2399H374IWPGjGHdunUuE+e9+OKLT+UopifFERPD6VatOd2qNY6Y+2tH+RLBdq++mKZUgskV4n4OLo27MwUtN9G2aEKWhRCyLIRoW3Q6ZS+EcNedO3do27Yt9evXJzo67m9Tp9NRsWJFD2cmhHC7z83BgwdZuDBh3xJ/f3+uX7+eJkllSA4HMYcOObfjldZcIKfm3mWoB2cYNuWC4i888rRKKS5FXnJuCyE87++//6Zt27acOnUKvV7Ptm3baNiwoafTEkLc43ZxkyNHDi5fvkzRokVd9u/bt48CBQqkWWIZjcZopOD0ac7teLk0cav82gtUR9d9vdvn9dJ5seiVRc5tIYTnKKX45ptvGDhwIFarlcKFC7NkyRKCg4M9nZoQ4gFuFzft2rVj8ODBLF26FI1Gg8PhYNu2bQwcOJDOnTunR44ZgkavJ3v9+gn259LcBUCZc6fqvDqtjvJ5yj9OakKINHDr1i3efPNNVqxYAUDLli2ZOXMmOXPm9GxiQogE3O5zM27cOJ555hmCgoKIiIigbNmy1K1bl1q1ajF06ND0yDFDy0lccUMqixshxNPh3XffZcWKFRiNRr7++mt+/vlnKWyEeEq53XJjNBr54YcfGDZsGIcOHSIiIoLKlStTsmTJ9Mgvw1B2O5H3OlRne+45NPcWxMsd33JjypWq89ocNn4/8zsAjYs0Rq+VqYmE8ITPPvuMkydPMm3aNKpWrerpdIQQyXD7k3Lr1q08//zzFCpUiEKFCqVHThmSio3l/FtvA/eWXzCbAcjpvCyVJ1XntdgtDNkyBIAXg16U4kaIJ+TGjRusWrWKrl27AlCoUCF27tyJRqPxbGJCiEdy+5PyxRdfpECBArRv356OHTtStmzZ9Mgr49Fq8XrmGed2vNzEdShW5tS13Gg1Wp4LfM65LYRIf9u2baNdu3ZcuHCB3Llz06xZMwApbITIINz+tLx06RLvv/8+mzdvpnz58lSqVIkJEyZw4cKF9Mgvw9B6e1NsxXKKrViO1vv+kO/Hbbnx1nvzQ6Mf+KHRD3g/OJRcCJHmHA4Hn376KfXq1ePChQuULFmSoKAgT6clhHCT28VNnjx56NOnD9u2bePkyZO0bt2aOXPmUKRIEV588cX0yDFDix8tRSr73AghnoyrV6/SpEkThgwZgt1up0OHDuzZs4dKlSp5OjUhhJse6zpH0aJF+fDDD/n000+pUKECmzdvTqu8Mo1cPN5QcCFE+tu8eTOVKlVi7dq1eHt78+OPPzJ//nyyZ8/u6dSEEKmQ6uJm27ZtvPvuuwQGBtKhQwfKly/Pr7/+mpa5ZSiOmBjOdurM2U6d7y+/YI3CrIkFUl/cRNuiabmiJS1XtJTlF4RIJ5cvX+by5cuUKVOG3bt389Zbb0n/GiEyMLc7FA8ZMoTFixdz6dIlXnrpJb766itatGiB+d7ooCzL4SBq927nNoAm6iYAFqUDY8q/AcbYYnAoB0adEaUUJ++cBGT5BSHSklLKWcC0a9cOi8XC66+/TrZsj17zTQjxdHO7uPnzzz/54IMPaNOmDXnypK6TbGakMRopMHmScxuA6BsA3MQXXze+BXb/X3f2X9vP5BcmU79gfWaGzARk+QUh0sqGDRsYOHAgv/32G/ny5QPI0jOsC5HZuF3cbNu2LT3yyPA0ej2+jRu77rvXcnNLZcc3lefVaXVUz1f9MbMTQgDY7XZGjhzJmDFjUEoxcuRIpk2b5um0hBBpLEXFzcqVK3n55ZcxGAysXLky2djmzZunSWKZgSYqbpX0myo7hd047odGPzgvSwkh0salS5fo0KGDc+DD22+/zZdffunhrIQQ6SFFxU3Lli0JCwvD39+fli1bJhmn0Wiw2+1plVuGoux2og8cAMBUsSJotVjvXscLuIl7Iy5kPhsh0tbatWvp2LEj169fx8fHh++++44OHTp4Oi0hRDpJUXHjuNdB9uFtcZ+KjeVshzcAKLXnb9rMPUD9i3/TVx/XcpMSMbYYuv7eFYDZjWdLkSNEGli6dClt2rQBoGLFioSGhlKqVCkPZyWESE9uDwWfO3cusbGxCfZbLBbmzp2bJkllSJZIDPkDMOQPIPbcbhzndlFCcxEAb78ATAbdI0/hUA7+vfEv/974F4eSIlKItNC4cWNKlSrFu+++y44dO6SwESIL0Cg3xxfrdDouX76Mv7+/y/4bN27g7+//1F+WCg8Px8/Pjzt37uDrm9puvok4vxtmNEz0LvXyBDTBPR55CpvDxl+X/gKgVv5askimEKm0Y8cOgoODnUO9w8PD0/bvXQjxxLnz+e12y82Dc0M86MKFC/j5+bl7usxDb4ScRSBnERw5inDW4c9Zhz/2/FXRlH75kYfH2GKw2C3Uzl+bugXrSmEjRCpYLBYGDhxIzZo1mTx5snO/FDZCZC0p/gStXLkyGo0GjUZDgwYN0OvvH2q32zl9+jSNHxoKnaUEVoR+cR2KYyw26g1fC8DhriGYjY9+mTuu6cixW8f47qXvqJW/VrqmKkRmdObMGdq1a8fOnTsBuHjxooczEkJ4SoqLm/hRUvv37yckJAQfHx/nfUajkSJFivD666+neYIZhSM2lgv/938A5P5iYrKxMbYYeq3vBcC0htOk47AQj2nFihV069aN27dvkyNHDmbNmpXsyE4hROaW4uJmxIgRABQpUoS2bdvi7S0fyC7sdiI3/wlArkf0O3IoB39f+du5DTC/yXwcyiGzEAvhhtjYWAYNGsTXX38NQHBwMIsXL6ZIkSKeTUwI4VFud+zo0qVLeuSR4WkMBgLHjXNuJ8eoM/JFvS+c2yBz2wiRGocPH2bq1KkAvP/++4wbNw6jUSa/FCKrS1FxkytXLo4fP06ePHnImTNnsqvl3rx5M82Sy0g0BgM5XnsVgCiLLdlYvVZPSJGQJ5GWEJla5cqV+eabbyhYsCBNmzb1dDpCiKdEioqbSZMmkT17dud2csWNEEKkl5iYGAYPHsxbb73Fs88+C8A777zj4ayEEE+bFBU3D16K6tq1a3rlkqEpu53Y48fjtosUSzbW7rDzz/V/AHg2z7PotI+e4E+IrO748eO0adOGAwcO8L///Y+DBw+6jNoUQoh4bs9zs3fvXg4ePOi8/csvv9CyZUs++ugjLBZLmiaXkajYWE6/+hqnX30NlcgMzg+KtcfS+bfOdP6tM7H25GOFELBw4UKqVq3KgQMHyJs3L5MnT5bCRgiRJLeLm549e3L8XgvFqVOnaNu2LWazmaVLlzJo0KA0TzDD0GjQ+/uj9/eHR1y202g0FMpeiELZC8klPiGSERUVRffu3XnjjTeIiIigXr16zukohBAiKW5/9Tl+/DiVKlUC4hakq1evHgsXLmTbtm20a9fOZVbQrERrMlHyz83AozsUm/Qmfn3t1yeRlhAZVlhYGC+99BKHDh1Co9EwbNgwhg0bJi02QohHcvtdQinlXBl8/fr1zhEKQUFBXL9+PW2zE0JkWXnz5sXf35+AgAAWLFhAgwYNPJ2SECKDcLu4qVatGmPGjKFhw4Zs3ryZadOmAXD69GkCAgLSPEEhRNYRGRmJTqfD29sbnU7HggULAMiXL5+HMxNCZCRu97mZPHkye/fupU+fPnz88ceUKFECgGXLllGrVtZdE8kRG8uFfv250K9/ijoUv7v+Xd5d/650KBbinkOHDlG9enXee+895758+fJJYSOEcJvbLTfPPvusy2ipeBMmTECny8JDmu127q6NWywzx8hRyYc67Gy5uMW5TRZ+2YRQSjFz5kz69OlDTEwMd+7cYcyYMeTOndvTqQkhMqhU98zbs2cPR44cAaBs2bJUqVIlzZLKiDQGAwFDh2J1OIh+RIOYQWdgdO3Rzm0hsqq7d+/Sq1cv5+WnkJAQ5s2bJ4WNEOKxuF3cXL16lbZt27J582Zy5MgBwO3bt3nhhRdYvHgxefPmTescMwa9nu53i7Ln7C04vDnZUIPWQMsSLZ9MXkI8pQ4cOECbNm04fvw4Op2OMWPGMGjQILRat6+WCyGEC7ffRfr27UtERAT//vsvN2/e5ObNmxw6dIjw8HD+7//+Lz1yzBCirfa4wuYB1QrnxGSQa05CPCw2NpYmTZpw/PhxChYsyObNm/nwww+lsBFCpAm3W25+//131q9fT5kyZZz7ypYty5QpU2jUqFGaJpeRKIeD/BHXAPhlXFuyeRswGXSJTtJnd9j57/Z/AJTMUVKWXxBZjpeXF9OmTeOHH35g9uzZchlKCJGm3C5uHA4HBkPCfiIGg8E5/01WpGJimLH+MwBMY17DbDQlGRtrj6X1qtYA7OywE7PW/ERyFMKT9uzZw61bt2jYsCEAzZs3p1mzZjJLtxAizbndBvziiy/Sr18/Ll265Nx38eJF3nvvvSw/yVaE3psIvfcj4zQaDf4mf/xN/vLGLjI9pRTffPMNtWrVom3btpw/f955n/z/F0KkB7dbbr799luaN29OkSJFCAoKAuD8+fOUL1+e+fPnp3mCGYXWbKZ10zEAHDYn3xJj0pvY0GbDk0hLCI+6desWb731FsuXLwegbt26+Pj4eDgrIURm53ZxExQUxN69e9mwYYNzKHiZMmWcTc1CCAGwc+dO2rVrx5kzZzAajXzxxRf06dNHWmuEEOnOreJmyZIlrFy5EovFQoMGDejbt2965SWEyKCUUkyaNInBgwdjs9koVqwYoaGhVK1a1dOpCSGyiBQXN9OmTaN3796ULFkSk8nEzz//zMmTJ5kwYUJ65pdhKIuFAXsW39t+AYxJv7Sx9liGbBkCwPg64/HSeT2RHIV4EjQaDUePHsVms9G6dWt++OEH/Pz8PJ2WECIL0SilVEoCy5UrR5s2bRgxYgQA8+fPp2fPnkRGRqZrgmktPDwcPz8/7ty5g6+vb5qdN+J2OOefCwYgaMdOfHIkfe4oaxTBC+Nid3bYidkgo6VExudwOJzz1ERHR/Pzzz/ToUMHuQwlhEgT7nx+p3i01KlTp+jSpYvzdocOHbDZbFy+fDn1mWYiGoOBH8u9wo/lXkGTyFD5Bxl0Bj4K/oiPgj+S5RdEhudwOPjss89o2rSpczoIk8nEG2+8IYWNEMIjUnxZKjY2lmzZsjlva7VajEYj0dHR6ZJYRqMxGPip5AsAjH5UcaM10P6Z9k8iLSHS1bVr1+jcuTO///47AL/88guvvvqqh7MSQmR1bnUoHjZsGOYHhjlbLBbGjh3rcj194sSJaZedEOKp9eeff9K+fXsuXbqEt7c33377LS1btvR0WkIIkfLipm7duhw7dsxlX61atTh16pTzdlZuglYOB7mj7zi3k+NQDs7fjZvILCh7EFqNrKcjMg673c748eMZMWIEDoeDMmXKEBoaSvny5T2dmhBCAG50KM4spEOxEI+nZ8+efP/99wB07dqVb7/91uWStRBCpId06VCcnqZMmUKRIkXw9vYmODiYXbt2pei4xYsXo9FonpqmcJtGiy2FrTDZDdnJbsiezhkJkfZ69epFrly5mDNnDrNmzZLCRgjx1HF7huK0tmTJEgYMGMD06dMJDg5m8uTJhISEcOzYMfz9/ZM87syZMwwcOJA6deo8wWyTpjWbadbic+DRyy+YDWb+6vDXk0hLiMdmt9vZtWsXNWvWBKBSpUqcPXtWllEQQjy1PN5yM3HiRLp37063bt0oW7Ys06dPx2w2M3PmzCSPsdvtvPHGG4wcOZJixYo9wWyFyFouXbpEgwYNqFevHrt373bul8JGCPE082hxY7FY2LNnj8u6VFqtloYNG7J9+/Ykjxs1ahT+/v689dZbTyJNIbKktWvXUqlSJTZv3oyXlxeXLl3ydEpCCJEiHr0sdf36dex2OwEBAS77AwICOHr0aKLHbN26lRkzZrB///4UPUZsbCyxsbHO2+Hh4anONznKYuHdAz/f205++QWL3cLI7SMBGFFzBEadMV1yEiI1bDYbw4YN49NPPwWgYsWKhIaGUqpUKQ9nJoQQKZOqlpstW7bQsWNHatasycWLFwGYN28eW7duTdPkHnb37l06derEDz/8QJ48eVJ0zPjx4/Hz83P+BAUFpUtuymaj2em/aHb6L5TNlmyszWFj5cmVrDy5Epsj+VghnqTz589Tv359Z2Hz7rvvsmPHDilshBAZitstNz/99BOdOnXijTfeYN++fc5WkTt37jBu3DjWrFmT4nPlyZMHnU7HlStXXPZfuXKFfPnyJYg/efIkZ86coVmzZs598dO96/V6jh07RvHixV2OGTJkCAMGDHDeDg8PT5cCR2MwML/0SwB8lIIZigdUHeDcFuJp8fPPP7Nt2zZ8fX358ccfad26tadTEkIIt7ld3IwZM4bp06fTuXNnFi9e7Nxfu3ZtxowZ49a5jEYjVatWZcOGDc7h3A6Hgw0bNtCnT58E8c888wwHDx502Td06FDu3r3LV199lWjR4uXlhZdX+q+6rTEYWFAmBICPU7C2VLfy3dI9JyHc1bdvXy5dukSPHj0SfFEQQoiMwu3i5tixY9StWzfBfj8/P27fvu12AgMGDKBLly5Uq1aNGjVqMHnyZCIjI+nWLe7Dv3PnzhQoUIDx48fj7e2dYBbUHDlyAMjsqEKkwtmzZxk2bBhTp07Fx8cHrVbLZ5995um0hBDisbhd3OTLl48TJ05QpEgRl/1bt25N1bDstm3bcu3aNYYPH05YWBiVKlXi999/d3YyPnfuHFqtx0esP5JSimyWaOd2chzKwbWoawDkNeeV5ReER/zyyy907dqV27dv4+Pjw9SpUz2dkhBCpAm3l18YP3488+fPZ+bMmbz00kusWbOGs2fP8t577zFs2DD69u2bXrmmCVl+QWR1FouFQYMG8dVXXwFQo0YNlixZkuALixBCPE3c+fx2u+Xmww8/xOFw0KBBA6Kioqhbty5eXl4MHDjwqS9sniZ6jccnhxZZ0KlTp2jbti1///03AO+//z7jxo3DaJTpCIQQmUeqF860WCycOHGCiIgIypYtm2FmLE2vlpvIWCvPDvsNgH9Gv0w2LxkFJZ4umzZtokWLFoSHhzvXhmratKmn0xJCiBRJ15abeEajkbJly6b28ExHo9Fg1+qc20I8bUqXLo23tzcVKlRg0aJF6TbnkxBCeJrbxc0LL7yQ7If3H3/88VgJCSHSzvXr150TXgYGBrJ582aKFy+O4RHTFQghREbm9jCdSpUqUbFiRedP2bJlsVgs7N27lwoVKqRHjhmCslp569Aq3jq0CmW1JhtrsVsYs2MMY3aMwWK3PKEMRVazaNEiihUrxrJly5z7nnnmGSlshBCZntstN5MmTUp0/yeffEJERMRjJ5RRKauVVic2O7fBlGSszWFjybElAAyoOkDWlhJpKjo6mn79+vHDDz8AMHfuXFq1auXhrIQQ4slJsyE7HTt2pEaNGnzxxRdpdcoMRWMwsKxEPQAGpGD5hV4Vezm3hUgrR48epU2bNhw8eBCNRsPQoUMZPny4p9MSQognKs2Km+3bt+Pt7Z1Wp8twNAYDM8rHrXn1fgqWX3i30rtPIi2RhcydO5devXoRFRVFQEAA8+fPp2HDhp5OSwghnji3i5vXXnvN5bZSisuXL/P3338zbNiwNEsss1BKYXPYMOgMztvRtriZjGXyPpFW9u7dS5cuXQB48cUXWbBgQaKLzwohRFbgdnHj5+fnclur1VK6dGlGjRpFo0aN0iyxjEYphc5hd27H/9v5t85U8q/E+9XeB8CmbAQvDObtCm/TrXw3shuyy9Bx8diqVKnC+++/j5+fHx999BE6nc7TKQkhhMe4NYmf3W5n27ZtVKhQgZw5c6ZnXunmSS6/kNgyC1aHlSrzqgBQ2b8ycxrPkeJGuE0pxdy5c2nQoAEFCxb0dDpCCJHu0m0SP51OR6NGjThy5EiGLW6eJIPWQNdyXbE6rM6Ow3qNnp0ddgJg0puksBFuu3v3Lr169WLBggU8//zzbNy4Eb1elvMQQoh4br8jli9fnlOnTlG0aNH0yCfD0phMtGoyGoBdprhh4AadwXk5yhmn0UhfG5FqBw4coE2bNhw/fhydTscrr7yCViurygshxIPcflccM2YMAwcOZPXq1Vy+fJnw8HCXn6xKo9EQaTQRaZTWGJH2lFJ89913BAcHc/z4cQoWLMjmzZv58MMPpbgRQoiHpLjlZtSoUbz//vs0adIEgObNm7t8iCul4tZXstvTPssMSimFTdmAuMtRUvSI1Lh79y5vv/02oaGhADRt2pTZs2eTO3duD2cmhBBPpxQXNyNHjuSdd95h48aN6ZlPhqWsVt44svbe9otg1BNti07QoVgId+l0Og4fPoxer+fTTz9lwIABUigLIUQyUlzcxA+qqlevXrolk5Epq5WOx9bd2x5DcssvCPEoSimUUmi1WsxmM6Ghody5c4fnnnvO06kJIcRTz60OxfJtMWkavZ5VRWsB0PveyBWT3sS29tuc20KkxO3bt3nrrbeoVq0aQ4YMAaBMmTIezkoIITIOt4qbUqVKPbLAuXnz5mMllFFpjEamVoybvbmPMW4hTI1Gg68x7ebSEZnfrl27aNu2LWfOnOG3337jzTffJCAgwNNpCSFEhuJWcTNy5MgEMxQLIR6fUorJkyczePBgrFYrxYoVY8mSJVLYCCFEKrhV3LRr1w5/f//0yiXTsdqt/HDwBwC6V+juXF9KiAfdvHmTrl27smrVKgBatWrFjz/+KF8khBAilVJc3Eh/m+Q5oqJY9cuguO0P64DRF6vDyrQD0wDoWq6rFDciAYvFwnPPPcd///2Hl5cXkyZN4p133pG/NyGEeAxuj5YSSdMrh+ttrZ62pds6t4V4mNFopH///kyePJnQ0FAqVark6ZSEECLDc2vhzMwgvRbOjIyxUHfwMgD+/KwV2byNaXZukblcv36dq1evUrZsWSDui0N0dDRms8yDJIQQSXHn81vmbU8jGq2WGyY/bpj80Mh0+CIJW7ZsoWLFijRr1ow7d+4A99Ybk8JGCCHSjHwKC/EEOBwOxo4dS/369bl06RJGo5Fr1655Oi0hhMiUpCNIGlFWK6//t/HedtzyC1HWKGovqg3AtvbbZPmFLOrKlSt06tSJdeviZrDu0qULU6ZMIVu2bB7OTAghMicpbtKIslp5+99f720PJ375hfiFM0XW9Mcff/DGG28QFhaG2Wxm6tSpdOnSxdNpCSFEpibFTRrR6PWsC6oGwFv3ll/w1nuzvtV657bIGux2O1arFYB58+bh5eVFw4YNmTRpEiVKlCAmJsbDGQohxNPJaDSiTYN+qzJaKo1EWWyUHR63KvjhUSGYjVI3ZjVKKcLCwrh9+7Zzn91ud/6fS4s/WCGEyMy0Wi1FixbFaEw44tidz2/5BBYijYSFhXHlyhXMZjNBQUEyEZ8QQrjB4XBw6dIlLl++TKFChR7rPVSKm3RktVuZf2Q+AB3LdJQZijOx2NhYTpw4gclkIjw8nJiYGHLmzOnptIQQIkPJmzcvly5dwmazYTCk/jNT2snTiCMqiqWrh7J09VAcUVEAWB1WJu6ZyMQ9E7E6rB7OUKSXCxcu0KFDByIiIoC4P05ZF0oIIdwXfznKbrc/1nmkuElDPrYYfGz3O4vqtXqaF29O8+LNZfmFTGrNmjVUqlSJPXv2oNVqKViwIIULF5b+NUIIkQppdTlfPnHTiMbbm7caDgZgnXfcyCijzsjY58d6Mi2RjsaNG8fHH38MQHBwMIGBgeTIkcOzSQkhhJCWm7Si0Wq55JOXSz55ZfmFLKJq1apoNBr69u3LokWLHuv6cEayadMmNBqNy6iwrKJu3bosXLjQ02mITObw4cMULFiQyMhIT6eSacinsBBuuHr1qnM7JCSEf//9l6+//jrRYYuZVa1atbh8+XKW61e0cuVKrly5Qrt27TydymNbunQpzzzzDN7e3lSoUIE1a9Y88pgpU6ZQpkwZTCYTpUuXZu7cuS73z549G41G4/Lj7e06v1dERAR9+vShYMGCmEwmypYty/Tp011iwsLC6NSpE/ny5SNbtmxUqVKFn376KUE+v/76K8HBwZhMJnLmzEnLli2TzSX+J/5v+PLly3To0IFSpUqh1Wrp379/os978uTJlC5dGpPJRFBQEO+9957LXFVFihRJ9HF69+7tjKlfv36C+9955x3n/WXLluW5555j4sSJyf8SRMqpLObOnTsKUHfu3EnT80ZERKnerYeo3q2HqIiIKKWUUpGWSFVzQU1Vc0FNFWmJTNPHE09WbGys6t+/v8qRI4c6efJkgvujo6PV4cOHVXR0tAeyy1hiY2M9nUKqNGjQQI0fP97TaTy2bdu2KZ1Opz7//HN1+PBhNXToUGUwGNTBgweTPGbq1Kkqe/bsavHixerkyZNq0aJFysfHR61cudIZM2vWLOXr66suX77s/AkLC3M5T/fu3VXx4sXVxo0b1enTp9V3332ndDqd+uWXX5wxL730kqpevbrauXOnOnnypBo9erTSarVq7969zphly5apnDlzqmnTpqljx46pf//9Vy1ZssR5f1RUlEsely9fViEhIapevXrOmNOnT6v/+7//U3PmzFGVKlVS/fr1S/C8FyxYoLy8vNSCBQvU6dOn1dq1a1VgYKB67733nDFXr151eZx169YpQG3cuNEZU69ePdW9e3eXuIc/g1avXq0CAwOV1WpN8veQFST3XurO57cUN2nk7q076nDpZ9Th0s+oa9evKIvdoiItkar87PKq/OzyUtxkYKdOnVLVq1dXgALUt99+myAmoxY39erVU3369FH9+vVTOXLkUP7+/ur7779XERERqmvXrsrHx0cVL15crVmzxnnMxo0bFaBu3brl3Ld161ZVr149ZTKZVI4cOVSjRo3UzZs3nY/Ru3dv1a9fP5U7d25Vv359pZRSmzZtUtWrV1dGo1Hly5dPDR48+JFv7Lt27VINGzZUuXPnVr6+vqpu3bpqz549zvvbt2+v2rRp43KMxWJRuXPnVnPmzFFKKRUeHq46dOigzGazypcvn5o4caKqV69eoh9u8a5evao0Go06dOiQy/4vv/xSlS9fXpnNZlWwYEHVq1cvdffuXef9I0aMUBUrVnQ5ZtKkSapw4cIu+2bMmKHKli3rfC169+6d7OvwONq0aaNeeeUVl33BwcGqZ8+eSR5Ts2ZNNXDgQJd9AwYMULVr13benjVrlvLz80v2scuVK6dGjRrlsq9KlSrq448/dt7Oli2bmjt3rktMrly51A8//KCUUspqtaoCBQqoH3/8MdnHetDVq1eVwWBIcN54Sf3+e/furV588UWXfQ8/74f169dPFS9eXDkcjkee/0GxsbHKy8tLrV+/Ptm4zC6tihu5LJVGNDodW/I/y1/Fc9BoxYssO74Mb703q19dzepXV8vyCxnUTz/9ROXKldm9eze5cuVi5cqVLs3NSVFKEWWxeeRHuTnp+Jw5c8iTJw+7du2ib9++9OrVi9atW1OrVi327t1Lo0aN6NSpE1H3pjh42P79+2nQoAFly5Zl+/btbN26lWbNmrkM5ZwzZw5Go5Ft27Yxffp0Ll68SJMmTahevToHDhxg2rRpzJgxgzFjxiSb6927d+nSpQtbt25lx44dlCxZkiZNmnD37l0A3njjDVatWuUclg+wdu1aoqKiePXVVwEYMGAA27ZtY+XKlaxbt44tW7awd+/eZB9369atmM1mypQp47Jfq9Xy9ddf8++//zJnzhz++OMPBg0alOy5HjZt2jR69+5Njx49OHjwICtXrqREiRJJxi9YsAAfH59kf7Zs2ZLk8du3b6dhw4Yu+0JCQti+fXuSx8TGxia4xGQymdi1a5dzqRGIu+xUuHBhgoKCaNGiBf/++6/LMbVq1WLlypVcvHgRpRQbN27k+PHjNGrUyCVmyZIl3Lx5E4fDweLFi4mJiaF+/foA7N27l4sXL6LVaqlcuTKBgYG8/PLLHDp0KMn8586di9lsplWrVknGJKZWrVrs2bOHXbt2AXDq1CnWrFlDkyZNEo23WCzMnz+fN998M8GonwULFpAnTx7Kly/PkCFDEvw9GY1GKlWqlOzvTqScjJZKIxovL8bV6Ix3gQUY9AcB0Gq0FPYt7OHMRGrExMQwcOBApkyZAsS9yS1atIhChQql6Phoq925HMeT5u7yHxUrVmTo0KEADBkyhE8//ZQ8efLQvXt3AIYPH860adP4559/eO655xIc//nnn1OtWjWmTp3q3FeuXDmXmJIlS/L55587b3/88ccEBQXx7bffotFoeOaZZ7h06RKDBw9m+PDhSQ6lf/HFF11uf//99+TIkYPNmzfTtGlTQkJCyJYtG8uXL6dTp04ALFy4kObNm5M9e3bu3r3LnDlzWLhwIQ0aNABg1qxZ5M+fP9nX6OzZswQEBCTI68F+GkWKFGHMmDG88847Lq/Fo4wZM4b333+ffv36OfdVr149yfjmzZsTHByc7DkLFCiQ5H1hYWEEBAS47AsICCAsLCzJY0JCQvjxxx9p2bIlVapUYc+ePfz4449YrVauX79OYGAgpUuXZubMmTz77LPcuXOHL774glq1avHvv/9SsGBBAL755ht69OhBwYIF0ev1aLVafvjhB+rWret8rNDQUNq2bUvu3LnR6/WYzWaWL1/uLPhOnToFwCeffMLEiRMpUqQIX375JfXr1+f48ePkypUrQf4zZsygQ4cOmEymZF+3h3Xo0IHr16/z/PPPo5TCZrPxzjvv8NFHHyUav2LFCm7fvk3Xrl0TnKdw4cLkz5+ff/75h8GDB3Ps2DF+/vlnl7j8+fNz9uxZt3IUiZPiJo3FXGrDtre+w9dbWmoysq+//tpZ2AwePJjRo0dn2tFQzz77rHNbp9ORO3duKlSo4NwX/0H4YGfqB+3fv5/WrVsn+xhVq1Z1uX3kyBFq1qzp8u22du3aREREcOHCBSCuk2W8jz76iI8++ogrV64wdOhQNm3axNWrV7Hb7URFRXHu3DkA9Ho9bdq0YcGCBXTq1InIyEh++eUXFi9eDMR9MFqtVmrUqOE8t5+fH6VLl042/+jo6AQtFwDr169n/PjxHD16lPDwcGw2GzExMURFRWE2m5M9J8S9ppcuXXIWWimRPXt2smfPnuL4tDBs2DDCwsJ47rnnUEoREBBAly5d+Pzzz50FX82aNalZs6bzmFq1alGmTBm+++47Ro8eDcQVNzt27GDlypUULlyYP//8k969e5M/f35na9KwYcO4ffs269evJ0+ePKxYsYI2bdqwZcsWKlSogMPhAOIK5Ndffx2IK1ALFizI0qVL6dmzp0vu27dv58iRI8ybN8/t571p0ybGjRvH1KlTCQ4O5sSJE/Tr14/Ro0czbNiwBPEzZszg5ZdfTlAs9+jRw7ldoUIFAgMDadCgASdPnqR48eLO+0wmU5ItpMI9UtykNWXApDdhkEn7MrR+/fqxceNG/u///o+XX37Z7eNNBh2HR4WkQ2Ype2x3PFy0aTQal33xBUj8h0qCx0vBt+Fs2bK5lVP+/PnZv3+/83b8t/EuXbpw48YNvvrqKwoXLoyXlxc1a9bEYrE4Y9944w3q1avH1atXWbduHSaTicaNG7v1+A/LkycPt27dctl35swZmjZtSq9evRg7diy5cuVi69atvPXWW1gsFsxmM1qtNsFlwgcv47jbkgBxlzce/gB/2G+//UadOnUSvS9fvnxcuXLFZd+VK1fIly9fkuczmUzMnDmT7777jitXrhAYGMj3339P9uzZyZs3b6LHGAwGKleuzIkTJ4C4AvGjjz5i+fLlvPLKK0BcYb1//36++OILGjZsyMmTJ/n22285dOiQs/WvYsWKbNmyhSlTpjB9+nQCAwMB1+LXy8uLYsWKOYvcB/34449UqlQpQYGdEsOGDaNTp068/fbbQFxhEhkZSY8ePfj4449dWvLOnj3L+vXrE7TGJCa+5e3EiRMuxc3NmzddbovUk0/gNBJ99xbz138Qt/1BDcy5E/+DF0+n6OhopkyZQv/+/dHr9Xh5efHbb7+l+nwajSbLrAz/7LPPsmHDBkaOHJniY8qUKcNPP/2EUspZPG3bto3s2bNTsGBBtFptov1Otm3bxtSpU519Hs6fP8/169ddYmrVqkVQUBBLlizht99+o3Xr1s5irVixYhgMBnbv3u28xHjnzh2OHz/ucmnkYZUrVyYsLIxbt2451wzbs2cPDoeDL7/80vkhFxoa6nJc3rx5CQsLc3meDxZt2bNnp0iRImzYsIEXXnghRa/d416WqlmzJhs2bHC5pLZu3TqXVpekGAwG5yWmxYsX07Rp0yQvIdrtdg4ePOj8XVmtVqxWa4J4nU7nLJzjWy2Si6latSpeXl4cO3aM559/3nnuM2fOULiwazeAiIgIQkNDGT9+/COfW2KioqISzQVIULTOmjULf39/Z+GWnPj/A/GFWrxDhw653S9IJCENOzlnCOk1Wura9Ssuo6VExnHkyBFVoUIFBbiM2nBHRh4t9fAojsKFC6tJkya57APU8uXLlVIJR0sdO3ZMGY1G1atXL3XgwAF15MgRNXXqVHXt2rUkH+PChQvKbDar3r17qyNHjqgVK1aoPHnyqBEjRiSbb+XKldVLL72kDh8+rHbs2KHq1KmjTCZTgnw//vhjVbZsWaXX69WWLVtc7nv77bdV0aJF1R9//KEOHTqkXn/9dZU9e3bVv3//JB/XZrOpvHnzqlWrVjn37d+/XwFq8uTJ6uTJk2ru3LmqQIECLq/N4cOHlUajUZ9++qk6ceKE+vbbb1XOnDldRkvNnj1beXt7q6+++kodP35c7dmzR3399dfJvg6PY9u2bUqv16svvvhCHTlyRI0YMSLBUPAPP/xQderUyXn72LFjat68eer48eNq586dqm3btipXrlzq9OnTzpiRI0eqtWvXqpMnT6o9e/aodu3aKW9vb/Xvv/86Y+rVq6fKlSunNm7cqE6dOqVmzZqlvL291dSpU5VScSPbSpQooerUqaN27typTpw4ob744gul0WjUr7/+6jxPv379VIECBdTatWvV0aNH1VtvvaX8/f2dI/Ti/fjjj8rb29tlZN+D9u3bp/bt26eqVq2qOnTooPbt2+eS74gRI1T27NnVokWL1KlTp9T//vc/Vbx48QQj8ux2uypUqJAaPHhwgsc4ceKEGjVqlPr777/V6dOn1S+//KKKFSum6tat6xJ3+vRppdFo1JkzZxLNNauQoeCplF7FzZ2ISNWg7xjVoO8YdSdChn1nFHPnzlXZsmVTgAoICFDr1q1L1XmycnGjVNyw7lq1aikvLy+VI0cOFRIS4rw/qWGwqRkKvnfvXlWtWjXl7e2tSpYsqZYuXZpovocPH1aAKly4sMuQXKUSHwpeo0YN9eGHHyb72IMGDVLt2rVz2Tdx4kQVGBioTCaTCgkJUXPnzk3w2kybNk0FBQWpbNmyqc6dO6uxY8cmGAo+ffp0Vbp0aWUwGFRgYKDq27dvsrk8rtDQUFWqVCllNBpVuXLlXAoHpZTq0qWLy5wwhw8fVpUqVVImk0n5+vqqFi1aqKNHj7oc079/f1WoUCFlNBpVQECAatKkicvcNEopdfnyZdW1a1eVP39+5e3trUqXLq2+/PJLl9/R8ePH1Wuvvab8/f2V2WxWzz77bIIh3BaLRb3//vvK399fZc+eXTVs2DDBMH2l4oawd+jQIcnXgXvTOzz48+Dvxmq1qk8++UQVL15ceXt7q6CgIPXuu+8mKJbWrl2rAHXs2LEEj3Hu3DlVt25dlStXLuXl5aVKlCihPvjggwSfQePGjVMhISFJ5ppVpFVxo1HKzXGjGVx4eDh+fn7cuXMHX1/fNDtvlMXmHB3j7mgV8eRFRkbSt29fZs2aBcSNwlmwYEGy/Q6SExMTw+nTpylatGiiHU/F0ysyMpICBQrw5Zdf8tZbbyUZFxYWRrly5di7d2+Cyx9CPA6LxULJkiVZuHAhtWvX9nQ6HpXce6k7n98yz43Ico4cOUKNGjWYNWsWWq2WkSNH8r///S/VhY3IWPbt28eiRYs4efIke/fu5Y033gCgRYsWyR6XL18+ZsyYkWinVSEex7lz5/joo4+yfGGTlqR5IY1YY6MIuRZ6b7sOGNOuVUikLYfDwenTpwkMDGThwoXOycFE1vHFF19w7NgxjEYjVatWZcuWLeTJk+eRxz24fpEQaaVEiRLJTtwo3CfFTRqxWmLov22XcxukuHma2O125yiHcuXKsXz5cipXroy/v7+HMxNPWuXKldmzZ4+n0xBCpCO5LJVGtDo9ewr7sKewD1qd1IxPkwMHDvDss8+ydetW576QkBApbIQQIpOS4iaNeJt9GVr5E4ZW/gRvs7TaPA2UUnz33XcEBwdz+PBhPvjgA7fXXRJCCJHxSHEjMqXw8HDat2/PO++8Q2xsLE2aNGHVqlUJFrMTQgiR+UhxIzKdvXv3UrVqVZYsWYJer2fChAmsWrUqRR1GhRBCZHzSOSSNRN+9xcxNg+9ty/ILnnLo0CHnWkOFChVi8eLFKZpWXgghROYhxU0aUSgCb9ud28IzypUrR9OmTbHZbMyaNcu54KIQQois46m4LDVlyhSKFCmCt7c3wcHB7Nq1K8nYH374gTp16pAzZ05y5sxJw4YNk41/UozePnzQ8HU+aPg6Rm8fT6eTpfz999/cuXMHiFuwcv78+axYsUIKG5Hubty4gb+/P2fOnPF0KkI89T788EP69u37RB7L48XNkiVLGDBgACNGjGDv3r1UrFiRkJAQrl69mmj8pk2baN++PRs3bmT79u0EBQXRqFEjLl68+IQzd6U3GDnkU5NDPjXRG4wezSWrUEoxadIkatWqRY8ePZwjoUwmk3QcFk/E2LFjadGiBUWKFPF0Ko9FKcXw4cMJDAzEZDLRsGFD/vvvv2SPuXv3Lv3796dw4cKYTCZq1arF7t27XWK6du2KRqNx+WncuLFLzNixY6lVqxZms5kcOXIkeJwDBw7Qvn17goKCMJlMlClThq+++sol5ueff+all14ib968+Pr6UrNmTdauXesSM378eKpXr0727Nnx9/enZcuWHDt2zCUmLCyMTp06kS9fPrJly0aVKlX46aefnPefOXOGt956i6JFi2IymShevDgjRozAYrEkeD2/+OILSpUqhZeXFwUKFGDs2LEuMZs2baJKlSp4eXlRokQJZs+e7XK/3W5n2LBhLo81evToBCM+jxw5QvPmzfHz8yNbtmxUr17dZRbtkydP8uqrrzpfmzZt2nDlyhWXcxQpUiTB7+nTTz91+znFxsby8ccfU7hwYby8vChSpAgzZ8503j9w4EDmzJnDqVOnSHdpt9xV6tSoUUP17t3bedtut6v8+fOr8ePHp+h4m82msmfPrubMmZOi+PRaODMy1qoKD16tCg9erSJjk1/8Tzy+GzduqObNmzsXu2vVqpWKiYnxWD4ZdeHMp4XFYvF0Cm6LjIxUvr6+avv27Z5O5bF9+umnys/PT61YsUIdOHBANW/eXBUtWjTZ/89t2rRRZcuWVZs3b1b//fefGjFihPL19VUXLlxwxnTp0kU1btxYXb582fnz8Mrdw4cPVxMnTlQDBgxQfn5+CR5nxowZ6v/+7//Upk2b1MmTJ9W8efOUyWRS33zzjTOmX79+6rPPPlO7du1Sx48fV0OGDFEGg8Fl4c6QkBA1a9YsdejQIbV//37VpEkTVahQIRUREeGMeemll1T16tXVzp071cmTJ9Xo0aOVVqt1nue3335TXbt2da5+/ssvvyh/f3/1/vvvu+Tct29fVbp0afXLL7+oU6dOqb///lv973//c95/6tQpZTab1YABA9Thw4fVN998o3Q6nfr999+dMWPHjlW5c+dWq1evVqdPn1ZLly5VPj4+6quvvnLGnDhxQuXKlUt98MEHau/everEiRPql19+UVeuXFFKKRUREaGKFSumXn31VfXPP/+of/75R7Vo0UJVr15d2e1253kKFy6sRo0a5fJ7evB1SclzUkqp5s2bq+DgYLVu3Tp1+vRp9ddff6mtW7e6xLRq1UoNHDgwwe85XqZYFTw2NlbpdDrnasPxOnfurJo3b56ic4SHhytvb2+1atWqFMWnV3Fz++5d1bl7D9W5ew91++7dND23cPXXX3+poKAgBSij0aimTJmSYOXnJy2jFjf16tVTffr0Uf369VM5cuRQ/v7+6vvvv1cRERGqa9euysfHRxUvXlytWbPGeYzNZlNvvvmmKlKkiPL29lalSpVSkydPTnDuGTNmqLJlyzpX/X7wSwygpk6dqpo1a6bMZrMaMWKEUkqpqVOnqmLFiimDwaBKlSqVYDXoxMydO1dVrVpV+fj4qICAANW+fXvnm7vdblcFChRQU6dOdTlm7969SqPRqDNnziillDpy5IiqXbu28vLyUmXKlFHr1q1zWQk9MUuXLlV58+Z12ZeS1yaxVdJbtGihunTp4rwdExOjBg0apAoWLKiMRqMqXry4+vHHHx/5WqSGw+FQ+fLlUxMmTHDuu337tvLy8lKLFi1K9JioqCil0+nU6tWrXfZXqVJFffzxx87bXbp0US1atEhRHrNmzUq0uEnMu+++q1544YVkY8qWLatGjhyZ5P1Xr15VgNq8ebNzX7Zs2RL8n8uVK5f64YcfkjzP559/rooWLeq8ffjwYaXX6xOsmv6gQYMGqXLlyrnsa9u2rcuq4K+88op68803XWJee+019cYbb7gc07FjxyQfZ+3atUqr1bp83t2+fVtpNBq1bt06577ChQurSZMmJXmelDyn3377Tfn5+akbN24kGaOUUnPmzFEFCxZM8v60Km48elnq+vXr2O12AgICXPYHBAQQFhaWonMMHjyY/Pnz07Bhw0Tvj42NJTw83OUnPVhjo/jwzz/58M8/scZGpctjZHUOh4PPP/+cOnXqcP78eUqUKMGOHTt49913n77LUEqBJdIzP25OVDhnzhzy5MnDrl276Nu3L7169aJ169bUqlWLvXv30qhRIzp16kRUVNz/a4fDQcGCBVm6dCmHDx9m+PDhfPTRR4SGhjrPOW3aNHr37k2PHj04ePAgK1euTLB2zieffMKrr77KwYMHefPNN1m+fDn9+vXj/fff59ChQ/Ts2ZNu3bqxcePGZPO3Wq2MHj2aAwcOsGLFCs6cOUPXrl0B0Gq1tG/fnoULF7ocs2DBAmrXrk3hwoWx2+20bNkSs9nMzp07+f777/n4448f+bpt2bKFqlWruuxLyWuTEp07d2bRokV8/fXXHDlyhO+++w4fn6T78r3zzjv4+Pgk+5OU06dPExYW5vIe6ufnR3BwMNu3b0/0GJvNht1uT7Bqs8lkcpkJHOIuv/j7+1O6dGl69erFjRs3UvISJOvOnTvJ9qlzOBzcvXs32Zj4fnoPxtSqVYslS5Zw8+ZNHA4HixcvJiYmJtn15x7OZdWqVRQrVozVq1dTtGhRihQpwttvv83NmzedMdu3b0/wmRUSEuLyeteqVYsNGzZw/PhxIO7y3NatW3n55Zedz/HXX3+lVKlSzhnXg4ODWbFihfMcsbGxaDQavLy8nPu8vb3RarUJfk+ffvopuXPnpnLlykyYMAGbzebWc1q5ciXVqlXj888/p0CBApQqVYqBAwcSHR3t8jg1atTgwoUL6d9P7ZHlTzq6ePGiAtRff/3lsv+DDz5QNWrUeOTx48ePVzlz5lQHDhxIMmbEiBHOSxcP/qR1y831G9dU6IuVVeiLldX1G9fS9Nwizo0bN1T+/PkVoNq3b6/Cw8M9nZJTgm8bsRFKjfD1zE9sRPLJPqBevXrq+eefd9622WwqW7ZsqlOnTs59ly9fVkCyl1969+6tXn/9deft/Pnzu3yDfxig+vfv77KvVq1aqnv37i77WrdurZo0aZLi56OUUrt371aAunuvBXXfvn1Ko9Gos2fPKqXut+ZMmzZNKRX3jVOv16vLly87z5GSlpsWLVok+GadmIdfm0e13Bw7dkwBLt+sH+XKlSvqv//+S/YnKdu2bVOAunTpksv+1q1bqzZt2iR5XM2aNVW9evXUxYsXlc1mU/PmzVNarVaVKlXKGbNo0SL1yy+/qH/++UctX75clSlTRlWvXl3ZbLYE50tpy822bduUXq9Xa9euTTLms88+Uzlz5nS24D3MbrerV155RdWuXdtl/61bt1SjRo0UoPR6vfL19U32cf777z/l6+urvv/+e+e+nj17Ki8vLxUcHKz+/PNPtXHjRlWpUiWXlqaSJUuqcePGuZzr119/VYCKiopy5jh48GCl0WiUXq9XGo3G5Zj4v0uz2awmTpyo9u3bp8aPH680Go3atGmTUiqudcrX11f169dPRUZGqoiICNWnTx8FqB49ejjP9eWXX6qNGzeqAwcOqGnTpqkcOXKo9957z63nFBISory8vNQrr7yidu7cqX799VdVuHBh1bVrV5fnGd/6Ep/jw9Kq5cajQ8Hz5MmDTqdL0LnpypUr5MuXL9ljv/jiCz799FPWr1/Ps88+m2TckCFDGDBggPN2eHg4QUFBj5d4Ikw+Ofig+mgADvvkSPPzi7hvWIsWLeLYsWO8/fbbT19rTQb14N+PTqcjd+7cVKhQwbkvvmX1wU7+U6ZMYebMmZw7d47o6GgsFguVKlVyxl26dIkGDRok+7jVqlVzuX3kyBF69Ojhsq927drOzqMLFiygZ8+ezvt+++036tSpw549e/jkk084cOAAt27dwuFwAHDu3DnKli1LpUqVKFOmDAsXLuTDDz9k8+bNXL16ldatWwNw7NgxgoKCXN5zatSokfyLBkRHRydouXjUa5MS+/fvR6fTUa9evRQf4+/v/8TXSps3bx5vvvkmBQoUQKfTUaVKFdq3b++yKGm7du2c2xUqVODZZ5+lePHibNq06ZH/PxJz6NAhWrRowYgRI2jUqFGiMQsXLmTkyJH88ssvSb4mvXv35tChQwlaL4YNG8bt27dZv349efLkYcWKFbRp04YtW7a4/E0AXLx4kcaNG9O6dWu6d+/u3O9wOIiNjWXu3LmUKlUKgBkzZlC1alWOHTtG6dKlU/RcQ0NDWbBgAQsXLqRcuXLs37+f/v37kz9/frp06eL8f96iRQvee+89ACpVqsRff/3F9OnTqVevHnnz5mXp0qX06tWLr7/+2tmSWaVKFbTa+xduHvyMfPbZZzEajfTs2ZPx48fj5eWVoufkcDjQaDQsWLAAPz8/ACZOnEirVq2YOnUqJpMJwPlvfEtwevFocWM0GqlatSobNmygZcuWQNx/jA0bNtCnT58kj/v8888ZO3Ysa9euTfAG+TAvLy+XJjmRcTgcDsaPH0/hwoXp2LEjAHXr1qVu3boeziwFDGb46JLnHtudcIPB5bZGo3HZF19Exr+ZLl68mIEDB/Lll19Ss2ZNsmfPzoQJE9i5cydw/83rUbJly+ZWns2bNyc4ONh5u0CBAkRGRhISEkJISAgLFiwgb968nDt3jpCQEJcRLG+88YazuFm4cCGNGzcmd+7cbj3+w/LkycOtW7dc9j3qtYG4S2XqoUuHVqvVuZ3S1+9B77zzDvPnz082JiIiItH98UXdlStXCAwMdO6/cuVKskVZ8eLF2bx5M5GRkYSHhxMYGEjbtm0pVqxYkscUK1aMPHnycOLECbeLm8OHD9OgQQN69OjB0KFDE41ZvHgxb7/9NkuXLk2yq0KfPn1YvXo1f/75JwULFnTuP3nyJN9++y2HDh2iXLlyAFSsWJEtW7YwZcoUpk+f7oy9dOkSL7zwArVq1eL77793OX9gYCB6vd5ZBACUKVMGiCu4S5cuTb58+RL9Uu/r6+v8/X/wwQd8+OGHzgKxQoUKnD17lvHjx9OlSxfy5MmDXq+nbNmyLucpU6aMS9HWqFEjTp48yfXr19Hr9eTIkYN8+fIl+3sKDg7GZrNx5swZSpcunaLnFBgYSIECBZyFTXyMUooLFy5QsmRJAOelrLx503eiW49P4jdgwAC6dOlCtWrVqFGjBpMnTyYyMpJu3boBcdeeCxT4//buOyqK6+0D+HfBXVi6SBdEul2KSsCCBQU1BnsjAhZMbEkkNhR7FBOjicYeBUtELLFFFMT6U8SGYgFEQZBoQGOh993n/YOXieMCLoogeD/n7Dk7M3dmnrni7rN37p3bFIGBgQCAH3/8EQsWLEBISAiaN2/O9c15231lpn55+vQpxowZg8jISKioqKBHjx5o2rRpXYclP4EAEFXvy7u+iIqKgrOzMyZPnsytS05O5t6rq6ujefPmOH36NHr06CH3cVu2bImoqCh4e3vzzlX+4a2urg51dXXePjExMXjx4gVWrFjBtchev35d5tijR49GQEAAYmJicODAAd4XlY2NDf7++288ffqUa6V6c0hzRezs7GQSirfVDVD2oZ6ens4tSyQS3L17l6urtm3bQiqV4vz585V+Qb9pyZIlmDFjhlxl32RmZgYDAwOcPn2aS2ays7Nx5coVTJo06a37q6qqQlVVFa9evUJERAR++umnSss+fvwYL1684CVR8oiLi0PPnj3h7e0tM/y43J49ezBu3DiEhoaif//+MtuJCNOmTcOhQ4dw7tw5mJmZ8baXtyS83qIBlLVmlif2QFmLTY8ePeDg4IDg4GCZ8p07d0ZpaSmSk5NhYWEBAFy/GVNTUwCAk5MTjh8/ztsvMjKS9zT1/Pz8KmMRiUTo2LGjzHD2+/fvc+d5Xfn0M2fOnMGzZ8/wxRdfyJQpFxsbCwUFBa7lS55r6ty5M/bv34/c3Fzuu/j+/ftQUFDgJZF3796FUCjkEsgP5q03rmrBb7/9Rs2aNSORSESdOnWiy5cvc9tcXFx4owhMTU0r7ENTPtribT7UaKnnL/6lsC5tKaxLW9bn5j2dOXOGDAwMCACJxWIKCgqq89FQb1OfR0u92f+jopETeK3/yZo1a0hDQ4PCw8MpMTGRAgICSENDg9q3b8+V3759OykrK9OaNWvo/v37FBMTQ2vXrq3weOUOHTpEQqGQNmzYQPfv36dVq1aRoqIinT17ttL4nz17RiKRiGbOnMkNzbW2tiYAdPPmTV7Zzp07U/v27UldXZ3r10BU1s/IxsaG3Nzc6NatW3Tx4kX67LPPCAAdPny40nPfvn2bGjVqxBvaLE/dbNq0iVRUVOjYsWOUkJBAvr6+pKGhwfuc8/HxIRMTEzp06BA9fPiQzp49S3v37q00lve1YsUK0tLS4vrHeHh4yAwF79mzJ2/4dXh4OJ04cYIePnxIJ0+epPbt25OjoyM3rD8nJ4dmzJhB0dHRlJKSQqdOnSJ7e3uysrLiPbbh0aNHdPPmTVq8eDGpqanRzZs36ebNm1yfqTt37pCuri59+eWXvKHKz549446xe/duatSoEa1fv55XJjMzkyszadIk0tTUpHPnzvHKlP8tFBcXk6WlJXXt2pWuXLlCSUlJ9PPPP5NAIKCwsDAiInr8+DFZWlpSr1696PHjx7zjlJNIJGRvb0/dunWjGzdu0PXr18nR0ZF69+7NlSkfCj5z5kxKSEig9evXywwF9/b2pqZNm3JDwQ8ePEg6Ojo0a9YsrszBgwdJKBTSli1b6MGDB9yQ8gsXLnBlgoKCKDo6mpKSkmjXrl2kra1Nfn5+3PZLly7RL7/8QrGxsZScnEx//PEH6erqkpeXV7WuKScnh4yNjWno0KEUFxdH58+fJysrK5owYQLvb23hwoXUs2fPN/8EOQ1iKHhd+FDJzb/Pn1K8TQuKt2lB/z6vuBMbU7XS0lJatGgRKSgoEABq3bo1xcXF1XVYcvmUkpvCwkLy8fEhTU1N0tLSokmTJtGcOXN4X+BEZV/iNjY2JBQKydDQkKZNm1bh8V73LkPBQ0JCqHnz5qSkpEROTk509OjRCpObDRs2EADeh3a58qHgIpGIWrRoQX/99RcB4H3ZVKRTp060adMmblmeuikuLqZJkyaRtrY26enpUWBgoMxQ8IKCApo+fToZGhqSSCQiS0tLCgoKemtdvCupVErz588nfX19UlJSol69elFiYiKvjKmpKe9H5N69e8nc3Jw31P/1ZCI/P5/69OlDurq6JBQKydTUlHx9fSkjI4N3XG9v7wp/sJYntZUNCjE1NeWO4eLiUmGZ1+u0ou0AKDg4mCtz//59Gjx4MOnp6ZGKigq1a9eO9zcYHBxc6XFe9+TJExo8eDD3eAIfHx+ZIdLlnXJFIhGZm5vz4iAqe8zJt99+S82aNSNlZWUyNzenefPmUVFREa/ctm3byNLSkpSVlal9+/YyCfns2bNJX1+fhEIhWVlZ0apVq3g/FmNiYsjR0ZE0NTVJWVmZWrZsScuXL5d5bpg815SQkECurq4kFovJ2NiY/Pz8eD8kiIhsbGwqfcQAUc0lNwKiao4breeys7OhqamJrKwsaGho1Nxx8/IxduYCAEDwyiXQUK1ev4dPXWlpKdzd3XH69GkAwPjx47F27VqoqNSPeiwsLERKSgrMzMwq7GTK1C9RUVHo0qULkpKSuGb4ioSFhWHmzJm4e/euzC0EhmH4Tpw4ge+//x63b99Go0YV94qp6rO0Ot/fdd7npqFoJBQhRqMH956pnkaNGqFjx464fPkyNm/eDE9Pz7oOifmEHDp0CGpqarCyskJSUhK+/fZbdO7cucrEBgD69++PBw8e4MmTJx9kFCbDNCR5eXkIDg6uNLGpSSy5YepMaWkpXr16xfWaX7JkCSZMmPDWLxSGqWk5OTmYPXs20tLSoKOjA1dXV6xatUqufb/77rsPGxzDNBBDhw6ttXOx5KaGlBQXwjHz5P+/dwFEbORWVR4/foxRo0ahqKgIFy9ehEgkglAoZIkNUye8vLzg5eVV12EwDFNDWHJTQ0qK8rHo3P8nN0XzADYsvVLHjx+Hl5cXXrx4AXV1ddy9exf29vZ1HRbDMAzTQLAecDVEIFBAkr4ISfoiCASsWitSUlKCWbNmoX///njx4gXs7e1x48YNltgwDMMwNYq13NQQsZoWpjktB8CmX6jIo0ePMHLkSFy+fBkAMG3aNKxcuZI9PZphGIapcSy5YWrFhAkTcPnyZWhqaiIoKAiDBw+u65AYhmGYBordP2FqxcaNG+Hq6oqbN2+yxIZhGIb5oFjLTQ0pyM3EL1cC/v+9I1S0deo4orqVkpKC06dPY8KECQAAS0tLREZG1nFUDMMwzKeAtdzUECIpWqQXokV6IYikb9+hAfvzzz9hZ2eHiRMn4tSpU3UdDvOOUlNTIRAIEBsbW9ehVEtiYiIMDAyQk5NT16EwH4lNmzZhwIABdR0GU4tYclNDhEoqWObSE8tcekKoVD+mDKhphYWFmDp1KoYOHYqsrCx89tln3DT3DFNdhYWFmDJlCpo0aQI1NTUMGTIET58+fet+/v7+mDZtmswM4vXNu1z/06dP4ePjAyMjI6ioqMDd3R0PHjzglenevTsEAgHv9fXXX/PKfPPNN3BwcICSkhI3U/ibbt++ja5du0JZWRkmJiYys4GXlJRgyZIlsLCwgLKyMtq3b4/w8HBemY0bN6Jdu3bQ0NCAhoYGnJyccOLECW57eYJd0Wv//v1cuYq2h4aGctvHjRuHGzdu4MKFC1XWH9NwsOSmhghFyrjYuB8uNu4HoejTm1soKSkJzs7OWL9+PQBg1qxZOH/+PExNTes4Mqa+mj59Ov766y/s378f58+fxz///PPW/lppaWk4duwYfHx8aifID6i6109EGDhwIB4+fIgjR47g5s2bMDU1haurK/Ly8nhlfX19kZ6ezr3eTEyAsoRgxIgRFZ4rOzsbffr0gampKWJiYrBy5UosWrQIW7Zs4coEBARg8+bN+O233xAfH4+vv/4agwYNws2bN7kyxsbGWLFiBWJiYnD9+nX07NkTHh4eiIuLAwCYmJjw4kxPT8fixYuhpqaGvn378mIKDg7mlRs4cCC3TSQSYfTo0Vi7dm3lFc40LG+dWrOB+VCzgucVlZDp7GNkOvsY5RWV1OixP3b79u0jdXV1AkBNmjShsLCwug6p1lU2k21ecR7lFefxZuEtLi2mvOI8KiotqrCsRCr5r6ykrGxhaaFcZatLIpHQjz/+SBYWFiQSicjExIR++OEHIiJKSUnhza5dWlpK48aNo+bNm5OysjJZW1vTr7/+yjve2bNnqWPHjqSiokKamprk7OxMqampREQUGxtL3bt3JzU1NVJXVyd7e3u6du1ahXFlZmaSUCik/fv3c+sSEhIIAEVHR1d6PStXrqQOHTrw1j1//pxGjhxJRkZGJBaLqU2bNhQSEsIrU9FM6O3bt+fNgv3q1SuaOHEi6enpkZKSErVu3Zr++uuvSmN5H+9y/YmJiQSA7t69y62TSCSkq6tLv//+O7euopngK7Nw4UKZ2d6JymZYb9y4MW+G6tmzZ5ONjQ23bGhoSOvWrePtN3jwYPL09KzynI0bN6atW7dWut3W1pbGjRvHW4dKZpl/3fnz50kkEsnMUs18XGpqVnDWclNDSkuKYZvzP9jm/A+lJcV1HU6tys3NRU5ODrp27YrY2Fj069evrkP6aDiGOMIxxBGvil5x64LjguEY4ojlV5bzynbf1x2OIY5Iz0vn1oXeC4VjiCMWRC3glXX/0x2OIY54mPmQW3ck6Ui14/P398eKFSswf/58xMfHIyQkBPr6+hWWlUqlMDY2xv79+xEfH48FCxZg7ty52LdvH4CyucIGDhwIFxcX3L59G9HR0Zg4cSIEAgEAwNPTE8bGxrh27RpiYmIwZ84cCIXCCs8VExODkpISuLq6cutatGiBZs2aITo6utLruXDhAjp06MBbV1hYCAcHB4SFheHu3buYOHEixowZg6tXr8pdT1KpFH379kVUVBT++OMPxMfHY8WKFVBUVKx0n759+0JNTa3SV+vWrSvd912uv6ioCAB4MykrKChASUkJFy9e5JXdvXs3dHR00KZNG/j7+yM/P1+ueigXHR2Nbt26QST6b5JgNzc3JCYm4tWrV1w8b87qLBaLZWIpJ5FIEBoairy8PDg5OVVYJiYmBrGxsRg/frzMtilTpkBHRwedOnVCUFAQiIi3vUOHDigtLcWVK1eqda1M/cRGS9WQ4sJcBJ4++v/vvwdUG3a/m9LSUm5mVx8fH6ipqWHQoEG1MtsrUzNycnKwZs0arFu3Dt7e3gAACwsLdOnSpcLyQqEQixcv5pbNzMwQHR2Nffv2Yfjw4cjOzkZWVhY+//xzbo6wli1bcuXT0tIwc+ZMtGjRAgCq7I+VkZEBkUgELS0t3np9fX1kZGRUut+jR49kkpumTZtixowZ3PK0adMQERGBffv2oVOnTpUe63WnTp3C1atXkZCQAGtrawCAubl5lfts3boVBQUFlW6vLLED3u36y5Mff39/bN68Gaqqqvjll1/w+PFjpKf/lzCPHj0apqamMDIywu3btzF79mwkJibi4MGDVV7Pm/GZmZnJxFa+rXHjxnBzc8Pq1avRrVs3WFhY4PTp0zh48CAkEglvvzt37sDJyQmFhYVQU1PDoUOH0KpVqwrPu23bNrRs2RLOzs689UuWLEHPnj2hoqKCkydPYvLkycjNzcU333zDlVFRUYGmpiYePXok93Uy9Rf7JqohAgjwd5Oy6tSDoI6j+bB27dqFwMBAXLhwAU2aNIFAIMCwYcPqOqyP0pXRZb8SxY3E3Lqxrcfiy5ZfopEC/7/fueHnAADKjf77tTuyxUgMsRoCRQV+C0H4kHCZsh6WHtWKLSEhAUVFRejVq5fc+6xfvx5BQUFIS0tDQUEBiouLuQ6n2tra8PHxgZubG3r37g1XV1cMHz4choaGAAA/Pz9MmDABu3btgqurK4YNG1bjE6UWFBTItBZIJBIsX74c+/btw5MnT1BcXIyioiKoqMj/AyQ2NhbGxsZcYiOPpk2byl22JgiFQhw8eBDjx4+HtrY2FBUV4erqir59+/JaMSZOnMi9b9u2LQwNDdGrVy8kJyfX6L/HmjVr4OvrixYtWkAgEMDCwgJjx45FUFAQr5yNjQ1iY2ORlZWFAwcOwNvbG+fPn5dJcAoKChASEoL58+fLnOv1dXZ2dsjLy8PKlSt5yQ1Q1nJU3VYqpn5it6VqiFi9MSZ2XYGJXVdArN64rsP5IPLy8jBu3Dh4eXkhISGBdc6Tg4pQBSpCFe7WDAAIFYVQEapApCiqsKzCa3OTCRXKyiopKslVtjrEYvHbC70mNDQUM2bMwPjx43Hy5EnExsZi7NixKC7+7zZscHAwoqOj4ezsjL1798La2pqbcmPRokWIi4tD//79cebMGbRq1QqHDh2q8FwGBgYoLi5GZmYmb/3Tp09hYGBQaYw6OjrcbZFyK1euxJo1azB79mycPXsWsbGxcHNz48WtoKAgcxujpKSEe1/dugLe77bUu16/g4MDYmNjkZmZifT0dISHh+PFixdVtjI5OjoCKBsUIC8DAwOZkVvly+Xx6erq4vDhw8jLy8OjR49w7949qKmpycQiEolgaWkJBwcHBAYGon379lizZo3MOQ8cOID8/Hy5Zm93dHTE48ePuVt15V6+fAldXV25r5Opv1jLDSOXuLg4DB8+HPHx8RAIBFi4cCECAgLqOizmPVhZWUEsFvMetliVqKgoODs7Y/Lkydy65ORkmXJ2dnaws7ODv78/nJycEBISgs8++wwAYG1tDWtra0yfPh2jRo1CcHAwBg0aJHMMBwcHCIVCnD59GkOGDAFQ9vyatLS0SvtjlJ87Pj5eJm4PDw98+eWXAMr6z9y/f5/XMqCrq8u7dZOdnY2UlBRuuV27dnj8+DHu378vd+vN+9yWetfrL6epqQkAePDgAa5fv46lS5dWWrb8OUblLWzycHJywrx581BSUsJdR2RkJGxsbNC4Mf/HnbKyMpo2bYqSkhL8+eefGD58eJXHlkqlMkkJUHZL6osvvpArOYmNjUXjxo15c9clJyejsLAQdnZ28lwiU8+x5IapEhFh+/btmDJlCgoKCmBgYICQkBD06NGjrkNj3pOysjJmz56NWbNmQSQSoXPnzvj3338RFxdXYYdNKysr7Ny5ExERETAzM8OuXbtw7do1ru9FSkoKtmzZgi+++AJGRkZITEzEgwcP4OXlhYKCAsycORNDhw6FmZkZHj9+jGvXrnFf3G/S1NTE+PHj4efnB21tbWhoaGDatGlwcnLiEqWKuLm5YcKECZBIJFxnXysrKxw4cACXLl1C48aNsXr1ajx9+pSX3PTs2RPbt2/HgAEDoKWlhQULFvA6C7u4uKBbt24YMmQIVq9eDUtLS9y7dw8CgQDu7u4VxvI+t6Xkvf4WLVogMDCQSxD3798PXV1dNGvWDHfu3MG3336LgQMHok+fPgDKvuBDQkLQr18/NGnSBLdv38b06dPRrVs3tGvXjjtuUlIScnNzkZGRgYKCAi4BatWqFTesevHixRg/fjxmz56Nu3fvYs2aNfjll1+4Y1y5cgVPnjyBra0tnjx5gkWLFkEqlWLWrFlcGX9/f/Tt2xfNmjVDTk4OQkJCcO7cOURERPDqIykpCf/73/9w/Phxmbr666+/8PTpU3z22WdQVlZGZGQkli9fzutnBZR1Njc3N6/xW6HMR6qmh3F97D7UUPDnL/6lPa72tMfVnp6/+LdGj12X1q1bRwAIAPXu3ZsyMjLqOqSPUlXDFz9mEomEfvjhBzI1NSWhUEjNmjWj5cuXE5HsUPDCwkLy8fEhTU1N0tLSokmTJtGcOXO4ocIZGRk0cOBAMjQ0JJFIRKamprRgwQKSSCRUVFREI0eOJBMTExKJRGRkZERTp06tsr4KCgpo8uTJ1LhxY1JRUaFBgwZRenp6lddTUlJCRkZGFB4ezq178eIFeXh4kJqaGunp6VFAQAB5eXmRh4cHVyYrK4tGjBhBGhoaZGJiQtu3b5cZCv7ixQsaO3YsNWnShJSVlalNmzZ07Nix6lV4Nchz/QAoODiYW16zZg0ZGxtz/5YBAQG84dppaWnUrVs30tbWJiUlJbK0tKSZM2fKfB66uLhw/+9ff6WkpHBlbt26RV26dCElJSVq2rQprVixgneMc+fOUcuWLUlJSYmaNGlCY8aMoSdPnvDKjBs3jkxNTUkkEpGuri716tWLTp48KVMX/v7+ZGJiQhKJRGbbiRMnyNbWltTU1EhVVZXat29PmzZtkinbp08fCgwMrLiymY9GTQ0FFxC9caO5gcvOzoampiaysrKgoaFRY8d9/uIZ/u3sAgDQjToPnSZ6NXbsupSZmYmOHTti7NixmDNnDhQUWDetihQWFiIlJQVmZmYyHVqZ2rV+/XocPXpU5tc/8+mKi4tDz549cf/+fe6WHfNxquqztDrf3+y2VA0RKqng5y5lwxNX1+PpF4gIp06dgqurKwQCAbS0tHDnzh32hc3UG1999RUyMzORk5NT76dgYGpGeno6du7cyRKbTwhLbmqIUKSM0zqDuff1UXZ2Nr766iuEhoZi8+bN3JBRltgw9UmjRo0wb968ug6D+Yi8/jBE5tPAkhsGAHDz5k0MHz4cSUlJaNSoUZWjPBiGYRjmY8aSmxpSWlIMm7xr//++ByCqH1VLRNiwYQP8/PxQXFyMZs2aITQ0VK7hpgzDMAzzMaof38D1QHFhLn6N3Fv2ftHkejH9QmZmJiZMmIA///wTAPDFF18gODgY2tradRwZwzAMw7w7NvSlhgggwDMNBTzTUICgnky/cOfOHRw6dAhCoRC//PILDh8+zBIbhmEYpt5jLTc1RKzeGN49fwIAxNeT6Re6du2KdevWoUOHDujYsWNdh8MwDMMwNYK13HxCXr58idGjRyMxMZFbN2nSJJbYMAzDMA0Ka7n5RERHR2PkyJFIS0tDUlISrly5wpvMkWEYhmEaCtZyU0MK87Ox6NYSLLq1BIX52XUdDkcqlWLlypXo1q0b0tLSYGFhgU2bNrHEhnmr1NRUCAQCbl6h+uLFixfQ09NDampqXYfCfCTCw8Nha2sLqVRa16EwtYQlNzVEKimFY0o2HFOyIZWU1nU4AIDnz59jwIABmDVrFkpLSzFixAjcuHED9vb2dR0aw7zVli1b0L17d2hoaEAgECAzM1Ou/ZYtWwYPDw80b978g8b3oRERFixYAENDQ4jFYri6uuLBgwdV7pOTk4PvvvsOpqamEIvFcHZ2xrVr13hlfHx8IBAIeK83J/9ctmwZnJ2doaKiAi0trQrPlZaWhv79+0NFRQV6enqYOXMmSkv5n33r169Hy5YtIRaLYWNjg507d/K2Hzx4EB06dICWlhZUVVVha2uLXbt28cq8GWv5a+XKlVyZ5s2by2xfsWIFt93d3R1CoRC7d++usv6YhoPdlqohQpEy1jk7AACWfwRPKE5KSkL37t3x5MkTKCsrY82aNfD19WUtNky9kZ+fD3d3d7i7u8Pf31/ufbZt29Yg5pX66aefsHbtWuzYsQNmZmaYP38+3NzcEB8fX+lTwydMmIC7d+9i165dMDIywh9//AFXV1fEx8fzZil3d3dHcHAwt6ykpMQ7TnFxMYYNGwYnJyds27ZN5jwSiQT9+/eHgYEBLl26hPT0dHh5eUEoFGL58uUAgI0bN8Lf3x+///47OnbsiKtXr8LX1xeNGzfGgAEDAADa2tqYN28eWrRoAZFIhGPHjmHs2LHQ09ODm5sbgLKpE1534sQJjB8/XmZG+SVLlsDX15dbfnPqDR8fH6xduxZjxoypuMKZhqWGJ/T86H2oWcHzikrIdPYxMp19jPKKSmr02O+iuLiYnJ2dycbGhm7dulXX4TR4lc1kK8nLI0leHkmlUm6dtKiobP1rszXzyr42m7G0uLhsfWGhXGWrSyKR0I8//kgWFhYkEonIxMSEfvjhByKSnRW8tLSUxo0bR82bNydlZWWytramX3/9lXe8s2fPUseOHUlFRYU0NTXJ2dmZUlNTiYgoNjaWunfvTmpqaqSurk729vZ07dq1t8Z49uxZAkCvXr16a9n9+/eTrq4ub508cbu4uNC3337LW+fh4UHe3t7ccmFhIc2aNYuMjY1JJBKRhYUFbd269a0xvQupVEoGBga0cuVKbl1mZiYpKSnRnj17KtwnPz+fFBUVZWYqt7e3p3nz5nHL3t7evBnRqxIcHEyampoy648fP04KCgqUkZHBrdu4cSNpaGhws5A7OTnRjBkzePv5+flR586dqzynnZ0dBQQEVLrdw8ODevbsyVtnampKv/zyS5XHffToEQGgpKSkKssxdaumZgVnt6UakH///RfFxcUAAKFQiAMHDuD69eto165dHUf26Uq0d0CivQMkr15x614EBSHR3gFPly7llb3fuQsS7R1Q8s9/v1RfhYQg0d4B6fMCeGWTerki0d4BxcnJ3LrMQ4eqHZ+/vz9WrFiB+fPnIz4+HiEhIdDX16+wrFQqhbGxMfbv34/4+HgsWLAAc+fOxb59+wAApaWlGDhwIFxcXHD79m1ER0dj4sSJXGuhp6cnjI2Nce3aNcTExGDOnDkQCoXVjrkqFy5cgIODQ7XilpeXlxf27NmDtWvXIiEhAZs3b4aamlql5b/++muoqalV+apMSkoKMjIyeHMiaWpqwtHREdHR0RXuU1paColEItOqIxaLcfHiRd66c+fOQU9PDzY2Npg0aRJevHghTxVwoqOj0bZtW97fipubG7KzsxEXFwcAKCoqqjCWq1evoqSkROaYRITTp08jMTER3bp1q/C8T58+RVhYGMaPHy+zbcWKFWjSpAns7OywcuVKmVtkzZo1g76+Pi5cuFCta2XqJ3ZbqoZIJKVoXhD3/+97obar9uzZsxg9ejQ8PT3x888/AwAMDQ1rNQamfsnJycGaNWuwbt06eHt7AwAsLCzQpUuXCssLhUIsXryYWzYzM0N0dDT27duH4cOHIzs7G1lZWfj8889hYWEBAGjZsiVXPi0tDTNnzkSLFi0AAFZWVjV+TY8ePYKRkVG14pbH/fv3sW/fPkRGRnIJh7m5eZX7LFmyBDNmzKjmFZTJyMgAAJlEU19fn9v2JnV1dTg5OWHp0qVo2bIl9PX1sWfPHkRHR8PS0pIr5+7ujsGDB8PMzAzJycmYO3cu+vbti+joaCgqKsodX0WxvR67m5sbtm7dioEDB8Le3h4xMTHYunUrSkpK8Pz5c+7zKSsrC02bNkVRUREUFRWxYcMG9O7du8Lz7tixA+rq6hg8eDBv/TfffAN7e3toa2vj0qVL8Pf3R3p6OlavXs0rZ2RkhEePHsl1jUz9xpKbGlKUn42NEWX3sIsW+EBdXDv9biQSCX744QcsWbIEUqkU4eHhWLJkCVRUPv7pHz4FNjdiAAACsZhb12TcOGh7eQGN+P/9rKPKfl0LXvu123j0aGgNGwa88aVjefqUTFmtQYOqFVtCQgKKiorQq1cvufdZv349goKCkJaWhoKCAhQXF8PW1hZAWf8JHx8fuLm5oXfv3nB1dcXw4cO5LzE/Pz9MmDABu3btgqurK4YNG8YlQTWloKCgwv4oVcUtj9jYWCgqKsLFxUXuffT09KCnpyd3+Zqwa9cujBs3Dk2bNoWioiLs7e0xatQoxMTEcGVGjhzJvW/bti3atWsHCwsLnDt3rlp/C28zf/58ZGRk4LPPPgMRQV9fH97e3vjpp5+goPDfTQN1dXXExsYiNzcXp0+fhp+fH8zNzdG9e3eZYwYFBcHT01Pm39jPz497365dO4hEInz11VcIDAzk9ScSi8XIz8+vsWtkPl7stlQNyhILkCWuvQ676enp6NOnDxYtWgSpVIpx48bh6tWrLLH5iCioqEBBRYXXkVsgEpWtF4kqLvvaB79AKCxb/0aHz8rKVof4tYRLHqGhoZgxYwbGjx+PkydPIjY2FmPHjuVuhQJAcHAwoqOj4ezsjL1798La2hqXL18GACxatAhxcXHo378/zpw5g1atWuHQO9xKq4qOjg5evXYLUN64FRQUQES8/V6/dVLdugLe77aUgYEBgLLbMK97+vQpt60iFhYWOH/+PHJzc/H3339zt4CqamUyNzeHjo4OkpKS5L42AwODCmN7PXaxWIygoCDk5+cjNTUVaWlpaN68OdTV1aGrq8vtp6CgAEtLS9ja2uL777/H0KFDERgYKHPOCxcuIDExERMmTHhrfI6OjigtLZV5HMDLly9552YaLpbc1BAVdW2MdFuJkW4roaL+4ednioyMhK2tLc6cOQNVVVXs3LkT27ZtY4kNIzcrKyuIxWKcPn1arvJRUVFwdnbG5MmTYWdnB0tLSyS/1uennJ2dHfz9/XHp0iW0adMGISEh3DZra2tMnz4dJ0+exODBg3kjdmqCnZ0d4uPjqx23rq4ub1SORCLB3bt3ueW2bdtCKpXi/PnzcseyZMkSxMbGVvmqjJmZGQwMDHj/NtnZ2bhy5QqcnJzeem5VVVUYGhri1atXiIiIgIeHR6VlHz9+jBcvXlTrNraTkxPu3LmDZ8+ecesiIyOhoaGBVq1a8coKhUIYGxtDUVERoaGh+Pzzz3ktN2+SSqUoKiqSWb9t2zY4ODigffv2b40vNjYWCgoKvJazwsJCJCcnw87OTp5LZOo5dluqHsrMzMSwYcOQlZWFtm3bYt++fVw/BoaRl7KyMmbPno1Zs2ZBJBKhc+fO+PfffxEXF1dhh00rKyvs3LkTERERMDMzw65du3Dt2jWYmZkBKOsEu2XLFnzxxRcwMjJCYmIiHjx4AC8vLxQUFGDmzJkYOnQozMzM8PjxY1y7dk1mOO/rMjIykJGRwbUo3LlzB+rq6mjWrFmlE7y6ubnB398fr169QuPGjeWKGwB69uwJPz8/hIWFwcLCAqtXr+Y9V6d58+bw9vbGuHHjsHbtWrRv3x6PHj3Cs2fPKu238z63pQQCAb777jv88MMPsLKy4oaCGxkZYeDAgVy5Xr16YdCgQZg6dSoAICIiAkQEGxsbJCUlcX2cxo4dCwDIzc3F4sWLMWTIEBgYGCA5ORmzZs2CpaUlN/QaKOsf9fLlS6SlpUEikXCJmKWlJdTU1NCnTx+0atUKY8aMwU8//YSMjAwEBARgypQp3G2g+/fv4+rVq3B0dMSrV6+wevVq3L17Fzt27ODOExgYiA4dOsDCwgJFRUU4fvw4du3ahY0bN/LqIzs7G/v378eqVatk6io6OhpXrlxBjx49oK6ujujoaEyfPh1ffvkl9zcAAJcvX4aSkpJcySHTANT0MK6PXUMZCr5nzx6aOHEi5efnf/BzMW9X1fDFj5lEIqEffviBTE1NSSgUUrNmzWj58uVEJDsUvLCwkHx8fEhTU5O0tLRo0qRJNGfOHGrfvj0REWVkZNDAgQPJ0NCQRCIRmZqa0oIFC0gikVBRURGNHDmSTExMSCQSkZGREU2dOrXK+lq4cCEBkHkFBwdXeU2dOnWiTZs2cctvi5uo7NEJkyZNIm1tbdLT06PAwECZoeAFBQU0ffp07vosLS0pKCioWvVdHVKplObPn0/6+vqkpKREvXr1osTERF4ZU1NTWrhwIbe8d+9eMjc3J5FIRAYGBjRlyhTKzMzktufn51OfPn1IV1eXhEIhmZqakq+vL29IN1HZcPGK6v7s2bNcmdTUVOrbty+JxWLS0dGh77//nkpK/vvsi4+PJ1tbWxKLxaShoUEeHh5079493nnmzZtHlpaWpKysTI0bNyYnJycKDQ2VqYvNmzeTWCzmXUu5mJgYcnR0JE1NTVJWVqaWLVvS8uXLqfCNxydMnDiRvvrqq8ornPko1NRQcAHRGzeaG7js7GxoamoiKysLGhoaNXbcl5kvsW9M2YOphu/6C9paNXtr6sSJE1BWVkaPHj1q9LhMzSgsLERKSgrMzMwqfcAaUzvCwsIwc+ZM3L17t8rbH8yn4/nz57CxscH169d5LXbMx6eqz9LqfH+z21I1RCophcuDl9z7mlJSUoKAgAD89NNP0NfXx61btyp9DgnDMED//v3x4MEDPHnyBCYmJnUdDvMRSE1NxYYNG1hi8wlhyU0NEYqUsbVTGwDAghqafiEtLQ0jR47kHto1dOhQaGpq1sixGaYh++677+o6BOYj0qFDB3To0KGuw2BqEUtuaohQSQV/GvkAAJYqvf+IpaNHj8LHxwevXr2CpqYmtm3bVmXnS4ZhGIZhyrAb0h8ZiUQCPz8/eHh44NWrV+jYsSNu3LjBEhuGYRiGkRNLbmqIRFIK/eKH0C9+CMl79LlRUFDgnh3x3Xff4eLFi299zDvDMAzDMP9ht6VqSFF+NrYf31D2ft6Iak+/UFpaikaNGkEgEGDjxo3w9PRE3759P0SoDMMwDNOgsZabGlQoLHtVR1FREaZNm4YhQ4Zwj39XV1dniQ3DMAzDvCPWclNDVNS1Mah/2Wzc8XJOv5CUlIQRI0bgxo0bAICLFy+ia9euHyxGhmEYhvkUsJabOrJ3717Y29vjxo0baNKkCY4dO8YSG4ZhGIapASy5qWUFBQX4+uuvMXLkSOTk5KBLly6IjY1F//796zo0hqmUQCDA4cOH6zqMatu+fTu0tLRq9Zznzp2DQCDgzU1VkdOnT6Nly5aQSCS1E1g9Eh8fD2NjY+Tl5dV1KEw9xZKbGlJUkIvp937C9Hs/oaggt9JyI0eOxObNmyEQCDB37lycPXsWxsbGtRgpw/BlZGRg2rRpMDc3h5KSEkxMTDBgwAC5Zwtn3s2sWbMQEBAARUXFug7lvbx8+RKenp7Q0NCAlpYWxo8fj9zcyj8DAaB79+4QCAS819dff81tb9WqFT777DOsXr36Q4fPNFCsz00NkZQWo8+9Z9z7ysydOxcxMTEICgpCnz59ais8hqlQamoqOnfuDC0tLaxcuRJt27ZFSUkJIiIiMGXKFNy7d6+uQ2yQLl68iOTk5Abx/CpPT0+kp6cjMjISJSUlGDt2LCZOnIiQkJAq9/P19cWSJUu4ZRUV/sNPx44dC19fX/j7+6NRI/ZVxVQPa7mpIY2EyvjDwRp/OFijkfC/YeD5+fk4f/48t+zo6Ijk5GSW2Hwi8vLyKn0VFhbKXbagoECustU1efJkCAQCXL16FUOGDIG1tTVat24NPz8/XL58mVf2+fPnGDRoEFRUVGBlZYWjR49y2yQSCcaPHw8zMzOIxWLY2NhgzZo1vP19fHwwcOBA/PzzzzA0NESTJk0wZcoUlJSUcGWKioowe/ZsmJiYQElJCZaWlti2bRu3/e7du+jbty/U1NSgr6+PMWPG4Pnz59W65iNHjsDe3h7KysowNzfH4sWLUVpa9myq0aNHY8SIEbzyJSUl0NHRwc6dOwEAUqkUgYGB3LW2b98eBw4cqFYMoaGh6N27N29iwOTkZHh4eEBfXx9qamro2LEjTp06xduvotuDWlpa2L59O7f8+PFjjBo1Ctra2lBVVUWHDh1w5cqVasUnr4SEBISHh2Pr1q1wdHREly5d8NtvvyE0NBT//PNPlfuqqKjAwMCAe705EWLv3r3x8uVL3ucnw8iLJTc1RKSsgt0mE7HbZCJEymW/QOLj49GpUye4u7vj9u3bXFklJaW6CpOpZWpqapW+3vzVrqenV2nZNx8N0Lx58wrLVcfLly8RHh6OKVOmQFVVVWb7m31VFi9ejOHDh+P27dvo168fPD098fLl/08WK5XC2NgY+/fvR3x8PBYsWIC5c+di3759vGOcPXsWycnJOHv2LHbs2IHt27fzvpi9vLywZ88erF27FgkJCdi8eTN3XZmZmejZsyfs7Oxw/fp1hIeH4+nTpxg+fLjc13zhwgV4eXnh22+/RXx8PDZv3ozt27dj2bJlAMpaIf766y/ebZWIiAjk5+dj0KBBAIDAwEDs3LkTmzZtQlxcHKZPn44vv/yyWl/CFy5ckJnrKDc3F/369cPp06dx8+ZNuLu7Y8CAAUhLS5P7uLm5uXBxccGTJ09w9OhR3Lp1C7NmzYJUKq10n9atW1f5d1rVYymio6OhpaXFuxZXV1coKCi8NaHavXs3dHR00KZNG/j7+yM/P5+3XSQSwdbWFhcuXJDz6hnmNfQRWLduHZmampKSkhJ16tSJrly5UmX5ffv2kY2NDSkpKVGbNm0oLCxM7nNlZWURAMrKynrfsHnyikrIdPYxMp19jHILiykoKIjEYjEBIAMDA7pw4UKNno/5uBQUFFB8fDwVFBTw1gOo9NWvXz9eWRUVlUrLuri48Mrq6OhUWK46rly5QgDo4MGDby0LgAICArjl3NxcAkAnTpyodJ8pU6bQkCFDuGVvb28yNTWl0tJSbt2wYcNoxIgRRESUmJhIACgyMrLC4y1dupT69OnDW/f3338TAEpMTKxwn+DgYNLU1OSWe/XqRcuXL+eV2bVrFxkaGhIRUUlJCeno6NDOnTu57aNGjeJiLCwsJBUVFbp06RLvGOPHj6dRo0YREdHZs2cJAL169arCmIiINDU1eeeoTOvWrem3337jlgHQoUOHZI4VHBxMRESbN28mdXV1evHixVuPXS41NZUePHhQ6evx48eV7rts2TKytraWWa+rq0sbNmyodL/NmzdTeHg43b59m/744w9q2rQpDRo0SKbcoEGDyMfHR+5rYeq/yj5Liar3/V3nNzL37t0LPz8/bNq0CY6Ojvj111/h5uaGxMRE6OnpyZS/dOkSRo0ahcDAQHz++ecICQnBwIEDcePGDbRp06YOrqCMVCqFZulTSIoL4TtuLPaE7AZQ1rS6a9cu6Ovr11lsTN2pqmPlmx1Jy6fdqIiCAr+RNTU19b3iAsA9NFJe7dq1496rqqpCQ0ODF/P69esRFBSEtLQ0FBQUoLi4GLa2trxjtG7dmnfdhoaGuHPnDgAgNjYWioqKcHFxqfD8t27dwtmzZytsoUpOToa1tfVbr+HWrVuIioriWmqAsltqhYWFyM/Ph4qKCoYPH47du3djzJgxyMvLw5EjRxAaGgqg7NlU+fn56N27N++4xcXFsLOze+v5yxUUFPBuSQFlfyuLFi1CWFgY0tPTUVpaioKCgmq13MTGxsLOzg7a2vI9awsATE1N5S5bUyZOnMi9b9u2LQwNDdGrVy8kJyfDwsKC2yYWi2VadBhGHnWe3KxevRq+vr4YO3YsAGDTpk0ICwtDUFAQ5syZI1N+zZo1cHd3x8yZMwEAS5cuRWRkJNatW4dNmzbVauyvK8zLxOIDS/F9+j/YU1wMBQUFLFmyBP7+/jJfTMyno6LbPbVdtjJWVlYQCARydxoWCvmP3xYIBNztjtDQUMyYMQOrVq2Ck5MT1NXVsXLlSplbE1UdQywWV3n+3NxcDBgwAD/++KPMNkNDQ7muITc3F4sXL8bgwYNltpUnG56ennBxccGzZ88QGRkJsVgMd3d3bn8ACAsLQ9OmTXn7V+d2s46ODl69esVbN2PGDERGRuLnn3+GpaUlxGIxhg4diuLi/wYoCAQCmaT09T5Lb6vDirRu3RqPHj2qdHvXrl1x4sSJCrcZGBjIJOWlpaV4+fIlDAwM5I7B0dERQFny+Hpy8/LlS94yw8irTpOb4uJixMTEwN/fn1unoKAAV1dXREdHV7hPdHQ0/Pz8eOvc3NwqfQZHUVERioqKuOXs7Oz3D7wSZ3Jz8bC4GAYGBti7dy+6dev2wc7FMO9LW1sbbm5uWL9+Pb755huZhCkzM1PuZ8RERUXB2dkZkydP5tYlJydXK562bdtCKpXi/PnzcHV1ldlub2+PP//8E82bN3/n0TP29vZITEyEpaVlpWWcnZ1hYmKCvXv34sSJExg2bBiXlLVq1QpKSkpIS0urtIVJHnZ2doiPj+eti4qKgo+PD9e3Jzc3V6aFTldXF+np6dzygwcPeC0b7dq1w9atW/Hy5Uu5W2+OHz/OS5DeVFXC5OTkhMzMTMTExMDBwQEAcObMGUilUi5hkUdsbCwA2ST17t27GDp0qNzHYZhyddqk8Pz5c0gkEplbNvr6+sjIyKhwn4yMjGqVDwwMhKamJvcyMTGpmeDfoKKujSPjtkHTaQQuX7/BEhumXli/fj0kEgk6deqEP//8Ew8ePEBCQgLWrl0LJycnuY9jZWWF69evIyIiAvfv38f8+fNx7dq1asXSvHlzeHt7Y9y4cTh8+DBSUlJw7tw5rlPylClT8PLlS4waNQrXrl1DcnIyIiIiMHbsWLkfhLdgwQLs3LkTixcvRlxcHBISEhAaGoqAgABeudGjR2PTpk2IjIyEp6cnt15dXR0zZszA9OnTsWPHDiQnJ+PGjRv47bffsGPHDrmv1c3NDRcvXuSts7KywsGDBxEbG4tbt25h9OjRMh2Be/bsiXXr1uHmzZu4fv06vv76a15r2KhRo2BgYICBAwciKioKDx8+xJ9//lnpj0Wg7LaUpaVlpa83W6he17JlS7i7u8PX1xdXr15FVFQUpk6dipEjR8LIyAgA8OTJE7Ro0QJXr14FUJb0Ll26FDExMUhNTcXRo0fh5eWFbt268W59pqam4smTJxUmugzzNg3+fom/vz+ysrK4199///1BziMWKiLhh37459wfaGYkf3Msw9Qlc3Nz3LhxAz169MD333+PNm3aoHfv3jh9+jQ2btwo93G++uorDB48GCNGjICjoyNevHjBa8WR18aNGzF06FBMnjwZLVq0gK+vLzfE3cjICFFRUZBIJOjTpw/atm2L7777DlpaWnLf+nVzc8OxY8dw8uRJdOzYEZ999hl++eUXmX4nnp6eiI+PR9OmTdG5c2fetqVLl2L+/PkIDAzkvtzDwsJgZmYm93V6enoiLi4OiYmJ3LrVq1ejcePGcHZ2xoABA+Dm5gZ7e3vefqtWrYKJiQm6du2K0aNHY8aMGbznw4hEIpw8eRJ6enro168f2rZtixUrVnzQBwXu3r0bLVq0QK9evdCvXz906dIFW7Zs4baXlJQgMTGRa2ESiUQ4deoU+vTpgxYtWuD777/HkCFD8Ndff/GOu2fPHvTp06dO+gQx9Z+AqtursAYVFxdDRUUFBw4cwMCBA7n13t7eyMzMxJEjR2T2adasGfz8/PDdd99x6xYuXIjDhw/j1q1bbz1ndnY2NDU1kZWVJfNcBYZ5V4WFhUhJSYGZmZlMR1GGqcjMmTORnZ2NzZs313UoH53i4mJYWVkhJCREJrlkGraqPkur8/1dpy03IpEIDg4OvMe8S6VSnD59utImcScnJ5nHwkdGRlarCZ1hGKauzZs3D6amplU+g+ZTlZaWhrlz57LEhnlndT5ays/PD97e3ujQoQM6deqEX3/9FXl5edzoKS8vLzRt2hSBgYEAgG+//RYuLi5YtWoV+vfvj9DQUFy/fp3XDMowDPOx09LSwty5c+s6jI9SeX8fhnlXdZ7cjBgxAv/++y8WLFiAjIwM2NraIjw8nOs0nJaWxruf7uzsjJCQEAQEBGDu3LmwsrLC4cOH6/QZNwzDMAzDfDzqtM9NXWB9bpgPgfW5YRiGeX8Nos8NwzQ0n9hvBYZhmBpVU5+hLLlhmBpQ/qwR9qh4hmGYd1f+RO73fXxBnfe5YZiGQFFREVpaWtyj6FVUVCAQCOo4KoZhmPpDKpXi33//hYqKyjs/hbwcS24YpoaUz6VT1QSYDMMwTOUUFBTQrFmz9/5xyJIbhqkhAoEAhoaG0NPTq3KuHoZhGKZiIpGoRiabZskNw9QwRUXFD/q4e4ZhGKZqrEMxwzAMwzANCktuGIZhGIZpUFhywzAMwzBMg/LJ9bkpf0BQdnZ2HUfCMAzDMIy8yr+35XnQ3yeX3OTk5AAATExM6jgShmEYhmGqKycnB5qamlWW+eTmlpJKpfjnn3+grq5e4w9Zy87OhomJCf7++282b9UHxOq5drB6rh2snmsPq+va8aHqmYiQk5MDIyOjtw4X/+RabhQUFGBsbPxBz6GhocH+49QCVs+1g9Vz7WD1XHtYXdeOD1HPb2uxKcc6FDMMwzAM06Cw5IZhGIZhmAaFJTc1SElJCQsXLoSSklJdh9KgsXquHayeawer59rD6rp2fAz1/Ml1KGYYhmEYpmFjLTcMwzAMwzQoLLlhGIZhGKZBYckNwzAMwzANCktuGIZhGIZpUFhyU03r169H8+bNoaysDEdHR1y9erXK8vv370eLFi2grKyMtm3b4vjx47UUaf1WnXr+/fff0bVrVzRu3BiNGzeGq6vrW/9dmDLV/XsuFxoaCoFAgIEDB37YABuI6tZzZmYmpkyZAkNDQygpKcHa2pp9dsihuvX866+/wsbGBmKxGCYmJpg+fToKCwtrKdr66X//+x8GDBgAIyMjCAQCHD58+K37nDt3Dvb29lBSUoKlpSW2b9/+weMEMXILDQ0lkUhEQUFBFBcXR76+vqSlpUVPnz6tsHxUVBQpKirSTz/9RPHx8RQQEEBCoZDu3LlTy5HXL9Wt59GjR9P69evp5s2blJCQQD4+PqSpqUmPHz+u5cjrl+rWc7mUlBRq2rQpde3alTw8PGon2HqsuvVcVFREHTp0oH79+tHFixcpJSWFzp07R7GxsbUcef1S3XrevXs3KSkp0e7duyklJYUiIiLI0NCQpk+fXsuR1y/Hjx+nefPm0cGDBwkAHTp0qMryDx8+JBUVFfLz86P4+Hj67bffSFFRkcLDwz9onCy5qYZOnTrRlClTuGWJREJGRkYUGBhYYfnhw4dT//79eescHR3pq6+++qBx1nfVrec3lZaWkrq6Ou3YseNDhdggvEs9l5aWkrOzM23dupW8vb1ZciOH6tbzxo0bydzcnIqLi2srxAahuvU8ZcoU6tmzJ2+dn58fde7c+YPG2ZDIk9zMmjWLWrduzVs3YsQIcnNz+4CREbHbUnIqLi5GTEwMXF1duXUKCgpwdXVFdHR0hftER0fzygOAm5tbpeWZd6vnN+Xn56OkpATa2tofKsx6713recmSJdDT08P48eNrI8x6713q+ejRo3BycsKUKVOgr6+PNm3aYPny5ZBIJLUVdr3zLvXs7OyMmJgY7tbVw4cPcfz4cfTr169WYv5U1NX34Cc3cea7ev78OSQSCfT19Xnr9fX1ce/evQr3ycjIqLB8RkbGB4uzvnuXen7T7NmzYWRkJPMfivnPu9TzxYsXsW3bNsTGxtZChA3Du9Tzw4cPcebMGXh6euL48eNISkrC5MmTUVJSgoULF9ZG2PXOu9Tz6NGj8fz5c3Tp0gVEhNLSUnz99deYO3dubYT8yajsezA7OxsFBQUQi8Uf5Lys5YZpUFasWIHQ0FAcOnQIysrKdR1Og5GTk4MxY8bg999/h46OTl2H06BJpVLo6elhy5YtcHBwwIgRIzBv3jxs2rSprkNrUM6dO4fly5djw4YNuHHjBg4ePIiwsDAsXbq0rkNjagBruZGTjo4OFBUV8fTpU976p0+fwsDAoMJ9DAwMqlWeebd6Lvfzzz9jxYoVOHXqFNq1a/chw6z3qlvPycnJSE1NxYABA7h1UqkUANCoUSMkJibCwsLiwwZdD73L37OhoSGEQiEUFRW5dS1btkRGRgaKi4shEok+aMz10bvU8/z58zFmzBhMmDABANC2bVvk5eVh4sSJmDdvHhQU2G//mlDZ96CGhsYHa7UBWMuN3EQiERwcHHD69GlunVQqxenTp+Hk5FThPk5OTrzyABAZGVlpeebd6hkAfvrpJyxduhTh4eHo0KFDbYRar1W3nlu0aIE7d+4gNjaWe33xxRfo0aMHYmNjYWJiUpvh1xvv8vfcuXNnJCUlcckjANy/fx+GhoYssanEu9Rzfn6+TAJTnlASm3KxxtTZ9+AH7a7cwISGhpKSkhJt376d4uPjaeLEiaSlpUUZGRlERDRmzBiaM2cOVz4qKooaNWpEP//8MyUkJNDChQvZUHA5VLeeV6xYQSKRiA4cOEDp6encKycnp64uoV6obj2/iY2Wkk916zktLY3U1dVp6tSplJiYSMeOHSM9PT364Ycf6uoS6oXq1vPChQtJXV2d9uzZQw8fPqSTJ0+ShYUFDR8+vK4uoV7Iycmhmzdv0s2bNwkArV69mm7evEmPHj0iIqI5c+bQmDFjuPLlQ8FnzpxJCQkJtH79ejYU/GP022+/UbNmzUgkElGnTp3o8uXL3DYXFxfy9vbmld+3bx9ZW1uTSCSi1q1bU1hYWC1HXD9Vp55NTU0JgMxr4cKFtR94PVPdv+fXseRGftWt50uXLpGjoyMpKSmRubk5LVu2jEpLS2s56vqnOvVcUlJCixYtIgsLC1JWViYTExOaPHkyvXr1qvYDr0fOnj1b4edted16e3uTi4uLzD62trYkEonI3NycgoODP3icAiLW/sYwDMMwTMPB+twwDMMwDNOgsOSGYRiGYZgGhSU3DMMwDMM0KCy5YRiGYRimQWHJDcMwDMMwDQpLbhiGYRiGaVBYcsMwDMMwTIPCkhuGYXi2b98OLS2tug7jnQkEAhw+fLjKMj4+Phg4cGCtxMMwTO1jyQ3DNEA+Pj4QCAQyr6SkpLoODdu3b+fiUVBQgLGxMcaOHYtnz57VyPHT09PRt29fAEBqaioEAgFiY2N5ZdasWYPt27fXyPkqs2jRIu46FRUVYWJigokTJ+Lly5fVOg5LxBim+tis4AzTQLm7uyM4OJi3TldXt46i4dPQ0EBiYiKkUilu3bqFsWPH4p9//kFERMR7H/tts8cDgKam5nufRx6tW7fGqVOnIJFIkJCQgHHjxiErKwt79+6tlfMzzKeKtdwwTAOlpKQEAwMD3ktRURGrV69G27ZtoaqqChMTE0yePBm5ubmVHufWrVvo0aMH1NXVoaGhAQcHB1y/fp3bfvHiRXTt2hVisRgmJib45ptvkJeXV2VsAoEABgYGMDIyQt++ffHNN9/g1KlTKCgogFQqxZIlS2BsbAwlJSXY2toiPDyc27e4uBhTp06FoaEhlJWVYWpqisDAQN6xy29LmZmZAQDs7OwgEAjQvXt3APzWkC1btsDIyIg3CzcAeHh4YNy4cdzykSNHYG9vD2VlZZibm2Px4sUoLS2t8jobNWoEAwMDNG3aFK6urhg2bBgiIyO57RKJBOPHj4eZmRnEYjFsbGywZs0abvuiRYuwY8cOHDlyhGsFOnfuHADg77//xvDhw6GlpQVtbW14eHggNTW1yngY5lPBkhuG+cQoKChg7dq1iIuLw44dO3DmzBnMmjWr0vKenp4wNjbGtWvXEBMTgzlz5kAoFAIAkpOT4e7ujiFDhuD27dvYu3cvLl68iKlTp1YrJrFYDKlUitLSUqxZswarVq3Czz//jNu3b8PNzQ1ffPEFHjx4AABYu3Ytjh49in379iExMRG7d+9G8+bNKzzu1atXAQCnTp1Ceno6Dh48KFNm2LBhePHiBc6ePcute/nyJcLDw+Hp6QkAuHDhAry8vPDtt98iPj4emzdvxvbt27Fs2TK5rzE1NRUREREQiUTcOqlUCmNjY+zfvx/x8fFYsGAB5s6di3379gEAZsyYgeHDh8Pd3R3p6elIT0+Hs7MzSkpK4ObmBnV1dVy4cAFRUVFQU1ODu7s7iouL5Y6JYRqsDz41J8Mwtc7b25sUFRVJVVWVew0dOrTCsvv376cmTZpwy8HBwaSpqcktq6ur0/bt2yvcd/z48TRx4kTeugsXLpCCggIVFBRUuM+bx79//z5ZW1tThw4diIjIyMiIli1bxtunY8eONHnyZCIimjZtGvXs2ZOkUmmFxwdAhw4dIiKilJQUAkA3b97klXlzRnMPDw8aN24ct7x582YyMjIiiURCRES9evWi5cuX846xa9cuMjQ0rDAGIqKFCxeSgoICqaqqkrKyMjd78urVqyvdh4hoypQpNGTIkEpjLT+3jY0Nrw6KiopILBZTRERElcdnmE8B63PDMA1Ujx49sHHjRm5ZVVUVQFkrRmBgIO7du4fs7GyUlpaisLAQ+fn5UFFRkTmOn58fJkyYgF27dnG3ViwsLACU3bK6ffs2du/ezZUnIkilUqSkpKBly5YVxpaVlQU1NTVIpVIUFhaiS5cu2Lp1K7Kzs/HPP/+gc+fOvPKdO3fGrVu3AJTdUurduzdsbGzg7u6Ozz//HH369HmvuvL09ISvry82bNgAJSUl7N69GyNHjoSCggJ3nVFRUbyWGolEUmW9AYCNjQ2OHj2KwsJC/PHHH4iNjcW0adN4ZdavX4+goCCkpaWhoKAAxcXFsLW1rTLeW7duISkpCerq6rz1hYWFSE5OfocaYJiGhSU3DNNAqaqqwtLSkrcuNTUVn3/+OSZNmoRly5ZBW1sbFy9exPjx41FcXFzhl/SiRYswevRohIWF4cSJE1i4cCFCQ0MxaNAg5Obm4quvvsI333wjs1+zZs0qjU1dXR03btyAgoICDA0NIRaLAQDZ2dlvvS57e3ukpKTgxIkTOHXqFIYPHw5XV1ccOHDgrftWZsCAASAihIWFoWPHjrhw4QJ++eUXbntubi4WL16MwYMHy+yrrKxc6XFFIhH3b7BixQr0798fixcvxtKlSwEAoaGhmDFjBlatWgUnJyeoq6tj5cqVuHLlSpXx5ubmwsHBgZdUlvtYOo0zTF1iyQ3DfEJiYmIglUqxatUqrlWivH9HVaytrWFtbY3p06dj1KhRCA4OxqBBg2Bvb4/4+HiZJOptFBQUKtxHQ0MDRkZGiIqKgouLC7c+KioKnTp14pUbMWIERowYgaFDh8Ld3R0vX76EtrY273jl/VskEkmV8SgrK2Pw4MHYvXs3kpKSYGNjA3t7e267vb09EhMTq32dbwoICEDPnj0xadIk7jqdnZ0xefJkrsybLS8ikUgmfnt7e+zduxd6enrQ0NB4r5gYpiFiHYoZ5hNiaWmJkpIS/Pbbb3j48CF27dqFTZs2VVq+oKAAU6dOxblz5/Do0SNERUXh2rVr3O2m2bNn49KlS5g6dSpiY2Px4MEDHDlypNodil83c+ZM/Pjjj9i7dy8SExMxZ84cxMbG4ttvvwUArF69Gnv27MG9e/dw//597N+/HwYGBhU+eFBPTw9isRjh4eF4+vQpsrKyKj2vp6cnwsLCEBQUxHUkLrdgwQLs3LkTixcvRlxcHBISEhAaGoqAgIBqXZuTkxPatWuH5cuXAwCsrKxw/fp1RERE4P79+5g/fz6uXbvG26d58+a4ffs2EhMT8fz5c5SUlMDT0xM6Ojrw8PDAhQsXkJKSgnPnzuGbb77B48ePqxUTwzRIdd3ph2GYmldRJ9Ryq1evJkNDQxKLxeTm5kY7d+4kAPTq1Ssi4nf4LSoqopEjR5KJiQmJRCIyMjKiqVOn8joLX716lXr37k1qamqkqqpK7dq1k+kQ/Lo3OxS/SSKR0KJFi6hp06YkFAqpffv2dOLECW77li1byNbWllRVVUlDQ4N69epFN27c4LbjtQ7FRES///47mZiYkIKCArm4uFRaPxKJhAwNDQkAJScny8QVHh5Ozs7OJBaLSUNDgzp16kRbtmyp9DoWLlxI7du3l1m/Z88eUlJSorS0NCosLCQfHx/S1NQkLS0tmjRpEs2ZM4e337Nnz7j6BUBnz54lIqL09HTy8vIiHR0dUlJSInNzc/L19aWsrKxKY2KYT4WAiKhu0yuGYRiGYZiaw25LMQzDMAzToLDkhmEYhmGYBoUlNwzDMAzDNCgsuWEYhmEYpkFhyQ3DMAzDMA0KS24YhmEYhmlQWHLDMAzDMEyDwpIbhmEYhmEaFJbcMAzDMAzToLDkhmEYhmGYBoUlNwzDMAzDNCgsuWEYhmEYpkH5P5EjYaj8mPbJAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.91      0.90        80\n",
            "           1       0.91      0.88      0.89        80\n",
            "\n",
            "    accuracy                           0.89       160\n",
            "   macro avg       0.89      0.89      0.89       160\n",
            "weighted avg       0.89      0.89      0.89       160\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LDA"
      ],
      "metadata": {
        "id": "4c3M7mz1CRjE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "-ahcz_GyxCX0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dd7b205-7b05-4344-ded6-53dfe42d06c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "lda.fit(x_feats_train, y_train)\n",
        "y_hat_lda = lda.predict_proba(x_feats_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plot_roc_curve(y_test_ohe, y_hat_lda)\n",
        "\n",
        "pred = np.argmax(y_hat_lda, axis=1)\n",
        "plt.figure()\n",
        "plot_confusion_matrix(y_test, pred, labels=np.unique(y))\n",
        "\n",
        "show_classifier_performance(y_test, pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bjTBZ0qfwRin",
        "outputId": "aca2e1af-8fdc-437d-d2e7-3af5c9b1e880"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.90      0.89        80\n",
            "           1       0.90      0.88      0.89        80\n",
            "\n",
            "    accuracy                           0.89       160\n",
            "   macro avg       0.89      0.89      0.89       160\n",
            "weighted avg       0.89      0.89      0.89       160\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Persistence\n",
        "\n",
        "Simply aim to save and load our models for use elsewhere. For your project, consider saving with easy to recognise naming."
      ],
      "metadata": {
        "id": "h6LdTUwNSYWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save models for later use\n",
        "save_model(fnn.model, 'fnn')\n",
        "save_model(lda, 'lda')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9YO-d9h2KUMZ",
        "outputId": "af2e67e0-42bc-4973-a7cb-0418944562b2"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved to  ./trained_models/fnn.h5\n",
            "saved to  ./trained_models/lda.joblib\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load FNN\n",
        "my_fnn_model = load_model('./trained_models/fnn.h5')"
      ],
      "metadata": {
        "id": "ZOTkg4EoQL09"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load LDA\n",
        "my_lda_model = load_model('./trained_models/lda.joblib')"
      ],
      "metadata": {
        "id": "GlyaWm1QNWk7"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_input = np.random.random((1, window_size*fs, 7))\n",
        "y_hat = my_fnn_model(my_input)\n",
        "preds = np.argmax(y_hat, axis=-1)\n",
        "print(preds)"
      ],
      "metadata": {
        "id": "-YMGRGWnXnqh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f238b2ed-ada3-4f2e-dab8-8c257eac1d44"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_feat_input = np.random.random((1, 21))\n",
        "lda_pred = my_lda_model.predict(my_feat_input)\n",
        "print(lda_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Hw4WEX5Q5Wi",
        "outputId": "2c49d725-1dbc-4f5a-e169-d4c14fed8b7c"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}