{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# CSC 3105 Project"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cda961ffb493d00c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Load and Clean the Data\n",
    "\n",
    "This code block performs the following operations:\n",
    "\n",
    "1. Imports necessary libraries for data handling and cleaning.\n",
    "2. Defines a function `load_data` to load the data from a given directory into a pandas dataframe.\n",
    "3. Defines a function `clean_data` to clean the loaded data. The cleaning process includes:\n",
    "   - Handling missing values by dropping them.\n",
    "   - Removing duplicate rows.\n",
    "   - Converting the 'NLOS' column to integer data type.\n",
    "   - Normalizing the 'Measured range (time of flight)' column.\n",
    "   - Creating new features 'FP_SUM' and 'SNR'.\n",
    "   - One-hot encoding categorical features.\n",
    "   - Performing feature extraction on 'CIR' columns.\n",
    "   - Dropping the original 'CIR' columns.\n",
    "   - Checking for columns with only one unique value and dropping them.\n",
    "4. Checks if a pickle file with the cleaned data exists. If it does, it loads the data from the file. If it doesn't, it loads and cleans the data using the defined functions.\n",
    "5. Prints the first few rows of the cleaned data and its column headers."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "73fe8802e95a784f"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\BenjaminLoh\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pickle\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pywt\n",
    "from skimage import restoration\n",
    "from tensorflow.keras.utils import to_categorical\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:00:23.218696Z",
     "start_time": "2024-03-20T10:00:06.670752Z"
    }
   },
   "id": "2aa3c6c09e8645d1",
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Define the directory where the dataset is located\n",
    "DATASET_DIR = './UWB-LOS-NLOS-Data-Set/dataset'\n",
    "\n",
    "def load_data(dataset_dir):\n",
    "    # Load the data\n",
    "    # file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames if 'uwb_dataset_part7.csv' not in file]\n",
    "    file_paths = [os.path.join(dirpath, file) for dirpath, _, filenames in os.walk(dataset_dir) for file in filenames]\n",
    "    data = pd.concat((pd.read_csv(file_path) for file_path in file_paths))\n",
    "    print(f\"Original data shape: {data.shape}\")\n",
    "    return data\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:00:30.430356Z",
     "start_time": "2024-03-20T10:00:30.423Z"
    }
   },
   "id": "7bcd7cfc8dd11cbb",
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def stat_analysis_and_plots(data):\n",
    "    # Statistical Analysis\n",
    "    print(\"Statistical Analysis:\")\n",
    "    print(data.describe())\n",
    "\n",
    "    # Plot Boxplot to check for outliers for the first 15 columns\n",
    "    print(\"Boxplot of the first 15 columns:\")\n",
    "    fig, axs = plt.subplots(15,1,dpi=95, figsize=(7,17))\n",
    "    for i, col in enumerate(data.columns[:15]):\n",
    "        axs[i].boxplot(data[col], vert=False)\n",
    "        axs[i].set_ylabel(col)\n",
    "    plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:00:53.814869Z",
     "start_time": "2024-03-20T10:00:53.808049Z"
    }
   },
   "id": "9e0b1ed6f23a17cf",
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Channel Impulse Response (CIR) Graphs\n",
    "\n",
    "This code block defines a function `cir_graphs` to plot the Channel Impulse Response (CIR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The CIR is a sequence of values representing the channel response to a single impulse. It is used to characterize the channel in wireless communication systems."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1dd92fe7b6881ea6"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def cir_graphs(data):\n",
    "    # Separate the data into LOS and NLOS\n",
    "    data_los = data[data['NLOS'] == 0]\n",
    "    data_nlos = data[data['NLOS'] == 1]\n",
    "\n",
    "    # Extract the CIR columns\n",
    "    cir_columns = [col for col in data.columns if 'CIR' in col]\n",
    "    data_los_cir = data_los[cir_columns]\n",
    "    data_nlos_cir = data_nlos[cir_columns]\n",
    "\n",
    "    # Calculate the magnitude and time for each CIR column\n",
    "    time_los = np.arange(len(data_los_cir.columns))\n",
    "    magnitude_los = np.linalg.norm(data_los_cir.values, axis=0)\n",
    "\n",
    "    time_nlos = np.arange(len(data_nlos_cir.columns))\n",
    "    magnitude_nlos = np.linalg.norm(data_nlos_cir.values, axis=0)\n",
    "\n",
    "    # Plot the magnitude vs time for LOS\n",
    "    plt.figure(figsize=(20, 10), dpi=300)  # Increase figure size and DPI\n",
    "    plt.plot(time_los, magnitude_los, linewidth=2)  # Increase line width\n",
    "    plt.title('Magnitude vs Time for LOS')\n",
    "    plt.xlabel('Time (ns)')\n",
    "    plt.ylabel('Magnitude')\n",
    "    plt.xlim([600, max(time_los)])  # Set x-axis limits\n",
    "    plt.ylim([0, 2e6])\n",
    "    plt.show()\n",
    "\n",
    "    # Plot the magnitude vs time for NLOS\n",
    "    plt.figure(figsize=(20, 10), dpi=300)  # Increase figure size and DPI\n",
    "    plt.plot(time_nlos, magnitude_nlos, linewidth=2)  # Increase line width\n",
    "    plt.title('Magnitude vs Time for NLOS')\n",
    "    plt.xlabel('Time (ns)')\n",
    "    plt.ylabel('Magnitude')\n",
    "    plt.xlim([600, max(time_los)])  # Set x-axis limits\n",
    "    plt.ylim([0, 2e6])\n",
    "    plt.show()\n",
    "    "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:00:57.022738Z",
     "start_time": "2024-03-20T10:00:57.010599Z"
    }
   },
   "id": "308d64639b199bc7",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def calculate_total_distance(data):\n",
    "    # Speed of light in meters per nanosecond\n",
    "    speed_of_light_ns = 0.299792458\n",
    "\n",
    "    # Extract the CIR columns\n",
    "    cir_columns = [col for col in data.columns if 'CIR' in col]\n",
    "\n",
    "    # Calculate the total distance for each row\n",
    "    data['Total_Distance'] = data[cir_columns].abs().sum(axis=1) * speed_of_light_ns\n",
    "\n",
    "    return data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:00.663980Z",
     "start_time": "2024-03-20T10:01:00.656826Z"
    }
   },
   "id": "80cfcfac265d9357",
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Signal to Noise Ratio (SNR) Graph\n",
    "\n",
    "This code block defines a function `snr_graph` to plot the Signal to Noise Ratio (SNR) for Line of Sight (LOS) and Non-Line of Sight (NLOS) data. The SNR is calculated as the ratio of the 'CIR_PWR' to the 'STDEV_NOISE' for each data point."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bfd97fbe797a7067"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "def snr_graph(data):\n",
    "    # Separate the data into LOS and NLOS\n",
    "    data_los = data[data['NLOS'] == 0]\n",
    "    data_nlos = data[data['NLOS'] == 1]\n",
    "\n",
    "    # Extract the SNR values\n",
    "    snr_los = data_los['SNR']\n",
    "    snr_nlos = data_nlos['SNR']\n",
    "\n",
    "    # Create a new figure\n",
    "    plt.figure(figsize=(10, 5))\n",
    "\n",
    "    # Plot SNR for LOS\n",
    "    plt.plot(snr_los, label='LOS')\n",
    "\n",
    "    # Plot SNR for NLOS\n",
    "    plt.plot(snr_nlos, color='red', label='NLOS')\n",
    "\n",
    "    # Set title and labels\n",
    "    plt.title('SNR for LOS and NLOS')\n",
    "    plt.xlabel('Index')\n",
    "    plt.ylabel('SNR')\n",
    "\n",
    "    # Add a legend\n",
    "    plt.legend()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "    "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:04.396705Z",
     "start_time": "2024-03-20T10:01:04.388228Z"
    }
   },
   "id": "4afc8d71b3271351",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "def plot_histogram(data, feature):\n",
    "    \"\"\"\n",
    "    Function to plot a histogram of a given feature in the data for 'NLOS' and 'LOS'.\n",
    "\n",
    "    Parameters:\n",
    "    data (pd.DataFrame): The data.\n",
    "    feature (str): The name of the feature to plot.\n",
    "\n",
    "    Returns:\n",
    "    None\n",
    "    \"\"\"\n",
    "    # Check if the feature exists in the data\n",
    "    if feature not in data.columns:\n",
    "        print(f\"The feature '{feature}' does not exist in the data.\")\n",
    "        return\n",
    "\n",
    "    # Separate the data into 'NLOS' and 'LOS'\n",
    "    data_nlos = data[data['NLOS'] == 1]\n",
    "    data_los = data[data['NLOS'] == 0]\n",
    "\n",
    "    # Create a figure with two subplots side by side\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "    # Plot the histogram for 'NLOS'\n",
    "    axs[0].hist(data_nlos[feature], bins=30, edgecolor='black')\n",
    "    axs[0].set_title(f'Histogram of {feature} for NLOS')\n",
    "    axs[0].set_xlabel(feature)\n",
    "    axs[0].set_ylabel('Frequency')\n",
    "\n",
    "    # Plot the histogram for 'LOS'\n",
    "    axs[1].hist(data_los[feature], bins=30, edgecolor='black')\n",
    "    axs[1].set_title(f'Histogram of {feature} for LOS')\n",
    "    axs[1].set_xlabel(feature)\n",
    "    axs[1].set_ylabel('Frequency')\n",
    "\n",
    "    # Display the plots\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Usage:\n",
    "# plot_histogram(data, 'First_Path_Power_Level')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:07.293218Z",
     "start_time": "2024-03-20T10:01:07.282060Z"
    }
   },
   "id": "22025d6c8281fc09",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def plot_features(data, labels, feature1, feature2):\n",
    "    reds = labels == 1\n",
    "    blacks = labels == 0\n",
    "    plt.scatter(data[reds][feature1], data[reds][feature2], c=\"red\", s=20, edgecolor='k')\n",
    "    plt.scatter(data[blacks][feature1], data[blacks][feature2], c=\"yellow\", s=20, edgecolor='k')\n",
    "    plt.xlabel(feature1)\n",
    "    plt.ylabel(feature2)\n",
    "    plt.title(f\"Plot of data: {feature1} versus {feature2}\")\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:13.412151Z",
     "start_time": "2024-03-20T10:01:13.405477Z"
    }
   },
   "id": "ac4db13fed3f9916",
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "source": [
    "## denoise_cir Function\n",
    "\n",
    "The `denoise_cir` function uses the Discrete Wavelet Transform (DWT) to denoise the Channel Impulse Response (CIR) values. The DWT is a linear transformation that operates on a data vector whose length is an integer power of two, transforming it into a numerically different vector of the same length. The DWT of a signal `x` is calculated as follows:\n",
    "\n",
    "1. **Wavelet Decomposition:**\n",
    "\n",
    "    The input signal `x` is passed through two complementary filters and emerges as two signals. The filter outputs are decimated by 2 (down-sampled) to get the approximation coefficients (cA) and detail coefficients (cD).\n",
    "\n",
    "    The approximation coefficients represent the high-scale, low-frequency component of the signal, while the detail coefficients represent the low-scale, high-frequency component.\n",
    "\n",
    "2. **Thresholding:**\n",
    "\n",
    "    The detail coefficients are thresholded to remove noise. The thresholding function `T` applied to the detail coefficients `x` is defined as:\n",
    "$$\n",
    "T(x) = x * I(|x| > \\text{{value}}) \\quad \\text{{for 'hard' thresholding}}\n",
    "$$\n",
    "\n",
    "$$\n",
    "T(x) = \\text{{sign}}(x)(|x| - \\text{{value}})_+ \\quad \\text{{for 'soft' thresholding}}\n",
    "$$\n",
    "\n",
    "where $I$ is the indicator function that is one if the argument is true and zero otherwise, $\\text{{value}}$ is the threshold value, and $(x)_+$ equals $x$ if $x > 0$ and zero otherwise.\n",
    "\n",
    "3. **Wavelet Reconstruction:**\n",
    "\n",
    "    The original signal is reconstructed from the approximation and detail coefficients."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "69413268ac5b549d"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "def denoise_cir(cir_values, wavelet='db1', level=1):\n",
    "    # Perform wavelet decomposition\n",
    "    coeffs = pywt.wavedec(cir_values, wavelet, level=level)\n",
    "\n",
    "    # Set the detail coefficients to zero\n",
    "    for i in range(1, len(coeffs)):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], value=0.5, mode='soft')\n",
    "\n",
    "    # Perform wavelet reconstruction\n",
    "    denoised_cir = pywt.waverec(coeffs, wavelet)\n",
    "\n",
    "    return denoised_cir\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:15.408124Z",
     "start_time": "2024-03-20T10:01:15.400949Z"
    }
   },
   "id": "fe3089568e99a58d",
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "source": [
    "## deconvolve_cir Function\n",
    "\n",
    "The `deconvolve_cir` function applies the Richardson-Lucy deconvolution algorithm to deconvolve the Channel Impulse Response (CIR) values. \n",
    "\n",
    "In the context of signal processing, deconvolution is the process of reversing the effects of convolution on a signal. Convolution is a mathematical operation that blends two functions together and is often used to describe the effect of a linear time-invariant system on a signal. Deconvolution, therefore, attempts to recover the original signal that was convolved with the system's impulse response to produce the current signal.\n",
    "\n",
    "The Richardson-Lucy algorithm is an iterative method for deconvolution. It is particularly suitable for cases where the impulse response of the system (also known as the Point Spread Function, or PSF) is known, and the noise is Poissonian (such as in astronomical images). \n",
    "\n",
    "The algorithm works by iteratively refining an estimate of the original signal. In each iteration, it performs a prediction step where it convolves the current estimate with the PSF to predict the observed signal, and a correction step where it computes the ratio of the observed signal to the predicted signal, convolves this ratio with the PSF, and multiplies the result with the current estimate to get the next estimate.\n",
    "\n",
    "This process is repeated for a fixed number of iterations, or until the estimate converges to a stable solution. The result is a denoised estimate of the original signal.\n",
    "\n",
    "The mathematical formulas involved in the Richardson-Lucy deconvolution algorithm are as follows:\n",
    "\n",
    "1. **Prediction Step:**\n",
    "\n",
    "    The current estimate of the latent image `x` is convolved with the PSF `h` to predict the observed image `y`. This can be represented as:\n",
    "\n",
    "    $$\n",
    "    y = h \\ast x\n",
    "    $$\n",
    "\n",
    "    where $\\ast$ denotes the convolution operation.\n",
    "\n",
    "2. **Correction Step:**\n",
    "\n",
    "The ratio of the observed image $y$ to the predicted image $y'$ is computed, then the PSF $h$ is convolved with this ratio and multiplied with the current estimate $x$ to get the next estimate $x'$. This can be represented as:\n",
    "\n",
    "$$\n",
    "x' = x \\cdot (h \\ast \\left(\\frac{y}{y'}\\right))\n",
    "$$\n",
    "\n",
    "where $\\div$ denotes element-wise division and $\\ast$ denotes the convolution operation.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "e1edd5ef4f54e752"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "def deconvolve_cir(cir_values, psf=None, iterations=50):\n",
    "    # If no point spread function is provided, create a simple one\n",
    "    if psf is None:\n",
    "        psf = np.ones((5,)) / 5\n",
    "\n",
    "    # Perform Richardson-Lucy deconvolution\n",
    "    deconvolved_cir = restoration.richardson_lucy(cir_values, psf, num_iter=iterations)\n",
    "\n",
    "    return deconvolved_cir"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:17.698394Z",
     "start_time": "2024-03-20T10:01:17.690697Z"
    }
   },
   "id": "670e8c2cf19126ea",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "def clean_data(data):\n",
    "    print(\"Starting data cleaning process...\")\n",
    "    \n",
    "    # print(\"Before Cleaning\")\n",
    "    # stat_analysis_and_plots(data)\n",
    "\n",
    "    # Calculate total number of missing values in the data\n",
    "    total_missing = data.isnull().sum().sum()\n",
    "    print(f\"Total number of missing values: {total_missing}\")\n",
    "\n",
    "    # Data has no missing values\n",
    "    data = data.dropna()\n",
    "    print(\"Missing values dropped.\")\n",
    "\n",
    "    # Data has no duplicate rows\n",
    "    data = data.drop_duplicates()\n",
    "    print(\"Duplicate rows dropped.\")\n",
    "\n",
    "    # Convert 'NLOS' column to integer data type (0 for LOS, 1 for NLOS)\n",
    "    data['NLOS'] = data['NLOS'].astype(int)\n",
    "    print(\"'NLOS' column converted to integer data type.\")\n",
    "    \n",
    "    # Print line where CIR_PWR is 0\n",
    "    print(f\"Line where CIR_PWR is 0: {data[data['CIR_PWR'] == 0]}\")\n",
    "    \n",
    "    # Calculate the expression inside the log10 function\n",
    "    expression = (data['CIR_PWR'] * (2**17)) / (data['RXPACC']**2)\n",
    "\n",
    "    # If the expression is 0, set 'RX_Level' to 0\n",
    "    zero_indices = expression == 0\n",
    "    data.loc[zero_indices, 'RX_Level'] = 0\n",
    "\n",
    "    # For the rest of the data where the expression is not 0, calculate 'RX_Level'\n",
    "    # First, update the 'expression' and 'data' to exclude zero_indices\n",
    "    expression = expression.loc[~zero_indices]\n",
    "    data = data.loc[~zero_indices]\n",
    "\n",
    "    # Now, calculate 'RX_Level' for the rest of the data\n",
    "    data['RX_Level'] = 10 * np.log10(expression) - data['PRFR']\n",
    "\n",
    "    # Calculate the median of 'RX_Level'\n",
    "    median = data['RX_Level'].median()\n",
    "\n",
    "    # Create the boolean mask on the same DataFrame 'data'\n",
    "    zero_indices = (data['RX_Level'] == 0)\n",
    "\n",
    "    # Replace zero values in 'RX_Level' with the median\n",
    "    data.loc[zero_indices, 'RX_Level'] = median\n",
    "\n",
    "    print(\"New feature 'RX_Level' created.\")\n",
    "\n",
    "    # Calculate new feature 'First_Path_Power_Level'\n",
    "    data['First_Path_Power_Level'] = (10 * np.log10(\n",
    "        (data['FP_AMP1'] ** 2 + data['FP_AMP2'] ** 2 + data['FP_AMP3'] ** 2) / (data['RXPACC'] ** 2))) - 64\n",
    "    print(\"New feature 'First_Path_Power_Level' calculated.\")\n",
    "    data.drop(['FP_AMP1', 'FP_AMP2', 'FP_AMP3', 'RXPACC', 'PRFR'], axis=1, inplace=True)\n",
    "\n",
    "    # Calculate SNR as the ratio of 'CIR_PWR' to 'STDEV_NOISE' for each data point\n",
    "    data['SNR'] = data['CIR_PWR'] / data['STDEV_NOISE']\n",
    "    print(\"New feature 'SNR' created.\")\n",
    "    data.drop(['CIR_PWR', 'STDEV_NOISE'], axis=1, inplace=True)\n",
    "    \n",
    "    plot_histogram(data, 'First_Path_Power_Level')\n",
    "    plot_histogram(data, 'RX_Level')\n",
    "\n",
    "    # One-hot encode categorical features\n",
    "    categorical_features = ['CH', 'FRAME_LEN', 'PREAM_LEN', 'BITRATE']\n",
    "    encoder = LabelEncoder()\n",
    "    for feature in categorical_features:\n",
    "        data[feature] = encoder.fit_transform(data[feature])\n",
    "    print(\"Categorical features one-hot encoded.\")\n",
    "\n",
    "    # Extract the 'CIR' columns\n",
    "    cir_columns = [col for col in data.columns if 'CIR' in col]\n",
    "    cir_data = data[cir_columns]    \n",
    "    print(\"'CIR' columns extracted.\")\n",
    "    \n",
    "    # Convert 'CIR' columns to float\n",
    "    cir_data = cir_data.astype(float)\n",
    "    print(\"'CIR' columns converted to float.\")\n",
    "    \n",
    "    # Denoise 'CIR' columns\n",
    "    denoised_cir_data = cir_data.apply(denoise_cir)\n",
    "    # denoised_cir_data = cir_data.apply(deconvolve_cir)\n",
    "    print(\"'CIR' columns denoised.\")\n",
    "    \n",
    "    # Replace original 'CIR' columns with denoised data\n",
    "    data[cir_columns] = denoised_cir_data\n",
    "    print(\"Original 'CIR' columns replaced with denoised data.\")\n",
    "    \n",
    "    # List of columns to check for unique values\n",
    "    columns_to_check = ['CH', 'PREAM_LEN', 'BITRATE']\n",
    "\n",
    "    # Iterate over the columns\n",
    "    for column in columns_to_check:\n",
    "        # If the column has only one unique value, drop it\n",
    "        if data[column].nunique() == 1:\n",
    "            data = data.drop(column, axis=1)\n",
    "            print(f\"Column '{column}' dropped due to having only one unique value.\")\n",
    "\n",
    "    # Print the shape of the cleaned data\n",
    "    print(f\"Cleaned data shape: {data.shape}\")\n",
    "\n",
    "    # print(\"After Cleaning\")\n",
    "    # stat_analysis_and_plots(data)\n",
    "    \n",
    "    print(\"Data cleaning process completed.\")\n",
    "    \n",
    "    # Return the cleaned data\n",
    "    return data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:20.299732Z",
     "start_time": "2024-03-20T10:01:20.284742Z"
    }
   },
   "id": "685463c2d6065b08",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pickle file not found. Loading and cleaning data...\n",
      "Original data shape: (42000, 1031)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
      "image/png": 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     "output_type": "display_data"
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    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
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9sx6Kt7M2X799c4OWFc/PArCFJBhN6JUmt54+M6BDnV7N5C9TbX6mvre3XjcvK1wQf9MX8nGXSDypl5uG9PjxPh3tGZ1w/I11+frunnqtLM2ag+oWp6Rh6EDHiB4/0a9j3T6NhGJqKHTqjlXF+uzmSrns1vkuEQCwxC3k3gUAAOD96F2wWBAUBiwgBIUBAICFjDfCAAAgndC7AJiM1uGA/v5Qt15rdssfWXjHZleXZun2FcW6fUWRqvIIBwMWM3oXAACQLuhbAABAOqF3Sa2RYFTPnRvSE6f6dWEoMKX7ZjusWluWrTXl2VpTmq1YIqkz/WM6MzCmcwP+GQU+pZJJ0vqKHN3UWKi9jQWqL3CmLDgmnjT0zJkB/c3bner1hVOyzaWiMjdDexoKtLuhQJur8uSwmjUSjOr/HujSI8d6FZ9BgN3Gihx9/6Z6bazMTWHFV2cYhrq8YR3u8uqdLq+OdPnkDkwvXMpikmrynWoscmlgLKyTfWMprnb6VhS79P2b6rWzrmC+S/kAwzDU4QnpSLf3csheQ+H441iRmzGpgLNwLKGXm9z6zal+HenyzXbJVzCbpI+uK9O3dtepyGWf030vBKf7x/TosV69cH5I4Xhq/55vrMjRD25u0IaKnJRud6oWYu/S7gnq8RN9eur0gHzTCKe8c1WxvrW7js+7p8ATjOqJUwN67ETfNXuG/EybvrqzRvdtLJfNYr7qGEzepfDO9uGgshxWbazM4XEFgElYiL0LAADAtdC7YLEgKAxYQAgKAwAACxlvhAEAQDqhdwFwPZF4Uv/37Q795FC3EjNYPDIbGgqdundtqW5bUaTKXCZLA0sFvQsAAEgX9C0AACCd0LvMnvODfj15ekDPnh2UNxS74jabxaSVJVnjwWBl2Vpblq3q/MxrBvAkDUOdnpDODIzpTP+Yzg74dX7Qr8g1QmAcVrNKsx0qzXao5OJlaZZdpdkZyrSb5fZHNTAW+cCXJxi76vYyrGbtrMvX3sZC7WkoUIFzdsN3Yomknjg9oL99q0OD/umFRC12VrNJm6pytbt+PBysNj/zmoFtPb6QHt7XoWfPDmomn/jc1Fio7+6tU0PhBxfXGIahIX9ULcMBNQ8F1DIcVMtQQH2jYSUNyW41y24xyW4xX/x+/NJhMctmMclhNStpSKf6RtPq/7nZJM3kY7TtNXn6/k31WlWanbqipiGeNHSi16fXmz16o3VYnSOhq45z2ixqLBoPDVtW5NKyYpcai1zKy7TJMAyd6hvTE6f79fy5oXkPN3TZLfrGrlp9+oYKWRd5kE04ltDz54f0yLFenR3wz/r+bltepO/urVdN/vx8TrtQepdoPKlXm9167ERfSgLxLGaTPrG+TF+9sXZJhtxNhmEYeqfbp0eP9+mVJvekQzArcjP0rd21unNVyaTCDnEldyCqZ84M6InTA2obDl6+3mW36PNbqvS5LZXKcljnscLFJRJPqsMT1EgwpnUV2XLZeWyBdLdQehcAAIDJoHfBYkFQGLCAEBSWev5IXOcH/RoYi2hTVa7KczLmu6RF5+zAmF5tHlarO6BCl117Gwt1Y10+HzKkWCyRVOdISKPhuNaWZctuXdwf6s6npGHw/AWugTfCAAAgndC7ALiWI11e/dcXmq65CGA+mCTdvKxQn9lUqS3Vuddc7AJg8aJ3AQAA6YK+BQAApBN6l9kXSyT1TpdPTe6AXHaLVpZkaXmxS7YZBtfEk4Za3QG1e4IKx5MqdNpVkm1XSZZDORnWaR1HjyWSGvS/GxwmScUuh9aVZyvDZplRvdMRiSf16PFe/fhAl0ZCVw8xW0psFpP2NhTqzlXF2l6bP+VQjguDfv3Fm23a3zYy7RrMJukja0p15+oSdY2E1OwOqNU9Hgw2Go5Pe7vpwmI2aXNVrvY2FmpvQ4EsZpP+z752PXNmZiFsd64q1rd216kqb+6ClwLRuA60j+j1lmG92eqRbwb//4pcdmXYzOr2hlNY4bhCl13DgemHxzUWOfVvbl+mzVV5qStqhkKxhA51evVWm0fnBwMym6TGIpfWlWdrXXmOaguuHRz5Xh2eoB470acnTg1oLDK3//4sZpPu21Cub+yqVV6mbW73PY+9i2EYujAY0FNnxoNAZ+NvU4bVrAe2VunBrVWEL13kC8X01JkBPXa8Tx0zmMOwvNil7+6t1666fOYbTCCWSOrNVo+eONWv/W0eJa7zRy43w6ov76jR/RvL56VfXAwMw9CJ3lE9cWpAL5wfUjA2vm7Sajbp9hVF+vaeOk6il0LN7oDeavOoyxtSWXaG9jYWaHlx1nyXhUWM4y4AACCd0LtgsSAoDFhAnj7ara2VORyUTYFzA2P6xdFePX9+6Iozit1QmaMf3tygteU581hd+ovGk3qpaUi/PNqrk31jH7h9TVm2fvvm+gX1oWO6anYH9OixXj19ZvDyAXGnzaJ71pTo67tqZ/3MdUvFpQ+T32ofUddISNX5mbplWaEe2FI15x/wAjN1ftCvd7p96vQEta48R7vq85WfotcK3gi/61KAY6bNoopcgkgBAFiI6F0AvN9YOK4/e6NVj5/on+9SLst2WPVb68v0yRvKmfgILHH0LgAAIF3QtwAAgHRC74J0EIjG9c/v9Oinh7oVWIInnN5YkaN71pToQyuLlZMx8/maR7q8+rPX23S6/4Pzm/FB2Q6rdtXn66bGQt1YV6DsjA8G91wY9OvP32jTW+3TD2Gzmk26f2O5vrqzZsL5jOFYQi3DQbUMBdTmCSoQjcthtSjDalaGzawMq+Wql3aLWecH/Xq9ZViHu7yKXS/5ZR7lZlj14ZXFuntNqdaXZ+toj09//Gqrzg74p73Nu1aX6LdvqldRliOFlU6OYRjq8IS0v92j/W0evdPtu+5jn+WwaG3ZeGjYuvJsrSvLUZ5z/N9+PGnojZZhPXq8Vwc6vHP0G1xbToZV37ixVvdvLJd1hgGYkzWV3iWRNNTsDsgTjKo026HafKcs5qmvRXL7I3rm7KCeOjOgFndw2rVPxaXwpU/eUCHHEjyBeiJp6FiPT0+c6teLF9xXrHmaqc1Vufre3nqtr2DN1Ps1Dfn1xKkBPXN2UN4pBuGVZNn1tRtr9dG1pXP2epDuhvwRPXV6QE+cHrjuifxsFpM+dUOFvrKjRrms3ZmWgbGInj83qGfODqppKPCB2/c0FOjrN9ZqTVn2PFS3OPX4QmoeCiqeTGpjZa6KXEt3bd9sHXdJGobaPUEN+aOqK3CqNHvu+9ylIpZIKmloSfZkWBySF9+TDvojynJYtbo0a1Lh1JiaeCKpvtGI4klD1fmZsk7jvScw35KGoS5fWJ7o+N++nQ0FSoZjfGaEtERQGLCAfPiPXlOGxawf3tKgDRyUnbJYIqmXLrj1i6O9Otk3et2x96wp0Xf31KuEgwRTMjAW0WMn+vSrE33yBCc+MH5zY6G+d1O96gqcc1Dd4hFLJPVq87B+eaxXR7t91xznslv0xe3V+tzmSs5OMg3xRFKvtQzrkeN9OtzpveoYHuPUCccSernJrefOjX/4kDSk7TV5+vSmCq0jvHHGDMPQ/vYR/fRQl450Xfm6kWE168FtVXpwW7UyZ/g8XuqTJw3D0NEen355tE+vNrsVT46/nWkscupL22t056piAl9TKGkY6vWFNRyIamVJFq/DAIApW+q9CzAfTvWN6tHjfXqn26dAJK7lxS59aGWx7lhZctVFBXPplSa3/uClZrlncEbuVKovdOqzmyp095rSGb9XA7A40LsAAIB0Qd8CAADSCb0L0okvFNPfH+rWz4/2pDQwJNUqczMUSyQ16J/+Zy7VeRm6e02p7l5doqq81J9IxTAMvdI8rL94o+26wRRLVUWOQ7csL9JNjYXaWJEz6cCTQ50j+rPX22YUaOWyW/Tgtio9sKVKdotZXd6QWtwBNQ8F1OwOqMUdULc3rMW20MhmMWlvQ6HuWVOiXfUFsr3vMU8ahp47N6i/eKNdA2ORae3DZbfoG7tq9ekbKmY9xCYUS+hQp1f72zx6q82j3tHp1XxJVV6GVpVk6UTv6IxeW66mJj9TDqv5qsEpk1Vf6NSPbmnQzrqCFFZ2dRP1LpF4Uoc6R/Rq07BebxnWyHvCjpw2i1aVZmlNWbbWlGVrbVm2ynMcV53XGo4l9FrzsJ46M6ADHSNKztM/uuq8DP3b25drR13+/BQwCeFYQh2ekDJsZlXmTX9BfDxp6EiXV680ufVKk3tS63Fm4pZlhfrunnrVFS7ttTy+UEzPnRvUE6cGdG5w+n+/LqnJz9Q3d9XqQyuLCaC4img8qTdah/XEqQG91e6Z0mtLtsOqL++o1qc3VRJWMwlj4bheujCkZ88N6p0u36R6p70NBfr6rlqtLiUwbKriSUMnen16s8WjN1s9avO8G6xpknTzskI9uK16Sa4HTuVxF7c/orc7RvR2+/iXLxy/fNu68mw9uK1atywr5PV3hoYDUR3q9OpQ54gOdXrVNxqRzWLSpspcfWZzpfY0FPAYz1AskdT5Qb+Odvt0vGdUZwfG5I8ktLLEpbvXlOoja0plXwR/6wzD0IneUb3c5FY4ltS68mzdtqJILvvszhUeDcd0qm9Mp/pGdbJ3TKf6R+WPvBu8X5xl1+e3VOm+jeWLYm5uImnoQMeIur0h5Tvt2lWfP6uP8aW1fC3uoFqHx4/TtLiDavcEL6+jzM0YP0HyA1uqVLhIwjK7vSEdvPjeeGtN3pxlIgSjCZ0dGLv8nD7TPyZPMKaSLLtuW1GsB7ZUqngeQuFnw/lBv546PSB3IKrynAx9ZG2JGgpnN+QoHEvodP+YTvSO6kTvqE72jl7RX1jMJt2zplRf3Vm9KE72bRiGzgz4dbzHJ6vZrD0NBarIzZiTfceThlrcAZ3sHdXJvlGdHfDLH4mrNj9Td68p1T1rSgkYTDGCwoAF5MN/9JqaLh54/NCKYn3vprpF8Ydltg1eDK96fJLhVZdkWM16aHu1HtxaRfDEdRiGoXe6ffrF0V691uzWVE+uZDFJn9hQrq/vqlXBBGeAWuoGxiJ6/ESffnWyX8NTWLRamu3Qd/bU6a7VJRyImYT+0bAeP9GnX58amPTjXJxl1zdurNW968poRqfg0huLJ07169mzg9c82+GehgJ9gw8cpiWWSOr5c0P6h8PdanZffxJDcZZd39pdp4+sKZ3WGcOkpTt5MhCN65kzg3rkeO91z5a2vjxbP7q1kfC7Ger2hvSrk/164lT/5d7OYTXrtuVF+vqNtarOpz9Ohf7RsJ45O6g3WobVMRJSSZZDu+oL9LnNFfNyZsnFqsMT1HPnBnVuwK9QPKlNlTm6d23ZnB1oWyoSFw8o9o9FVJOfqdr8TIIbZ0kwmlDSMJTlmN+wn8laqr0LMNdiiaRevDCkn7/Te80zszusZt2yrFAfXVembTV5c3r8Ysgf0f98uUWvNLlTsr0Mq1mbq3O1qiRLnmBM/WMRDYxG1DcaVniCRTsmSTc1FurTmyq0rSaPv1cArkDvAgAA0gV9CwAASCf0LkhHbn9E//dAlx4/0Xd5Ad58qszN0JbqXG2pztOW6jyVXjxJ82g4phZ38HK406Wgp2vNE8zNsOpDK4v1kTWlWleePSefk8SThn5zql9/vb9jwZxMZr4UOG368Mpi3bmqZEaPf9Iw9NIFt/7yzTZ1e8PTrifbYVU0kVzQoXipsKkyR3evKdXtK4qUk2GbcHw4ltA/vdOjnxzsuua/pYk0FDr1b25fpi3VeVe9PRpPqtMbUvvw+ELfdk9QQ/6oDMOQyWSS2WySxSSZTSZZzCaZTSaZTbr8vTcU07Een2JTXVQwh3IzrLpjVYnuWVOitWXZMiQ9d25Q//vNdvXNINRsb0OBfnhLo2pmcQ7j1XoXXzCqfa0evdrs1v62EQVjk39u5GXatKYsS2svhodlWC169uygXrwwNO3n2Gy4c1WxfnhLo4oWyCLz4UBUr7UM67Vmtw51ei8/3zOsZq0seTeMbU1ZtqrzMq75mhpLJHWw06uXLwzptebhKxYkzwWzSfrYujJ9Z0+d8hfoWp5wLKEjXT4d7/UpEEmotsCp7TV5qi2Y/hzA4UBUr7cM67XmYR3sHJmV16vlxS59Z0+ddtcXpM3cj1giqR5fWEUue8rn/10Y9Os3F9eMzPR5Xpbt0LfTdH2UYRjq8YUVTxqqmkGw4LVE4knta/PomTMD2tfmmfZzO50DwwzDULc3rE5vSNV5mdd9DZ4pXyimt9pH9GbrsN5qH9HoJJ7bN1Tm6KFt1dqdxkFLhmHo3KBfx3tG5bRZtKY8W8uKrh1AMJPjLtF4Usd7fXqrbURvd4xMKti1Jj9TD22r0t2r0ztoacgf0YGOEQ2MRdRY6NLWmrxZm5sdiMZ1tNungx1eHer0Trj2rKHQqS9ur9YdK4tnPYB4NnV4gnrxwpCO9YyqJMuuzVV5+tDK4lkJowxE4zrZO6pjPaM61uPTqb6x677XLc6y64EtVfrEhrJZD9WaLd3ekP7Hi816u2PkiuvzMm362s4a3bex/AMB2dORSBpqHb4UPDMepNTumVwgfG6GVZ/bUqlP31A57yc6ng7DMPR6y7D+6NVW9frePf7itFn0+a2VemBL1YxfN8KxhE70jl4+tncpHCwUm9zruMNq1sfXl+nBbdWXjxemG7c/oj9+rVXPnRu64vqddfn67p46rUphv5RIGmobDupU36hO9Y/pdN+YWocD1w3XtVtM+ti6Mj20vVrlOem5Ds0TjOpPXmvV02cGr7jeJOnetaX6zp66lK1jHBiL6ETvqI73+HSid1QXhgJKTOL4usVs0m+tK9NXdtak7XP5dN+o/tcrLTrZd+VajrtWl+i7e+pUluLnjzcY08m+8VCwk72jOt0/dt3XjsUSuj0ciOoXR3v0dodX/aNhrS/P0W0rinTnqpJpr9mfLoLCgAXkvUFh0viZYz6zqVJf2VGTlo3obDIMQ8d6RvWLoz16pWnq4VXvVZrt0Pf31uuOVcVpc4B2LgSjCT1zdkC/ONqr1uFrh6JMlstu0Re3V+tzmysJZnsPwzB0qNOrR4736fVpBLG916qSLP3wloZrfrC7lCWSht5uH9Ejx3u1v21qZyZ5r7qCTH13T71uXlbI68V1eIMxPX12QE+cGpjwAOJ73dxYqG/sqtWKkqxZrG5xCETj+tWJfv3sSPeUz562otilH97SoG01Uz/71lKbPNniDuiRY716+szglCZW3L26RN/bW6+SND0wMB/iiaTeaPXoseN9HzhQ/F4Wk/TRdWX66s6alB+gWAoC0bhevuDW02cGdLjLd9UxdotJn9hQntYHaufbWDiuF84P6snTgzrZN/qB2y1mk+5aXaIvbate8mfrm4l4IqnDXV693OTWq01Xnh2zscipb9xYq1uWF6X1AcSFot0T1JOnB/TC+SH1+sIym6T15Tn63JZK3ba8aEH3xenUuxiGoZN9Y3qny6v+sYhWlmRpb0MB4Y0pFk8aOt03qo6RkLIdVm2qylVe5sSTsXF1bv94eP+jx6cW3l+W7dBH1pbq3rWls3KG+EuShqFfnezXn73eesXZwqbKJGlVaZZ21uVrR22+1pfnXHWSkWEYGg3H1T8WUf9oWP2jkYvfRxRPJrWiJEt3ry6Z1d8ZQHpLp94FAAAsbfQtAAAgndC7IJ31+EL667c69cyZgUnPeXRYzarIzVBlboYKnDaNRRLyBqPyBGPyhmKTCm4oz3FcDAUbDwebyiI0wzA0MBZR88XgsN7RsBxWi7ZW52pXfUFKFolOx6Xwpb8/1DWjz43STZbDotuXF+uOVcXaUp2X0sVKsURSj5/o19+81XHFnA2M/xu6d22pPrK2dNonjfcEo/qr/R361Ym+ac8tv3NVsT6xoVw93rDaPUG1eYLq8ATV4wtPex71QmY1m7SnoUAfWVOq3Q1Xf72JxJP65bFe/d+3OzUWmV6QjdVs0uc2V+orO2smvSh7LBxXjy+kSDypDJtFGVazMm0WZdjGL61m0+X5N5d6l8GxsF44M6Anj/XoUId3QQRHXo3Dar4YQlisl5vc+s3J/mk/Z112i76zp173byyf88WV0njYwStNbr3WPKwTvaOa7K+R7bBqdem74WErS7LUNBTQy01Der1leEH83cl2WPXtPXW6b8P8PLbvNzAW0b7WYb3R6tGhTu9VgzRKsuzaVpuv7TV52l6bP2GIXLc3pFebx8PdjvdM/v/fTG2syNF39tZpc1XeHO1xaoLRhF5vGdbz5wb1dse7oWkril26dXmRbl1epIZC55TnABqGofODfr3WPKxXm4entGZkslYUu/SDmxu0o3bqax7mWq8vrMdP9OmJ0wMavhhO67RZdOvyQt29ulRba6bfh0XjSR3q8uqVC2691DSU0teUmxoL9fUba1IagDFb/JG4njw9oF8e61XnyLsBMVV5Gfro2jJ9ZG3pjOe9G4ahNk9Qb7Z49GbrsI73jk67Z2oodOrBbVW6c1XJvL0Hmqp40tDLF4b044NdHwjsWl2apfs3luuOVSXKfN+a0KkcdzEMQ13esN5u9+it9hEd7vROeGLOaynOsutzmyv1iQ3laXPyY0nqGw3rb9/q1JOnr+yZnDaL7llTok/eUKHG6wSzTUY8kdSpvjEd7BzRwQ6vTvWPTSok5f3Ksh36wtYq/db6srRaC9w3GtZf7+/QU1c5llLosuuBzZW6b+PMnjej4ZgOdXp1tNun4z2jujDkn9brRU6GVZ++oUKf2VyZNnOZ40lD/3SkWw/v77huGFpVXoa+s6deH1ox9bUGwWhCL54f0gvnh3Sid3RK6/muxmW36JM3VOiBLZUqWKDhue/X7Q3pf73SojdbPdcck5dp05d3VOv+jRVTDsBrGvLrF0d79ezZwWm/Dr+X1WzSvWtL9cXt1WkzRzqRNPTIsV79733t1w3R/vDKYn1rd920AsMNw9DZAb9ebXbrRO+ozvb7p/18tphN+siaEn1xe82shpenUiJp6PETffrLN9uvewwm02bWl7bX6IEtU8+euJQ18sSpfh3s9GpgbPrh8NK7ayq/tKNmwQSZT2TIH9Gfv9H2gSC293JYzfritmo9uK1q2n/TL72PP947qlN9Y1f05FOxsiRL395Tp111+Qt6Ldr7+SNx/fRwt/7pSPdVA9HqCjL19RtTE4SWNAwlDU0YPE1QGLCAvD8o7JLcDKu+fmOt7t9YntYpzKkwEozqpQtuPXaib1Ip4VOxvjxbP7q1UevKc1K63XSSSBo62u3Tc+cG9cL52TlLTEmWXd/ZU6+716Tf2R1SyRuM6Zlzg3r0WK86ptkQXcvehgL94KYGgic0vmj5idMDevxE34zOAPV+68tz9IOb6nVDVW7KtpnuEklDb3eM6IlT/XqteXhGH0jfvqJIX7+xdsYHNxcjdyCqn7/To0eO9874Q56bGgv1/ZvqVVcw+deKpTB5Mp5I6tXmYT1yvFdHrhGkNBkZVrMe2l6tB7dO/w30fIolkurwhFSUZZ/VA879o2H96mS/fn2yf0pn7bRbTLpvY4W+tL1ahWly4OVqYomkkoZm5awklySShg51juipM4N6pck96bNw2i6edeBL26sJZZuERNLQgY4RPXl6QK81uxWdxGwnk6TbVhTpy9trtLKUkMzJiMaTOtg5opcvuPVay/CEZ+daUezSt3bXaU9D+py1b6HwR+J64fyQnjg1cNXAu0tWl2bpu3vrF+xknHToXcKxhJ49O6hfHOv9wHEOu8Wkj64r00PbqlWRy2vxdMUTSR3p8umlpqEPBAvaLCbdvbpEn99apYZC3n9M1qm+Uf38aK9ePD8048nIW6pz9dG1ZbptRdEHJhHNRNtwUP/txSYd7Z5eT1+e49CO2vFgsK01eWkzCQNAekuH3gUAAECibwEAAOmF3gWLQdtwUD891KV9bR6FYgkVuuyqyMlQRe7Fr/d8X+C0Xffz8XgiKW84Lm8wJk8wKm8oJk8wplgiqUKXXRsrc6YdbJQOvKGYfnygS7881jOpuR1zKdth1eaqXG2+GNBWmZuhdk9QzUOB8eC1i+FrE4W9Oaxm3dRYqDtXFevGuoKrngAnlQLRuP7xcLf+4fDVF0ktFXaLSbcuL9LH1pVpa01eyubItw4H9Gevt113cfBSt748W/esKdWHVhZP+nNdXyimvzvQpV8c67kcmjNVBU6bvrOnTveuLZPFbFI8kVS3L6wOT0idI0F1jITU6Rm/nOjEWxaTxgPEbBZl2syy2yxqcwe0kFfcNRY5dd+Gct29ulTZGe+GLLR7gnp4X7tevOCe9rZXl2bpdz68XKtnObjGMAxdGAzolWa3Xm12q8UdnNX9zVRxll02s0m9M1gTsaokS//m9mVaXzG3a6YSSUOn+8cuh4NNZy1YY5FT22vytb02T5ur8pRpM+vCUECvNrlnLahqKm5uLNQPb2lYEMEI4VhC+9tH9MK5Qb3R6plw3nBNfqZuWVakW5cXak1Z9jX/hsUSSb3T5dNrLcN6vWV4xovwJ2tnXb6+v7d+wZ2MPpE0tK9t/CTZ+9s81w2nK3TZdcfKYt29pkSrSrImnM/qDcW0v82j15qH9Xb7yIwDUiaykAPDWocD+uXRiU/8bpK0oy5fH1tXppsaCyc9N98bGg/7OdAxooMdIylddyaNr6F8YEuVPr6hTC77wgyzisSTevJ0v356qFs9vvB1x2Y5LPrImlLdt7H88nzLiY67ROJJvdPt1b5Wj/a1edTtvf4+pirLYdEnN1bos5srF/S6kiF/RH93oEuPn+ibcM7l5qpcfeqGCt2yrHDSa8mH/BHtb/PozVaPDnZ4U/q6kZdp06c3VejTN1QodwHPo3QHovrxgU49dqJvwh4/22HVpzZV6LObKpQ/yeCoS+Gbz50b1NvtIykN8s2wmvWJDeX6/NaqGYcezqbT/WP6r89f0IUp9HJryrL1g5vqtaU677rjDMPQid5R/eZUv144PzQrxxccFx/nLyzgxzkST+rvD3bpxwc7J33cqjTboW/cWKt71pZeN1QmnkjqleZh/fJoj472XHt9yExYTNKdq0v0pe01ql/Aa9tP94/pv7/QpHNXyfO4GotJ+tj6Mn1tZ61KJvHc6R8N65mzg3r6zIDaPanNDjCbxsPLvryjZkGvvT47MKb//mKzzvSPTfo+ZdkOfW9vve5YVTypfvnpM+O5Aal+jKXx14tP31Chh7ZVK8+5MP/2hWMJ/exIj358sHPSr5klWXZ9/6YG3TmJx1gafw/4RqtHvz7Zp7faRlIayL2pMkff3VuvjZULO6MhEk/qkWO9+rsDnZM6CcvyYpe+uatWNzUWTnod43uzXd5qH5E7EJXLbtG9a0v15R011zzeSVAYsIBcKyjskpr8TP3gpvopvTjMF8Mw1DES0oVBv1wOq1aWZE07PdMbiunVJrdevDCkw53eaZ9hZLLuXl2i7+6tX7DN/iWGYehk35jeavMomjBUmevQ2vIcNRa5JkyJfP92TveP6blzQ3rx/NCUQjpmYmVJln5wU722L9AF5ZckDUPNQwF1jIRU4LRpTVn2tBeuxhJJ7W/z6MnTA3qz1TOrZ/axmKRPbCjX13fVpkXSda8vrGM9PlnNJjUUudQ4jbOSXOKPxPVKk1vPnh3U4S7vrJ71am9Dgb67t35Bv6m6JBRL6ETPqPrHwqrIzdCGitwZB9MYhqFmd0AvnB/SU6cHNOhP3euHSdIdq4r1tRtrpxRkNV8Mw9CRLp+ePTeocwN+Oe0WrSnN1t7GAm2szJ3S6/L7JS+eceex43166szAtCclXI3FbNInN5brazfWTmqCxHxPnuzxhfTU6QEd7vLJG4qpvsCpPQ0F2ttYOKOF+4ZhqHU4qBfPD+lXUwysmkhptkPfn+RBivl2KUX9F0d79XrLu0FHDYVO3bu2VHevLlFR1sz7o0sfjD5+YvyD0Zm8TmdYzfrs5ko9uK1KORkL88DL+4VjiYvhO/063T+meNJQbYFTH15RrLtWl6g6Rcn+ze6Anj49oGfODs7oOX3pzA5f2lGdVhNBm90BPXt2UPvbPOrxhlWW49DOunzduapEq0sn/pB9slrcAT2Vgsd5V32+vry9Jq1CSC9NlHrxwpDODfplMZm0tixbH15ZnNLA2nAsobfbR/RSk1tvtAxPK8x4XXm2vrWrTttr8xb8a/H7+SNx7W8bP3PicCCq6vxM7azL15aqvJRP5k0ahg53evXE6YEpBQtK0taaPH1vT53WLrDg7cn2LsFoQkd7fOrwBOW0WbSxMld1BZmz+nzpGgnpkeO9euLUwIRnqbWYpLvWlOpL26oJhJ6kWCKpQ51evXRhSK81D0/qoPyehgJ9YWuVNlflpsVrRSyRlC8Ukzccl+/i2d99oZh8oZhC8aTyM21aVZKlVaVZKQmujcaTeqlpSD9/p1enp/Ch3WS57BZ9aOX4Ga3Xlk1/0lm7J6i/fbtTz58bnFafubIkS7/zoWVaU5adFs8DAIvLfB93AQAAmCz6FgAAkE7oXQBcTf9oWA/v79BTpwdSusBpKrIcFm2qzNXWmjxtqcrTsmKXLBPMMzQMQ8OBqJouhoa1uANqHQ4qGE2oocilW5cX6ubGIjntc39iy+FAVH/zVoceP9mvxGxO3E0hl92iaCI5ozmZq0uz9LF1ZbpjVfGszl97q92jP3y5RZ0pPjl1urq0WO69IRXT0esL6y/fbNNz54amvY3a/EwZknp84bR57k+Xw2rWh1YU6RMbyrWhIue6n6mf7h/TX7zRpkOd3mnty2ySPnVDhb61u05ZjqsHqxiGoYGxiFrcQTUN+dXsDmg4GJNhGDJJksmkSxWaxn+USSZd/E/tnmDKA2FSzaTxkKT7N5Zrd0OhDMPQ4yf69Ldvd04YQHc9H1tXqu/trZ90MMZUhGMJjYRiGgnG1O0NaX+bR/vaRuQNTb/e97OYTcrPtM3Z2qfJsllMemBLlb68o3rOA4FiiaQOdIzo+XNDen2a8yyl8YXjN18MDdtUlTceOnYxsGpfm2fa250pk6R71pbqW7tq5/3kx0P+iH59sl+/Otk/rbC02vxM3bW6RHetLrkiWK7bG9JrzeMhbMd7fLO+dvJqbm4s1Hf21s37CTfjSUNvtgzr58d6dXgaf0dyMqy6a1WJPrquVCvfF8wWjSd1vNenAx1eHewY0bkB/5y8H1iIYVb+SFyPHu/Tz450T+tvyqaqXH1yY7luX1mispJ35/t5PAH1jAS172Jo1eFOr8JTmI88XZdOzPuFrVULIrTxkpFgVD852K1Hjvf+/+zdd3yb5b3//7csecm25CHvPbN3SCAJEAh7Q4FCB6UbKKfztD09v/M9o2f2tKUtBToOq1DaQgstlDJSRvbee3jvvbdl3b8/ZKtxYjseki0nr+fj4Yd1y/d9+7Zy59ZH131d72tc/bIlyREWpDsXJujOhYmKPWscT7/L0JGqVs/77Mkxht1MRmigO2TpY8v8K2SptbtPL+4u1+/2VYz7XAu2BOiOBe7zZrj3lx6neyzw+hN12lzYMO5/w/GyBJh009w4ffKSVL8aS9nZ26+fby3Wy/srJjz+a83AGNycs8bg1nf06u1jNXrjSLVPwn6GMzhG6lMrUv3qerG1qFE/+CB/woGKGdGhenh1hq7KdQx576vv6NUfD1Xpj4eqVOfFsb+jMUm6Os+hT69M0yw/Cntt63bqqS1FevVg1YTe/4MtAfroEnd41NnBie09Tn1wql5vHa/R3rKJTXI9XlflOvSZlal+Ffba1u3Uz7cW6w8HKyd8vViQGKGvrc0+J+DaMAztK2/RHw9V6YPT9V4dYz0Sa6BZ9y1N0seX+8+4VcMw9MHpej2+sXDCQeILEiP09auyNX+E8V/FDZ16/Ui1/nK0Rk1e/Cw/nDVZ0Xp4dYbfBUM7XYbeOlajX24rmdDnvjnx4XpodYYuy4gatv1srNkui5NtevLuhcOOHyQoDPAj5wsKG7Q0xa6/vzpbubH+ddGT3Kmy752q12/2lut4zdC/JdEWrAWJNs1PsmlBYoTyYsNHHNjc2t2nDfkNeu9knXaVNk/5TZNgS4A+tSJVn7ok1eczKY2XYRjaXtykp7eX6HDVuQMzQywBmpMQoQWJEZqX6H6tz24MkKT8ug69e6JW60/WqfI8ie++dP3sWH1rXY7fFEmDOnqdevNIjV45UDnkpqolwKT5iRFanhqp5WmRmp9oGzVsaTBA4c9Hq/XuiTqv3uAYi7Agsz57aZruX5YyqaAiX3C6DG0qaNDvh2m8jbYG6pK0yIGvKCXZR2/Md4ewNemd47VT0vBypgCTdMs8390sm6yTNe364+EqvXO8dsiNmRBLgC7NiNLlWTFanRU95sZmwzB0orZd75+q1wen6lTm5dkczhZgcgc4fvbSdK+F53hTfUev3jxSrTeOVI/4WkQEW7Qq0/1aX5YZNabrXWVL98CsJM3aXdo0pmCDyYgItugzl6bp3sVJo77vTUfnyR6nSxvz6/X64WrtGuFGj9kkLU2N1FW5Dq3NiRn2fW+4/e4rb9aWgkZtKWyY1AxbY7Eg0aZvXJXldwEqkvum/DvHa/XKgcpRZ+kKMEkr06N0y7x4XZEdM67AifYepw5WtGpvWbPWn6zz+ixS4cFmfWJ5iu5bmuy3s+0UNXTqtUNV+svR0QNhFiRG6Ma58bo2L3ZcqfPdff06WNGqPWXN2lbUOK5ZOsbCbJJumutOIvfH67Hk7sT57ok6vXO8dtTZ4VIjQ3Td7DhdPztu3LNU9DpdOlnbrgMVLfrrybpzPvNM1pJkmz59aZouTR++IcgfFDd0av3JWq0/UaeSETof5sWG6YY5cbp2Vuy4O2UYhqGKlm7tK2vR9uImbS1q8NqMMEtS7HpodbqWpkR6ZX++Ut/Rq0357pkOd5c2DxswbA0069KMKF2RHaPVmdGTmqWivLlLbx6t0V+O1qh6ktfnq3Idenh1ht/MADNS7TIYxLqjuEk7S5p0sKL1nNfZPVNijK7McWh+4sgzJY6HyzC0vahJrxyomNCMGv560+x8nC5D5c1dKqx3h3AHmEyKDQ/SkhS7Er3YcavX6dKu0ia9d6pem/IbzhvANpI58eH6xPIUXZ0XO+2fo/v6XdpV2qztRY0qbuxUS5dTLd19aulyjnnGN3OASbmOMM1LjNCCRJvmJUYoLSp01HO6tbtPp+s6dLK2XafqOnS6tl2FDZ0+DTw/05z4cN21MFHXz4kbc1h7cUOnntk58YCwYEuAvrgq3S/bTwBcPBi0CgAAZgrqFgAAMJNQuwAYTX59h57aXKTNhY1j3sZsktKircqOCVNOrFWRoYHq7TfU63Spt991zvcep0u9/Yb6+l0KH5h8e1mqXXmx4ecNBpuJSpu69LMtRXrvVP10H8qwkuwhujI7Rldkx2hxsk0ymVTW1KX8+g7l13eooM79vWKUvvX2EItumhuvW+fHT+l4jl6nSy/tLdczO0qntJ+0P8mNDdPdi5N0w+w4rwbiHa9p0+MbC7VnigbzzjSZ0VbduShRN82JO2cw9PnsLGnSk5uLJtzPzhEWpK9fla3VmdEqqO/Q6YH/p4OBiRPtG+Lvoq2BunV+gu5YkDBseEFHr1O/2VuhX+8uH3P/kbNFBFv08JoM3bUwcUzvR119/Z6+Pw0dvWrq7PMEgjV19am5s1eNnX1TEgbjCwEmaVVmtJanRuq1Q1WTCmaMCQvSo5dn6Ka58V7pc3Y2Z79LjZ19auzsVU1brzYXNujD0/Vq9fJ4g4hgi7r6+qesv9BYBA9MMP3gitQRQwR9wWUY2l3arNcOVmljfr3XQrwGx/3tLGlSUUOnd3Y6SQEm6bb5CfriqnSvTDA+Hs2dffrT4Sq9erBq0n1ZB+XGhumWefFyGe73pP3lLdNaRwWZTbp7cZI+e2natI2jbOjo1e/2Vej3Byq9Ev4XZQ3URy9J06VZ0dpe2KD3j9aM2o/f1wJM0rWzYvXwmoxpnSS9tbtPL+0p12/3VUy6H7zZJK3NdejOBYlq6urTlsIG7Sj2/TizkVgCTO5xfpelTetr3Nnbr5f3V+iF3WVq75ncuWwe+Js+dUmqUiJDtLusWe+eqNOG0/XTEpLp6S++Ik2z4qe3v/jmggZ97/18r4wBGxyD+7nL0nW6rkNvHK7WlsKGaQnHHDye62bH6aHV6dN6Lle1duuxDwu0Ib/BK/ubEx+uL63JVEhggH5/oFLvn6qf1npuTVa0Pn9ZuuZOYhLpyTIMQ++cqNWPNxROKnB5UHiwWQ9ckqp7FifpYEWr3jpWo40FUzum/UxrsqL1mZVp5wRrTSXDMPTuiTr9aEOBV15jyZ098ejlmQqxmPXmsRr96VDViGPYfC082KyPL0vR/cumd9zqydp2PfZhgfaVe6f96sY5cXr08kzFRQSrq69f752s0xtHqnWgotUr+x8rk6TrZsfqi6sypn3MqmEY2pDfoJ9tKVZR4+Q/oy1Msunh1RlanhYpwzCUX9+h9SfqxpXt8umVqXpkTeY5zxMUBviRsQaFSe4PH59ZmapPr0xToHn6g6xau/v0x0PVemV/hWrHmCobZDZpVlyEFiRFaH6iTXmxYTpa3aa/nqzTjuImv2hMzIy26v+7LleLku3TfSgyDENbChv19I5SHas+NyBsNHHhQVqQZNO8hAj19ru0/kSdCv2kEVFyH98/3zBLK9OjpvtQVN7cpZf3V+rPR6rH9EE+2BKghUk2T3DY3PhwWcwBaujo1TvHa/XmNDdwDZodF65/uj7PLwaT13f06k8DKdRjvV4k20POCA6LVJQ1SC7D0KGKVr1zolbvnaybtgauQZGhgfrm1dm6dlbstAd7dPQ6tf5Enf54qGpMN1ZNkuYnRujy7Bhdnh2j7BjrkL/BZRg6WtWm90/V68PTdT4PVBqO2STduTBRD6/JmPZgwX6XoR0lTfrToSptLmwcV5il2SQtSra7X+usaKUPJPy3dvdpT2mzdpU2a2dJ04TT3ycrJTJE/7AuVyszhr8eT2XnydN17Xr9cLXePl477huXCxJtuio3RlflOobcIK9v7/HMTLKzpMlrwTPjcdNc9wfosYSZ+Vp5c5dePVilN45Uj/s1Dg8269pZsbp5bvywM8M1d/Zpf0WL9pW3aH95i07XtU84CX48IkMD9eCKVH1kUeK4gsx8pdfp0oen6/XqoSrtH2cjjDnApNWZ0bpxTpwuz445J5i0u69fh6tataesRXtLm3W0um1K6ucAk3TDnDh9emWaX8xS0tzVpw9OucPB9k+gISo3NkzXz47TdbNjhw2raers1aHKVh2qbNXBilYdr2lT7xTciZgTH64HV6ZpbU6MTzqKjFdFS5f+OtAQNVqg4HAWJ9t03ew4XZPnGDFUtaKlS3vLWrSvrFl7y1q8doN/JCvTI/XQ6owRZz+YDmVNXdowEA52uLJ1XAFSASZ34+EVA7XccP83DcNQU1efypq6VNbcpbLmbpU3dam4sdPrwYIBJunmufH6gh/M3ndm7VLd0q23D5Rre2GjdpU2jyvEOdoaqCsHQsMuSY0cd6B4S1ef/ny0Rn84UDlqZ+LxWJ0ZrU+vTPWL9opBZ87UWjDYibq+Q8WNnSNeO5NswVqSGqllKXYtTbUryRYyps9TZ88Ke7K2Q9uLvTtrZaItWPcvS9Ht8xOmdMbt7r5+7Shu0gen67W5sGHSHRiGYwuxaG5ChOYnuNvkevtdOjUYClbX7jcz5oYHm3Xz3NFngS5u6NTTO0q0/kTdhGd3XJ4Wqf/v2ly/mhkMwMWJQasAAGCmoG4BAAAzCbULgLE4UN6ip7YUndP3JNEWrGxH2MCXVTmOMKVHWf1uEmp/dLSqVT/dXKS9fhC8tGCgf+wV2THKOqt/7Eg6e/tV2OAOIsqv71Bde69sIRZdlhGly7NjpnX8RlVrt360oVAfnvbPMLYzzY4LV2RooI5Wt0040CnQbNI1ebG6e3GSFiRG+KyPtmEY2pjfoB9vLPRa346ZzBZi0XWzYnXzvHjNS5jc6+4yDL1/ql4/31o8qfCli8HyVLvuWpSktTlju840dfbq2Z1l+sOBygn3H50dF65vrcvxDCrvH5gQsKC+Q6fr/tb/p7y5e8L9MvxZRnSobp2XoJvmxnlCkZz9Lv35aI2e3l4y5vEuw5mXEKFvXJU9rgH7LV19Olnbruq2HjV0uMPXGjt61djZq4aBx9M9bmYyoq2Bun52nI7XtE1q8Lk9xKLPXpauuxcleu092TAMtXQ5Vd/Rq/qOHvf39l7Vd/Rqe3HTRXf9CrEE6BPLU/TJS1J90neuu69f1a09qmjtVlVLt45UteqvJ+umpI+2P7CHWPS5gXPYMgV1pcswVNrYpZf3V+jPR2suitDZQLNJ9y9N1qdXpk1psGBHr1Mv76vUr/eUX7CBpoMCzSbduzhZn7k0dUrH+fU4XXrtUJWe31nqtTCaQSZJESEWr4dvTsa1s2L10OoMpU1xcEp9e49++GGB34aAe9N0XS/6+l369Z6LJwz81nnxeuTyTDnChh9T5CvFDZ363gf52lPa7PV9myS/+ox0Va5Dj16eOeXXC1++xsGWALkMQ31+UiNGWwP18OoM3To/YUongGjs7NXPthTr9cPVXj/nQiwBWp0VrR3FTdMSjnkmc4BJt89P0OcuS5uWsdd7y5r1xOYiHakaX4bLWCxJtqm52zmhgGhroFlvfH7FOeH9BIUBfmQ8QWGDcmPD9M/X52l2/PSkqZY2del3+yr05yPVfjnzQ2hggG6aG6+Gjt4JJ+qaJN29OEmPrMmY0kJ/kGEY2lTQoKe3l+rEOM+PqZAeFap7lyTpkrQovbC7TH85WjPhQuOjS5LcCa9THOxhDMzu8Lt9FdpS2DipQskaaFZmjFUnatq8mmYdbAnQNXkOHa9pn3DIm9kkPbAiVZ+9NP2csBFfMwxDByta9fsDlfrg9ORTqHNjw9TR4/R6YFWQ2SRHWNCk9rs2J0bfXpcz5bNnSO4Zrf54qErvHq+b8OxAknuw/uXZMVqcbNeBihZ9eLp+Uje5vCkqNFBfvjJTN8+Nn/JAturWbr1xpFpvHKnxShK+JKVFhSo82KITNW1TEqI0VjfNjdNXr8w6J9DF150n23ucevdErV4/XD3h2cPOlhsbpqUpdh2qbPXaPifLGmjWF1al66NLkqbkxs6ZXIahXSVNemV/5aTf8walRYXqprlxSrGHesLBpntWo4SIYP391Tm6MidmWn5/WVOX/nioSn8+WjOuIJqRhAWZdU1erFZlRul0XYf2lrfoSFXrtDZ0BZikjyxK0kOr06c8wLG7r1+bChr0zvFabfdiwPCCRJuunx2rIEuADla26nBl67Tf2J8dF65vrsvRwmmY2aGuvUfvnarXX0/U6rAXGrnMJmlFepSunx2neQkROlzVqr3l7nCw6QqkWZMVrYdWZUzLTDsuw9Cp2nZtyG/Qhvx6FdR777qZFhWqNVnRCrYEqKypW2XNXSpv7pryRttAs0l3L0rSp1emjhgS50ut3X06Wt2ugzXt2ny6Tqe8VAdYA81alRmttTkxWppqV2dvv5q7+tTY2aemgZkxz16uaOn22Q28Zal2fXplmlakRU5pfWwYhkqaunSwokVHqtpUUN+pwoaOSZ9nCRHBWppq17KUSC1NtSvZHqKO3n4VDHQ6PF3XMRBC1jllnSgigi36yKJEfXRJks8+53X29mtrUaM+OFWnrUWN0xJo6++WpNh196JEXZXrUKA5QEUNnXpmkgFhthCLvnJllm6dN/WfLwFgOAxaBQAAMwV1CwAAmEmoXQCMR3lzlypauj19gaej3/iFxDAMbStu0hObiiY86XK0NVBpUaHqdxnqdrrU3dc/8N2lbmf/sP2ngi0BWpEWqSuyY7QmO2bKB6FOle3FjfrBBwXT3r/pTLYQi1amR2lVZpQuzYj2vPYuw1BpU5eOVLXqSFWbjlS1Kb+ufdS+9km2YN21KEm3zY+f0n4nvU6XfruvQs/uKJ1UX+yZaHBy05vnxWtNZrTXQxGd/S69vL9Sv9hWTL+IMyTZgnV1Xqxun5+gjJiJTd5a2dKtX2wr1tvHaifch2JVZpSaOvtU2NB5wQcVhAW5J0u+bX6C5o8SQNjjdOkPByr13M7SSYVzjTTJdEevUydq2nWsuk3Ha9p1vKZt2iYb9yWzSVqTFaNb58drdWa0LOaAgXFyjXpic6GKGyf+PpZsD9GXLs/UNXmOMfX9cboM5de163BVmwrrOwZCwf4WCDYVEzb7UqD5b5NUZ8RY9eLucr19rGZSY9uirYH6wqp03b4gUZZxhCEYhqHK1m6VN7uDwCpbu1XZ0q3Klh5VtnarocM/xidNt7SoUH3lyixdnhU96f5rhmGorr1XZc1dKm3q8kwsXNLUpYrmrosmhO1skaGB+uKqdN2xcHzn8Pm09zhV3dajmrO+thY2emX8yExiC7Hos5em6Z7FSV4PVO53Gaps6VZhQ4enn/L+8ha/GeM4Vcwm6fYFiT4JTnEZhlq6+tTQ0af6jh41dPSpvLlLv9tf4ZOJjv1ZtDVQD63O0G0+CAByDVyjK1q6VNHcrYqWbr13sk4lfvR5fipYA8367KVpum9psk9C+F2Gofr2XlW1umuP49Xt+v0kQpVnInOASXcvStTnLk1XpNX7Y/26+/pV2tSl4sZOFTd2qrChUxvzGy6q11iSchxh+uraLK1Mj/L6vgcnLxgcN3O6rkPHa9ouqnaUYEuA7l2cpAdWpCoy1LvncWdvvypbuj33AdxfXSpv7varNtazfe7SNH1xdcaQ5wgKA/zIRILCJHeh/6mB8J+pmKHIMAztK2/Rb/ZWaHNBg18lng5KiwrVvYuTdPO8eM9N2j2lzXpsQ4FO103sRmNceJD+4ZpcXZ49NYETLsPQhtP1enpH6YSP2VcCTNLlWTG6Z0nSOYORT9a26/GNhdo1wfTXjOhQ/duNszU3wffhd919/XrreK1e3lcx4fAtX0uNDNHdi5N0y7x42UIC5XQZ+vORav18a/GEU8/To0L1/67P06Jku5eP9lxdff16+3it/nCg0u/O4zOlRYXqroWJnmvGW0dr9IttxRNuOLKFWPT1tdm6aW6czwcbt/c4tf5Erf54qNovwwSHkxEdqrKmrkndcFicbNO31+UqJ9Z3hezgjEgnatr11vEabS9q8sv3vLPZQiy6e3GSEiKC9cttJaqf4E0Ue4hFX12bNSSUzdudJ3udLpU0daqwvlPbixv13qn6GXNz2R5i0SVpUdpYUD/hsKasGKu+tS5Hy1IjvXtwZ+nodadNH6ps1asHq/z6Q6u3XZkdo7+/OlsJthCf/h6ny1BxY6eOVbdp/Yla7Sxp9unv8ydRoYF69PJM3TI/XgE+es9r7OzVkao2HR3oKHa4qvWiauCSpJvnxetRH86e0dHrVH5dh07Vdeh0XbtO1XboWHXbjHjfmyyTpFvnx+uRNZmK8WHH0ObOPh2tdp+/R6vadKS69aK5eWYNNOv+Zcn6+LIURYT4phO1YRiqaOnWwYpWHaxs0cGKVr/9jOcrCxIj9NDqDK3wwc0Gyd1Z82Rtuw5UtOpAhfs1bpqCzgwRwRa/mVUt0GzSDbPj9PHlKcp2TP5zSFu3U5sLG/TBqXrtKGmaMXXwWIUFuQdPeHtGlWhroGbHh0/689k1ebH6xtXZF+ygAAAzE4NWAQDATEHdAgAAZhJqFwCYfv0uQ+8cr9XPtxareoRJWkMsAcp2hCnHEabs2DDlOKzKcYSdNyDK6TLU4+xXV587RMwSYFJseLDXB/X6q16nSy/tLdczO0qn7Z7znPhwXZYZrVUZUZqXaBtzAEN3X79O1LS7+9JUtym/rkO9/S7NS4jQLfMSdGlG1LT+O9a39+ipLcX689GaaTuG88mNDdPanBitSItSTVuPjla36Vh1m07Uto/rfJgTH66b5sbr+tmxUxLKVt3arcc2FOrD0/U+/13+Ki0qVFfnOnR1nkOz48K9Nt7gdF27frqpSNuLm7yyvwvN8rRI3TovXlfnOhQSaB7zdu09Tv1mb7le2lMx4QDB0MAAPXBJqiKCLTpW06bj1e0qbuy8oPtoZsZYddv8BN04J27EvplOl6E3DlfpF9tKJjw+SpLmJkToK1dmamlK5JDnmzp7dajS3W/zcGWrjlW3qfsC66NlkrQ01a4bZsfp6jzHOZM/lzV16dmdpZMODMuIDtWjl2fpiuxzA60GQ8HcwXfu0LsTNe1+0+/P25LtIbo8O0aXpEVqZ3GTXj9SPek67JK0SH31yizlxY1t4uPuvn4dqWrTgYoW5dd3qLTJPaHwhdK/Pshs0pIUuy7LiFa3s18v76ucdH/VzGirvrI2S6syosb8vtve49ShylYdr2lTVevfwsBq23qmfPJmXwg0m7QoyabMmDCtP1E7qVBMSUqJDNGjl2fq6tyxhTeeyWUYqm7tUWFDhwrrO1Uw8L2oceYHmCbagrUg0aYj1W2qbJlcIGiwJUAfXZKsT61IOed6P5rmzj7tr2jR6bp21Q2EYzYMfnX2qX+GBvwEmKRsR5gWJNq0IClChiG9uLtcRY2TG0OQGxumr16ZNe4++S7DUEljl0qbOt3BM81/C6CpbOmekYGNgWaTMqOtynaEKdsRJkdYkF4/XKX9Fa2T2m9KZIi+emWWrsiOGff1or3HPUayqtX9+la1dqtqIIi0qrV7wmM8/UFaVKjmJ0aotq1He8paJrWvsCCzPrMyTR9dmqzgCeSONHX2qqixU8WNXSpp7FRRQ6dKGjtV1dozYz/DpEeFamGSTYtT7MpKsuvZLUXaPMl2iTVZ0frKFVkTCv3udxkqa+7yhIENfq+Y5HuFP4i2BmpuQoQOV7ZOqr4ICzLrk5ek6P6lKbIGjf0zvOQO/t5X3qxDFa0qa3Zfhytauif1uXM6hQeb9efPrxwysQlBYYAf2XW6Vv/+1kkdrppYkZQVY9U/X5+neYk2rx2TJxG4s08NHb2qaO7Sa4eqddIPw2hMki7PjtE9ixO1Ij1q2KCC/oGQpZ9NImTp2lmx+sZV2T4ZRO4yDPU6Xdpc2KhndpSooN6/BjbbQyy6Y2GiPrIoUYmjBG4YhqHtxU36ycbCCQ3ONgeY9NlL0/TplWnjTms3DENdfS519jrV0duvzr5+dfb2ux/39nuer2nr0TvHJ9+I4QueILbFSbokPXLYc7mj16kXd5fr13vKJ9TgYZJ0z+IkPXJ5hsKCJjdY32UYauzsU+1AY1dte49q2npV3dqtbcWNfhuAYA4waW1OjO5amKjlaee+zt19/Xplf6We31U24QbqVZlR+s41uRMOqHG6DDV19qquvVd17T3u7x29qmvrUd3AzCVlzV0zotHLFmLRjXPidOv8BM2KC1dpU5ee2VGid47XaqLtSWaT9NGlyfr8ZemTmrnPMAw1dPQqv75D+fWdyq/vUGF9x4ybESkhIlgfX56i2+YneD74dPX168XdZXph98SuFZL7puh3rslVWlTohDtP9vW7VNLY5W60bXCnhRfWd6i8eXKBcdNhfmKE7l6UpGtmxSrYEqDy5i79ZGOhNuQ3THifN8yJ01euyJRjkjM7dPf1q6SxSwVnzJZRWN+hytbhO1f5O0uASVflOtTe45xUp4XQwAB9YVWG7luSJIsXZihx9rtU2NCpE7XtOlHTrhM1bTpV1zGjrhdnCjSblB5lnfCsnYPmJ0boW+tyNCd+cmGvPU53EM2RwTClqtYZew6fKchsmnTDfliQWV9Yla57F0/8XB4MUzo9EAjm/n5hNCROVliQWZ+7LF0fXTL52Yz6+l06Vdeho1WtOjwQcld2Ac70N17hwWZ9fFmK7luaPOlZlwdDqw5Wtg6Eg7Uyw9yA5al2PbwmUwuTJtc21N7j1JGqVh2oaNXBihYdrmqbse91vrA6M1qfWJ6iZan2cd2gbO9xamN+g/56sk47S5ouyNlz0qJC9dEl7vD+sCCLypu79OejNfrL0RrVjNDpfyrFhQfp29fk6oopmggAAMaDQasAAGCmoG4BAAAzCbULAPiPHqdLH56u1+7SJrkMd8hCjiNMObFhSrKH+GySwotBVWu3fjSB4CVroFkZMValR4UqPNgil2Go32W4vxuSa+Cxy5DnZ4YhOcKDtCDRpkszonw6MZ8/OFbdph9+WKBDlZMbCO0NJkmLkm1am+PQlTkxSokMHXY9p8tQUUOHjla16VhNm45Vtyu/rn1If93Y8CDdOCdON86NV44XJkubiC2FDfr++/l+3T8w0GzSJWmRWpvjUHiwRccGw9hq2scdGJUVY9XVuQ6ty4tVtsPqs8nIDcPQpoJGPfahf7+2wwkNDPBqyI0lwKTlaZG6KidGV2THTLqfdnNnn57bVao/HKickUEPUyEsyKzrZ8fptvnxmpsQMebzvLO3Xy/tKdeLe8omdQ5cnhWtSzOidbS6VYcqW1V+AffbzI0N041z4nTd7DjFR5z/3C5r6tIzA4Fhk+mytiTFrs9dmqaO3n4dHwi9O17T5pdj87zFbJIWJdu1Jital2fFKD06dMi53dTZq98fqNQr+ysn9TqYJN02P0EPrck4Z/LL9h6nDla2an95i/aXt+hYddsF1/cwM9qqSzOidGlGlJam2IcEOnb39evPR2v06z3lkw5aujQ9Sl+5Mks5sefWP4NhSoOv86m69kn9f/E3Jkmz4sK1Ij1SK9KitCjZ5nmdu/r69afD1XppT/mk+3kuSLTpq2uzRu3HXN/uDrk9UtXmCbu9EMLXTJJyYsO0KMmmxcl2LUq2ecaUOl2G3jtZp1/tKpv02J3wYLMeuCRV9y1NVugw4af1Hb3aV9as/eUt2lfe4vPJt9OiQnX97NhJXwfPJyo0UAuSbJqfGKEFiTbNTYg4JzjGZRjakN+g53aU6sQkMxCuyI7Rl6/IVHr08AFAvU6XjlW7QxsHxzT4MiTz2lmxOlHT5rNxKWlRoQMB7gPBYDFhSokKHXZ8/77yZj23o0w7SiYXULwiLVJfuyp71M+knb39OlDRor1lzdpT1qITNW0+uzYn2UM0Ky58SkKt7SEWzU+0aV5ihOYnRmheQsSQAMCDFS16bmeZthY1Tur3JEQE65HLM3T97LgR290Gx5kNvv/tr2jxWR2dEBGsjy1P0euHq3yanxFsCdDc+HAtTLZrYZJNCxNtirS6X98z7xntLGzQ9946rn3lEw9mM5ukjyxK0udXpSsydOQQx75+9zVj38C1+VBF64SDqM/n8qxoVbf16HTd5N5vxsJskvLiwjV/ILRxQaJNyfYQmUwmtfc49dKecv1m78RDtyV38NinV6bproWJChol+K6xs1dbChu1uaBBO0uafB6iaw4wTWnY5sOrM/SZS9P+9vsJCgP8R0tLp3p6nHr/VL1+urloQh9cA0zSx5el6Aur0s87w0Ffv0tlzV0qbuhUdVuPJwG4cSANuLGzT02dvX4f4GEPsej2BQm6a1Giku3D3+w4W3uPU8/tLNVv91VMKCHWFmLRV6/M0i3z4kdsuGzrdroTUwcCUSpautTZ268ep+tvX/2Dj93P+2ta7Zz4cN27JEnXzoobV3qs02XozSPV+vm2kgkN2p6fGKF/vWHWsB+mXIahsqYunaxt18naDp2qa1d+XYcaO3tnbCNMtDVQdyxI0J0LE8ccLFXb1qOfbS3WX47WTCiJNyEiWN+5NlerMqNHXMdlGKpt61FpU5dKmrpU0dyt2va/hYLVtvfOqOTw+Ihg3bkwQbfPTxjTzZ6Wrj79aleZXt5fMaEbOmFBZn35yizduSBhxOtFd1+/Ttd16ERtu07Wus/l2nb3dXkGvbTnMElamR6lW+fH68ocx7DXj+KGTj29o0TrT9RNOE3aERakr63N0rWzYs97M6mj16mCgTCwgroO9/f6jhl9YyI3NkwPXJKqa/IcIwbH1Lb16KmtxXprgteKILNJn7ssXZ9amab42L+FAA3XebK9x6lj1e7G2hM17Sps6FBZ08wLBDtTsCVA182K1T1LkkYMQdpT2qzHNhRM+AO0JwBoSfKYQjKbu/o8IUr5A8F25c1dM/qaMSglMkR3LkjULfPjFT0wO92B8hY9tbVY+yfR8JIbG6Z/uCZ3XMEpLsNQYUOnjla16niNOxgsv37mhoKdaVGSTTfNi9c1A7NH7S1r1jM7SrW7tHnC+zRJumtRoh5enSH7KI1bZ2rs7NW+Mndj4pHqNp2qbb+gblwuSbHrlnnxWpfnUHlzt57fWab3T038PU9yz7L291dlj2mGkn6XoRO17dpV0qSdJU06UdN+QdxEO1t6VKjW5Tkkk0kv76uY1N+YHhWqr1+VPWp9fDbDMFRQ36n3T9VpZ0mzTta2XZAdgUIsAVqRHqVdJU2TmlXPHmLRJy9J1b1Lkoa9OTkcwzBU3NilzQUN2lbcqCOEVp3X6sxoPbw6Q7PixzbDnOSuzTfk12tjQYOOVfvuJtqFZHZcuD55SYquzosdsYbr6HVqc0Gj/nqyTtuLG/227WeyVmdG694lSbo0Y+Tw/j2lzXrjSLU25NdP+XXSJOnuxUl6ZE3GpMMKAcBXGLQKAABmCuoWAAAwk1C7AAAuJtuLG/X4xqJzBp7HhgcpPdqqzGirMqJDPY9jw4N8FlZ0ITEMQ+tP1OnxTYWqbZ/aiewCzSatTI/SldkxuiInxtOncrwG+6o3dPQqLiJYs+LCZR7nhO6+0N3Xr2d2lOrFPeV+Mx4hLMisVZnRWpsTo1WZ0cP2Meh3GSpp6hwIDmvXseo2naprP6dPSF5smNblxerqXIcyYoYPGPCV7r5+/WpXmV7YXea3fdmCLQG6JC1Sl2dFa1VmtGLDg3VyoL/jrtJmHaxoGfexWwPNWpUZpbU5Dq3OGv7fb7Jq2nr01JYivXWs1uv79qUFiTatzYlRcWOnNhU0eG38RLQ1UFdkx+jKnBhdkhY1rjFnZ6vv6NX/bSvR64erZuTYA3OASVflxCg5MlR/PVk36UCjs+U4wnRFToyumxWr7AmGPJY2delZLwSGTacFiRG6bnac9pW3aFNBg0/eP2whFq3KjB4In4saEtwxkq6+fr1xuFov7S1X1SSCGq2BZn1qRaoyY6zTFljlCAvSx5a5JwV+7WDVpMN2hhMebNaKNHcw2GUZUWMaR+l0GfrglDto6dQkgi8CTO5Qto8uSVZBfYf2V7gDO4p8HKZ0tkvSIvWRRYnaVtSod0/U+aRfdEpkiFakRWlFeqSWpUaOGmAiucebv3uiVi/sKldR4+Rej3V5Dj16eaairUE6XuMOAzsyMAZqqmrq5al2PbQ6QzVtPXp+V5nXA1NMkhYk2bQkxa4lyXYtSIo47/XCMAxtKWzU87vKJh1GHBMWpM9dmqbLMqN0qLJV+8rc53JpU9ek9jtWlgCTHlyRqgdXpinYEqD2Hqde2F2m3+yt8Nr5vCAxQrfMi9eK9ChP8MxYGIahbcVNem5HqQ5O4nU2B5h07+Ikfe6yNJlk0sHKFs8k3Meqp2bMSG5smL69LkeLku1y9rv0xpFqPb2jVHVe+H8UbAnQTXPjdM/iJOXGjn3MwaCj1W16bkepNhY0TPgYBkOWvrAqXfbQQHX39etwVav2lLqDwY5Wt/n8s6IlwKQHLknRp1emKSTQrJM17XpyS5G2F08uCO1MkaGBWpfn0OJku+YnRoz5fD5e06Znd5RqQ/7EX2PJnRHxlSuztCw1Ui7DUFFDp6fOOFDR4vPrsjnApE8sT9FnL01TaKBZTpehNw5X6edbS9TU1eeV3xEVGqhb58frqlyHZsWFK3CEcdZn3zNqaGjX9qJG/WJrsQ5XtU3490cEW/TZS9N075IkBZoD1N3Xr6PVbQPX5mYdnoIxUNkOq76+1j3esN9l6M9HqvWzrcVq7PTOayy5/y0vy4jS4oH3vbnxEefNs2nq7NVzO8v0h4OVkxpLk2gL1ucvS9dNc+NlDjB5xvJtLmzQ5oIGHalqm9R4zbFKiwrVw6szdGlGlH63r0K/3lM+JWM17SEWvfH5lZ6QTILCAD/S0tKp3oELQa/TpVcOVOqZHSVq7xn/xSE9KlT/7/o8LUq2q7uvXyVNXSpq6FRRQ4eKGrtU1NChsuZuv2lMHy9zgEkr0iJ13exYXZMXe943kZGUN3fp8U1FE054vSQtUl+5MkvtPU4VN3aqaCAUrLix0yuF9nRKsgXr2tlxun52rHIcYZO6CTg4u8MLu8vGPaA82BKgr1yZpYWJtoFQMPfX6boOn6WlTqUzg5SuynWMWHyez8nadv1kY+GEwz1umhunL6xKV1NnnzsQrLHTEwxW1tQ1qSAAf2AOMGlVRpTuWJioVZnRYwrhOVt1a7d+ua1Eb04waGl5WqT+6bpcRQRbdKr2b6FgJ2vbVdLYOWMb2YeTZAvWLfMTdMu8eCWOMfSuoL5DT28v0XunJp64vTwtUt++OkcZMVb19btU0tTlCQMbDASbTIO7v1mRFqlPXpKilelRY75Gn6hp0483Fmpv2cTClnIcYfrePYu0bCCcpqauTScGQsGOVrfpaFWrihunpkFxKqREhugji5J067z4MYUe9bsMvXGkWj/bUjzhRopsh1XfWpejpSmRnuec/S6dru/Q4co2Ha1u1ZGqtilruJ0qZpN0ZY5Ddy1M1CXpkcOGSxiGoZ0lTXpqS7GO10z8ZtOdCxP0pTWZw/6bugxDBfUd2jvQQL6vrHlGBwmeLckeopvnxunGOfFKjRo+YPdgRYue3lGqHZNo0LSHWPTI5Zm6fX7COZ2Jmjp7ta+8RXvLWrSnrHnKb6hNhSR7iG6ZG68b58YNO2tjSWOnfrWrTG8dr53U57F1eQ599cqsc26MVrV2a2dxk3aWNGt3adMFdQ6fKdth1brcWF2d51BWzN9mWGzu6tOLu8v1yv6KSdWwl2dF62trs0f8v2IYhk7Xdej9U3V6/1S9Si6w6/KZliTbdMu8BK2b5VBYkEX1Hb16dkepXjtUNalzOCo0UJ9akaqPLEoc9jN9X79L+8pbtLmgQVsKG1Xh5U4z/igvNkz3LklSSmSofr2nXFsKJzcDjOS+VnxhVbqyYs5tAHYZho5Xt2lDfoM25NdfUHXcSNKiQnVNnkOLku1af6JW75yo80rbWKItWPctTdbtCxIUFmRRV1+/thS6w8G2FTVesMF2YUFm3TIvXvcsThpxxqzhtHb3af2JOr1xpHpSdd1Yrc2J0ecvS1de3PhvYgPAVGLQKgAAmCmoWwAAwExC7QIAuNgM9mlp6uxTWLBZGdFWJlPykq6+fr2wq0wv7ikftR9AkNmk1KhQpUVZlR4VqvToUKVHWeUID1JPn0vdzn5197nUNfi9r1/dTpe6+/rV02/IZAlQaKBZOXERmhcTqpBJhO7MFAX1Hfree6e1v2JyYQkTNRh0tDbXoUtSIxU0gde8r9+l/PoOFTV0KsBk0vzEiGH7D0618uYu/eCDAm0tmnw/JG9IiAjWmqxorcmK0bJU+6hjwbr7+nWwolW7Spu0q6RZJ2vbhx3HERUaqCtyYrTWC0FV43G4slU//LBAR6snPpjc12bHhbvH3c2KHTKuw+kytL+8WRtOu/uNjTcYIDPaqityYnRldozmJUYM2+97MooaOvXk5qJJhU5MpURbsO5cmKhb5yfIEeYOdDQMQ4cqW/XO8Vr99WTdhPrymk3uCYuvyHHoiuxoJdu9d00pberSMztK9Pax2ikZTD9ZQWaTrpvtDjKZm/C3id+bOnv19vFa/flIzTlBqeNhDjBpQWKEVqRHaWV6lOYmRExo3JnkHvvx3ql6vbDb+6FEvpYaGaJPXpKqm+fGe94LDcPQsZp2vXawctJhVjmOMK3OitbqzGgtSLJN+DU2DEM7Spr0wq4y7Zng2KjptDDJpodXZ2h5WqTnuZauPr15tEZ/OFip8uaJ95cOtgRoWapdqzPdIZwTrUVchqHNBQ361a6ySYWmDIZ4TPWYyXkJEXpkTcaQidkNw9C2oiY9v6tUByZZcw4GFl6T55AjPHhC+zAMQ/srWvT8zjKvhhFNlUVJNv3jdbnD9k+vaevRL7cV682jEwuljAoN1E1z43Xr/PgJB2OeaV95s57bUaYdJRN/nUMsAepxuqb0PTMsyKyHVmfo7sVJ51wvu/v69cr+Sv1qd5laJ1BjpESG6J7FSbplXvyYwjDPJ7+uQ8/tLNVfT9ZN+DWyhViU7QjT0arWKQ06Xp5q17fX5Q4b7Ly3rFlPbi6a8HUwyGzS5dkxumluvFZlRMkywewAScqv79DzA6/xZK6pcxMiVNHcNaXjzJam2PXta3KGvV609zj13M4y/XZf+YQDpFakRerOhYm6MidmTPkMI90zMgxDW4sa9YutJZMKSU2JDJEjLEhHq9umbIJ5e4hFD63O0B0LE8+5XnjjNZbcuTW3L0jQTXPjFRM2sRD76tZuPb29VH8+Wj2p8zgz2qqlqXZtL2pU5RTmBTjCgvT5Vem6bV78kP/Prd19emlPuX67r0Jdfb69//jlKzL1yUtSJREUBviVM4PCBjV39enp7SX6/YHKcV/0THI3NlW19syIRpvzCTBJy1Mjde2sWK3NdZw3wXo89pY16wcfFEyqUWg6mSSv/BvHhAXpmjyHrp8dp/mJEV6fIai2rUf/vv7UpIInLhQZ0aG6eW68bpwbr/iIiTUInG2wweKxDQUXXIDMRC1OtumGOXFalxfrtWtGfn2HfrqpUNuKxn8emwNMMzag8XzsIRatzXHo2tmxuiRt+JCfsThd165fbiuZcMq1JcCktKhQlTZ1yTlNr3VMWJCsgQEqm0Tj7EgcYUFanRmtuxcnanZ8xPk3GIZhGNpU0KDHNxVN6FphMknXzY1XbVuPjk5gpiZvCDSblBsbrpM1bV6fLcg+MPvLDXPidGlG1ITO5fYep57dUarf7quY8Hl4/exYxYYH60hVq47XtE95uIQtxKIb58SpqrVHW4safXbtSohw3xi9bX78mBvIDcPQxvwG/XxbsQrqJxYyFRUaqK+uzdL1s+OUX9ehveXNniT8qWzsyo0NU0a0VZsLGnwWyhkWZNY1s2J189x4LU62jbm+O1LVqmd2lE4qpGZOfLi+fEWWWrv7PMFghVMcDBYebNby1EgdrGj12iwDwwkLMmtdnkM3z4vX4mT7mK4d1a3denF3uV4/Uj3h/+PBlgA9uCJVubHh2lXSpB0lTdNSB9pDLHK6DJ8n4M+OC9fVeQ5dletQxnkCaRo6evWrXWV69WDlhN+rAs0m3b80RZ+5NFVhQRYZhqFTtR1671SdPjhdP6WvtUlSfESwqtumphEzPiJYN8+L1y1zRw4WLG/u0i+3leid45PrKBITFqRPr0jVnQsT1d7r1LaiRm0uaNTOkqYpmVVhulkCTFqX59A9i5O0MGnodfpkbbue31mm909N/KaZ5G5PuWFOnD5/WboSIoK1t7xFG/MbtHECnbxmoswYq9blOrQuL1bZDuuQ17imrUcv76vQa4eqvHK+RQRbtCjZpj2lzdMSuB1sCZA9xKIAk8mn14usGKvuWpiom+fFT7pDeX5dh/54qEp/OVbj9f/zV+U69LlL0wgIAzBjMGgVAADMFNQtAABgJqF2AQAA3lbd2q3XD1frSFWbZJKS7SFKj/5bKFhCRMg5E2yO1cVcuxiGoTeP1ugnGwvH3Y/TEmBSZoxVWTHuYLzB0YqGDBnGwFgbY+hygElKtIXokrRIzU+0TfjfbCZw9x1v1GMf5k/pIFrJPTh+TnyEJxzs7L4749Hc2ac9Zc06UNGiho4+pUaFaFWGO2xmuv79XIaht4/V6qebi9TQ4R/9sDKjrbpudqyunRU7pon/XIahY9Vt+nAgNGy4fpEmSYuSbboiO0ZX5jiUNkKfQm/bX96ixzcVuq+3fsYkaXVWtO5elKRLM6JGPQed/S7tKGnS28dqtbGgYdQ+w2FBZl2WEaUrcmK0OjPaK+Edo8mv69BPN09sfNRUSIgI1kcWJeqOBYmKtI78WhiGoeM17frzkWq9e6JObT3nfx/JiA7VyvQorUiP0rJUu8KCvBusOhhm9cz2Uh2snJ4gzLGaFReuT61I1dW5jlHP5dbuPr11rFavHaxSUeP5xwSEWAK0Ij1Kq7OitSoj6pzJsb3haFWrnt9VNuExaFNpdly4HlqToVUZUSO+F7oMQztLmvT7/ZXaUtg4pr7DibZgrc6M1uqsaC1PjRw1hHO8BsOsfrWrzG+vE2fKdlj18OoMXZEdM2q9sb+8Rc/vKh3X35QbG6brZsXq2tmxXg1ulKSTNe16ftfk+4tPhfBgs/7u8kzdsTDxvGNm8us79OTmojGNSQowSasyo3Xb/AStyYoeU9jPeB2tbtPzO0tnxPXixjlx+vKVWZ4A0pG0dTv14p4y/XZvxXn7rZvkfo3vWZKkyyY4XvJ8ihs79atdZXr7eK3fj6GOtrrHFd4wO27U68XgONwntxSraIzj4RYn23TT3HhdkxeriBDv1hcljZ16fleZ3j5W4/Uxtd4WbQ3UV67M0o1zRn+NJamipUtPbCrSe6fqx7zvW+cn6I4FCeMOxTxfu4thGNqQ36BfTGLc6lQxB5h0z+Ikff6ytPN+bihv7tLjm4r04emxvcaSu5a7Zlasbp+foEXjGJN6PsWNnfrF1uIx/3tPt4hgiz61IlUfXZI0ap3V1NmrX+0q1x8OVo5rnOhgtku0NUg/21o86rrR1kC9/rkVCgk0ExQG+JPhgsIGHa1u03ffOTnlA+snI8keoo8uSdK8hAgdrW7Tkao2Ha5sHddARZOkpal2XTsrVlflui9yvuLsd+nFPeV6envJtISeTERoYIDuWZysjy9PVnefS0eqWnWkqk1Hqlp1orZ9TOmethCLrsp16LpZsVqWGunzBnLDMPSHg1X6ycbCKQ89mag58eG6b2mygi0B2l3arL1lzSpuHH8oQESwRdfNjtUt8+I1L8H7QWyDepwuPb29RC/uLvP7Yn9Qki1YH1mUpKWpdh2saNXuUndgS2ff+AcJZ0ZbdePcOF0/O05Jdu83Ikru83j9iTp9/4P8KQ2UmYywILNumBOna2fFqqC+U5sLG7SntHlSYVrR1kBdlevQ1bkOLU2NnPBsDsM5UdOmn2wq0p7SZq/t05cGG6TuWJCg1VkxMpukkqYubS5o0ObCRh2qaJnQ/8fQwAAtS43UivQorUiLVFbMxG+Onq2v36VX9lfqF9uKfZ5U7C05jjDdviBBN8yJU2RooJq7+rS5oEEfnq7XzpKmCb9/5zjCBm5AR3v1Zn9ZU5d+srFwxsxmJLkbr+9dnKQb5sR5Prg2dvbqneO1+svRGp2a5Kw2ASYpLzZcS1LsuiwzSivSRr8xOpp+l6G/nqzTL7cVTziYL3hgVoepFGwJ0HWzYnXXokRPPdDR69TG/Aa9daxGu0ubJz1jSrbDquWpkVqeGqlLM6ImdbPneE2bntleOqPO48FZF66fHafVmdEKsgTI6TK0p7RJ756o04en670SRJJkC9aCJJtWZ0XrqhzHhF/nho5e/XZfhf5woHJGhSKFBZm1duBzxIq0SDld7tkT3j1Rp62FDV75TBUWZNbiZLuWp0VqbU7MhGY1qmnr0XM7S/X64eoJ1z2OsCBdnevQtuLGSc3SNF6BZpMuSYvU2hyHLs+OkSMsSOXNXdpc2KjNBQ3aV97i1RsowZYArc2J0a3zErQ8beyfDU/XteupLcWTChaU3J9N27qdU36DMzY8SCvTo3RpepSWptpV3NipjfkN2pDfoBofBi3FhgfproWJumNh4nlv4nnrppk5wCRroHlMnWB8JdgSIJPk8xCtHEeYrs5zaF2eY9jZZ87W3uPUnw5X67d7y/02PC0rxqpL0iIVZQ2UPSRQ9tBA2UMsnu+RoYFD3ouaOns97XFHqlp1tLpN7T3jf59JiwpVbmyY8mLDlRfn/h7npcDzM3X19evd47V69WDVpGbhkQgIAzBzXcwDPwAAwMxC3QIAAGYSahcAADCTULtIzV19emJzkV4/XD3sz+PCg5QTG6YcR5hyYsOU6whXenSoT8IELjTdff361a4yvbC7bFL9+yJDAxUTFqio0EBFhgYpyhro/goNPONxkKJCA2ULtfgkhMDfdPQ69dzOMv1mb/mYxnF5U4DJPZHiFdkxum5W3KTC2AzDUGFDpzYVNCi/rkPWILMWJNq0Jjvap+P5zndMH5yu1xObi6a0/+ZIYsKCdPsCdyhB4gRCjwb7jL99vFYHylvU7XQp0RasVZnRujInRstSIhVkmfrr2a6SJj2+qUgnJ9lvy1suSYvUvYuTtCY7ZtzjlLr7+rUxv0F/PlqtXSXNnj6pUaGBWpHuHpuzMj1K8T7oAzccwzD04el6Pb6pSBUt038On2lZql2fWpGqS9NHDq4ajmEY2lfeolcPVunD0/VD+menRYVqVWa01mRGa0mKfcrO50OVrfrxhgId9sNgwawYqx5anaG1OaOHV52tsqVbrx2q0uuHq9V8xoTp5gCTliTb3K9zVowyokN9Nkb1TIcrW/WTjYV+GXyXbA/RF1en67pZceMan3Sytl2/GgjoGq57dlpUqK6bFavrZscpM+b84ZuTVdzYqV9uK9FfT9b5/HdNxDV5Dn3jqmw5wsd3/dxb1qzHNxXpWPW5/z9TI0N06/wE3Tw33id9k4dzuLJVj20o8Msg0qwYq761LkfLUiPHtV19R6+e21Gq1w5VnTNmJiLYolvnx+vuRUkjTiDvbaUD4yk3+eE4NJOkuxYl6pE1GeMKZO13GXr7eI1+sbVk2IyM1MgQ3TQ3XjfOjfN6mOBwKlq69MttJXrrWK3Pf9d4mSR9ZFGiHh7nayy5gxx/tKFAx2uGr0lXpkfqzoWJuiI7ZsJtAGNtd3EZht45XqsnNxf55fiSyzKi9LW12eN+f9pb1qwfflig06OMEZ6bEKHbFyToulmxk55UfjTHa9r01JZi7Sj2zzDSYEuAProkWZ9akTKuc7muvUfP7ywb9po8aDDb5frZsVqa4h6/1+8ydN+v9pw3v+QbV2XrvqXJBIUB/mS0oDBJ6nW69MzOUv1qZ6lfh/8sSrLpY8tTdGV2zLAfrOraezyDFA9XtupYTfuQgAaT3Imp18yK09V5jvMOmPW24sZO/ddfT2t/ecuU/t7xCAsy694lSfrY0pQRE/F7nS6drmvX4YHX+khVm6dByRpo1uXZ0bpudpwuy4ialpsixY2d+te3T+roMB/w/IHZJF2dF6v7liZrQeK5oV517T3aU9asvaUt2l3WrMoRGuvMJumyzGjdPDdel2fHKHgKG2xP1rTru++enHSoiy+tyozS3YuStCoz+pzrhbPfpaPVbdpV2qzdpc06XNk6YlHkCAvS9bPjdOOcOOXFhU1JA5fkDs/5/vv5fp1cOz8xQncuSNS1s2MVelaASUevUzuLm7SpoEFbChvHFHoWFx7kDgfLc2hRkt2n4YKG4Q4B+tGGQtX7yQw7Z0uICNZtCxJ02/yEUW8UtHT1aXtxkzYXNGhbceOIA/TNJmleok0r0iK1Mj1K8xIjfH6Nrm7t1vfez590uIevhAWZdd3sWN2+IFFz48NH/P/d0evU1sJGfXi6QVuLGkYNPwsym7Q8LVJrsmK0Jit6QjfpxmNbUaN+8EH+hMOsfM1skq7KjdW9S5K0+Dzp3qdq2/WXYzV653itGjv7RlzPs+8Ak+bGR2hJil1LU+xalGzzegOBs9+lPx6u1lNbiiYUfjFVMqOtumtRom6aGzdq40Bde4/ePVGnt4+NPZgtM9qqZanuMKUlKXaf3Iw/WdOuH20s0N4y/6yRA0zS8tRI3TAnTlflOkY9z3qcLm0ratT6E7XaXNg4prA4c4BJs+PCtTDJpkXJNi1Msil2nDc4zqe1u08v7SnXr/eU+21wcbAlYKAzSawuy4wesbZt73FqQ3693j1Rp90lTWP+/GoNNGtxik3LUiK1LNWuWfERXgsirWjp0jPbS/WWn8+cERZk1qrMaK3NidGqzOhRz+W2bqe2FzdqU0GDthU1jSv8KSYsSKmRIUqJDFVaVKgyo61anhY5qWv0wYoWPbG5SAcq/O8G8JmCLQFammLXpRnuzh4jBbEahqGTte3akN+gjfkNyq/3zueqpSl23bskSVdmx8gyzjqvqrVbL+wq0xtHqv32OjHIHGBSRnSosmPClO0IU7bDqmxHmJLsIXK5DB2rade+smbtK2/RwYrWCYVEn/27chxhmj0wA2nGGGbDHI6z36W/nqrTi7vLR735MVVmx4Xr6jyHrsp1TPhvGuQyDJU0dg0JmS+o7/BcE4MtAcpxhCkvLky5seHKi3V3qPX2TInnYwzMUvrqwSqtP1k3rlDXq3Md+txlacqNJSAMwMzEwA8AADBTULcAAICZhNoFAADMJNQuf1Pe3KX3T9Wrs9epKGuQcmPdfVAiQ8c30BjnKm/u0g8+KNDWotH7jodYApTt+FsoW87A45HGMMH92v5kY6E25E8uFMEkdx+/aGugosOCFGMNVLQ1yP1c2MBjq/uxPSTQp2M6/EVfv0uvHazS0ztKhwTm+IpJUpQ1UI6wIDnCg5RsD9WKtEityYoed7+/kbgMQ71O16QmhPamwTCEn20pHjYAw1fMASYlRAQr0R6ihYkRun5O3JgmBx2Ltm6nihs7FRkaqOTIkGkNLux1uvTKgUo9s6NkWsc8hFgCtDorWh9flqIFSbZJ76+jr19Frb3qdxnKiQuXLUDTVrsMjkF7YnORqlqn7hweSVpUqL5wWbqumRU7qet0j9Olw5WtKmvuUlxEsBYleX9MzlgZhqEP8xv0xKZCvxgfFRcepM9emqbb5idM6tpc2tSlN45U60B5i0IDzcqLC9O1s2I1K27kMWy+dKKmTU/6UXBKXHiQvn1Nrq7IjpnwPgzD0LaiJm0udE/knR5l1eXZ0VqaYp+W19gwDL17wn298OXE4mNlDTTr86vSdd+SpEmdy5Ut3XrvZJ32V7QoNjxI8xNtunbWuWOKp8qO4kY99mGhiho7p+X3n212XLj+4ZoczUuc+Ptfr9Ol907VaUtho7r7+pURbdVVuQ7NHyaDYCocr2nTTzYW+s1Yv3kJEfrmuhzNS4iY8D5chqH3TtZp/Yk6HatpU7I9RIuT7bp9QYJSIicfwjbedpfuvn69tLdcv9pVNup45akyKy5cD6/O0Oqs6Anvo99l6I0j1Xp5f4UK6t3/P+0hFt0wJ063zU+Y8sna95Y168nNxTpc5R9j0YItAbppbpw+d2n6pEIsq1u79atdZdqQ36D6jl5FhgZqZXrkqNkubx2r0b+8fXLU/caFB+mPn12h0GALQWGAvzhfUNigkzXt+rd3T/rFgMVBZpN0zaxY3b8sZdxv4M5+l/LrO1TS2CWL2eSTQffj5TIMvX64Wo9vKvSrwInwYLPuW5Ks+5Ymyz6BGxztPU619TgVFx7sFw3BTpeh53aW6pntJX4zWN8eYtFdixL1kUVJ45odoLKl2x0cVtas8uZuRQRbtDzNHVYx1WF3Z3L2u/TrPeX6v+0lfjOQ3BZi0a3zEnT34sRxFeZdff06UNGi3SXNOl7Tpq4+l2bFhWtdnkPLUiOn9Zz+4FSdvvd+/phCc6ZCWJBZN82N1x0Lxl6U97sMHa5s1ebCBm0uaBzyATzRFqyrch1alxer+YkRU9443t7j1P9tL9HL+yr84lphDjDpyuwY3bEwQSvSosZ97jn7XTpQ4X6tB9Otcx1hWpEepWWp9mlpsDUMQ++fqtcPPixQg5+Esi1Otun2BQlalzf+Bqkep0s7S5r04el67SppUn1HrxIigrUiPUprsmK0Ij1yyhu5ep3u6/GzO0vHFXbgS9HWQN25MFF3LUwc94dWZ79L24ub9JdjNdpU0OCZ/SrIbNL8RJuWpti1JMWuBUm2KXutGzp69eONhXrnuP+k4QeaTbo616G7FiVqSfL4G7Dz6zv09rFavXO8Zkj6fHpUqJaluoOUlqVGKmaKao3Bm2c/3lioOj9Jw58TH64b5sTpulmx456ZRPrbzFx/PVmn7cVN6h8IJbWHWLQgyaZFSTYtTLZpbnzElN2IL2/u0mMfFmiznwQ4WgJMWpUZretmxery7BhZg8b3OjR29ur9U/V693jtOTMIhQYGaHGy3XM+z44L91oHipEUN3bqJxsL/SogM9oaqCuyY7Q2x6FL0iY2G9yZ9cXu0mY1DdSlqVGhQwLBUiJDlRIZ4rPgn8GbeE9tKfKbwOIAkzQn0aY1uQ4tTojQgoSICQU4lzd3aWN+gzbm1+tARauGK0uDzCZFW8+YJdMapOiBmTJjw4O1MMmmJPvkA0rr23v00t4K/f5ApV/UFeHBZi1Msml2fISyY9yBYGlRY5+p1ekydLKmTXvLWrSvvEUHKlrUMUL7WFx4kHJjw92dEGOtPpsV1jAM7Spt1q/3lE/5jfYFiRGecGZfzyjU1devxk73e3p8RIjXwhm9pbW7T28erdFrB6tU0jTyzCjr8hz67KUEhAGY+Rj4AQAAZgrqFgAAMJNQuwAAgJmE2gVTxTAMbS9u0qsHq1TY0KFAc4Ayo61DAsGmO1RnJttZ0qTHPixQYcPYQhHSokI1Jz5ccxMiNCc+QrPiwsfdV/Ni0d7j1Au7y/SbvRWT6jsXExakOfHhigsPliMsSDHhQe5QsIGv6LAgv+tLNVV6nC69sr9Cz+4s9cqYSpOkuIhgJdlDlGRzf0+0hSjJHqJke4gc4cEX1Wvd1Nmr/9teqtcOVvp0fFR8RLBSo0KVNtB/ePBxcmSIV/tb+mPt0uN06Xf7KvTcztIR+6J6k9kkxYYHKz7C/ZVgC9HK9EgtTY28YM/twfDG/9teopbusU90PRnBlgBlRFuVFeP+mp9o0+IU+wX7GkvSntJmPbmlSEeq2qbsdw4GZbonJQ/VVbkOXZkTM21BU742GAD0/M4ydU9hn3xHWJCS7e73wmWpdl2dG6uIkOkJAPQ1Z79Lrx6s0i+2laitZ2quF4MiQwOVaAtWlsMd/ndp+vjHAc8EhmFoS2GjHt9UqOLGkfvbe1t4sFmZ0ValR1uVEW3VZRlRUx4wNRETrV3q2nv0863F+vORmmHHEvlCbHiQ5/N5TmyY5sZHKCNmcpPPn62qtVuB5gDFWAOnJexukGEY2lzYqJ9tKVZ+/dSMRQu2BCh54DNJcmSoku0hSo0M1ZIUu1c/jxuGoa4+15j26XQZuue53So/TyDrP1yTo3uXphAUBviLsQaFSe4PU8/vKtOzO0rldE3ff6WIYIvuXJigexYnKcE2+cGu/qauvUf/+37+pGdzmKyIYIvuX5as+5YkX5AF/7HqNv3L2yemtAg9W44jTPctTdL1s+P8ZjYGbypu7NR/rj+lAxXTl6g6Oy5c9yxO0nWzYy/I17i5q08/2lCgt45NX0DNgkSb7lyYoGu8kPRd3dqtytZuxYYFKyUyZFqL/EGn69r1v+/nT8t5HGIJUF5cuNbmxOimufFTFsoz1dq6nXpic5FeO1Q15b87LMisbEeYlqXaddOceK9+aO13GX7TkFPV2q3HPiyYttoiJixIc+PDde3sWK3LjZ1QGM3Zuvr6VVDfoSCzu+HdG/ucjF0lTfre+/kqHSVMwpdsIRbNjgvXqsxo3TQ3TlHWyV8vXIah0qYutXT1KTkydFpDSCWps7dfz+wo0W/2VkzpZxFzgEl5sWGan2jT/MQILUq2eTVApauvX6VNXQoPNivJNv3vfVuLGvXYhwVTfi4nRAQrNzZMuXHhmhUXruWpdtlCvDMDYHVrtw5Vtqq336X0KKvmxPs+GGwk0/X6mk1STmy45idGuL8SbEqLDr3gOlS5DEMb8hv0y23FnhkmpkpYkNl9jUiya0mqXZfPS/QEsXqrA0JzZ59KmjrV2u1UZGigJxjMGmie0mtHfXuPnt9VptcOVXlCO6dCQkSwFiXbtDjZrsXJdmU5rF49h50uQ6dq23W0uk1dvf0KCTQrJ9aqHEeY165H43G6rl0v7SnXOyfqPIGS3mSStDjFrnW5Dq3NdYwrtPxiYRiG9pa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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting data cleaning process...\n",
      "Total number of missing values: 0\n",
      "Missing values dropped.\n",
      "Duplicate rows dropped.\n",
      "'NLOS' column converted to integer data type.\n",
      "Line where CIR_PWR is 0:       NLOS  RANGE  FP_IDX  FP_AMP1  FP_AMP2  FP_AMP3  STDEV_NOISE  CIR_PWR  \\\n",
      "4343     1   7.02   757.0     30.0    214.0    413.0         36.0      0.0   \n",
      "837      1   4.88   739.0    112.0    323.0    227.0         40.0      0.0   \n",
      "1356     1   6.33   747.0    293.0    311.0    187.0         28.0      0.0   \n",
      "\n",
      "      MAX_NOISE  RXPACC  ...  CIR1006  CIR1007  CIR1008  CIR1009  CIR1010  \\\n",
      "4343      412.0   192.0  ...    252.0    271.0    190.0    292.0    271.0   \n",
      "837       322.0   128.0  ...    161.0    219.0    295.0    242.0    279.0   \n",
      "1356      310.0   160.0  ...    197.0     84.0    246.0    353.0    196.0   \n",
      "\n",
      "      CIR1011  CIR1012  CIR1013  CIR1014  CIR1015  \n",
      "4343    239.0    210.0    260.0    223.0    256.0  \n",
      "837      67.0    153.0    177.0    159.0      0.0  \n",
      "1356     38.0    228.0     42.0    173.0      0.0  \n",
      "\n",
      "[3 rows x 1031 columns]\n",
      "New feature 'RX_Level' created.\n",
      "New feature 'First_Path_Power_Level' calculated.\n",
      "New feature 'SNR' created.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features one-hot encoded.\n",
      "'CIR' columns extracted.\n",
      "'CIR' columns converted to float.\n",
      "'CIR' columns denoised.\n",
      "Original 'CIR' columns replaced with denoised data.\n",
      "Column 'CH' dropped due to having only one unique value.\n",
      "Column 'BITRATE' dropped due to having only one unique value.\n",
      "Cleaned data shape: (41997, 1025)\n",
      "Data cleaning process completed.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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Zs2fj9OnTiI2NxQ033IBBgwbhoYcecti2Tp06mDdvHsaMGYOFCxfil19+gSAIqFu3Ll555RU8//zzigYdISIKdgaBw+AQERERERF5xD5PREREREREMrDwREREREREJAMLT0RERERERDKw8ERERERERCQDC09EREREREQysPBEREREREQkAwtPREREREREMrDwREREREREJEOk1hHQWmZmPrSeJthgAKpXTwqKuJA6eE1DE69r6OE1DU28rqGH1zT0BNs1rYiPJ2FfeBIEBMUFA4IrLqQOXtPQxOsaenhNQxOva+jhNQ09erumbLZHREREREQkAwtPREREREREMrDwREREREREJEPY93kiIiL1CYIAq9UCq9WqdVTcMhiAkpISmM2moG9zbzQaYTRGwGAwaB0VIqKwxcITERGpqqzMjNzcLJjNJVpHRZasLGPQF/IqREfHIjm5GiIjo7SOChFRWGLhiYiIVCMIAjIzz8NoNCIlJRUREZFBX1MSEWGAxRLc1U6CIMBiKUNBQQ4yM8+jZs06QX9eiYhCEQtPRESkmrIyMwTBipSUGoiOjtU6OrJERhpRVqaHmqcYREREICvrAsrKzIiKitY6QkREYYcDRhARkeoMBr5e/IHnlYhIW0yFiYiIiIiIZGDhiYiIiIiISAb2eSIiIgLQtm0rDBv2M266qZXkOlu2bMTkyRNx4MA+REZGoWHDRnjuuZfQokVLl/UmTBiLgwf/Q2RkJJo0aYbXXuuOG25o6O/DICIiP2LNExERkQyLFv2Bjz9+D82b34Rx46Zg1KhxaNCgId57rwcWL15kW+/Agf349NMPcO+9D2DSpBkYPXo8LrvsMrz99hs4d+6shkdARES+Ys0TERGRBxkZ6Rgy5Du8//4nePjhTrbfu3XrgZSUFAwZMgj/+18bVK+ein/++RutW9+Mzp0ft6334Ye9kJa2FcuWLcVzz70Y+AMgIiJVsOaJiIj8ThAEFJstAf1PENSbu2np0r+RmJiIhx56xGVZ165PISIiAsuXLwVQPiLe4cOHkZ2dZVvHYDBg6NCRePTRx1SLE8kXN3woknq8DuhkMmQiCl6seSIiIr8SBAGvztiJXWfzArrfZpcn45enmqkymeyBA/tRv/4NMBpdvzlGRkaiUaMm2LdvLwCgY8dHsWDBHHTp8jBat26DVq1a45Zb2uKKK+r4HA/yTuI3XwAASp58Bubb22sbGSLSNdY8ERGR3/lefNFWXl4ukpKSJZcnJSUhLy8XAHD11ddg7NhJaN/+LuzYsQ1Dhw7Gk092wueff4qSkpJARZlEGIqLtY4CEekca56IiMivDAYDfnmqGUrKAttkKjbSqEqtEwAkJ6cgMzNTcnlGRjpSU2vY/r7mmmvxxRffoKysDHv27MKyZUuxcOE8VK+einff/VCVOBER+WrvuTz8tu0Mera7Bpclx2odHV1g4YmIiPzOYDAgLipC62h4rVGjxpg+fQrMZjOioqIclpWWluL48aNo1+4OAMCIEUNx//0P4vrr6yMyMhLNm9+E5s1vQkJCAtatW6NF9ImIRL04fQcA4GJ+KcY+1VzTuOgFm+0RERF5cM89HVBSUoJ582a5LJsz53eUlppw9933Ayif4+mvvxa6rJeYmIQqVar4O6rkjt7bjxL5yckcNimWizVPREREl+zfvxcmk8nht+bNb0Jqaio++OATfPddPxQWFuLuu+8FACxbthRTp/6Kjz7qjdTUVADACy+8ii+/7I3o6Gjcd98DiIqKxK5dOzF9+mT06dM34MdERETqYeGJiIjoktGjh7v8NmPGPNSpcyXuv/9B1KhRE1OmTMTMmdMBAI0aNcEPPwxHixYtbevfddc9iI6Owm+/TcX8+bNhNpehXr3r0KvXF2jb9o6AHQsREamPhSciIiIAa9du9bjOTTe1wk03tfK4Xtu2d7CgRES6oea8eKGOfZ6IiIgoPHg5+qKVGUsiuoSFJyIiIiIJff8+gE7jNqPIZNE6KkQUBFh4IiIiovDgRQ3SX/su4lxeKVYdzvBDhIiCg1pz4oUDFp6IiIiIPGDLPQpl7PMkHwtPREREFB74dZ2IfMTCExEREZEHAvhlnohYeCIiIiIiIpKFhSciIiIiD9glhIgAFp6IiIgoXLDPExH5iIUnIiIiAG3btsKXX/Zx+f2vvxaia9eHHdbbtm2r27C2bNmIt97qhnvvbYcHHrgL77/fE9u3p4mu1737y7j77ttw//134IMP3saBA/t9PxhSHSuegl/c6BGo2v5WGDIztY4KhTAWnoiIiC5ZtmwJ0tK2+BTGokV/4OOP30Pz5jdh3LgpGDVqHBo0aIj33uuBxYsX2dY7cGA/Pv30A9x77wOYNGkGRo8ej8suuwxvv/0Gzp076+uhEIWdxL69EblvD+KHDtY6KhTCWHgiIiK6pHbtyzFkyHcwm81ebZ+RkY4hQ77D++9/glde6Ya6da/GNddci27deuCNN3piyJBByMwsn2z1n3/+RuvWN6Nz58dRp86VuPba6/Dhh71QrVp1LFu2VM3DogpsthcWDKZSraNAIYyFJyIi8j9BAAoLA/ufFz38X3utO9LT0zF9+mSvDnPp0r+RmJiIhx56xGVZ165PISIiAsuXlxeMDAYjDh8+jOzsLNs6BoMBQ4eOxKOPPubV/omIyL8itY4AERGFOEFAlY73IWrLpoDu1tz6ZuQsXKKotiE1tQZeeeV1jB07Cvfe2wGXX36Fon0eOLAf9evfAKPR9dtkZGQkGjVqgn379gIAOnZ8FAsWzEGXLg+jdes2aNWqNW65pS2uuKKOon2SAr4MmcdOT0ReMZVZsf9CPprUTkaEUf+1v6x5IiIi/9NRc6muXZ9CnTpXYagX/Sby8nKRlJQsuTwpKQl5ebkAgKuvvgZjx05C+/Z3YceObRg6dDCefLITPv/8U5SUlHgdf6Kwp6P0Jhz0/nM/Xp2xE+M3ntA6KqpgzRMREfmXwVBeA1RUFNj9xsd7lYmKiIjAhx9+ijfffBX//rtK0bbJySnIdDPSV0ZGOlJTa9j+vuaaa/HFF9+grKwMe/bswrJlS7Fw4TxUr56Kd9/9UHHcyQMfMtUCq56IvLL6SHmaOGPbWbx+69XaRkYFLDwREZH/GQxAQoLWsZDtxhub4aGHHsFPPw3GM888L3u7Ro0aY/r0KTCbzYiKinJYVlpaiuPHj6JduzsAACNGDMX99z+I66+vj8jISDRvfhOaN78JCQkJWLdujarHQxQyBAGJH7wLyzXXorjnO1rHhsIQm+0RERGJ6N79LZSUFGPGjKmyt7nnng4oKSnBvHmzXJbNmfM7SktNuPvu+wGUz/H0118LXdZLTExClSpVvI43+Ycv3aVIPZFbNyNuykQkfv251lGhMMWaJyJvCQLbVXvpeGYRqiVEITk2yvPKRBpJSamC7t3fwsCB/XDZZbUdlu3fvxcmk8nht+bNb0Jqaio++OATfPddPxQWFuLuu+8FACxbthRTp/6Kjz7qjdTUVADACy+8ii+/7I3o6Gjcd98DiIqKxK5dOzF9+mT06dM3MAdJpDOGwkIZK4XXu9l49AisV14FRAX3OzVUmr6y8ETkhYSvv0DM/DnIXvYvhGrVtY6OrhzPLMLjv24FAGz54HaNY0Pk3kMPPYpFi/5Aenq6w++jRw93WXfGjHmoU+dK3H//g6hRoyamTJmImTOnAwAaNWqCH34YjhYtWtrWv+uuexAdHYXffpuK+fNnw2wuQ71616FXry/Qtu0d/j2wMCX41OeJdCOMqgmjFy1EykvPwtTuDuTOca3JJvWx8ETkhfgRQwEAcePGoOjj3tpGRme2nsrROgpEotau3erym8FgwOjREzyu5+ymm1rhpptaeVyvbds7WFAiIq/FTRgLAIhes1rjmIQP9nki8kUYfd0iIiLShTBrtkeBxcIThYzY8WMQ85v8jt2qYAJNREQUtGbtOIuPFuyFqcyqdVT8gx9xA47N9igkGM+fQ1KvjwAA6U88DUREaBwjksLyJhHpEbOo+jRo+WEAwJ/7LqBz09oe1ibyjDVPFBIMBQUa7Vh5SUAQBLw3bw++WfKfHyJEREQU3sQGBiksLdMgJgGg0hdJVmDJx8JTuCouRvy3XyNym+eOz7oT5CnAkcwirD2ahT/2XNA6KppgxVN4EIL8OdQrnlcN8dxTMOJ9GXAsPIWp+J8GI2HoYFTtcJfWUQFQPnz17B1nUWb1MhHQKkfuxRcfq7fHGCLC++hDX8SlJrMmU6nGMQlNFec1IoKt7r3DzzdE5BumvmEqcv9+raPgoGLeH5PFimda1vEtsEB+hWEHHiIHRmME4uISUVCQDQCIjo6BIcifE6vVAIsluIv1giDAZCpFQUE24uISYTTy22egBfcdQmFLpfQ1yJPpoMLCU7gK0mre3WfzgZae13Nh99Qv2H0Ov+2+iKGPNcFlybHqRY5UEWrpc+S2rbDUuQpCzZpaRyVoJCdXAwBbASrYGY1GWK36GIkrLi7Rdn6JSL4gzfYEDZ4f+Vh4Cluh+5QMXHYYZRGR+HHVUXz3SCOto0MhLHLbVlvT1/SLeRrHJngYDAakpFRHUlJVWCzB3UnbYACqVk1AdnZh0GceIiIiWeNERKQxTQtP//zzD3r27Onw2/33349hw4Zh37596Nu3Lw4ePIjrrrsOX331FZo0aWJb788//8TQoUORnp6Otm3b4ptvvkG1avwap3/q5V5KQ3VOB70LobYBUWvXaB2FoGY0GmE0RmsdDbcMBiA2NhZRUeagLzyRCkIo/SEibWj6Cevw4cO48847sXbtWtt//fr1Q1FREV5//XW0atUKc+fORYsWLdCtWzcUFRUBAHbt2oU+ffqgZ8+emDlzJvLy8tCrVy8tD4WCiCGQtWp8EYc3Xn+isMHCtY6IpM28fKQWTQtPR44cQf369VGjRg3bf8nJyfjrr78QExODjz/+GPXq1UOfPn2QkJCAxYsXAwCmTp2KBx54AJ06dcINN9yAQYMGYfXq1Th16pSWh0MaEkR60gQkX8vMc3jj9SciIr2yWBBnKtE6FrqjeeHp6quvdvl9586daNmypW2EJoPBgJtuugk7duywLW/VqpVt/dq1a+Pyyy/Hzp07AxHt0BDCn9AMoXtoFGxYeCIKG3y16BvnSHNV9a7bsP/HrkguKdA6KrqiWZ8nQRBw7NgxrF27FmPGjIHFYkGHDh3w9ttvIz09Hdddd53D+tWrV8ehQ4cAABcvXkRNp5GtqlevjvPnzyuORzDkfSrioFVcguEc2PMmPgaj+EZ+PzaDQXQf7q6pfX9vseWCICDtVC6ur5GAlLgodeIZROwPOdjuPU+cr6v9fae3Y6FyWqe/5B9S19Vg8P5aG0TCo8ARu6ZS10PsWvly7fWi4vgiN22E5eprINSq5Xb9yP37AAC3ntiJTVXb+/38CIL49QuW6yI3HpoVns6ePYvi4mJER0dj6NChOH36NPr164eSkhLb7/aio6NhMpkAACUlJW6XK1G9epL3B6GygMYlKsL2z9TU4DkHMTFR3sUnN9H2z4o+T9HRkX4/toSEGCS42YfYNU03V379Eovf3G2n8f7vu1ArOQabet+jTkSDSFJS5fDVwXTvKWG7rgkxtt/0eixULpjeBaQe5+uakhIPePmsJiTGhNxzXmQqw/AVh3F/48vQ/MoqWkdHluTkeNu/pa5HXHwM4pyWxcWH3vUDAERWfpFNTU0CVq8GOt5X/oOC2jaj0eD382M0iO9Db+mvZoWnK664Aps2bUJKSgoMBgMaNmwIq9WKjz76CK1bt3YpCJlMJsTGls/ZExMTI7o8Li5OcTwyM/M1b8FmMJTfOGJxidi/D8nPP42iT3qjtOuTqu0z2VSGiuJnRka+auH6qqTU7FV8jFkFcB5r0WQq89uxpV76f2GRCcUi+3B3TbOzC23/FovfH9tOAwAu5JV6H3+LBYkfvANzy/+h9LkXvAvDTwoKKttXB9O9J4fzdY0tMqGi2K63Y6Fy7p5V0i/n61qRZufmFsHs5bNaUOBDmhykRvx7DL9uPoXRq45g64e3ax0dtyquaV5eEZIv/eZ8PSquc3GxCYVOy4qKQu/6AUCy2eKQn4v/YxHi7f52J9Xu31ar4PfzYxUc9xFs6W9FfDzRdKjyKlWqOPxdr149lJaWokaNGsjIyHBYlpGRYWuqV6tWLdHlNWrUUBwHQQie7j9icUns0Q0Rx48hqftrKOmiXuHJfjfBcvwVvImPIHFA/j42wWBwuw+xa2r/t9Uq2Pr2SW3vjehFCxE7bTJip01Gyf8FV+HJ/piC7d6Ty9N1Jf0JpncBqcf5uvpynQVBCLl75HBG+ce8mDITjLt3w9K4SfC0oZIg5x0iiCwL2Wfc7noJgvf5O7Fz5g9i+9DbtdFswIg1a9agTZs2KC4utv22f/9+VKlSBS1btsT27dttnfsEQcC2bdvQrFkzAECzZs2QlpZm2+7cuXM4d+6cbXkoMZQUe17JG0F6l3qdZAd5Ym8vEFE15uT4fyde0s+VkkHiYh7PKkJOkTnAkSEij/TyrhAEJL32IhLf7RGQ3U2b0QfV7roNMbNmBGR/pCIV8nOGIM0TBivNCk8tWrRATEwMPvvsMxw9ehSrV6/GoEGD8Oqrr6JDhw7Iy8tD//79cfjwYfTv3x/FxcV44IEHAABPP/00FixYgFmzZuHAgQP4+OOP0b59e1x55ZVaHQ6pRI3HN6DzPPkB0zB9O51TjMcnbsW9ozdoHRUi0injqZOIXTAXcdOnAKWlft9fqzP7AQCx0yb7fV8UnHTyWSEoaFZ4SkxMxPjx45GVlYUuXbqgT58+ePLJJ/Hqq68iMTERY8aMQVpaGjp37oydO3di7NixiI8vb8XZokULfP311xg5ciSefvpppKSkYMCAAVodiu59OH8vLFad59hFviYyISC/E7nvdp3N0yAiRBRSLBatYxByQvbDpF5qU0OIpn2err/+ekycOFF0WdOmTTFv3jzJbTt37ozOnTv7K2rBIwAPxeojmdhyMhs3X+085II+BXaeJ9+uj+BzCKSpEH9pGS+cR8R/B2Bud0fIHyuFCR/u45DNfJO+qXRj8vaWT9NJcklDTg+byRIcj43uXk5+yFCqcgp0dyL1SQjxAkW1pg1QpesjiP5nsdZRISKSTyRtFlR4uxryclH19jaIH9jP57BIv1h4IgChldfWe5+nUBdS5Y2QOhhXFZ2Io1av1DgmRNoLxTdLaKdg6ov9dTwiD+xHwpBBWkelUoi/h4IRC0/hKkhLS16nAVolHn7YbygngxmFJpSWBee9R27w5UxEOqdGtsdgDsJRVIM0PxfKNO3zRDKEWaZFlcTtUiDu5k8KFoJIp6dQTQbP5ZXgkV82ax0NlQX/PaYKHTxLRP4WqmlzaGKaRf7Dmqdgxy8K8uio5skQpon6hmNZWkdBfSxUEGnObLHir30XcDHf/0N6a4L5gOARjNeC76GAY81TmArlCdGYjFDAhM1LK1yOk/Ro0uZTGLP+BFJiI7Gsx61ehRG5PQ1CZBQsNzZVOXZe0iptCZm8getxhMqRuQiZa6YfrHkKdmGTOVNfQM6cF9dHjRF/dEkH9/K6Y1noOHYTtp7M0ToqwUUH147C1/pLtdq5JWVebW/Iz0PV++9EtbvbAmXehaE6ZojVp8YpDdG0MDSPyn9YeApXLglz6CTUehptTyymQhi+NAVBQL+lBzE97bSm8Xh37h5cyC9F91m75G0Qoi9SF+FynBT6xCZUz7JrUuxmQADN0mY/Pn966BvsHT8dVwi/n8Mx7+EtNtuj0KCjF0C49nlyd9RbT+Vgwe7zAIBnWtYJTITUIDaXSCi+f3T0fBGRfMwwEynHmie6JDgyR6rUqgfyZRCsmUqdvRCLTBato+CdYL3+aguX4yQKdyH8rIdtk3lSHQtPwS5gCZnOE5UQTvDDg06vn1gTIJ0eilsheVBEFLLN9sKlVYBKBATuXoidNhnVmt2AiH17A7I/f2DhKVyFQSoSkHTA132EwXWQI5Te37ykREHMh8RGs2dbR4lKxJFDiJ04DjCZtI4KKWBA4JpwJr3XExHnziLp3TcDsj9/YJ+nMBNx5BAsda/RVWIc1EIp1+9n7s6Ubs9iuFz/cDlOAgBE7tiGiBPHUfpoZ62jEhjB+D7U6TNX7ZaWAABDQQGK33pX28hQcCvTaXN9sPAUVmLmzkLyG6+g9O57tY6KX+l9Dit9xz7MsNkehaCq97UHAGRfeRXKbmqlbWSCCNNm+aK2bESx1pFwosr103n+gtTBZnthJG7sKABAzPJ/XJbpPj3QKnPHTKVs7k6Vbk+jhu3qOUoW+VvE4UNaRyEwdJsA+QHTFQoQPX/oZuGJgooaGUK9z/NE5MnG41m4d9QGrDqUEZgdMnNJREQEgIWn8GJfMAm1XLueap6YD3Wh17mvBI3uu7fm7EFuSRk++mNfQPan1XESqUKPX7gDFGe/PdlBeM6DL0bBQ8+1QFpg4SnYhVmmRY2hMoM+EQjy6GlCr7d5uDyfYXKYFKZkvjM0ayrrx3QmZF9HYucs2PMGQSB2/FikdHoQhvw8v4QfKpeAhScKKmq+nFYfzlQtLPKdXmuXiCiEeCqIBOMHkVDJcVLQS+r1IaLXr0Xc6BFaRyWosfAUrkI4Ma549QXiCH1tzuS3y6Cz6xuE2RV5gjGj5Q/hcpxEbqw7loX+Sw+ixByAIZYD9MzxySbANS9jKCrSKCb6wMIThQY9Ze50FNVA0dPlc6DbiCsVLsdJIUmlj0lpp3Ixf/d5TEs7rUp4IS0I00Z9fVIMAzr7yGuPhaeg568ESHDzl74FtM9TEL4gKIDC5fqHy3H6KGr9WsR/1x8oK9M6KuoIx+su4/1xPq/Up13ETp6IqDWrfQoj6GmdMQ7He9cHLvkmra9fkGPhKZyE8sOg2bGpm0AbLl7E7ev+RKy5xMeAgvDF4W6ep8DFQl3BeJ79IVyO00dVOj2IhB++Q+y0yVpHRR2h+M4Qu5cDeH9Hpm1B0ofvoEqXh73aPu1UDp74dSu2n85VOWbubTudg2//OYj8khD5MKAGiwWJ77+F2KmTtI6JPuk4fWHhiUKOnud5qvJoB7w++Vt8tmK8JvHxp5DMfodLoSJcjlMlEUePaB0FkiKWYQtgJi7i1Emftn/j9104llmE12fuVClG8nSbuQvzdp3HyLXHArpfNalyme0Cif57EeKmTkLS+2+pEHCQYZrvFgtP4STAD4Px1EkYcrIDus+AU/mcRh45DAC4/9AG3wLS2RcdjsRHQPkImV0nbMH+C/laR8U3Onv+Qp6S68FMo1unsou1joLX1H4qjbk5KocYZnT8rLHwFE4cJslVORmxWhG19l8YLiUmhgsXUL1lE6TWr6vufmQwhEK+JRSOwYnbdFKvaaj9QYVyhjlAL7kPF+zFiexivD9vb0D25zehci/44bobsjKR+OkHiNy5XfWwAyXgV9eP95OO86/aCJcTFog0TMfpJAtPpIrYKb+iSueOqHL/nQCA+J/DZI4APyWkemp6qAbdvo5YePKL4kAMBe1PglXrGKjDD/d0Yq8PETfhF1S99w7Vw5ZFjXvZl9MSZOlEkEUnKBxKL8CecxKTxIboCQu3PIevWHgKVyonADHz5wAAIi+19Y9esUzV8JXRTyKg1qTAZRYrdpzOhdkSIpk2vWDhKSC7U3Py7IDQW3wDKPLAfq2joD9BVNuhZVTm7z6He4esRkaByavt5T6Wz0zehpem70BOkdn9ikF0XVQXysemAhaegl2AbmDdv+rDPLMyaMVhvDZzJ75fcVjrqISXcCk8aehsbgkeHLMJEzf51tGevBAqGSglzyaf46DVb8khHLpYgNk7z3peWYV7N6PQu0KaHgmatP/Q77PGwhOFnFCY50npMczbdd7h/8GYAXA3KERI5NGC8Jyr5tIFih/0bfk8Rn4QuWkjbj65y+X3kWuOIaPQhFFrj/tlv34RyveCzszfdQ5/7DlX+YMKiY2g40yfMz2mvSaL/86/klpuQY8nL0CMZ88g8eP3EHHwP62j4heRWkeANOLvl7uXiQrzHOXCLUnW62h7gkjNUyhlrGwMBhhyc5AweCAAoLjbmxCqVFUvfLMZVR++DzMA3PjuTCAmxbbIqsPTGdAPOBoxW6zIKylD9YRoraMiKbfYjH5LDyHCasErcjcKswxxGNyqdhQerIdbIRyec28lv/IcotK2ImbWTGQek6op1O+zxpqncBLI51zDRCWgHR+DpObJNYDgS5TcRcmxDOLdscf/8B2q3nELDHkBnDwynJrtmcvE/+0lQ3o6qtzdDrHjxwKmyuYxySWFPoetuQDeC5uOZ+OjBXuRUVAasH0CwFOT0tDh541BPXR16aWBR3SVyS0rQ/KzjyN+6ODK3/QU/2Ah8sKRcxorVrn9aBpqzpqqbpy8VVqK6MV/wZAvMYiFCtTON0Xu3AEAMBYWuFlLv/c1C0/BLggzwSQtctNGRO7Y5nYdXlH3vE1OE77rj8j9exE3drSq8XHPtfCk11o0dxK+/Rrxw39UN8zBAxC1eyeSen3oYU0dvmADmNntOWc3Vh3OxHfLA9vf8eSlQtPqI5kB3a8S/roKal3eKve3R9wvjulV9N9/IuafJYibPkWdnXgQslkMmRcpu8iE1YczUOZUxT15Vl/U6/sRIvbslgw3UM32Er75AinPP4Xk/3vSq+0jDuxH3MhhQEmJ2/V0mNJqhoWnILZg9znklXgY7UWusjJE+XNejSD6MhbQeZ7sEk9DdhaqPnwfqt7XHrD6Nuqd3oYNjdy4AXHDfvT5uFVRptIzo5ROm+0N//coOo/fjIJS97VJ8aOHq7pfQ7HnGouP/9iHZQczVN1vQGiQHl7ID2zNk24FUWkhavs2JPb5xOE3Oc9FuFPzCr4wbTs+XLAP07eeBuD66EZcOCeyVWDF/jYNABC9YZ1X21e7vQ0Sv/oM8SOGqhgr7+jt/SiFhacglV1kQr+lh3Ahz/2XArliJ01w/ME5hVD9ZR/gB0Srwpvdi9iYafcF1k18AhJTp/0nP/8UUh5/1G/nqeoj9yOxX1/EzJ3lc1hBVA73LASa7U3echqnckowZ6f2mQSg/MNBxWldeUiHBSdAN59wy6wC9pzLc/nqrgSHkQ8veq5ZF7vy5/LKPzqsOiwzrQmiwrdSnlrFBJyOn0UWnoJU0aW22mpVC0evX+v4g683rcfttUtg9FRrIxVTVdvoFxQgZvFfiF69EsbTp9QLV0TEEe+aDjmUQVSKS0CIFJ70mrmwBvhF5pC26ThDIkrrTIHM/f+48ghemr4DgwM6xUHgr7Wid4KMc+fT1fUUfqg9C1rxdtAqlx/cfGgOoWtlELQZrFyvWHgKUupXBKkXYPygb1G9yfUwnjld+aNKiYiuMs6A9HG7Od+BTqAMQmVTOk9Ns7RiEOk7pAs+jLY3dv1xvDV7N8qCZGJjTU+7nq65HFoej8WCKg/cheRXnve46u87ykfBCmytY+DOTajdVuQbd/cDbxUN6LjwycJTCDmVXYwuE7Zg/i7/vggTBg+EMf0i4r8f4Nf9eEuzmicV0wF/JSmzdgRH0yxn9scrmM22t1yJ2RLwGhFF7Pu8KbzvftlwEhtPZHvucF9UhIS+fRC5cYM3MdSe2Yy4YUMQuT1N1uqCbuvu7InfC4bcHBjPnvHrniN3bkfUtjTELJzv1/3olo4zbFTOr5fQ+X0TSveLu+4EBg1yTsH8bveAhacQ8u2yQziZXYz+/xzSOipeC6FkyjdqJip2YZWUWdQL1w/iTcW4rHE9pHTuiIyCUrQbtg49ZrlOnBqUvGy2Z/JQ8xQ/7AfEjx6Oqo/c73XU5PBXR964X8chsd+XqHr/nbLW11OzW0kSz2/q9VehevOGMFy86L99B8OgLWHOYhW87wsmllnXcSZTMyLnsd6uDYgfMojnk3zGwlOQk9sKtaC0DFtP5sgOV5U+NX7oLK9GKF6PticIiP5nsbJ+QV4025Ozmt8ykEb5j3zU2n+R8kQnGI8dVWXXcuZ5and8O4z5eYhetwZL/0sHAGw9FcB5m5RSYZJcT7dK5CG7jyGWICn8KnjeI/bv87xSKH3dlSFyj04+CPhF4K51xbOo9gis7oIrMVvwyC+b8OECiftexXvdkJ6u+iAABrNGo5UGwMv9uiNhYD9EL1oouY4AINZsN1AXC1okgoWnELHkgMpfMgUByS8/h4SvPpdex+1LQH+j7UX/vQgpzz6B6jc19i4AL16KpjIr/j2SiSKTHzPFdudGSQyrdO6I6FUrkNztJfXjJMXuMiafP43kEncT7AWBAI+2V61NC7/VLDCPoCINTiavnw9UPHmbTmTjYoEJ/0o1x/VyXxar4PKeSG1cD1Xva4/IbVu9ClNMVNoW9yvIfYkE8Q0Zcfqk5LKonTtwYEhX5YHqvNCpyUTSQXyPeMLCU4jweA96GJrcefPIbVsR8+cCxI/8yee4BZq3tTZR69d4sTPfMs+DVhzGB/P34rNF+x2DVR4TWQxeFPCM588r2YH0IoVH9dJL92HXT0+hXoZ/Rwj0iUNnLf+Mtmc/Kl3EyeMwnlen39rpnGKc92UqBF9rwUK5tkkHmQLt+hJqfG5KSxE3bAgi9u31S/D+Oq3PT92GO4aLz/MTtWa1f3YapJxTDjXv5cTB3vXlTr2qpksrDePZM34f4Za0wcJTiFA7vTaYTDJWCrHMjyHwj8M/l5qmrTmaFZgdBuk1c1eoWz6+u7ymX3byS8rw87rjOJ5Z5GvU5AvQJLnJr77gcxiFpjI8Nn4LHv5ls+03JbGO3rAWqddejtipk3yOS/nOK/c+ZeZnuDrztJuVdcAfw1GXlHhVYC0xW/D6zJ2YtNkxE6eD8p1q7D+oxQ8fisR+X6Ja+1u8D1DtkycjvIPphdILgzRdD4RzeSXoMHojxqw77lM4si+pxIoGiwXxI+w+NpvNqN68YXlLFp8nPvYQOR09zA5R1fF9y8JTiPA87VLAB8gO8P7seJuQREQo35Wf5qrxVxW6XpOqqHX/Klr/h5WHMX7jSTz+q3rNWURpMElu1NbNnldyErlrBxI/fg+GjPKJINMLRD6OKIh+8msvwlBcjKT331IcF0+uyT6HH2d8pXq4YrKLTJi29TQyC0pVDfd0dhEsPkw868xQkI/Ua2qjyr13eF7Z6T6cv/s8tp/OxYg1x7zYsV5TjHJij2Tkzu3KN3JexctlUiI3baz8w6tzHrjrFGx3xJh1x5FdbMa4jdJN8VyodV+7a2lRXPnhzpjlYURVD8yW4CgcCYKAFQfTcTbXrsWC2YyIo17OE6ejQp8zFp5ChMeRfTw029NUQZD0a1EwmIKNF4mw1CZHM918WVTK/vra93mS2PfOM7nYeUZiYIYAZKDU3sPuc/kqhyhBhdl9/f3hI7HXh6h6z+2I+3U8kj5699JOReKhZXMqp2Oske9bZkOujxbsw4+rjqL7VHU73e86nYN5Kk4ZEbVhHQwWC6K8GGiitMyHPnJ+eU9onP0We55UTOM8njGRc2rM93FQnAAWct0d3x97FDTxDjKy73S5H8wCmcfyx/UXif8//6Xjk4X78ei4yg94KU8+hmo334TohQvUj0MQY+EpRGiT7XFX6yIvRvHfD0CNay/HfQfL57DxenhX8Vgp403hSYoXx1Fsrszk+Gu0PbHmcaVlVrw6YydenbETxWb/DVzh7rqonfarcR/JIuNFuuVkNmZuOyM/TmXqTmQcN36s7d8RB5Q1f1SFxMWNPCL9tVLt0dGk7DybBwDYfFz9ZrPbT7vJEOu8RkfXgvDU/7DyCErcpb3+aAaqshKzBd8sOah1NCp5e06Ufoh2XKhuXCpC9bS93HeLh/U8BbNNJE2LXlveKiTu13Hy4hAiWHgKch4fmor1FAccHEOVJ1yaaLff0lG+xUWF4xF8LDzJHVZeenhyu3+rmXn0cG7sC0z+HPVPzq2sVqFRxRZT8kmc5zdn7cbglUeQJjHkun2NT1LPbqh+wzUwZAam5iUgJM5L1CbvJ/01HjuKKne3Q8y82V6H4VeC4GEw0sDdoFLRUCsGyg9F/WP3prZH0XLfVvfoVE5J5Yi53mS0g6DwVCaS6FqsAs75MiiNDGpdCq/OoLvz7qe5GgPG13tKzvbB1AJKIRaedMiQnYWYWTOAoso2tQH70q4DXvcXMmr/AqoQyJqnYBCcsVLIw3131kMmwnjuLGJ//w3GvFzE/jZVzZhVcjfDfAAL7J54uv+TPnwHUbt3Irnby+U/lJWpegAWq4D+Sw963QzJgMA/a1JHbzAAMeZS3H14E1CosGlwkKYXQc2L+1CAeOGjwu87zrgPwIfrpPQ+VbJ2n0X78cgvm7FU7alU/ED1N679faDic1RaZsWzk9Pw/XK7mnuVwlergkv5yvrDwlOQErvvJmw8iTKLFSlPd0Fyj9eR2Psj3wJ0t1jOja/iVxe/TQqriIoZBTfHL3Xa/JZPcejzFPqZoTIrcCbXv187Rfn4skjoLzFIQoAumUvsvTwe4/lzqNbsBsQP7OdznKQYCuz6tJlMqHZTY1R58G7Vwl91OAPzd5/3uhmSQfBQD+3P51Dksn27dCTGz/kGyT272a0n4/rqpM+TohC1TgNF9+8+TiP+Pe5FmOq742gaeg99B8aTJ2RFYfnB8gFqpmzRYPRMObOy+3Efh9ILZK2n1MpDGTiYXojfd5yt/NEfz2kgCj9aP4s+CJrC0+uvv45PP/3U9ve+ffvw+OOPo1mzZujSpQv27NnjsP6ff/6Je+65B82aNUOPHj2QlRWgoZ41NHrdcfy+4yyitqUBAGLnz7EtC7pCvoYPhdcFsQANGCErWH+NtmcX3aUHLqL77zuRVSRjWHp/k3ke3X2drbD9VI6PkfGOzx8AzAG4DkruK4X34C/rT+D1GTsQ88P3iDh/DglDBpUv8GZQFU/n0i7MyN07EXH+HKLS1BtZMbdY+YSXMbNm2P5tgIdmewHWZc8KAEDMoj80jklgid1FpX4euczbJoRB9w4XMWlWX9z431Ykvd1d66jYBON5yysx443fd/slbH/Oz6Y4aF8nbA/GiydTUBSeFi1ahNWrKyd5Kyoqwuuvv45WrVph7ty5aNGiBbp164aiS83Udu3ahT59+qBnz56YOXMm8vLy0KtXL62i71fOt9aRjMpmF2aLFQWl5Z3LFT9QqvR58j2ICnL7C/mVxgNGBOIM2O+jz6ID2HoqF8P/9WIIY5XJOfZFey/g1h/X4N8jjv2BjEePIO6X0ba/T+cEsNZJYlRDMbsvDU7gLggbDXLevtY8jd1wAtvP5OFUttPcWv54Qap0ftod24bJMz/HFblOzYq8CD+5x+uVmwsBqOU1mZD8wjN4casfC0TBVAL0gX1h3H5gHlG+DlXu7aib3m1WLsCXyZgeuGZ4MTOmIfHj93zPqHugZteH9AKT6wegggKkPPkYYidPVG0/Nio8py4hiIRp3z83at0aVL/+Kp/3q1eaF55ycnIwaNAg3Hjjjbbf/vrrL8TExODjjz9GvXr10KdPHyQkJGDx4sUAgKlTp+KBBx5Ap06dcMMNN2DQoEFYvXo1Tp0KnZmc5TzGFouAwSuP+GcPAf4iUJHQSO01fsggxE6fIi8su7grKlR6Mc8TAD917vfT+RdJEPNLREZ382IyTl94SvuN6ReR89kXqJWXgQ/m73VYVv3mFkjs84ntb+eatJ1nct2PZKUWD/fa/N3nRef+8XSlDSXqzkEkm5dpQGCmJFEntzjl9y9w+/HtGPzXj6qH7rdme4KAiH17Efv7b4j5+098uXys523EgvE+BrometzBVEjU6YARBj+V4JLf7o64X8cj+u9FXodxTdYZxJTZvRfcnC/fis52u3Dq8xQ/dhSiVy5H0ofvyNreKbTKvYvtXoManJRnH4cxX/yDYDjQvPD03Xff4dFHH8V1111n+23nzp1o2bKl7cudwWDATTfdhB07dtiWt2rVyrZ+7dq1cfnll2Pnzp0BjXswqPia7euz49XmAZogNGL/PiQM7Iekd3tIriM1yaVzRtsd2aPt2R1r8ltvILXhNYj6dxXUzJ6LjbZnyM5C/A/fibY3d8s+LJFjFHuPpHTu6H4FKe4mDZQRjlRzxYQfB+Pddb9h6szP5Mflkldn7MS78/Z4XtFXcr5Ye/GcxCz2PtPgEy+faTlfMF03Upbxikrbomh9T2oUZMtaL+Lgf4gfPNCxz5UIgx/r0uN+GY1q7W/xywTF3vBntm3U2mPo/ed+VWsFREew1bj5kGAwuI2CkmasgWI8fw4JfT5GxOFDostjli5G22Pb1dtftnddM6JW/IOVv3TDnKnu+4jb3k9+mBNTgAGGPF8KGirdn35stu0VNtvzzoYNG7B161a8+eabDr+np6ejZs2aDr9Vr14d58+Xj3x08eJFt8uVMBiC4z+xuHiMOwQIl4bEdb4FXcNyf5Ma3OzfYCjvA7DuWJZLTY5tfaff5J5jl3iIrGPMzfEY1vsSmeO1R7PkXwe7goXb9UTOZfzwHx3iULGO+PbixAoX9tslvdsDCd/1R9UOd7o9j2LXr4LRw/KKZdEb1knGw+11dHvexI7Z/Tlxdl3WaZf4yJF2Klf2c+jpmO2XOawvWD0/C0aD6/mH6zkQW0+KomOqiJNouILH+1zMl38fwKK9dmmv3aiVYudD6lw7/C0x1Lf4MQkuy+X+Jxa22PNR8V+1tv9DwqBvkfDNF27DMgqC2/35Es+4ET+5nhiUN6lxvbcEGJ0CkLqvPMWz0FSm+FkRPRYF52HiplP457907Dmf73Y9QSIuFYUu+49R9sVauddG7GzLuV6iyyWeq8rzI7pD9wwGt/ExGICk7q8gqfsrMo5N3jlIfvUFxP/yM6p0uAsGg/hgtVN//1z6PCi+9+2ec4fIOsa7QrHZAoMBiPttGgCgyQXHVjou4buZXsAxTo73j8N6guszWLmeOumU2PMDpzjZb+KYHsi5tytXdruuSMHHYV27Zpbu9lOxzJd7RO3/5IiUt5r6SktL0bdvX3zxxReIjY11WFZcXIzo6GiH36Kjo2EylVe7lpSUuF2uRPXqSYq38Rf7uOQJ4lcwNjbK8QejAampSYhPcDwfqalOxxXteKmjIh2bqCUnxzlukxLvENbDA5ZDOHUav++ZZvs9Li4acRXb2IWfmpoERFQWRFKrJ8q+I6OjIl3jnhInfVyX5Ng1PXPek9Q2LpI87weAaNvr6KgInMiprP2quJZi91e2RBVV1aqV57ziBesQj3VrAADGjAykjh4KvPEGUKOGSzipqUlAQkxlXKol2P4dlxDjcmxRkeXXLqG0CKm5F1G9bkOH5RGX7jE5EuJjkCCxblKy69f6inCrliprzy77mvqwjbv1bdfV7jmpVjUBSE1CYlKuZBip1RMRGeH4zSoxKbZ8vZjKZ3vZoQw8KjO+So4rIsKI1NQk5Iic7ri4aMewSuU1F1y07yIW7avsAxHlnBbYpVlScU2tnuj0i/g9J/ZblSoJbpfLYYDjs5qYWFkTJRZm3M7tlWmfGEFAvPP5tJOSEg8oiavdfRZx/pzoKpEREeX7c0ovE+zSgorfAKC0zCL6u72kpDgkpSbhmz/3YfzaY5j6yNVoW8UANGpkWychIQapiVFAdLS8fqP27waZ5yA+Mda2blahCd2npqFryzp4vNWVAIBIu/eZfZinnfvfObGtm5vo+FtCgsQW5WJioiTjnni2Mp0TXcfuPWMvoSJtThZf7k5iUhwS3Z3L9HRgziwAQOzPo4DUVNuimGjXbKCn6xIZGQFsKx+gxZiXi9TUJBSZpCf3joyM8PrZrJBkdx9HRlVe79gY8fRlxrYz6JGaBMS4Hl9iYqzL+YqMKo9jmdP5cHlW4yrzWknJ8Q7rxsZGITY1CVkWx3xI9eqJDtspPRf2KXFyUmV+2RZOlNP9Hx/jug6A6GiR/JUd+2xafHwM4p3WjY2tPAbnfFZ0lNM1tssn2f9uX/iMjDC6zSsFM80KTyNGjECTJk3Qrl07l2UxMTEuBSGTyWQrZEktj4tTnuhkZuZrXnNoMJTfOPZxycoST/RLShxHgSorsyIjIx+FhY4ZnYwMx4xqktkC+1eo2VwG+2JYXl6xwzZRuUVIsQvrXG4JJv/9E646XlkNX1xiRuGlbVLMFlt4GRn5qGKx2m6ujIx8ycJTqtPfJlOZS9wjc4pQReK45JC7TVyRCRWvTLfbWCyi8baXmZmP6nXiRO+v7GzxuVZyciqveUXzNft4VLMKlVXFX3wB04qVyJu1wCWcjIx8xBaUoCI7kJlZgOqX/l1SbHI5NnNZedw3jHoRyUOLkL12M6raH65VQLaHc1hxPgqLSlEssW6+yDxHZ87n4lxuCUrKlBWe/tlxGofTC9G1eW24Fh/FeboPsp36Somt7/ysRuUU2p6TrMx8WBPyUZBfLBlGZmYBIpw+0ebnlyAjIx+JpjJUvBZ3ncnDbZe2db7XlB6X/faWMguyM/KRLZKpLCpyujeKiz3uW4zZ7lpmZOQjscRsO66Mc1n4L7sUtZJicJ3dNhkZ+Q77MkAQPa4MkXOSk+t9+mAfTmZmPpCeAaF6dRQWVN6r9mFWrF9WZkWO076c419aanaJT8U6ublFMCuIq316LKXMYkFGRr5Lein1bjA5PXNix5mfX4zSjHyMX1s+qEzb25oAALI276jcb0YmhMQGKLuxKcy3tkXkzh3ImzUfiIrC0YxCZBWZ0eqqKrb1Xd4NMuTlFiEjozzj9t2yQ9h0LAubjmXhzqvLw7XYFQQrwjQYABjd92OtWDf+x2GIt/+t2H16JHZtbXHNk37+ASAmvxjO2UQBQEFBeToQnVeMZLd7d1VQWIoSqXS3pAzxOTmodenvzIx8CHa5gVKRQo+n63IutxiXoTIDnZGRj2I3E6yXlVm8encDlffiij3ngPgrysOz68NqnyeyT0cEgwEZGflIKi2D4+eD8nPtfL7KzOVxjDeVOWSMc/OKHZ7V+KJS272S73QtS0rLUJCR7/KOz8wqRFyxyfEeU8D+01JevmvalGwqQ7Tdb/ZxtD8nJlMZ8tykW/Z5laJiE4qc1i0pMdmt69g02WS2VIYtCA7h2h+vfRPcMosVuZn5LvlfLVW84z3RrPC0aNEiZGRkoEWLFgBgKwwtWbIEHTt2REZGhsP6GRkZtqZ6tWrVEl1eQ+QrvCeCEDzNLu3jImNEZtt6guBaGeJyTB4CFJy2EezWr/j98rx0x40Mle207UN3aTJsFWRPQOscD5flMs6Lc78Z25+C4LYGTDBUfhF1ux/RDptOf146f+L3l3gcHPqXyohH1OpVosud9+k8V59UfJJN5RnqqBXL3MbNLYXP02szdmLf+Xy8fmtd+Rtd2g4ArqwSh45Oy0T7NMB9vJYeuIg+iw54XL/MzXUVrEL5727CsAqA0XkQptKy8oEkpO5bD7wZaFPOfSM7EfK0L7u/q9epgT6vjMaZGnVwyGk9sW2V/uZLWh65cgVSHu+Eks6PAz0q56gS3afBQx8VofI+EaPkvRO5YxsSP/nA84qCyLNvFVySK6l3jKx7oiJO29KAS9nxOjs3w2CxIGrHdkTtKP+4FrV8GUz3P4Anfi2fVmP2S61Qt1q8LZ7u9imu8nznl1ZmnOXeI05BuawbP3KY42+XfjdbrBi/8aToPtxdW7fxc5P+ez9Sn/j9mFNsxr2jNuAGFGKxxH7EYuMpHgWllvJ+PPZheoqjj8nJ8v8uAi0uhWUfrpt9SMVL7PmVzHsIguM1dbc/VJ5f+3yIIMDhgfPpXHi6v9y8gzzlrxxYBdHz6TZql3aQ0vUR2dvZ8pA+3P9a0KzP05QpU7Bw4ULMnz8f8+fPx1133YW77roL8+fPR7NmzbB9+3ZbCVUQBGzbtg3NmjUDADRr1gxpaWm2sM6dO4dz587ZloeCileeVEbQtl7FOQrmsZOC4ImIXvwXqje8RrRgYCOzgOfr8ah1rWTPBRUE51/KvvPlX6QW7lHeXxEATuUUe15Jhp9WH/W4zu/bz+LWH9dg/ZEM8RW8PM8/rjqKnrP9MyeIT7w8HndbGaxW9NgwC2ZPQ/Ip2bcq97eAuKE/AABi585SITy4H31MbsN6AFXva4+I0wpGknU6H/L35F5Ksd1XaPs+FmLn3+zYQuJ4ljrPqVIVUVM0B5vd8czYdka08OQPPt3FEvfT9tPlTYgzCn2fQy6pVLzFRAVvH8OojetR9ZabELVqhdv1lN7HhsqL72Ld0Sz8vv2MwhDFduIUuMRJqNL5IcT/PMKXHblf7OXouEaJJsA+KSlB9JrV6ocbZDQrPF1xxRWoW7eu7b+EhAQkJCSgbt266NChA/Ly8tC/f38cPnwY/fv3R3FxMR544AEAwNNPP40FCxZg1qxZOHDgAD7++GO0b98eV155pVaHozq5H30rVvOYcCn9hCBn3waRT3hyWK3qz9lgt3+xF2XK80/BmJWFKk91lg7Dp3mefD+famVy3DEaREbbc/5B4lAMebniC2Ryl19Us3znnJmrUpyHy/N8n5fk+xWHYRWAd2fsEF/Bh4PYcjJHUYbaH1xi7+3xiNxjDouD9EOPwSEN8e1aOPXh1o6ba6h0BLudw56u/MNT2q/yBxupU3k4o1DWxNn2lIyDeDTTfZ8pr/hwbr5dPBy//dYLRmtgp5IAgOe2OY766enDbuWK7o835dEHEHnkMKo80Ul2XOyfT6n7uMdG6Y8gG45n4/sV3k7zIo99Ohd58D/1whU57dEb13sVVvJL/+fwdy37+e78lIA5XK0g/rDrieZDlYtJTEzEmDFjkJaWhs6dO2Pnzp0YO3Ys4uPLq/1btGiBr7/+GiNHjsTTTz+NlJQUDBgwQONYq0zGPWUQBNu8Mb7egnJepF4/SvZhW62o2v4WVL29jfwClMIHrE7uRSSUKn/pCR7axyuKjxeJgsOoOD4kKr9vP4tZO86KxkVOepjYt7dLxGIWzEXqdVci/rv+XsdLitFqscXRH8n1jmHPYP3ol2HIkKgxgmMnVkXUajMWYGK1n87R97qQExSlBqUUxtnD6mJDlas53LYYqdD9cjkchymTsYFvx+48YliFpyelofef+xWFJTvT7y4MlS+lYLB72kTiV/E+eGbnEtxycjdannE6ZqmmyirGMcri1DdK5nn0lI748q4LNhfy/DUnn0pN/pw4T/nw4oY5vgcaQtfTHc36PDkbOHCgw99NmzbFvHnzJNfv3LkzOnd2U4ugc3KbdtnuU6X3q6zGq94xnj2DyP37RJcZsrIQeaA84XeXmfXFpFl9URQVg0bvK0wIvJjnyeFnmbuR81Xb6MNr7/sVh/GcxIAjYhkHWQWqj98DACT88B2KPukjvaJTYHE/j4AQn4CS518SXT3GXIrl497A0cuvw/OPuglX3u7cijywD+a2t4uHI/JbxJFDsNaoCSE5xeF3yUyqmlkVfxVAAtEcTkbBQrV9qUbdBvflQxY7nohVhzPxhGp7kMlP59VzAcRxv6rWLDvteuWhDNxY2/uRuvJKzLIHnang7nB8PdbIXTs9rhMhOH14DIIPFlJ5FqNaF18iHK8/fCncj5izuSUOg3tsOJ6F5BPZqJ4QLTpXo9bcF1QFx7RZZF0BQNWiXBRFxwVBmq2toKx5ospme87fLx0z3oJt3iVN+jxJJFoJA75x/EFWTY0K8bETb/biC5BPzfZ8568Z2h2bNCoXcea0V/synjuLxC96l8+oLtEmu+2JHaiTl47bD2zwImb+c13GSVS7pSWqN77O88oV/FQjGTjqNO1V4z5WVBD1R8bRm170Tps7B7H+mHeTfGpK4tw6dllzXWfX6RyHfjZa3/UOnfedlimZSF0Ob4+1omYyfviPHtd1zphnl0gPEy6yIyXRqtzM7l5Q0vRx509P4f8WT/Rqn/ak0gRvanTFtrANXKAgnNHrjrv8tudcRd9Are96Dwpd+7B5KvAlZ2dg+/Bnsfbnl/0UKf1g4SmI5BSZ8f68PVh9OMOWIDgnGM6FJNl9npyokoeTyrQ4dRaWNSyRO4HKcKrUhlv2OhqQ1edJJQa7xHnR3vMB6dOllnaXhuQ3iMx1JHk7qzA6na+MR4/AeMG7wTdceHsPexp4RUa4MWVmj+soCU8plzlMlv6N6k2ul7/9pQmT7QXq7lC1BlTi3G4/k2e3jmvz6xlpZ/D81G2ydhG1ZjUid7uvbfFUu3BOQXMp54z/Dvtjcd6v7FDlM2a7FqKVFEYqtrC37L90ifW0l2gqxrNLJ/kcTjA271OjCagosxmxE8ch4kj5mKTK7w/3Yub8jhrX1BZZYneORY7t+r3lzfxqFOYEbR4nUFh4CiLD/j2KNUez8OGCfTIHjDDY1Twp89/FAqXRE9m9QfzfXoXl2+aq8LXmyd9NJ5R8+ZZa19/nWeIcfL3koLzN1R7IxGGBdNjuLl2vhfL6VHT/fSdyihRk+t3w5mVpyMhA9ZtboPqN9d0FLGnV4UwcTrf7GhlGL0dP913K/z0JY7qyQUdcMvwiuzBcuIDoRQuBMgU1B0q4GzDCh2BNdlVPUl+r0ws81zwZT51ElS4Po+rd5fM9xk74BdVaNoHxqLLO/EpGk1OS4fV0jgRBACwWJD/TFQnf9L20jfutEvt8Inv/clmVpBdevqdcjkreQHOqqdjdHUfT0GvYu37Zh/HUSSSsdDMib4DEjRmFpE/eR7VbWvol/OTur4r+LpUOGi5eRLVWTXH/gnF+iY8esfAURByaOdgKRe4Suspx+JVWXfu787LTzmSs49suVCkT2BeepBKR9HREiL3Y1TifwVCA9JW78yDy0lb7i5o/LDuYLl0osjvczAITftumoImjB9O2nsbec9JfxZ1FHvJtRKeT2cV4enLlFBDe39PBf01FKRhYRYDBZZJZewZBZMAIka+61drfjJSXnkXchLFKYyubwWBQlrn2B4l7KeKU4zDgSZ9+gIhTJ5HU+yOHd5R97JUciT9fc0czC9Hh541YN2EOYpYtLW9uV+T96HxK4uoyl2EQ9Hnyt4pjnjSrr8INRd47Euer6n13eA5P5oXy5UNg1GYtmrFLl4bjh36PiJPHUevcCc/BBHyaCW2w8BSk5E+SK7fmSY12fc6/eTFUuZfzEQSCIKPwlNq4HqrdcbPHsPwyHLOXL0j7uIg2f5ETbpAmckrOSMyfC1CtRSNEbtmkOByr5LSM9mEIHp9bJWdx6OqjeHH6DgVbyCfrcnrdbM/5B+d+m/r338UC3DNqveQw2WJDlYudTmNmJgAgeukSlWPouFOvM9dSo7jZpZViZ8C1ublCZRaHbRyi4cWxKNkiasc2VL3lJkQv+VtyHUEABi47jKwiMxZuq8xQJnz7leK4qcGb6ztx00l8tmi/D2+qwD7Jar5TpQo2Fc+j1+H6tLU76r5/xe8XNy0zgjjfphUWnoLU0Uz3E9JVsA0sEZx523KXIhdx6CBSb6zsNyCWGGp6HHYJyuh//TsHhOjuPa2gwskR646iaoIv8RIX60AP+OeLqdSLMW7iOEScOY2U/6sc86zfkoPo+/cBReFLtoisyKjaLTdbvJvPTPDmtNjtuMwqeL3vyvC83dD5taLfPoJSLAJQbLYit1i8RrK85ik4iokGuO8g75bkSu6HKve5+a3Bf7eEp9rulKe7IPLIYaQ896TLmo0uHMXLWxbAaCkTbb0RtX6dV/F2GKpchHM8ps/8DB/8O0VGwNKhjlp7HGlpB1F97gzEeDPAUpD48NvXEWcq8d8OFF5Qv4xfYxcFVQYHk+iiYH8XnrHvRxgGNZtKsfAUpPadF++TZP9CNgjSNU9Ttrifkb7p+cM+xc9esdmC9AL7xFf84Y4f9K13OwhUxiqicp6nKZv9N6u8VDKkZvrkMDKSyHxaDk2O5JxfHyOXUlKAuHSVBjLwlak8w1toKsOCPefx176LSHfqM+EugyWdnXRd0n74OgUR8/EGsLuOXSdswf2jN3osQF2RexFViiWaBno7kbWHe8WfHb+jVJrZ3vdJcgVlA0aoeU7EhhhWOfNjH56ckOUcXkGpY78vh5onedGSxdOHCbFBYir89evb+GLFL7j93/ni23o7kh2UN6V/a8PMyu29vL6zp36MwX8NRa9VEz2u67yPQDcVlOpbd/2hnXh6p5uaW5nN9g6o0Q/cfrdBMtpe4kfvuf4oVXiyuwdXHkzHrrNumo073686+wDmKxaegpStUOTyAhac1rv0u9N9O+zfY4r3ue10Dj6Yvxdncx2/4kh2xr2UAA1bfRSnc9x8+ZGcFynIvmbYJSi+zk1h8fWrv4/sE0H7lkXGS9fs50tDrN5zaBPGffow2h7b7tf4bBv+LB7udCuqFuX6dT+yXDoH9pfYufWV8/WXvB1E+mXYv5dNFiX3kWD3Ly+eDbu4nMktwZUnDyJjs/N1rVwntTAb635+GTuGPeMxPN94+Zx7UXir0uVh7/YFeHW87vKPrn2epFn8Vs1S8XFNrL+hSh9NROIu+9jtwn984lbpcC+tdzqnGOuPKh/yXe2M7JWnDtn+HQzvMW+P7uqccwCA+w5tdF1YVISo1StdR8+VkF/qp0FPLnF3DaOsbuLo5tnqI2eCZbfPgFOB0n6JL7ecS582+z0qu9/iJo13DVey5snRgQuXCpRKCsphUohi4SlIWWXcgAaHiR19u2EFAeg2cxf+PZLp0oyp0y+ufUTKI1D+QG08ke0SM5fAxTYPki8zNvaFJy8ybvYv0T92X3Cznrrif/wer2yeJ70/kQz+kgPlI4eNm/sNUgpyMPX3z93uw6yoECCtfob7Gr1A3hMO/VWc7tEvVvwiuZ3UF2KxZnsBZbfjhNIi/PXr22j66J2Sqze6cFR2eIp4+rIv8xon9ezm3f694BwnnwcPFUTCcHM+t53KRqmbASh85VUzUHccWu2JNgp0/EukIOTM+SOdWKiPjd+CbImmklpx6d+lxfMvMgWFElaRa5Lc7SVUefxRJHxd/m6wf7+J3U/DViv/YKuImxNrUXj8nfauAgAslTPEu5JRbh3+7b8PIsklPtaSSY0sLIing6K1jM7nJcya9rHwFKTE+iGv+fkV1N+zSXQ9jwNMKEgAzjvNmeHppe7tI2M8ftzLLUXioMpod5VHYhSZu8Qtp/2vUzohpiAg9pjyflbG8+eQMOAbfL5yPKIl5sZxLDx5d56KTfK+Km46kY1tp3NgtlglO9M7xM2r2PjIYEBusRn3jlJ3RCM559b9bapes72qUk3xUD7s7JVD+uOqHPFmlPGDByJ23M/ev/x9zMhViJ090/NKgPRJLSwUH/2stBTJrzyP2OlOfUYUpCENLx7Dp6smIiJP/DyXN9uT/iotltG4kO9bvxPRD262Uf8q91diVtD5W7LVgHtu0+MANBOOOHQQ0Yv/kqhd8z2TJ3UEkXt3o/Hv4yWW+o/vhWPXAGIuDZgRN158JMjIkmKHv49lyeun7S136ZHFGCG5TEzLswfQ2NuuC4LoP124i2/ktq2Sy5ytOJjuEFL7vj2x66envI8/IJpGazKCo45rqVh4CiYOH8Jdb6orcy+g53c97VavfDx9vQddNlfytcXjuL7ibQvFJgvUkv0IUkYfs/VncoowecNx0T4nYmfrrfUz0OzBtsp3VFz5AjMK4pkixzKMfxPE7Wfy0G3mLjzw80a8PXuXX/cFQNEwtPbbLD+obFJJOXeDQShfLxg+wElGQRCQ3P0VXDFuBPr9M9plcZ2c80gY9C2Sen/sQ81TEJwAsxk1rqmN1GsvdxnhM3bqr4hZOB9J7/bwOvgYixlvbJqDGt9+IbpcdKhyP+cTjmSUFxSjNjl+FKh+9ABiLJUfVu4aud73+ATgGtvHb8SaY9h+Wn6T32q3tULyc08hZv1a13D9HPf/jR6IVqf3KtpGMBh8uh6+Nh2Uc07s12kg0oLA3/e3u2ZwbmueJI7tylzp1iH+VrXDXe5XsIvzJwv3o8yu5cfVa5YCAF5O+8O7nQuC48jCUlFQ8YJe9PHDUDBi4SlIyRmq3AABNQqyEbV6JQSlNSVO3LV/l/yC4oeXkH08DHm5iBs7CsZzZ1Xfjyi7BNjXhONcXim+WLAXk7fIm/fng7XTvNuRfcdtGf1yxEbbU1NF8LklZbhY4NpXzvVe0iaj7RwL73omOao4NjXeOf87vRf3HJJoLitF5o6dM9f2EsyVfRdjZk5Xtv9L8kqUN6tSu4mL8WJ5xshgtcJQ7Fj7ZMx2bmbsff+EmL27RX83CJUDRsROmoAq992BpLwAfCwqLETCt19X/i0IePrNxxxWUdQEVyqNt08rfa5xldjG7t9bT+bg9Zk7FYcRvbt8G/v0XO08vljB5bJ85UNe+xQvL97FNQoqnwOxZntKaVmHoLTmyScyT5VXaUpRESKOqjeYl7PolctR9eYWMBbkiy53ab5s+4fngz6VXYxD6eLNCdNO5yiIpT6w8BSk5D53a38ub5fcaMtK1fYt+b6U7LvkzGk9QUBOsRkbnZuyeXijJn70LhI/+xTJPV53u55iUvt1GDDCt2Z7FXYq+FrqiVWkH5ZULO2vidWuJB4MlQK+emrHYtRPP+59AKoOayjdb8oXD/23DuPmfoPL8y6qFqYc9s94Yv+vvArD04SsPnWkFg1QpespePGcuOnPWRFE0kfvImrHdnScO8ZuBf88iFKZIrV5qqlwO2CEzGNXYyL39HzXDziq1Dx5iJqvAw4581RAlfxu5mabK+2a7QbDoBcAsPqwdKHT3TkoC2DhySKzb6I3H4Sqtf0fqt18E6LSHJv1xZuKXdb19ppFHhPv6yoYDC5ps5JH5Xx+CZ6ZvC34pqDxExaegpTcF0eMpbwvyvXb1/u4P08/iJD5ZBkg4LdtZ1BgcmpW5mEf0cv+kRW+UimdHhRf4MNoe85f2/0xHLNY3zM5/ZkcavO83LfchFrpUXuTkRm4ZASWTujpeUU12Z3nnCIzpm08DkO6Y9M/sdH2fFXT7uuwR/b3grv7LxRK0HYi9ylrIiWV7lws8KJpidvT7HieY0pcM0C2dQWt6mC9ZRdbsdH2fJ7nSZ2zMWur65QdgSgoGKQ+vrmZN0uNwqIS9u8L5z5TcgascqFC9D9coPBZvsSqUj9LT8wWK6amyWtN4il/k/xMV8T9PMLht4jT5ferMb3yo1m7Y9sQ6WPLItnsa2jtn0E5A0aEGRaegoj9rVh5X0on9Gpm0Pv+/Z/y/Vx6oIxy2ksLgtsXYkVCvv5YdmU/IRntcr0RvUFi7h0fBow4nF6o/ohWMlgc5v2SKDzJGOkqWKhxT/uj4Pr23D0Of7d/rQtSG9dD5P7Kl72c0facF/3w5w+YPfUjGK0qzOAu47jlDOLhczQ8tA1Vu4le0ofvKNtA4jydsxsoR/ZwwDJqnjSlwrNg8mIUQJcR6Bw79NqtKH2WvI35R3YZ8DI/3u5JBTnosns54kQmmFW75skT+4zuvvP5WLn/PCI3b0KE2QSDYMWUmZWjqe44k+OSDjjfrc6DRgUD9wNGiOcVYn7/TdU4XMgv9dilQu5zH7NsKRK/6O1xvT4rxAcgUTuFcX5vGhTmG1RvURDkIrWOAIlT/uVHwzvX03MlCIgwGGS/yDccz8bt9aor+hSrep8JqxWC4DpiVrCxz9bYx7Tp+cp5SBzmebr0f6VnS7wq3vX8RHo7saqXvLo6Xmx0KN1xJKlml85vjN2IcN7cg132lje3vensARSZLYh1Wq4oRLvn641Nc0RXSS8oxRXKoqiYp2dGSeH21c1zMa51Z1+jBADIKTZj1vazeLnEjASnZVfnnENhdJziMKWuudhQ5UrvjoLSMszZeQ4fKthGSYZKzmX4e/9FfH12g8vIXp4KyHL3FTt1kuJtPFnlpumXUvaFjBi7QtJt6/9C3SN7cM2Zw7iYUNVlO28KT768wewLTy9M247eK8aj6pZ5eCSlKlJqNkBDu2bOnyzYh86FkZLbA971jZU1b5gPvBkwIrlnN5R07qpaHJwvq7uWE8GdcxAn2edJjBofCHRce8WapyCl9AOxx5emH29S5z1HHDrksk6EgtTYFtVAF1zsB1YQfHsV3JB+HL9P+xg3HNrhc7TcsUhUd3XZs8L2b/tLX+XUUdHJRz31U3G241QO7hu9EUv2O/bJeWd95Ze+QM9AL5vIyFZePx4ymspdmXMerU/tEV1mv+2/R7zL9G06no30glLYZ7+e2blYfGWZNcC+Ue+6f7ZygmphfbX4P4zdcAJ/7BYfor3xRfG+AHJGitp91nHI8vKaJ4PLb1LElv2w8ghGrDnmcd9u+ZjurzuWhUKTBYsmvesYrEONt+t2br9C291/sVI1AwaD3zPjcvxtl761sXuGo8pMuOZMeYGyZqFr01rJlgt+ew873muvb5kHAIjJzRadAHfeLudnwOle9WYUUz9Tc6hyb3m6egYZ7wNdUXDNtWh5oyUWnoKU0ponNR9T52dA6pnYf7EABaVl5c+XXXyjdjuNiiQIigpPtqPRMLE2wqr4PWefoZj0e1+0Pr0PXwzxfjjkCsVmC9Yfy5Ks/fHIriT+8HvPIrH3R66rKDjXkbt34n9tG6Pj+gX47K8DkuvJeXn44/Vi/5KX7Hug2s4qj6B+xgnRVdaMeRW/T//U86S0Xuo5ZzceHLMJyw8oG37db7x5bAOQ0dh6MgcAUGz2fE/YH0LnCVukV7wU753OhSe70fYqtNm4RE40bbadylG0vlI+FU48Dhjh+/X01y0RmEyeeOSl3uu+xknJ9oKh/OpULa4cXMQ5VsH4CLt7n7ifJFf8aNRo9uYujEBO+K4W+ylaHAvLSm4w1+MuKFWhWXqQYeEpSNmmRtKq8ldGSrjpZA76Lz3kuX+AICBSrZqnAH3NUVrz5BzTuDL5bcYf2bfK7fLPFx3AO3PFay7sK5GkEmur0+9xE35R1PzAWVLPN1ClOB/9lo4SXW4QrLjh4jHRl12g20Xb18BVRsI/z1TrU/vc3p4tzh5wW9j15VmvVpSLLl++4XG9QJx/f6VYySUFMJ4UL6Aq4/kk2N8ibicJl2imaoCH20xOP1GPa2hJog9TkGl1eh9+mfNNwEeWk2q2t0Ci1hPw7TR6c0zj5n5j+7fzgAtir+sg6cUnypuaJ9mFG4XXxXCpcOrvmid/fASIMVeOTmmLv9WK+DEjfQp3wibXecH0jn2egoj9oyan5sn+2QnE66t6Ua7Lb2uPZuLKqnEeMwOiNU9SX+G8ip26DIJwKX7qp1DOxzds4WC366++1JxL7OUl53uOVUYbUGUvxsrwGqQfR8R/NR2W9l45Aa9tmY/l9f7nOSQ/1y62Oem+uZwtHirsy9PLOKbM/fxHSr5UxpSZUBoZbfv7zQ2/y97WbRxUORHuv8l5e8V3/fQU8BOQuX2flyH4gVQ6Lbg22/PEL4+CvzJwdpF1uwdBwBV56RCs9RXFKXrFMpS+9BJw06s+nZiO/5VPknttlswR0txQklbFlLkOkQ4A645mwpuJNzxmxA0GGAryISQkehG6a0ZcaV/fyG1bcdXFEzgRVdPzyl5ydw4qhio/l1eCGn6LgcyWHoCqmZhA1WDVLMjGg3tXO+0biJ3yq7KARM5RZqH486BnrHkKAv8eycTAKWsQWVg5wZjWw0VEHD1i+3dFopVS6thpXoBB3ntNouapxFzmfrsANtszZGYiduqvtr8Vz/PkB4mlRYhav9bW9EwsEbUvZEu/XGQUnrw81Usm9ES1dq0dfntty3wAwN1H3DR3ChDp4dtVYnfOIzyMmBdjKX+BHLxYgHN5JW7XtQUv8Qz890NndNs02/Z3tMXDs2QLD/5/rpzDV3l/kWme7yt3H59U/SLsbrQ9JQPeBHp0NkHwrabDUwH5UuCfrpqIdT+/jMYzxynexw3LFuCms9LNgpWok1vZdykQfXe+XjZG9HepPQvwrY/XDYd2IPXaK5D4yftebe/88SzC0zDVTqp2uAtThrwsOh+RWtwVIipqzh75ZbPs8GR/MLRbTe4VEoK6nk7cI/tXi/6e9NG74huIJCARejtoH7DwFAT6ztiMT5+/HVM/vB8AUKMgC/DwlVoxhW/KxL6eh9CsIOd5iTAaXAokc+06rYrPAO8a8u/bzmDFoQyH35YeuOhz5iPl/55AzNLKTva+Dhhh7+J/R2E8o/zL56xpH6NKpwfx7A6Jzv8ALHK6PMmpeVIwT0ax83xdKlKa9npaX/SF66fM02Meml/GlJlwPr8Uz07ZJvqSV/q67bXqV0XrAwFqNul8GKqNziHf0FWu/ctsrYHV3JFU4UmFQ1TjNAXiq3WG6IAa5ft9Y/NcAMD/xgyyi5TvNThKxVjs36fa5fDcHbogADB5d7z3ryofXTPuV/FhrV335XhfuBQovTxF9v2oAklqqHIAmvWdVuuDSKCKYc73gGBQVncuN82zbwat50E1WHgKAvXTK9vxNz13EFtGPo++33f3uJ2/bjyD1QqDzETcAIPnKn5BQEyZCQ8edJzI9/DFAtHVzWVWbDyeBbPI4Q1eeRif/FHZbGfX2Tz0WeT718kop6/Z5UOVKwtDKpFr3K45qrdoBBQr+ypXMbzsY2L9di6RM0qenKYGShLo00E4B4giGr1Moy1mHM0s8kvYimoOA13zJENCvy9V233klk0uH1jsBaJAYYAga/479Xcsb58C5H5Fl+hsb7efXWddm3OrVUj/bMV4NLAbZlvvpN6VFecz5cnHAhkdWawC0PvP/bLSGKXP1urD0s+ps/ZH0/DGxtmeV3SS5WuTsUuHtOJgOg5cKJA3EJIg/1w4T5QrP1r+TV+8zl/quECkBAtPQeaJXf8AAK4/rqxdv5q365Thjq2yJZs+XUrwDYD7B0YQcNXWta4/i4QFAJ/9dQBvzdmD3BLPTZGOZRZ6XMcbRsGKg+kFmLLlFMwWKw6lF+C5Kdt8CzOrvO+S0va/bpsryGi2J6vwFMRNjJTy9mv1FbkX0WnvSo/N7+yJnYtis/j2MWVm+W3mL5H7glQrs+qXocplZOjjJipv1iUl5u9For8rK1/KPe/l58v5uhoEZVkbv2WDJNME2QGI/+whwk/uWoo4k7ymqe40vngUSyb09Dkce97e4WpkVt3N/yQAiF63xud9eCKWZjV0U0C1WAX8859/RvL8cIH8fE6rM/vx6epfFe9DrHCv1L7z+fhk4X587jS6rFQTUCX3mJyJcgNB6XtdbP3gzhmoiwNGhIpLd22suQQlUeVTbZaWWRETqaB8LAi47cROXHtR3ohWFX2e5OQzjCJ9nuwzmaIPokjABkFwyOj7Kx9vhICXpu+w/T17x1mcdVfjoiAianaePJRRiBsv/VuykCsjbs6jLQWKP76evbF5Lgbe+fKlHYgdu+s8TwCw7ufybaoW57kuVOD7FUdEf4+2mBXP36a2gHQ+9jCyphZD+Bpyc9B1+2L8cf2t6gbsbZ8nf9VKyUyHfL0CnvoNtTx7AJ+tFC8QR+6TN4iLv2g9X1Ew8Oa9Gcy9eNxl/DvsWulT2MVlVhyT2WLAUHGW5FftAgDMFitKzFZFg108ufsf5GxcD/PN6qRpLq2tDQZFkyXb8mVB/nFVLax5CnJyawQEA9Dq9F4cGNIVny//BQDQ9qe1mKhgiMh7Dm/GtJmfyY+bghVFJ91zesiuTz+BrruX2R4+LR9B+/5Z/10skFUL5ulaCZfCKpSomZDirlZhwHLXgT1c9iuSY3f+Rcm59i3z4WlPAbjqHuJ/64ldDn9HWcx4eN9q3HJiF15IW4hI+8EZFLwoor2oeZLrqhzpIZADLggzp0lvvIJ+fw3DqPkDZH1htT+CKIu7/qc6zSgIgkPU40wlSHznTUQv/dtpRalrWZl1kDqf9x7a5PJbxH8qDAAhCHht01zcdnyHd5trWAiQyoxGWK2ocu5UQOIgK/3W6W3ti/fWTBX9fUbaaa8G81DykajTuM24a+R60WXuwqnySAfF8ZLivBeDFyOGhhPWPAWZJhfEv1qLcZjQTAA+Xj0JAPDK1gX45u7XAACj1h7HS22ukhXe7cfEm6V5ahIkp4lLhEiHTodBIgwG/DOhfELZ4sgYLGrYTnxfHvekDvvmFbZRy320+WQO3lyvfKJU94mwQeRfjuSMG6hVhsLXr8CeNpdeLP+CvrFxNj5YO832d/XCHDfBSIcbYzGJ9uPz1Z1HtqD9sTT1A/aSfXpgPHsGUWudhr/V4MtkzPLy5tBtT+zEztr1PaxdKcpixtbh/ye9wqVjcU4DK9JMXwrLXm3p/EDInA6i2+Y5iFv3G+J+m4r0i77VvLpjyM72OYz2R9PQZ9UEAMDVn/ypPA4algw2HMsS/f2Xud8gcYb/RqpTQzDXPKnhnfUzRH/fcy4Pt0rcMi+kLXT4u+IcCRAUpXMXC7QfyjvV/r1WwZtLzpon0kLzcwe92k7Lpgge9ywI4pPu2cXZPqG58cLh8uUKnly1j9454fP05UnOC3mVm07s7l3KoHmbJskabS+0X4zOJm6S/5X3nsOOI+O9vWGm7d8RJ4/LDiemzOwyYXGF8pGN5DVddfbs9r9kx6Gca5gRVgtqFGR79eJLcRphyz706s0bIvKo/A9CchgkJqaVvb2CzPO1WWdcpmhwINXnydM+NH7eyiueKuNYO08qbZK4X432H23kn0+DClNA1Mm76HklN7wtBPiaRhaaylBsFj/+RD8O8R0O/Pk0dd29HI+9/JDDRMsV2pzeK7qNAG0L6d6oXZDpcxhKXx/Gc2d93qdWWHgKER6/cnhYHmcqkWx2llKSj1lTP3YN0mDX58nTJLlevHg8vaz+3n8BEzf7p6mDfbM9Adp+THFXaLK/ZtIDRnjeh1aFJ3+fVqm+dBkaTNoXbTFDKt9vEITADCMuYcb0Xtgy8jk0O39I8bY7hz2N6zIqmwd7apTa8uyB8ua5Xkro/5XX2wLyPkJUPA73Htro5T48zKMUqOHb/TYRuf2HL/E1ahaK1DL5WPAF4PO5itBoDr8iP07xoJTnxtOBSYw0HUlR5iuvw8H1qHr8EL7652cZQVZ8TPElYo60qvF7fcs8v+/DUKrfkXtZeApySiZr9fYh67p7Gfb/2BUvbBMfparnhpn43xnpUXHk1DyJFa4kC3y2Pk/ut/nir/9wNtf3EZ0MF12/ZEY4veQ9v2z8l8C5+4Jln3GpWO/TVRMd1rHKuIeqlIgPGx9QIV7dH1NmcpspCcgQ2oIgWlCueL4rRvtUyn67dUfFmyZVqFWQhcF/DfVqPwAQceK419sC8s5zxQiiH0r0g7CRCErplKfuRmFTRO5Q5bL353mockWC4Bmvl6V8zj2vOLde0P7QbZQ0J422liHGLDOTq/Ag/5j0nqL1VaXwesSbleU1lDwhNQqyVKmVVZtX8zx5uAdCZeoBFp6C2NQZfdD8nLwvwe4y79O2nsZF0YkMy3nKyIh1/C1nqPy/h6HKlbxsK74MKpq7RmK/nsR/PwCpTa5z+d0gCEgqLcTkmZ/j1n//8BiOnAzZoA8fwQMHXIdsV0tF4vXGpjkOv/tzUlul7jyy1eG6JNs1iaoh9qXaHzzci/cedrzfbzx/2Otd1cuorBktH6pcfL2OB9bgkf3/uvzuj/yW1FDqvrC///0xcmPd7MA377j/0AbPK0lOkut+0JtjWY6jd/3vzD5U+aey+WXCZ59g8JTP/Jqhso9bwJoYeVnzlGqXNuipOVT0X38i5dEHYDx9KmBNueI9NAGU0xdn9o5zDn8v+vUd39/HImLcDsYSXOQevsFwqdmegoLklpHP48c/f/AqXsFEzhE/tnelw9+mMivMluArOHrCwlMQa3tip+x13d20Q1cfxaF07+dDujxfvD28YAAMMKDuuaOSbX+98dqW+ZcyS/JT69gy18Jhv6Wj0OiC+wEaEr4fIPq7EVZ02zQHtx/fjpd+7a/aKGmjFwxUvI38RFh8vV4LXWsNlR6PWp38X98yDw/8t04yXLUHExDNrCj8am70MsOTUFqE5eMrJ7uOsZgkC09Stb7+4O/sm9qZrBsuHsPqsa97XlGmF9JkDDJgMKCZjP6n0W76vEUWFSL+J/EM0fA/XdP2a3q8ZPt3/NjRaPffJrQ4+5/nuNoLVHNA++bCSu4oL+Pz1M4lbpe3OHMA/4zrjnYSgx5pwQABKS8+g+gN65Dy3FMB2+++Hx/3uI7Fw3UYu8FxupLrAlVb5yWv3hteplOy7ndBUFxQ7rRvtejvSsNJers7YubPVbSNFK8qmBVeituGrkXr/suQW6yfgjTAwlPI0OpbnMEAfDj5a/crCQLEYujuufxAormMVELSzam2BQD+b8ff+OvXt93HTYJREJBcUlngDIZvnZ4S0WCfvLaC/aiOPo+25+ENGOh+RGdzKwvx1YscJ2eMLjM7TGqsBX99+fbnvTdmXn9Vw4sT+dAiRu69YxQZEMMAoOP3HyHpW/G0UW6hSMmEzUr43I8zwM32PN1f02b2wfWZpzDl9y+8Ct+d5JICRFrKfHpyIvfu9ts0Bd4oKFV+XwW6702ZVcDRTO8/+qpOwfUrr3nyX1TciZ0xTfVBeuSo+Gi25oj7gbGc7yMBQHaRGSey9TVoCocqDxHxRflul/uzuUCsCjPJi1GSsa6bre48N0bBqmgyXn8mlO7PQuWOpa6x2PZGP2XK5IhUo9O4L/w4OMayg+mSy2IsJlnDxuuRw9D+Kmey6iqdw0qlTKrcNNOYmwOgpuO2goDrtoh/SQb8+DFG7lDlKkZAUcHZ28KTh+XxcvvkKFQzPxObR70AAMiIT1G0rdh58fY9oebHiWAfWTXivwOIOH4Mn5TWxYpDGTiudYQu0VNzUS19s+QgHlOwfq2kaFzI136odqVY8xQi2mxcEvDhb21zGnjYrwHK+jwBlzq2i/6uKBgAQOxE8ZnuPe+/Ms5aJpsVzcY8ZUqVnJvH0pwnw3RPzRduhFBZcNNiJKEIjYZHjSkzwypj2Hh/Ut6YRJ7GF47gMsnhrvVHyV1ZpeN9qHnYsWmsVhmtqF3bHf9eI16AUzKcxbWZYk22vK158u7zgX3hwf7fdx+W6o+rjvZHK+dPS3WqSdarKsX53o12GaD8RbV2rZHy3JPIXqWkf3BwFWwEAbgm64w6YWk4v9aEjSc9r+TkhlqJbpc7N+uuGIk5uIv0rlh4Ip/IuuH93FwhwmpBlRLpiR2TPnlfcZguo1/JOAZ/1T5VhOux2Z5UzZNI3BueVT4ktVqiLGUB21cwfS2MtpiVx0blDIu/zkeb03uxcfSL+ODfKfjfaemROXVFZrplMJvRtc8rDr9Fl7lvvy/3Y4TSq5/ydFfHv199QXz/Vuni00vTHQtgf0z2fUS0iuP1tgbFAAF3HE3DN0tHIbas8iv1+DnfIMlpHq7bjwbHhNHO1y5YUqJf5vbzabRLb0WtsBvJU8YQ1Q3Tj/kxNv4lABj5x3daR8Nnp3KUtyoa1vlGt8u1nJJDTWy2R15TUhvhadhxMe5G7bLvOzJvyge4Jvuc5LrecB4iXsvn3f1Q5Z6b7YmJ1LDZnkM/Dr19bvJBTJn0gBHBw7cIvmU3gbBmJCYcVhSEwvVjCxw/3njqYK94IAgVpRTn48o2NyLvvoeAyx51Wb7nnGMTcLEJXM/nlQBIuPSXgnPtZZNdgyBg0qy+AIDjVWo7LHMeXW7ypfXU0Oii+wGHlNC6v2MFfw7V7u65qfJUF6RfzENC748QP24MslbJGM1SphqFOfh5Xn/81qyDamE6kzU/nB8ucTB9AJRF4j6PLjOj4cWjLiOIVqwd5K1JXbDmKUz446GWHaScWhuxzUR+rEhIFu+vnJupqQ9DSUvGx2l49ee3LkSd3AuqhD3g72GoJTGCoWhcZJ5pqWsstn2kwtofNdvcR0o03fHHiElaDqIh9nEhGDJQSW6HMtbZG0yGCX1GKt7G3y/y249v97ySn/zf9r8Qee4sqk36xfab0veDyduhhVUYMCLZqabJn1+yX9wmY2RGmQRBhxlhP4gfNwYAkPCD+5oZoyCgdp50H1J7n68Yhw4HN9gK2FpTc3CQBhnum86lncpRbV++8PQc/rTweyyY8gHe2KzOSIBaY+EpCASiTWutgky/hFtmFTzmNCwS/Tw8vUjc1SZtPpnjMW6+cD6iL5eNwZLxPVQJ++ldS/HTwsGqhCWLyGmOtAau6ZwztUYQizcV438j1B2JTU1iBbclvy3BXxPfUnVIZSU1wKFXNBLh1F/x49++VRzEjSMHanKunOfj80dWW60MfLdNs/H0jsXKCi/eDphg9++suGSnZcFZINHL6Kdy3Xg+sE29+y0dhQ2jX/K8IlwL1P4QrPfZG7/v0joKNkarRfIDyQMH14v+rtfHhIWnIKDWQ+kulIZ+mtVZTr5ty4ls3dXJir34EtzMMK70Gtb38DXJMS4y14OAWJmzoEdavC/A+Dp4hLs+T0pCfm/tNDSdO8ntOiklBbLCSpa5nq9+ndUXjS4ekz2kspadhcNZg2ljXZruqm3OTteBSx4Zt9mrsLpvnIXpv/VWvJ23HzKqXzyDXqt+xYAlI2Stb0szvMwpRdlNploQE+dVGOQbqXmIwoVBEHDDxWOok3tRch3Bt9vcK1dWiQ3czty49eQu7B/SFbEzpinaztZsT2fvOhaeyCdybniTWfkcGZKhVqRKfk6dUoty/JpxVRJ7t32e7JcJAi4XGfFMjWZ7DuH5eO7tR9tzCFfhXVJPdBSwSsklBbKaR918chd2/RSYSSzlFub8JdS+houJ3BE8E6W6M3CZa3Nj51p6uSnQJ6sn4daTyr9A//vzq16lpf8d8bKPqZd9nlwG8LFTO0hHeXROz8qnO/R+wAw9COX0pWZhNhZPfAuP7P9Xch2DAMzaEdjRXGe99L+A7s+dGIsZif2Co+mkv7HwFAT0+nW5Ym4Nsb5J9iZsOgWxbEAwJ7RajEYkRZA5vLWSu+i6iyccvuZ6jIOKNYf28zzZ3/uvbpmPlmf2qxanG2X2hXt37XTZ+1Qi2OdTCVXR69ZoHYWgZp/uXpGfjpgyxzlW+i1R1kdMUcbeyzTfXS3g3GkfeRWmv7209Q+to0AqulJmn+dzeaWYvk2dYcrl6D4reJrteac8TdDb65KFpyCgl69Kzrpvmi1rvczCUpzLc21O5s2zsmnUC3jwwFrbGWt8IfAzaYtRWhBU1k9FkLUPgyA+i4/YdpfnXsBX/4yRHwc1B4yQqPV6bct8PLf9L9nheIqR3DiXRMbI3qcSaszz8fLWBSrEpBILdPIF88cdf/q/HZ7ngLP/6CHnPBkrPpi4q3lyE467mqdg1XvVRIe/9XcEyukpffHnR+uVhwJXG7r9dPDPP9bq9F6P6+jnzinHwlMIifEwt4g/GAyem+49s2MxFu9W50tMlZICjFow0Pb3ol/fUSVcMYon9lXwelSScLvLnNj3h1JaCH9q5xJF61fw9QXpMEluELxsS6Ki/RJuyzMHfA6j657lKsSkkueMbjhk8eTR/s4MHKm0Q87dYFR0z4R2szV38krMIXEc7ujpg8P1GSfxw58/4CqVpzkhV7OnfSK5zGzRzz1jj/M8hZAW5wI/d0heief+TD03/I5/r24hskSfD42YYCgESL244srEJyRUlulRT6SX/R6UkptRKfZTzZMYPWUuwp1W1yqn2IwafgzfJaXyIu2yPzNyz1NBaRmqqXBO9TrJ5g8rg6OVRLCI2ig++lqgfLRmCgCg+bmDuPs1+a0wSF1ZReUf/YMgC6UIa57IZ8VlnjPDLc66foV3mOBVabO3ALxAFQ3qoDRClxKKFjJqJ+SmKVLrjZo/UGKJNtQaqtxTgdVdU586Oedt8SiJVK/mqd2xbfh49a/lQ7bqks7eYH6kVS3Bd8v8OyR03Wz5Hdql7oak0qLKdWSmfX/tu+C+Ft3N+bbv86TX2pvYndrN7aXUzV4MPqKUMV161LpAqqdC82pSg77ePax5ooAwRUQBcDc5pxMPL+RAvD79WZskwIDG5w9j3tQPfQrHPiMhlTGpk6fuS+oKN0O1yhEZBAWLtWNexcprW+Klx79CSZR6NU8VQ5DHm0qQE5fkslwwGDStcNVrxjNctL+uOvZf8O+IjF32rnT5TWnz0D6rJtj+LXdId0GAKqPt+WNS9EC48eRe7KlaR+toyDLDi2HvicIJa54oIMoLT8Fj3bEszAjgiDjOBIMBrWSPLFeecYjxMDqePzPGKXaTEKb4OCGh/QS9vnXadb+tpy/idx5NAwCUqljzVOHFbX/qtnkRaSc2KiLgzVdq5UtPoC7nFr730CZZ+xEAv4y2pyfepgl6ae7r6R0UcTiwE+1q4dbjO/DWut+0joZu3HlkCwD9NdtjzVMQ8NeoL40uHMVdRzbjl9ad/RJ+Jc/xN0e43mr2fV+UZvwFH18m787dAwB4y6dQKvmz4CL7xamP96tqfZ481wwGX58ntak9ua9ep03wBy0yrL6ma974atnPPm1/rcxmTwLgdc2TXgoP7ggw4PL8dK2j4VfjZ3/tdnnyqy8EKCbaMEDA9JmfaR0NXZk4+ytc/cmfWkdDMRaegoC/Mt5//fo2ACC1MMcv4SthNbhWcr66ZZ7X4fm7aQvg34ykWpPk6pH9UOWe5gjzhdyg/TXanqdjU6Pg895aZbO5exIq95oaxxEM58J5DiZ/qOHm/aDm43nfgA9hvuZy6X25HVVU+2uhhuXjumsdBb+6Lsv9xOWW8+fdZjo/Wj1J3QiRbujts52mzfZOnDiBV155BS1atED79u0xbtw427JTp07hxRdfRPPmzfHggw9i7dq1DtuuX78eHTt2RLNmzfD888/j1KlTgY6+bry4TftS/dU5rsOBNkw/bvu30oLKyWwF/aeCkLKhyqWXPWs3J0swZPbkiBRUGjDCw3I5Ga5mZ//Dy36azFLsGtvHacSC73zeh7uMrzeanzuoanh6pkWzS0FwzERU9KHzJ3fPiZzBJeR+ALlh9SJU//UXudFy4M95npRMFu6LYBiRVWsxWe7nP+qxcVaAYuIfak8tEU709nhoVniyWq14/fXXUbVqVcybNw9fffUVRo8ejYULF0IQBPTo0QOpqamYM2cOHn30UfTs2RNnz5Yn5GfPnkWPHj3QuXNnzJ49G9WqVcObb76pSZMHNei9qYwaZz0YM/7+nOcJkP811V24PTf8rjg8rRntmu5ofe8vmPIBahVk+SXs99ZNd7v89uMajL6lj1vEZ2rcV1qkSVpcHnfH2eKs5+kvAlHIlJpuQQ2DFw31W9j2vlrG4bCJQoXqzfbMZjOiojwPDpCRkYGGDRviyy+/RGJiIq6++mrccsstSEtLQ2pqKk6dOoUZM2YgPj4e9erVw4YNGzBnzhy89dZbmDVrFpo0aYKXX34ZADBgwADcdttt2Lx5M9q0aaP2IfldMBYcAs0549/xv7USawanq3LO46PVk2Wv74+vkHq5j6JUGm3P0xkMxvOh9Wh7JJ82fZ4Cvkuvhw9Xso6seLhZ9sj+f1XZh5hH96/2W9hEJI9BZ5UIimqeCgsLsWLFCqxevRqFha4jbq1atQodO3aUFVbNmjUxdOhQJCYmQhAEpKWlYcuWLWjdujV27tyJRo0aIT4+3rZ+y5YtsWPHDgDAzp070apVK9uyuLg4NG7c2LacQl8DuyZ/wSC1KBeP7Vsle31lzfbk1lDpg1rzPHkSDqPddTywBvce3qx1NEg1AgxB1H5FzjOklxpvIiK1yK552rp1K3r06IHc3FwAQGpqKiZMmID69evj7Nmz+Oqrr7B69WqHQo1cd911F86ePYs777wT999/P7799lvUrFnTYZ3q1avj/PnzAID09HS3y5UIhveU1k2X9GjJhJ5+H6HFn1kCJQMlyJ4kVxBg0EGJwX6ocl/cd2ij2+UNLx5VZT+hJBhr44KVv8+VQWT47TVHs1AqY9JxNYXKMODeiDh2ROsoyGIA0JT9ESmEGQzBkR+XGwfZhadBgwahSZMm+PbbbxEVFYVBgwahf//+ePPNN9GjRw/Ex8dj8ODBsmue7A0bNgwZGRn48ssvMWDAABQXFyM62nEErOjoaJhM5SMPeVquRPXqrhNZkjIh2xE2SI7rytwLGPj3MI/r6SVjbD9Uub/uncTSInyg8kh0alCrySK51/iC7wVnf3+H+GHRjy6/BbrgBLg/TjlpSu0C6Xmigl21Ni20joJs3TfO1joKIateJgcc01rVqglITdVPflx24enQoUOYPn06atWqBQDo3bs3br31Vrz33nt46KGH8NFHHyExMdGrSNx4440AgNLSUnz44Yfo0qULiosdR1MzmUyIjY0FAMTExLgUlEwmE5KTkxXvOzMzX5N25hWKTcxMhaMoqwVfLh8re/2ndi31uI4OKp0AABEB+NKdWpjt933oUbjUcg/+y7Vgopx/H6jOe1f6NXy5gqXmic3/3KtelKN1FEJWbACmBCBpb637DbnP3oiMCK1jUv7NXE6liuw+T8XFxQ5N5ZKTkxEVFYWnnnoKX331leKCU0ZGBpYtW+bw23XXXQez2YwaNWogIyPDZf2K/deqVUt0eY0aNRTFASjvoKvlf/8e8c9Xu5TifL+E66xaUa4q4QRjlu7KHOXNQOXyxwhvNQqzUa04T/Vw/clf2aWr/Hjt9EwvtZO+UiMjHi6Z+WBMe8lV7Xz91vAFu2CYCzOcfbB2GlLWrtI8P17xnxw+D1X+8MMPe7Xd6dOn0bNnT1y4cMH22549e1CtWjW0bNkSe/fuRUlJiW1ZWloamjVrBgBo1qwZ0tLSbMuKi4uxb98+23I98Vfn4J3DnvZLuM7+mviWKl+zk0tdByDR2kP/rdM6CopMmtUXv0//VOtoBIXJs/pqHQXSuXApVIj1vaLgc2XuBc8rkVdqsKWC5iJK9DV3p+zCk8FgEM3oR0Z6N9r5jTfeiMaNG6N37944fPgwVq9eje+//x5vvPEGWrdujdq1a6NXr144dOgQxo4di127dqFr164AgC5dumDbtm0YO3YsDh06hF69eqFOnTo6HaZc31+DL/PTHDkULsIlixocwqU2JSUIP8YEK3fvn0DeL3p+D5K+hUtzZqVant4XsH3pre+87JKPIAjo168fYmJibL+ZzWZ8//33SEhIcFh3wIABHsOLiIjAqFGj8M033+DJJ59EXFwcnnvuOTz//PMwGAwYNWoU+vTpg86dO6Nu3boYOXIkLr/8cgBAnTp1MHz4cHz77bcYOXIkWrRogZEjRwbVEK8U3O47uAGvbF2gdTSIGSYKUuFS0NRLX8lw9vSOxVpHIaQpGf02nMyZ9nHA9qW3/LvswlOnTp1cDs7bJnsVatWqhREjRoguq1u3LqZOnSq57R133IE77rjDp/0Hg/I5M/V10ziLsWjT2fKHRUO83nbsvP4qxoS8p+97X4nwOdLQEC41IeFynHr20ZopWkchpFkNPvdgIZ/p6w0pu/A0cOBAf8YjbOnrdhFXV6PO+V32rNBkv0QU+sKlRsZd+q23pjRE3giTR10xkzES0SrNy+iJYNRXWiO78HT27FnZgVY0ryMZ+HKiMHRlznmcqnKZ1tEIKL6g9SXKatY6CmElXAqrRGr62Y+taI5WuwI3ZJzwW/j2QrbZ3l133eVycMKlNuHOv+/fv1+FqBFRqFoz5lXc8P7ssPqyHT5HGho67VutdRQ0Z7RyJD4KfXp+D3U4uMFvYQe0Sa/OroHswtPy5csll+3fvx8DBgzAhQsX8Morr6gSsXChr9uFSD1VAzQXGVViehPcPvh3Cn5p/ZjW0bBpd2KH1lEgCgD9pox97+mGr5aN8UvYxkAOmhOqhacrrrjC5beioiL89NNPmDZtGlq1aoWxY8eiXr16qkYw1OnrdiFST7h1VA+voyVvvLVhJi7PT9c6GkSkE8eq+q+bTECnKgjVwpOzv//+GwMHDoTFYsG3336LRx55RM14hZXUohyto0BEfhZnLtU6CqQDD+hscm61GAWL1lGgsKXfT1uhMiG83ppOKi48nTx5El999RU2bNiAp556Cu+99x6SkpL8EbewYDAAV2fLH4yDiPQpUtC+/0i4zF2kZ/FhWsjePOJ5raNAYarZuUNaRyEoBbI4E7I1TyaTCWPGjMG4ceNQv359zJo1C40bN/Zn3MKEQffzPBEREfki2VSkdRQoTL2ydYHWUQhS7PMkRXbh6aGHHsLp06dxxRVX4Pbbb8fKlSuxcuVK0XV79uypWgRDnc7uFyIiIiIKccZAtpbQWWZYduHpsssuw2WXlc/LsnnzZsn19Fb1RkREREREGtFZ2UF24WnKlCkAgIyMDFSpUgWRkeWb7t27F5s2bUK1atVw3333IT4+3j8xDVH6ul2I1FMrPwvf/zVU62iElaolHB6eiIg8C+jE1TorPBnlrlhUVITu3bujXbt2OHGifMbhefPm4fHHH8fUqVMxZswYPPzww7hw4YLfIhuqBH3dM0SqmDf1Q9QszNY6GkREROTk6pxzgdtZqBaehg0bhtOnT2Pq1Km49tprUVRUhH79+qFp06ZYsmQJ/v77b7Rt2xbff/+9P+Mbcow6u2GIiIiIiFSjs7yw7MLT0qVL0adPH7Rs2RIGgwFr165FYWEhnnvuOURFRQEAOnfujLVr1/otsiHJAI62R0REREThKVQLT+np6bjqqqtsf69fvx4RERFo27at7bfU1FQUFxerG8NwoLObhoiIiIhIFTrLB8suPNWqVQunTp0CAAiCgNWrV6NZs2ZISUmxrbN9+3bUrl1b/ViGMH3dLkREREREKgrVwtOjjz6K/v37Y/ny5fj2229x7tw5PPPMM7blBw4cwJAhQ9ChQwe/RJSIiIiIiEKMzgpPsocq7969OwoKCtC7d28YDAa8/fbb6NixIwDgu+++w8SJE9G+fXt0797db5ENRTq7X4iIiIiIVCPoLDMsu/AUGRmJXr16oVevXi7LOnXqhIcffhiNGjVSNXLhQF+3CxERERGRmvSVG5ZdeHKnQYMGagQTlgw6u2GIiIiIiNRi0FnNk+w+T+QnHKqciIiIiMIVC09ERERERESe6a3PEwtPGtPX7UJEREREpCIWnkipBFOR1lEgIiIiIgo8fZWdWHjSmsEAPLlrqdbRICIiIiIKOA4YQYoYYECdvHSto0FEREREFHDs80SKGM2lWkeBiIiIiEgbLDyREkazWesoEBERERFpg4UnIiIiIiIiz4xFhVpHQREWnjSms8I2EREREZFqos6d0zoKirDwpDFBb+MzEhERERGpJCIvR+soKMLCk8ZKyqxaR4GIiIiISBMROTlaR0ERFp40VmoRtI4CEREREZEmSho11joKirDwpLHCkjKto0BEREREpAlTnbpaR0ERFp40xgEjiIiIiChcGYz6ygyz8KQ1nd0wRERERESq0VlNAgtPGjOzzxMRERERhSmBhSdSYs+5fK2jQERERESkERaeSAGLwJonIiIiIgpPBSaL1lFQhIUnjbHLExERERGFqz0XCrSOgiIsPGnMYOAlICIiIqLwZNU6Agox564xI6ueiIiIiChMGXXWg4WFJ42x7ERERERE4arGsf+0joIiLDxpzGDkJSAiIiKi8BRZZtI6Coow566xCJ2NbU9EREREpBZBZ/3/9RXbEMSyExERERGFK4POMsMsPGnMCJ31kiMiIiIiUovOurDoK7YhqMbZE1pHgYiIiIhIGyw8kRIcbY+IiIiIwpWgs7ywpoWnCxcu4O2330br1q3Rrl07DBgwAKWlpQCAU6dO4cUXX0Tz5s3x4IMPYu3atQ7brl+/Hh07dkSzZs3w/PPP49SpU1ocAhEREREReYkDRsgkCALefvttFBcXY9q0afjxxx+xcuVKDB06FIIgoEePHkhNTcWcOXPw6KOPomfPnjh79iwA4OzZs+jRowc6d+6M2bNno1q1anjzzTchCPrrP3Q2r1TrKBARERERaUJvA0ZEarXjo0ePYseOHVi3bh1SU1MBAG+//Ta+++473H777Th16hRmzJiB+Ph41KtXDxs2bMCcOXPw1ltvYdasWWjSpAlefvllAMCAAQNw2223YfPmzWjTpo1Wh+SVi4X6GtueiIiIiEgtAvs8yVOjRg2MGzfOVnCqUFBQgJ07d6JRo0aIj4+3/d6yZUvs2LEDALBz5060atXKtiwuLg6NGze2LdcTvZW2iYiIiIjUorc+T5rVPCUnJ6Ndu3a2v61WK6ZOnYqbb74Z6enpqFmzpsP61atXx/nz5wHA43IltC67GHXWzpOIiIiISDUGo+b5cUB+mUCzwpOz77//Hvv27cPs2bPx66+/Ijo62mF5dHQ0TKbyJm7FxcVulytRvXqS95FWQVR00FwCIiIiIqKAiomLRmqqtvlxJYIi5/79999j0qRJ+PHHH1G/fn3ExMQgJyfHYR2TyYTY2FgAQExMjEtByWQyITk5WfG+MzPzoeU4EzUvntZu50REREREGio1WZCRka91NGAwyKtU0bzw9M033+C3337D999/j/vvvx8AUKtWLRw+fNhhvYyMDFtTvVq1aiEjI8NlecOGDRXvXxCgaeEppoyj7RERERFReLIajJrmxZXStMPNiBEjMGPGDAwZMgQPPfSQ7fdmzZph7969KCkpsf2WlpaGZs2a2ZanpaXZlhUXF2Pfvn225XpyRcZZraNARERERKQJvQ2eplnh6ciRIxg1ahRee+01tGzZEunp6bb/Wrdujdq1a6NXr144dOgQxo4di127dqFr164AgC5dumDbtm0YO3YsDh06hF69eqFOnTq6G6YcAGDU1w1DRERERKQWDlUu0/Lly2GxWDB69Gi0bdvW4b+IiAiMGjUK6enp6Ny5M/744w+MHDkSl19+OQCgTp06GD58OObMmYOuXbsiJycHI0eO1F3JFQCsxgito0BERERERDIYBEFPrQzVl5Gh7YARf73RGy/MHaFdBIiIiIiINPL3wHFo9fITWkcDBgNkjfqnr3qyEGSNYM0TEREREYWn7OIyraOgCAtPGrNGaD7gIRERERGRJnJKLVpHQREWnjSWEBfteSUiIiIiohBk4IARpMRlVeK1jgIRERERkTZ0NuAbC09aY58nIiIiIgpXrHkiJQwsPBERERFRmDLobM5TFp40JrDwREREREThSmd5YRaeNFZm4CUgIiIiovBkZJ8nUuJErknrKBARERERaYKj7ZEiVp3dMEREREREamHhiRSJiOIkuUREREQUngQOGEFKcLQ9IiIiIgpXMZH6yguz8KSx6NhoraNARERERKSJK6vFax0FRVh40lhiXIzWUSAiIiIi0oS+Gu2x8KQ9nVVVEhERERGpxWwRtI6CIiw8acxqZOGJiIiIiMLTiaxCraOgCAtPGkuIjdI6CkREREREmoiK0FdxRF+xDUGRUSw8EREREVF4uiIlVusoKMLCk9bY54mIiIiIwpRVX12eWHjSmoGFJyIiIiIKUxZBX6UnFp40xklyiYiIiChcWa1WraOgCAtPWouI1DoGRERERESaEHTWbo+FJ40Z2WyPiIiIiMKUhYUnUsIg6KuqkoiIiIhILWUWfeWFWXjSWITOOskREREREamlrIyFJ1LAaLVoHQUiIiIiIk2YLfrKC7PwpLEInd0wRERERERqsbDZHikRYSnTOgpERERERJpgnydSxMCaJyIiIiIKU2Vl+soLs/CkMaHMrHUUiIiIiIg0USMhSusoKMLCk8aSIw1aR4GIiIiISBN1kmK0joIiLDxpzGAyaR0FIiIiIiJNCDqb85SFJ40ZzGy2R0REREThyWrV15ynLDxpzMA+T0REREQUpopNHDCCFBAiIrWOAhERERGRJi7mFmsdBUVYeNJYQcvWWkeBiIiIiEgT0RFax0AZFp40JkTqa3hGIiIiIiK11EyI1joKirDwpDF9dZEjIiIiIlLPDTXitY6CIiw8ac3AeZ6IiIiIKDxFGfWVF2bhiYiIiIiINKKvdlgsPGlMX7cLEREREZF6SjlUORERERERkWfFpfqa85SFJ40J7PNEREREROFK0Fc7LBaeNKav24WIiIiISD1Wi1XrKCjCwhMREREREWlCYM0TERERERGRZ4KVNU+khL4K20REREREqmHNExERERERkQwC+zyREhxtj4iIiIjClSCw8EREREREROSR1cpme4qZTCZ07NgRmzZtsv126tQpvPjii2jevDkefPBBrF271mGb9evXo2PHjmjWrBmef/55nDp1KtDRJiIiIiIiX7DZnjKlpaV4//33cejQIdtvgiCgR48eSE1NxZw5c/Doo4+iZ8+eOHv2LADg7Nmz6NGjBzp37ozZs2ejWrVqePPNN3XX4QwABI4YQURERERhis32FDh8+DCeeOIJnDx50uH3jRs34tSpU/j6669Rr149dOvWDc2bN8ecOXMAALNmzUKTJk3w8ssv4/rrr8eAAQNw5swZbN68WYvDICIiIiIiLwhstiff5s2b0aZNG8ycOdPh9507d6JRo0aIj4+3/dayZUvs2LHDtrxVq1a2ZXFxcWjcuLFtORERERERBb/q8VFaR0GRSC13/swzz4j+np6ejpo1azr8Vr16dZw/f17WciW0H+xO8wgQEREREWkiIcqIkiDIDsstE2haeJJSXFyM6Ohoh9+io6NhMplkLVeievUk7yOqguy8Ak33T0RERESklcSEGCSmapsfVyIoC08xMTHIyclx+M1kMiE2Nta23LmgZDKZkJycrHhfmZn50HKciZzsIu12TkRERESkoYK8IpRk5GsdDRgM8ipVgrLwVKtWLRw+fNjht4yMDFtTvVq1aiEjI8NlecOGDRXvSxCgaeEpdet67XZORERERKQhwSpomhdXSvOhysU0a9YMe/fuRUlJie23tLQ0NGvWzLY8LS3Ntqy4uBj79u2zLdeT1BLtS9pERERERNrQUckJQVp4at26NWrXro1evXrh0KFDGDt2LHbt2oWuXbsCALp06YJt27Zh7NixOHToEHr16oU6deqgTZs2GsdcOUH7ESuIiIiIiLQRE6N1DBQJysJTREQERo0ahfT0dHTu3Bl//PEHRo4cicsvvxwAUKdOHQwfPhxz5sxB165dkZOTg5EjR8LAgggRERERkW6Y2t+tdRQUMQiCnloZqi8jQ9sBI2JmTEPy2921iwARERERkUYyj56GNVH5oG9qMxiAVBmj/gVlzRMREREREYUBnbUcY+GJiIiIiIhIBhaetKaz0jYRERERkVoE6CsvzMITERERERGRDCw8acyYlaV1FIiIiIiItKGzVlgsPGksav0araNAREREREQysPCktcgorWNARERERKQN1jyREkJUpNZRICIiIiLSBgtPpEgEC09ERERERHrAwpPWIll4IiIiIqIwxZonUkJg4YmIiIiISBdYeNIam+0RERERUbhizRMpEhmhdQyIiIiIiEgGFp40xmZ7RERERBS2WPNEigiC1jEgIiIiIiIZWHjSmMFq1ToKRERERETaYM0TERERERFR6GHhSWtstkdERERE4Yo1T6QIC09EREREFK5YeCJFWHgiIiIiItIFFp40Zq1WXesoEBERERFpgzVPpAjneSIiIiIi0gUWnrTGocqJiIiIKFyx5okUsVq0jgERERERkTZ0VpHAwpPWrBwwgoiIiIjCU9T6tVpHQREWnjRm4Gh7RERERBSmDAUFWkdBERaetKazqkoiIiIiIrUIsbFaR0ERFp60xsITEREREYUpITZO6ygowsKT1lh4IiIiIqJwFceaJ1KChSciIiIiClOseSJlBBaeiIiIiCg8sc8TKWJp1ETrKBARERERaYI1T6SItVYtraNARERERKQN9nkiJYT4BK2jQERERESkCdY8kSLm1jdrHQUiIiIiIm1ER2sdA0VYeNJaRITWMSAiIiIi0obBoHUMFGHhiYiIiIiISAYWnoiIiIiIiGRg4YmIiIiIiEgGFp6IiIiIiIhkYOGJiIiIiIi0IQhax0ARFp6IiIiIiEgTEYcOah0FRVh4CgLmW9tqHQUiIiIiooATIiO1joIiLDwFAcsVV2gdBSIiIiKigBNq1NA6Coqw8BQECr/sj3V1m2JH7fpaR4WCQPFzL2kdBQoRxU//n9ZRICIicktITNI6Coqw8BQEhJo18exT36Lrs4O0jgoFgcI+X2gdBQoRBT8M0zoKLl7s+qXPYZjatUf+gO99j0yIyp0wVeso6I6p/V0B36f5f21Q8NlXAdtfwRffBGxfWrFcdbXWUXBhqXOl7d8LGt6hYUyCmMGgdQwUYeEpiJRFiLf5FFS6qayp6leLljz+lOphaiFv7MSA7OfJpwd4XEeoVh1F3d8KQGyUK+n6pCb7tVx1NQq+8XzupOR/P1S9yIgou+56dcOrd506AUVGIvvPf9QJSyV5sQk+h5E75w+UvNJNhdhow+r0ldXU9nbfw6xRszK8jo/4HF4wsCYli/5e2uEh1fdlukNe4clatarsMIve+cDt8rLGTVD89nvIHT9Zdphyme682+U3c7vbUdawser7ChaWOlciZ96fLr+bmzTVIDaVcqfNsv37QmI10XWyFy5VL9134s8WCCWdOtv+nf/dEJQ1uMFv+womLDzpQMaFXFUeqtxJ030Ow5qc4vC3+eZbZW0XbBk458SktOOjAdnvpqtuxPRm93teMQiH7Szp1Bn5o35RLTwhXn4mOmvrLhS//LrX+ypr3MTrbcUIMTEOf+csWelxm5yZ82SFnbU+Ddkbtjn8VnqvjHtGQlnrNl5v6xCHu++VfQzunKxyGUojolSIEZA7fZZLQcQfnNM9uUrvvR/pF3JdfjdYrbZ/W2pdhtypvysKt+Dzr1HyaGeH33Lm/gnzTS2R+9tsWWFkrd6oeqHfIT5zFiJz43bZ66efz4EQF+fwm0UiI6b2R7vsRf9ASJF3jT1dq7xho23/9vThs/Dz8lon0133ytq3EparrkbxC6+4/J7/3RDV96Wlom5vInfyDGQcPIGstD2wXnmVyzq5f/zt8pul1mUOf4sVNtViadgIWSvXo+/PyzC1xYMuy0se64KyNje7pPtqKGvYCAU/jVI9XAAofulV5A8ZYfvb3O4OWGtf7pd9BRsWnoKUEB8PwPOXK7lyf5sNg8nk8rv5f8oyVtn/bnT423K9vH5aZa3boOSJp21/m9rejtIHOkqun34+x+U3ITbW5TfTHXei8N0PZcUBAB5/ZiAK3/8YBYN/clwQoJFeBj3SCCe6vee5MCxSeCrp3NVPsSovyJTe/4Dbdcw331a+roxzVfyK54KOEBPt8ltZ/QYofbiT7e+cPxYjc/elIUwlMiLFT/+fS5wyDp9y+NtarbrH+EgRq7HNnfsnCvr2q/zBKW7OX3fLGtwA8x13etxX+tGzsIhkaC3XXOvym7llK7cZ5ezFKzzuTwmhWnWY77wb6RfzkLV8LXKn/S6aOfMYDgy4+f0ZKOn8uM9xMt1zPzKdrrXD8nbtfd4HABjKyrzarvDrbwGDwZaeV7J7vqOjy/+TUPTaG6K/CylVHP62NLgBOYtXwnT3fbLiZmnYSNZ6FQr69FW0vrndHbBeW89zPGrWQv7QkYDRCMEY4bBMSEhAzuw/kDvtdxS9+bbtd2vNWori4knZ/9rA6nQ+3a2bfvKi5HJTx0cgxMRAiIqCUEW6lspy+RUQKmrWXO4PFURGwHL1NQ4/WatWU+1jii+KXu/u/cZWKwoGD3X4ydThwfJzLfGOcO5Pk3HgGCx2H9TST15Engq1f/k/jnD5LXfiNACApXETPPdAC1x5U2PM/GeP43Yj1fso6azgy/5+CTfv5/Eo6PcdEOH4zAbjh19/0HXhqbS0FL1790arVq3Qtm1bTJgwQesoqab0wYeRfuwcCi+9sEwKa0ay1mx2+Nt0931AaanH7cwtW7ldbr38CuSOn2L7u+z6BvIjZZew5c6c576Nq9GIzC27kDtlJvJ++RXWqlWRO901o1jS5QkU9f4CWWs2I2vFOo9R2HJlExR9+hkQJf3lu+SxLgAAyxV1UPrgw5Lr5cxb5HF/zu68PhWvdr1Vxhcm1wSotMsTKOr5ruJ9OjPdfCtKHu2M4mefh6ndHchatxUZx88hf9hoycxw6UOPoOSFlwHIK4gUvf6m6O+F739s+7cQ7Vh7k/3HEmQvX4vSS02OLLUvh/nmW2Gt+EIo9WKsURMZpzMcf3OqKRASEiXj6lAIElH83Asuv5W1boPSRzrZ7aDyeuUPGY7s1Rsc1s/+dxNglJHcJkrH01nu9NluM8plN1U+y56ageYN/9nj/iq+kgOA5camMN3bAYWffwlzsxai6xd1exO5035H/sAfXO6roug45A8ZjnznjxjeEDmvJV2eQM6Mucj7ZSIK+g1E7uQZkpvnLJFRyLSIF56cv167LK9XURB2unftap6sVauJfrwp+GYASro8gcKvXZurClWrwtSh8mNH5qYdbuNh36QPKP8aLYdgl04KCu5Ne7nTfoe5ZSvJptE5y/5FyTPPSW5vvr09TPd2ACwW229lbt5TJi+n/hCqVPG4ju0+EvmQB5RnnoXEJGQcPImMw6cBo/Q7rujDTyv/MBjc1gjlTp+FvJFjJZdbU6rAfGMzh99Kuj4JS92rHde7qq68dMhO+rlsRevLIbeAL8pgsL2LAIh+FLYn9oFJqFYdgv15iI2FkJgkq0mj8zs4Y/8xZK3dgvwfhqHkqWcdlpnuuBOmhyrzEClxURjauQnuanaVLW9hbtVa9sfb3PFTYL6ppcNvFR9iTXfdY/utoG8/WKtWRd7YiTDLrFGzptZA4bsfwpqYhJw/Fntc33Rfh/J8lP15NACwsvAU9AYNGoQ9e/Zg0qRJ6Nu3L0aMGIHFiz1f9GCUFOv48Jhb3wwkVDZrKrRPaD2wplQRbe4gmsg4fSXI+Wt5+QvbrjanpPPjSD+dYWt+Ynr4URS//BoK3/9I5IuqSHyql2e2zS3sHvqoKBT2+tz2p7nl/1y+8FvrXg3T/Q+g9NHOyDxwHGaxfgGX4m9pcAMsTW50WFTQpy+y1m31GL8Khb0+R9FrbyB/+BjkjZ2I7CWrHOJU2sGxut0i0jzAFi03X5JF993baZAIqa83dgWI0vs6IP1Mpvj+3TWJi4tD/i+/ouDHEcids9BWeyhUrYaC738U/VJV8uQzti9MYpkMc+ubHf52zqwB5YWjok8/q/whJhZFb78PS63LkL18DcpuvgWIiUFppy7I+WOxSwHE/tiLn3vR9u+yho1EMwSWy2oDKD+3Qi3xL9XZC5eiuMfbyNy80yGjkT/ge2T/sQQ5v89H0Ye9RLeVLPyLXbtL68ptUiV7XzIVftUfptsra74sV16FrFWV59d66VzZy9yyy3YOpdYRklMc2vNXyBs9DoXfDITp3g4oefk1h4x3VEVBJD4eJc+/5PGDDYDyAtjz5RkmOU2Y80ePg/mueyBUq47i19+EqYNrU5kKZTe1Aq51rdlzWEeigJg3Ud7ADC7NtwQBub9Oh7lpc+T/PL58H061i8XdeiB/9DjXL7sofx5Nd9+H7MUrkHH4FKwiNZMWu9qZzO37UPD51+W/X1EHeaPL9+nuvjLdcSey1m6pjLKXtbemezsg5+8VKO3URXwFu496Budnxy5+FvtarMhI5I0YI/pRIHfeIocO+vaKurl+1MnakAYAsprtubuP8sZOrCwExsUBcXGi/agK+g1E5tbdKPk/x48yzrVpBV98A9PtdyJzyy6Y7rkfpSJNFUu6PIGi7m8ha91W5Cxfg5wFfyNv7ERkpu1BWcv/OdQ82Z+T3Omuz6wkkfuv+P9cPygpYb7zbpcPGiVdnnD427ngJ8VdSwhr1arSBTUFaap9wbTw496O+69eHZb6DVDy3ItARITDoBzu0of8YaOQ/8Mw5E6d6XbfFf37cmbMhenhR1HWxLGQnLN4BfKHjkTemMrKA0v9+sj874TDM1cR7/wBg5Ez17VfWObewyjq/QUyD58S7Y5R1six6btQ8UHI5TzKKzyJtSTSE90WnoqKijBr1iz06dMHjRs3xr333otXX30V06ZN0zpqXpnx+s3431VVsOmPf5E/dGT5g2gvJsalCUr6uWzk/zAM1tQayFq+1tZvp/iNHgAqXxQVbePNbcozuJar6jqEk/338vLtXnq1/AvY6HEoss/MG43lzUrsHpKCgT+g6NPP3X7FMrf8H0rvuQ85c8of1LJW/3NYbmlwAzL2H0P+90PL25ELVrFgynmZebRvVjijqfuvXUXvfYTC/oOA6GiUduoCoWZNhwKekJjk0FHZXdMRa63LgKVLbX+XXXe95Nccc6vWKHrnA+SNnYisjZdqpKQKT3bNh/Km/u5QgybYveRKH3gIRa9KdKj3cC6LX3kdxc+/jKJuPcS3EdnevllZ2TXXAomJLhmVsptvcfhbiIlG4WdfImv3QZTZfzU1GGC++VbXJi/2haeXX0f2kpUo6P8dSh9zbM5YUUjI2robGUfPoOjShwDnZmIV7cwBwHr1NcjasgtFPd+F6bZ2KHn+ZZTdfAvM7e9yW0tZeTCu10us2Zzp7vts8RCiopA/ZDgsl5fP82bfXBEA8kb9AiEiArm/TkdZ4xudg3LbPKL4RdcaxNIHyjN+1tQayErbA0ujxsgbOxFFb78P8+3tUfhxbxQ/8xzSz2Yh4+AJWOVmXmrWRMlTz9rSH3OzFih1ygi5G6wmd9IMFHz+NTL2HpGszS15+TUUfP0t8oaNRs4fS2TFS46Cz750+c2+FqfkkceQtW4rzG1ucVkPgPyv+CKFJ9ODHZGz7F/RZprm5o6FNXNLx7QTUVGAwYCym1q51LJWyJs0HZbLapdnqqKjUfzWu+VNLrfvg6WR5y/s5latYb36GpQ88TSKXu+O0kceQ+GnnyFngWv/EWcWJf0eHM6h4z1daH99nM5h6RNPo/ArkSZJBgOy1mwurykdOdLhA1/hNwNRYtd/MP+7IbbaQU/N9jwN6lHaqYtLHC0NGyFr7RaHgoJgNJbXADmH71QwK+75DnJnL3B4DrMXLnXom2O+5TYUftUfQs2atr9LO3Wx9fux1q3cj6GkuHK75pUfMk3t7kDWxm3IdtNvUxApQGWt2eyQYa9gP3pgxQcPMc7Hm//DMOROnIb0o2eRfjHP8WOPyP1U8PW3KLuhIYrekd9s34HIsyskVw5QUtC3H/6/vTuPi6rc/wD+GZgYQERQkF/SvWYZhIVsKiqYaaY3l7S0utc0bbMF03LL9OaGhVtqLiVdc+1mZhaFN1Pp5jUzyTQptxAlU1MBBVlmgZn5/v44cJhh0TGFcfDzfr14vZjnbM9zvnNmzvec5zyTm1OI3JxCFGzaCsPwp1Hw0UbA0xP6hNEAYNeVtIIhYRQK334HF77eecnht8W3CYxDh1e7KFFSfoHx4vLVyP8yDYWr/o3cnEKUVdxZsjlPyjt2CtLED8bBQ+268Vpurv7bofpxE5GbUwjj0yNq7Aauvndr2C/nf/wFpv4Pqa+tgc0ru5raJK/WZgEwOzg4R172mWsyUI6zuNZP+to4cuQIzGYzoqIqv2RiYmKwbNkyWK1WuF3hrWlnu6tFE7z7aFuIAMaOkZedP/fcReX29dDhyhUsjQaFy96H9uDL6lXSkimJKH2gL8rKu++If1PlYPP0QmBw+QErAnNMe+TmFNa+sUudbOt00CeMhsagh+HxYWh6X2WXiaLFy+xODGo6YKVZM/UWfGnX7vD8dIN6MlkT8fKCxmCodbqtqtvLbmr/AZy/5Rv49+p2yYfgpVkzGPs/DM/PP4Xh+QSYb78Dga1uhjkkFPD0xIX/7cZNe/dA+9M+eK1diZJJU+CRthXF8xbCv0ss8r/5DpbAIPXLzVZp127Q/rQPFz9WujDaXiWqqa+8Nej/AHNZ7Q3WaFAyZgK81qxAyeSpsN7yF1hCw9B4/Mv2bbpcIqrTKX3KReCdvBQAUBZn0xXGZvmCjanwXPcBiqe/Ac/19gOSlMxIgndy9QdVTb0egG7LZhiudERB23qLwBwVA7PN3cy8Q8eh2/IljBWDgXh42N0BLFr0LrRHDkN76AAKF71bLekCgJIpM/5cfWxUnGibo9vhwu591bo5Fs+aB8utt8I08DFY7ghRjl+zudoVXtOgx5T3hFYLWK2wzJwG95xzl62WJfgWFCfNq1ZuHP4MrLf8Vf08AJQTvor3nW03IvX950jiCGXfKivR19ilyfDUs/CZ/k+YvBrhrG8AZj1Q2d1XmjeH4aWXAQBlNj+UaPnrrTA+9g+U9ii/6OHtDVOVbjFXwtI8CO455yDe3tDo9TAMHgrDqDFVO9TZ3cUxt42A5Y4Q6F8ZB+/FC6qvs4ZnekSjqXYHxdT/IXj9u/KZipqSybKOnaHNPg4AKEixT1AKUr6Ez7TJ8Hq/9q5bVZlj2uPCz79ecp6SydPQZPhgGJ58Bl4rlwMoH2jgmREwDC+/mLYkWZ1fb9Pt9lLy076tsdw48FF4bvwY+pEvQ3x8oCkosH/A32a/5Z7Ks38W7EouoDVqBMOoV9AooDFKN30J3ebKpFxTW/ffKnfUi+a9jcbjRsNyayuUTJrypwcUsISEKn8tguH+x+ka7yABANzdYW3iB7eLBbWuyxzbERfXfwbdZ5/gph3bKz/raiE2oxXadrGXgAD1/8JlK9QfKC2ekgifGZUXDPXPjwQA5J0+D5hMCGxZecHQEnonLKF34nxsJ+hSPoXPtMnKdmLa4cLOPdClpsDw3IsofaA3dBvWQ/vzfmizjtrVr/if0+EzcyqK3loEeHvbdXGDtvLz8OIHH6PxKyOh/+dUVFwqMDw/Eoby+tW+Ay5xF0RT/Ryx6O2laPzskzCMegUm2wFZvLxQPKfy+C95fTqMjw2GJaSGxxY0GpiuYnQ7/ZgJyoXLRjX3HilrHwuvtasA2McXUO5+up09U60XTlXWm1ugLKYdNEVFMD34EEwPPlRtnvwt38B73iyUTJ2pJPs2x835/Ycrkyw3N+R//S1gMkH8/FEyYRLEyxOmPv3hsXMHtPt+hOfnn1avhLs7rDWcF7kKl02ecnNz4e/vDw+bD9eAgACYTCYUFBSgadOah4Os6noYWr6iDperizWw8gNPY9uXumJBnQcs0TGVJwMeN8EcF29/cuDra/fa0uauy2637J6ul5xHP025Te3+6xG17Hz2aYhP42rbvvDzEYhOV+P6SubOhzkyCqUPDqh1ewWpX6HRtNfhsXMHAKWLbU3zWoL+D2Xl67Hcdjvcjx9D1AtD8M7td6jzW6JjkJdbqK6nNsX/WomSBYsgjZV9l3euANBooNEA1jZtYGrTBqahw1CS+Kbypf3KOHUb1vBwQGpef+GGFOWk+aabqk03vjgSjeYqzzpYAwOhHzMBlohIaGxOwKpdzPb1heG1f8IwcbJSPwBwr/4FoXF3d+x9r9EobS0rA3Q6tY7FcxfCr18vlEyaAvM9XVF8j/3vVqgx0WhQsDkNjV94FiUzZ6nbLFqxFvpjWbDcGfanjz+Npoa4BwbANOSJ2mOp80DB/3Yp3YRs2uOIioTPdvti+xnj7Y3zh4/DLeccrKGh6rqt5V3M7Lbl7w9D+R1NtfymWj6KK8rd3ZB/8CjcDx6A/72dK5et0oiCTVthjoiERlv9SjG07igrf07G0bYXvb8ajYf+A/qpMxyLVaNauvF6e6nH2s4yCzxvqqF+VVib+sMw4TWH6nvxk8/R+PmnYerbH6ZH/15jXS/+Zys8/70WhmefV+5GNGpU+T5q1Qo4riQuGg1wcf2n8Nj6FYzPvahMb9wY+Wk74DP5VdyUbtOd1N8fF/YdgHh6oVkbJZEyvDwWug3rYfrH42o9St6YDXPHThDvRvBathTFc+ZXq6P+jVmw3toKpv4PQVN1P3rqUHp/LzV5ulbfW2V9+uL8kWxI06aVydNtrWB8YaRD7xHjw4MgAYEwvPQy3A8egLlDrPIsYy3HV/Gid2B86lmYo2PUK9a285XFdYFH2lZYbm4BTZUBZcxR0er/tu0v+HIb/Horo9UZnk+o9n0qN99st5zpiSeh2/qV+lpdl81dB1PvvjANexJlPXspFz/KR9esbZ8Ynn3+sjHJzzisPOvm5lbregq2bYfP+Fegf3XSJddX+vAglD48yKEYibs7NBYL9NNn2q0zL7dQSS4qvisAGBNeAnQeymAft9yifufB3Q3wrhwJUfybVu7f4GAYE15Cae8+cM86CnN8FwCAIfRVAEBZj54o69ETTXpV3kmqWNY4+hWYhg2H+PlXa4t+zAR4bNsC4+NDYW3bFhe/3uHwuVJlRavPW7T4XeXz2yZZVr+vb2+Ni/9VEv9LbsLdDdawsCv6DrkiPrV3uy997B8o0rqjLKZ9tbaVlj+vfdl6ubvh4uavlfiXJ0FVl7FEx6CovHunBoDx8SfgPX8OSh/oA42H/UU1S9uIynX4NFK/34xtlbtQNSVPGg3sLq5fD+fiwJW8t1zUZ599Jvfee69d2e+//y4hISFy5swZJ9Wqjv3xh0iPHiIbN179uvbuFRkzRiQ/v/Z5fv9d2ZbF4tg6zWaRiAiRbt1ErNarr+OlBAaKACInTtiXf/edyNSpIqWllWVGo4grvicKC5X9r9dXlh09qrR7+PDKsi+/FGnTRuSHH6qv4/RpZf6wMJE5c5T9lpl59XUzmaqXvfGGsq2vv7769ddmwACRTp0cf09eK2Vlyn57+22RffsqyzMzRbKy6q8eVqvIffeJ3H9/5TGWlKTs9+XL626b9enZZ5X2pKZe2XJXU8+TJ0UGDhTZsePS8xkMIl27KvVbuNB+2gsviPz1ryIXL9bNPrNaRRYsEPnmm2u/bhGRV15R2pWefvl5t2wRGT1a+Wy9lnJzRWbMEMnOrnn69u0ix49XLw8IUOp+8GD1aRcuiDzyiMgXX1SWBQcr8589az/v6tUivr4in39++bquXSvi73/594yzFRSI/PLLtVnXhx+KPPig8h6/Uh99pOzzvn0dX+bPHkfDhinbeuutyrLDh0W2bq18ffasSFycEnNyTGnpn4vJN9+IdO8usnix8rm5apVSrteLTJwosmvXNa1mfdCIuOa4gps3b8bMmTPx3XeVI6wdO3YMvXv3Rnp6OvwcGDkHAM6fL3L6yIoaDdCsWeProi5XzWqt5bbANWYwQFNYWOtAAM5WpzE1GpUroQ7uY83FAmW0Oa1WvdJYZ8rv6tSpum7DJVwXx2oN7dcU5F9yWGSXIgLN+fN2XYvq0jWNqRPfm9eEwaAMduBqSkrgdj7P7lmiS8ZVRPkcramt5XeHHOLq8a5nbtnHlW6af/KnQRw+Vi0WuB/LUp55Znyua9fFd2oN9bkcl+22FxQUhPz8fJjNZmjLD8Tc3Fx4enrC19f3MktXUi5P1FUtr8z1VJc/raIfcV23w9ML4ulV99u5SnUSU135MyUOrld8/Wzm19TtPvPQ1UNM6rgNDnDusVq9/dLE3+n75NrRQJoF1Ht7rk1Mnf/evCou8JlaI+9GsHg3qrHuNcdVU3tbNW5XsA9cPN71zHJr+fOEV7nPLnusurlXPuPF+LgEVzv/da1RFWyEhYVBq9Vi//79atnevXsRHh7ucoNFEBERERHR9c9lswwvLy8MGDAA06ZNw88//4y0tDSsWLECTzzxhLOrRkREREREDZDLdtsDgNdeew3Tpk3DsGHD4OPjg5deegk9e17FL1cTERERERHVwqWTJy8vL8yePRuzZ892dlWIiIiIiKiBc9lue0RERERERPWJyRMREREREZEDmDwRERERERE5gMkTERERERGRA5g8EREREREROYDJExERERERkQOYPBERERERETmAyRMREREREZEDmDwRERERERE5gMkTERERERGRA7TOroCzaTTOrkFlHa6HutC1wZg2TIxrw8OYNkyMa8PDmDY811tMHa2HRkSkbqtCRERERETk+thtj4iIiIiIyAFMnoiIiIiIiBzA5ImIiIiIiMgBTJ6IiIiIiIgcwOSJiIiIiIjIAUyeiIiIiIiIHMDkiYiIiIiIyAFMnoiIiIiIiBzA5ImIiIiIiMgBTJ6czGQyYdKkSWjXrh3i4+OxYsUKZ1eJalFaWoq+ffsiPT1dLTt58iSGDx+OyMhI9O7dGzt37rRbZteuXejbty8iIiLwxBNP4OTJk3bTV61ahS5duiAqKgqTJk2CwWCol7YQcO7cOYwaNQodOnRAly5dkJSUBJPJBIBxdVUnTpzA008/jaioKNx7771Yvny5Oo0xdX0jRozAxIkT1deHDh3CI488goiICAwcOBAHDhywm3/Tpk3o0aMHIiIikJCQgAsXLqjTRATz5s1Dx44d0aFDB8yZMwdWq7Xe2nKj27ZtG0JDQ+3+Ro0aBYBxdVWlpaWYPn062rdvj86dO2P+/PkQEQANMKZCTjVjxgzp16+fHDhwQLZu3SpRUVGyefNmZ1eLqjAajZKQkCAhISGye/duERGxWq3Sr18/GTt2rGRlZcmyZcskIiJCTp8+LSIip0+flsjISHn//fclMzNTRo8eLX379hWr1SoiIl999ZXExMTIf//7X8nIyJDevXvL9OnTndbGG4nVapVHH31UnnnmGcnMzJQ9e/bI/fffL7NmzWJcXZTFYpGePXvK2LFjJTs7W7Zv3y7R0dHyxRdfMKYNwKZNmyQkJEReffVVEREpKSmRuLg4mTVrlmRlZUliYqJ07txZSkpKREQkIyND2rZtK5999pkcPnxYhgwZIiNGjFDX9/7770vXrl1lz5498v3330t8fLwsX77cKW27Eb3zzjvy3HPPSU5Ojvp38eJFxtWFvf7669KzZ0/JyMiQXbt2SWxsrKxbt65BxpTJkxOVlJRIeHi4ejIuIrJ06VIZMmSIE2tFVR09elQefPBB6devn13ytGvXLomMjFQ/AEREhg0bJosWLRIRkYULF9rFUq/XS1RUlLr84MGD1XlFRPbs2SNt27YVvV5fH826oWVlZUlISIjk5uaqZampqRIfH8+4uqhz587J6NGjpaioSC1LSEiQqVOnMqYuLj8/X+655x4ZOHCgmjxt2LBBunfvria4VqtV7r//ftm4caOIiIwfP16dV0Tkjz/+kNDQUPn9999FRKRr167qvCIiKSkp0q1bt/pq0g1v7Nix8tZbb1UrZ1xdU35+vrRp00bS09PVsuTkZJk4cWKDjCm77TnRkSNHYDabERUVpZbFxMQgIyPD+bckSfXDDz8gNjYW69evtyvPyMhAmzZt4O3trZbFxMRg//796vR27dqp07y8vHDXXXdh//79sFgs+OWXX+ymR0ZGoqysDEeOHKnbBhECAwOxfPlyBAQE2JUXFxczri6qefPmWLhwIXx8fCAi2Lt3L/bs2YMOHTowpi5u9uzZ6N+/P1q3bq2WZWRkICYmBhqNBgCg0WgQHR1da0xvvvlmtGjRAhkZGTh37hzOnDmD9u3bq9NjYmJw+vRp5OTk1E+jbnDHjh3DrbfeWq2ccXVNe/fuhY+PDzp06KCWjRgxAklJSQ0ypkyenCg3Nxf+/v7w8PBQywICAmAymVBQUOC8ipGdwYMHY9KkSfDy8rIrz83NRfPmze3KmjVrhrNnz152emFhIUwmk910rVYLPz8/dXmqO76+vujSpYv62mq14oMPPkDHjh0Z1wage/fuGDx4MKKiotCrVy/G1IV9//33+PHHH/Hiiy/alV8upjk5ObVOz83NBQC76RUXUhjTuiciyM7Oxs6dO9GrVy/06NED8+bNQ2lpKePqok6ePIng4GCkpKTgb3/7G+677z4sXboUVqu1QcZU67QtEwwGg13iBEB9XVpa6owq0RWoLX4VsbvUdKPRqL6ubXmqP3PnzsWhQ4fwySefYNWqVYyri1u0aBHy8vIwbdo0JCUl8Vh1USaTCVOnTsWUKVPg6elpN+1yMTUajVcUU3731p8//vhDjd/ChQtx6tQpzJw5E0ajkXF1UXq9HidOnMBHH32EpKQk5ObmYsqUKfDy8mqQMWXy5EQ6na5a8CteV/2ioOuPTqerdoewtLRUjV1t8fX19YVOp1NfV51e9Q4X1a25c+di9erVWLBgAUJCQhjXBiA8PByAcvI9btw4DBw4sNroeIzp9W/JkiW4++677e4SV6gtZpeLqZeXl93JV9X4MqZ1Lzg4GOnp6WjSpAk0Gg3CwsJgtVoxfvx4dOjQgXF1QVqtFsXFxXjrrbcQHBwMQEmS161bh5YtWza4mLLbnhMFBQUhPz8fZrNZLcvNzYWnpyd8fX2dWDNyRFBQEPLy8uzK8vLy1NvLtU0PDAyEn58fdDqd3XSz2YyCggIEBgbWfeUJAJCYmIiVK1di7ty56NWrFwDG1VXl5eUhLS3Nrqx169YoKytDYGAgY+qC/vOf/yAtLQ1RUVGIiopCamoqUlNTERUVdVXHaVBQEACoXYJs/2dM64efn5/6DAwA3H777TCZTFd1rDKuzhMYGAidTqcmTgDQqlUrnDlzpkEeq0yenCgsLAxarVZ9aA5QHroLDw+HmxtDc72LiIjAwYMH1dvKgBK/iIgIdfrevXvVaQaDAYcOHUJERATc3NwQHh5uN33//v3QarW48847668RN7AlS5bgo48+wvz589GnTx+1nHF1TadOncLIkSNx7tw5tezAgQNo2rQpYmJiGFMXtHbtWqSmpiIlJQUpKSno3r07unfvjpSUFEREROCnn35Sf0dGRLBv375aY3rmzBmcOXMGERERCAoKQosWLeym7927Fy1atKj27AVde99++y1iY2Pt7gYfPnwYfn5+iImJYVxdUEREBEwmE7Kzs9Wy48ePIzg4uGEeq84b6I9ElHHx+/TpIxkZGbJt2zaJjo6WLVu2OLtaVAvbocrNZrP07t1bXn75ZcnMzJTk5GSJjIxUfzvm5MmTEh4eLsnJyepvx/Tr108drnPTpk0SHR0t27Ztk4yMDOnTp48kJiY6rW03kqysLAkLC5MFCxbY/c5ITk4O4+qizGazPPzww/LUU0/J0aNHZfv27dK5c2dZtWoVY9pAvPrqq+qQxkVFRdKxY0dJTEyUo0ePSmJiosTFxanD0e/bt0/uuusu+fjjj9XfjnnuuefUdSUnJ0t8fLzs3r1bdu/eLfHx8bJixQqntOtGU1RUJF26dJExY8bIsWPHZPv27RIfHy/vvfce4+rCRowYIY899pgcPnxYduzYIR07dpTVq1c3yJgyeXIyvV4vEyZMkMjISImPj5eVK1c6u0p0CbbJk4jIb7/9Jo8//rjcfffd0qdPH/nuu+/s5t++fbv07NlT2rZtK8OGDVN/t6BCcnKydOrUSWJiYuS1114To9FYL+240SUnJ0tISEiNfyKMq6s6e/asJCQkSHR0tMTFxcm7776rJkCMqeuzTZ5ElB/XHDBggISHh8ugQYPk4MGDdvNv3LhRunbtKpGRkZKQkCAXLlxQp5nNZnnzzTelXbt2EhsbK3PnzlXfK1T3MjMzZfjw4RIZGSlxcXGyePFidf8zrq6psLBQxo8fL5GRkdKpU6cGHVONSPl9NCIiIiIiIqoVH6whIiIiIiJyAJMnIiIiIiIiBzB5IiIiIiIicgCTJyIiIiIiIgcweSIiIiIiInIAkyciIiIiIiIHMHkiIiIiIiJyAJMnIiIiIiIiBzB5IiIilzZ06FAsXrz4Ty0bGhqK9PT0a1wjIiJqqJg8EREREREROYDJExERERERkQOYPBERUYPw6aefYujQoVi0aBFiY2PRrl07JCUlQUTUeZYsWYJOnTohNjYWGzZssFu+tLQUM2fORGxsLGJjYzFu3DgUFBQAADZs2IC7774bJ06cAAAcO3YM4eHhSEtLq7f2ERGR8zF5IiKiBuOnn35CdnY21q1bh9dffx1r1qzBrl27AADr16/HmjVr8Oabb2LVqlXYuHGj3bLz58/HgQMH8K9//Qtr1qxBcXExRo8eDQAYNGgQoqKi1GRsypQp6NmzJ3r06FHvbSQiIudh8kRERA2GxWJBYmIibrvtNvTv3x933nknfvnlFwDAxx9/jGHDhqFbt24ICwvDzJkz1eUMBgM++OADTJ8+HW3btkVoaCjmzJmDH374Ab/++is0Gg1mzJiBXbt2Ydy4ccjOzsbkyZOd1UwiInISrbMrQEREdK00a9YMPj4+6msfHx+YzWYASle7hIQEdVrr1q3h7e0NADh58iTKysrw97//3W59VqsVv/32G0JDQ9GqVSuMGDECixcvxuzZs9G0adN6aBEREV1PmDwREVGD4eHhUa3M9pkn2/8BQKtVvgYtFgsA4MMPP1QTqgrNmjVT/z9y5Ajc3d2Rnp6OAQMGXKtqExGRi2C3PSIiuiHccccdahc+ADh16hQKCwsBAH/5y1/g7u6OgoICtGzZEi1btoSPjw+SkpJw/vx5AEBaWhp27tyJZcuWITU1Fd9//71T2kFERM7D5ImIiG4IQ4YMwZo1a7BlyxZkZmZi8uTJcHNTvgZ9fHzwyCOPYNq0aUhPT0dWVhYmTJiAEydO4JZbbkFxcTESExPxwgsv4J577sGQIUMwdepUmEwmJ7eKiIjqE5MnIiK6IfTv3x+jRo1CYmIiBg8ejLi4OPj6+qrTJ06ciE6dOmHUqFF49NFHodVq8d5778Hd3R0LFiyAp6cnnnzySQDAyJEjodfrsXTpUmc1h4iInEAjVTuAExERERERUTW880REREREROQAJk9EREREREQOYPJERERERETkACZPREREREREDmDyRERERERE5AAmT0RERERERA5g8kREREREROQAJk9EREREREQOYPJERERERETkACZPREREREREDmDyRERERERE5ID/B7VdIFlQl5TnAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BenjaminLoh\\AppData\\Local\\Temp\\ipykernel_6952\\2998122009.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data['Total_Distance'] = data[cir_columns].abs().sum(axis=1) * speed_of_light_ns\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      NLOS  RANGE  FP_IDX  MAX_NOISE  FRAME_LEN  PREAM_LEN        CIR0  \\\n",
      "0        0   3.90   745.0      967.0          2          0  128.646447   \n",
      "1        0   0.66   749.0     1133.0          0          0  117.353553   \n",
      "2        1   7.86   746.0      894.0          0          0  433.353553   \n",
      "3        1   3.48   750.0     1127.0          2          0  465.646447   \n",
      "4        0   1.19   746.0     1744.0          0          0  259.353553   \n",
      "...    ...    ...     ...        ...        ...        ...         ...   \n",
      "5995     0   2.43   745.0     2379.0          0          0  395.646447   \n",
      "5996     0   1.39   745.0     1810.0          2          0  183.353553   \n",
      "5997     1   5.48   747.0     1092.0          2          0  202.646447   \n",
      "5998     1   3.40   745.0     1077.0          0          0  484.353553   \n",
      "5999     0   2.43   747.0     1224.0          0          0  747.646447   \n",
      "\n",
      "            CIR1        CIR2        CIR3  ...     CIR1010     CIR1011  \\\n",
      "0     311.646447  330.646447  140.646447  ...  197.646447   87.353553   \n",
      "1     163.353553  199.353553  136.353553  ...  187.353553  212.646447   \n",
      "2     240.353553  233.353553   81.353553  ...  124.353553  329.353553   \n",
      "3     246.646447  406.646447  224.646447  ...  433.646447  396.646447   \n",
      "4     239.353553   57.353553   19.353553  ...   87.353553  178.353553   \n",
      "...          ...         ...         ...  ...         ...         ...   \n",
      "5995  277.646447  279.646447  278.353553  ...  193.353553  417.646447   \n",
      "5996  304.646447  247.646447  162.353553  ...  211.353553  144.353553   \n",
      "5997  242.353553  228.353553  188.646447  ...  253.646447  191.646447   \n",
      "5998  210.353553   75.353553  246.353553  ...   86.353553  123.353553   \n",
      "5999  366.646447  744.646447  717.646447  ...  726.646447  366.646447   \n",
      "\n",
      "         CIR1012     CIR1013     CIR1014     CIR1015   RX_Level  \\\n",
      "0     295.646447  504.646447  306.646447    0.000000 -27.806709   \n",
      "1     202.353553   89.353553  103.353553    0.000000 -23.050836   \n",
      "2     207.353553   96.353553  218.353553    0.353553 -28.334789   \n",
      "3     289.646447  154.646447  341.646447  255.646447 -33.611812   \n",
      "4     313.646447  247.353553  292.353553  256.000000 -21.081660   \n",
      "...          ...         ...         ...         ...        ...   \n",
      "5995  242.646447  225.646447  256.353553    0.000000 -21.648732   \n",
      "5996   47.353553  110.353553   82.353553    0.000000 -20.809477   \n",
      "5997  180.646447  445.646447  299.646447    0.000000 -41.462381   \n",
      "5998  522.353553  295.353553  223.353553  256.353553 -41.296123   \n",
      "5999  802.646447  818.646447  466.646447  767.646447 -23.907380   \n",
      "\n",
      "      First_Path_Power_Level         SNR  Total_Distance  \n",
      "0                 -32.018158  185.234375   120371.292417  \n",
      "1                 -34.255986  296.375000   135265.933614  \n",
      "2                 -43.509715  244.983333   153365.885081  \n",
      "3                 -41.706556  115.105263   125802.050979  \n",
      "4                 -23.834943  167.352941   110697.999893  \n",
      "...                      ...         ...             ...  \n",
      "5995              -24.337520  104.031250   110297.192886  \n",
      "5996              -28.059910  165.250000   106982.743632  \n",
      "5997              -43.758684   19.930556   122190.602961  \n",
      "5998              -42.910043   20.708333   132871.634243  \n",
      "5999              -32.863387  162.785714   234026.967141  \n",
      "\n",
      "[41997 rows x 1026 columns]\n",
      "Data loaded and cleaned successfully.\n",
      "Saving cleaned data to pickle file...\n",
      "Cleaned data saved to pickle file successfully.\n",
      "First few rows of the data:\n",
      "   NLOS  RANGE  FP_IDX  MAX_NOISE  FRAME_LEN  PREAM_LEN        CIR0  \\\n",
      "0     0   3.90   745.0      967.0          2          0  128.646447   \n",
      "1     0   0.66   749.0     1133.0          0          0  117.353553   \n",
      "2     1   7.86   746.0      894.0          0          0  433.353553   \n",
      "3     1   3.48   750.0     1127.0          2          0  465.646447   \n",
      "4     0   1.19   746.0     1744.0          0          0  259.353553   \n",
      "\n",
      "         CIR1        CIR2        CIR3  ...     CIR1010     CIR1011  \\\n",
      "0  311.646447  330.646447  140.646447  ...  197.646447   87.353553   \n",
      "1  163.353553  199.353553  136.353553  ...  187.353553  212.646447   \n",
      "2  240.353553  233.353553   81.353553  ...  124.353553  329.353553   \n",
      "3  246.646447  406.646447  224.646447  ...  433.646447  396.646447   \n",
      "4  239.353553   57.353553   19.353553  ...   87.353553  178.353553   \n",
      "\n",
      "      CIR1012     CIR1013     CIR1014     CIR1015   RX_Level  \\\n",
      "0  295.646447  504.646447  306.646447    0.000000 -27.806709   \n",
      "1  202.353553   89.353553  103.353553    0.000000 -23.050836   \n",
      "2  207.353553   96.353553  218.353553    0.353553 -28.334789   \n",
      "3  289.646447  154.646447  341.646447  255.646447 -33.611812   \n",
      "4  313.646447  247.353553  292.353553  256.000000 -21.081660   \n",
      "\n",
      "   First_Path_Power_Level         SNR  Total_Distance  \n",
      "0              -32.018158  185.234375   120371.292417  \n",
      "1              -34.255986  296.375000   135265.933614  \n",
      "2              -43.509715  244.983333   153365.885081  \n",
      "3              -41.706556  115.105263   125802.050979  \n",
      "4              -23.834943  167.352941   110697.999893  \n",
      "\n",
      "[5 rows x 1026 columns]\n",
      "Column headers:\n",
      "Index(['NLOS', 'RANGE', 'FP_IDX', 'MAX_NOISE', 'FRAME_LEN', 'PREAM_LEN',\n",
      "       'CIR0', 'CIR1', 'CIR2', 'CIR3',\n",
      "       ...\n",
      "       'CIR1010', 'CIR1011', 'CIR1012', 'CIR1013', 'CIR1014', 'CIR1015',\n",
      "       'RX_Level', 'First_Path_Power_Level', 'SNR', 'Total_Distance'],\n",
      "      dtype='object', length=1026)\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# File='data_original.pkl'\n",
    "File = 'data.pkl'\n",
    "\n",
    "# Check if the file exists\n",
    "if os.path.exists(File):\n",
    "    # If the file exists, load it\n",
    "    print(\"Loading data from pickle file...\")\n",
    "    with open(File, 'rb') as f:\n",
    "        data = pickle.load(f)\n",
    "        # plot_features(data, data['NLOS'], \"First_Path_Power_Level\", \"RX_Level\")\n",
    "        # plot_features(data, data['NLOS'], \"SNR\", \"RX_Level\")\n",
    "        # plot_features(data, data['NLOS'], \"SNR\", \"First_Path_Power_Level\")\n",
    "        snr_graph(data)\n",
    "        cir_graphs(data)\n",
    "    print(\"Data loaded successfully.\")\n",
    "else:\n",
    "    # If the file doesn't exist, load and clean the data\n",
    "    print(\"Pickle file not found. Loading and cleaning data...\")\n",
    "    data = load_data(DATASET_DIR)\n",
    "    cir_graphs(data)\n",
    "    data = clean_data(data)\n",
    "    plot_features(data, data['NLOS'], \"First_Path_Power_Level\", \"RX_Level\")\n",
    "    snr_graph(data)\n",
    "    cir_graphs(data)\n",
    "    print(calculate_total_distance(data))\n",
    "    print(\"Data loaded and cleaned successfully.\")\n",
    "    print(\"Saving cleaned data to pickle file...\")\n",
    "    with open(File, 'wb') as f:\n",
    "        pickle.dump(data, f)\n",
    "    print(\"Cleaned data saved to pickle file successfully.\")\n",
    "\n",
    "print(\"First few rows of the data:\")\n",
    "print(data.head())\n",
    "\n",
    "# Print Headers\n",
    "print(\"Column headers:\")\n",
    "print(data.columns)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:01:51.078701Z",
     "start_time": "2024-03-20T10:01:30.817851Z"
    }
   },
   "id": "79c2c23691b26753",
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "MODEL_DIR = './models'\n",
    "\n",
    "\n",
    "def train_and_save_model(classifier, X_train, y_train, file_name):\n",
    "    if not os.path.exists(MODEL_DIR):\n",
    "        os.makedirs(MODEL_DIR)\n",
    "\n",
    "    file_path = os.path.join(MODEL_DIR, file_name)\n",
    "\n",
    "    # Check if the file exists\n",
    "    if not os.path.exists(file_path):\n",
    "        print(f\"Training the model and saving it to {file_path}\")\n",
    "        # Train the classifier\n",
    "        classifier.fit(X_train, y_train)\n",
    "\n",
    "        # Save the trained model as a pickle string.\n",
    "        saved_model = pickle.dumps(classifier)\n",
    "\n",
    "        # Save the pickled model to a file\n",
    "        with open(file_path, 'wb') as file:\n",
    "            file.write(saved_model)\n",
    "\n",
    "    # Load the pickled model from the file\n",
    "    with open(file_path, 'rb') as file:\n",
    "        loaded_model = pickle.load(file)\n",
    "\n",
    "    return loaded_model"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.171209Z",
     "start_time": "2024-03-19T14:36:34.167079Z"
    }
   },
   "id": "12e16974341e6266",
   "execution_count": 39
  },
  {
   "cell_type": "markdown",
   "source": [
    "The selected code is performing data standardization, which is a common preprocessing step in many machine learning workflows. \n",
    "\n",
    "The purpose of standardization is to transform the data such that it has a mean of 0 and a standard deviation of 1. This is done to ensure that all features have the same scale, which is a requirement for many machine learning algorithms.\n",
    "\n",
    "The mathematical formulas used in this process are as follows:\n",
    "\n",
    "1. Calculate the mean (μ) of the data:\n",
    "\n",
    "$$\n",
    "\\mu = \\frac{1}{n} \\sum_{i=1}^{n} x_i\n",
    "$$\n",
    "Where:\n",
    "- $n$ is the number of observations in the data\n",
    "- $x_i$ is the value of the $i$-th observation\n",
    "- $\\sum$ denotes the summation over all observations\n",
    "\n",
    "2. Standardize the data by subtracting the mean from each observation and dividing by the standard deviation:\n",
    "\n",
    "$$\n",
    "\\text{Data}_i = \\frac{x_i - \\mu}{\\sigma}\n",
    "$$\n",
    "Where:\n",
    "- $\\text{Data}_i$ is the standardized value of the $i$-th observation\n",
    "- $\\sigma$ is the standard deviation of the data\n",
    "- $x_i$ is the value of the $i$-th observation\n",
    "- $\\mu$ is the mean of the data\n",
    "\n",
    "The `StandardScaler` class from the `sklearn.preprocessing` module is used to perform this standardization. The `fit_transform` method is used to calculate the mean and standard deviation of the data and then perform the standardization.\n",
    "\n",
    "**Note:** By setting the explained variance to 0.95, we are saying that we want to choose the smallest number of principal components such that 95% of the variance in the original data is retained. This means that the transformed data will retain 95% of the information of the original data, while potentially having fewer dimensions.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "b36814c942066d6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Mining / Machine Learning\n",
    "\n",
    "### I. Supervised Learning\n",
    "- **Decision**: Supervised learning is used due to the labeled dataset.\n",
    "- **Algorithm**: Random Forest Classifier is preferred for its performance in classification tasks.\n",
    "\n",
    "### II. Training/Test Split Ratio\n",
    "- **Decision**: 70:30 split is chosen for training/test dataset.\n",
    "- **Reasoning**: This split ensures sufficient data for training and testing.\n",
    "\n",
    "### III. Performance Metrics\n",
    "- **Classification Accuracy**: Measures the proportion of correctly classified instances.\n",
    "- **Confusion Matrix**: Provides a summary of predicted and actual classes.\n",
    "- **Classification Report**: Provides detailed metrics such as precision, recall, F1-score, and support for each class.\n",
    "\n",
    "The Random Forest Classifier is trained on the training set and evaluated on the test set using accuracy and classification report metrics.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8fefd253728ea2f0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Split the data into training and testing sets\n",
    "\n",
    "The next step is to split the data into training and testing sets. This is a common practice in machine learning, where the training set is used to train the model, and the testing set is used to evaluate its performance.\n",
    "\n",
    "We will use the `train_test_split` function from the `sklearn.model_selection` module to split the data into training and testing sets. We will use 70% of the data for training and 30% for testing, which is a common split ratio."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7d64d6490fa1c2c2"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "# Assuming 'NLOS' is your target column\n",
    "# y = data['NLOS']\n",
    "\n",
    "# Convert labels to categorical one-hot encoding\n",
    "# y_categorical = to_categorical(y, num_classes=2)\n",
    "\n",
    "# Now split the data\n",
    "# X_train, X_test, y_train, y_test = train_test_split(data, y_categorical, test_size=0.2)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.176671Z",
     "start_time": "2024-03-19T14:36:34.172145Z"
    }
   },
   "id": "54d2a6506b584a03",
   "execution_count": 40
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train a Random Forest Classifier\n",
    "\n",
    "The next step is to train a machine learning model on the training data. We will use the `RandomForestClassifier` class from the `sklearn.ensemble` module to train a random forest classifier.\n",
    "\n",
    "The random forest classifier is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.\n",
    "\n",
    "We will use the `fit` method of the `RandomForestClassifier` object to train the model on the training data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ab55160e30fd6f99"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# \n",
    "# # Initialize the classifier with parameters to prevent overfitting\n",
    "# classifier = RandomForestClassifier(n_estimators=200, max_depth=10, min_samples_split=10, min_samples_leaf=5, max_features='sqrt')\n",
    "# \n",
    "# loaded_model = train_and_save_model(classifier, X_train, y_train, 'random_forest_classifier.pkl')\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.180663Z",
     "start_time": "2024-03-19T14:36:34.178318Z"
    }
   },
   "id": "dc485f3de9f8936f",
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Evaluate the Model\n",
    "\n",
    "To evaluate the performance of the trained model on the testing data, we will use the `predict` method of the `RandomForestClassifier` object to make predictions on the testing data. We will then use the `accuracy_score` and `classification_report` functions from the `sklearn.metrics` module to calculate the accuracy and generate a classification report.\n",
    "\n",
    "- **Accuracy:** The accuracy score function calculates the proportion of correctly classified instances.\n",
    "\n",
    "- **Precision:** The ratio of correctly predicted positive observations to the total predicted positive observations. It is calculated as:\n",
    "\n",
    "  $$\n",
    "  \\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}\n",
    "  $$\n",
    "\n",
    "- **Recall:** The ratio of correctly predicted positive observations to all observations in the actual class. It is calculated as:\n",
    "\n",
    "  $$\n",
    "  \\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}\n",
    "  $$\n",
    "\n",
    "- **F1 Score:** The weighted average of precision and recall. It is calculated as:\n",
    "\n",
    "  $$\n",
    "  \\text{F1 Score} = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}\n",
    "  $$\n",
    "\n",
    "- **Support:** The number of actual occurrences of the class in the dataset.\n",
    "\n",
    "The classification report provides a summary of the precision, recall, F1-score, and support for each class in the testing data, giving insight into how well the model is performing for each class.\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "424cc5954c9e81cc"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "# Make predictions on the test set using the loaded model\n",
    "# y_pred = loaded_model.predict(X_test)\n",
    "# \n",
    "# # Evaluate the loaded model\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "# classification_rep = classification_report(y_test, y_pred)\n",
    "# cross_val_score = cross_val_score(loaded_model, X_test, y_test, cv=5)\n",
    "# \n",
    "# print(f\"Accuracy: {accuracy}\")\n",
    "# print(f\"Classification Report:\\n{classification_rep}\")\n",
    "# print(f\"Cross Validation Score: {cross_val_score}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.183956Z",
     "start_time": "2024-03-19T14:36:34.181545Z"
    }
   },
   "id": "702b4f40dda16736",
   "execution_count": 42
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Visualize a Decision Tree from the Random Forest\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "41957f9babb74a3"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.tree import plot_tree\n",
    "# import matplotlib.pyplot as plt\n",
    "# \n",
    "# # Select one tree from the forest\n",
    "# estimator = loaded_model.estimators_[0]\n",
    "# \n",
    "# plt.figure(figsize=(100, 100))\n",
    "# plot_tree(estimator,\n",
    "#           filled=True,\n",
    "#           rounded=True,\n",
    "#           class_names=['NLOS', 'LOS'],\n",
    "#           feature_names=data.columns,\n",
    "#           max_depth=5)  # Limit the depth of the tree\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.187211Z",
     "start_time": "2024-03-19T14:36:34.185001Z"
    }
   },
   "id": "1f6f826d6234591c",
   "execution_count": 43
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Support Vector Machine (SVM)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "eef3be2c3026a909"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# import os\n",
    "# from sklearn.svm import SVC\n",
    "# import pickle\n",
    "# \n",
    "# svm = SVC(kernel='linear', random_state=42, verbose=True)\n",
    "# loaded_model = train_and_save_model(svm, X_train, y_train, 'svm_classifier.pkl')\n",
    "# \n",
    "# # Predict the labels for the test set with each model\n",
    "# y_pred_svm = loaded_model.predict(X_test)\n",
    "# \n",
    "# # Calculate the accuracy of each model\n",
    "# accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "# \n",
    "# # Print the accuracy of each model\n",
    "# print(f\"Accuracy of SVM: {accuracy_svm}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.190410Z",
     "start_time": "2024-03-19T14:36:34.188127Z"
    }
   },
   "id": "c970b0c1593d955c",
   "execution_count": 44
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Logistic Regression"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "cccaf1db0d5060a8"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.model_selection import cross_val_score\n",
    "# \n",
    "# # Logistic Regression with L2 regularization\n",
    "# log_reg = LogisticRegression(penalty='l2', C=0.1)\n",
    "# \n",
    "# # Use the train_and_save_model function to train and save the model\n",
    "# loaded_model = train_and_save_model(log_reg, X_train, y_train, 'logistic_regression_model.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.193604Z",
     "start_time": "2024-03-19T14:36:34.191388Z"
    }
   },
   "id": "ee7506f4aa805faf",
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "# # Predict on the test set\n",
    "# y_pred_log_reg = loaded_model.predict(X_test)\n",
    "# \n",
    "# # Calculate accuracy\n",
    "# accuracy_log_reg = accuracy_score(y_test, y_pred_log_reg)\n",
    "# print(f\"Accuracy of Logistic Regression: {accuracy_log_reg}\")\n",
    "# \n",
    "# # Perform 5-fold cross validation\n",
    "# scores = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
    "# print(f\"Cross-validated scores: {scores}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.196677Z",
     "start_time": "2024-03-19T14:36:34.194470Z"
    }
   },
   "id": "a44d38efa4b86d93",
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.metrics import roc_curve, auc\n",
    "# import matplotlib.pyplot as plt\n",
    "# \n",
    "# # Compute ROC curve and ROC area for each class\n",
    "# fpr, tpr, _ = roc_curve(y_test, y_pred_log_reg)\n",
    "# roc_auc = auc(fpr, tpr)\n",
    "# \n",
    "# plt.figure()\n",
    "# lw = 2\n",
    "# plt.plot(fpr, tpr, color='darkorange',\n",
    "#          lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "# plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "# plt.xlim([0.0, 1.0])\n",
    "# plt.ylim([0.0, 1.05])\n",
    "# plt.xlabel('False Positive Rate')\n",
    "# plt.ylabel('True Positive Rate')\n",
    "# plt.title('Receiver Operating Characteristic')\n",
    "# plt.legend(loc=\"lower right\")\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.201209Z",
     "start_time": "2024-03-19T14:36:34.198983Z"
    }
   },
   "id": "a3646a4965b0707c",
   "execution_count": 47
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Gradient Boosting Classifier"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "aeaf5eeffa7ec104"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import GradientBoostingClassifier\n",
    "# \n",
    "# # Gradient Boosting Classifier\n",
    "# gbc = GradientBoostingClassifier()\n",
    "# \n",
    "# # Use the train_and_save_model function to train and save the model\n",
    "# loaded_model = train_and_save_model(gbc, X_train, y_train, 'gradient_boosting_classifier.pkl')\n",
    "# "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.204277Z",
     "start_time": "2024-03-19T14:36:34.202108Z"
    }
   },
   "id": "c7ecae5d021ad44f",
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# y_pred_gbc = loaded_model.predict(X_test)\n",
    "# accuracy_gbc = accuracy_score(y_test, y_pred_gbc)\n",
    "# print(f\"Accuracy of Gradient Boosting Classifier: {accuracy_gbc}\")\n",
    "# "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.207167Z",
     "start_time": "2024-03-19T14:36:34.205128Z"
    }
   },
   "id": "4a8a1c3a7289ef7a",
   "execution_count": 49
  },
  {
   "cell_type": "markdown",
   "source": [
    "# K-Nearest Neighbors (KNN, K=15)\n",
    "\n",
    "This code block is implementing the K-Nearest Neighbors (KNN) algorithm for classification. The KNN algorithm is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until function evaluation.  \n",
    "\n",
    "The KNN algorithm works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression).  \n",
    "\n",
    "The number of neighbors, K, is set to 15 in this case. This means that the algorithm looks at the 15 nearest neighbors to decide the class of the test instance.  \n",
    "\n",
    "The mathematical concept behind KNN is the Euclidean distance. Given two points P1(x1, y1) and P2(x2, y2) in a 2D space, the Euclidean distance between P1 and P2 is calculated as:\n",
    "\n",
    "$$\n",
    "\\text{Distance} = \\sqrt{(x2 - x1)^2 + (y2 - y1)^2}\n",
    "$$\n",
    "In higher dimensional space, the formula is generalized as:\n",
    "$$\n",
    "\\text{Distance} = \\sqrt{\\sum_{i=1}^{n} (x_i - y_i)^2}\n",
    "$$\n",
    "Where:\n",
    "- $n$ is the number of dimensions\n",
    "- $x_i$ and $y_i$ are the $i$-th dimensions of the two points\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "25102568a6e5c457"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "# \n",
    "# # K-Nearest Neighbors\n",
    "# knn = KNeighborsClassifier(n_neighbors=13)\n",
    "# loaded_model = train_and_save_model(knn, X_train, y_train, 'knn_classifier.pkl')\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.210206Z",
     "start_time": "2024-03-19T14:36:34.208116Z"
    }
   },
   "id": "705c62e64bf6d614",
   "execution_count": 50
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# y_pred_knn = loaded_model.predict(X_test)\n",
    "# accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "# print(f\"Accuracy of K-Nearest Neighbors: {accuracy_knn}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.213117Z",
     "start_time": "2024-03-19T14:36:34.211022Z"
    }
   },
   "id": "cf4df4ef7bbfd74",
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "# \n",
    "# # Define the parameter values that should be searched\n",
    "# k_range = list(range(1, 31))\n",
    "# \n",
    "# # Create a parameter grid: map the parameter names to the values that should be searched\n",
    "# param_grid = dict(n_neighbors=k_range)\n",
    "# \n",
    "# # Instantiate the grid\n",
    "# grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n",
    "# \n",
    "# # Fit the grid with data\n",
    "# grid.fit(X_train, y_train)\n",
    "# \n",
    "# # View the complete results\n",
    "# grid.cv_results_\n",
    "# \n",
    "# # Examine the best model\n",
    "# print(grid.best_score_)\n",
    "# print(grid.best_params_)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.216452Z",
     "start_time": "2024-03-19T14:36:34.214045Z"
    }
   },
   "id": "faabcf63e34005a9",
   "execution_count": 52
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# import numpy as np\n",
    "# \n",
    "# # Apply PCA to reduce dimensionality to 2D\n",
    "# pca = PCA(n_components=2)\n",
    "# X_test_2d = pca.fit_transform(X_test)\n",
    "# \n",
    "# # Print the number of features\n",
    "# print(f\"Original number of features: {X_test.shape[1]}, reduced number of features: {X_test_2d.shape[1]}\")\n",
    "# \n",
    "# # Create a scatter plot\n",
    "# plt.figure(figsize=(10, 7))\n",
    "# \n",
    "# # Create a color map\n",
    "# cmap = plt.cm.viridis\n",
    "# \n",
    "# # Plot NLOS points\n",
    "# nlos = plt.scatter(X_test_2d[y_pred_knn == 1, 0], X_test_2d[y_pred_knn == 1, 1], c='blue', label='NLOS')\n",
    "# \n",
    "# # Plot LOS points\n",
    "# los = plt.scatter(X_test_2d[y_pred_knn == 0, 0], X_test_2d[y_pred_knn == 0, 1], c='red', label='LOS')\n",
    "# \n",
    "# # Add labels\n",
    "# plt.xlabel('Principal Component 1')\n",
    "# plt.ylabel('Principal Component 2')\n",
    "# plt.title('2D Scatter Plot for LOS and NLOS')\n",
    "# \n",
    "# # Add a legend\n",
    "# plt.legend(handles=[nlos, los])\n",
    "# \n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.219790Z",
     "start_time": "2024-03-19T14:36:34.217307Z"
    }
   },
   "id": "2ed22b3fc59f74e6",
   "execution_count": 53
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# import matplotlib.pyplot as plt\n",
    "# \n",
    "# # Define the list of numbers of neighbors (from 1-20)\n",
    "# num_neighbors = np.arange(1, 100, 2)\n",
    "# \n",
    "# # Initialize the lists to store the accuracies\n",
    "# train_acc = []\n",
    "# test_acc = []\n",
    "# \n",
    "# # Loop over the different numbers of neighbors\n",
    "# for k in num_neighbors:\n",
    "#     # Initialize the KNN classifier\n",
    "#     clf = KNeighborsClassifier(n_neighbors=k)\n",
    "#     \n",
    "#     # Fit the classifier on the training data\n",
    "#     clf.fit(X_train, y_train)\n",
    "#     \n",
    "#     # Make predictions on the training and test data\n",
    "#     y_pred_train = clf.predict(X_train)\n",
    "#     y_pred_test = clf.predict(X_test)\n",
    "#     \n",
    "#     # Calculate the accuracies\n",
    "#     train_acc.append(accuracy_score(y_train, y_pred_train))\n",
    "#     test_acc.append(accuracy_score(y_test, y_pred_test))\n",
    "# \n",
    "# # Plot the accuracies\n",
    "# plt.figure(figsize=(10, 5))\n",
    "# plt.plot(num_neighbors, train_acc, 'ro-', num_neighbors, test_acc, 'bv--')\n",
    "# plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
    "# plt.xlabel('Number of Neighbors')\n",
    "# plt.ylabel('Accuracy')\n",
    "# plt.title('Training and Test Accuracy for Different Numbers of Neighbors in KNN')\n",
    "# plt.grid()\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.224221Z",
     "start_time": "2024-03-19T14:36:34.220898Z"
    }
   },
   "id": "4ac86c268055c1b8",
   "execution_count": 54
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Naive Bayes"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5b9b66f92968957c"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.naive_bayes import GaussianNB\n",
    "# \n",
    "# # Naive Bayes\n",
    "# nb = GaussianNB()\n",
    "# loaded_model = train_and_save_model(nb, X_train, y_train, 'naive_bayes_classifier.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.308314Z",
     "start_time": "2024-03-19T14:36:34.225481Z"
    }
   },
   "id": "3d984228fb1d3026",
   "execution_count": 55
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# y_pred_nb = loaded_model.predict(X_test)\n",
    "# accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
    "# print(f\"Accuracy of Naive Bayes: {accuracy_nb}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.391920Z",
     "start_time": "2024-03-19T14:36:34.309741Z"
    }
   },
   "id": "98cd350871bc3201",
   "execution_count": 56
  },
  {
   "cell_type": "markdown",
   "source": [
    "# K-Means Clustering"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "92c8498137a5e32e"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.cluster import KMeans\n",
    "# \n",
    "# # K-Means Clustering\n",
    "# kmeans = KMeans(n_clusters=2, max_iter=600)\n",
    "# loaded_model = train_and_save_model(kmeans, X_train, y_train, 'kmeans_clustering.pkl')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.395785Z",
     "start_time": "2024-03-19T14:36:34.393071Z"
    }
   },
   "id": "305a796294814705",
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# y_pred_kmeans = loaded_model.predict(X_test)\n",
    "# accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n",
    "# print(f\"Accuracy of K-Means Clustering: {accuracy_kmeans}\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.399666Z",
     "start_time": "2024-03-19T14:36:34.397013Z"
    }
   },
   "id": "494bb537046bf5a7",
   "execution_count": 58
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# labels = loaded_model.labels_\n",
    "# # Print the data table with the cluster labels\n",
    "# print(f\"Data table with cluster labels:\\n{pd.concat([X_test, pd.DataFrame({'Cluster': labels})], axis=1)}\")\n",
    "# \n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.403493Z",
     "start_time": "2024-03-19T14:36:34.400833Z"
    }
   },
   "id": "62401c8d1a4d61cc",
   "execution_count": 59
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.cluster import KMeans\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# from sklearn.decomposition import PCA\n",
    "# from mpl_toolkits.mplot3d import Axes3D\n",
    "# import matplotlib.pyplot as plt\n",
    "# \n",
    "# # Define the range of cluster numbers\n",
    "# cluster_range = range(1, 15)\n",
    "# \n",
    "# # For each number of clusters\n",
    "# for n_clusters in cluster_range:\n",
    "#     # Create a KMeans model\n",
    "#     kmeans = KMeans(n_clusters=n_clusters, max_iter=600)\n",
    "# \n",
    "#     # Fit the model to the training data\n",
    "#     kmeans.fit(X_train)\n",
    "# \n",
    "#     # Make predictions on the test data\n",
    "#     y_pred_kmeans = kmeans.predict(X_test)\n",
    "# \n",
    "#     # Calculate the accuracy of the model\n",
    "#     accuracy_kmeans = accuracy_score(y_test, y_pred_kmeans)\n",
    "# \n",
    "#     # Print the number of clusters and the corresponding accuracy\n",
    "#     print(f\"Number of clusters: {n_clusters}, Accuracy: {accuracy_kmeans}\")\n",
    "# \n",
    "#     # Apply PCA to reduce dimensionality to 3D\n",
    "#     pca = PCA(n_components=3)\n",
    "#     X_test_3d = pca.fit_transform(X_test)\n",
    "# \n",
    "#     # Create a 3D scatter plot\n",
    "#     fig = plt.figure(figsize=(10, 7))\n",
    "#     ax = fig.add_subplot(111, projection='3d')\n",
    "# \n",
    "#     # Create a color map\n",
    "#     cmap = plt.cm.get_cmap('viridis', n_clusters)  # We use 'viridis' colormap and we specify that we have n_clusters\n",
    "# \n",
    "#     # Plot the points with colors according to their cluster assignment\n",
    "#     scatter = ax.scatter(X_test_3d[:, 0], X_test_3d[:, 1], X_test_3d[:, 2], c=y_pred_kmeans, cmap=cmap)\n",
    "# \n",
    "#     # Add labels\n",
    "#     ax.set_xlabel('Principal Component 1')\n",
    "#     ax.set_ylabel('Principal Component 2')\n",
    "#     ax.set_zlabel('Principal Component 3')\n",
    "#     plt.title(f'3D Visualization of {n_clusters} Clusters')\n",
    "# \n",
    "#     # Display the plot\n",
    "#     plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.408739Z",
     "start_time": "2024-03-19T14:36:34.404634Z"
    }
   },
   "id": "f0f5284581e70e6e",
   "execution_count": 60
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# from sklearn.decomposition import PCA\n",
    "# import matplotlib.pyplot as plt\n",
    "# \n",
    "# # Apply PCA to reduce dimensionality to 2D\n",
    "# pca = PCA(n_components=2)\n",
    "# X_test_2d = pca.fit_transform(X_test)\n",
    "# \n",
    "# # Predict the cluster labels for the data points you're plotting\n",
    "# labels = loaded_model.predict(X_test)\n",
    "# \n",
    "# # Create a scatter plot\n",
    "# plt.figure(figsize=(10, 7))\n",
    "# \n",
    "# # Create a color map\n",
    "# cmap = plt.cm.get_cmap('viridis', 2)  # We use 'viridis' colormap and we specify that we have 2 clusters\n",
    "# \n",
    "# # Plot the points with colors according to their cluster assignment\n",
    "# plt.scatter(X_test_2d[:, 0], X_test_2d[:, 1], c=labels, cmap=cmap)\n",
    "# \n",
    "# # Add labels\n",
    "# plt.xlabel('Principal Component 1')\n",
    "# plt.ylabel('Principal Component 2')\n",
    "# plt.title('2D Visualization of Clusters')\n",
    "# \n",
    "# # Display the plot\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-19T14:36:34.412341Z",
     "start_time": "2024-03-19T14:36:34.409690Z"
    }
   },
   "id": "82c7ba8cbb2aa17a",
   "execution_count": 61
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Convolution Neural Network\n",
    "\n",
    "This code block is implementing a Convolutional Neural Network (CNN) for a classification task using TensorFlow.  The CNN is a class of deep learning neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics.  Here's a step-by-step breakdown of what the code does:  \n",
    "1. Data Preparation: The target column 'NLOS' is separated from the rest of the dataset. The target values are then encoded from categorical to numerical values using LabelEncoder. These numerical values are then one-hot encoded to create binary variables for each class.  \n",
    "2. Data Reshaping: The input data is reshaped to fit the model. Each data instance is reshaped to a 3D array where the third dimension represents the number of input channels, which is 1 in this case.  \n",
    "3. Data Splitting: The data is split into training and testing sets using a 80:20 ratio.  \n",
    "4. Model Creation: A Sequential model is created using Keras. This model is composed of the following layers:  \n",
    "5. Conv1D layers: These are convolutional layers that will convolve the input data with a set of learnable filters, each producing one feature map in the output. The kernel size is set to 3, and the activation function used is ReLU (Rectified Linear Unit).  \n",
    "6. MaxPooling1D layers: These layers are used to down-sample the input along its spatial dimensions (height and width). The pool size is set to 2.  \n",
    "7. Dense layers: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second Dense layer has a number of units equal to the number of classes and uses the softmax activation function to output a probability distribution over the classes.  \n",
    "9. Model Compilation: The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and accuracy as the evaluation metric.  \n",
    "10. Model Training: The model is trained on the training data for 10 epochs with a batch size of 32. The validation data is set to the testing set.  \n",
    "11. Model Evaluation: The model's performance is evaluated on the testing set and the accuracy is printed.  \n",
    "\n",
    "12. The mathematical concept behind the Convolutional layer (Conv1D) is the convolution operation, which is a mathematical operation on two functions that produces a third function. In the context of a CNN, the two functions are the input data and the kernel or filter. The convolution operation involves sliding the kernel across the input data and computing the dot product at each position.\n",
    "\n",
    "The mathematical formula for the convolution operation is:  $$ (f * g)(t) = \\int_{-\\infty}^{\\infty} f(\\tau)g(t - \\tau) d\\tau $$  Where:  \n",
    "$f$ and $g$ are the input data and kernel respectively\n",
    "$t$ is the position of the kernel\n",
    "$\\tau$ is a dummy integration variable\n",
    "In the context of a CNN, the integral is replaced by a sum over the discrete spatial dimensions (height and width) of the input data and kernel."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "862a9b7ee430a667"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-19 22:36:35.691708: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-03-19 22:36:35.692208: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2256] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1050/1050 [==============================] - 26s 23ms/step - loss: 0.6720 - accuracy: 0.6929 - val_loss: 0.5328 - val_accuracy: 0.7787\n",
      "Epoch 2/20\n",
      "1050/1050 [==============================] - 24s 22ms/step - loss: 0.5543 - accuracy: 0.7698 - val_loss: 0.4913 - val_accuracy: 0.7969\n",
      "Epoch 3/20\n",
      "1050/1050 [==============================] - 28s 26ms/step - loss: 0.5277 - accuracy: 0.7864 - val_loss: 0.4936 - val_accuracy: 0.7923\n",
      "Epoch 4/20\n",
      "1050/1050 [==============================] - 24s 23ms/step - loss: 0.5123 - accuracy: 0.7893 - val_loss: 0.4852 - val_accuracy: 0.7951\n",
      "Epoch 5/20\n",
      "1050/1050 [==============================] - 28s 27ms/step - loss: 0.5005 - accuracy: 0.7973 - val_loss: 0.4921 - val_accuracy: 0.7888\n",
      "Epoch 6/20\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.4895 - accuracy: 0.8001 - val_loss: 0.4799 - val_accuracy: 0.7936\n",
      "Epoch 7/20\n",
      "1050/1050 [==============================] - 27s 26ms/step - loss: 0.4809 - accuracy: 0.8041 - val_loss: 0.4597 - val_accuracy: 0.8007\n",
      "Epoch 8/20\n",
      "1050/1050 [==============================] - 26s 25ms/step - loss: 0.4758 - accuracy: 0.8053 - val_loss: 0.4803 - val_accuracy: 0.7943\n",
      "Epoch 9/20\n",
      "1050/1050 [==============================] - 27s 26ms/step - loss: 0.4697 - accuracy: 0.8102 - val_loss: 0.4687 - val_accuracy: 0.7975\n",
      "Epoch 10/20\n",
      "1005/1050 [===========================>..] - ETA: 1s - loss: 0.4632 - accuracy: 0.8108"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Drop the target column 'NLOS' from the data and assign the remaining data to X\n",
    "X = data.drop('NLOS', axis=1)\n",
    "# Assign the target column 'NLOS' to y\n",
    "y = data['NLOS']\n",
    "\n",
    "# Split the data into training and testing sets with a 80:20 ratio\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize a Sequential model\n",
    "model = Sequential()\n",
    "\n",
    "# Add a Conv1D layer\n",
    "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1), kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Add another Conv1D layer\n",
    "model.add(Conv1D(filters=32, kernel_size=3, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Add a Flatten layer\n",
    "model.add(Flatten())\n",
    "\n",
    "# Add a Dense layer\n",
    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Add the output Dense layer\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Define early stopping\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
    "\n",
    "# Generate a classification report\n",
    "report = classification_report(y_test, y_pred_classes)"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2024-03-19T14:36:34.413470Z"
    }
   },
   "id": "1c1dd203ad7db076",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "\n",
    "# Plot the training and validation accuracy over epochs\n",
    "plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
    "plt.title('Training and Test Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
    "plt.show()\n",
    "\n",
    "# Plot the training and validation loss over epochs\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss Over Time')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the testing loss and accuracy\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])\n",
    "\n",
    "# Print the classification report\n",
    "print('Classification Report: \\n', report)\n"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true
   },
   "id": "89aa08d7d1866179",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Visualize Weights and Biases\n",
    "for layer in model.layers:\n",
    "    if 'dense' in layer.name:\n",
    "        weights, biases = layer.get_weights()\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.hist(weights.flatten())\n",
    "        plt.title(f'{layer.name} weights')\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.hist(biases.flatten())\n",
    "        plt.title(f'{layer.name} biases')\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred_classes)\n",
    "plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion matrix')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.ylabel('True class')\n",
    "plt.show()\n",
    "\n",
    "# ROC Curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true
   },
   "id": "dd49203934ca9cf6",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Plot the model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "# Generate the plot\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true
   },
   "id": "81f2d793ada5c410",
   "execution_count": null
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# Save the model\n",
    "model.save('CNN.keras')"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true
   },
   "id": "6b7329b28452b82a",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Multi-Layer Perceptron (MLP)\n",
    "\n",
    "This code block is implementing a Multi-Layer Perceptron (MLP) for a binary classification task using TensorFlow. The MLP is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training.\n",
    "\n",
    "Here's a step-by-step breakdown of what the code does:\n",
    "\n",
    "1. **Data Preparation**: The target column 'NLOS' is separated from the rest of the dataset. The remaining data is assigned to X and the target column to y.\n",
    "\n",
    "2. **Data Splitting**: The data is split into training and testing sets using an 80:20 ratio.\n",
    "\n",
    "3. **Data Scaling**: A StandardScaler object is initialized and fitted to the training data. The training and testing data are then transformed using the fitted scaler.\n",
    "\n",
    "4. **Model Creation**: A Sequential model is created using Keras. This model is composed of the following layers:\n",
    "    - Dense layers: These are fully connected layers. The first Dense layer has 64 units and uses the ReLU activation function. The second and third Dense layers have 32 and 16 units respectively, and also use the ReLU activation function. The final Dense layer has 1 unit and uses the sigmoid activation function for binary classification.\n",
    "    - BatchNormalization layers: These layers are used to normalize the activations of the previous layer, which speeds up learning and provides some regularization, reducing generalization error.\n",
    "    - Dropout layers: These layers are used to prevent overfitting. They randomly set a fraction of input units to 0 at each update during training time.\n",
    "\n",
    "5. **Model Compilation**: The model is compiled with the Adam optimizer, binary cross-entropy loss function, and accuracy as the evaluation metric.\n",
    "\n",
    "6. **Model Training**: The model is trained on the training data for 20 epochs with a batch size of 32. The validation data is set to the testing set. Early stopping is used to stop training when the validation loss has not improved for 10 epochs.\n",
    "\n",
    "7. **Model Evaluation**: The model's performance is evaluated on the testing data and the loss and accuracy are printed. The model also makes predictions on the testing data, converts the predicted probabilities to binary outputs, and generates a classification report.\n",
    "\n",
    "8. **Visualization**: The training and validation accuracy and loss over epochs are plotted.\n",
    "\n",
    "The mathematical concept behind the Dense layer is the dot product operation, which is a mathematical operation that takes two equal-length sequences of numbers and returns a single number. In the context of a MLP, the two sequences are the input data and the weights of the neurons. The dot product operation involves multiplying each pair of input and weight and summing the result.\n",
    "\n",
    "The mathematical formula for the dot product operation is: $$ a \\cdot b = \\sum_{i=1}^{n} a_i b_i $$ Where:\n",
    "- $a$ and $b$ are the input data and weights respectively\n",
    "- $n$ is the number of dimensions (length of the sequences)\n",
    "- $a_i$ and $b_i$ are the $i$-th elements of the input data and weights respectively."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "42eff9445377f73c"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "1050/1050 [==============================] - 6s 4ms/step - loss: 1.1248 - accuracy: 0.5219 - val_loss: 0.8407 - val_accuracy: 0.6114\n",
      "Epoch 2/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.9929 - accuracy: 0.5490 - val_loss: 0.7929 - val_accuracy: 0.6896\n",
      "Epoch 3/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.9030 - accuracy: 0.5894 - val_loss: 0.7408 - val_accuracy: 0.7452\n",
      "Epoch 4/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.8242 - accuracy: 0.6418 - val_loss: 0.6877 - val_accuracy: 0.7770\n",
      "Epoch 5/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.7582 - accuracy: 0.6933 - val_loss: 0.6468 - val_accuracy: 0.7981\n",
      "Epoch 6/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.7035 - accuracy: 0.7320 - val_loss: 0.6120 - val_accuracy: 0.8099\n",
      "Epoch 7/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.6612 - accuracy: 0.7629 - val_loss: 0.5832 - val_accuracy: 0.8183\n",
      "Epoch 8/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.6318 - accuracy: 0.7824 - val_loss: 0.5609 - val_accuracy: 0.8215\n",
      "Epoch 9/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.6083 - accuracy: 0.7946 - val_loss: 0.5442 - val_accuracy: 0.8268\n",
      "Epoch 10/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5877 - accuracy: 0.8022 - val_loss: 0.5297 - val_accuracy: 0.8308\n",
      "Epoch 11/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5729 - accuracy: 0.8093 - val_loss: 0.5164 - val_accuracy: 0.8344\n",
      "Epoch 12/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5526 - accuracy: 0.8206 - val_loss: 0.5062 - val_accuracy: 0.8352\n",
      "Epoch 13/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5422 - accuracy: 0.8222 - val_loss: 0.4985 - val_accuracy: 0.8380\n",
      "Epoch 14/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5254 - accuracy: 0.8285 - val_loss: 0.4871 - val_accuracy: 0.8410\n",
      "Epoch 15/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5127 - accuracy: 0.8314 - val_loss: 0.4795 - val_accuracy: 0.8427\n",
      "Epoch 16/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.5025 - accuracy: 0.8361 - val_loss: 0.4744 - val_accuracy: 0.8423\n",
      "Epoch 17/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4925 - accuracy: 0.8385 - val_loss: 0.4685 - val_accuracy: 0.8433\n",
      "Epoch 18/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4860 - accuracy: 0.8403 - val_loss: 0.4615 - val_accuracy: 0.8455\n",
      "Epoch 19/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4750 - accuracy: 0.8425 - val_loss: 0.4589 - val_accuracy: 0.8411\n",
      "Epoch 20/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4700 - accuracy: 0.8436 - val_loss: 0.4528 - val_accuracy: 0.8448\n",
      "Epoch 21/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4596 - accuracy: 0.8456 - val_loss: 0.4482 - val_accuracy: 0.8444\n",
      "Epoch 22/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4539 - accuracy: 0.8471 - val_loss: 0.4451 - val_accuracy: 0.8454\n",
      "Epoch 23/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4461 - accuracy: 0.8509 - val_loss: 0.4432 - val_accuracy: 0.8444\n",
      "Epoch 24/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4469 - accuracy: 0.8486 - val_loss: 0.4459 - val_accuracy: 0.8412\n",
      "Epoch 25/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4337 - accuracy: 0.8550 - val_loss: 0.4365 - val_accuracy: 0.8463\n",
      "Epoch 26/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4315 - accuracy: 0.8530 - val_loss: 0.4346 - val_accuracy: 0.8464\n",
      "Epoch 27/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4291 - accuracy: 0.8527 - val_loss: 0.4321 - val_accuracy: 0.8462\n",
      "Epoch 28/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4229 - accuracy: 0.8544 - val_loss: 0.4285 - val_accuracy: 0.8460\n",
      "Epoch 29/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4189 - accuracy: 0.8575 - val_loss: 0.4280 - val_accuracy: 0.8455\n",
      "Epoch 30/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4168 - accuracy: 0.8572 - val_loss: 0.4257 - val_accuracy: 0.8468\n",
      "Epoch 31/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4108 - accuracy: 0.8586 - val_loss: 0.4251 - val_accuracy: 0.8457\n",
      "Epoch 32/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4064 - accuracy: 0.8599 - val_loss: 0.4226 - val_accuracy: 0.8473\n",
      "Epoch 33/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4054 - accuracy: 0.8584 - val_loss: 0.4217 - val_accuracy: 0.8461\n",
      "Epoch 34/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.4074 - accuracy: 0.8576 - val_loss: 0.4196 - val_accuracy: 0.8486\n",
      "Epoch 35/40\n",
      "1050/1050 [==============================] - 4s 4ms/step - loss: 0.3921 - accuracy: 0.8633 - val_loss: 0.4232 - val_accuracy: 0.8456\n",
      "Epoch 36/40\n",
      "1050/1050 [==============================] - 5s 4ms/step - loss: 0.3896 - accuracy: 0.8644 - val_loss: 0.4233 - val_accuracy: 0.8462\n",
      "Epoch 37/40\n",
      "1050/1050 [==============================] - 5s 4ms/step - loss: 0.3898 - accuracy: 0.8643 - val_loss: 0.4181 - val_accuracy: 0.8468\n",
      "Epoch 38/40\n",
      "1050/1050 [==============================] - 5s 4ms/step - loss: 0.3863 - accuracy: 0.8647 - val_loss: 0.4189 - val_accuracy: 0.8471\n",
      "Epoch 39/40\n",
      "1050/1050 [==============================] - 5s 4ms/step - loss: 0.3854 - accuracy: 0.8666 - val_loss: 0.4176 - val_accuracy: 0.8471\n",
      "Epoch 40/40\n",
      "1050/1050 [==============================] - 5s 4ms/step - loss: 0.3794 - accuracy: 0.8672 - val_loss: 0.4179 - val_accuracy: 0.8461\n",
      "263/263 [==============================] - 1s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.4178744852542877\n",
      "Test accuracy: 0.8460714221000671\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.90      0.85      4192\n",
      "           1       0.89      0.79      0.84      4208\n",
      "\n",
      "    accuracy                           0.85      8400\n",
      "   macro avg       0.85      0.85      0.85      8400\n",
      "weighted avg       0.85      0.85      0.85      8400\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, auc\n",
    "import seaborn as sns\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Drop the target column 'NLOS' from the data and assign the remaining data to X\n",
    "X = data.drop('NLOS', axis=1)\n",
    "# Assign the target column 'NLOS' to y\n",
    "y = data['NLOS']\n",
    "\n",
    "# Split the data into training and testing sets with a 80:20 ratio\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize a StandardScaler object\n",
    "scaler = StandardScaler()\n",
    "# Fit the scaler to the training data and transform it\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "# Transform the testing data using the fitted scaler\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Initialize a Sequential model\n",
    "model = Sequential()\n",
    "# Add a Dense layer with 64 units, ReLU activation function and L2 regularization\n",
    "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "# Add a BatchNormalization layer to normalize the activations of the previous layer\n",
    "model.add(BatchNormalization())\n",
    "# Add a Dropout layer to prevent overfitting\n",
    "model.add(Dropout(0.5))\n",
    "# Add another Dense layer with 32 units, ReLU activation function and L2 regularization\n",
    "model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "# Add another BatchNormalization layer\n",
    "model.add(BatchNormalization())\n",
    "# Add another Dropout layer\n",
    "model.add(Dropout(0.5))\n",
    "# Add another Dense layer with 16 units, ReLU activation function and L2 regularization\n",
    "model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
    "# Add another BatchNormalization layer\n",
    "model.add(BatchNormalization())\n",
    "# Add another Dropout layer\n",
    "model.add(Dropout(0.5))\n",
    "# Add the output Dense layer with 1 unit and sigmoid activation function\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Define early stopping to stop training when the validation loss has not improved for 10 epochs\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "# Compile the model with Adam optimizer, binary cross-entropy loss function and accuracy as the evaluation metric\n",
    "model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=0.0001), metrics=['accuracy'])\n",
    "\n",
    "# Train the model on the training data for 20 epochs with a batch size of 32 and validate on the testing data\n",
    "history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test), callbacks=[early_stopping])\n",
    "\n",
    "# Evaluate the model on the testing data and store the loss and accuracy in 'scores'\n",
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "\n",
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "# Convert the predicted probabilities to binary outputs\n",
    "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
    "# Generate a classification report\n",
    "report = classification_report(y_test, y_pred_classes)\n",
    "\n",
    "# Plot the training and validation accuracy over epochs\n",
    "plt.plot(history.history['accuracy'], 'ro-', history.history['val_accuracy'], 'bv--')\n",
    "plt.title('Training and Test Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Training Accuracy', 'Test Accuracy'])\n",
    "plt.show()\n",
    "\n",
    "# Plot the training and validation loss over epochs\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss Over Time')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the testing loss and accuracy\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])\n",
    "# Print the classification report\n",
    "print('Classification Report: \\n', report)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T11:06:16.563126Z",
     "start_time": "2024-03-20T11:03:23.792203Z"
    }
   },
   "id": "c8745832a585d5ec",
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Multi-Layer Perceptron (MLP) visualization\n",
    "This code block is used to visualize the performance of a trained Multi-Layer Perceptron (MLP) model. It generates three types of visualizations:\n",
    "\n",
    "1. Weights and Biases Visualization: This visualization is used to understand the distribution of weights and biases in the model's layers. For each layer in the model, if the layer is a dense layer, it retrieves the weights and biases, and plots histograms of their values. The x-axis of the histogram represents the value of the weights/biases and the y-axis represents the frequency of these values.\n",
    "\n",
    "2. Confusion Matrix: A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It gives a more detailed breakdown of correct and incorrect classifications for each class.\n",
    "\n",
    "3. ROC Curve: The Receiver Operating Characteristic (ROC) curve is a plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. It is created by plotting the true positive rate (TPR) against the false positive rate (FPR).\n",
    "\n",
    "## Weights and Biases Evaluation\n",
    "\n",
    "The weights and biases of the MLP model layers were visualized to understand their distributions. The weights in all layers (dense, dense_1, dense_2, and dense_3) are not close to zero, indicating they are likely being updated during training and contributing to the model's learning. The weight distributions show a spread around zero, suggesting the model is capturing complex relationships in the data.\n",
    "\n",
    "The biases in dense and dense_2 introduce a slight positive bias to the activations in subsequent layers, potentially affecting the model's predictions. The biases in dense_1 and dense_3 are centered around zero, with a slight spread towards positive values, introducing a small positive shift in the activations of the next layer.\n",
    "\n",
    "The impact of these biases would depend on the network architecture and data. Overall, the model's weights and biases suggest that it is learning effectively from the training data."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4114f5c851874555"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Visualize Weights and Biases\n",
    "for layer in model.layers:\n",
    "    if 'dense' in layer.name:\n",
    "        weights, biases = layer.get_weights()\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.hist(weights.flatten())\n",
    "        plt.title(f'{layer.name} weights')\n",
    "        plt.subplot(1, 2, 2)\n",
    "        plt.hist(biases.flatten())\n",
    "        plt.title(f'{layer.name} biases')\n",
    "        plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T10:38:19.011672Z",
     "start_time": "2024-03-20T10:38:17.692289Z"
    }
   },
   "id": "41091791008ff727",
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Confusion Matrix Evaluation\n",
    "\n",
    "The confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It gives a more detailed breakdown of correct and incorrect classifications for each class.\n",
    "\n",
    "The confusion matrix shows that the model has a good performance in distinguishing between positive and negative instances. The majority of the instances are correctly classified, with a small number of false positives and false negatives."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "618bc6deb5ea296b"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 2. Confusion Matrix\n",
    "# Convert the predicted probabilities to binary outputs\n",
    "y_pred_classes = (y_pred > 0.5).astype(\"int32\")\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_classes)\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.title('Confusion matrix')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.ylabel('True class')\n",
    "plt.show()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6b7d586ea49a858a"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## ROC Curve Evaluation\n",
    "\n",
    "The Receiver Operating Characteristic (ROC) curve is a graphical representation that illustrates the performance of a binary classification model at all classification thresholds. It is commonly used in machine learning to evaluate the performance of a classifier system for two-class problems.\n",
    "\n",
    "The ROC curve has two axes:\n",
    "- The X-axis represents the False Positive Rate (FPR), which is the proportion of negative instances that are incorrectly classified as positive.\n",
    "- The Y-axis represents the True Positive Rate (TPR), which is the proportion of positive instances that are correctly classified as positive.\n",
    "\n",
    "A perfect classifier would classify all positive instances correctly (TPR = 1) and all negative instances correctly (FPR = 0). This would be represented by a curve that goes straight up the left side of the ROC graph and then along the top to the right corner.\n",
    "\n",
    "The Area Under the Curve (AUC) is a numerical measure of the ROC curve’s performance. A larger AUC indicates a better performance. In our case, the AUC is 0.91, which is considered to be very good.\n",
    "\n",
    "In summary, the ROC curve shows that our binary classification model has a good performance in distinguishing between positive and negative instances."
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8645f7e159d38f0a"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "# 3. ROC Curve\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "4d080bef0cf9bec4"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Learning curve"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5d1867cb3af9788d"
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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MlStXcS8HBLhak+x2ewH2DXDve/ToYYKCgomKinZvb9iwcaHeS1JSIikpydxyS0P3Op1OR716t3Dy5AnuvHMA7dvHMHLkcKpUqUpMTCfuuKM/JpOJNm3aUaNGLQYNuo86deoSE9OJfv3uQqe7ucoH+b7uZalZdv6zaDv3zf8TVVV9HY4QQgghRJFQFAU/vdarj6IulAAMBoN7ecWK5Tz33DCsVgudOnVh9uz3KV8+8qrH5jcz3tW+7+VXdKiqilary3NMYb8zGgzGfNc7nQ6cTgeKojBt2iw+/PATbrutK7//vplHH32Iw4cPYjKZ+PDDT/jf/z6gWbMWrFy5gkcffYj4+AuFiqW0kmLJy/RaBY0Cx+LTOZuc5etwhBBCCCHEVSxfvpRHHhnKM8+8QK9efQkODiExMaFY/+BdrVp1UlNTiI0961538OD+Qp3LbDYTFhbOvn173OvsdjsHDx6gSpWqnDx5gnffncUttzTkiSeG8dlnXxMZGcmWLX+wd+9uPvvsY5o3b8nTTz/Pl18uxWq1sHv3zht9i6XKzdWOVgKY9FoaVAhk59kUtp9OJipY5tsXQgghhCiJgoOD2bbtL2JiOpGRkcGHH76H3W7HZrMW2zWrVKlK69btmDLldZ599kWSkhIKdOPbP//8Pddrg8FA8+Yt+c9/HmDBgnlERJQjOroyX3yxCKvVQpcuPXA6HSxf/i1ms5kePXpz/Pgx4uJiqVOnHkajkY8/nk9YWDgtW7Zm584dZGZmUrNm7eJ66yWSFEs+0Cw6mJ1nU/j7TDL9GlbwdThCCCGEECIfzz77Im++OZEhQx4gNDSUrl27YzL5cejQwWK97pgxrzFt2hs88cQQypUrR58+d/Dll59e85gXX3wm1+ty5crz3Xcrue++h0hPT2fatMmkp6fRsGET5syZ577v0+TJ03n//Tl8+unHhIaG8uSTI2jdui0Ao0eP55NPPmLmzGlERlbg1Vdfp1q16sXzpksoRb3JBs5cvJiKr9/xnycSeXrpXqKCTSwf2tq3wZRhigIREYEl4mde1kmuvUPy7B2SZ++RXHtHUefZZrOSkBBHeHhF9HrD9Q+4ieh0Guw3OMlFVlYW27ZtoW3bDu5xTevWrWXu3Nl8++2Kogiz1Ctonq/1Wc35vbgeGbPkA42jgtBqFM4mZ3EuRcYtCSGEEEIIF4PBwJQpr/Pxx/OJjT3L3r27+fjjD+ncuZuvQ7spSbHkAwEGHQ0rBQHw99lkH0cjhBBCCCFKCo1Gw5tvzmDr1i08/PBAxox5iTZt2vP440/5OrSbkoxZ8pHW1cPYdSaZv88k07v+1aegFEIIIYQQN5cmTZry4Yef+DoMgbQs+Uzr6uEA/H1GWpaEEEIIIYQoiaRY8pHW1cJQgBOJmSRmFN/0k0IIIYQQQojCKRHFktVq5fbbb2fLli3X3Xfbtm107drVC1EVr2B/PbXKBQDSuiSEEEIIIURJ5PNiyWKx8Pzzz3P48OHr7nvw4EGeffbZYr1rsjc1iw4GpFgSQgghhBCiJPJpsXTkyBEGDhzIqVOnrrvv4sWLue+++wgPD/dCZN7RPLtY2iHFkhBCCCGEECWOT4ulv/76izZt2rBkyZLr7rtp0yamTp3KkCFDij8wL8lpWToSn05Kls3H0QghhBBCCCGu5NNi6YEHHmDMmDH4+fldd9+5c+fSo0ePG76mopSMB0CE2UDVMD9UYFdsis9jKouPkvQzL+sPybXkuSw9JM+S67L2KOo8l0bDhg1l4sRx+W5bvfonevXqjNV69Um34uJiiYlpSVxcLAAxMS3ZsWNbvvvu2LGNmJiWBY5t3bq1JCUlArBgwTxGjHiiwMd64p577mDlyhXFcu6S7EY+xzfdfZbCwwN9HYJbeHgg7WtFcPKv0xxIyGRAm5ITW1lSkn7mZZ3k2jskz94hefYeybV3FFWes7KySEzUoNUq6HQ+H/5eYD169OKDD95FVR3o9fpc29avX0vnzl3x9zdd9XitVuN+1uk0/PjjaoKCgvPkQKfTuPctSH7i4mIZP/4Vli37AZ1Ow8MPD+a++x4ottxqNKXr53Y1BXkPTqeCRqMhNDQAk+nqP9trXqdQR5ViCQmp+Hp+CEVx/YOVkJBK/Qh/AH47FM/F1tG+DayMuTLPvv6Zl3WSa++QPHuH5Nl7JNfeUdR5ttmsOJ1OHA4Vu9154yf0kk6dujJz5nT+/PMP2rWLca9PT09jy5Y/mD599jXfj8PhdD/b7U6Cg8MAch2j02mw253ufQuSn5x9cs5rMJgwGEzFlluns3T93PKTk+frcThUnE4nSUnp6PW5h7zk/F5c91qFjrKUUlVKzD/IqgrNolzjlg6cTyXNYifAcNP9SIpdSfqZl3WSa++QPHuH5Nl7JNfeUVR5vuo5VBXsmTd+AU/o/Arcnyo0NJSWLduwceP6XMXS5s0bCQoKplmzFsTHX2D27LfZtm0rFksW1avX4LnnXqJx46Z5zhcT05L//e8DmjdvSXp6GtOmvckff/xKWFgE/fr1z7Xv7t07ef/9ORw6dABFUWjatDmvvDKeiIgI7r23HwD33tuPMWNeIy4ulr//3s67734IwN69u3nvvdkcPnyQ0NAwHnxwEP373wPA5MkTCAoKIj4+nt9+20RwcAhPPDGMXr36FiKZ177WuXPnmDp1Env37sZoNNG1a3eefvp5dDodhw8fYsaMtzh8+CCBgUHceecAHnnk8ULFUBxu5LMv38x9rEKQiUpBRmJTLOyJTaFttTBfhySEEEII4RlVJWTZXejP5T+Gp7jYKrbi0l3LClwwdevWg/fem4XDMQatVgu4xgt17dodjUbD66+/itkcyLx5H+N0OvnggznMmPEWixYtvuZ5p0+fwqlTJ5g7dz4JCYlMnjzBvS0tLY2XX36O//znQV599XUuXoznzTdf5/PPP+a5515i/vxFPP74YObPX0SNGjX5/PNF7mNPnDjOM888xX/+8wCjR7/Kvn17mTHjLUJDw+nUqTMAS5d+zeOPP8WTTw7n22+XMH36m8TEdMJsNnuUy+tda9asafj5+fPxx1+SlJTIuHEvU7VqdQYMuJc33niNxo2bMn78JE6dOsm4cS9Tr179XEVpaVViOyzGx8eTlZXl6zC8Qu63JIQQQohSrxTM/NCpU2cyMjLZtetvwFXIbN36J92790ZVVTp2vI2RI1+iatVqVK9egwEDBnL8+LFrnjMtLY3169fy3HMvUa9efdq0aceQIUPd2y2WLAYPHsqQIUOpVCmKxo2bctttXdznDQkJdT8bjbnH1axY8R116tTlySeHU6VKNXr3vp277/4PX375qXufWrXq8OCDg4mKimbo0CexWCwcP37U49xc71pxcXGYzWYqVKhIo0ZNmD59Nu3adQDg3LlYgoODqVChIm3btmfWrLnUqVPP4xhKohLbshQTE8OUKVMYMGCAr0Mpds2ig/nxnwtSLAkhhBCidFIUVwtPCe6GB+DvH0D79jFs2PALzZu3ZPPmDVSsWIl69eoDcNdd97B27c/s3bubkydPcPDgAZzOa4+NOX36JA6Hg9q167jX1a9/i3s5PDyC3r1vZ8mSLzh8+BAnThznyJFDNGrU5LrxnjhxgltuaZBrXaNGjfn++6Xu19HRld3LAQGu1iS73X7dc3t6rQcfHMSbb05k06b1tGnTnq5de7gLoocffoR5897j+++X0b59DD179iE8PMLjGEqiEtOydPDgQdq0aZPrdX6F0oABA1i3bp03Qyt2zaNDANh7LpUsm8O3wQghhBBCFIaigN7fu49CtGZ1796LzZs3oqoq69atoVu3ngA4nU5GjhzO4sVfEBlZgQceGMS4cRMLfF71ikExOt3l2fbi4y8wePB/2LFjG3Xr1ueZZ57nvvseKtA5DQZDnnUOx+UJJIA8M/v9O5aCut61evTozdKlP/Df/44gMzODV18dxYcfzgXgoYeGsGTJch58cBCxsWd59tmnWLFiuccxlEQlpli6mUWHmIgIMGBzqOw7l+rrcIQQQgghyqx27TqQmZnBjh3b2L59K9279wLgxIlj7Ny5g1mz5jJo0KO0bx9DQsJF4NrFR5UqVdHpdOzf/4973eHDB93LmzatJzAwmGnTZjFw4P00adKM2Niz7u3KNQq+KlWqsm/f3lzr9u3bTZUqVT170wVwvWvNm/ceiYmJ9O9/D9OmzWLo0KfYuHEdFouFWbPeRq/Xc999DzFnzjz69buLDRvKRuOGFEslgKIoMm5JCCGEEMILDAYDt97amXffnUmNGrWoXLkKAGZzIBqNhl9++Zlz5+JYv34tCxfOA7jmzWoDAsz06tWXWbOms3fvHnbs2MbChR+6twcFBXP+/Dm2bfuLs2fP8Pnnn7Bx4zr3OU0mPwCOHDlERkZGrnPfdde9HD58iHnz3uPUqZP89NMPLFv2DQMG3Fvo93/06BH+/PP3XI/k5EvXvdapUyeYOXMaR44c5tixo/z552/Url0Xo9HI7t07mTlzOqdOneDAgX/Ytetv6tSpW+gYS5ISO2bpZtMsOpg1B+OlWBJCCCGEKGbdu/dk5coVPP30SPe68uUjeeGFV/jkk4+YN+89KleuyrPPvsgbb7zG4cMHrzkGZ+TIl5g5czrPPDOMwMBA7rnnPt57bxYAXbp0Z9euvxk3bhSKolC//i2MGPEcCxbMw2q1EhISQs+evRk/fjRPPfV0rvNWqFCBadNmMnfubBYv/pzIyAqMGDGSvn37Ffq9L1nyBUuWfJFr3cyZ79GqVZtrXuvFF0czY8ZbjBjxBA6Hg/btO/Dccy8B8PrrU3jnnakMHToYrVZLly7dGDLksULHWJIoamE6NZZiFy/6/sZ3igIREYG5Yjl6MZ37Fm3HpNOwfkR7dFpp9LtR+eVZFA/JtXdInr1D8uw9kmvvKOo822xWEhLiCA+viF6fd5zLzaygN0sVN6ageb7WZzXn9+J65Bt5CVE93J9gk44su5P959N8HY4QQgghhBA3PSmWSgiNjFsSQgghhBCiRJFiqQRxF0tnpVgSQgghhBDC16RYKkGaX9Gy5HBK520hhBBCCCF8SYqlEqR2OTMBBi3pVgdH4tN9HY4QQgghhBA3NSmWShCtRqFJVBAAO6QrnhBCCCGEED4lxVIJ0zw6BJBJHoQQQgghhPA1KZZKmCtnxLvJboElhBBCCCFEiSLFUglTP9KMUafhUqaN44kZvg5HCCGEEEKIm5YUSyWMXquhUSXXuCXpiieEEEIIUTQmT55ATEzLqz527Njm8TlHjHiCBQvmFWjfe+65g5UrV3h8jYJauXIFMTEt+eGH5cV2jZuRztcBiLyaRwez7dQldpxO5u4mlXwdjhBCCCFEqffssy/y3/+OAOCXX9awePHnzJ+/yL09KCjY43O++eZ0dDp9gfadP/9T/P39PL5GQa1d+zNRUdGsWrWS22/vX2zXudlIy1IJ1PyKm9PKuCUhhBBCiBtnNpsJD48gPDwCs9mMRqNxvw4Pj0CvL1jRc6WgoGD8/f0LtG9oaChGo8njaxREUlIi27dv5ZFHHmfXrr+JjT1bLNe5GUmxVAI1qBCITqMQn2blbHKWr8MRQgghhLguVVXJtGd69VGUf1SOi4slJqYln3zyEb16deadd6aiqiqffrqQe+/tx223teXOO3uxcOGH7mOu7IY3efIEZs2awfjxo+natQMDBvRl1aof3fte2Q1vxIgnWLRoAc8/P4IuXTpw330D2LLlD/e+ycmXGDPmJbp378i9997J8uXfEhPT8qqxr1u3FrPZTI8evYmIKJfrugCZmZlMmzaZPn260qdPV6ZOnYzFYgFchdb48aPp0aMT/fr1ZN6891BV1Z2PuLhY93kWLJjHiBFPAK5uf0899SijR79Iz56dWL36J9LT03jzzYncfnt3brutLQ88cDebNm1wH3+1a02d+gajRo3MFfPMmdOYNOnVAv3sipN0wyuBTHotDSoEsis2hR1nkokOKb4mWyGEEEKIG6WqKs/8+V/2Je3x6nUbhjZmdtv3URSlyM65e/cuFiz4DKfTyapVP/L1118xYcJkoqKi2bLld95++y06dLiVunXr5Tn222+X8PjjT/Hkk8P59tslTJ/+JjExnTCbzXn2/fTThbzwwiu88MIrfPDBu0yd+gbffrsCjUbDa6+NwWq1MnfuAi5evMBbb026Zsy//LKadu1i0Gg0dOhwK6tW/cgjjzzuzstbb03i6NEjvPXWDIxGE5Mmvcr8+e8zYsRzjB79IlqtlnffnUdGRgavvTaaiIgI2rfveN1c7dmzm0GDHuXJJ4cTEhLK7NkzOH36JDNnvovJ5MeXX37K1KmTaNeuA3q9/qrX6tatJy+99Czp6WkEBJhxOp1s2LCOUaPGFfCnVnykZamEal7Z1RVvh0zyIIQQQohSQKHoChZfGjjwfqKioqlcuQqRkRUYM+Y1WrZsTcWKlejf/x7Cw8M5fvxovsfWrl2HBx8cTFRUNEOHPonFYrnqvu3axdCnzx1ERUUzePBjXLhwnsTEBE6dOsm2bX8xduwEateuQ7t2MTzyyBNXjff8+XPs2bOLjh1vA6BTp87Exp5l9+6dAKSkpLBhwy88//zLNG7clLp16/HSS2OoUKECR44cZu/e3YwdO4E6derRtGlzXnxxNIGBQQXKlaIoDB78KNWqVSckJISmTZvz0ktjqF27LpUrV+H++x8iOTmZxMSEa16rWbMWBAYG8dtvmwHYtetvbDYbrVu3LVAcxUlalkqoZtHBfLzltMyIJ4QQQogST1EUZrd9nyyHd4cPmLSmIm1VAqhY8fLkWs2bt2Tfvr188MG7nDx5nEOHDpKQkIDT6cz32MqVq7iXAwJcrUl2u70A+wa49z169DBBQcFERUW7tzds2Piq8f7yy2oMBgNt2rQDcBceP/30A02aNOPs2dM4HA7q1avvPqZJk2Y0adKMdevWEhQUTKVKUe5tOUXXld3vriY0NCzXOKxevfqyefMG/u//vuPkyRMcPHgAAKfTyalTJ696LYAuXbqzfv1aevTozbp1a+nUqTM6ne9LFWlZKqEaVwpCo0BschbnUmTckhBCCCFKNkVR8NP5efVR1IUSgMFgcC+vWLGc554bhtVqoVOnLsye/T7ly0de9dj8Zsa72riq/AoBVVXRanV5jrnW2Ky1a3/GYrHQs2cnOnVqQ9euHUhNTWH9+rVYLFnXLDiutS2/3Docjlyvr8wVwBtvvMa7784mMDCI/v3vYdq0WQW6FkC3bj3ZsuVP0tPT2LRpHV279rjm/t7i+3JN5CvAoKNueTP7z6ex82wKvYKKZ/YUIYQQQgiRv+XLl/LII0N54IFBAKSmppKYmFCssxVXq1ad1NQUYmPPulthDh7cn+++p06d5NChgzz33Is0b355Aojjx4/x2mtj2LhxAx06xKDVajl8+DBNmjQFYPPmDXz88XzGjXudlJRkzp8/R2RkBQC++WYxO3Zs5YUXRgOQkZHhPu+1ZtlLT09jzZpVfPjhJ9Sv3wCAP/74FXAVe9HRla96rSlTZtCgQUPKlSvHF198iqq6WshKAmlZKsGaR4cAsOPMJZ/GIYQQQghxMwoODmbbtr84deokBw7s57XXRmO327HZrMV2zSpVqtK6dTumTHmdI0cOs3Xrn1e98e3atT8TFBRMv34DqFGjlvvRtWsPqlWrwapVPxAQYKZXr77Mnj2df/7Zy4ED/zBv3lxatGhNjRo1adGilXsCiB07tvH555/QsmUbwsLCKF8+ki+//JSzZ8+wcuUKd/GTH4PBiMnkx4YN64iLi2XLlj94553pANhstmteK0fXrj1YvPgLOnfuilarLdrEFpIUSyVYs5z7Lcm4JSGEEEIIr3v22RdJT09nyJAHGDv2JWrVqs2tt3bm0KGDxXrdMWNew8/PjyeeGMLbb79Fnz535HsfqF9+WU2PHr3zdIcDuOuuu9m27S/i4y/w7LMvUKtWHUaOHM6LLz5D8+YtePzxpwB49dVJmEx+PPnkECZOHEe/fncxYMC9aDQaRo9+lf379/HwwwNZv34tgwY9etWY9Xo948e/zoYNv/DQQ/cyZ85MBg9+lPDwCA4dOnDNa+Xo2rUHVqulxHTBA1DUm+yupxcvpuLrd6woEBEReN1YkjNtdJvrmnP/56faEuaf9xdBXF1B8yxunOTaOyTP3iF59h7JtXcUdZ5tNisJCXGEh1dEr5fvJlfS6TTY7flP/lBQWVlZbNu2hbZtO7jH+axbt5a5c2fz7bcriiLMEmvr1j+ZOnUy33zzf9ccj1bQPF/rs5rze3E90rJUggX76akV4ZodZae0LgkhhBBClHkGg4EpU17n44/nExt7lr17d/Pxxx/SuXM3X4dWbC5evJhdEP6P22+/s1gm7igsKZZKuObRcr8lIYQQQoibhUaj4c03Z7B16xYefnggY8a8RJs27d3d5sqitLRUpkx5neDgEO677yFfh5OLzIZXwjWLDubrnbFSLAkhhBBC3CSaNGnKhx9+4uswvKZateqsWbPJ12HkS1qWSrim2S1LR+LTScmy+TgaIYQQQgghbh5SLJVwEQEGqoT6oQK7zqb4OhwhhBBCCCFuGlIslQIybkkIIYQQQgjvk2KpFJD7LQkhhBBCCOF9UiyVAjktSwfOp5Jhdfg4GiGEEEIIIW4OUiyVAhWCTFQMMuJQYXestC4JIYQQQgjhDVIslRLNpSueEEIIIUShDRs2lIkTx+W7bfXqn+jVqzNWq/Wqx8fFxRIT05K4uFgAYmJasmPHtnz33bFjGzExLQsc27p1a0lKSgRgwYJ5jBjxRIGP9VRmZibdusUwbNjQYrtGWSLFUikh45aEEEIIIQqvW7ee/PHHr9hseW/Fsm7dGm67rQsGg6HA5/v++1U0atTkhuM6dy6O8eNfISsrC4D773+YN9+cfsPnvZpff91IeHgEe/bs4uzZM8V2nbJCiqVSoll0CAB7z6VisTt9G4wQQgghRCnTuXM3MjMz2bZtS6716elp/PXXn3Tv3suj84WHR6DX6284LlVVc7329/cnKCj4hs97NWvX/kzHjrdRo0YtVq36sdiuU1ZIsVRKVA4xERFgwOZQ2Rsn91sSQgghRMmiqipqZqZ3H/8qNK4lNDSUli3bsHHj+lzrN2/eSFBQMM2atSA+/gLjxr1Mr16d6dy5HY8++iC7d+/M93xXdsNLT0/jtdfG0KVLDPfdN4ADB/7Jte/u3Tt56qnH6Nq1A926xfDii89w8eJFAO69t5/7eeXKFXm64e3du5unnnqMbt1iuPfefixf/q172+TJE5gz5x3Gjx9N164dGDCg7zULoJSUFP7660+aNm1G+/YxrFq1Mk8Of/55JQ88cDddu3bgv/99lEOHDri3LV78Offccwfdu3fk+edHEBt7FoARI55gwYJ57v3y67L40Ucf0LdvV0aNGgnAihXLeeCBu7nttrb07duVGTOm4nA4rnmt3bt30qlTG5KSktz7HTiwn65dO5CRkX7V930jdMVyVlHkFEWhWXQwaw7G8/eZZFpUDvF1SEIIIYQQgKtQSh72OPa9u716XV2jJgS/9yGKohRo/27devDee7NwOMag1WoB13ihrl27o9FoeP31VzGbA5k372OcTicffDCHGTPeYtGixdc87/TpUzh16gRz584nISGRyZMnuLelpaXx8svP8Z//PMirr77OxYvxvPnm63z++cc899xLzJ+/iMcfH8z8+YuoUaMmn3++yH3siRPHeeaZp/jPfx5g9OhX2bdvLzNmvEVoaDidOnUGYOnSr3n88ad48snhfPvtEqZPf5OYmE6YzeY8cW7atA6NRkPLlm0ICwvns88+Zteuv2natDkAW7b8wZQpr/Pccy/SsmUbvv12MS+/PJJvvvk/fvzx//j44/m8/PJY6tSpx7x57/Hqq6+wYMFnBcr9b79t4v33F+BwOPn77+3MmjWd8eMnUadOPQ4c+IdJk8bTsmUrOnXqwvLlS/O91kcffUpERDk2blzH7bfflf3zW0O7djH4+wcUKA5PSctSKSLjloQQQghRYhWwYPGlTp06k5GRya5dfwOuQmbr1j/p3r03qqrSseNtjBz5ElWrVqN69RoMGDCQ48ePXfOcaWlprF+/lueee4l69erTpk07hgy5PHmCxZLF4MFDGTJkKJUqRdG4cVNuu62L+7whIaHuZ6PRlOvcK1Z8R506dXnyyeFUqVKN3r1v5+67/8OXX37q3qdWrTo8+OBgoqKiGTr0SSwWC8ePH8031jVrVtOqVRtMJhP16zegfPlIfvrpB/f2779fRvfuvejf/x6ioyszfPhzdOvWk5SUZP7v/5YxcOADdO3ag8qVq/D88y/TvHlLLJasAuX+zjsHUKWKK69+fv688sqrdOrUhYoVK9G5czdq167rzsnVrmW1WujatQe//LLWfd7163+hW7ceBYqhMKRlqRTJKZZ2x6ZgdzjRaaXWFUIIIYTvKYpC8HsfQlbBvjgXGZOpwK1KAP7+AbRvH8OGDb/QvHlLNm/eQMWKlahXrz4Ad911D2vX/szevbs5efIEBw8ewOm89ljx06dP4nA4qF27jntd/fq3uJfDwyPo3ft2liz5gsOHD3HixHGOHDlUoMkhTpw4wS23NMi1rlGjxnz//VL36+joyu7lgABXa5Ldbs9zroSEi+zcuZ2XXx4LuH5mt956GytX/sDIkS9jMpk4deok/fsPcB+j1+sZMeI5AE6dOsmjj9Z3bwsLC2f48Gev+x5yVKhQyb1cr159jEYjCxbM4/jxoxw9eoQzZ07TunXb616re/eeLFnyJcnJl4iNPUty8iXatYspcByekm/bpUiNcH+CTTqy7E72n0/zdThCCCGEEG6KoqD4+Xn3UYjWrO7de7F580ZUVWXdujV069YTAKfTyciRw1m8+AsiIyvwwAODGDduYoHPe+XYH53u8sQP8fEXGDz4P+zYsY26devzzDPPc999DxXonPnNzudwOHE4Lhdw+U0ykd9YrnXr1uJwOJg2bTKdOrWhU6c2LFv2DRkZ6WzatD477qu3o1xr279/DleOPcrvvWzZ8gePPfYwCQkXadu2PW+8MS1X8Xita9WuXZfo6Gg2b97A+vW/0LHjrRiNxqvuf6OkWCpFNNnjlkC64gkhhBBCFEa7dh3IzMxgx45tbN++1T0L3okTx9i5cwezZs1l0KBHad8+hoQE1yQM15pIokqVquh0Ovbvvzypw+HDB93LmzatJzAwmGnTZjFw4P00adLMPTEC5C00/n3uffv25lq3b99uqlSp6tmbBn75ZTUtWrTm44+/cD8++eRLoqKi3V3xoqMrc+TIYfcxDoeDe+/tx+7dO4mOrsKRI4fc25KTL3H77d2Ii4tFr9eTkZHh3nbl+8vPihXf0bdvP15+eSy3396fqlWr5ZrG/FrXAujZsze//baZP/74la5de3qcC09IsVTKuIuls1IsCSGEEEJ4ymAwcOutnXn33ZnUqFGLypWrAGA2B6LRaPjll585dy6O9evXsnCha4a3a92sNiDATK9efZk1azp79+5hx45tLFz4oXt7UFAw58+fY9u2vzh79gyff/4JGzeuc5/TZPID4MiRQ7kKDoC77rqXw4cPMW/ee5w6dZKffvqBZcu+YcCAez16z3Fxsezdu5v+/QdQo0atXI877xzA9u1biY+/wD33/IfVq3/ip59+4MyZ08yZ8w5Op5O6detxzz3/4euvv2Lz5g2cOnWS6dOnULFipexujLewfv1a9u/fx/79+/joow+uGU9QUDB79+7i6NEjHDt2lDffnEhCwkV3Tq51LXB1xduy5U8SEhLcXfeKS4kolqxWK7fffjtbtmy56j7//PMP9957L02aNOHuu+9m7969V923LMsplnaeTcbhLPh0mUIIIYQQwqV7954cPnyI7t0vt0qULx/JCy+8whdffMrDDw/ks88+4dlnX0Sr1eZqKcrPyJEv0bBhY555ZhiTJ0/g7rv/497WpUt3evbszbhxoxg6dBA7dmxjxIjnOHnyOFarlZCQEHr27M348aP54Yfluc5boUIFpk2byZYtvzN48H0sWrSAESNG0rdvP4/e79q1qwkJCSEmplOebX369EOn07Fq1UqaNm3O88+P4uOP5zN48H0cPnyIadNmYTSa6NmzD/ff/xAzZkzlsccewmq1MGnSNADuu+9B6tSpy/DhTzBhwthcE1zk59FHnyQ0NIwnnxzCyJHDMRgM9O9/jzvP17oWQOXKVahWrTqdOnW+Zpe9oqConkxQXwwsFgsvvPACa9as4dNPP6VNmzZ59snIyKBHjx7ccccd3HPPPXz11Vf89NNPrFmzBn9/f4+ud/FiKr59x67JYiIiAgsVi8Op0vW930m3Ovj8oebUjcw7LaRwuZE8C89Irr1D8uwdkmfvkVx7R1Hn2WazkpAQR3h4RfT6vGNqbmY6nQa7/doTQogbp9FA//59GTduIs2bt7zqftf6rOb8Xlz3Wjcc7Q04cuQIAwcO5NSpU9fcb+XKlRiNRl5++WVq1qzJ2LFjCQgIYNWqVV6KtOTQahSaRAUBsEO64gkhhBBCiJvI77//ysyZb2MwGN33hypOPi2W/vrrL9q0acOSJUuuud+uXbto0aKFewCcoig0b96cnTt3eiHKkqdZlEzyIIQQQgghbj5fffUZ69evZfToV9Foir+U8el9lh544IEC7RcfH0+tWrVyrQsPD+fw4cNXOeLqSsL90nJiKGwszStfWSyphZo282Zwo3kWBSe59g7Js3dInr1Hcu0dRZ1n+XkJX5ozZ57H3R0VJe/ntqCf41JxU9rMzMw888wbDIZrzkxyNeHh1++b6C2FjaVjSAAm/R4uZdq45FSoHVly3lNJVJJ+5mWd5No7JM/eIXn2Hsm1dxRVnrOyskhM1KDVKuh0JWKusBJFcuIdBcmz06mg0WgIDQ3AZDIV7jqFOsrLjEZjnsLIarUW6k0nJPh+EKmiuP7BupFYGlUMYuupS/yyJ5ZQbaXrH3ATKoo8i4KRXHuH5Nk7JM/eI7n2jqLOs81mxel0Yrc70WhkMoMryQQP3lHQPNvtTpxOJ0lJ6ej1tlzbcn4vrnutQkfpRZGRkVy8eDHXuosXL1K+fHmPz6WqlJh/kG8klmZRwWw9dYm/zyRzdxMplq6lJP3MyzrJtXdInr1D8uw9kmvvKKo8azRaAKxWCwaD8cZPKEQxsVotAGg0ukJ/9ktFsdSkSRPmz5+PqrrG56iqyo4dO/jvf//r69B8Jud+SzvOJLvzIoQQQghR3DQaLX5+ZtLSkgAwGIzyPSSb06ngcEjlX9yul2dVVbFaLaSlJeHnZ76hiSBKbLEUHx9PYGAgJpOJXr16MWPGDCZPnsx9993H4sWLyczMpHfv3r4O02caVgzEX68lPs3K7tgUmmTPkCeEEEIIUdyCgsIA3AWTcNFoNDid0g2vuBU0z35+ZvdntbBKbLEUExPDlClTGDBgAGazmXnz5vHaa6/x9ddfU7duXT788EOPb0hblpj0Wm6rHc7Kfy6wav8FKZaEEEII4TWKohAcHE5gYCgOh93X4ZQIigKhoQEkJaVLt9JiVNA8a7W6IplavMQUSwcPHrzm68aNG/Pdd995M6QSr3f98qz85wJrDsbzQuea6LQy+4oQQgghvEej0aDRGK6/401AUcBkMqHX26RYKkbezrN8uy7FWlYJJcxfT3KWnT9OSDO4EEIIIYQQRUmKpVJMp1HoUc81I+Cq/Rd8HI0QQgghhBBlixRLpVyv+q5iaePRBNKt0mdYCCGEEEKIoiLFUil3S6SZKqF+WOxONhxO8HU4QgghhBBClBlSLJVyiqK4W5ekK54QQgghhBBFR4qlMqBX9rilv04lcTHd6uNohBBCCCGEKBukWCoDKof60ahiIE4VVh+Q1iUhhBBCCCGKghRLZYR0xRNCCCGEEKJoSbFURnSrWw6tAvvPp3EyMcPX4QghhBBCCFHqSbFURoT5G2hTLRSQ1iUhhBBCCCGKghRLZUjv+pEA/LT/Aqqq+jgaIYQQQgghSjcplsqQTrXC8dNrOJucxd64VF+HI4QQQgghRKkmxVIZ4qfX0qlWBCBd8YQQQgghhLhRUiyVMb2zZ8VbfTAeu8Pp42iEEEIIIYQovaRYKmNaVw0lzF/PpUwbW05e8nU4QgghhBBClFpSLJUxOo1C97rlAPhp/3kfRyOEEEIIIUTpJcVSGZRzg9qNRxLIsDp8HI0QQgghhBClkxRLZVCDCoFUDjGRZXey4chFX4cjhBBCCCFEqSTFUhmkKIq7dUlmxRNCCCGEEKJwpFgqo3pl36D2r5NJJKRbfRyNEEIIIYQQpY8US2VUlVA/GlQIxKHCmoPxvg5HCCGEEEKIUkeKpTJMuuIJIYQQQghReFIslWHd65ZDq8C+c6mcSsr0dThCCCGEEEKUKlIslWHhAQZaVQ0FYJXcc0kIIYQQQgiPSLFUxvW+oiueqqo+jkYIIYQQQojSQ4qlMu62WhGYdBpOX8rin3Opvg5HCCGEEEKIUkOKpTLO36ClU61wAH6SiR6EEEIIIYQoMCmWbgK9s++5tOZgPHandMUTQgghhBCiIKRYugm0qRpCiJ+exAwbf51M8nU4QgghhBBClApSLN0EdFoN3euWA+SeS0IIIYQQQhSUFEs3iZwb1G44cpFMm8PH0QghhBBCCFHySbF0k2hUMZCoYBOZNicbjyT4OhwhhBBCCCFKPCmWbhKKorhbl6QrnhBCCCGEENcnxdJNJKdY+vNEIokZVh9HI4QQQgghRMkmxdJNpFqYP/UjzThUWHsw3tfhCCGEEEIIUaJJsXSTka54QgghhBBCFIwUSzeZHvXKo1FgT1wqZy5l+jocIYQQQgghSiwplm4yEQEGWlUJAeAnaV0SQgghhBDiqqRYugn1rh8JuLriqarq42iEEEIIIYQomaRYugndVjsco07DqaRM9p9P83U4QgghhBBClEhSLN2EAgw6bq0ZDkhXPCGEEEIIIa5GiqWbVO/sWfFWH7iA3Sld8YQQQgghhPg3KZZuUu2qhRJs0pGYYWPbqSRfhyOEEEIIIUSJI8XSTUqn1dCtbjlAuuIJIYQQQgiRH58WSxaLhTFjxtCyZUtiYmJYuHDhVff99ddf6devH82aNWPIkCEcO3bMi5GWTTld8TYcTiDL5vBxNEIIIYQQQpQsPi2Wpk2bxt69e1m0aBGvvfYa7777LqtWrcqz3+HDh3nyySfp2rUrS5cu5ZZbbmHw4MGkp6f7IOqyo3GlICoFGcmwOdh0NMHX4QghhBBCCFGi+KxYysjI4JtvvmHs2LE0aNCA7t27M3ToUL744os8+3711Vc0a9aMZ599lho1avDSSy8RGBjIihUrfBB52aEoCr2yW5ekK54QQgghhBC5+axYOnDgAHa7nWbNmrnXtWjRgl27duF0OnPte/r0aRo3bux+rSgKderUYefOnd4Kt8zqlX2D2j9OJHEx3erjaIQQQgghhCg5dL66cHx8PKGhoRgMBve6iIgILBYLly5dIiwsLNf68+fP5zr+3LlzBAcHe3xdRSl8zEUlJ4aSEEuNCH8aVQxkT1wqP+w7xyNtqvg6pCJTkvJc1kmuvUPy7B2SZ++RXHuH5Nk7JM/eUVR5LujxPiuWMjMzcxVKgPu11Zq7haN3794MGzaM22+/nY4dO7JixQr27NlDmzZtPL5ueHhg4YMuYiUllsExNXjxm118v/c8z/e+Ba2mbP2Wl5Q83wwk194hefYOybP3SK69Q/LsHZJn7/BWnn1WLBmNxjxFUc5rk8mUa/2tt97K8OHDefrpp3E4HLRp04Y777yTtLQ0j6+bkJCK6uN7sCqK6wdcEmIBaFvJTKBRx5mkTFZsO0VMjbDrH1QKlLQ8l2WSa++QPHuH5Nl7JNfeIXn2DsmzdxRVnnPOcz0+K5YiIyNJSkrCbrej07nCiI+Px2QyERQUlGf/p556iscee4zU1FTCw8N59tlniYqK8vi6qkqJ+QCXlFiMOi23N4jkqx1nWbozlg7Vy0axlKOk5PlmILn2Dsmzd0ievUdy7R2SZ++QPHuHt/Lsswke6tevj06nyzVJw/bt22nUqBEaTe6wfvjhByZPnozBYCA8PJysrCy2bNlSqG54In8DGlcE4LfjiZxLyfJxNEIIIYQQQviez4olPz8/+vfvz4QJE9i9ezdr165l4cKFDBo0CHC1MmVlub60V6tWjcWLF7N69WpOnDjBCy+8QMWKFbn11lt9FX6ZUy3cn5aVg3GqsHzPOV+HI4QQQgghhM/59Ka0o0ePpkGDBgwePJiJEyfy9NNP06NHDwBiYmJYuXIlAA0bNmTChAm89dZbDBgwAIB58+blaYESN2ZAk0oAfL/nHHaH8zp7CyGEEEIIUbYpqnpz9aq8eNH3g+4UBSIiAktELFeyOZzc/uEWEjNsTO13C11qR/g6pBtSUvNcFkmuvUPy7B2SZ++RXHuH5Nk7JM/eUVR5zjnP9UjTjHDTazX0a1gBgGW7Yn0cjRBCCCGEEL4lxZLIpX/jCijAlpOXOJ2U6etwhBBCCCGE8BkplkQuUcF+tKseCsCy3XE+jkYIIYQQQgjfkWJJ5DGgsWuihxV7z2Gxy0QPQgghhBDi5iTFksgjpkYYkYFGkrPsrDsc7+twhBBCCCGE8AkplkQeWo1C/0Y5Ez1IVzwhhBBCCHFzkmJJ5OvORhXQKrDzbApHLqb7OhwhhBBCCCG8Toolka9yZiO31nLdZ+k7aV0SQgghhBA3ISmWxFXd3bgiAD/+c55Mm8PH0QghhBBCCOFdUiyJq2pVNYToEBPpVgerD1zwdThCCCGEEEJ4lc7XAYiSS6MoDGhckf9tOs7SXXHc2aiir0MSQgghhBAliKqqOFUHdtWBQ7Vjdzqwq3YcTjsO1bVsd9pxqHb8dQFU9K/k65A9IsWSuKY7GlTg/d9OsP98Gv+cS+WWCoG+DkkIIYQQQnjIqTrJtGeSbk9zPWzppF2x7FqfTrrNtS7NvZxOpiMDhzN34WO/ohjyxKQWU+kQ2bGY3mXRk2JJXFOIv56udcqxav8Flu2Kk2JJCCGEEGWOw2nH4rRgdVixOl0Pi8OSvWzBmrPsyG9b9rPTit6oITPTmuvcCkreCyrKNfdRUXGqTpyqA4fqyLXsUJ04VWf2escVy87s7Tn7O7E5ra4CyJ5Ghj0DFbXIc3c1OkWHVtGi0+jQKjp0Gh3B+mAqScuSKGvublyRVfsv8POBCzzbqQaBJvnYCCGEEKL0yXJkcTL1OMdSj3Is9SjHU49yLPUIl6yXfB2a1+gUHQF6MwG6AAJ0ZgL0rmfzFcsBuoDsfVzLfjp/9NkFj9b9rHUVRBpdrsJIp2jRKFoUJZ8isRSSb73iuppEBVEj3J9jCRn8tP88A5tF+TokIYQQQniRw2kny2FBo2gwao1olJI9R5hTdRKXEZtdFB3JLoqOcTb99HVbV/QaPQaNAYPGiEFrwKAxYNQar1iXs3zFeq1rOTDAn8wMKyqusTzXcmUc+cWkQYNWo0WLFo1GixYNGkWDVtGhUXKWte7Hles02a/1Gj3+ugACdAGYs4sfg8ZQZgoZb5BiSVyXoijc3aQi09cdZemuOO5tWkl+yYQQQogSxOa0keXIJMueRaYjkyxHlut19rMhWSH+UhJZ9iyynFlk2bOwZD/n7GNxWC5vc7jOY3FYyHJk5hmXYtL64af1w0/nh5/WP/vZ9frf20y6f73WmjBqjeg1BvQaHTpFj16jR6fR5Vqn0+gKVJQlW5OvKIiOcizlKCfSjpPlyMx3/2BDCDUCa1I9sCY1A2tRPbAGFf0rYdSa0Gv0aBVtoX4GigIREYFcvJjKdeokUYpIsSQKpM8tkczZdJxjCRnsOptC0+hgX4ckhBBC+IzDaSfRmkRi1kUSLYkkWC6SaEkg0ZJAkiUJh2pHUTRo0KAoCgoKGkVBQeN6vmLblfu41rueFS4XQZlXFj/2Kwsh17JD9e79EF3XzyTJev19b4RW0V6lqHItJ1mSSLBczPdYvcZANXP17MKoBjUCa1EjqCahhjD5o68oMCmWRIGYjTp61i/P93vOsXR3nBRLQghxE1JVldiMs5zNOINRa8SsC8SsN2PWBeKn8yvxXbMKItOe6S56EiwJJFoukpCVkGtdkiWBS9ZLXh0sX1BaRYtJ64dJa8JP64dJZ8Kk9SPIz4zGoctu1XE9/LKfXfsbMWn9sl9fuZzz8MOoNbpmVMsulDLtmWQ6Msm0Z7jW2TPJdGS4Cruc5X/tk/Pa6rRgd9qxOa3YnHbsThs21Y7zX0WfQ3XgcGSSdZ1asKJfJWoEuVqLamQ/ovyj0Wrkq664MfIJEgV2d5OKfL/nHL8ciuf522oQ6m/wdUhCCCGKiVN1EptxlkPJBziUfJBDKQc4nHyIdHtavvtr0BCgD8CsCyRAnzNY3PVs1ucUVVcuBxKg9ydJG0xyamZ2y4sWBQVt9uBwTfYYjZzWFtc4DMW1Lnuba1khy5FJuj2dDHsGmfYM13TH9gwyHBlkZK93PbL3cVyxT/Yj3Z5+1a5b+b5nRUuoIZQwYzjhxnDCTOGEGcMJM4Sj0+jcM5qpqooTJ6DiVFVU1YmTnOcrtqvkeq2qTvQag7tYySl8rixg/l0U6TX6PHEWdfcwP53fjZ/kKhyqI7uIsrkLKLvThi378e91/jp/qgfWwF8XUGwxiZubFEuiwOpHBlI/0sz+82n8sO88D7eq7OuQhBDipuBUncXaauNUnZxJP83h7KLoUPJBjqQcIt2enmdfvcZA5YDK2Jw20mxppNlTsTltOHGSaksl1ZYKBa83SiST1uQqerIf4cZwwo0RhBrDCDdFEG4MJ9QYTrAhuNDjW0T+tIoWrVaLUWv0dShCAFIsCQ/d3aQib6w+zLLdcTzYMhqN9PkVQogb4lSdJFgSuJB5jguZ5zmfeY7zWa7nnNfp9jQCdAEE6oMI0gcTaAgkSB9MkD6IIEOwa70hKM86s96c58u8Q3VwJv20u8XocIqrMMqwZ+SJzaAxUDOoNnWC6lInuB51gutS1Vwd3b+6NlkdFtLsaaTZUrMLqMvL6fa0XOvSc21LR1WcOJzO7PvFZLfE4HTfJyanBaYgXd5yZv7y1/rjp/N3T3nsn/PQ+uOfvS5A59rHXxuAv/7ythBDKP46fxnTIoQApFgSHupRrzwzNxzjzKUstp68RJtqob4OSQghSjSLw3K58Mm6XADlPMdnXcgz01h+XDeWTOdcZlyBr62gYNabXQWWPgiNouFY6tF8u5oZNUZXYRRcj9pBdagTXI9q5moFGvNh0BoJ0xoJM4YXODbwrHtYTtc0V7c2VxGVU2CpOK/aBU2UXqrFgjMlGTX5Es5Ll1CTk3EmX7r8OiUZVQVNWBia8HA0YVc+wlBCQlG00vInbowUS8IjfnotfW+J5OudsSzdHSfFkhCiTLI5bCRbk7PHv7gGs+eMhcnMWc4e0J5hT3cPXM9wZOQa2J5iSy7QzS41ipZypnKUN0US6RdJpF8FyvtVyH5dgSBDMOm2NFJsyaTaUkixppBiSyHFlkyKNe+6VFsKGfYMVNTLXeOuYNKa8rQYVQmoes3CSHU6cV44j+PkCRxxseBw4K5wVDXXw133XLkeleybz1x+DRBgIiPThqoooHHNCodG43qA69m9TQMaBbKfXWOWFNAoOBUNFve2K4+54nw5y4oC2TPSXXk+FA2KTgsaLWhdD0WrdR2j1bmWtVrQarK36S7vp8nbTVK12VCzslCzMiErC9WShZqZ85wJ2c+qxZL9fMX6rCzULAtoFBSjEcVoBIMRxWBEMRpcy0bXa4xGFIMhex8DitF0xbIRjdGAzZaG/Ww8zvQM1MwM1IwM1zXdy65n8ll35X6goJhMrmtmX+d6rzFlr7/iNVbr1Quh5Euol5JRM/O2dnpEo0EJDnEVU2HhroIq1LWshF8uqjRh4Sj+AeBwoNrtkP1QHfZ81jmuvt1hJ9lPR9aldFRb9vZ/H2N3HeM+3p77HO7tjuzjVNX12ukEpxPV6QCnCk7XOtXhdC9f3sd5+ZiimsNcUVAMhnw/X4rBkP0ZzP6c5nwe3Z/Ny59ZTXg4ulsalqqWWymWhMcGNKnI1ztj2XTkIvFpFsqZpV+xEKWZqqqQmYkzNRU1NQVnagpqagpqaqp7nerelupan5KMmpqKYjSgqRSNNioKbVTl7OdoNJWi0QQGFm/cVivO+As4LpzHef4czgvnUdPScCoKdsWBTbVjVexYVTsW1YYVGxbVhgUbWaoVCzYynRYsWMl0WshSrWQ6s8hULdhVOwY76BxgsIPeDnqH6nq2g97heg61QznHletUDA7Q2V3HaVSwacGh04BBj2IwojX6oTP6YTAGYPALxM8vCJMpEI3R5Pryoc/+QmKwoBjOgSERxeRHYFg4lSIi0ITXcX0huQ6b00aqLfVyMWVLxua0Uc1cg8rmKlcda6PabDjOnHYVRSeP4zh5AvvJkzhOnYCsrCL+KcINfh0uWbRa0OpcxZTV6vrCWkIkFOG51LTU6+9UVLRalOBgNMEhrsInOARNSLB7GacTZ1IizsREnIkJ7od66ZKrcEhKxJGUiOPoEa+E68XMlFqBEydj7NLd12EUmBRLwmM1IwJoFhXE32dT+H7POYa2q+rrkITwOdXpdP3lNS0dNSMNNT3d9chwPeNUL/8FWnf5L9Xuv1y7/zqtzb2f9vJfuTU6LZYUE7Zzia6/RFssYLGgWq2uZatrnWq1XrE+C7K3q1bL5eX09CuKn5RCf6lTAeeFC9h37sizTQkKchdQmqhotNlFlSYqGkdoMGn2NFJtKaTaUsm0p5PlsGB1WLA4LWTZMiEpCW18AtqEJAwXkzEmpuKfkIZfUiaBSZkEpNquGZsGMGU/fM8JWLIfKXm2Wjw8mxIYhCYiAk14RPZzuStel3P99TY8gjBjGGHGsPwjykjHcfJkdkF00l0YOc6eufrnQadDG10ZbXRl0OtdLTTktNKQ6zU5fzjOtS5nfc4xCiaTjqwMC6pTBdXp+l1RnVe8zv7rePaz6ry8D06n6xoOB6DmOYf7WVWvej6crtnnXMsOcLj+Kq86HNmvs5dzHjnXzE/OPv+m0aCY/FwtLCYTiskv+9mUve7y68vr/VxFsdOZz++0BdVizf07bf33vweufXOOVXQ68PdH8fNH8fNDyW/5qtsDUPz9wOTn+hlmZWX/G+RqAcNicbWEWS1Xf53TWmaxgCULdHo0wdlFT8gVhVBwMEpIiLs4UszmQrVCqA6Hq5UqMQFnQoKroErILqQSE3IVV2pK3t9J17/RWtDpXC2IuuyWRd2/lrU6V261WhSdDr2fAZtTca3L2X7lcVfsizb3Ovdyzvk1Gte//xoFNNmtl9qcdZrs11rXz0R7xTqN1r2foimaFhzVqbo+d//+/8Zy5WfQ6moVzfP/kAWsrs+iYjShq1u/SGLyFimWfECTehYCIinN6R/QpBJ/n03hu91xDGlTBV0R/TIK4Uuq3Y4zKRHV/Z9ooqugyC54XI+0XEXQlUWRNyQW58l1OteX8KAglMBAFHMQmsBA13JgzrJrmyYwENUcQHpaIlmnj2E9cwrn2TNo4s6jP5+AITkdNSUFe8o+7Pv35blUlh4uhMD5EIVzoWDXQngKRKSoVEh1Leuu8Z00h1UHFwPhYrBCQiCk+blacxQVdKoWI3oMig4Drode0aFHi0HVoUODHi06VYMOLTo0aFUFnapgNBhQNQYUgwnFoM9u6TGC3tU65F7nbgXKfx0aDdisri8MNpv7S26edVar6wtH9iNnu3tdRgbOhIs4ExJc+6em4EhNwXH82DXzowQF5SqgFD//7Faj4zgvXLj6cX7+aKtVQ1u1Gtqq1dFWqYq2WnW0laJcX+aKSFFPae0Nak63KIfj2kWVXu8qNIwm17JPux2plCsXVKryfKMUrRYle/wSta69b053yVxFSz7dKq97zVL4eRbXV3q/rZdSiiWF0C86QUB5lIE/oRpK581du9SOYIafngtpVn47lkinWp4N6hXCW1Sn09UX3t09I/HyXxaz/8qY8xdHNfnSjV9Qq3X9JdQ/ACUgACXA7PoLrVaLanf1Q3f1M7/iS5XDfrmP+b+/cF35ZczuQKPXoeoN1x6jcOWYhpw+5Dl9y41GnAY9VqOGLD89WX5aMkwKqSaVdK2NDEcG6fYM0u1pZNjTSbelk2FPIsN+hrTsdRnWDNLPp5MVmz1JgBaomv3IZrRqibwEkUkqFS5BhSSVyCSIvKQSkQwmG1SJhyrxV/9G4VQgM8SPzLAAssICsUYEYw8PwVEuDLVcBEr58miDQzHqTJTXmqisMWDS+mHWmwnQmTFoC3cvuJL8hUdVVdS0VJwX412f34vxOC9edBVSV65LuOgqtFJScKRcvahSwsLQXVkMVXUVSJpy5UvVmAJvUhTF3UoAlxvQSjL5WV6botej6GVyEJE/KZa8TNUH4AyoiDb5OAGbJ5La9R1fh1QoBp2GOxpE8tm2MyzbHSvFkvAq1WJxDQi+lIgzKclV7Fy6hJqU/fpSkqv4SUzAeSnJsy5mWi2a0DCU0DDXwN+gIFfhk6sAyi6CAgLQBATkKowwGIrti0l+X+KtDmv2IH/XmJRk6yXXIH9rcvbrxCuWXc9ptjTUDLXIBov4af0J1AcSqA/K9WzWBxKkD8KsD3Sv1+kDsSt++CVlYjifiBob5+r2ZbOhKR+JNrICmvKRaCIruFpCirAVoyxQFCW7lS8Iqte86n65iip3MXURNT3dNa6sanW0VauiCSqdf7ATQghvkf+FvE2jJbXbO4QsHYDpwNdYavbFWq2rr6MqlLsaV+SzbWf443gSZ5MziQouvjt6i7LD3YXFakW1ZXdDstnc3ZFcRdAlV8GTXfyoSYnZr5NQLyW5xgB5SAkO+dfUsmHuLhq5ZkgKDi5U94vikGnP5GzGac6kn+Z0+inOpp8hTU0mPi3BXSBlOgpf8Rg0BgJ0AfjrAgjQmfHX+7tf++sCMF+xHJDPPjnr/n3PnQIJBqoVOnRxHQUtqoQQQlybFEs+YK/YCtoNhz/exbz+ZZLu/wXVFOLrsDxWOdSPNlVD2HLyEst3n2N4x+q+DkkUMzUzE0f8BZwXzuO8kP0cfwHnxXjSVTu2jCxUqw3VZr1cANlsrrEX9suvi6Rvk07nagEKCUETEprdGhSKJjTU9Tq7EFLCw9GEhJbYFgqb00ZcxlnOpJ/hTPopzqRfLo4SLBcLdA6NoiVIn32TUkMwwYZg93KQPohgQ4j7RqU56wP1gXJPGiGEEOI6PP72MGrUKPr27UuHDh3Qyo2+Cq/LOOz7f0J36SjmX18jtdtsX0dUKHc3qcSWk5f4v73neKJ9VfTakvEXeeG5qxVCjpzX8Rdcs6ZdhbWwF9ZoXIOf9a7B8Zqg4MuFT4jr4SqCwtyFkBIaVugZknzBqTq5kHWeM2muQuhMdmvRmfRTnMuIw8nVZzIINoQQHVCZaP/KRJsrU6NcFTQWo6vo0QcTZAgiQGdGo8jvnhBCCFHUPC6WzGYzY8eOxWaz0aNHD/r06UObNm1KzZeWEkPvR1q3dwheehemg0td3fGq9/B1VB7rWCOMcmYD8WlWNhxJoHvdcr4OSeRDVVXU5GQc52JxxsXiiIvDeS7O9ZxdFF2rELqSEhCAplx517iScuXRRkaiiShHULlQ0iwOVJ0+e7CsAfS67JnB9Ci6nJnC9NnFkcH1XEr/6KKqKim2FJIsiSRaEkiyJpJkSbri9eXlS9YkHOrVx02ZtH5EB1SmckBlogOquIqjgMpE+VcmyBDk3q8kTzwghBBClEUeF0uvvvoq48aNY+vWraxatYoXX3wRgN69e9O3b1+aNm1a1DGWWfYKLchs+iT+f7+PecMrJFVshWoK9XVYHtFpNdzZsAIf/XmKZbtipVjyEfdg7thYHOfisgui7OVzcTjj4gp0J3TFz99VBJUvn/2IRJtTGOWsDzDnPU6B4IhAbGXkS7xTdRKXEcuRlEPEZ8XnKYBchVHiNQugf9MpOir5R2UXQlWyiyPXc5gxXP7gJIQQQpRAherErygKrVu3pnXr1jz//PN89NFHfPzxx3z++edUqlSJgQMHMmTIEIwFuMP4zS699QsYTqxFl3QY86ZXSe3xrq9D8tidjSqwcMsptp1O5kRCBtXC/X0dUpmkqirOC+dxnMi+ceQVRZHzXFyBJj3QhEegqVgJbcWKaCpURFuhomvWsfLl0ZSLRGPOWwiVdQ7Vwem0UxxOOcjh5IMcTjnEkZRDpNsLNolEoD6QUEMYodk3/ww1hhFqCCPMGE6oMdS9LdwYjrYwEyEIIYQQwmcK9T93eno669evZ9WqVfz6669ERkbyyCOP0KdPH+Lj43n77bf566+/WLBgQVHHW/boTKR2fYeQpXdiOrwcS62+WGv09nVUHqkQZCKmRjibjiawbHccz3eWmZduhOpw4DwXh/3EcRwnjrmKoxPHcZw8ed3WISUsDG2FSq5CqGIlNBVdBZG2YiU0kRVc99u5idmddk6mHedQdlF0OOUgR1MOk+XIyrOvXmOgRmBNKvlXumoBFGIILfS9fIQQQghR8nlcLD311FP8/vvvBAUF0bt3bz799FMaN27s3l6nTh1SUlIYO3ZskQZaltkjm5HZbBj+O94lcMNoEiu2QfUL83VYHhnQpCKbjibww77zPBVTDT996RyH4k2q3Y7j7JnsYshVFNlPHMdx6hRYLfkfpNOhja7sukdKpUrZrUSV3K1Eisnk3TdRglkdFo6nHstuMTrEoZQDHEs9hs2ZdyoKk9aPWkG1qR1Uh9rBdakdVJeq5mqFmxJbCCGEEGWGx98EIiIimDdv3jUndWjZsiXffPPNDQd3M0lvPRLDiTXoEg9i3jSO1J5zfR2SR9pWDSU6xMSZS1l8tzuOB1pE+zokn1OdTtTkS+6bQTovxuM4dw7HyeyWojOnwW7P/2CDEW3VquiqVkNbrTraajVcz1HRJXYK7KLkcNrJcGSQYXc9shyZ7uXM7OcMRzqZ9kwyr9gv055BhiODNFsaZ9JP5TumKEAXQO2gutQOrkudoLrUDq5DVEBltIoU+EIIIYTIzeNvXZMmTeKLL77g4sWL3H777QAMHz6cmJgY7r//fgDKlStHuXIy0N8jWqOrO963/TAd+T8sNftgrXW7r6MqMK1GYXCrykxec5jPt53h7iaVMOrK5lTGriIoGWdCPM6EBJwX410P9/JFV4GUcNF189Vr8fNDV6062iuKIl216mgqVCy1s8R5yqk62ZO0i3Vn1/Bn/O8kWy9hzaf1pzCC9MHUyW4pcrUY1aGifyWZZlsIIYQQBeJxsTRz5kyWLVvGxIkT3evatGnD3LlzSUxMZPjw4UUa4M3EXr4JGS1GELBtNoEbx5BYqS2qf4Svwyqwvg0imf/HSS6kWflh3znublLJ1yHdENVux37gH2zbtmI/fNDdOuRMTLh6i9C/KQpKSCiaiAjX5ArlyqOrUhVtdVdLkaZceRTNzffFXVVVjqUe4ZfY1ayLXcuFrPP57qfX6PHT+uGn88df6+96zn74Xfn6imU/rR/+ugAqm6tQ3hQps8wJIYQQotA8LpaWLl3KrFmzaNmypXvdoEGDqFu3Li+99JIUSzcoo+WzGI//jC7hAIGbxpLSa56vQyowvVbD4NaVmb7uKIv+Os2dDSugK0U3qVVVFceJY9i2bcW2fSu2v3egZlx9RjQlNAxNeDiaiHKuQiinILrydVj4TdFtrqDiMmJZF7uGtbGrOZl23L0+QBdAxwq30bliNyoHVHEXPnqN3ofRCiGEEOJm5/G3uMzMTMz5TC8cGhpKampqkQR1U9MaSO06k5Bv78B49EeMh1dgqX2Hr6MqsH4NK7Dgz1PEpVhYuf8C/RpW8HVI1+Q4f85dHFm3b0VNTMi1XQkKQt+8JfrGTV1TbOcUQmFhrpuriutKsiSyMW49a2N/5p9Le93r9Ro9bct1oGul7rQt3x6D9uaeqU8IIYQQJY/HxVLHjh2ZPHkyU6dOpVIlVzer8+fPM3XqVGJiYoo8wJuRvVwjMlo8TcDWmZg3jcEa1RbVv3SMATPptTzcqjKzNx7jky2n6HtLJFpNyekG5UxJxrZjO7Ztf2HdvhXnmdO5dzAa0Tduir5FKwwtW6OtXeem7CZ3ozLs6fx2fjO/xK5m28WtOLMnWlBQaBbegq6VetCxQifM+kAfRyqEEEIIcXUeF0vjx49n2LBhdO3aleDgYACSk5Np27Yt48ePL/IAb1YZLZ7GeOxndAn/ELhxNCm95kMpGXsxoHFFPtlyitOXslhzMJ5e9cv7LBZnVhbWrVuwbv0L2/at2A8dBFW9vINGg67+LehbtELfsjX6Bo1QDHLfnMKwOWyuAunsan4//ysW5+Xpz+sG16drpR50rtiVcFPpGYcnhBBCiJubx8VSWFgYixcv5sCBA5w4cQKdTke1atWoVatWccR389IaSOk2i9Bv+mA8tgrj4e+x1Onv66gKxN+g5YEW0bz/2wkWbjlFj3rl0Hix0FMtFqy/b8ayaiUXt/2Fas09s5q2WvXLxVHT5mjy6VYqrk9VVc5nnuOfS3v5O2E7m89vIMWa4t4e7V+ZrlE96FKxO5XNVXwYqRBCCCFE4RRq5Lndbic0NJSgoCDA9aXp+PHj7N+/nz59+hRpgDczR8QtZLR8joC/3sa8aSy2qHY4AyJ9HVaBDGxWic+2neZ4QgYbjiTQpXbxtiaoqop9z24sP6/Esm4Nalqae5umXPnLxVGLlmgjSkeXxpIm057JoeQD/HNpr+uRtI8ka2KufcKNEXSu2JVuUT2pHVRXZqITQgghRKnmcbG0du1aXn31VS5dupRnW7ly5TwqliwWCxMnTmT16tWYTCYeffRRHn300Xz3XbNmDe+88w7nzp2jXr16jBs3jgYNGngafqmT0Xw4huM/o4/fg3nDaFL6LCgV3fHMRh3/aRbFgj9PsfDPU3SuFV4sX5wdsWex/PwTWT+vxHn2jHu9pnwkpp69qTBwACkhkUDJz1lJ4lSdnEk/zf5L+/jn0j72X9rLsZSjOHHm2k+raKkVVIdbQhvQp05PquvqoeHmuD+UEEIIIco+j4ulGTNm0L17d4YMGcL999/Phx9+yKVLl5g0aRLDhg3z6FzTpk1j7969LFq0iNjYWEaNGkWlSpXo1atXrv0OHz7MCy+8wOuvv07z5s355JNPePLJJ1mzZg1+fn6evoXSRasntes7hH7dB+OJ1RgPLcNS925fR1Ug9zWP4svtZzh4IY3fjicSUyO8SM7rTEvDun4tWT+vxL5r5+UNfn4YO3XB2Luvq3udVoMxIhDlYmquYUoir1RbCgcu/ZNdGLkeqba8s1uWN0VSP6QBt4Q0oH5oQ2oH1cGoNaIoEBERyEXJtRBCCCHKEI+LpdOnTzNv3jyqVKlCw4YNiY+Pp1u3bmg0GqZNm8aAAQMKdJ6MjAy++eYb5s+fT4MGDWjQoAGHDx/miy++yFMs/fbbb9SqVYv+/fsD8Pzzz/PFF19w5MgRGjVq5OlbKHUc4fXJaPU8AVumYt48Hlt0B5wBJXtKboAQPz33NKnEZ9vOsODPU3SoHlbo1iXVbse2dQtZq1Zi/XUTWLMnD1AU9C1bY+zZB+Ott6GU9eK5CKiqypn00+xJ2sWexF3sv7SPU+kn8+xn0BioG1w/V3FUziRdGIUQQghx8/C4WAoKCiIzMxOA6tWrc+DAAbp160aNGjU4c+bMdY6+7MCBA9jtdpo1a+Ze16JFCz744AOcTieaK6ZrDgkJ4ciRI2zfvp1mzZqxbNkyzGYzVarcPIPGM5o/heH4KvQXdmFeP4qUvp+Uiu54D7aM5uudseyNS2XrqUu0rhrq0fH2w4fIWvUjlrU/oyZeHh+jrVYdY6++GLv3RFu+dIzj8hWH6uBYyhH2JO1id6KrQPr3WCOAKP/o7MKoIbeENqBGYC10GrmhrhBCCCFuXh5/E+rUqRMTJ07k9ddfp02bNkybNo3OnTvz888/U758waeIjo+PJzQ0FMMV0zRHRERgsVi4dOkSYWFh7vV9+vRh3bp1PPDAA2i1WjQaDfPmzXNPXe6JklBf5MTgUSxaHWld3yFkSW+MJ3/BdPBbLPXvLZb4ilKE2cBdjSuweEcsC7ecok216xdLzqQkslatJGvVjziOHnGvV0JCMHXribF3X3R1rj95QKHyXAZYHVYOJh9gd+JO9iTuYm/SbtLt6bn20Wv01A+5hYahTWgY2oj6IbcQYvSskL3SzZprb5M8e4fk2Xsk194hefYOybN3FFWeC3q8x8XS2LFjmTx5Mnv37uXOO+/k559/5p577sHf35/p06cX+DyZmZm5CiXA/dr6r6mek5KSiI+PZ/z48TRp0oSvvvqK0aNH89133xEe7tk4mPDwknMTTI9jiWgJXcbA2gkE/jqBwMY9ITiqeIIrQs/2rMfSXXFsP53M8TQbraqF5bufqqokL/uO82+9hTPVNV5G0esxd+lC8J13Yu4Yg6LXe3z9kvQzLw7ptnR2XdjF9gvb2X5+O3sv7sXisOTaJ0AfQNNyTWkR2YLmkc1pGNEQo9ZY5LGU9VyXFJJn75A8e4/k2jskz94hefYOb+VZUVXPhmP/8MMPdOjQgdDQy3+FTktLw2g0ovfgi+xPP/3EG2+8wW+//eZed/ToUfr06cOWLVsICQlxr3/ppZfw9/dn4sSJADidTnr37s3dd9/NE0884Un4JCT4fgC6orh+wIWKxWkneOld6M//jbXKbaTc8Vmp+BPG5NWH+G73OdpVC2XOPXnHmTnOnSN12pvY/voTAG3NWvjddQ/GLt3QZE9R76kbynMJlmnPZPvFv9iV3XJ0OOUwTtWRa58QQwiNwprSOKwJjcOaUjOwJtpi7FJXVnNd0kievUPy7D2Sa++QPHuH5Nk7iirPOee5Ho+/PU2cOJElS5bkKpbMhbipZ2RkJElJSdjtdnQ6Vxjx8fGYTCb3/Zty7Nu3j4cfftj9WqPRUK9ePWJjYz2+rqpSYj7AhYpF0ZHadSahS3piOLUB4z9LyLrlvmKJrygNbl2Z/9tzjj9OJLE3LpUGFVwfTtXpJOv/viNj7hzUzAwwGPB/9An8/vMASvbn4kZ/XiXpZ15YqqqyL2kPP535gQ1x68h0ZOTaXsGvIo3CmtAo1FUcVQ6okqebojdyUBZyXRpInr1D8uw9kmvvkDx7h+TZO7yVZ4+LpTZt2vDDDz/w3//+N083Ok/Ur18fnU7Hzp07admyJQDbt2+nUaNGuSZ3AChfvjxHjx7Nte748eM3xUx4+XGE1iK9zUuYf3+DgN8mYq3aucTfrDYq2I9et0Ty477zfPznKd7u3wDH2TOkTZuMbcd2AHQNG2Me/Sq6KlV9HG3JcTErntVnf2LVmZWcST/lXl/RrxIty7WhcWgTGoU1obxfyf75CyGEEEKURh4XSwkJCcydO5cPPviAsLAwjMbc4x5++eWXAp3Hz8+P/v37M2HCBN58800uXLjAwoULmTJlCuBqZQoMDMRkMjFw4EBeeeUVGjZsSLNmzfjmm2+IjY3lrrvu8jT8MiOzyeMYj6xAf2EXAb+9TmqP93wd0nUNaV2ZlfvOs/lIPKc+XoT/lwsgKwtMJgKeHI7prntQtHJDU6vDyh8XfmXVmR/ZGr/FfSNYk9ZEpwpd6F35dhqFNimWm/wKIYQQQojLPC6WBg4cyMCBA4vk4qNHj2bChAkMHjwYs9nM008/TY8ePQCIiYlhypQpDBgwgD59+pCens68efM4d+4c9evXZ9GiRR5P7lCmaLSk3fYWId/0xXT4e7Lq/wdb5Vt9HdU1VQvzZ2CEjVbL5uGfeAIAffMWmEeNQ1up5E9UUdyOpBxi1ZkfWXt2NSm2ZPf6RqFN6BXdl04VO+OvC/BhhEIIIYQQNxePJ3go7S5e9P2gO0WBiIjAIoklYPN4/HcvxB5cnaT71oDOVDRBFjHVbidzyRekL/gQxWYjQ2dE8/hwou4biPKvbpdFpSjzXFySrcn8EruaVWd+5EjKIff6cGMEPaP70DOqD5XNJf9+YqUh12WB5Nk7JM/eI7n2Dsmzd0ievaOo8pxznuvxuGXp4Ycfvmb3n08//dTTU4obkNHmJYxHfkSXfBz/HXPJaP28r0PKw370CGlvTcJ+YD8KcKxaQybUuZPWoXWZUEyFUknmUB1sv7iVVWd+5Lfzm7A5bYDr3kfty3ekV3RfWpZrjVaRLolCCCGEEL5UqAkermS32zl9+jQbN27kqaeeKrLARMGohkDSYyYQtPop/He8R1adu3CGVPd1WEB2a9Jnn5Dx6UKw21HMgQQ8PZLA5rcS/8VOVu2/wNB2VYkO8fN1qF5xMSue708u5eezP3ExK969vlZQHXpF96VrpR4EGzy/0bIQQgghhCgeHhdLI0aMyHf9smXLWL16NY899tgNByU8Y6l1O9b9izGc3kjgpnEk3/G5z++9ZD94gNS3JuE4chgAQ8ytBLwwCm1EOeoD7auH8vvxJBb9dZqxPer4NNbiZnfa+e7EN3xyeIF7yu8gfRDdonrSK7ovtYLK9vsXQgghhCitiuwula1atXLfNFZ4maKQeusbhC3uhuH0RoxHVmCp3c8noahWKxmffETml5+Bw4ESHIz5uRcxdO2Rq/vmo22q8PvxJH7Yd57H2lahQlDJHGt1o/Yk7mLW3ukcTzsGwC0hDbi3+v20Kx+DQVv4qfeFEEIIIUTx87hYyu9GsOnp6SxYsICoKJnRzFecIdXJaDGCgL9mEPDrRKxVbkM1Bl3/wCJk+2cfaW++juPkcQAMXbphfu5FNKFhefZtEhVMy8rBbDudzOfbzvBil1pejbW4JVkS+fDAXH4+uxKAIH0wT9YbTs/oPmiUm2+clhBCCCFEaeRxsdSlSxcURUFVVXdLgaqqVKxYkTfffLPIAxQFl9F8GMaDy1yTPWyZTvqtk7x2beuWP0gZ8xJYrShhYZifH4WxU+drHvNo2ypsO72H5XvOMaRNFSICSn9Li0N18OOp/+Ojgx+QZk8FoG/lfgyt+5SMRxJCCCGEKGU8Lpb+fdNZRVHQ6/VERETITTJ9TWskrdObhPzf/fjtXYSl/kDs5RoV+2WvLJT07ToQOPY1NMEh1z2uZeUQGlcKYndsCl9sO8OznWoUe6zF6VDyAWbunc7B5P2Aa+KG5xq8yC2hDX0cmRBCCCGEKAyP+wNFRUWxYcMG/v77b6KioqhUqRITJ05k8eLFxRGf8JCtckeyat+Jojoxb3gFnI5ivZ71j99IGf0iWK0YOnYiaPK0AhVK4Cq0H23ruofQ0l2xXMqwFWOkxSfVlsLsvW/z1G+PcTB5PwG6AEbcMpL3238khZIQQgghRCnmcbE0c+ZM3n//ffz9/d3rWrduzdy5c3nvvfeKNDhROGkdXsNpCEJ/YRemfZ8X23Wsv20mZezLYLNh6NSZwNenoOj1Hp2jfbVQ6keaybQ5+ervs8UUafFQVZXVZ35iyMb7+f7UMlRUulXqwaJOixlQ7V60miKbP0UIIYQQQviAx8XS0qVLmTVrFl26dHGvGzRoEG+//TZLliwp0uBE4agB5Ulv+zIAAX9ORUm/UOTXsPy2mZRxo7ILpS4ETpiMovO8OFAUhUfbuFqXluw4S2qWvahDLRbHU48ycstw3to9iSRrElXN1ZjRZg5jmk4gzBju6/CEEEIIIUQR8LhYyszMxGw251kfGhpKampqkQQlblxWg4exlW+CxpqC+feinejBsnkjqeNGgd2OoXNXAie8UahCKcettcKpGeFPutXB1ztLdutSpj2DD/a/yxO/DmF34k5MWhOP132KD2MW0Sy8ha/DE0IIIYQQRcjjYqljx45Mnjw51xTi58+fZ+rUqcTExBRpcOIGaLSkdZqCqmgwHfoO/elfi+S0lk3rSX31FVeh1KU7geMn3VChBKC5onXpq+1nybAW7zirwlBVlU1x6xmy6QG+Pv4lDtVBTGQnPr71S+6v+TB6jWfdD4UQQgghRMnncbE0fvx4bDYbXbp0oW3btrRt25ZOnTrhcDh47bXXiiNGUUj28o3JajgIAPOmMeCw3ND5LBvWkTp+DDgcGLv1IPDViTdcKOXoWqccVUL9SM6ys3RX3nt5+VJcRiyvbH2eCX+PJT7rAhX9KvFmy+m83mIKkX4VfB2eEEIIIYQoJh5/0w0LC2Px4sUcPHiQ48ePo9PpqFatGrVqla2bipYV6W1exnB0JbpLx/Df8T4ZrZ4r1Hks69eSOvFVV6HUvRfmMeOLrFAC0GoUHmlTmYmrDvH5tjPc27QSJr22yM5fWMdSjvLSX8+QZE1Cr9Fzf42Hub/mwxi1Rl+HJoQQQgghipnHLUtWq5Vp06axbds2evXqRbdu3Xj55Zd5++23sdlK59TPZZlqDCI9xtXi5799DprkEx6fw7JuzeVCqWdvzGNfK9JCKUeveuWpFGQkMcPG93vOFfn5PXUo+SDPbxlOkjWJWkG1WdDxc4bUGSqFkhBCCCHETcLjYumNN95g48aN1KtXz71u2LBhbNiwgalTpxZpcKJoWGr1wxrdEcVhIXDTOFDVgh+7djWpr493FUq9+mAePR5FWzwtPjqthsGtKwPw6dbTWO3OYrlOQexL2sMLW54mxZZC/ZAGvNPmXaIDKvssHiGEEEII4X0eF0urV6/m7bffpkWLyzN/devWjSlTprBy5coiDU4UEUUhrdNkVK0Rw6kNGI7+WKDDstasInVSdqHU+3bMr7xabIVSjtsbVKC82cCFNCtf7fDNzHi7Ev7mpb+eI92eRuOwpkxvPQuzPtAnsQghhBBCCN/xuFhSVRWLJe9EAaqqSje8EswRUoOM5sMAMP/6Gor12tO8Z/38E2lvTACnE2PffphfGVfshRKAQadheMfqACz48yQXUm9sUgpPbY3fwitbnyfLkUmL8Fa81eod/HUBXo1BCCGEEEKUDB4XSz179uTVV19l27ZtZGRkkJGRwY4dO5gwYQLdu3cvjhhFEcloPhx7cDW06efx3/L2VffLWvUjaZMnuAql2+/E/PIYFI3HH5VC612/PI0qBpFpczJn83GvXff3878ybvvLWJwW2pZrz+SW0zBpTV67vhBCCCGEKFk8/gY8evRoateuzeDBg2nRogUtWrTg4Ycfpn79+owdO7Y4YhRFRWcirdObAPjt+Rhd/N48u2T99ANpb74Oqoqp312YXxrt1UIJQFEUXupaEwVYtf8Cu84mF/s1N8St47Udo7E5bdxa4TYmtpiCQSZyEEIIIYS4qXk8pZmfnx/vvPMOKSkpnDx5Er1eT3R0NGazmQsXLhAQIF2WSjJb5VvJqtUP05H/w7zhFS7d/T1oXN3rsn5cQdrUN1yF0p0DCHj+Za8XSjnqRwZyZ6MKLN9zjunrjrLowWZoNUqxXGvN2VVM3fUGTpx0q9SDUY3HodUU/Wx/QgghhBCidCn0N+GgoCAaNWpEjRo12LhxI0OHDqVz585FGZsoJukx43EaAtFf2Inpny8ByPrh+8uF0l33EPDCKJ8VSjmGxVTDbNRy8EIa3+8tnqnEfzj1PW/tmoQTJ32i72BUk1elUBJCCCGEEEAhWpZybN++neXLl7Nq1SrS0tKoWbMmY8aMKcrYRDFxBlQgvc1LBG4eT8Cfb3HpoELarP8BYLp7IAHPvoCiFE8rjidC/Q082b4aM9YfZe7m43SrE0GQSV9k51924mve/WcWAHdWvZunbxmJRvFtgSiEEEIIIUoOj4qls2fPsnz5cr7//ntOnz5NUFAQaWlpvPPOO/Tu3bu4YhTFIKvhYEwHvsF5ZD9pn2YXSvfeR8DTI0tEoZTjniYV+W53HMcSMvjw95O82KVWkZz3q6OfMf/g+wAMrP4AT9YbXqLetxBCCCGE8L0C/Rl96dKlPPzww3Tr1o2vv/6aDh06sHDhQn777Tc0Gg21a9cu7jhFUdNoSb31Tc7tCAYVjC0alrhCCVw3qn2hc00Avt0Zy5H49Bs6n6qqfHLoI3ehNKjWo1IoCSGEEEKIfBWoZWns2LFUrVqVqVOn0q9fv+KOSXhJxpFUMs4bUTQqFesdIc1pA63B12Hl0bpqKF1qR7Du8EVmrD/C3HsbF6q4UVWV+QfnsvjYFwAMrftfHqg5qKjDFUIIIYQQZUSBWpbefPNNoqOjGT16NO3atWP06NH88ssv+d6cVpQOqtVK+nuzAQht4MTPcQy/nR/6OKqre7ZTDYw6DdtOJ7Pu8EWPj3eqTt79Z6a7UBpe/1kplIQQQgghxDUVqFgaMGAACxYsYPPmzYwYMYJTp04xYsQI2rZti9PpZMuWLdhstuKOVRShzG+X4DxzGiUsHOOTLwAQsG02mtSzPo4sf5WCTQxqFQ3ArA3HyLI5CnysQ3Xwzt6pfHfyWwBGNnyZu6v/p1jiFEIIIYQQZYdHU3+FhYXx4IMP8sUXX7B+/XqGDx9O/fr1mTRpEh07dmTKlCnFFacoQs7EBDIXLQQg4Mnh2Brfj7VSGxR7JuZfJ/g2uGsY1KoyFQKNnEu18OnW0wU6xu60M3XXG6w8vQINGkY1HscdVfoXb6BCCCGEEKJMKPQ8yRUqVGDo0KEsW7aMVatW8dBDD7F58+aijE0Uk/QP30fNSEdXrz7GXn1AUUi7dTKqosV47Cf0J9f7OsR8mfRaRt5WA4BPt54hNjnrmvvbnDZe3vQya87+jFbRMq7ZRHpG9/FGqEIIIYQQogwokpvKVKtWjREjRrBy5cqiOJ0oRvaDB7CsXAHgup9S9o1nHeH1yGwyFIDATePAfu1CxFc6146gZZUQLHYnszceu+p+TtXJG3+/xpqTa9Br9ExoPpnbKnb1YqRCCCGEEKK0kztw3kRUVSXtfzNAVTF274m+YeNc2zNajcQREIk25ST+f3/goyivTVEUXuhcE60C6w5f5K+TSfnu9/GhD9l0bgN6jZ43WkylQ+StXo5UCCGEEEKUdlIs3USs69Zi370LTCb8/zsiz3bVYCa9w2sA+G+fgybllLdDLJBaEQHc07QSADPWH8XucOba/svZ1Xxx9FMAJrafSOvybb0eoxBCCCGEKP0KVCw99thjfPTRR+zbt6+44xHFRM3KIn3u/wDwf3Aw2vKR+e5nqXUH1ugYFIcF8+bx3gzRI0+0r0qIn55jCRl8uyvOvX7/pX1M2/MmAPfXfIg7at7hqxCFEEIIIUQpV6Bi6YknniAtLY1JkybRoUMHnnnmGb788kuOHz9e3PGJIpL51ec4L5xHE1kBv/sfvPqOikLarW+gavQYT6zFcHy194L0QJBJz7CYagDM+/0EiRlW4jMv8Or2V7A5rbQrH8PQuv/1bZBCCCGEEKJU0xVkpzZt2tCmTRsA0tLS2Lp1K3/88QdfffUVqamptG3blrZt29KuXTsiI/NvsRC+4zh/nowvFgEQMOxpFKPp2vuH1iKz6RP473gP8+bxJEZ3BL2fN0L1SL+GFVi2K44DF9KYs/kgsQHvkGhJoLq5BmObvoZGkV6mQgghhBCi8Dz+Nmk2m+ncuTNjxoxhxYoVLF26lI4dO7Jt2zYeeuih4ohR3KCMD+aAxYKuSVMMnbsV6Jj0ls/iMFdCm3oG/x3vFnOEhaPVKLzYpSbgZG3SuxxOOUiwIYTJLafjrwvwdXhCCCGEEKKUK1DL0rWEh4fTt29f+vbtWxTxiCJm270Ly9rVoCiYn3kBRVEKdqDen7SYCQSvegL/He9jqXs3jpAaxRtsITSJCqZB/S2cYg+oWiY0m0wF/4q+DksIIYQQQpQB0k+pDFOdTtL/NwMAY99+6OrU9eh4a43eWKvchuK0Yt70KqhqcYR5QzbEreMU3wOQFdefs+cr+TgiIYQQQghRVkixVIZZfvoR+8EDKAEBBDxeiMkOFIXUjpNQNQYMpzdiOFaybjp8KPkgU3dNAuAWU19sya3436bjpFvtPo5MCCGEEEKUBVIslVHO9DTSP3wPAL/Bj6EJCy/ceUKqk9H8KQDMv04Aa3pRhXhDErIuMm77y1icFlqXa8v0mJeoHGIiId3Kwj9L5v2hhBBCCCFE6VKoYuno0aOkpqYCsHnzZiZOnMg333xTpIGJG5P52SeoiYlooivjd89/buhcGS1G4AiqgjYtjoDts4sowsKzOCy8uv0VLmbFUyWgKuOavo6fwcDznWsC8OX2s5xMzPBxlEIIIYQQorTzuFhasmQJ/fr1Y//+/fzzzz889dRTnD59mtmzZzN7tu+/SAtwnD1D5tdfAWAe8RyKXn9jJ9T5kdbxdQD8dn6INvHwjYZYaKqqMmPPFA4k/0OgPpDJLadj1psBiKkRTofqYdidKjM3HPNZjEIIIYQQomzwuFj66KOPmDp1Kq1bt2bp0qXUr1+fjz76iJkzZ0rrUgmR/t5ssNnQt2qDvn1MkZzTWq0blmrdUZx2zJvG+myyh6+Ofsba2NVoFS2vNZtMVEB0ru0jb6uBTqPw2/FEfj2a4JMYhRBCCCFE2eBxsXT+/HlatGgBwPr16+nWzXXfngoVKpCe7tl4FovFwpgxY2jZsiUxMTEsXLgw3/0efvhh6tatm+cxevRoT8Mv86zb/sK6eSNotQQ8PbLgU4UXQFrHiahaI4azv2M88n9Fdt6C+u38Jj469AEAT9/yPM0jWubZp2qYPw+0iAJgxvqjWOwOr8YohBBCCCHKDo/vs1SjRg1WrFhBWFgYsbGxdOvWDZvNxsKFC6lXr55H55o2bRp79+5l0aJFxMbGMmrUKCpVqkSvXr1y7TdnzhxsNpv79a5du3juued44IEHPA2/TFPtdtLnzATAdNc96KoX7X2RnEFVyGj5DAFbphPw6+tYq3ZBNQQW6TWu5mjKESbvnAjAnVXvpl/Vu66676Ntq/DjPxc4fSmLhb+e4N6G5b0SoxBCCCGEKFs8blkaNWoUCxYsYNy4cTzwwAPUrFmTKVOmsGbNGsaOHVvg82RkZPDNN98wduxYGjRoQPfu3Rk6dChffPFFnn1DQkIoV64c5cqVIywsjJkzZzJ06FAaNWrkafhlWtaK5TiOHUUJCsL/kaHFco2Mpk9iD66GNuM8/n+9UyzX+LckSyJjt71EliOT5uEtGV7/2WvuH2DQ8cyt1QGYs+4wcclZ3ghTCCGEEEKUMR4XS+3ateOPP/5gy5YtjB8/HoBhw4axfv16GjZsWODzHDhwALvdTrNmzdzrWrRowa5du3A6nVc9btmyZSQnJ/P44497GnqZ5kxJJuMjVxc1/6H/RRMUXDwX0plI6+i6t5Hf7oVoE/YXz3WyWR1WXtsxhgtZ54nyj2Z8szfQaa7fINqrfnmaRAWRYXXwyor92BxX/0wJIYQQQgiRH4+74QH8+uuvNGjQAIBvv/2W1atXc8sttzBs2DAMBkOBzhEfH09oaGiu/SMiIrBYLFy6dImwsLA8x6iqykcffcSgQYMICAgoTOgU4RCeQsuJoShjyfz4I9SUFLQ1auLXr3+xvk97tc5YavTGeOwnAjeNI/mub4slsaqqMmvfdPYm7SZAZ+bNVtMINgYV6FitovBG33o8+OkO9p1LZc6m47zQpWaRxyhciuMzLfKSPHuH5Nl7JNfeIXn2DsmzdxRVngt6vMfF0nvvvcdHH33EJ598wtGjRxk/fjz33nsva9asITk5mddee61A58nMzMxTWOW8tlqt+R6zZcsWzp07x8CBAz0N2y083DtjbAqiqGKxHDlC/HffAhD16lgCKoQWyXmv6c634d2N6GO3EHH2R2h6f5FfYtG+Raw68yMaRcOM296meZRn3S4jIgKZMbApj3+6ja92nOXW+pH0blSxyOMUl5Wk36+yTPLsHZJn75Fce4fk2Tskz97hrTx7XCx9/fXXzJkzhyZNmjB27FhatWrFxIkT2bNnD0OHDi1wsWQ0GvMURTmvTSZTvsf8/PPP3HrrrYSEhHgatltCQqqvZr12UxTXD7goYlFVleTX3wCHA0PHTmTWbkjmxdSiCfSagvFr+SwBf0zB+fM4ksp1RDUWXde/Py/8zjvbXGOinqr/NHWNjbno4ftSFOh+SySDWkXz6dYzvPjNLiqYtFQO9SuyOIVLUX6mxdVJnr1D8uw9kmvvkDx7h+TZO4oqzznnuR6Pi6Xk5GRq1KiBqqps2LDBPXbIbDbjcBR8mubIyEiSkpKw2+3odK4w4uPjMZlMBAXl39Vq8+bNjBgxwtOQc1FVn90iKI+iiMX626/Y/toCej0Bw5/16nvLaPI4xgPfoEs6gv+f00m79Y0iOe+J1ONM+ns8Tpz0rdyPAVUH3tD7GhZTjV1nU9gVm8Ko//uHhQ80w6jzeLieKICS9PtVlkmevUPy7D2Sa++QPHuH5Nk7vJVnj78x1qtXjwULFvDuu++SmJhI9+7dOX/+PO+88w5NmzYt8Hnq16+PTqdj586d7nXbt2+nUaNGaDR5w0pMTOT06dPuezwJUG020t6dBYDfwPvRRkVf+4CipjWQdutkAEx7P0UXv+eGT2l1WHhtx2gy7Bk0DmvKMw1euOF7Rem0GibfXp8QPz2H4tOZsf7IDccphBBCCCHKPo+LpQkTJrBt2zYWLVrE888/T1RUFB999BFnz54tcBc8AD8/P/r378+ECRPYvXs3a9euZeHChQwaNAhwtTJlZV2e8vnw4cMYjUaio71cEJRgmUu/xnnmNEpYOH6DHvFJDLboDmTVvhNFdWLeOAbUG5t17sujn3E6/RRhxnAmNn8TvUZfJHFGBhqZ1KcuCvDd7nOs/Od8kZxXCCGEEEKUXYVqWfr+++/Ztm2buwveSy+9xLJly6hcubJH5xo9ejQNGjRg8ODBTJw4kaeffpoePXoAEBMTw8qVK937JiQkEBQUdMOtDGWFMzWVzE8+AiDgiWFo/As3O2BRSG8/Dqc+AP35vzHtX1Lo85xKO8lXxz4DYMQtzxFsCCmiCF3aVgvjsbZVAJiy5jDHEtKL9PxCCCGEEKJsUVTV895+//zzDwsWLODYsWM4HA6qV6/Ogw8+SOvWrYsjxiJ18aLvB90pimumthuJJfPbJaTPnoG2eg1CPvkSJZ+ui97kt/NDzL+9jtMUStJ/VuM0ezbrnKqqvPDX0+xM2EHrcu2Y0vLtGy6M88uzw6kyYuketp26RPVwfxY92Aw/vfaGriOK5jMtrk/y7B2SZ++RXHuH5Nk7JM/eUVR5zjnP9Xj8DXvNmjUMHDgQVVUZMGAAAwYMQFEUHn30UdauXVuoYIVnVFUla/kyAEx33ePzQgkgs9Ej2CIaoMlKIujn/4Ij/+nfr2bN2VXsTNiBUWPk2SIYp3Q1Wo3CG33qER5g4HhCBm+tPUwh/l4ghBBCCCFuAh7Phjd79mxefPFFhgwZkmv9J598wpw5c+jWrVtRxSauwrZzB46Tx8HPD2OPXr4Ox0WrJ6XXPEK/6Yv+3HbMv010T/5wPcnWZN4/MAeAh2s/QkX/SsUZKeEBBib3rcewb3az8p8LNI8O5k65/5IQQgghhPgXj5skTp8+TefOnfOs79y5M8ePHy+SoMS1ZS1fCoCpRy80AWYfR3OZM7gaqd3+B4DfnkUYD35boOPmH5hLsvUS1czVGVj9geIM0a1F5RD+26EaANPXHeXQhTSvXFcIIYQQQpQeHhdLNWvWZNOmTXnWb9y4kaioqCIJSlydM+Ei1o3rATD1v9vH0eRlrdaV9FYjAQhcPwpt/L5r7r87cScrz6wA4PmGo9BpPG7sLLTBrSvToXoYFruT0T/sJ81i99q1hRBCCCFEyefxN9Onn36ap59+ml27dtGkSRMAdu7cyc8//8y0adOKPECRW9aP/wcOB7oGDdHVquPrcPKV0Wokugu7MJ5cR/Cqx0m6dyWqKSTPfrb/b+++w+Oozr6Pf2f7SqveLMndcm+4YFNMsenFYCCF0AMBUoA8CQkEJy8YQnACKSSBPAR4IPQWQjEdh94MuHfLcm/qXVu0u/P+sfLawgI3aXYl/z7XtdfOnpmduXXraKVbM3NOtJW/LIv1mTP6nMWo7DGWxmkzDGadNpSLHlvAplo/v3trDXecOVwjLoqIiIgIcABnlqZOncoDDzxAMBjkqaee4j//+Q+mafLkk09y+umnd0WM0saMRAi8/CKQnGeV4gwbjSf+jUh6P+wNm0ibe12H8y89u+5JNjZtINOVyZVDf5yAQCHT62T2mcOx2wzmrqniuUXbEhKHiIiIiCSfA7rm6cgjj+TII49s1xYMBtm8efN+z7Uk+y702SdEy3dgpKfjnprcA2mYnkzqT72frOfPwr3xHVK++Astk66Pr9/avIXH1j4MwI+GX0e6Kz1RoTK6KJ3rjh3AX95bx1/eW8fIwnRG9tr7UJIiIiIi0rN12pjTn3/+eXxCWeka8YEdTp+O4XYnOJq9i+SNpHHqHwBI/eIvuDb8F4gNff635X8iFA0xPmciJxadksgwAfje+GKOL8khHDW5ac4KGgKtiQ5JRERERBIs8RP0yD6JbNtK67xPAfCcdU6Co9l3waHfwj/6UgDS5l6HrX4D726fyxdV83DaXPzPqF8mxT1ChmFw8ylDKc7wsL0hyKzXV2v+JREREZFDnIqlbiLw8otgmjgPn4y9T99Eh7Nfmo6+hdZeE7AF67G9/gPuXXE3ABcOuoTeqclz2Waax8Hvpw/HZTf4cF0Nj3+5JdEhiYiIiEgCqVjqBsxQKDYKHuCZcW6CozkAdhcNp9xH1JvLvZRTG6qlT2pfzh94UaIj28OwgjSunzoIgHs/XM/irfUJjkhEREREEmWfBnj44osv9rrN6tWrDzoY6Vjog3cx62qx5eXjOuqYRIdzQKK+Qj6d8iueLf0rADd6R+OyuxIcVcfOGVPIgi31vLmqkpmvrOTxi8eTlZKcsYqIiIhI19mnYuniiy/ep50lw70nPZF/58AO08/GcFg3aWtnCkfD3Fn+GqZhcFZjE8dufIC6PicTLjw80aHtwTAMZp40hNUVTWyo8XPza6u5+9xR2G3q3yIiIiKHkn36y3vVqlVdHYd8jfC6MsKLF4HdjvvMsxMdzgF7fsOzrGtcS7oznWszRmFUvUb6Gz+k9juvY6bmJzq8PaS47MyePoLLnljIZxtreXjeJn5wZL9EhyUiIiIiFtI9S0ku8NJ/AHBNORZ7XvIVFftih387j5Q+CMDVw67BPu0vhLOHYm8pJ/3NH0EkOYfpLslN5VcnlgBw/ycbeW1FeYIjEhERERErqVhKYmZLC8E3XgPAM+O8BEdzYGJzKv2ZQCTAmOzDOLX3GeBKpeG0B4i60nBtn0fqp79LdJhf68yRvThvbCEmcMvrq3lqwdZEhyQiIiIiFlGxlMSCc9/EbGnG1rsPzvETEx3OAfmw/H0+q/gYh+HgZ6NuiN/XFskcSOMJdwOQsvhB3GteTFyQe3HDCSWcP74YgD+/W8Y/PlqvOZhEREREDgEqlpKUaZrxgR28Z5+LYet+36qWcDP3rPgLAN8deCH9fP3brQ8NPIXmCdcCkPbuL7FXr7Q6xH1iMwx+fvxAfjylPwAPz9vM7LmlRKIqmERERER6su73F/ghIrxiGZHSNeBy4z79zESHc0AeWvMAVYFKilKKuajksg63aZn0C0J9jsUI+0l//UqMYHLOa2QYBt+f3JebThqMzYAXluxg5isrCYWjiQ5NRERERLqIiqUkFWg7q+SediK29IwER7P/1tSv4sUN/wbgpyN/gdvu7nhDm52Gk+4h4ivGUb+BtLn/A2byFiDnjilk9pnDcdoN3imt4qcvLKMpGE50WCIiIiLSBVQsJaFofR3Bd+YC4JlxboKj2X8RM8Kfl95JlCjTCk/i8LzJ37i96c2m4bQHMO1u3BveJmX+PRZFemCmDcnjr+eOIsVp58tNdfzo2SXUtIQSHZaIiIiIdDIVS0ko8PqrEAphHzwEx4hRiQ5nv7208XnWNKwi1eHjxyOu26f3hPPH0HRsbFS8lHl34dz0XhdGePAO75vF/35nDJleJ6sqmrjy6cVsqw8kOiwRERER6UQqlpKMGY3G51byzjgvPnpcd1EZqOShNfcDcOXQH5Htztnn9wZGnI9/xIUYmKS/dQ322rVdFWanGNErjQfPH0thuptNtX5+8PQi1lY1JzosEREREekkKpaSTOuCL4lu2YyRkor7xFMSHc5+u3fFX2gJtzAicyRn9j17v9/fdOxttBaMwxasI+PlC7A1buuCKDtPv+wUHjz/MAbmpFDZFOKqpxezeGtyDlIhIiIiIvtHxVKSiQ/scOrpGCkpCY5m/3xa/jEf7HgPm2HnZ6NuxGYcQPeyu6k/41+EMwdhb9pGxpwLMQK1nR9sJ8pPc3P/d8cyujCdxmCYn/x7KR+vq0l0WCIiIiJykFQsJZFIZQWhjz4Aut/ADv6wn7+t+BMA3x5wPoPSSw54X6Y3h/qzniTiK8RRW0rGnIshlNyXt2V4ndz77dEcNSCLYDjK9S8t5/WV5YkOS0REREQOgoqlJBJ85SWIRHCMPQzHgEGJDme/PL72X5T7d5DvKeCSkssPen/RtGLqpz9J1JOFs2IRGW9cCZFgJ0TadbxOO386eySnDMsjEjW5+bXVPLVga6LDEhEREZEDpGIpSZjhMIE5LwGxgR26kw2N63l2/ZMAXDvy53gd3k7ZbyR7MPVnPorpSMG1+YPYHEzRSKfsu6s47DZuO30Y3x1XBMCf3y3jHx+txzTNBEcmIiIiIvtLxVKSCH3yIdHKCozMLFzHTk10OPvMNE3+uvyPRMwIR+VP4eiCYzp1/+GCcdSf/n+YNieetXPwffBrSPLCw2YYXD91ED86uj8AD8/bzOy5pUSiyR23iIiIiLSnYilJBF6MDRfuOeMsDJcrwdHsu7e3vcHimoW4bW6uGfGzLjlGa59jaDjp75gYeJc/Tsrnf+yS43QmwzC4/Ii+3HRiCQbwwpIdzHxlJaFwNNGhiYiIiMg+UrGUBCKbN9H6xTwwDDxnn5PocPZZY2sD9638OwAXD/4+vVIKu+xYoZIzaTpuNgCpX/4V7+IHu+xYnencsUXMnj4cp93gndIqfvrCMhoCrYkOS0RERET2gYqlJBB4+QUAnJOPwl5YlOBo9t2Dq/9JXaiOfr7+fHvA97r8eIFRF9E8+QYAfB/Nwr36+S4/Zmc4YUged58zihSnnS831XHhows0F5OIiIhIN6BiKcHMYIDAa3MA8Haj4cJX1q3glU0vAvDTkb/AaXNactyWCdfSMuYKANLeuR7Xhv9actyDNalfFvd/dyy9Mz3saAxy9TOLeeizTbqPSURERCSJqVhKsOC7/8VsaMDWqxDnEUclOpx9EjEj3L3sLkxMTio+lcNyxlt3cMOgecotBIacixENk/7m1Ti2f2Hd8Q/C0AIfj100Pja0uAn/+/EGrnl+KZVNyT0kuoiIiMihSsVSggVejF1K5pk+A8NuT3A0++bljS9Q2rAanyONq4ddY30Aho3GaX8i2O8EjHCAjFcvw161wvo4DoDP7eC3pw/j5lOG4HHY+HJTHRc8uoCP19UkOjQRERER+QoVSwkULl1DePkysNvxnDE90eHsk+pAFQ+t+ScAVwy9mmx3dmICsTtpOOU+WgsnYQvWkzHnImz1GxMTy34yDIPpo3rx2MXjGZyXSp2/lf95YRl/ea+M1ohGyxMRERFJFiqWEsj/Quyskuu4qdhychMczb7535V/pznczNCM4ZzZ9+zEBuP0Un/Gw4RzhmNvqSDz5QswmisSG9N+6J+dwsMXjItPYPvk/K1c8dQiNtf6ExyZiIiIiICKpYSJNDURePsNADwzzktwNPtmQdWXvLP9bWzY+NmoX2I3En/ZoOnOoH7640TS+2Fv2EjmnIswgt1npDm3w8YvppXwx7NHkOFxsLK8iYseW8AbK7tP0SciIiLSU6lYSpD6l18Gvx97vwE4D7NwgIQDFIqEuHt5bDLYs/qdy5CMYQmOaJdoagF1Zz1B1JuHo3oF6a9eDuHudXbmuJJcHr94POOK02lpjfD/XlvFrW+spiUUSXRoIiIiIocsFUsJYJomdU89DYBnxrkYhpHgiPbumfVPsKV5E1mubC4fclWiw9lDNKN/rGBypePaPo/0N38Mke41+WuvdA//+M5YrjyyLzYDXlleziWPL2B1RVOiQxMRERE5JKlYSoDwksUES0vB48F9yumJDmevtrVs5Ym1jwDw4+HX4XP6EhxRxyK5I2g442FMuxv3hrfxvfNLiHavARMcNoOrjurPP749hnyfi421fr7/5EKeXbgV09ScTCIiIiJWUrGUAP6dw4WfeDK2tLQER/PNTNPk78v/TCgaYnzORKYVnZTokL5Ra9FkGk79J6Zhx7P63/DWb6AbFhkT+mTyxMUTOGZgNq0Rk7veKeOXL62gzt+9zpaJiIiIdGcqlixmtrQQfO8doHsM7PBh+fvMq/wUh+HgupHXd4tLBkP9T6Rx2p9iLz67l9QPfgPR7nfvT2aKkz/NGMn1UwfhtBu8X1bNhY/OZ8GWukSHJiIiInJIULFkNbsNe99+pJ9+Gs5hwxMdzTfyh1u4d8XdAHx34IX09fVLbED7ITjsWzQddwdg4F36COlv/QjCgUSHtd8Mw+D88cU8/L1x9M3yUtEU4kfPLuGBTzYSjna/M2YiIiIi3YmKJYsZbg/ZjzxJ8Z//nOhQ9uqR0oeoDFRQ6C3iopLLEh3OfguMvgS+/TCmzYW77DUyutmw4rsbWuDjsYvGc8bIAqIm3P/pRi5/ciFrNPiDiIiISJdJaLEUDAaZOXMmEydOZMqUKTz00ENfu+3q1av53ve+x5gxY5g+fTqfffaZhZEeetY1lPHvDc8AcO3In+O2uxMc0QEaeQ4NZz1G1JWGa9tnZL5wHram7YmO6oCkuOzMOnUot542FJ/bzsryJi55YiH/+Gg9wXD3GshCREREpDtIaLF05513smzZMh555BFuueUW7rnnHt544409tmtsbOTyyy+npKSEOXPmcNJJJ3HNNddQXV2dgKh7vqgZ5e7ldxE1I0wpOI4j8o9KdEgHpbX30dSd8zyRlAIc1avIfH4G9prSRId1wE4fUcBzl01k6uBcIlGTh+dt5sJH57NwS/c8ayYiIiKSrBJWLLW0tPDcc8/x61//mpEjR3LSSSfxgx/8gCeeeGKPbV944QVSUlKYNWsW/fr147rrrqNfv34sW7YsAZH3fG9ueY1ltUvw2L1cM+J/Eh1Op4jkjqDuvBcJZw7C3rSVzP+cg2PH/ESHdcByfW7uPGsEfzhrBDmpsSHGr3pmMb+fW0pTMJzo8ERERER6BEeiDrxq1SrC4TDjxo2Lt02YMIH77ruPaDSKzbarjvv888854YQTsNvt8bbnn3/+gI6bDIO57YwhGWL5qvpQPf9cdS8Alw2+goKUggRHdOC+mmczow/1571A+iuX4ixfSOZL36XxlP8lNCC5h0P/JicMyWVS30z++v46Xly6g+cXb+ejddX86sTBHDMox7I4krlP9yTKszWUZ+so19ZQnq2hPFujs/K8r+83zATNdPnmm29y22238fHHH8fbysrKOP300/n000/Jzs6Ot5999tmcccYZbN68mXfeeYfi4mJuvPFGJkyYkIjQe7RZn8zi+dLnKcks4dnpz+K0ORMdUucLNcNz34fSN8GwwZl3w4RLEx3VQftkbRW/+s9SNtW0ADB9bBG3TB9Brq+b3m8mIiIikmAJO7Pk9/txuVzt2na+DoVC7dpbWlq4//77ueSSS3jggQd49dVXueKKK3j99dcpLCzcr+NWVzcmfI5Sw4CcnLSkiGV3y2uX8nxp7IzddcOvp74mAHS/4bZ3+sY8n/RPfI4b8ax8BuZcR3PFJvwTf9qt/x00JNPNkxeP458fb+SJ+VuYs3gbH6yu4OdTB3H6iPwunSMrWft0T6M8W0N5to5ybQ3l2RrKszU6K88797M3CSuW3G73HkXRztcej6ddu91uZ/jw4Vx33XUAjBgxgo8//piXXnqJH/7wh/t1XNMkaTpwMsUSiYb5y9I/AnBq7zMYlTU2aWI7WB3m2XDQOPWPRFJ7kfrlX0md90dsTeU0HXs72Owd7qc7cDvsXHfcQE4alsdv31xDaWUzt7y+mtdXVnDTiYMpyvDsfScHIZn6dE+mPFtDebaOcm0N5dkayrM1rMpzwgZ4KCgooLa2lnB4183olZWVeDwe0tPT222bl5fHwIED27X179+f7du75xDQyeiFjc9T1lhKmjONq4b+ONHhWMMwaJn8SxqP/R0mBt7lj5H+5tUQ9ic6soM2vCCNRy8cx4+n9MdlN/hsQy3nP/IlTy3YSkST2YqIiIjsk4QVS8OHD8fhcLBo0aJ42/z58xk9enS7wR0ADjvsMFavXt2ubd26dRQXF1sRao9XGajk4TUPAHDl0B+T6c5KcETWCoy+lIZT78O0u3Gve4PMly/ECNQlOqyD5rDb+P7kvjxxyQTGFafjb43y53fLuPLpRZRVNSc6PBEREZGkl7Biyev1MmPGDGbNmsWSJUuYO3cuDz30EJdccgkQO8sUCMTulzn//PNZvXo1f//739m4cSN//etf2bx5M2effXaiwu8xTNPk3hV344+0MCJzJKf3mZ7okBIiNOgM6s96gqgrHef2z2OT1zZuS3RYnaJ/dgr3fXcsvzqxhFSXnaXbG7nosQXc/8kGQprMVkRERORrJXRS2ptuuomRI0dy6aWXcuutt3Lttddy8sknAzBlyhRee+01AIqLi3nwwQd59913OfPMM3n33Xe5//77KSjovsNaJ4tXNr/EBzvexYaN/xn1S2xGQrtEQrUWHUHduc8TSe2Fo2Y1mf85G3v16r2/sRuwGQbnjS3imcsmcszAbMJRkwc+3cRFjy/g/bVVRHVxtYiIiMgeEjZ0eKJUVSV+hBLDgNzctITHsrx2KT/77CeEzTA/GPpDLhh0SeKC6QIHmmdb41Yy5lyEo7aUqDuD+jP+Rbjw8K4L1GKmafL26kr++E4Ztf5WAPplebn48N6cNrwAl2P/C+Zk6dM9nfJsDeXZOsq1NZRnayjP1uisPO/cz94cuqcRDnHVgSpmLfg1YTPMsb2m8r2BFyc6pKQRTSum7tz/0NprArZgPZkvnY9r3RuJDqvTGIbBycPyefb7E7nk8D743HY21vq5/a1Sznrwc/41bxONgfDedyQiIiLSw6lYOgS1Rlu5deFvqA5W0c83gBvGzOzSOXi6I9OTRd1ZTxPsfzJGJEj6G1eR8tkfIBLa+5u7iUyvk2uPHcCcKyfzP8cNJN/noro5xL0fbeDM++fxl/fK2NHQfefZEhERETlYKpYOQf9Y+TeW1S4h1ZHKbeNnk+JITXRIycnppeG0+/GPvBjDjJI6/+9k/vss7DVrEh1Zp/K5HVw4sTcv/mASt542lEG5KbS0Rnhy/lZm/N8X3PL6KtZWavQ8EREROfSoWDrEvLHlVV7a+DwAM8fOoo+vb4IjSnI2B03Hz6b+1H8S9WThrFpG1rOn4V38IJg9ayQ5p93G6SMKeOqSCdx97igm9skgEjV5bUUF33t0Pj/9z1K+3FTHIXabo4iIiBzCHIkOQKyzum4lf1l2FwCXDr6CIwuOTnBE3Udo0BnU9pqI751f4N70Lr6PZuHaMJfGE/5M1FeU6PA6lWEYHD0gm6MHZLN8RyOPf7GZd0qr+GR9LZ+sr2V4gY9LDu/D1MG52G26fFNERER6Lp1ZOkTUBWu5ZcFMWqMhjsyfwsUl3090SN1ONLWAhjMfpfG4OzAdHlxbPiLr6ZNwr3kx0aF1mZG90pg9fQTPX3443xpbiNthY2V5Eze9spLzHvqC5xZtI9AaSXSYIiIiIl1CxdIhIBINc9vC/0dFoJzeqX25aezNh/R8SgfFMAiMuoTa775Fa/5h2IL1pL99DWlv/QQjUJvo6LpM70wvN544mDlXTuLKI/uS4XGwtT7Anf9dy/QHPuf+TzbSEGhNdJgiIiIinUp/MR8C7l/9DxbVLMBrT+G28bPxOX2JDqnbi2QOpO68F2medD2mYcdT+hJZT5+Ec/MHiQ6tS2WluLjqqP7MuWoyv5w2iKIMD3X+Vu7/ZCMn/ul93llTpXuaREREpMdQsdTD/XfbWzy3/mkAbhz7G/qnDUhwRD2IzUHL4T+j7ryXCGcOxN68g8yXLyD1w5sh7E90dF3K67TznXHFPH/54fzujGH0zfJS0RjkhpdX8MuXVlDeGEx0iCIiIiIHTcVSD1bWUMofl8wG4IJBl3Bsr+MTG1APFS44jNrvvIl/9KUApCx5iKxnT8NRsSTBkXU9hy02we1Tl07g2mkl2G0G75dV891/fcmzC7cSieosk4iIiHRfKpZ6qIZQAzfPv4lgNMjhuZP5/pArEx1Sz+b00nTs76g78zEiKQU4ateS+fxZpHz5V4iGEx1dl3M7bFx/8lCeuHg8owvTaQ5FuOudMn7w9CJKK5sSHZ6IiIjIAVGx1ANFzAi/W3QL2/3bKPQW8evDbsVu2BMd1iGhtd9Uar83l+CgMzCiYVLn3UXmC+dhq1uf6NAsUZKXyoPfG8uNJ5SQ6rKzbHsjFz++kHs+XK9R80RERKTbUbHUAz285gG+qJqH2+bmtgmzSXelJzqkQ4rpyaLhlPtoOPGvRF1pOHfMJ/uZU/AsfxwOgcEPbIbBtw4r4rnvT2Ta4FwiUZNHPt/M9x6dz7yNPXfEQBEREel5VCz1MB/seI8nyx4F4BdjbmJQ+uAER3SIMgyCQ8+j9vy5hIqPxAi3kPber0h/9bJD5ixTns/NH84awR/PHkG+z8WWugDX/Hsps15fRV2LhhkXERGR5KdiqQfZ0LiePyy+HYBvDzifE4pOTnBEEk0rpv7sZ2g6+mZMmwv3xv+S/eTx+N69AVvjtkSHZ4njSnJ55rKJfHdcEQbw6ooKvvXwF7y2olzDjIuIiEhSU7HUQzS1NnHz/F/hj7RwWM54rhr640SHJDsZNvyHXUXtd14n1Pd4DDOCd8WTZD9xDKkf3oLRUpnoCLucz+3gF9NKeOiCwyjJTaU+EOaW11dzzb+XsqWuZw+zLiIiIt2XiqUeIGpGmb34Nra0bCbfU8DNh/0Wu82R6LDkKyI5Q6mf/ji15/yHUNFkjEiQlCX/R85jR5H66e8xAnWJDrHLjSpM57GLxvGTKf1xO2x8vqmO8x+ZzyOfbyYciSY6PBEREZF2VCz1AI+v/RefVnyE0+bitgmzyXRnJTok+QbhoknUz/g3ddOfoDV/LEbYT8qCe8h+7ChSvvwrRqhnD7XtsNu4bHJfnr50ApP6ZhIMR7nnw/Vc8sRClm9vSHR4IiIiInEqlrq5T8s/5l+lDwLws1G/ZEjGsARHJPvEMGjtexx133qF+tP+j3D2UGyhBlLn3UX2Y0fhXXQ/hHv25Wm9M73c863R3HraUDI8Dkorm/n+k4v46X+W8tLS7RoEQkRERBJOxVI3tqV5M3csvhWAs/udx6m9z0hwRLLfDIPQwFOoPf9tGk66h3BGf2yBGnwf30b241PwLHsMIqFER9llDMPg9BEF/Pv7h3PGiHxM4JP1tdz+Vimn3vcpP3puCc8t2kZVUzDRoYqIiMghyDAPseGoqqoaEz7VjWFAbm7aQcVSGajkhs9/ysamDYzKGsOfJv8dp83ZuYF2c52RZ8tFWvGsfo6UL+7G3hQbLS+S3pfmw39OcMg5YEvOyYU7K9cbqlv4b2kl76ypYk1l8679A2OK0pk2JJepg3MpTPccfNDdULfs092Q8mwd5doayrM1lGdrdFaed+5nr9upWLLewX6T19Sv4tdf3kB1sIocdy7/nPIw2e6czg+0m+vWH1qRIN5lj5My/x5s/thoeeGsEpon/YLQoNPBSK6Twl2R6y11ft4treKd0iqWbW9st254gY9pg3OZNiSPvlnezjlgN9Ct+3Q3ojxbR7m2hvJsDeXZGiqWulgydOCD+SZ/sOM9Zi+6lWA0SH/fAO6Y+Ed6pRR2TaDdXI/40Gptwbv0YVIW/ANbsD7WlDuKlsm/JNRvWuyLTAJdnesdDQHeW1vNO6VVLNpSz+6HKMlNZdrgXKYOyWVQTgpGkuSkK/SIPt0NKM/WUa6toTxbQ3m2hoqlLpYMHfhAvsmmafLUusd4cPV9AEzKO5L/d9htpDpTuzDS7q0nfWgZwQa8i+7Hu/gBbK2xy9PCOSNoGXc1wZKzwJ7YSzCtzHV1c4j318bOOH25qY7Ibsfrm+VtO+OUy7B8X48rnHpSn05myrN1lGtrKM/WUJ6toWKpiyVDB97fb3JrtJU/L/0Db259DYBz+3+bHw27VnMp7UVP/NAy/DWkLLgX77JHMdpGy4v4CvGPuYLAiAsw3emJiStBua73t/JBWeyM07yNtbTuVjmN7JXGj6f0Z1K/njOUfk/s08lIebaOcm0N5dkayrM1VCx1sWTowPvzTa4P1XHLgpksqVmEzbBz7Yj/4ex+51kTaDfXkz+0jEAt3mWP413yUPyepqjTR2DkhfjHXEE0rcjaeJIg103BMB+vq+Gd0io+Xl9DMByb5Pbwvpn8ZEp/RhYmppDsTMmQ50OB8mwd5doayrM1lGdrqFjqYsnQgff1m7ypaQMzv/wl21q2kupI5eZxv+XwvCOsC7SbOyQ+tCJBPKtfwLvonzhqSwEwbQ6CJdPxH3Y14bxRloSRbLmuag7xr3mbeH7xdsLRWEDHl+Rw9dH9KcntvpeuJlueeyrl2TrKtTWUZ2soz9awulhKriG1JG5B1Zdc88nVbGvZSqG3iL8feb8KJdmT3U1gxPnUfu+/1J/xCKHiozCiYTxrXiDr2VPJeOl8nBvf5VD71M5NdfGLaSU8f/nhnDmyAJsB762t5oJH5nPL66vYUtezJ/wVERGRzqFiKQnN2fQiN3zxM5rCjYzKGsO9Rz1A/7QBiQ5LkplhI9T/BOpnPEvtt18jMPhsTMOOa8tHZL5yMVlPn4h75bMQObQmdy3K8HDLqUN56tIJTBuciwm8tqKCbz/8JX+YW6rJbkVEROQb6TK8BPi604cRM8J9K+/h+Q3PAHBi0Sn8YvRNuOyuBEXavR3qp8NtDVvwLvk/PCuejI+gF0kpwD/m+wRGXoTpyey0Y3WXXK/Y0cj/frSBzzbWAuB22PjuuGIuObw3Gd7kn9S5u+S5u1OeraNcW0N5tobybA3ds9TFkqEDd/RNbgk3c/vCW/is8hMALh9yFRcOurTHDX1sJX1oxRjBejzLn8C75P+wN5cDYDpS8I/4Hv6xPyCa3ufgj9HNcj1/cx33friBpdsbAPC57Vw8sQ/njy8mxWVPcHRfr7vlubtSnq2jXFtDebaG8mwNFUtdLBk68Fe/yeX+Hfz6y1+yrrEMl83Fr8bezPGF0xIbZA+gD62viIRwl75MyqJ/4qheCYBp2AgNOBn/qEtp7X00GAd2ZW53zLVpmny4rob//WgDa6tiZ96yU5x8f3Jfzh1TiMuRfFcpd8c8d0fKs3WUa2soz9ZQnq1hdbGkiXoSbGXdcn7z5Y3UhmrIcmVz+8Q7GZ45ItFhSU9kdxEc9i2CQ8/DueVDUhb+E9fm93GvewP3ujcIZw4kMOoSAkO/1amX6CUrwzA4dlAOUwZm89aqSv75yQa21AX407tlPDl/C1ce2Y/TRhTgsOnsroiIyKFKZ5YSYGcl+9ySF/n94t8SioYYmFbC7ybeSYG3V2KD60H0H569s9eswbvsUdyr/o2ttQkA0+EhMHgGgVGXEM4fs0/76Qm5DkeivLxsBw9+tonKphAA/bO9XDqpDycOycPjTPzleT0hz92B8mwd5doayrM1lGdr6DK8LpYcHdjk+W1Pcu+iewE4Iv9ofnPYLFIc3Xf+l2SkD639EGrGs+YFvMseiV+iB9Cafxj+0ZcSLDkTHN6vfXtPynWgNcJzi7bxyOebqQ+Egdg9TacNL2DG6F4MyfclLLaelOdkpjxbR7m2hvJsDeXZGiqWulgydOA7l/yON7a8CsC3B5zPVcN+gt1I/H+texp9aB0A08SxYz7eZY/gXvsqRjR2hiXqziQw/Lv4R11MNKP/Hm/ribluCoZ5btE2Xly6g231gXj7yF5pnDOmFycNzbd8MIiemOdkpDxbR7m2hvJsDeXZGiqWuliiO3BjawNnv30qDsPBdaN+zpl9ZiQumB5OH1oHx2ipwrPyabzLH8feuCXeHup7PP5RlxLqNw1ssWKhJ+c6app8sbGOF5du57211YSjsS8wxWnnlOF5nDOmkOEFe/+w7Qw9Oc/JRHm2jnJtDeXZGsqzNVQsdbFk6MDzqz5nQEFvcqJFCY+lJ9OHVieJRnBtehfP0kdwbXoPg1gyI75i/KMuJjD8fEjNPSRyXdMS4tXl5bywZDub63adbRqa7+OcMb04ZVg+PnfXjZujPm0N5dk6yrU1lGdrKM/WULHUxZKhA+uHyRrKc+ez1W/Au/xxPCuexhasA8C0OQkOOh3P4RdSlTEB0+ZObJAWME2TBVvqeWHJdt4praI1EutgHoeNk4flMWN0IaMK0zp9njT1aWsoz9ZRrq2hPFtDebaGiqUulgwdWD9M1lCeu1A4gLvsFbxLH8VZviDeHHX6CPU/geDA0wj1nQqunj9oSV1LK6+tLOfFJTtYX9MSby/JTWXG6F6cNiKfdI+zU46lPm0N5dk6yrU1lGdrKM/WULHUxZKhA+uHyRrKszUclUvxrHwG74Y3oXF7vN20uwn1OY7goNMI9T8R05OVwCi7nmmaLN7awItLtzN3TRXBcBQAt8PGCUNyOX1EARN6Z+CwH/hkt+rT1lCeraNcW0N5tobybA0VS10sGTqwfpisoTxbxzAgNzuVuhUf4ip7HXfZa9gbNsbXm4ad1uKjYoXTgFOIphYkMNqu1xBo5Y2VFbywZAdrq5rj7ekeB1MGZnN8SS5H9s/a77mb1KetoTxbR7m2hvJsDeXZGiqWulgydGD9MFlDebbOHrk2Tew1q3CXvY573evt5m4yMQj3mkBw4GkEB55KNKNf4gLvYqZpsnxHIy8v28F7pdXU+lvj69wOG0f2z+L4klymDMwmw7v3S/XUp62hPFtHubaG8mwN5dkaKpa6WDJ0YP0wWUN5ts7ecm2rW4973Ru4173e7h4ngNbckYQGnkZw4GlEsofEdtYDRaImS7Y18N7aKt4rrWJbQzC+zm7A+D6ZHF+Sy/ElOeSndTxIhvq0NZRn6yjX1lCeraE8W+OQKpaCwSC33norb731Fh6Ph8svv5zLL7+8w21/9KMf8c4777Rru++++5g6dep+HTMZOrB+mKyhPFtnf3Jta9qOa/2buMtex7ntMwwzEl8XzhhAa59jaS2aTGvRJKKpvbo48sQwTZM1lc28V1rFe2ur212qB7GJb48vyeH4wbn0z06Jt6tPW0N5to5ybQ3l2RrKszWsLpa6bkKQfXDnnXeybNkyHnnkEbZt28aNN95IUVERp5566h7blpWVcdddd3HkkUfG2zIyMqwMV0Q6QdRXSGD0ZQRGX4bhr8G14W3c617HtekDHPXrcdSvx7vsEQAi6f1ihVPhJFqLJhHJGNAjzjwZhsHQfB9D831cfXR/Ntf6Y2ec1lazdFsDy3c0snxHI/d+tIEB2SkcPziH40tyGdHLl+jQRUREDikJO7PU0tLCEUccwQMPPMDkyZMB+Mc//sGnn37KY4891m7bUCjEYYcdxquvvsqAAQMO6rjJUO3rPw/WUJ6t0xm5NkJNODd/gHPbPJzb5uGoXoFhRtttE0nJjxdOrYWTieQMA9v+DZKQ7KqaQ3ywtop311bz5aY6wtFdCS1Ic3Pa6EIm907jsOJMHLbuXzgmI312WEe5tobybA3l2RqHzJmlVatWEQ6HGTduXLxtwoQJ3HfffUSjUWy2XcPrrlu3DsMw6NOnTyJCFRELmC4foUGnExp0OgBGsAHHjvm4ts3Duf1zHOWLsLdUYC97BU/ZKwBEXem0Fk5sK6AmE84fA/buPSlubqqLc8cWce7YIpqCYT5aV8N7a6v4ZH0N5Y1B/vXJBv4FZOw2st4RBzCynoiIiOxdwoqlyspKsrKycLlc8bbc3FyCwSB1dXVkZ2fH29etW4fP5+OGG27g888/p1evXlx77bUcd9xx+33cZLiCZ2cMyRBLT6Y8W6dLcu1JJ9x/KuH+bfclhgM4KhbvOvO0fT62UAPuje/g3hi7n9G0uwkXjKO1aDKhAScSzj+sW3eANI+D00bkc9qIfILhKJ9vquXTTfW8tbycOn8rr66o4NUVFbuNrJfDlEE5ZO7DyHry9fTZYR3l2hrKszWUZ2t0Vp739f0JK5b8fn+7QgmIvw6FQu3a161bRyAQYMqUKVx11VW8/fbb/OhHP+KZZ55h9OjR+3XcnJy9n26zSjLF0pMpz9bp2lynQa8TYcyJsZeRMJQvhY2fwqZPYOOnGC1VOLd9hnPbZ6R8+VfIHgSjvw1jvgM5g7owNmuc0yuDcybB7HNNvtxQw1srynlz+Q621Pp5b201762txm4zmNQ/m1NGFnDSyF4UZ3oTHXa3pc8O6yjX1lCeraE8W8OqPCfsnqXXX3+d22+/nY8//jjeVlZWxumnn868efPIzMyMt0ejURobG9sN6PDDH/6QvLw8fvvb3+7XcaurE38dqWHEvsHJEEtPpjxbJylybZrY68pwbJuHa8vHuNa/hREOxFe35o8lOOQcgoPPwkzNT1CQB6ejPJumSWllM++2jaxXWtl+ZL3hBT6OK8lh6uBcBuakYOhfnnuVFP35EKFcW0N5tobybI3OyvPO/exNws4sFRQUUFtbSzgcxuGIhVFZWYnH4yE9Pb3dtjabbY+R7wYOHMjatWv3+7imSdJ04GSKpSdTnq2T2FwbhDNLCGeWEBhxIYSaca9/A/eaF3Ft/gBnxWKcFYtJ/fg2WntPITDkHEIDT8V0db//ALbPs8HgPB+D83xcdVR/ttT5eX9tNe+vrWLR1gZWljexsryJ+z7eSJ9MD8eX5HJcSQ6ji9KxqXD6RvrssI5ybQ3l2RrKszWsynPCiqXhw4fjcDhYtGgREydOBGD+/PmMHj263eAOAL/61a8wDIPZs2fH21atWsWQIUMsjVlEuhFXKsGh5xEceh5GSxXutXPwrHkBZ/kCXJs/wLX5A8z3fkWw/0kEh5xDqN9UsLv2vt8k1zvTy4UTe3PhxN7UtIT4sCx2ed7nG2vZXBfgsS+38NiXW0h12Rle4GNYQRrDC3yM6JVGcYZHZ55ERER2k7Biyev1MmPGDGbNmsUdd9xBRUUFDz30ULwgqqysJC0tDY/Hw7Rp0/j5z3/O5MmTGTduHHPmzGH+/PncdtttiQpfRLoRMyWXwJjvExjzfWz1G/CseRH3mhdw1JXhaRtdL+rOIDjoTIJDZtBaNBkM2953nOSyU1ycPbqQs0cX0hwK8+n6Wt5bW8VH62poDkX4cnM9X26uj2+f5nYwrMDH8LYCangvH0XpKqBEROTQlbB7liA2yMOsWbN466238Pl8XHHFFVx22WUADB06lNmzZ3PuuecC8Nxzz/Hggw+ybds2Bg8ezE033cThhx++38dMhrHvNQ6/NZRn63TLXJsmjqpluFe/gLv0Jewt5fFVEV8RwcFnExhyDpHcEQkMsr3OynM4arK+upmVO5pYWd7IyvImSiubCEX23GmGZ7cCqlesiOqV5u7RBVS37M/dlHJtDeXZGsqzNayeZymhxVIiJEMH1g+TNZRn63T7XEcjOLd+invNC7jXvYYt1BhfFfEVxedxai2cRCR7cMLOOnVlnlsjUdZVtcSLp5XljZRWNrebFHenTK8zduapwMe43hlM7JOJw979z8Tt1O37czeiXFtDebaG8mwNFUtdLBk6sH6YrKE8W6dH5TocwLXxv3jWvIBrwzsY0fZTGUTdmbHiqfBwWosmEc4bA3Zr5jWyOs+hcJSy6mZW7miMDxSxtqqZyFcKqEyvk2mDczl5WB6HFWdgt3Xvs049qj8nOeXaGsqzNZRna1hdLCXsniURkaTk8BAadAahQWdAqBln+QKc2z/Hue1znOXzsQXrcG94C/eGtwAwHR5aC8bvOvtUMB5cqQn+IjqHy2Fru39p1y+TYDjK2qpYAbV8RyMfr6uh1t/Kf5Zs5z9LtpOb6uLEoXmcPDSPUYVpPfpyPRER6flULImIfB1XKq19jqG1zzGx15FWHJVLdxVP2z/HFqzDtfUTXFs/AcA07ITzRrUVT5NoLZyE6c1J4BfRudwOGyN7pTGyVxrfInb/0/xNdby1uoJ3S6upag7x9IKtPL1gK0Xpbk4cms/Jw/IYkpeqwklERLodXYaXADpNaw3l2TqHbK7NKPaa0ljx1FZA2Zu27rFZOKuEcN5oIlmDCWeVEMkaTCSj/35fvpfseW6NRPlsQy1vra7k/bVV+Fuj8XX9srycNDSPk4flMyAnJYFR7l2y57knUa6toTxbQ3m2hi7DExHpLgwbkZyhRHKGEhh1MQC2xq04t82LF0+O2jU4atfiqG0/ibZpcxDJ6N9WQA0mklVCJHsw4cxB4EzuYuLrOO02jhmUwzGDcgi0Rvh4fQ1vrark4/U1bKz18+Bnm3jws00MzkvlpKF5nDQ0j96Z3kSHLSIi8rVULImIdKJoWjHBoecSHBqb9sDw1+DcMR97zWoctaXYa9fiqCnFCLfEiyg3r7fbRyStN5GsEsJZQ4hkl8SLKbxZifiSDojHaeeEIXmcMCSPpmCYD8qqeXt1JZ9uqKW0spnSymb+8dEGRvZK4+RheUwZmENxhqfbDw4hIiI9iy7DSwCdprWG8mwd5Xo/mVFsTdux15biqF2LvaatiKotxRao+dq3Rb252HqNwJ8+lNbcEYRzRsSGMre7LAz+4NT7W3lvbRVvrarky8117D6wnstu0CfLy4DsFPplp9A/O4X+2V76ZqWQ4rJbFqP6s3WUa2soz9ZQnq2hy/BERHo6w0Y0rZhoWjGtfY9vv8pf03YGalcBZa9Zi71pKzZ/Faz/AC8fsPPiNdPmiJ2Fyh1JOGcE4dzYI1kHlcjwOjl7dCFnjy6kujnEf9dUMXd1Bct3NBKKmJRVtVBW1bLH+wrS3PTP9tI/XkjFlnNTXRo4QkREuoyKJRGRJGJ6s2n1Tqa1aHL7FaFmnHVryQxtwL9+PvaqFTiqV2IL1uOoXoWjehXwfHzzSEoBkdzhseKprYiKZA4EW/J87OekuvjOuCK+M66ISNRkR2OADTV+Nta0sKGmJb5c09JKeWOQ8sYg8zbWtdtHqsverngaku/j8D6ZuBw9Z5JcERFJnOT5rSkiIl/PlUq4YCzkTqG5z9mxSw9ME1vTNhxVK3BUr8BRtQJ71XLs9Ruxt5Rj31SOa9N78V2Ydjfh7KGEc4cTzhtDuNd4wtnDLJtU95vYbQbFGV6KM7wcPSC73bo6fysba1rYWONvK6Ja2FjrZ0udn+ZQhBU7GlmxozG+farLzpSB2UwbnMuRA7LxOq27hE9ERHoWFUsiIt2VYRBNKyaUVkxowEm72kPNOGpWxYqoeCG1EiPcgrNyCc7KJbDyGaBtUt28sYQLDqO113jCBeOJ+goT9AV1LNPrJLM4g7HFGe3aQ+EoW+r98TNQ66tb+HJzHZVNId5cVcmbqypxO2wcNSBWOE0ZmI3PrV97IiKy7/RbQ0Skp3GlEu41gXCvCbvazCi2+o04qpbjqF6Js2IRjvJF2IL1uLbPw7V9XnzTiK+QcMF4WgvG0VownnD+aHAk3xDfLoeNgTmpDMxJjbdFTZNl2xt5t7SKd0qr2FYf4N3SKt4trcJpN5jUN4tpg3M5tiSHTG/iz6iJiEhy02h4CaDRUqyhPFtHubZGp+fZjGKvW4djxwKc5QtxlC/AUb0Sw4y238zmiN331GscrQXjCBeMJ5IxIBZQEjNNkzUVzbxTWsk7pVVsqPHH19kNGN8nk2mDczl+cC65qbtGFFR/to5ybQ3l2RrKszWsHg1PxVIC6IfJGsqzdZRra1iS51AzzsolOMrbCqgdC7C3VOyxWdSTRWvBOCJZg4l6czA92US92UQ92ZjebKKeLEx3BhjJM9DCuupm3lkTO+NUWtkcbzeAscXpTB2cy7TBuRRmeNSfLaLPDmsoz9ZQnq2hYqmLJUMH1g+TNZRn6yjX1khIntsGkXDuWLCrgKpcihEJ7v2thh3Tk0XUk03UmxUrqNqKKrOtLerJwfRmE/EVYabkWfAFxWyp88cLp+W7DQ4BMKLAxymji+ib5qQkN5WCNLeGJ+8i+uywhvJsDeXZGppnSUREkkfbIBLBtGKCg6fH2iKh2MAR5QuwN27F5q/GCNRg89dgC9TGlkONGGYEw18Vmx+qdu+HiqTkx+aLyhsVf46m9+uSy/16Z3q5ZFIfLpnUhx0NAd5bW807pVUs2lLPivImVpSviW/rc9spyU2NPfJiz4NyUzVYhIjIIUCf9CIisn/sLsIFhxEuOOzrt4mEdhVO/tjDCNRgC9Rg+GPPtkBt23I1tqYd2FsqsG+qwL3p3fhuoq60tol2R8WLqEjW4E4d7rxXuofzxxdz/vhiqptDvF9WxYrKFpZvqWd9TQtNwQiLtjawaGtDu/cVpbspyfNRkptCSZ6Pwbmp9M7y4rDpLJSISE+hYklERDqf3UU0tQBSC4jsy/atLTiqV+KoXIajahmOyuU4qldhCzXi2jYP17Zdo/XF54vKG7mriMoZDs6Ugw47J9XFeWOLuLrtEo9QOMrGGj+lVU2srWxmbVUzayubqWgKsa0hyLaGIB+UVcff77IbDMzZdQZqSH4qowrTNdeTiEg3pWJJREQSz5my53DnkVbstaWx4c53FlFVK7CFGnfNF9XGNGxEMgcSzhlOJHMgkYwBRDL6E8kcgOnJPuBL+Zx2W6zwyUuF4bva6/2t8cJpbdWuIioQjrKqoolVFU3xbe02gxEFPsb1zmR87wzGFqfrEj4RkW5Cn9YiIpKc7E4iuSOI5I4gOOzbsTYziq1hU1vxtDz+bG+pwFG7Fkft2j12E3WlE8lsK54yBrQtD2grpLIOKLQMr5MJfTKZ0Cdz13FMk231AUordxVRy7Y3UNEUYun2RpZub+TRLzZjM2BIno9xvTNij+IMMlM055OISDJSsSQiIt2HYSOa0Z9QRn9CJWfuam6uwFm1DHtNKfb69djr1seem7ZhCzVgq1iMs2LxHruLujP3KKSimQMgZSSYDmIDi+8bm2HQO9NL70wvUwfnArG5nrY3BFm4pZ4FW+pYuKWezXWB+NmnpxZsBWBgTgrjemcwvu2R63MfXJ5ERKRTqFgSEZFuz0zNJ5Q6DfpNa78i7Mdev7F9AbXzubkcW7AOW/lCnOUL99hnjs1FNCWPaGo+0ZR8oqkFsdfx5fy213lg6/jXqWEYFGV4KMrwcMbIAgAqm3YWT/Us3FLPuuqW+OP5xdsB6JvlZVxx7MzT+D4ZFKZ72u03apqEwlFaIyat0Wh8ORSJ0hqJEoqYbc9t27QtO202juifpcsARUT2kT4tRUSk53J4ieQMI5IzbM91rS3Y6zd8pZDagKN+PbaWCoxoCHvTVuxNW7/xECYGpjenrYjKI5oSK6QiqfmY3lyirjRMdzqmKw3TlUaeK52Th+Zx8rB8AGpbQiza2hAvoNZUNLGp1s+mWj8vLdsBQIYn9us61FYIRaLtJxcxiJJBMzlGA9k0kmM0xB40kN223JcGXEaYuQwn2O8EJkw+gZKCjE5IsohIz6ViSUREDk3OlPg9UbszDMjNcFKzeT1Gczm2lgpsLZXY2i1XYGspx9ZS1X4+qeqvOdZXmIYd0+XDdKWR5Uqjvyuds91pmL3SCBX72B50sanZTmmjnTUNdgIhB9lGIznUk21rJNdeTzaNZBsN5BoNZNGIw4ju07EnsgY2v0TtJh9L3BOwl5zMoEnTcaRm728GRUR6PBVLIiIiX+X0EE3vjZnW+5u3i0YwArWxIqq5PFZI7b7sr8IINcYm6d35iIZjBVawHoL1e+zSA6QDQ4GTYL9+U0dd6US92bEzWt6c+MNseybSStPquWRs/5CsaCPHhd6HFe8TWfEbtqaMwjnkZNxDTyGSM7xLJgMWEeluVCyJiIgcKJsdMyWXSEruHmeoOmSaEA5gCzXECqdgQ7yIihdUbW2x1w0YwUaMaIioJztWCHlyiKbkxl6n5BL15GB6Y+uw731gCOfw79ASDVOxfh7bF84hv/wDSthE35alsGgpLPoTLZ5eMOhEWvufSKj4aHB6OyFZIiLdj4olERERqxgGOL1EnV5ILUhcHDYHvkFHM3jQ0YQjUeYsX8aOha8ysP4TjrYtIyWwA5Y/DssfJ2p301p8FKH+JxDqdwLR9D6Ji1tExGIqlkRERA5hDruNI8aMgTFj2FDTwu8WbqBq5TscGZnPNPtCekeqcG96F/emd4HfEM4eSqjPcURTCzDdaURduwavMF1p8TYcXl3KJyLdnoolERERAaB/dgrXnTAC/7FDeWtVBVcs3IZZtYpptoVMtS9iom0NjprVOGpW73Vfps2B6fRhutNjIwJ2VFC50yG3EFckhYg7O3Y5oScL050Bhs2Cr1hE5JupWBIREZF2vE47Z48u5KxRvVi+YzD/XjyRi1dV4Ak1cpxtCZMdpeQ7/WTa/KQbfny0kBJtxms24460YCMaG8giWAfBOux7OV76V16bhh3Tk9V2n1YWpid7t3u2Ym3xe7Xa1uFK7aJsiMihTMWSiIiIdMgwDEYVpjOqMJ3/OW4gc5bt4PnFucypPwpCX/cukxSCpNFCmtFCOi2kGX7SaCHTHiDXESDbESDbFiDT1kKOvRlfpB5ftJ6UcD3uSHP74dhr9y3WqCuNqK+IiK+IqK+IaNqu5dhzITg8e9+RiMhuVCyJiIjIXmV6nVx8eB8unNib9dUt1PlbaQiEaQyEaQiGaQy0Ur/b69i6VsoCYRqDYaImEAVav/k4TsJk0Ui20UiW0Ug2jfRyNNHL2Uy+vZlcWxNZNJBhNpAWbSAlXIfDDGELNWLbyyWCUW/OVwqonUVVcWw5NR9s+tNIRHbRJ4KIiIjsM5thMCh3/y55i5omLaHIbsVVrNBqCIQJ221sq2qmzt9KvT9WcNX709jub2XNziIrxDeeyUolQC+jhr72GsamNTHc20A/Zw35ZhVpoQocTdswwn5s/mps/mqoXNrxngwb2JyYNkesaDJsYDgwbXYw7GBztG3jiA0b39YWW7fba5udqDMN05PZdg9WJlFPZuzSQnfmbu0ZYHftVy5FxFoqlkRERKRL2QwDn9uBz+2AjF3thgG5uWlUVTVimnu+L2qaNAbC1AfCuxVTrdT7w9QHWtvawlQ3h1hb5aMsVMy7tbS7dM9uwMCcFMblmoxLb2KYt56+jlq8/h3YmrZha9yGvWkbtuYdGNFWiAQxIsEuz0n8a3SmdlBEtRVX7kxMhydWpO0s4HYuG46O220OTJszVsDZnZiGHcPuBF8vMA1AIxSK7A8VSyIiIpKUbIZBhtdJhtdJ36xvnhg3appsrQuwqqKJVeVNrKloYlVFE3X+VkqrWiitgmexA9kYZNM3axRD830M6+NjaL6PoXkpZNKAEQ6CGcYwoxANgxnBiEbiy0QjGGYYotHYdtFIW/vuy62xiYUDdRiBWmzBOoxAHbZALUawLtYerMfAxNbaDK3N2Bu3dHk+c2wOop7sWHEWHywjJ1agtQ2gEfVk7xo4w5sdGwL+UGKaYEaB3Z/bHpixfrFzXVtbfB1RCKoY7WlULImIiEi3ZzMM+mR56ZPl5aSheQCYpkl5Y5DVFc2srmhkVXkTqyuaqGgKsbHWz8ZaP2+trozvozDdTZ9ML4XpHnqluynKSKVXupvCdA95PjcOWyf+IRyNYIQa4kXUVwuq+HI0BJFwW2EWbivKYs/tls0O2s0IRqS17XUIIxrG3lIBLRX7HKbp8LYbidB0pWLa3Zh2N7Q9mw432F0dtLkx4+2u2FmynW3OVEx3OqbT17XzcZlmrHBt2o6teTv2pu0dLO/ACDZg0MHpzQOQ5Ssikj2YcNaQXc9ZJZiezE7Zv1hLxZKIiIj0SIZh0CvdQ690D8eV5MTba1pCrG47A7W6IvbYUhdge0OQ7Q0dX4JnNyA/zU2vdA+FbQVUYXrsdVG6h4I0Ny7HfswNZYsNj256sogy4GC/1G9kGJCb4aBm6yZoqcUWqMEWqMHwx55t/hqMtmdbYNeyEW3FCPuxN23F3rS1S2IzDVvb3FuZRN3pmK702Nxc7nRMV8au5a++blsm2oqtqa3waW4rfpp2tF1a2fa6tblLYv869qbYpZ2uTe+3a4+kFLQVT4OJZLcVUtlDMT1ZlsYn+0fFkoiIiBxSslNcHNk/myP7Z8fbGgNhSqua2FYfK5p2NATY1va8oyFIOGrGi6mFX7Pf3FRXvIDKTnGS5naQ5nGQ5naQ7tm1HHvtxOu0YXTlWZXdOb1EfUWYqUVE9mV708RobW5fRPlrYm2RUOzernAgduYrHLvPy4gEIRKKte9cjgTb1rctRwIY4WBsP9FWDDOKEayHYP1e5+M6GFF3BlFfIZHUQqK+tkdqIZGdy+7MtgE9jF0TIhs2wADDwGTnOiPeBrtvb2DYDHJTw9StXYS9Zg32mjU4akqx167B3rQde0s59pZyXFs+ah+bN5dw9mAiWUPangcTSSuOnR1sy1ss56Fdud/tdTzX0dCunEdaY9uYEUyHFxxeTKcX05GC6fBiOj2xZ0cKOL3xZTO+nTc21H5nTQ6985LFeF67DxVLIiIicshL8zgY3zuT8b33XBc1TaqbQ2yrjxVO2xt2noXa9ToQjlLVHKKqOcTS7Y37dEy7zdhVSLUVUWmeXa/TPQ6KM70MyUulOMNjXWEFsQLB5cN0+Yim9+38/ZsmRALYgg2xS+BCDdiC9fFlI7jzdazNFmrYczkaBiDqyd5V9PiK2hdBvkIiqb3AmdL5X0NHvFmECyfS2mtiu2Yj1Ii9phR7bSmOmjVtz6XYGzdj81fh2loFWz+1Jsb9YDraiiq7m9j9WbTdtxVtu29r93u3orF18bZo/B6wnZc4Rl1p1J/5GOHCid9w1OSiYklERETkG9gMgzyfmzyfm7HFe643TZM6f2v8jNT2hiD1gdjw6E0755xqe975Ohw1iURj76vz72XyKSDVZWdIvo8heakMyfcxNM/HgJyU/bv0L5kYBji8RB1eSC3Y//ebJoT9sbMU3WCyYdOVRrjXeMK9xtPuQs9QM466tdhrSnHUrok916zB1lLR/n4vm3O3+8JcmHZX/PWu5bZn227bGDaMcADCfoxwC0arHyPsj539C7dA/LUfo7Ul9rzbaJCx7QKdlgdj5xmwbkTFkoiIiMhBMAyDrBQXWSkuRvRK2+v2pmkSCEfjRVRjYGdB1UpjMEJjW6FVHwizobqFsupmmkMRFm6pZ+GW+vh+7DaDgTkp8QJqSJ6PwXmpZHidXfnlJgfDsO5sUVdypRLOH0s4fyzWDVi/F2YUwoFdxVNbAdX+UkRb22WLtrbLFI3dXtt2u1zRFpubrO1h2j3g7F4jLKpYEhEREbGQYRh4nXa8TjsFae69bh+ORNlQ42dNZWwwijWVzaypaKIhEKa0spnSymZeXbFrhLteae72Z6HyU8nJ8XXllyQ9iWEDZwqmM6WTxgfs3lQsiYiIiCQxh91GSV4qJXmpnD4idsnazmHRdxZOO5+31gfY0RhkR2OQD8qq4/vwOG0UpXvok+mlODP23DvTQ+9ML73SPZ07LLpID6JiSURERKSb2X1Y9GMH7RoWvSkYO9sUK6CaWFPRTFl1M4HWKOuqW1hX3bLHvuw2g6J0N8WZ3ngRVZzhpU9W7NmdhPdFBVojNAXD+NwOPM6uHEdPDnUJLZaCwSC33norb731Fh6Ph8svv5zLL7/8G9+zZcsWpk+fzn333cfkyZMtilREREQk+fncDsb1zmBc74x4WyQaJehwsGRdFZvrAmyp87OlLsDmOj/b6gMEw1E21wXYXBfgM2r32Ge+z0XvtkIqK8WJy2HDbbfhctjaL9tteBwdtRu4d7Y57DhsBpGoSVOw7Z6ttvu22j3vNiDG7u0720KR2AViKU47544t5MIJxeT69n5Jo8j+SmixdOedd7Js2TIeeeQRtm3bxo033khRURGnnnrq175n1qxZtLTs+V8REREREdmTw26jV04qqWaUI75yE0rUNKlsCrUVULEiavdiqjkUoaIpREVTiAW7DS5xMOwGRDrpZpiW1giPf7mFZxduZfqoXlw0sTe9M7vXAAKS3BJWLLW0tPDcc8/xwAMPMHLkSEaOHElpaSlPPPHE1xZLL7/8Ms3N1s7CLCIiItJT2QyDgjQ3BWluJvTJbLfONE3q/WE21/nZUh8roBoDYUKRKMFwlFA4Gl8O7ra8e3sosvP1rupo90LJ67S1m7zX18G8Uz63g/Tdttn5nOKy8+n6Wh6at4kl2xp4fvF2XlyynZOH5XPppD4Myk21KIvSkyWsWFq1ahXhcJhx48bF2yZMmMB9991HNBrFZmt/fWxtbS133XUXDz30EGeeeabV4YqIiIgcUgzDIDPFSWaKk9FF6Qe1r6hptiuidk7I67Qf3P1QRw/M5qgBWSzcWs/D8zbz2YZaXl9ZwesrKzi+JIfLJvdl5D4M5y7ydRJWLFVWVpKVlYXL5Yq35ebmEgwGqaurIzs7u932v//97znnnHMYPHjwQR3Xysmv9xZDMsTSkynP1lGuraE8W0N5to5ybY1kyLPdMPC67Hjp/MEYDMNgQp9MJvTJZOWORh6et5l3S6t4b201762tZlK/TC6f3JcJfTIwujAJyZDnQ0Fn5Xlf35+wYsnv97crlID461Co/cy+n3zyCfPnz+eVV1456OPm5CTPfxeSKZaeTHm2jnJtDeXZGsqzdZRraxwKeT4mN41jRhWxtqKR/31vHS8u2srnG+v4fGMd4/pm8pPjSzhheH6XFk2HQp6TgVV5Tlix5Ha79yiKdr72eDzxtkAgwM0338wtt9zSrv1AVVc3YiZ4hi3DiH2DkyGWnkx5to5ybQ3l2RrKs3WUa2scinnOtMFN0wZy6YQiHvtiCy8t3c7CTXX84NEvKclN5bLJfThxaF6nzi91KOY5ETorzzv3szcJK5YKCgqora0lHA7jcMTCqKysxOPxkJ6+67rYJUuWsHnzZq677rp277/yyiuZMWMGt912234d1zRJmg6cTLH0ZMqzdZRrayjP1lCeraNcW+NQzHNhuocbTijh8iP68tT8rTy/eBtrq5r5zauruO/jDVxyeB/OGFGAqxPnkjoU85wIVuU5YcXS8OHDcTgcLFq0iIkTJwIwf/58Ro8e3W5whzFjxvDWW2+1e+/JJ5/M7bffztFHH21pzCIiIiLS/eSmurj22AFcOqk3zy3axlPzt7KlLsAdb5fywKcbOXNkAXk+N5leJ5leB1leF5leBxle50EPQiHdW8KKJa/Xy4wZM5g1axZ33HEHFRUVPPTQQ8yePRuInWVKS0vD4/HQr1+/Pd5fUFBATk7OHu0iIiIiIh1J9zi54oh+XDChNy8s2c4TX26hoinEw/M2f+17Ul32tiLKGS+mMtuKqd3bs1KcRFxOqhuDRM3Y0OsmbWdAMONnQWKv29bvXGbXMiY47Ab5Pjcprs4fEEP2T0Inpb3pppuYNWsWl156KT6fj2uvvZaTTz4ZgClTpjB79mzOPffcRIYoIiIiIj2M12nnggm9+dbYIt5YVcHSbQ3UB8LUtYSo84ep87dSH2glakJzKEJzKMLW+oDlcfrcdvJ8bgp8bvLTXOT73OSntT18sdfpHkeXDlhxqDNM89C6qrKqKvE33RkG5OamJUUsPZnybB3l2hrKszWUZ+so19ZQng9M1DRpDMQKp9gjTH3bcm28rTXeVucP09IawSCW89hzrIjZ1WbEnndfbjueYRjx7YLhKM2hyD7F6XbYKNhZPKW5yfe5YwVWmoucVBcuuw2X3YbTYcSe21677AZ2m9HtCq3O6s8797M3CT2zJCIiIiKSjGyGQYbXSYbXyZ43hOyps4vS5lCYysYQ5U1BKhqDVDQFqWgMtT0HqWgKUedvJRiOsqnWz6Za/34fwwCcdiNeQDntBi5H+4LKabeR4rIzKDeVIXmpDMn30TfLi62bFVkHSsWSiIiIiEiSSXU5SM1x0D8n5Wu3CYajVDbtVki1FVXlbcVUbUuIUMSkNRIlFI7SGokS2a2QM4FQxCQUidDMN5/J+mhdTXzZ67RRkutjSH6seBqa72NQTgoeZ8+7x0rFkoiIiIhIN+R22Oid6aV3pnef3xOJthVPkehXCimTUCTafl04tlwfCLO2spk1lU2UVjbjb42ydHsDS7c3xPdrM6BfdgpD8lIZmu+LFVF5PjJTnF3xpVtGxZKIiIiIyCHCbjOw2+wHfBYoEjXZVOtnTUUTayqbWFPRzOqKJmr9rayvbmF9dQtvrqqMb5/vczGkrXgaXZjG0QOyu9V9UiqWRERERERkn9htBgNyUhiQk8Ipw/OB2DDoVc0h1lQ0txVQTayuaGJzXYCKphAVTTXxy/huP31Y/H3dgYolERERERE5YIZhkNc2Ct/RA7Pj7c2h2OV7q9uKqHp/K2OL0xMY6f5TsSQiIiIiIp0u1eVgbHEGY4szEh3KAbMlOgAREREREZFkpGJJRERERESkAyqWREREREREOqBiSUREREREpAMqlkRERERERDqgYklERERERKQDKpZEREREREQ6oGJJRERERESkAyqWREREREREOqBiSUREREREpAMqlkRERERERDqgYklERERERKQDKpZEREREREQ6oGJJRERERESkAyqWREREREREOqBiSUREREREpAMqlkRERERERDqgYklERERERKQDjkQHYDXDSHQEu2JIhlh6MuXZOsq1NZRnayjP1lGuraE8W0N5tkZn5Xlf32+Ypmke3KFERERERER6Hl2GJyIiIiIi0gEVSyIiIiIiIh1QsSQiIiIiItIBFUsiIiIiIiIdULEkIiIiIiLSARVLIiIiIiIiHVCxJCIiIiIi0gEVSyIiIiIiIh1QsSQiIiIiItIBFUsWCwaDzJw5k4kTJzJlyhQeeuihRIfUI7399tsMHTq03eO6665LdFg9RigU4swzz2TevHnxts2bN3PZZZdx2GGHcfrpp/PRRx8lMMKeo6Nc33777Xv078cffzyBUXZf5eXlXHfddUyaNIljjjmG2bNnEwwGAfXpzvRNeVZ/7lwbN27kiiuuYNy4cRx//PE8+OCD8XXq053nm/KsPt01rrrqKn71q1/FX69YsYJvf/vbjB07lvPOO49ly5Z1yXEdXbJX+Vp33nkny5Yt45FHHmHbtm3ceOONFBUVceqppyY6tB5l7dq1TJ06ld/+9rfxNrfbncCIeo5gMMj1119PaWlpvM00TX7yk58wZMgQnn/+eebOncs111zDa6+9RlFRUQKj7d46yjVAWVkZ119/Peecc068zefzWR1et2eaJtdddx3p6ek88cQT1NfXM3PmTGw2GzfccIP6dCf5pjzfeOON6s+dKBqNctVVVzF69GheeOEFNm7cyM9//nMKCgo488wz1ac7yTflefr06erTXeDVV1/l/fffj+e0paWFq666iunTp/P73/+ep556iquvvpq3336blJSUTj22iiULtbS08Nxzz/HAAw8wcuRIRo4cSWlpKU888YSKpU5WVlbGkCFDyMvLS3QoPcratWu5/vrrMU2zXftnn33G5s2befrpp0lJSWHQoEF8+umnPP/881x77bUJirZ7+7pcQ6x/X3HFFerfB2ndunUsWrSIjz/+mNzcXACuu+46/vCHP3DssceqT3eSb8rzzmJJ/blzVFVVMXz4cGbNmoXP56N///4ceeSRzJ8/n9zcXPXpTvJNed5ZLKlPd566ujruvPNORo8eHW977bXXcLvd3HDDDRiGwa9//Ws++OAD3njjDc4999xOPb4uw7PQqlWrCIfDjBs3Lt42YcIEFi9eTDQaTWBkPU9ZWRn9+/dPdBg9zueff87kyZN55pln2rUvXryYESNGtPtvzoQJE1i0aJHFEfYcX5frpqYmysvL1b87QV5eHg8++GD8D/idmpqa1Kc70TflWf25c+Xn53P33Xfj8/kwTZP58+fzxRdfMGnSJPXpTvRNeVaf7nx/+MMfOPvssykpKYm3LV68mAkTJmAYBgCGYTB+/Pgu6c8qlixUWVlJVlYWLpcr3pabm0swGKSuri5xgfUwpmmyfv16PvroI0455RROPPFE/vjHPxIKhRIdWrd3wQUXMHPmTLxeb7v2yspK8vPz27Xl5OSwY8cOK8PrUb4u12VlZRiGwX333cexxx7LWWedxQsvvJCgKLu39PR0jjnmmPjraDTK448/zhFHHKE+3Ym+Kc/qz11n2rRpXHDBBYwbN45TTjlFfbqLfDXP6tOd69NPP+XLL7/kxz/+cbt2K/uzLsOzkN/vb1coAfHX+kO+82zbti2e67vvvpstW7Zw++23EwgE+M1vfpPo8Hqkr+vb6tedb926dRiGwcCBA7nooov44osv+H//7//h8/k46aSTEh1et3bXXXexYsUK/v3vf/Ovf/1LfbqL7J7n5cuXqz93kb/97W9UVVUxa9YsZs+erc/pLvLVPI8cOVJ9upMEg0FuueUWbr75ZjweT7t1VvZnFUsWcrvde3wTd77+aieQA1dcXMy8efPIyMjAMAyGDx9ONBrll7/8JTfddBN2uz3RIfY4brd7j7OjoVBI/boLzJgxg6lTp5KZmQnAsGHD2LBhA0899ZR+ER+Eu+66i0ceeYS//OUvDBkyRH26i3w1z4MHD1Z/7iI77+8IBoP84he/4LzzzsPv97fbRn364H01zwsWLFCf7iT33HMPo0aNandmeqev+5u6K/qzLsOzUEFBAbW1tYTD4XhbZWUlHo+H9PT0BEbW82RmZsavYwUYNGgQwWCQ+vr6BEbVcxUUFFBVVdWuraqqao9T5HLwDMOI/xLeaeDAgZSXlycmoB7gt7/9LQ8//DB33XUXp5xyCqA+3RU6yrP6c+eqqqpi7ty57dpKSkpobW0lLy9PfbqTfFOem5qa1Kc7yauvvsrcuXMZN24c48aNY86cOcyZM4dx48ZZ+hmtYslCw4cPx+FwtLv5bP78+YwePRqbTd+KzvLhhx8yefLkdv9BW7lyJZmZmWRnZycwsp5r7NixLF++nEAgEG+bP38+Y8eOTWBUPdNf//pXLrvssnZtq1atYuDAgYkJqJu75557ePrpp/nzn//MGWecEW9Xn+5cX5dn9efOtWXLFq655pp2f5gvW7aM7OxsJkyYoD7dSb4pz4899pj6dCd57LHHmDNnDi+++CIvvvgi06ZNY9q0abz44ouMHTuWhQsXxkeMNU2TBQsWdEl/1l/oFvJ6vcyYMYNZs2axZMkS5s6dy0MPPcQll1yS6NB6lHHjxuF2u/nNb37DunXreP/997nzzjv5wQ9+kOjQeqxJkyZRWFjITTfdRGlpKffffz9LlizhW9/6VqJD63GmTp3KF198wf/93/+xadMmnnzySV588UUuv/zyRIfW7ZSVlfGPf/yDK6+8kgkTJlBZWRl/qE93nm/Ks/pz5xo9ejQjR45k5syZrF27lvfff5+77rqLH/7wh+rTneib8qw+3XmKi4vp169f/JGamkpqair9+vXj1FNPpaGhgd/97nesXbuW3/3ud/j9fk477bROj8MwO5rEQ7qM3+9n1qxZvPXWW/h8Pq644oo9/gMhB6+0tJQ77riDRYsWkZqayvnnn89PfvKTdpfmycEZOnQojz76KJMnTwZis5n/+te/ZvHixfTr14+ZM2dy1FFHJTjKnuGruZ47dy5/+9vf2LBhA8XFxfzsZz/j5JNPTnCU3c/999/Pn/70pw7XrV69Wn26k+wtz+rPnau8vJzf/va3fPrpp3i9Xi666CKuvvpqDMNQn+5E35Rn9emu8atf/QqA3//+9wAsWbKEW265hbKyMoYOHcqtt97KiBEjOv24KpZEREREREQ6oMvwREREREREOqBiSUREREREpAMqlkRERERERDqgYklERERERKQDKpZEREREREQ6oGJJRERERESkAyqWREREREREOqBiSUREREREpAOORAcgIiKyL6ZNm8bWrVs7XPfoo48yefLkLjnuV2eNFxGRQ4eKJRER6TZmzpzJ6aefvkd7RkZGAqIREZGeTsWSiIh0G2lpaeTl5SU6DBEROUToniUREekRpk2bxr/+9S+mT5/OYYcdxlVXXUVlZWV8fVlZGVdccQXjx4/nmGOO4Z577iEajcbXv/TSS5x66qmMHTuW888/nxUrVsTXNTU18bOf/YyxY8dy/PHHM2fOHEu/NhERSQwVSyIi0mP8/e9/5wc/+AHPPPMMfr+fa6+9FoCamhouuOAC8vPzee6557jlllt4/PHHefTRRwH48MMP+fWvf82ll17Kyy+/zKhRo7j66qsJhUIAvP3224wcOZJXXnmF0047jZkzZ9LY2Jiwr1NERKxhmKZpJjoIERGRvZk2bRqVlZU4HO2vIC8qKuLVV19l2rRpnHjiicycOROAzZs3c+KJJzJnzhw+++wzHnroIebOnRt//1NPPcW9997LRx99xDXXXIPP54sP4hAKhfjLX/7C5Zdfzp/+9Cc2bNjA008/DUBjYyMTJ07k2WefZezYsRZmQERErKZ7lkREpNu47rrrOPnkk9u17V48jR8/Pr7cp08fMjMzKSsro6ysjJEjR7bbdty4cVRWVtLQ0MD69es5//zz4+tcLhc33nhju33tlJaWBkAwGOy8L0xERJKSiiUREek2cnJy6Nev39eu/+pZp0gkgs1mw+1277HtzvuVIpHIHu/7KrvdvkebLswQEen5dM+SiIj0GKtWrYovb9y4kcbGRoYOHcqAAQNYvnw5ra2t8fULFy4kOzubzMxM+vXr1+69kUiEadOmMX/+fEvjFxGR5KJiSUREuo3GxkYqKyv3eLS0tACxyWn/+9//smrVKmbOnMnRRx9N//79mT59OqFQiJtvvpmysjLmzp3L3//+d773ve9hGAYXX3wxL7/8Mi+88AIbN25k9uzZmKbJyJEjE/wVi4hIIukyPBER6TbuuOMO7rjjjj3af/rTnwJwzjnn8Oc//5lt27Zx3HHHceuttwLg8/l48MEH+d3vfseMGTPIzs7m0ksv5eqrrwbg8MMP55ZbbuHee++lsrKSUaNGcd999+HxeKz74kREJOloNDwREekRpk2bxjXXXMO5556b6FBERKSH0GV4IiIiIiIiHVCxJCIiIiIi0gFdhiciIiIiItIBnVkSERERERHpgIolERERERGRDqhYEhERERER6YCKJRERERERkQ6oWBIREREREemAiiUREREREZEOqFgSERERERHpgIolERERERGRDvx/NIWZVKVOZvkAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(history):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(history.history['loss'], label='Training Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss / Accuracy')\n",
    "    plt.title('Learning Curve')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curve(history)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-20T11:09:20.185477Z",
     "start_time": "2024-03-20T11:09:19.972639Z"
    }
   },
   "id": "c67bb53e5a864293",
   "execution_count": 21
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}