{
 "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": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-19 22:42:15.775694: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-03-19 22:42:15.778739: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-03-19 22:42:15.827945: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-03-19 22:42:15.827988: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-03-19 22:42:15.829288: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-03-19 22:42:15.836619: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-03-19 22:42:15.837262: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-03-19 22:42:16.891151: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\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": "2024-03-19T14:42:17.607868Z",
     "start_time": "2024-03-19T14:42:15.282745Z"
    }
   },
   "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": "2024-03-19T14:42:17.614254Z",
     "start_time": "2024-03-19T14:42:17.609300Z"
    }
   },
   "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": "2024-03-19T14:42:17.620906Z",
     "start_time": "2024-03-19T14:42:17.616617Z"
    }
   },
   "id": "9e0b1ed6f23a17cf",
   "execution_count": 3
  },
  {
   "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": "2024-03-19T14:42:17.628854Z",
     "start_time": "2024-03-19T14:42:17.622188Z"
    }
   },
   "id": "308d64639b199bc7",
   "execution_count": 4
  },
  {
   "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": "2024-03-19T14:42:17.634307Z",
     "start_time": "2024-03-19T14:42:17.630037Z"
    }
   },
   "id": "80cfcfac265d9357",
   "execution_count": 5
  },
  {
   "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": "2024-03-19T14:42:17.640022Z",
     "start_time": "2024-03-19T14:42:17.635459Z"
    }
   },
   "id": "4afc8d71b3271351",
   "execution_count": 6
  },
  {
   "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": "2024-03-19T14:48:53.165594Z",
     "start_time": "2024-03-19T14:48:53.158283Z"
    }
   },
   "id": "22025d6c8281fc09",
   "execution_count": 16
  },
  {
   "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": "2024-03-19T14:42:17.655155Z",
     "start_time": "2024-03-19T14:42:17.651346Z"
    }
   },
   "id": "ac4db13fed3f9916",
   "execution_count": 8
  },
  {
   "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": "2024-03-19T14:42:19.014933Z",
     "start_time": "2024-03-19T14:42:19.010553Z"
    }
   },
   "id": "fe3089568e99a58d",
   "execution_count": 9
  },
  {
   "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": "2024-03-19T14:42:20.753526Z",
     "start_time": "2024-03-19T14:42:20.748700Z"
    }
   },
   "id": "670e8c2cf19126ea",
   "execution_count": 10
  },
  {
   "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": "2024-03-19T14:49:10.815666Z",
     "start_time": "2024-03-19T14:49:10.805408Z"
    }
   },
   "id": "685463c2d6065b08",
   "execution_count": 17
  },
  {
   "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>",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x3000 with 1 Axes>",
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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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OnUyP3333XWNYWJixUaNGxmeffdb4/vvvG/fu3WtRTKWFoeYOMHr0aCUkJFzx79JhZNfi6+urpKQk/fXXX1Yf9+eff1aFChWuGFb0zDPPyGg06ueff5Yk0/Cay4eJFA5FuZpu3bpdsezS655ycnKUkZFhGsr5xx9/XNG+cCiWdHGIZ2RkpIxGo9lyX19f1alTR8nJydeMRbp4UxpJevrpp82WP/PMM2brS8LLy+uK5+7SGy9dTUhIiFq2bGm2zNfXV5K0fv16FRQUlDie4owYMUIxMTFq2bKl+vbtq6ysLE2ZMsV03dWlz9PZs2eVkZGh5s2bKzk5WWfPnrX4OI899pjZLx/R0dHKz8/X33//bXXMvXr1UkxMjOLj4/Xyyy+rSpUqmj17tmrUqGHxcxsQEKBbbrnF9C34//3f/6lChQrq3bu30tPTTX9DO3fuVJMmTUyxf/3116pbt65uueUWZWRkmP4VDpG7fFqOZs2aqV69elb3sTiJiYn666+/1LFjR508edIUx/nz5xUTE6Pt27ebXjcPPPCADAaD2RC1b775RgUFBabrkk+dOqUtW7bogQceMP1Sl5GRoZMnTyouLk5//fWXjh8/ft1xnzhxQomJierUqZPZN84RERGKjY296t/e1d4/iuPj46OnnnpKGzZs0J9//lnieM+dO1fsjdAK12dmZkq6+AvRf//7X3Xr1k1nzpzRsmXLNHjwYMXExGjOnDnX9U0/XAe5nFwukcuLQi4nl5fEuXPnJMmi3FyYl6WLN/ObPn266tevr19++UXvvPOOOnfurE6dOl0xPN5RGGruAFFRUVedgsTPz++Ka6YuN3DgQA0YMEBt27ZVWFiY4uLi9NBDDykiIqLY4/7999+qXr36FcM26tata1ovSampqXJ3d79iCMlNN910zX1f3la6+OYwe/ZsrV279oqbGlwtCVw+dYGPj4+8vLwUEBBwxfJTp05dM5bCvri7u+vGG280Wx4cHCxfX98SJZBCFSpUUGxsrFXbXO38tGvXTv/5z380atQoTZ8+XTExMWrdurXuv/9+ubvb7jux559/XtHR0XJ3d5e/v7/q1q2rihX/96e/c+dOzZo1S7t371ZWVpbZtmfPnrX4ZhuXP3+FH0bOnDljdcyjR49WnTp1VKFCBQUFBalOnTqmc2LNcxsdHW1KDjt27FBkZKQaNGigatWqaceOHQoKCtK+ffvMbvJy9OhRHTp0SDExMVeN7fLX8tWeW1so/DBR1AfBs2fPys/PT3fddZd8fHy0du1aU9xr165V/fr1VadOHUkXh/gZjUa9++67evfdd6+6P4PBYNXdca8mNTVVkkzHvVTdunX1yy+/XHHTlZKew549e+rjjz/WrFmz9P7775doH1WqVCn2ffdqHwKqV6+ucePGaezYsfrrr7/0yy+/aP78+Zo5c6aqV6/uVHeGhX2Qy8nlErm8KORycnlJFObawtx7LefOnbvi2u0OHTqoQ4cOyszM1G+//aaVK1dq9erVeu6557R69WqH34OFwtvFNGvWTN99953Wr1+vX3/9VcuXL9fHH3+scePGOfSD3tVeyC+99JJ27dql3r17q379+qpcubIKCgr07LPPXvUXoaslqGvdVdTSX5Ssnb7FXq5211Nvb28tWbJEW7du1Y8//qiNGzdq7dq1+uyzz/Thhx8We0dVS4WFhV3zw8WxY8fUq1cv3XLLLRo+fLhuuOEGeXh46KefftJHH31k1bf31/qAUZJf/671gfZSljy3TZs21eeff67k5GTt2LFDTZs2lZubm5o0aaKdO3eqevXqKigoUHR0tGmbgoIChYWFmc1BeqmaNWuaPbbXHW0Lz9vQoUOvOf1MYcLz9PTUfffdp++++05jxoyRwWDQ//3f/+mVV14xtS18Lp955pkrfrEpdPkHoNJS0kTo4+Ojnj17atasWSX+1btu3bpKTExUamrqNect3b9/vyRd9dcQNzc31alTR3Xq1NHdd9+tNm3a6Msvv6TwRpHI5ReRyy1HLieXS2Uzl1+u8EvEwtx7NX///bcyMzNNbS9XtWpV3Xnnnbrzzjvl4eGhVatW6bfffrvi+vnSRuHtgqpVq6YuXbqoS5cuOnfunJ588knNmjXLlKyv9SZWu3Ztbd68WZmZmWbflB8+fNi0Xrr4bWdBQYFSUlJ08803m9odPXrU4hhPnz6tzZs368UXX9QLL7xgWl6SYXUlUbt2bRUUFOjo0aNmf5Tp6ek6c+aMTW+acT3c3d0VExOjmJgYvfbaa5o7d67eeecdbd26VbGxsXb/sLFhwwbl5ubq/fffNys6Lh+CJTnPBx9rntvCm9j8+uuv+v333003FWrWrJmWLl2q6tWrq3LlymY357rxxhu1b98+xcTEOLTPoaGhki4mD0t+lXnggQe0atUqbd68WYcOHZLRaNQDDzxwxf48PDys/pXHGoWvoyNHjlyx7vDhw/L397fpFEWFv3rPnj3b9MuMNe6++26tXr1aX3zxhQYMGHDF+szMTK1fv1633HJLkb8UShfPsa+vr9LS0qyOA+UPubx45HLLkMvJ5bZW2rn8UnXq1NHNN9+s9evXX/E+V+iLL76QJN1zzz3F7i8yMlKrVq1yitzMNd4u5vLha1WqVNGNN95oNjVCpUqVJF05LOiuu+5Sfn6+aYqSQh999JHc3NxME8vHxcVJ0hXTUCxevNjiOK/1De/HH39s8T6uR3x8/FWPl5CQYLbeka42xK7w29DC5/Naz6WtFD5Pl36TffbsWa1YseKKtpUqVbJbHNaw5rkNDQ1VjRo19NFHH+nChQumu8hGR0fr2LFj+vrrr9WwYUOz4XoPPPCAjh8/rs8///yKY2dnZ+v8+fM279PVREZG6sYbb9SHH3541eFWl9+NNzY2VtWqVdPatWu1bt06RUVFmRK0dHEqjebNm+uzzz7TiRMnit1fSVWvXl3169fXF198YfZ6OXDggH799Veb/+0V/uq9fv16JSYmWr1927ZtVa9ePc2fP1+///672bqCggKNGTNGp0+fNis6fvvtt6u+Dvbs2aNTp05ddWgecClyuWXI5ZYhl5PLr7W/kirtXH65559/XqdPn9aYMWOUn59vtm7v3r1asGCBwsLC1KZNG0lSVlaWdu3addV9Fd73whlyM794u5j27durefPmuv3221WtWjX9/vvv+uabb8xullL4jd/EiRMVFxenChUqqH379mrVqpVatGihd955R3///bfCw8P166+/av369erZs6dpaErhnKAff/yxTp06ZZqCpPAbbku+OaxataqaNWumBQsWKC8vTzVq1NCvv/5qNiemPUVERKhTp0767LPPdObMGTVr1ky///67Vq1apfvuu890cw1HmjNnjnbs2KH4+HjVrl1bBoNBn376qWrWrGn6dvfGG2+Ur6+vli1bpipVqqhy5cpXvAlfj8IhOM8995y6deumc+fO6T//+Y8CAwOv+Gbw9ttv19KlS/Xee+/ppptuUkBAwDWvnbIna5/b6OhorVmzRmFhYfLz85Mk3XbbbapcubLphieXeuihh7Ru3TqNGTNGW7duVZMmTZSfn6/Dhw/r66+/1oIFC4odOmepvLy8K6Y0kS5eI9q9e3dNnDhRffr0UYcOHdS5c2fVqFFDx48f19atW1W1alXNnTvXtI2Hh4dat26tNWvWKCsr66rXk40ZM0ZPPPGEOnbsqMcee0yhoaFKT0/X7t279e+//+rLL7+0Sb+GDh2qPn36qGvXrnrkkUdMU5D4+PiYFbC28tRTT+mjjz7Svn37rvoNfEZGxlXPc0hIiB588EHNnDlTPXv21BNPPKHOnTsrMjJSZ8+e1erVq/XHH3/omWeeUfv27U3b/fe//9VXX31lmnvWw8NDhw4d0ooVK+Tl5WWapge4FnK5ZcjlliGXk8tdMZd/++23ppE6l+rUqZMefPBB/f7771q0aJEOHTqkjh07ytfXV3/++adWrFihatWq6d1335WHh4eki4V3t27d1KhRI7Vs2VI1a9bU2bNn9f3332vHjh267777dNttt113zNeLwtvF9OjRQxs2bNCvv/6q3Nxc1apVSy+99JJ69+5tatOmTRv16NFDa9as0Zdffimj0aj27dvL3d1d77//vmbOnKm1a9dq5cqVql27toYOHWq6i2ShN998U0FBQVqzZo2+++47xcbG6p133tH9998vT09Pi2KdPn26JkyYoE8//VRGo1F33nmn5s+ff81rUmxt4sSJCgkJ0apVq/T9998rKChI/fr1s8sH/5Jo1aqV/v77b61YsUInT56Uv7+/mjdvrhdffNF0ExQPDw9NmTJFb7/9tsaOHasLFy5o8uTJNkvWt9xyi2bOnKkZM2aYnvPHH39cAQEBZnPBShe/fUxNTdWCBQt07tw5NW/e3CHJWrLuuW3atKnWrFlj+gAkSRUrVlSjRo20adMms+XSxSGDc+bM0UcffaT//ve/+u6771SpUiWFhISoR48eNv3GNC8v76o3R7nxxhvVvXt3tWjRQp999pnee+89LV68WOfPn1dwcLCioqLUtWvXK7YrvMmPm5ub2dC0QvXq1dOKFSs0e/ZsrVq1SqdOnVJAQIBuu+02Pf/88zbrV2xsrBYsWKCZM2dq5syZqlixopo1a6ZXX33VZq/dS/n6+qpnz56aPXv2VdcbDIarnueYmBg9+OCDqlu3rr788kt98MEH2rBhg1auXClvb29FRkbq/fffV6tWrcy269q1q7y9vbVlyxZt2LBBmZmZ8vf315133ql+/fo5RXKHcyOXW45cXjxyObncFXP5mjVrrrq8efPmuuGGGzRy5Ei1aNFCn376qebNm6esrCzdcMMN6t69u/r06WN2w0ZfX19NnDhRP/74o1auXKm0tDRVqFBBderU0dChQ6+YBcJR3IzMewILJSYm6uGHH9Zbb72lBx980NHhAAAAK5HLAcAxuMYbV5WdnX3Fso8//lju7u5q1qyZAyICAADWIJcDgPNgqDmuasGCBdq7d6/uuOMOVahQQT///LN+/vlnde3aVTfccIOjwyvXsrOzrzp36qX8/PwsHkZYGs6dO1fsjUwCAgJsNu2KI506dUp5eXnXXF+hQoUr5rMFAHsglzsvcrlzI5fDHhhqjqv69ddfNXv2bB06dEjnz5/XDTfcoIceekjPPfec2V0jUfpWrlx5zXkpCy1atEgtWrQopYiKN2vWrGtef1to/fr1CgkJKaWI7KdHjx7atm3bNdfXrl1bGzZsKMWIAJRX5HLnRS53buRy2AOFN+BiTpw4oYMHDxbZ5vbbbzfd9dMZJCcnKzk5ucg2TZs2lZeXVylFZD979+4tcqoWLy+vK24CAwAoX8jlzo1cDnug8AYAAAAAwI64uRoAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEfc0tIKBsNZOcMV8W5uUmCgj9PEYy3idyzidxxXjl0i/tJUGCtKzpmeZ1d67VmLvrmmstq3stovib45K2vyNYW3FYxGOdWLwdnisRbxOxbxO44rxy4RP1yDMz7PzhiTrdA311RW+1ZW+yXRN1fGUHMAAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsKOKjg4AgG2kpCQrI8NgUVt//yo6efKcAgICFRISaufIAAAAAMtY85m2kCt8pqXwBsqAlJRkxcRGKyc7y6rtvLwrafOmHU7/RgUAAICyryx/pqXwBsqAjAyDcrKzFNhhsDwCLXvDyTMky7B6ujIyDE79JgUAAIDyoSx/pqXwBsoQj8BQedWs5+gwAAAAgBIri59pubkaAAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHXOMNlHNJSfstbusKUzUAAAAAzobCGyin8jNPSm5u6t+/j8XbuMJUDQAAAICzofAGyqmCnEzJaLR4ugZXmaoBAAAAcDYU3kA5VxanawAAAACcCTdXAwAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOHFp4b9++Xc8995zi4uIUHh6u77//3rQuLy9Pb731ljp27KhGjRopLi5OQ4cO1fHjx832cerUKQ0ePFhNmjRRdHS0RowYoXPnzpm12bdvn5544gk1aNBA8fHxmj9/fqn0DwAAAAAAhxbe58+fV3h4uMaMGXPFuuzsbP3555/q37+/Vq5cqdmzZ+vIkSPq37+/WbshQ4bo4MGDSkhI0Ny5c7Vjxw6NHj3atD4zM1O9e/dWrVq1tHLlSg0dOlSzZ8/WZ599Zvf+AQAAAADg0Hm84+PjFR8ff9V1Pj4+SkhIMFv2+uuv69FHH1Vqaqpq1aqlQ4cOaePGjVq+fLkaNGggSRo1apT69u2roUOHqkaNGvryyy+Vl5enSZMmydPTU7feeqsSExOVkJCgrl272r2PQEmlpCQrI8NgUdukpP12jgYAAABASTm08LZWZmam3Nzc5OvrK0natWuXfH19TUW3JMXGxsrd3V179uxR69attXv3bkVHR8vT09PUJi4uTvPnz9fp06fl5+dn8fHd3GzXl+tRGIezxGMt4i9eSkqyYmKjlZOdZb+DXAdHPneu/Ppx5dgl4i9NrhAjAACwnMsU3jk5OZo2bZrat2+vqlWrSpLS09MVEBBg1q5ixYry8/NTWlqaqU1ISIhZm6CgINM6awrvwECf6+mCzTlbPNYi/ms7dixbOdlZCuwwWB6BocW2zzq8Q6c3LrZbPJfy96+ioCDHP3eu/Ppx5dgl4gcAALCWSxTeeXl5GjRokIxGo8aNG+ewOAyGszIaHXZ4Eze3ix8cnSUeaxF/8U6evHiDQI/AUHnVrFds+zxDsn0CuYqTJ88pPf1sqR3vcq78+nHl2CXiL02FsQIAgLLB6QvvvLw8vfTSS0pNTdXHH39s+rVbuvjLdUZGhln7Cxcu6PTp0woODja1SU9PN2tT+Ljwl29LGY1yqg9rzhaPtYjfdTlDv135/Lty7BLxAwAAWMup5/EuLLqPHj2qjz76SP7+/mbrGzdurDNnzmjv3r2mZVu2bFFBQYGioqIkSY0aNdKOHTuUl5dnarNp0ybVqVPHqmHmAAAAAACUhEML73PnzikxMVGJiYmSpJSUFCUmJio1NVV5eXkaOHCg9u7dq2nTpik/P19paWlKS0tTbm6uJKlu3bpq2bKlXn/9de3Zs0c7d+7UhAkT1L59e9WoUUOS1LFjR3l4eGjkyJFKSkrS2rVrtWjRIj399NMO6zcAAAAAoPxw6FDzvXv36qmnnjI9njx5siSpU6dOeuGFF7RhwwZJ0kMPPWS23aJFi9SiRQtJ0rRp0zRhwgT17NlT7u7uatOmjUaNGmVq6+Pjo4ULF2r8+PHq3Lmz/P39NWDAAKYSAwAAAACUCocW3i1atND+/deef7iodYWqVaum6dOnF9kmIiJCn376qdXxAQAAAABwvZz6Gm8AAAAAAFwdhTcAAAAAAHZE4Q0AAAAAgB1ReAMAgCJ9+umn6tixo5o0aaImTZqoa9eu+umnn0zrc3JyNG7cOLVo0UKNGzfWiy++qPT0dLN9pKamqm/fvmrYsKFiYmL05ptv6sKFC2Zttm7dqk6dOikyMlKtW7fWypUrS6V/AADYG4U3AAAoUs2aNTVkyBCtXLlSK1as0B133KHnn39eSUlJkqRJkybphx9+0IwZM/TJJ5/oxIkTeuGFF0zb5+fnq1+/fsrLy9OyZcs0ZcoUrVq1SjNnzjS1SU5OVr9+/dSiRQv997//Vc+ePTVq1Cht3Lix1PsLAICtUXgDAIAitWrVSvHx8br55ptVp04dvfzyy6pcubJ2796ts2fPasWKFRo+fLhiYmIUGRmpSZMmadeuXdq9e7ck6ZdfftHBgwf11ltvqX79+oqPj9egQYO0ZMkS5ebmSpKWLVumkJAQDR8+XHXr1tWTTz6ptm3b6qOPPnJcxwEAsBEKbwAAYLH8/HytWbNG58+fV+PGjbV3717l5eUpNjbW1KZu3bqqVauWqfDevXu3wsLCFBQUZGoTFxenzMxMHTx40NQmJibG7FhxcXGmfQAA4MocOo83AABwDfv371e3bt2Uk5OjypUra86cOapXr54SExPl4eEhX19fs/aBgYFKS0uTJKWnp5sV3ZJMj4trk5mZqezsbHl7e1sVr5ubVc3tqjAWZ4rJVuibayqrfSur/ZLom7X7Ki3WHI/CGwAAFKtOnTr64osvdPbsWX3zzTcaNmyYFi9e7Oiwrikw0MfRIVzBGWOyFfrmmspq38pqv6Sy3zd//yol3t7fv4qCgpz3/FB4AwCAYnl6euqmm26SJEVGRur333/XokWL9MADDygvL09nzpwx+9XbYDAoODhY0sVfrvfs2WO2v8K7nl/a5vI7oaenp6tq1apW/9p98fhnZTRavZlduLld/EDpTDHZCn1zTWW1b2W1X1L56dvJk+dKvJ+TJ88pPf2sDSMrXmHslqDwBgAAVisoKFBubq4iIyPl4eGhzZs3q23btpKkw4cPKzU1VY0aNZIkNWrUSHPnzpXBYFBgYKAkadOmTapatarq1atnavPzzz+bHWPTpk2mfVjLaJTTfTh1xphshb65prLat7LaL6ns980Z9mEv3FwNAAAUafr06dq+fbtSUlK0f/9+TZ8+Xdu2bVPHjh3l4+OjLl26aMqUKdqyZYv27t2rESNGqHHjxqaiOS4uTvXq1dPQoUO1b98+bdy4UTNmzFD37t3l6ekpSerWrZuSk5M1depUHTp0SEuWLNG6devUq1cvx3UcAAAb4RdvoBSkpCQrI8NgcfukpP12jAYArGMwGDRs2DCdOHFCPj4+Cg8P18KFC3XnnXdKkkaMGCF3d3cNHDhQubm5iouL05gxY0zbV6hQQXPnztXYsWPVtWtXVapUSZ06ddLAgQNNbUJDQzVv3jxNnjxZixYtUs2aNTVx4kS1bNmy1PsLAICtUXgDdpaSkqyY2GjlZGc5OhQAKJFJkyYVud7Ly0tjxowxK7YvV7t2bc2fP7/I/bRo0UJffPFFSUIEAMCpUXgDdpaRYVBOdpYCOwyWR2CoRdtkHd6h0xud827B1v4aHxAQqJAQy/oNAAAAlEUU3kAp8QgMlVfNeha1zTMk2zka6+VnnpTc3NS/fx+rtvPyrqTNm3ZQfAMAAKDcovAGYJGCnEzJaLTql/s8Q7IMq6crI8NA4Q0AAIByi8IbgFWs+eUeAAAAANOJAQAAAABgV/ziDcDurLkhGzdjAwAAQFlD4Q3AbkpyQzZuxgYAAICyhsIbgN1Ye0M2bsYGAACAsojCG4DdcUM2AAAAlGfcXA0AAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyooqMDAIDLJSXtL3K9v38VnTx5zvQ4ICBQISGh9g4LAAAAKBEKbwBOIz/zpOTmpv79+1i1nZd3JW3etIPiGwAAAE6JwhuA0yjIyZSMRgV2GCyPQMuK6DxDsgyrpysjw0DhDQAAAKdE4Q3A6XgEhsqrZj1HhwEAAADYBDdXAwAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOuKs5UALHjh1TUtJRi9omJe23czQAAAAAnBmFN2CllJRkxcQ2U3bWeUeHAgAAAMAFUHgDVjIYDMrOOq/ADoPlERhabPuswzt0euPiUogMAAAAgDOi8AZKyCMwVF416xXbLs+QXArRAAAAAHBW3FwNAAAAAAA7ovAGAAAAAMCOHFp4b9++Xc8995zi4uIUHh6u77//3my90WjUu+++q7i4OEVFRalXr17666+/zNqcOnVKgwcPVpMmTRQdHa0RI0bo3LlzZm327dunJ554Qg0aNFB8fLzmz59v764BAAAAACDJwYX3+fPnFR4erjFjxlx1/fz58/XJJ59o7Nix+vzzz1WpUiX17t1bOTk5pjZDhgzRwYMHlZCQoLlz52rHjh0aPXq0aX1mZqZ69+6tWrVqaeXKlRo6dKhmz56tzz77zO79AwAAAADAoTdXi4+PV3x8/FXXGY1GLVq0SP3799d9990nSZo6dapiY2P1/fffq3379jp06JA2btyo5cuXq0GDBpKkUaNGqW/fvho6dKhq1KihL7/8Unl5eZo0aZI8PT116623KjExUQkJCeratWup9RUAAAAAUD457V3NU1JSlJaWptjYWNMyHx8fNWzYULt27VL79u21a9cu+fr6mopuSYqNjZW7u7v27Nmj1q1ba/fu3YqOjpanp6epTVxcnObPn6/Tp0/Lz8/P4pjc3GzTt+tVGIezxGOtshI/nEtS0n6r2gcGBiokpPjp4GyprLz2id/+XCFGAABgOactvNPS0iRd/HB8qcDAQKWnp0uS0tPTFRAQYLa+YsWK8vPzM22fnp6ukJAQszZBQUGmddYU3oGBPtZ1ws6cLR5ruWr81apVcXQIuER+5knJzU39+/exajvvSpW1f1+ibrzxRjtFdm2u+tovRPwAAADWcdrC2xkZDGdlNDo6iou/hAQG+jhNPNZy9fhPnTpXfCOUmoKcTMloVGCHwfIItOwX7DxDsgyrpysp6agqV/a3c4T/4+qvfeIvPYWxAgCAssFpC+/g4GBJksFgUPXq1U3LDQaDIiIiJF385TojI8NsuwsXLuj06dOm7YOCgky/kBcqfFz4y7eljEY51Yc1Z4vHWq4avyvGXB54BIbKq2Y9q7dzxPPpqq/9QsQPAABgHaedxzskJETBwcHavHmzaVlmZqZ+++03NW7cWJLUuHFjnTlzRnv37jW12bJliwoKChQVFSVJatSokXbs2KG8vDxTm02bNqlOnTpWDTMHAAAAAKAkHFp4nzt3TomJiUpMTJR08YZqiYmJSk1NlZubm5566im9//77Wr9+vfbv36+hQ4eqevXqpruc161bVy1bttTrr7+uPXv2aOfOnZowYYLat2+vGjVqSJI6duwoDw8PjRw5UklJSVq7dq0WLVqkp59+2mH9BgAAAACUHw4dar5371499dRTpseTJ0+WJHXq1ElTpkxRnz59lJWVpdGjR+vMmTNq2rSpFixYIC8vL9M206ZN04QJE9SzZ0+5u7urTZs2GjVqlGm9j4+PFi5cqPHjx6tz587y9/fXgAEDmEoMAAAAAFAqHFp4t2jRQvv3X3sKIDc3Nw0aNEiDBg26Zptq1app+vTpRR4nIiJCn376aYnjBAAAAACgpJz2Gm8AAAAAAMoCCm8AAFCkefPmqUuXLmrcuLFiYmI0YMAAHT582KxNjx49FB4ebvZv9OjRZm1SU1PVt29fNWzYUDExMXrzzTd14cIFszZbt25Vp06dFBkZqdatW2vlypV27x8AAPbmtNOJAQAA57Bt2zZ1795dDRo0UH5+vt5++2317t1ba9asUeXKlU3tHnvsMQ0cOND0uFKlSqb/5+fnq1+/fgoKCtKyZct04sQJDRs2TB4eHnrllVckScnJyerXr5+6deumadOmafPmzRo1apSCg4PVsmXL0uswAMBmUlKSlZFhKLKNv38VnTx5TklJ174M2dVReAMAgCItXLjQ7PGUKVMUExOjP/74Q82aNTMt9/b2VnBw8FX38csvv+jgwYNKSEhQUFCQ6tevr0GDBmnatGl64YUX5OnpqWXLlikkJETDhw+XdHH2kp07d+qjjz6i8AYAF5SSkqyY2GjlZGc5OhSHo/AGAABWOXv2rCTJz8/PbPlXX32lL7/8UsHBwbrnnns0YMAA06/eu3fvVlhYmIKCgkzt4+LiNHbsWB08eFC33Xabdu/erZiYGLN9xsXFadKkSVbH6OZm9SZ2UxiLM8VkK/TNNZXVvpXVfkmu27eMDINysrMU2GGwPAJDi22fdXiHTm9cXOLjlfb5seZ4FN4AAMBiBQUFmjRpkpo0aaKwsDDT8g4dOqhWrVqqXr269u/fr2nTpunIkSOaPXu2JCk9Pd2s6JZkepyWllZkm8zMTGVnZ8vb29viOAMDfUrUP3tyxphshb65prLat7LaL8n1+ubvX0WS5BEYKq+a9Yptn2dIvq5jBQU57/mh8AYAABYbN26ckpKSrpims2vXrqb/h4eHKzg4WL169dKxY8d04403lnaYMhjOymgs9cNelZvbxQ/LzhSTrdA311RW+1ZW+yW5bt9OnjxXqsdKTz9baseT/ve8WILCGwAAWGT8+PH68ccftXjxYtWsWbPItg0bNpQkHT16VDfeeKOCgoK0Z88eszbp6emSZLouPCgoyLTs0jZVq1a16tduSTIa5XQfTp0xJluhb66prPatrPZLKtt9swVnPjdMJwYAAIpkNBo1fvx4fffdd/r4448VGlr8dXqJiYmS/ldUN2rUSAcOHJDB8L87227atElVq1ZVvXr1TG22bNlitp9NmzapUaNGNuoJAACOQeENAACKNG7cOH355ZeaPn26qlSporS0NKWlpSk7O1uSdOzYMc2ZM0d79+5VSkqK1q9fr2HDhqlZs2aKiIiQdPEmafXq1dPQoUO1b98+bdy4UTNmzFD37t3l6ekpSerWrZuSk5M1depUHTp0SEuWLNG6devUq1cvR3UdAACbYKg5AAAo0tKlSyVJPXr0MFs+efJkde7cWR4eHtq8ebMWLVqk8+fP64YbblCbNm00YMAAU9sKFSpo7ty5Gjt2rLp27apKlSqpU6dOZvN+h4aGat68eZo8ebIWLVqkmjVrauLEiUwlBgBweRTeAACgSPv37y9y/Q033KDFi4uf/qV27dqaP39+kW1atGihL774wprwAABwegw1BwAAAADAjii8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADuq6OgAAEdLSUlWRobB4vZJSfvtGA0AAACAsobCG+VaSkqyYmKjlZOd5ehQAAAAAJRRFN4o1zIyDMrJzlJgh8HyCAy1aJuswzt0euNiO0cGAAAAoKyg8AYkeQSGyqtmPYva5hmS7RwNAAAAgLKEm6sBAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEfc1RxAuZWUtN/itgEBgQoJsWzKOQAAAOBSFN4Ayp38zJOSm5v69+9j8TZe3pW0edMOim8AAABYjcIbQLlTkJMpGY0K7DBYHoHFF9J5hmQZVk9XRoaBwhsAAABWo/AGUG55BIbKq2Y9R4cBAACAMo6bqwEAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgR05deOfn52vGjBlq1aqVoqKidN9992nOnDkyGo2mNkajUe+++67i4uIUFRWlXr166a+//jLbz6lTpzR48GA1adJE0dHRGjFihM6dO1fKvQEAAAAAlEdOXXjPnz9fS5cu1ejRo7V27VoNGTJECxYs0CeffGLW5pNPPtHYsWP1+eefq1KlSurdu7dycnJMbYYMGaKDBw8qISFBc+fO1Y4dOzR69GhHdAkAAAAAUM44deG9a9cu3Xvvvbr77rsVEhKi+++/X3FxcdqzZ4+ki792L1q0SP3799d9992niIgITZ06VSdOnND3338vSTp06JA2btyoiRMnqmHDhoqOjtaoUaO0Zs0aHT9+3JHdAwAAAACUA05deDdu3FhbtmzRkSNHJEn79u3Tzp07ddddd0mSUlJSlJaWptjYWNM2Pj4+atiwoXbt2iXpYvHu6+urBg0amNrExsbK3d3dVMBbys3Nef45WzyuGj9grbLy2id+5/8HAADKjoqODqAoffv2VWZmph544AFVqFBB+fn5evnll/Xggw9KktLS0iRJgYGBZtsFBgYqPT1dkpSenq6AgACz9RUrVpSfn59pe0sFBvqUtCt24WzxWMsZ4vf3r+LoEOBC/P2rKCjo+l+3zvDavx7EDwAAYB2nLrzXrVunr776StOnT1e9evWUmJioyZMnq3r16urUqVOpx2MwnNUl93VzGDe3ix8cnSUeazlT/CdPcpM9WO7kyXNKTz9b4u2d6bVfEsRfegpjBQAAZYNTF95Tp05V37591b59e0lSeHi4UlNTNW/ePHXq1EnBwcGSJIPBoOrVq5u2MxgMioiIkCQFBQUpIyPDbL8XLlzQ6dOnTdtbymiUU31Yc7Z4rOXq8aN8ssVr1tVf+8QPAABgHae+xjs7O1tul13oVqFCBdN0YiEhIQoODtbmzZtN6zMzM/Xbb7+pcePGki5eJ37mzBnt3bvX1GbLli0qKChQVFRUKfQCAAAAAFCeOfUv3vfcc4/mzp2rWrVqmYaaJyQkqEuXLpIkNzc3PfXUU3r//fd10003KSQkRO+++66qV6+u++67T5JUt25dtWzZUq+//rrGjRunvLw8TZgwQe3bt1eNGjUc2T0AAAAAQDng1IX3qFGj9O6772rcuHGm4eRdu3bV888/b2rTp08fZWVlafTo0Tpz5oyaNm2qBQsWyMvLy9Rm2rRpmjBhgnr27Cl3d3e1adNGo0aNckSXUApSUpKVkWGwqG1S0n47RwMArm/evHn69ttvdfjwYXl7e6tx48YaMmSIbrnlFlObnJwcTZkyRWvXrlVubq7i4uI0ZswYBQUFmdqkpqZq7Nix2rp1qypXrqyHH35YgwcPVsWK//s4snXrVk2ZMkVJSUm64YYb1L9/f3Xu3LlU+wsAgK05deFdtWpVjRw5UiNHjrxmGzc3Nw0aNEiDBg26Zptq1app+vTp9ggRTiYlJVkxsdHKyc5ydCgAUGZs27ZN3bt3V4MGDZSfn6+3335bvXv31po1a1S5cmVJ0qRJk/TTTz9pxowZ8vHx0YQJE/TCCy9o2bJlkqT8/Hz169dPQUFBWrZsmU6cOKFhw4bJw8NDr7zyiiQpOTlZ/fr1U7du3TRt2jRt3rxZo0aNUnBwsFq2bOmw/gMAcL2cuvAGrJWRYVBOdpYCOwyWR2Bose2zDu/Q6Y2LSyEyAHBdCxcuNHs8ZcoUxcTE6I8//lCzZs109uxZrVixQtOmTVNMTIyki4V4u3bttHv3bjVq1Ei//PKLDh48qISEBAUFBal+/foaNGiQpk2bphdeeEGenp5atmyZQkJCNHz4cEkXLxfbuXOnPvroIwpvAIBLK9HN1ZKTk20dB2BTHoGh8qpZr9h/Ff24zh9A2WaPnH327MVp9fz8/CRJe/fuVV5enmJjY01t6tatq1q1amn37t2SpN27dyssLMxs6HlcXJwyMzN18OBBU5vCwv3SNoX7AADAVZXoF+/WrVurWbNmeuSRR3T//febXU8NAACch61zdkFBgSZNmqQmTZooLCxMkpSeni4PDw/5+vqatQ0MDFRaWpqpzaVFtyTT4+LaZGZmKjs7W97e3hbHedmkKA5VGIszxWQr9M01ldW+ldV+SWW7b7ZU2ufHmuOVqPBetWqVVqxYoSlTpmjChAlq166dHnnkEabnAgDAydg6Z48bN05JSUn69NNPbRypbQUG+jg6hCs4Y0y2Qt9cU1ntW1ntl+R6ffP3r1KqxwoKct7zU6LCu379+ho1apSGDx+uDRs2aOXKlXriiSd08803q0uXLnrooYcUEBBg61gBAICVbJmzx48frx9//FGLFy9WzZo1TcuDgoKUl5enM2fOmP3qbTAYFBwcbGqzZ88es/2lp6dLklmbwmWXtqlatapVv3ZfPPZZGY1WbWI3bm4XPyw7U0y2Qt9cU1ntW1ntl2S/vqWkJMtgsGw2IOniSKaQkOLvo1To5MlzJQmrRE6ePKf09LOldjzpf8+LJa7r5moVK1ZUmzZtdPfdd+vTTz/V9OnT9eabb+rtt9/WAw88oCFDhqh69erXcwgAAGAD15OzjUajJkyYoO+++06ffPKJQkPNP3RFRkbKw8NDmzdvVtu2bSVJhw8fVmpqqho1aiRJatSokebOnSuDwaDAwEBJ0qZNm1S1alXVq1fP1Obnn3822/emTZtM+7CG0Sin++DtjDHZCn1zTWW1b2W1X5Jt+1aS2YA8vbyU8OFi1ahh2X2SSnvqXmd+3q+r8P7999+1YsUKrV27VpUqVdIzzzyjRx55RMePH9fs2bM1YMAALV++3FaxAoBLudqc8v7+Va757W9AgHXfIgPWuJ6cPW7cOK1evVrvvfeeqlSpYrom28fHR97e3vLx8VGXLl00ZcoU+fn5qWrVqpo4caIaN25sKprj4uJUr149DR06VK+++qrS0tI0Y8YMde/eXZ6enpKkbt26acmSJZo6daq6dOmiLVu2aN26dZo3b16pnCMAKE+snQ0oO+UPndqwQN27P1oK0ZU9JSq8ExIStHLlSh05ckR33XWX3nzzTcXHx8vd/eJN0kNDQzVlyhS1atXKpsECgKsoybfIXt6VtHnTDopv2JQtcvbSpUslST169DBbPnnyZHXu3FmSNGLECLm7u2vgwIHKzc1VXFycxowZY2pboUIFzZ07V2PHjlXXrl1VqVIlderUSQMHDjS1CQ0N1bx58zR58mQtWrRINWvW1MSJE5lKDADsqHA2oOLkGZIlo9HiQl1i6t5LlajwXrp0qbp06aJOnTpdc1haQECA3njjjesKDgBclbXfIucZkmVYPV0ZGQYKb9iULXL2/v3FDxX08vLSmDFjzIrty9WuXVvz588vcj8tWrTQF198UezxAACOYWmhLv3/Yh2SSlh4f/vtt8W28fT0VKdOnUqyewAoM6xJToA9kLMBAHA895JstGLFCq1bt+6K5evWrdOqVauuOygAAGAb5GwAAByvRIX3Bx98IH9//yuWBwYGau7cudcdFAAAsA1yNgAAjleioeapqakKCQm5YnmtWrX0zz//XHdQAOCMrJkSo7SnzwCuhZwNAIDjlajwDgwM1P79+69I5Pv27VO1atVsERcAOI38zJOSm5v69+/j6FAAq5GzAQBwvBIV3u3bt9cbb7yhKlWqqFmzZpKkbdu2adKkSWrfvr1NAwQARyvIyWT6DLgscjYAAI5XosJ70KBB+vvvv9WrVy9VrHhxFwUFBXrooYf08ssv2zRAAHAWpTF9hrVD1AMCApl+DEUiZwMA4HglKrw9PT01Y8YMHTlyRPv27ZO3t7fCwsJUu3ZtW8cHAOVCSYeze3lX0uZNOyi+cU3kbAAAHK9EhXehOnXqqE6dOraKBQDKrZIMZ88zJMuweroyMgwU3igWORsAAMcpUeGdn5+vlStXasuWLTIYDCooKDBbv2jRIpsEBwDljTXD2QFLkLMBAHC8EhXeb7zxhlatWqX4+HjdeuutcnNzs3VcAADABsjZAAA4XokK7zVr1mjGjBmKj4+3dTwAAMCGyNkAADiee0k28vDw0I033mjrWAAAgI2RswEAcLwSFd7PPPOMFi1aJKPRaOt4AACADZGzAQBwvBINNd+5c6e2bt2qn3/+WbfeeqtpXtBCs2fPtklwQEpKsjIyDBa3t3YOZAAo68jZAAA4XokKb19fX7Vu3drWsQBmUlKSFRMbrZzsLEeHAgAui5wNAIDjlajwnjx5sq3jAK6QkWFQTnaWVfMaZx3eodMbF9s5MgBwHeRsAAAcr0SFtyRduHBB27Zt07Fjx9ShQwdVrVpVx48fV9WqVVWlShVbxohyzpp5jfMMyXaOBgBcDzkbAADHKlHh/ffff+vZZ5/VP//8o9zcXN15552qWrWq5s+fr9zcXI0fP97WcQIAgBIgZwMA4Hgluqv5G2+8ocjISG3btk1eXl6m5a1bt9aWLVtsFhwAALg+5GwAAByvxHc1X7p0qTw9Pc2W165dW8ePH7dJYAAA4PqRswEAcLwS/eJdUFCggoKCK5b/+++/XCsGAIATIWcDAOB4JSq877zzTn388cdmy86dO6dZs2YpPj7eJoEBAIDrR84GAMDxSlR4Dx8+XP/3f/+ndu3aKTc3V0OGDFGrVq10/PhxDRkyxNYxAgCAEiJnAwDgeCW6xrtmzZr673//qzVr1mj//v06f/68HnnkEXXs2FHe3t62jhEAAJQQORsAAMcr8TzeFStW1EMPPWTLWAAAgB2QswEAcKwSFd5ffPFFkesffvjhkuwWAADYGDkbAADHK1Hh/cYbb5g9vnDhgrKysuTh4aFKlSqRxAEAcBLkbAAAHK9Ehff27duvWPbXX39p7Nix6t2793UHBQAAbIOcDQCA45XoruZXc/PNN2vw4MFXfLMOAACcCzkbAIDSVeKbq111ZxUr6sSJE7bcJQCgGElJ+y1uGxAQqJCQUDtGA1dBzgaAsiUlJVkZGQaL21vz+QHXr0SF9/r1680eG41GpaWlacmSJWrSpIlNAgMAFC0/86Tk5qb+/ftYvI2XdyVt3rSD4rscIWcDQNmXkpKsmNho5WRnOToUXEOJCu/nn3/e7LGbm5sCAgJ0xx13aNiwYTYJDABQtIKcTMloVGCHwfIILL6QzjMky7B6ujIyDBTe5Qg5GwDKvowMg3Kysyz+TCBJWYd36PTGxXaODIVKVHjv27fP1nEAAErIIzBUXjXrOToMOClyNgCUH9Z8JsgzJNs5GlzKZjdXAwAAAAAAVyrRL96TJ0+2uO1rr71WkkMAAAAbIGcDAOB4JSq8//zzTyUmJurChQuqU6eOpItzgrq7u+u2224ztXNzc7NNlAAAoETI2QAAOF6JCu9WrVqpSpUqevPNN+Xn5ydJOn36tF577TVFR0frmWeesWmQAACgZMjZAAA4Xomu8f7www81ePBgUwKXJD8/P7300kv68MMPbRYcAAC4PuRsAAAcr0SFd2ZmpjIyMq5YnpGRoXPnzl13UAAAwDbI2QAAOF6JCu/WrVvrtdde07fffqt///1X//77r7755huNHDlSbdq0sWmAx48f15AhQ9SiRQtFRUWpY8eO+v33303rjUaj3n33XcXFxSkqKkq9evXSX3/9ZbaPU6dOafDgwWrSpImio6M1YsQIPmwAAMqF0szZAADg6kp0jfe4ceP05ptvavDgwbpw4YIkqUKFCnrkkUc0dOhQmwV3+vRpPf7442rRooXmz58vf39/HT161Gy43Pz58/XJJ59oypQpCgkJ0bvvvqvevXtr7dq18vLykiQNGTJEaWlpSkhIUF5enkaMGKHRo0dr+vTpNosVAABnVFo5GwAAXFuJCu9KlSpp7NixGjp0qI4dOyZJuvHGG1W5cmWbBjd//nzVrFnTbCqU0NBQ0/+NRqMWLVqk/v3767777pMkTZ06VbGxsfr+++/Vvn17HTp0SBs3btTy5cvVoEEDSdKoUaPUt29fDR06VDVq1LBpzAAAOJPSytkAADhSUtJ+i9sGBAQqJCS0+IY2VKLCu1BaWprS0tLUrFkzeXt7y2g02nQ6kg0bNiguLk4DBw7U9u3bVaNGDT3xxBN67LHHJEkpKSlKS0tTbGysaRsfHx81bNhQu3btUvv27bVr1y75+vqaim5Jio2Nlbu7u/bs2aPWrVvbLF4AAJyVvXM2AACOkJ95UnJzU//+fSzexsu7kjZv2lGqxXeJCu+TJ0/qpZde0tatW+Xm5qZvv/1WoaGhGjFihPz8/DR8+HCbBJecnKylS5fq6aef1nPPPafff/9dEydOlIeHhzp16qS0tDRJUmBgoNl2gYGBSk9PlySlp6crICDAbH3FihXl5+dn2t5SzvL5pDAOZ4nHWq4eP1AWlOTvz9X/dl0pflvGWFo5GwAARyjIyZSMRgV2GCyPwOIL6TxDsgyrpysjw+D8hffkyZNVsWJF/fjjj3rggQdMy9u1a6cpU6bYLIkbjUZFRkbqlVdekSTddtttSkpK0rJly9SpUyebHMMagYE+pX7MojhbPNYqLn5//yqlFAlQvvz77zGr/r6CgoJ04403mh6X9feesqa0cjYAAI7kERgqr5r1HB3GNZWo8P7111+1cOFC1axZ02z5zTffrNTUVJsEJknBwcGqW7eu2bJbbrlF33zzjWm9JBkMBlWvXt3UxmAwKCIiQtLFD4yXT6Ny4cIFnT592rS9pQyGszIare6Gzbm5Xfzg6CzxWMvS+E+e5M7zgC0VDsV68sknrdrOy7uStmzeodDQ0HLx3uMMCmO1hdLK2QAA4NpKVHifP39e3t7eVyw/deqUPD09rzuoQk2aNNGRI0fMlv3111+qXbu2JCkkJETBwcHavHmz6tevL+nifKW//fabHn/8cUlS48aNdebMGe3du1eRkZGSpC1btqigoEBRUVFWxWM0yqk+rDlbPNZy9fgBV2PtUCzpf8OxDIb/Dcdy9b9dV4/fWqWVswEAtpWSkqyMDIPpsb9/lWv+MGXNjcXgGCUqvKOjo/XFF1/opZdeMi0rKCjQggUL1KJFC1vFpp49e+rxxx/X3Llz9cADD2jPnj36/PPPNX78eEmSm5ubnnrqKb3//vu66aabTNOJVa9e3XSX87p166ply5Z6/fXXNW7cOOXl5WnChAlq3749dzQHUC45+1As2Jatcvb27du1cOFC7d27V2lpaZozZ44p10rS8OHDtWrVKrNt4uLitHDhQtPjU6dOacKECfrhhx/k7u6uNm3aaOTIkapS5X+XPuzbt0/jx4/X77//roCAAD355JPq08fyG+YAQFmQkpKsmNho5WRnOToU2EiJCu9XX31VvXr10t69e5WXl6e33npLBw8e1OnTp7V06VKbBRcVFaXZs2fr7bff1pw5cxQSEqIRI0bowQcfNLXp06ePsrKyNHr0aJ05c0ZNmzbVggULTHN4S9K0adM0YcIE9ezZ05ToR40aZbM4AQBwVrbK2efPn1d4eLi6dOmiF1544aptWrZsaTYF6OW/qA8ZMkRpaWlKSEhQXl6eRowYodGjR2v69OmSLo5a6927t2JiYjRu3DgdOHBAI0aMkK+vr7p27VqC3gOAa8rIMCgnO8viUWpZh3fo9MbFpRAZSqpEhXdYWJi++eYbLV68WFWqVNH58+fVunVrde/e3exaa1u45557dM8991xzvZubmwYNGqRBgwZds021atVMSR0AgPLEVjk7Pj5e8fHxRbbx9PS85v1TDh06pI0bN2r58uWmKT5HjRqlvn37aujQoapRo4a+/PJL5eXladKkSfL09NStt96qxMREJSQkUHgDKJcsHaWWZ0guhWhwPawuvPPy8vTss89q3Lhx6t+/vz1iAgAANlDaOXvbtm2KiYmRr6+v7rjjDr300kvy9/eXJO3atUu+vr6moluSYmNj5e7urj179qh169bavXu3oqOjzX4pj4uL0/z583X69Gn5+flZHIszTRvnSlPZWYu+uaay2rey2i/Yz/W+VqzZ3urC28PDQ/v3c/E+AADOrjRzdsuWLdW6dWuFhIQoOTlZb7/9tvr06aPPPvtMFSpUUHp6ugICAsy2qVixovz8/JSWliZJSk9PV0hIiFmboKAg0zprCm9nnDbOGWOyFfrmmspq38pCv5hW1/78/asoKKj0XislGmr+4IMPavny5RoyZIit4wEAADZUWjm7ffv2pv+Hh4crPDxc9913n+lX8NLmTNPGudJUdtaib66prPatLPWLaXXt7+TJc0pPP3td+7Bm+s8SFd75+flaunSpNm3apMjISFWqVMls/WuvvVaS3QIAABtzVM4ODQ2Vv7+/jh49qpiYGAUFBSkjI8OszYULF3T69GnTdeFBQUFKT083a1P4uPCXb0s547RxzhiTrdA311RW+1ZW+wXbK83XiVWFd3JysmrXrq0DBw7otttuk6Qr5tl246IKFKFwPsKi5iEsxHyEAFByjs7Z//77r06dOmUqqhs3bqwzZ85o7969ioyMlCRt2bJFBQUFioqKkiQ1atRIM2bMUF5enjw8PCRJmzZtUp06dawaZg4AgLOxqvBu06aNfvnlF33yySeSpJdeekmjRo2y+ltolE/MRwgApcfWOfvcuXM6duyY6XFKSooSExPl5+cnPz8/zZ49W23btlVQUJCSk5P11ltv6aabblLLli0lSXXr1lXLli31+uuva9y4ccrLy9OECRPUvn171ahRQ5LUsWNHzZkzRyNHjlSfPn2UlJSkRYsWMZIOAODyrCq8jZf9Fv/zzz8rK4siCpZhPkIAKD22ztl79+7VU089ZXpcOF93p06dNHbsWB04cEBffPGFzp49q+rVq+vOO+/UoEGDzO5QPm3aNE2YMEE9e/aUu7u72rRpo1GjRpnW+/j4aOHChRo/frw6d+4sf39/DRgwgKnEAAAur0TXeBe6PKkDlmA+QgAofdebs1u0aFHkHdIXLlxY7D6qVaum6dOnF9kmIiJCn376qdXxAQDgzNytaezm5sY13AAAuAByNgAAzsPqoebDhw83DRvLzc3V2LFjr7hD6uzZs20XIQAAsBo5GwAA52FV4d2pUyezxw8++KBNgwEAALZBzgYAwHlYVXgX3kgFAAA4N3I2AADOw6prvAEAAAAAgHUovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO7Lq5moAAAAAAOukpCQrI8NgcfukpP12jAaOQOENAAAAAHaSkpKsmNho5WRnOToUOBCFNwAAAADYSUaGQTnZWQrsMFgegaEWbZN1eIdOb1xs58hQmii8AQAAAMDOPAJD5VWznkVt8wzJdo4GpY2bqwEAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFR0dAADA+SUl7Zck+ftX0cmT54psm5OTIy8vL6v2HxAQqJCQ0BLHBwAA4MwovAEA15SfeVJyc1P//n0s38jNXTIWWHUcL+9K2rxpB8U3AAAokyi8AQDXVJCTKRmNCuwwWB6BxRfFWYd36PTGxRa3l6Q8Q7IMq6crI8NA4Q0AAMokCm8AQLE8AkPlVbNese3yDMlWtQcAACgPuLkaAAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHXOONEktJSVZGhsHi9oXTEQEAAABAeULhjRJJSUlWTGy0crKzHB0KAFgkJSVZx45lFzsPeSHmFgcAALZC4Y0SycgwKCc7y6opgwqnGQKA0laSLwuZWxwAANgKhTeuizVTBhVOMwQApc3aLwuZWxwAUBRrLrnkcktIFN4AgHKE+cUBANeLSy5REhTeAAAAAGAha0dRcbklJApvAAAAALCapaOouNwSEvN4AwAAAABgVxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHbkUoX3Bx98oPDwcL3xxhumZTk5ORo3bpxatGihxo0b68UXX1R6errZdqmpqerbt68aNmyomJgYvfnmm7pw4UJphw8AAAAAKIdcpvDes2ePli1bpvDwcLPlkyZN0g8//KAZM2bok08+0YkTJ/TCCy+Y1ufn56tfv37Ky8vTsmXLNGXKFK1atUozZ84s7S4AAAAAAMohlyi8z507p1dffVUTJ06Un5+fafnZs2e1YsUKDR8+XDExMYqMjNSkSZO0a9cu7d69W5L0yy+/6ODBg3rrrbdUv359xcfHa9CgQVqyZIlyc3Md1CMAAAAAQHnhEoX3+PHjFR8fr9jYWLPle/fuVV5entnyunXrqlatWqbCe/fu3QoLC1NQUJCpTVxcnDIzM3Xw4MFSiR8AAAAAUH5VdHQAxVmzZo3+/PNPLV++/Ip16enp8vDwkK+vr9nywMBApaWlmdpcWnRLMj0ubGMpNzermttNYRzOEg8A2Iozvq85IiZnPA8AAKDknLrw/ueff/TGG2/oww8/lJeXl6PDUWCgj6NDMOPIePz9qzjs2ADKJn//KgoKss/7Wknfs+wZk6vZvn27Fi5cqL179yotLU1z5szRfffdZ1pvNBo1c+ZM/ec//9GZM2fUpEkTjR07VjfffLOpzalTpzRhwgT98MMPcnd3V5s2bTRy5EhVqfK/52ffvn0aP368fv/9dwUEBOjJJ59Unz59SrOrAADYnFMX3n/88YcMBoM6d+5sWpafn6/t27dryZIlWrhwofLy8nTmzBmzX70NBoOCg4MlXfx1e8+ePWb7LbzreWEbSxkMZ2U0lrQ3tuPmdrHodmQ8J0+ec8yBAZRZJ0+eU3r6Wbvtu6Tb2SumohS+zzuT8+fPKzw8XF26dDG7iWmh+fPn65NPPtGUKVMUEhKid999V71799batWtNX54PGTJEaWlpSkhIUF5enkaMGKHRo0dr+vTpkqTMzEz17t1bMTExGjdunA4cOKARI0bI19dXXbt2LdX+AgBgS05deN9xxx366quvzJa99tpruuWWW9SnTx/dcMMN8vDw0ObNm9W2bVtJ0uHDh5WamqpGjRpJkho1aqS5c+fKYDAoMDBQkrRp0yZVrVpV9erVsyoeo1FOUXgXcrZ4AOB6OeN7mjPG5Ajx8fGKj4+/6jqj0ahFixapf//+pl/Bp06dqtjYWH3//fdq3769Dh06pI0bN2r58uVq0KCBJGnUqFHq27evhg4dqho1aujLL79UXl6eJk2aJE9PT916661KTExUQkIChTcAwKU59c3VqlatqrCwMLN/lStXVrVq1RQWFiYfHx916dJFU6ZM0ZYtW7R3716NGDFCjRs3NhXecXFxqlevnoYOHap9+/Zp48aNmjFjhrp37y5PT0/HdhAAgDIgJSVFaWlpZjc79fHxUcOGDbVr1y5J0q5du+Tr62squiUpNjZW7u7uppFpu3fvVnR0tFl+jouL05EjR3T69OlS6g0AALbn1L94W2LEiBFyd3fXwIEDlZubq7i4OI0ZM8a0vkKFCpo7d67Gjh2rrl27qlKlSurUqZMGDhzowKgBACg7Cm9WWjiyrFBgYKDp8q709HQFBASYra9YsaL8/PzMbogaEhJi1qbwhqjp6elmU4oWx5luUFeWb4pK31xTWe1bWe0X7Od6XyvWbO9yhfcnn3xi9tjLy0tjxowxK7YvV7t2bc2fP9/eoQEAACfhbNfIS84Zk63QN9dUVvtm735xk+GyobRvoOpyhTcAAHAuhTcrNRgMql69umm5wWBQRESEpIu/XGdkZJhtd+HCBZ0+fdrshqiFv5AXKnx8+dSgxXGWG6JKznFTVHuhb66prPattPrFTYbLBlvcQNWam6E69TXeAADA+YWEhCg4OFibN282LcvMzNRvv/2mxo0bS5IaN26sM2fOaO/evaY2W7ZsUUFBgaKioiRdvCHqjh07lJeXZ2qzadMm1alTx6ph5tL/bkDqLP+cMSb6Rt8cHYOr9gtlR2m+Hii8AQBAsc6dO6fExEQlJiZKunhDtcTERKWmpsrNzU1PPfWU3n//fa1fv1779+/X0KFDVb16ddNdzuvWrauWLVvq9ddf1549e7Rz505NmDBB7du3V40aNSRJHTt2lIeHh0aOHKmkpCStXbtWixYt0tNPP+2wfgMAYAsMNQcAAMXau3evnnrqKdPjyZMnS5I6deqkKVOmqE+fPsrKytLo0aN15swZNW3aVAsWLDDN4S1J06ZN04QJE9SzZ0+5u7urTZs2GjVqlGm9j4+PFi5cqPHjx6tz587y9/fXgAEDmEoMAODyKLwBAECxWrRoof37919zvZubmwYNGqRBgwZds021atU0ffr0Io8TERGhTz/9tMRxAoC1UlKSlZFhsLh9UtK13wuBa6HwBgC4HD4kAQBsISUlWTGx0crJznJ0KCjjKLwBAC6FD0kAAFvJyDAoJztLgR0GyyMw1KJtsg7v0OmNi+0cGcoaCm8AgEvhQxIAwNY8AkPlVbOeRW3zDMl2jgZlEYU3TKwZusmwTQCOxockAADgKii8IYmhmwAAAABgLxTekGT90E2GbQKwNUtH0jDiBgAAuBoKb5ixdOgmwzYB2Ep+5knJzU39+/dxdCgAAAB2QeENAHCogpxMyWhkxA0AACizKLwBAE6BETcAAKCscnd0AAAAAAAAlGUU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdlTR0QEAAAAAgK2kpCTLYDBY1DYpab+dowEuovAGAAAAUCYcO3ZMd8REKyc7y9GhAGYovAEAAACUCenp6crJzlJgh8HyCAwttn3W4R06vXFxKUSG8o7CGwAAAECZ4hEYKq+a9Yptl2dILoVoAG6uBgAAAACAXVF4AwAAAABgRxTeAAAAAADYEYU3AAAAAAB2ROENAAAAAIAdUXgDAAAAAGBHFN4AAAAAANgR83iXUSkpycrIMFjcPilpvx2jAQAAAIDyi8K7DEpJSVZMbLRysrMcHQoAAAAAlHsU3mVQRoZBOdlZCuwwWB6BoRZtk3V4h05vXGznyADAtVg7GiggIFAhIZa97wIAgPKDwrsM8wgMlVfNeha1zTMk2zkaAHAd+ZknJTc39e/fx6rtvLwrafOmHRTfAADADIU3AACXKcjJlIxGq0YO5RmSZVg9XRkZBgpvAABghsIbAIBrsGbkEAAAwLUwnRgAAAAAAHZE4Q0AAAAAgB0x1BwAAACAU0pJSVZGhsHi9v/+e8yO0QAlR+ENAAAAwOmkpCQrJjZaOdlZjg4FuG5OXXjPmzdP3377rQ4fPixvb281btxYQ4YM0S233GJqk5OToylTpmjt2rXKzc1VXFycxowZo6CgIFOb1NRUjR07Vlu3blXlypX18MMPa/DgwapY0am7DwAAAJRbGRkG5WRnWTXDRNbhHTq9cbGdIwOs59SV57Zt29S9e3c1aNBA+fn5evvtt9W7d2+tWbNGlStXliRNmjRJP/30k2bMmCEfHx9NmDBBL7zwgpYtWyZJys/PV79+/RQUFKRly5bpxIkTGjZsmDw8PPTKK684snsAAAAAimHNDBN5hmQ7RwOUjFPfXG3hwoXq3Lmzbr31VkVERGjKlClKTU3VH3/8IUk6e/asVqxYoeHDhysmJkaRkZGaNGmSdu3apd27d0uSfvnlFx08eFBvvfWW6tevr/j4eA0aNEhLlixRbm6uA3sHAAAAACgPnLrwvtzZs2clSX5+fpKkvXv3Ki8vT7GxsaY2devWVa1atUyF9+7duxUWFmY29DwuLk6ZmZk6ePBg6QUPAEAZNmvWLIWHh5v9u//++03rc3JyNG7cOLVo0UKNGzfWiy++qPT0dLN9pKamqm/fvmrYsKFiYmL05ptv6sKFC6XdFQAAbM6ph5pfqqCgQJMmTVKTJk0UFhYmSUpPT5eHh4d8fX3N2gYGBiotLc3U5tKiW5LpcWEbS7m5lTR62yqMw1niAQCYu973Z1d9f7/11luVkJBgelyhQgXT/7k0DABQnrlM4T1u3DglJSXp008/dVgMgYE+Djv21VwrHn//KqUcCQCgkL9/FQUFOVe+KC0VKlRQcHDwFcsLLw2bNm2aYmJiJF0sxNu1a6fdu3erUaNGpkvDEhISFBQUpPr162vQoEGaNm2aXnjhBXl6epZ2dwAAsBmXKLzHjx+vH3/8UYsXL1bNmjVNy4OCgpSXl6czZ86Y/eptMBhMiT8oKEh79uwx21/h0LarfTgoisFwVkZjSXthO25uF4vua8Vz8uS50g8KACDp4ntwevrZ69pH4fu8qzl69Kji4uLk5eWlRo0aafDgwapVq1axl4Y1atTompeGjR07VgcPHtRtt91mVSzONGqgLI9Uo2+uqSz3DbBGaY5Qc+rC22g0asKECfruu+/0ySefKDTUfBqByMhIeXh4aPPmzWrbtq0k6fDhw0pNTVWjRo0kSY0aNdLcuXNlMBgUGBgoSdq0aZOqVq2qevUsuzvi/+KRUxTehZwtHgDAReXxvTkqKkqTJ09WnTp1lJaWpjlz5qh79+766quvSv3SsIv7dr4vLpwxJluhb67J2fvGKE7YU2mPUHPqwnvcuHFavXq13nvvPVWpUsWUeH18fOTt7S0fHx916dJFU6ZMkZ+fn6pWraqJEyeqcePGpsI7Li5O9erV09ChQ/Xqq68qLS1NM2bMUPfu3Rm2BgCAjcTHx5v+HxERoYYNG+qee+7RunXr5O3tXerxOMsoNan4kWqujL65JlfpG6M4YU+lPULNqQvvpUuXSpJ69Ohhtnzy5Mnq3LmzJGnEiBFyd3fXwIEDlZubq7i4OI0ZM8bUtkKFCpo7d67Gjh2rrl27qlKlSurUqZMGDhxYeh0BAKCc8fX11c0336xjx44pNja2VC8Nk5xzVJgzxmQr9M01leW+AZYozde/Uxfe+/fvL7aNl5eXxowZY1ZsX6527dqaP3++LUMDAABFOHfunJKTkxUcHFzql4YBAOBsnLrwBgAAruHNN9/UPffco1q1aunEiROaNWuW3N3d1aFDBy4NAwCUexTeAADguv3777965ZVXdOrUKQUEBKhp06b6/PPPFRAQIIlLwwAA5RuFNwAAuG7vvPNOkeu5NAxASkqyMjIMFrdPSir+slPAVVB4AwAAALCrlJRkxcRGKyc7y9GhAA5B4Q0AAADArjIyDMrJzlJgh8HyCAy1aJuswzt0euNiO0cGlA4KbwAAAAClwiMwVF41LZupIM+QbOdogNLj7ugAAAAAAAAoyyi8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOKLwBAAAAALAjCm8AAAAAAOyIwhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6ro6AAAAAAAuJ6UlGRlZBgsapuUtN/O0QDOjcIbAAAAgFVSUpIVExutnOwsR4cCuAQKbwAAAABWycgwKCc7S4EdBssjMLTY9lmHd+j0xsWlEBngnCi8AQAAAJSIR2CovGrWK7ZdniG5FKIBnBc3VwMAAAAAwI74xRsAAAAo56y5UZrEzdIAa1F4AwAAAOUYN0oD7I/C20Vc/i2kv38VnTx57qpt+QYSAAAAlrL2RmkSN0sDrEXh7QL4FhIAAAD2ZumN0iRulgZYi8LbBTBdAwAAAAC4LgpvF8J0DQAAAADgephODAAAAAAAO6LwBgAAAADAjii8AQAAAACwI67xBgAAAMqYy6eivdylU9MyFS1gfxTeAAAAQBnCVLSA86HwBgAAAMoQpqIFnA+FNwAAAODEihs2frnCoeNMRQs4DwpvAAAAwEkxbBwoGyi8AQAAACdl7bBxiaHjgDOi8AYAAACcnKXDxiWGjgPOiHm8AQAAAACwIwpvAAAAAADsiMIbAAAAAAA7ovAGAAAAAMCOuLkaAAAAUIqsmZe7cE5uAK6NwhsAAAAoJczLDZRPFN4AAABAKbF2Xm7m5AbKBgpvAAAAoISsGTYu/W/ouKXzcjMnN1A2UHg7QEnfoAEAAGBfl35O8/evopMnz12z7fHjx/X0Mz2Um5NdWuEBcFEU3qWM63oAAACcU0k/p1k6bFxi6DhQXlF4lzJrr+uReIMGAAAoDSW9/trSYeMSQ8eB8qpcFd5LlizRwoULlZaWpoiICL3++uuKiopySCy8QQMAcHXOlK/hXKy9XC8nJ0deXl4Wt+f6awD2Um4K77Vr12ry5MkaN26cGjZsqI8//li9e/fW119/rcDAQEeHBwAARL7GtZVoGLibu2QssF9QAGChclN4JyQk6LHHHlOXLl0kSePGjdOPP/6oFStWqG/fvg6ODgAASORrV1Yav0aXZBg4l/cBcAblovDOzc3VH3/8oX79+pmWubu7KzY2Vrt27XJgZAAAoJAz5mtbFZNF3R3b2gLU2vaSFBAQqJAQy4pPybp++/tX0b59h62/u3cJf422dhg4l/cBcAblovA+efKk8vPzrxiiFhgYqMOHD1u8H3d3yWi8vlgqVKggHx8feZ0/IY9TnhZtUynvtFXbWNueY3CM8nQMZ42LY7j+MXT+hHx8fFShQgW5u1u2ybW4uV3f9q7KVvlask3O/vvvv9Wm7T32H9ps7TYlOIanl7fef+8DBQdXL7btiRMn9PwLfZWTbd0UWV6eHqoe100V/IKLbZubekCZf2yQX/MuFrW/dBtXf6/gGOR4juHYYzgqX7sZjdeblpzf8ePHddddd2nZsmVq3LixafnUqVO1fft2/ec//3FgdAAAQCJfAwDKruus8V2Dv7+/KlSoIIPBfMiUwWBQUFCQg6ICAACXIl8DAMqqclF4e3p66vbbb9fmzZtNywoKCrR582azb9QBAIDjkK8BAGVVubjGW5KefvppDRs2TJGRkYqKitLHH3+srKwsde7c2dGhAQCA/498DQAoi8pN4d2uXTtlZGRo5syZSktLU/369bVgwQKGrgEA4ETI1wCAsqhc3FwNAAAAAABHKRfXeAMAAAAA4CgU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4u6Aff/xRjz76qKKiotSsWTMNGDDAbH1qaqr69u2rhg0bKiYmRm+++aYuXLjgoGjNtWrVSuHh4Wb/PvjgA9P6lJSUK9aHh4dr9+7djgv6EsXFL0n79u3TE088oQYNGig+Pl7z5893ULTXlpubq4ceekjh4eFKTEw0LXf28y9dO3bJuc/9c889p7vvvlsNGjRQXFycXn31VR0/fty03tnPfXHxS857/lNSUjRixAi1atVKUVFRuu+++zRz5kzl5uaatXHm8w/X5ep5ryhlJScWxZXzZVFcNZcWxdXzbFFcOQcXpbzl53IznVhZ8c033+j111/Xyy+/rDvuuEP5+fk6cOCAaX1+fr769eunoKAgLVu2TCdOnNCwYcPk4eGhV155xYGR/8/AgQP12GOPmR5XqVLlijYfffSR6tWrZ3pcrVq10gjNIkXFn5mZqd69eysmJkbjxo3TgQMHNGLECPn6+qpr166OCPeqpk6dqurVq2vfvn1XXe/M5/9asTv7ub/jjjv03HPPKTg4WMePH9fUqVM1aNAgLVu2zKyds5774uJ35vN/+PBhGY1GjR8/XjfddJMOHDig119/XVlZWRo2bJhZW2c9/3Btrp73ilIWcmJRXDlfFsVVc2lRXD3PFsWVc3BRyl1+NsJl5OXlGVu2bGn8/PPPr9nmxx9/NEZERBjT0tJMyz799FNjkyZNjDk5OaURZpHuueceY0JCwjXXJycnG8PCwox//vln6QVlheLiX7JkibFZs2Zm5/qtt94ytm3bthSis8yPP/5ovP/++41JSUlXnGtnP/9Fxe4K5/5S33//vTE8PNyYm5trNBqd/9xf7vL4Xe38z58/39iqVSvTY1c7/3Adrp73ilIWcmJRXDlfFqUs5dKiuHqeLYqr5+CilOX8zFBzF/Lnn3/q+PHjcnd318MPP6y4uDg9++yzZr947969W2FhYQoKCjIti4uLU2Zmpg4ePOiIsK8wf/58tWjRQg8//LAWLFhw1WHw/fv3V0xMjB5//HGtX7/eAVFeW1Hx7969W9HR0fL09DQti4uL05EjR3T69GlHhGsmPT1dr7/+uqZOnSpvb+9rtnPG819c7M5+7i916tQpffXVV2rcuLE8PDzM1jnjub/c1eJ3pfMvSWfPnpWfn98Vy13h/MP1uHreK4or58SiuHK+LEpZyqVFcfU8W5SykIOLUpbzM0PNXUhycrIkafbs2Ro+fLhq166thIQE9ejRQ998842qVaum9PR0s6JbkulxWlpaqcd8uR49eui2226Tn5+fdu3apbfffltpaWl67bXXJEmVK1fW8OHD1aRJE7m5uenbb7/V888/rzlz5ujee+91cPTFx5+enq6QkBCzbQrPf3p6+lXfSEqL0WjU8OHD1a1bNzVo0EApKSlXtHHW829J7M587gu99dZbWrJkibKystSoUSPNnTvXtM5Zz/2liorfFc5/oaNHj2rx4sVmw9hc4fzDNbl63iuKK+fEorhyvixKWcmlRXH1PFuUspKDi1LW8zOFtxOYNm1asTdAWLt2rQoKCiRdvMFC27ZtJUmTJ0/WXXfdpa+//lrdunWze6xXY2n8devW1dNPP21aFhERIQ8PD40ZM0aDBw+Wp6enAgICzNpERUXpxIkTWrhwod3+uGwZvyNYGv+vv/6qc+fOqV+/ftdsV9rn35axO4I1rx1J6t27tx555BGlpqZq9uzZGjZsmObNmyc3Nzenf+0XF78jWBu/JB0/flzPPvus7r//frPrUh1x/uG6XD3vFcXVc2JRXDlfFsXVc2lRXD3PFsXVc3BRyM9XR+HtBJ555hl16tSpyDahoaGmX6wvfZF6enoqNDRU//zzj6SL327t2bPHbNv09HRJUnBwsC3DNrE0/qtp2LChLly4oJSUFN1yyy3XbLNp06brjvNabBl/UFCQ6XwXKnx8+UgEW7E0/i1btmj37t1q0KCB2bouXbqoY8eOevPNN6+6rT3Pvy1jd+ZzXyggIEABAQGqU6eO6tatq/j4eO3evVuNGze+6rbO9tovKn5XOP/Hjx/XU089pcaNG2vChAnF7t/e5x+uy9XzXlFcPScWxZXzZVFcPZcWxdXzbFFcPQcXhfx8dRTeTqDwD6k4kZGR8vT01JEjRxQdHS1JysvL099//61atWpJkmnoicFgUGBgoCRp06ZNqlq1qtmdAB0R/9UkJibK3d3dFOu12tjrSwPJtvE3atRIM2bMUF5enum6m02bNqlOnTp2G+ZjafyjRo3SSy+9ZHp84sQJ9e7dW++8844aNmx4ze3sef5tGbszn/urKRzBcumUGZdz5tf+5fE7+/kvTOq33367Jk+eLHf34m9xYu/zD9fl6nmvKK6eE4viyvmyKK6eS4vi6nm2KK6eg4tCfr46Cm8XUrVqVXXr1k2zZs3SDTfcoFq1amnhwoWSpPvvv1/SxRsp1KtXT0OHDtWrr76qtLQ0zZgxQ927d3f4sK9du3bpt99+0x133KEqVapo165dmjx5sh588EHTm8KqVavk4eGh+vXrS5K+++47rVixQhMnTnRk6JIsi79jx46aM2eORo4cqT59+igpKUmLFi0yXe/mSIVfzhSqXLmyJOnGG29UzZo1JTnv+bckdmc+97/99pt+//13NW3aVL6+vjp27Jjeffdd3XjjjaZv4Z313EuWxe/M5//48ePq0aOHatWqpWHDhikjI8O0rjBxO/P5h+ty9bxXFFfPiUVx5XxZFFfPpUVx9TxbFFfPwUUpb/mZwtvFDB06VBUrVtTQoUOVnZ2thg0b6uOPPzYluQoVKmju3LkaO3asunbtqkqVKqlTp04aOHCggyO/OCx+7dq1mj17tnJzcxUSEqJevXqZXbchSe+9955SU1NVoUIF3XLLLXrnnXdMXyw4kiXx+/j4aOHChRo/frw6d+4sf39/DRgwwKnnULycs57/4jjzuff29ta3336rWbNm6fz58woODlbLli01YMAAsy/EnPXcWxK/M5//X3/9VUePHtXRo0d11113ma3bv3+/6f/Oev7hulw97xWlvOTEorji81YcV33OXD3PFsXVc3BRylt+djMajUZHBwEAAAAAQFnFPN4AAAAAANgRhTcAAAAAAHZE4Q0AAAAAgB1ReAMAAAAAYEcU3gAAAAAA2BGFNwAAAAAAdkThDQAAAACAHVF4AwAAAABgRxTewHXq0aOH3njjDUeHYRPDhw/XgAEDHB1GuZaSkqLw8HAlJiY6OhQAKFPI17Al8jWsReENWGj48OEKDw+/4t+rr76qQYMGXde+w8PD9f3331vcfuXKlabjR0RE6K677tJrr70mg8Fg0fb2ShY9evQwxdWgQQO1a9dOS5Yssekx7K0sfTADgPKIfF088jVQ+io6OgDAlbRs2VKTJ082WxYQEKAKFSpcc5vc3Fx5enraPJaqVavq66+/VkFBgfbt26cRI0boxIkTWrhwoc2PZY3HHntMAwcOVHZ2tr744guNHz9efn5+6tChg0Pjkuz3XAAAnAv5unjka6B08Ys3YAVPT08FBweb/evVq5fZN66tWrXSnDlzNHToUDVp0kSjR49Wbm6uxo8fr7i4ODVo0ED33HOP5s2bZ2ovSc8//7zCw8NNj4vj5uam4OBg1ahRQ/Hx8erRo4c2bdqk7Oxs/fzzz3r88ccVHR2tFi1aqF+/fjp27Jhp23vvvVeS9PDDDys8PFw9evQw2/fChQsVFxenFi1aaNy4ccrLy7P4HHl7eys4OFihoaF68cUXdfPNN2vDhg2SpNTUVPXv31+NGzdWkyZNNGjQIKWnp0uSzp49q/r16+v333+XJBUUFKh58+Z67LHHTPv+73//q/j4eNPjf/75R4MGDVJ0dLSaN2+u/v37KyUlxbS+cCje+++/r7i4ON1///0W9+NaduzYoSeeeEJRUVGKj4/XxIkTdf78eUnS22+/rUcfffSKbR588EHNnj3b9Pg///mPHnjgATVo0ED333+/y/3KAADOjnxdPPI1+Rqli8IbsIMPP/xQERER+uKLLzRgwAB98skn2rBhg2bMmKGvv/5ab731lmrXri1JWr58uSRp8uTJ+uWXX0yPreXt7a2CggJduHBBWVlZevrpp7VixQp99NFHcnNz0/PPP6+CggJJFxOJJH300Uf65ZdfNGvWLNN+tm7dqmPHjunjjz/WlClTtGrVKq1atarE58LLy0t5eXkqKCjQgAEDdPr0aX3yySdKSEhQcnKyXn75ZUmSj4+P6tevr23btkmSDhw4IDc3N/355586d+6cJGn79u1q1qyZJCkvL0+9e/dWlSpVtGTJEi1dulSVK1fWs88+q9zcXNPxN2/erCNHjighIcH04amkjh07pj59+qhNmzb68ssv9c4772jnzp2aMGGCJKljx47as2eP2YempKQk7d+/Xx07dpQkffnll3r33Xf18ssva+3atXrllVc0c+bM6zrHAICSIV//D/mafA37Yqg5YIUff/xRjRs3Nj1u2bLlVdvdcccdeuaZZ0yP//nnH910001q2rSp3NzcTElcujj0TZJ8fX0VHBxcorj++usvLV26VJGRkapataratm1rtn7SpEmKiYnRwYMHFRYWZjpmtWrVrjimn5+fRo8erQoVKqhu3bqKj4/X5s2bzb7JtkR+fr5Wr16t/fv3q2vXrtq8ebMOHDig9evX64YbbpAkTZ06Ve3bt9eePXsUFRWl5s2ba9u2berdu7e2bt2q2NhYHT58WDt37tRdd92lbdu26dlnn5UkrV27VgUFBXrjjTfk5uYm6eKHoWbNmmnbtm2Ki4uTJFWuXFkTJ060yZC1efPmqWPHjurVq5ck6eabb9bIkSPVo0cPjR07VrfeeqsiIiL01Vdf6fnnn5ckffXVV2rYsKFuuukmSdKsWbM0fPhwtWnTRpIUGhqqgwcP6rPPPlOnTp2uO0YAAPnaGuRr8jVKB4U3YIUWLVpo7NixpseVKlXS4MGDr2gXGRlp9rhTp0565plndP/996tly5a6++67TYmmpM6ePavGjRuroKBAOTk5atq0qSZOnCjpYmKfOXOmfvvtN508eVJGo1HSxQ8UYWFhRe63Xr16ZtfABQcH68CBAxbHtXTpUi1fvlx5eXlyd3dXr1699Pjjj2vx4sWqWbOmKYkXHsvX11eHDx9WVFSUmjVrpuXLlys/P1/bt2/XnXfeqaCgIG3btk3h4eE6evSomjdvLknat2+fjh07piZNmpgdPycnx+wb7LCwMJtdJ7Zv3z7t379fX331lWmZ0WhUQUGBUlJSVLduXXXs2FErVqzQ888/L6PRqNWrV+vpp5+WJJ0/f17Hjh3TyJEj9frrr5v2ceHCBfn4+NgkRgAA+doS5GvyNUoXhTdghUqVKpm+CS2u3aVuv/12rV+/Xj///LM2bdqkl156SbGxsZo5c2aJY6lSpYpWrVold3d3BQcHy9vb27TuueeeU+3atTVx4kRVr15dBQUF6tChg0XXflWsaP624ObmZvogYImOHTvqueeeM1075u5u+RUtzZo107lz5/THH39o+/btevnllxUcHKwPPvhAERERql69um6++WZJF5Pi7bffrmnTpl2xn8JfCKQrn4vrcf78eXXr1u2Ka+wkmT6gdOjQQdOmTdMff/yh7Oxs/fvvv2rXrp1pe0maMGGCGjZsaLa9NecJAFA08nXxyNfka5QuCm+glFStWlXt2rVTu3bt1LZtWz377LM6deqUqlWrJg8PD+Xn51u1P3d396t+qDh58qSOHDmiiRMnKjo6WtLFG4xcysPDQ5KsPqYlqlatetW46tatq3///Vf//POPKekdPHhQZ86cUd26dSVdHL4XHh6uJUuWyMPDQ3Xr1lVgYKBeeukl/fDDD6Zvz6WLH47WrVunwMBAVa1a1eb9uJrbbrtNBw8eLPLDXM2aNdWsWTN99dVXys7OVmxsrAIDAyVJQUFBql69upKTk/Xggw+WSswAAOuQr8nX5GvYA1/ZAKUgISFBq1ev1qFDh3TkyBF9/fXXCg4Olq+vrySpdu3a2rx5s9LS0nT69OnrOpafn5+qVaumzz77TEePHtXmzZs1ZcoUszaBgYHy9vbWxo0blZ6errNnz17XMS0RGxursLAwDRkyRH/88Yf27NmjoUOHqnnz5mrQoIGpXfPmzfXVV1+ZbspSrVo11a1bV+vWrTMtky5+U+/v76/+/ftrx44dSk5O1tatWzVx4kT9+++/1xVrRkaGEhMTzf6lp6erT58+2rVrl8aPH6/ExET99ddf+v777zV+/Hiz7R988EGtWbNGX3/9tekmLYUGDhyoDz74QIsWLdKRI0e0f/9+rVixQgkJCdcVMwDg+pGvydeFyNewNQpvoBRUqVJFCxYs0COPPKJHHnlEf//9tz744APTcKVhw4Zp06ZNuvvuu6/7hh3u7u5655139Mcff6hDhw6aPHmyhg4datamYsWKGjVqlD777DO1bNlSAwYMuK5jWsLNzU3vvfeefH199eSTT6pXr14KDQ3VO++8Y9auefPmys/PN/u2/GrLKlWqpMWLF6tWrVp64YUX1K5dO40cOVI5OTnX/Y366tWr9fDDD5v9+/zzzxUREaFPPvlEf/31l5544gl16tRJM2fOVPXq1c22b9u2rU6dOqXs7Gzdd999ZuseffRRTZw4UStXrlTHjh3Vo0cPrVq1SiEhIdcVMwDg+pGvydeFyNewNTejNReDAAAAAAAAq/CLNwAAAAAAdsTN1QAn1L59e6Wmpl513bhx4xxyo48dO3aoT58+11y/a9euUoymZMpCHwAAzoN8bR9loQ/A5RhqDjihv//+WxcuXLjqutK8K+ilsrOzdfz48Wuut2TaFkcrC30AADgP8rV9lIU+AJej8AYAAAAAwI64xhsAAAAAADui8AYAAAAAwI4ovAEAAAAAsCMKbwAAAAAA7IjCGwAAAAAAO6LwBgAAAADAjii8AQAAAACwIwpvAAAAAADs6P8BJXlUz3AviLAAAAAASUVORK5CYII="
     },
     "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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QoahXrx5ee+01nDx50u+g5/LLL8f48eMxbNgwjBo1ChdccAH69u2LjIwMv9cJlJdKfffddzh27Jjo7ytWrBANUOvWrYvGjRvjqaeeQrNmzfDjjz/i559/RmFhIWrXro077rgDL774IurWreuy3JAhQ/DPP/9gwYIFOHfuHARBwEUXXYQXX3wRzz//vORYX0RE4cAgBLN1KxERERERUQRiGykiIiIiIiKFGEgREREREREpxECKiIiIiIhIIQZSRERERERECjGQIiIiIiIiUoiBFBERERERkUIMpIiIiIiIiBRiIEVERERERKQQhxR3kpVVAK2HJzYYgFq1UnWRFlIHz2lk4nmNPDynkYnnNfLwnEYmPZ3XyrT4wkDKiSBA8xNXSU9pIXXwnEYmntfIw3MamXheIw/PaWQKp/PKqn1EREREREQKMZAiIiIiIiJSiIEUERERERGRQmwjRUREQWe322GzWbVOhlcGA1BaWgqLxaz7+vlGoxFGowkGg0HrpBARRS0GUkREFFRlZSXIyckAoPPoBEB2thF2u13rZMgSF5eAatXSEBMTq3VSiIiiEgMpIiIKGrvdjpycDMTFJSAlpbruS1BMJgNsNn0HfIIgwGazorAwF1lZZ3DeefV0f1yJiCIRAykiIgqa8up8AlJSqiMuLl7r5PgUE2OE1RoOJVLxMJlMyM4+C6vVgtjYOK0TREQUddjZBBERBR1LTNRnMPAVTkSkJT6FiYiIiIiIFGIgRUREREREpBADKSIiIiIiIoXY2QQREZGbjz9+H4WFBRg27AuP38rKSvHLLz9j0aK/cPbsaSQlJaF166vwzDM9cemlDR3zlZaW4qefxuHffxchMzMDSUlJuOSSS/Hoo4/j+utvDOHeEBFRMDCQIiIikslsNuPll1/C2bNn0bv3y7jyymbIzs7CpEk/4YUXnsJXX32LZs2aAwA++2wodu3agVdeeR2XXHIp8vLysGPHVuTl5Wm8F0REpAYGUkREFFKCIKA0xF2MJ8QYVek5cPr0KdixYzt+/HEyLrusMQDgggsuxMcff4qePZ/C8OEfYdKkaTAYDPjvv+Xo3/81tG/fEQBw4YV1cPnlTQNOAwUmftoUxM/7A/nfjQeSk7VODhGFMQZSREQUMoIg4LmpW7HtVH5It9uyTjWMfaxlwMHUokV/4eqr2zmCqEpGoxGPPPI4PvzwHRw4sA+XXdYEaWm1sHr1f+jU6SYkJfGDXS+q9X0RAJD0wzcofvUNjVNDROGMnU0QEVFIhfOIUsePH8PFFzcQ/e2SSy4BABw7dgwA8MYbb2PHjm24665b8NxzPTBq1BfYtm1LiFJKvhhyc7VOAhGFOZZIERFRyBgMBox9rGXYVu0DykvV5GjVqg2mT/8dO3dux/btW7Fx43rMmDEVzz77Ap566jlV0kJEpJbjOSX4/r8j+N81F6HxeSlaJycsMJAiIqKQMhgMSIw1aZ0Mv1x0UX0cPXpY9LcjR44AAOrXr++YFhMTg5YtW6Nly9Z44omn8NNP4/DTT+PQvfv/EBsbG4okExHJ8vLsHTiWU4IlBzKx6uXrtU5OWGDVPiIiIpk6d74NGzasw/79+1ym2+12TJ8+BZdccikaNWossTTQoMGlsNlsMJvLgp1U8kWlEkqiSHEspwQAYLHJK3UnlkgRERGJKiwsxP79e12m3XbbnVixYhnefPMV9OnzCq64ohlycrIwceIEHD16GF999a2jCmGfPj3RufPtuPzyK1C9enUcOXIIP/zwDdq0uQrJyaw2Q0QU7hhIERERidi8eSOefrq7y7R77rkPo0Z9j4kTf8SYMd/gzJnTSEpKRps2bfHDDxNw6aWNHPO2a9ceCxfOw5gx36K0tBTp6eno0OF6PP0020cREUUCBlJERERu3n77fbz99vuSv/fs+RJ69nzJ6zqefPJpPPnk0yqnjIiI9IJtpIiIiCj6+NlGyi6z10YiinwMpIiIiIhk+O6/I7jt29U4nV+qdVKISAcYSBEREVH08aNk6cc1x5BXasW41UeDkCAiCjcMpIiIiIgUYO0+IgIYSBEREVE04jhSRBQgBlJERERECrBAiogABlJERERERESKMZAiIiIiUoAlUkQEMJAiIiKiaMQ2UkQUIAZSREREREqw2z7di5/9G2p2vBqmfXu1TgpFMAZSREREbj7++H107HgVJk36yWX68uVL0bHjVQCATZs2oGPHq1BQUCC5nv/+W4E+fXri1ltvwC23XIfnnuuB+fP/9Jhv2bIl6NnzKdx+eyfceusNeOKJRzBy5Beq7hNRNKn2wjOI2bcXqX16ap0UimAxWieAiIhIj+Li4jF58s+4776uqFatmuLlf/ttKkaN+hLdu/8PAwYMRGxsLFasWIbPPx+GQ4cOok+flwEAGzasw+DBb6Fnz5fQseMNAAw4cuQw1q9fq+4OkStW7YsKhpISrZNAEYyBFBERhZYgAMXFod1mUpLiD+errroGJ08exy+/TMBLL/VXtOzZs2cwevQIPPxwN7zwQm/H9G7dnkBsbAxGjPgcN93UGVde2Qz//bcCzZu3xOOP93DMV7/+xbjhhhsVbZOIiEKLgRQREYWOIKDGPbchNsSlLZZrrkXun38pCqZMJiN69uyNDz54Bw899BjOO+982csuXfoPrFYrunV70uO3++57ED/88C0WL/4LV17ZDGlptXD48CEcOnQAl17aSPY2KEABtHNiCyki/9jsAnaczkfT81MRFxP+LYzCfw+IiCi8hFGVqk6dbsJllzXG+PE/KFru+PFjSElJQXp6usdvsbGxqFOnLo4fPwoAeOihR9G06RXo0eMxPPRQFwwe/Bbmzv0dZrNZlX0gimph9LyJBt/9dwTPTd2KwQsioxMQlkgREVHoGAzlJUNhULWvUq9efdG/fy/R0iU1JCYm4rPPRuLkyRPYtGkDdu7cjtGjR2DGjKn44YcJSEhICMp2o14AH9jstI/IP79sOAEAWLwvA8PQVOPUBI6BFBERhZbBACQna50K2Vq1aoNrrrkWP/wwGnfe2UXWMhddVB+FhYXIzMxAenptl98sFgtOnTqBNm2ucplet2491K1bD1263I8ePZ5Bt25d8c8/f+Puu+9VbV+IIk3Sp0NhKChA0UfDtE4KRSFW7SMiIvLhxRf74r//VmDHjm2y5u/U6RbExMTg119/8fhtzpyZKCkpQefOt0suf+GFdZCQkIDSUvY4pkcskNIJqxXJnw9H0g/fwHjsqNapoSjEEikiNQgC62H76XR+KWKMBtROidc6KUSSGjZshFtvvQO//TbN47dDhw4gKSnJaYoBl13WGC+91A+jR49AXFwc7rjjbsTExGDFiqUYM+ZbPPbYE7jyymYAgPHjf0BZWSmuvfY6XHDBhSgsLMBvv02D1WrF1Ve3C8n+EYUlpzqWBqk2hVH2bjYePwZ7rfTy6swUdAykiAKUMO57JI34Anmz5sLWuInWyQkrRWYr7h27DgCw7tXrYYiyFx6Fl+eeexH//rvIY3rv3s+7/G0ymbBs2Vo88sjjqFOnLn799Rf89ttU2Gx2NGhwKQYMGOhSXa9167aYNWs6hgwZjJycbKSmVsNllzXBl19+g/r1Lwn2bkWvgNpIsUwqbETRuTLt2om0G9vDdmEdZG/do3VyogIDKaIApQ56AwCQ8uaryJs9T+PUhJdzBVU5iHYBMDGOIp14++33PaZdeGEdLFmy2vF3mzZXYeXKDV7X07FjJ3Ts2MnrPG3aXOXRXoqISKn4BXMBAKbTpzROSfRgGykitdjtWqeAiIiInLGmAwURAymKSPFzZiJx9MjQbpQP64BET+ULIiLSwr/7MtBv5nZkF0foGG1RVI1RLxhIUUSq1vNppHz4Lkzb5fWwRdpg6ElERKHy5p+7sfpIDr5efljrpFCEYCBFEc2Ykx26jflZIjV88X70/W077NGekxTt+09EREHg+W7OLbFokI4QYM2YkGMgRYAgIOmrzxD772KtU6K+MPg4n7n1NNYczcHO0wVaJyX0+MyPGuzlTH08ptrhoSddCoMLM9Je++y1jxC3YB6Sh30EAMg4l69xaoBzBWVYeiATd195PpLjwugSDTAnKCpLpKJwl6ON0VieX2ezWQFwrDA1mc1lAACTKYyek3rC3HsiChCfvgTTiWNaJ8HFM79uwdmCMuw+W4jBdwQ4LlMogxO+lIk8GI0mxMYmoLAwFyaTCQaDvitC2O0G2Gz6jvAFQYDZXIbCwhwkJqY4glUKHX1fIeRLxOZb8jsk5BhIke6eKGcLynNZVx0OvH3TjtP5eHfCBrx/ZxNccUFqwOsjlUXYM9+0ayeE5GTYL75E66TohsFgQPXqacjKOoPs7LNaJ8cno9EIe5gMZZCYmIJq1dK0TgZR2BEYCpNKGEiR7gIpNX3/31EcbpCGV2bvwF+92mudHK8i+DTIEu67b8jMRNqN5deYHqrI6klMTCzOO68erFZ9N/A2GICaNZORk1Ok+/vRZIphSRQRkcYYSJFuqfkhU2y2qbcyUk0kFUiZjrI7XW8MBgNiY+O0ToZXBgOQkJCA2FiL7gMpUgGrQRFRgDTNzvr666/RpEkTl//uuOMOx+9lZWX44IMP0K5dO7Ru3Rp9+/ZFZmamyzpOnTqFnj17omXLlmjfvj0++eQTWK3WUO8K6ZQhpOUcgb2U+U4PczyBRFGDgXYYEXk28/yRWjQvkbrsssswYcIEx98mk8nx76FDh2LZsmUYMWIEUlNT8dFHH6FPnz6YOnUqAMBms+GFF15Aeno6pk6dinPnzuHNN99EbGwsXn311ZDvC+lXSL5x+SEdkLB/sfH8ExFRuBIEJJcVoyg+SeuUhBXNK1ibTCbUrl3b8V9aWnnD2YKCAsycORMDBw5E+/bt0axZMwwdOhSbN2/Gli1bAAArV67EgQMH8Nlnn6Fp06bo1KkT+vfvj8mTJ8NsNmu4V2Em3D9gvTCE0dd5GCWVxDCQIooifGCHM549T9X+9zh2jngEDTOPa52UsKJ5idTRo0fRsWNHxMfHo1WrVhgwYADq1KmDHTt2wGKxoEOHDo55GzZsiDp16mDLli1o1aoVtmzZgsaNGyM9Pd0xT8eOHfH+++/jwIEDuOKKKxSlRQ/fQZVpCGlanLalh2PgTM30BH3fjOLbkHtODQbxebadyscFqfE4LzXyxuBx2V+J/dcrj/NqNHj8RuFFk+cvBZ238xrIueZ1oh2xcyr1DhV7txgQoedP5HvOtG0rhOrVffYmG79wHgDgyc3z8P6tL4bk+HicFx09g+WmQdNAqkWLFhg2bBgaNGiAjIwMfPPNN+jevTv+/PNPZGZmIjY2FtWqVXNZplatWsjIyAAAZGZmugRRABx/V86jRK1a+ukeO6RpSapqAJ6erp9jYDQaVEuPwaDeuqTExcZ43Yavc1q9RpLH8ttP5OGZKVsAAEeG3x1wGvWmwKlQvFatFCTEmrzMrU+O81oj2TFNT/cRKaendwGpx/28JiXFIcnPezUu3vvzPhzZ7QJG/bsfrevXRKfGtbVOjizO57RmzWRA5JzExJg8zlVMrOe0iJDo9j138iRwy/XlExRWewnW8XEOUKS2EU7PYE0DqU6dOjn+ffnll6Nly5a46aabsGDBAiQkJIQ8PVlZBZpXrzIYyi8gsbQYMjJQ/b47UdbtCZT0fVm1bSYWlaHyEzAzs0C19QbKZhf8Tk+629+C4P+65G7LbLEhX2Qb3s6ps7zcYmRmxrpMW7rztOPfgaQ/6aP3ISQloWTAG36vIxhyckoc/87KKkR8jOa1jWVzP6+m3GLUrPgtMyNfH1lqpIjce5XCi/t5rXxmFxebUeznc7WszKqr96UaFu/NwIjF+wEAG167QePUeOd8TmtVTMvJKYLN6ZxUnmer1Y5ct3Nlsdgi7vwBQGKx6/dc7NpNqO70tzfu303BOj7Oz1b3bejpGVyZFl80r9rnrFq1arjkkktw7NgxdOjQARaLBfn5+S6lUllZWahduzynJD09Hdu2bXNZR2WvfpXzKCEI+mmnIpaWpK8+Q8z+fYj58D0U93lZ1W2J/VsPAk2PwfH/huDvm8H7NnxdX2K/C26/+8N48gSSRn0JAOXXTZx+uqB23ie7XdDd9SeH47w5BU6CXXCp6kfhRU/vAlKP6DPWz/McidfI6fwyAIDJboNp6xZYm7UAdD5Wmfv3i9Q58Tzv4fm+8c3pPSQE9n0XiuPj7XyFy/nR1R1SVFSE48ePo3bt2mjWrBliY2OxevVqx++HDh3CqVOn0KpVKwBAq1atsG/fPmRlZTnmWbVqFVJSUtCoUaNQJz/oDOYgDWap06uVn6HqMJSVVv2hs3MdSedYcN4bp+N8Or8UZ/JLRZYgIi0JYVRqnDz4bVR/rCtgC/6YiEMXjkbNzjcgafiQoG+LVKbCOz60w8aEP00DqU8++QTr1q3DiRMnsGnTJvTp0wdGoxH33HMPUlNT8eCDD2L48OFYs2YNduzYgUGDBqF169aOQKpjx45o1KgR3njjDezZswcrVqzAiBEj0L17d8TpKNed/KPGrRxOvfaJEcI8/VGr4ryVWmy4d+w6dBm7DlY7zyUR+Sfpu68R9+9ixK5aGfRtPbp9EQAgecTnQd8WUbjTtGrfmTNn8OqrryI3NxdpaWlo27Ytpk+f7ugCfdCgQTAajejXrx/MZjM6duyIwYMHO5Y3mUz4/vvv8f777+PRRx9FYmIiHnjgAfTr10+rXQp7vaZvxWf3XYmUeF3V+gxYGGU8UrgyeJZI5ZZUlSKXWW2IiYus+4qIQswSpJopUSZi8yj5sRNymr7Vv/rqK6+/x8fHY/DgwS7Bk7u6deti7NixaidNn0Jwf2w4nocpG0+gZ4dLgr+xSBPgAyxSn+tRQySQiiSG3BzEblwPc6ebgRgGhBQBAnhkR94dThEhAt89eqerNlKkEbcbr8gc/DrYcoRbtTbd1rcPk+MYHqn0IsIDqRr33Ynq3R5C4rdfa50UIiLZxN7NqjyhbTZUv/cOpPbrpcbaKEwxkKIIFz4ftCEJw3QW7OksOYGJ8EAqZvcuAEDCrBkap4RIe+GW0SdHJD2OQyFm4wbErVmFhKmTtU5KlTB4qYZBEhVhIEW6/egzhNvdFm7p1VheiQVFZVWlnzq9DP0TUTvjhtc5EYU5VTqzsuuj9o6LSH736BQruoeTKPuAUSPHL5yOWDQ9/kotNnT+drXvGcNJhJdIVdJtFVYiIjF8ZlEQsUQqnETwx1lECIeHtU6uoTMFZVonQX1REkgR6ZkgCFi0NwNHs4u1TkqQ8NmiG3p8zofDd0iEYSBF+nwYUNQRwv0DIVoCKb6oSceWHcjCoLm78dCEDX6vw7R/H2LXrFIxVaQpsedxpD6jI3W/dIyBVDjhB4zfQnLowuH86CSN+kiFd3vPFqLLmLVYuPuc4mUjemR4nVxDRGJ2nCkIeB1p112FGvfeAeORwyqkiILKz8BBlSd0hD4LDRH8+goGBlIE90dKJGVoGMJ8Z8I8+Yo47+t3/x3B18u1/YgZNG83zhSU4d35e+QtwBIpovDi41qOObhf8jftbvHg3X8Re2sHa8ci+TlPsrGzCSIKOW89MhabbfhxzTEAQPer6iItKS5UyXJhttqVLRA1gZTWCSCiYIjkxxZRsLBEijxEUq5USKtY6fXAhdnL0e70NrfYwijxzqc/kr9I9HqdE4VQBN/hUSGSH9EUWgykwkmIPmD4gIlgYfYRrOWgl4oPVdSUSIXXNURE8kTsrS2yYxH8hA6YEMLrIO6fv5HWvDFil/4buo2qjIEURfRHX2WjSUMo6iNF7FsotCLiMEbwPUUUOfx/2GiXyRM+zxbjmdNIGPc9DAX5WieFFAh2ZxPOd131bg/BdPYMajxyf3A3GkQMpKKY8egRoKQk7Dtk0I9IiABCQ+xICaEMemVQmgoBLJGiyGM6uB8JUyYBNpvWSaEwU/3+u5A66A2kvP6K1kkhChp2NhGlYrZsQs3bboTt4ktQ2r2H1skJunD99lP9czzMPvC1TK3ibbtU7VMzJXoTpjcT+SWtfdvyf9jtKH3if9omRkci+hZXScyhgwCAuEV/aZwSEWq8C8PsfUrBwRKpKBX/xxwAgOnoEY/fIvHREJLnXbhGaxrwdqjC9jA6J9yusMe/AGnZloyiQ8yGdVongYhIdxhIkW6p8WkYTgOjCmGU1mAQ238t44OA4jmRhAeryuKBjCLc/t0aTN98Kijr9xC2kS4REZG6GEhFK+cPvSjIzQ7Jtx8/MFXh0ot4uAaXIvdUsPZlyN/7kFNiwWf/HgjK+j3wOiciJYL5jeHnusP0zRIS4ZQBrQcMpMJJlH3AqLG37EgjfOjuVAVwv4XyRWQP9YGLsucQkb7w/lNMrPtzvb1vdChu7h+ocVdnGI8d1TopusZAinRLzedcfqlVxbUFh156q9OTsH3ZhTDhYXuMiLTmI1NA0GWmAW94Co3qzzyB2A3rkDqgn9ZJ0TUGUsQvMbUE+NINWjU2nt/Q8FFdNmJOgy4/LolCa/upfHywcC+yisxaJ4VIVYJbpq4hL1ebhIQJBlJEOhZNvbFF1J6GskQqZFuq2B4DKSLklVoxd+dZfPz3Pq2Ton86fGZE1PuGNMVAKowE7QPG7aMvkj7eQ9poUocvi3AXtpdi2CZcBl7nsph2bEfykPdhyM/TOinqiMLzLqeN7dGckoC2EffnHMT9OSegdeie1s/D6Lt0A+Lx3RTBrzM1MJCKVlo/2CKRyh8ahoJ8XLloNmqU5Ae4IueBYvVx3kUPlT6S5qB29+cRIwo/qP2RdvN1SBr1JZLff0frpKgjEq9pja9lQ2EBqj/bA9Wf7QEUFSle/nBWMR79aQMW780IQuqkHcgswpC/9uF0fmlIt6t3SUM/RNKXn2qdDAoxBlIU2cL43Z/6ch/cPuIdjJv5kdZJUZ3cjjXC6vRpNKRAyEuQGUgpErN9m9ZJIClaB4fFVaVZhjLlQcl78/fgUFYx3pq7W81U+dTjl034fccZvPH7rpBuV02qnHqnlRhPn0LyiM+RPHwIUBphASYf+V4xkIpWIf4YMpw7B+OZ04qW0fodp5TaVS/jK6p7XHUywJdkuB1IJ1pWMw3odEZwG6lQ2Xk6Hw/9uB4rD2VpnZTAhPH9F+303h6w2GLTZLsWW/k1vT9TeSmafqh8XzoHT7znowoDqWgV5NzzmHVrYTh7tvwPmw3pzRqhVosmQElg9ckpMoh9n4j1Whi2ryP22hewfrN24GhOCV6ZvTMk2wuaSDnxwTjvZWVIfncgYpf8o/66QyTUmT3BHBvRoPPAUXei5XhFyCMsWBhIkepi16xCzXtuRXrzywAApsOHHL8ZM0NblzukouWhGkrh+gCPlI9nMSG6zovK9D/2mxwGu13rJKgjCNd04vgxSPrhW9R49AHV1y2LCtdyQEdFZ8+JSOpoSi0nckuw7miO+I9SxyvMj2Mwg3Ug8gJ2BlKk+k0fu2KZy9+mPU5V00KdexdGX+JqHRq7IGDryTyUaFTtw1+C2/+HtTB/kXqn7Usw7D72wi29IWQ6flTrJOiHzI9LPVU31DIlKw9l4+YvlmLXaf96xZR7Vz4wfj16/7YdO0776PRJR+dFdRG8a2pgIBVOIvlGJdVM23wKz03din4zt2udlKjiErSLVe2LjBBRU8VmG7r+uB7DF+/XOinRJwrfP8HOmSf/vTxrBw5lFOGV2TI6vFDh2t15ukD+zGF+3egpWA8HDKQotCKoPrnnxgJcXKVn1+yt5Z16bDlZkYOmw4e63F3VMuWBnI5QloSG/PRWXKgJP41HyuuvBCUBpl07cfP+tR7TF+w+ixO5pZi5VVnHNZrS4f0XrZbsz8TP645XTVCjal8End5wrHKlVYcbHsLw2IWKoSAfKW+9hph1ns/0SMBAioL/JvBzHCPm4EcXsUsjbK+Bip0JRepDfowqbufUN15B4s/jEbv0X9U3kXZje4yd+RGan3YteQrPj9awTLQiNruAjMIyrZPh0+u/78LoFYdlzx/SnHkdXNxhV202AEp31eelEEXHTqnkoR8icfwY1LznVq2TEhQMpKJVKG/6aHnABPjSDdphCuOcMn+PScKUSajZoS2Mhw6qmyC5xILCCL0NjPn+tVFwYTaj+r13IPnjD1wmN848Fvi6tRbCE7/3bCEGzNmJgyHulvq133firh/WYu0RiUb55LeUl3uXl/xWYHVDP4i8A5UcxVan9qLVjB8BPXQcY7cjbvFfMGQEr+Muta8x0/7IrorNQCqchPEHsUMUvQRMO3cgduXykG/XawmFjo+/WMr8TW3qy70Rc2A/Ut94NZAkKSMypEAE3LEe4pYtQcpbr6m6zvi5vyNuzSokjfzC63z6vXq9COE99/Svm7H8YBZ6/xba9pErD2UDAKZuPhnS7eqBWme3+kP3IfnD91ymGc+eQeKUSUj8ebxKW/EuHKv2ySLzHiw227BkfyZK3aoLzpk0ANeN+wzx03+VXm+Ijl3ClEmo/vjDSOt4lV/LG08cR+KoL2HIyVY5ZdGLgVSYWHYgE3vPKmjs6EP8vD9UW5cHHX2sh/S14PYgTbupA2p0vQfGo0dCmQrND79p9y4kffEJUORfrria6TeUaTTCfCir9qm4kembT+KeMWtxLMf7eG+J48eot1EAMJt9zvLV0oP49J8D6m43FEJ4Q1YOlJpV5Pt4EnSVORm7YxuSRo9wnWiNjCEAgknNM/j2vN14449dGCbRmU3Mvr0qbs0/cX8vAAAYc/wr/a1xz21IGfI+Ul/uo2ayohoDqTDx2u+7sOWEClVoUD7Ok+mYU7ezbi961d/7Wn/Zh4z4I93kJZDyViddtfr5bttIees11LyxQ9AGR07rdC2SP/kYyZ98HJT1h0pAubOivfbp32f/HsTZgjJ8sUQfAYtzpx1TNoZpaUeYPP8EQcDOMwUeufERLUzOjV7pKA5VTOzdW1myOn/XOXkrkToAOr6uTKfKn6Oxy5dqm5AIwkAqjAgq5b2Ydu5wW3GAN72S5SO51z61BektlTh+DGJ27UD8n3OCsv5KMVs3S/4mGqSI9zYRnkJYtS8Yh8hmD3UHFmH8ReaLHp5BMtIwY8tpPDV5M16evcPnvGqJiG6WAzi/Pnv3jITjowdBypTUompfKITT+Jt6wEAqKql3kyRMnohaTRsgZvPGqokqPVD08P2hiE5yp+RuLTsMetmKll77Zm07jWd/3YLcEosf21K+iM9Vhulh1yWND2a1Z55EjbtuAWzeS5pmbTsFANh4XJ2aD3KEdUaXRiIi+CSKIAykIlRBqRWP/bwBY1cFd+T41Ff6wJidjWo9n5Y1P3M69GP29jNaJ8GFaGcTVpvjA9BstcMa6pISJZyTprB0bdii/dh2Kh8/rvHRS50gIGn4R4hbON+vJOpBwvgxsrtMV6sUXlNSwUJxMYyHDwV98/Fzf0fsxg2I2bEt6NsKO2EelERTd+VSjKH8pgjz68WZt0yMiHjuhhADqQg1ddNJHMwsxpjVwQ2k5PHvQRdBzyz/BPkAlFp10JWrFwbBjjb3dkJau9YoK7Pg5m9W4YFx67ROljx+Vu3zNbhk3IJ5SP7yM1Tv8ZifCZMnWJ8mMWtWI/Wt11DjkftlzR8RGS8SHyxp11+DWu1auZbmU8SxCwLs/gY8Iu+AYJbiRewrV+Q4Njq2B0nDPgSKizVIEEUSBlJhRG6Rvs0uKAygVHgwext0V6RbaDnUeF8E8iEW+98KmJT00qN21T4/A6lg5FKadu1E9YfvQ8zG9aqsz9ueVVbnSy0rRuLRQzAdO4JTe4+gzGrHmQL9V0cE4H+vfT4WMJ4+VfVHRU93mld/VHC9mU5EwLhQKjEdLz8W8fP+1Dgl2omEamrern5BEPD0lC14YtIm0WBKzZx/Q0E+Ytau0bwqabj4esSLSP7qcySN+NzrfPEWp3eOzGMbERlAJBsDqQh03EfXxf5IfvsNpL74jOSDxNsLMRzrwZsO7EeNB+5GWserg7od5yNjswtYeSirqp1MsD8yFKy++iP3I27ZEtS885agJcf9MnG+bmIzM1C7MIzGvRD9aPJ9HygJitIbXwxDQb6iZMkVfnesfoXL8+9gZuA582GyqyFTZLZh15kC7M8oQmahZ5f0/n5wC4KAwjLXrtFr3H4Tana5DfHTpvi1Tn/IfoXo+MKI2bNL8jfj2TPY++WD8lbk/L62+NHWVUcYCCrDQCoC+bwFvJUYSSyfNPZ7JMz6Dab9+/xIkHY3pb8fMYpKohwbCyzwmb75FF6ZvRP/myzd213AXI6H/NvfdO6s8m15C64VHqrWj92B9d/0QKtTe0N2PSk+m07pqnwRqR4KOx04Q3ER4v/8XZXDkVlYhqPZAXxI++jIwKcIKJmQpJePSB0eYz0EmYk/fIPYNaskfg3smAVr996auxs3jV6F47lV4+TFHCgf+yhh1ozgbFSn3M+Q39UoRcTP9O9Ypje5BLGr/3OZZsjOgumg+PhUFN4YSEWgoFb1kRogUO5LWvv3pjzG0N8ayw5mAQBO5VW8HIP84aO37yo5l8acSQMQN3+uovVabXaMW30U208Fp/RGVIgG5E19ubcq27jzh7V4aMKGqglKqusdOohal9VH0vAhKqTE1efzR6DtCekc47Dg61D6cyNaLLIGMfZIiiDg9d934rNwHNg4COL+XoCUd99CjXvv8Hsdcm8V2ae5zHf15X/2ZaqwochTYrHhgXHr8P7CEA2c6+Xkp7za1+Xv9MsbIK1924A7mPF5vekgcyLaMJCKQD7vo0AetNHykDb5cWuofGxsfj4P5S6m5ZkM5Fmf8Ns0RfPP2HoaP6w6imd+3eL/RpXS8YC8xiOHkfL6KzAeOig5j5K0Jg8fAmNhAZK//DTwxImYOfmNoKzXXYnFhikbT+B4ICVzIorNVnUHuRUEpLVthlrNGklnbEnYe64QSw9kYfqWU75ndheBz37TIV8ftXKq43r7Tfldnzh+TNUf/hzzEJ6ngAYtD4LVh7NxKr8M83YqqUGh0j7IPBax69cGtBn3Kp1aWn0kGwcyi6om2O3ltZaiLJhjIBWBFOdYBDuHQ0lnE4WFgW1LLf6USKkdSPnZ1bfHIZY45lLJPZBZhP8Oh1F7JB8OOT/oQ6WyRErhKQxqJgiAxFFfodY1LZH483jUeLCL/+mIQF8vP4wvlxzCnSNXqLrevOIyjFimYjfnxcUwnTkNY26ua+cjMgQ0fEAQLgrNO5sI8vZdxmv1NUMFoz/VqJ1XGcJj6q1jo+UVNSzCmq9r3lsnW0HiszOpYJx/kU0eyCxCv5k70O3nql5HU94cgLTrrkLSyC/UT4OOMZCKQJr04uV887rfyDIfMPHTpqD2pXXwzPrf1UuWn8sJBhVvDT8fsEF/IUqsv9vPG/HyrB04kBHaAETwM/jwuV4/lwvo8EvsxJ6zBfhp7TFYbDK7nldY4uBLypDBjn+bTp5Qdd2ySBxU49nAPh7VsP5YDoDg5PguPSD9URnU+zwaI2IlQhnHyTzPf+w4g3OB9E6qdXBaYcCcnVonwUmQjom3+0vqtwDPj8/nhex7PrBng1gGZeLP4wEgKNW89YyBVBiRfXsovD98NviVs0K5OTNefqvW90UAwHv/jvW9vWALUYmUnr9zDmVpUJLjRK2eg0I2aKWPkldBAJ78ZTO+WXkE0zaLlyQ4L5X0ycdIb3AhTLu9txEKq0E5JdKa8tF7fq/SkJ+HGnfejMTvRvu9DiB496JBAIxeHg1B7XDBpUjEEPS4QWkmXjD2PdS3g9r3X16pFd/+d6T8D51X7VNCEASczCsJr+eVEqE67locv0B3TZ+XpGoYSIW74mLEz5gKQ3ZVjqfuHlOaPjj93LYGnU2483eMEdl7rNMXbkRwdDYhfjYOZPiowlpcjOQvPoGhrAzJAQQZ/lL1jg3y/Z/43WjEbtyAlMGDyifY7aqX5H278jB+WuvfGFgGVUcLCpDBAJPdhpsOrochR2H13ah8XgRYeuDnMmYvg6XP2X7G+woCOE9Kl1TSRuq7/47g/nHr8aOf95FkGvT3xeOdiveRIAjoO3O7a+mfHu/TMDtFSmn/tUh+GbvqKApKrUh5+w1U690T1bs5jXXgqwqtjw8bjxwjpSVS7uwyqzHpiZoPI7+r9qmXBDF6fN6qTTAY8McO/6qNBZR/H2DwkDD7N/EfRE5aUN5RagU/JSWoed1VSO3zgjrrE2EoLXX5u8ZdtyCtbTO/erUTcya/FBPWHsc3K4/41W7R5/M2xFX7nls/GxN++wA17r414HUFKhj7rmiVoeyYQe72faRpibce+2Qsr5ZmZw7g518GImbLJlnzT1h7HADw/X9HVU2HrGDO2zwhOF4uQ0uouL1zhWasOZKD5QezUGSuyDwKRsZVhAdCgWIgFabGrD6Kz/494Pjgit1c9TBTvY2UW/UQ1dalZ84lUjLTrPZHgd/52LJHX6+y4Vguek7dErLqfGpcBXIazucWq/MxrVigvfapXKISanO2nUaPXzah7I8/EXNgPxKm/1r+QzA+WtzWGbtpI0ynTyFmt7I2GlLnp9SpdEDuOXQel8gAQbvezZyuw8rnyT27yzvTqBx3KJqV+ds1qlx+duQUDq/JGZPfxLVHtykPyINIk/bhMrgMLaEiXVWTDMcMc5XoJpAaM2YMmjRpgo8//tgxraysDB988AHatWuH1q1bo2/fvsjMdM2NOXXqFHr27ImWLVuiffv2+OSTT2AN848QKe4f6tvcxsWpbKCqONNUjZsx0oo3/AikwoLLvlSds14ztmHzyXy8/rt2Y/bIbyIrYP2xHFw3YgVmuHXjbDh7FonfV7WVKSgN4bPApY2U5yRnq4/kiK9CbKIG91agV/zHi/Zj99lC/L3nnNuKg3AvqXR8Lju5HxOnvYsrz7iOseSydpnpT37/bZflvbWRUoUgIOXVvnhz6U++Z/X3eEXaMx5AicXXx19g16vzh72ywxfAdkN0nhKt5d8bBoslJNsDgLjFfyG1zwswFBaEbJtBYbcjtedTSPpsmOJFfV4ZKpx/g/tGRFbp/Cg0HdiPWpdfEvB2w5UuAqlt27Zh6tSpaNKkicv0oUOHYsmSJRgxYgQmTZqEc+fOoU+fPo7fbTYbXnjhBVgsFkydOhXDhw/H7NmzMWrUqFDvgi70m7UdgB+PYDU6m5C7vIJ1Sc2a8POPSBz1lax1OFerscrtKQ2AYDRV/SE3p8VggCFLWZev3o6GFr32ZRWJlODoLJA0FBXh6Ovv4NKMY/jUbWDRGo/ch5T3Bjn+znIrkdp5piA0wZWPY5ZdbMGJ3BLFy4ldizo7PS7MocikVOk++fqHV3DDkc2Y9cvrbqtX4cPE7x99M+3bC9POHUj85Wf0WutWJdTt4jAggJLuCKTjWweAv+cqcs9v9ccfRsL0X5H01ed+r6Ne7hkkl6lU1c6fh6/BgNj/ViBhziwk+xFI+Ty/GrwQkt97C8bc3JBvVy80D6SKiorw+uuvY8iQIahevbpjekFBAWbOnImBAweiffv2aNasGYYOHYrNmzdjy5YtAICVK1fiwIED+Oyzz9C0aVN06tQJ/fv3x+TJk2FWqX68/lXdVAczKx4OAd5Ifi2tQq99cqW+/jJShgyG8egR0d/zS8VzyB5zGu/AJ+csZJmBVOKUSUhv2gAJEycEv8i9tBSJI7+Aacd2j58C6WxC7J3iPkK7bApfUO6HTKptSfzSf9FrySQsHv+Sx28xPnq4e2ryZjw0Yb28BKnd/bnbtJO5pZ7z+JDw6y+eq1W8ltDxOIRByByIm/eHKutJMpcHtvE2eTnsxlMnkfTpUBjPijT+dzrXBkEIWilB3JJ/kNbxaqTdfJ3sZYLZ9jKYj73pm0+h72/bVR3cOJRjLsndkuCz+wRfjaBDH0gZCguQ/O5biNko/mw1FhXirj0rVdue8dRJv5YzHTqAlT88h/XfPClvASXtxeVe/AYDDKUimWiyiW9HcZteJTernl8yOqB5IPXhhx+iU6dO6NChg8v0HTt2wGKxuExv2LAh6tSp4wiktmzZgsaNGyM9Pd0xT8eOHVFYWIgDB1xzquUwGPTxn3talFzFBgM8ntge6/dxAxncl3GrnlBisWHFoSyY3Up43PfBMc1teW/77S3d7vMZi4tEfx80d7fofh3NKZF/HkxVt4ZBsCtKd8rAAXj9j6oP+sr9l7t85bwepetOyyR9MwIpH3+AtJuv81ife5M29/Mhtr6qtBo8fk+cPFEyHb72x9/zrYTS5bKLLfKuARn7IbV952vGeR0u6TZ6P2bOEyqnxW7d7Ln/MtLp65i7E0TWJceIpQcxcf1x0cSJPhsk9l8snVJpjzl4QHw+ufe6jOtWbJ3VH+uK5M+Ho9pTj3tflyB4PlNd7jll6XRe1tH2TET5Nez67DUaAMHptS9n/8XSWWS2qrMvXo65+3+f/XsAa47mYObW036dRzGCyPPO9Ro1eF1/+TrknSvx/Zd+FxsMgMGfOqFGzzS7/5f80Xuo/mAXGKyuz0Kxb3FfxxgAkocPQdIP36DmnbdIzvPt78NxUe4Z6WOh4LqX803kPL/ZVv48jlu+FACQZHEap8vHteM60S1NTudH9L6XWAdkfhPJub6dNlj+t1ua3K9BsX30do9Izuu8XpFvSsl5fV3zBvHtafWfHDHyZguOefPmYdeuXfjtN88eqjIzMxEbG4tq1aq5TK9VqxYyMjIc8zgHUQAcf1fOo0StWqmKlwkW57TYJRo9mUxGjwdtenoqqhVbPaa5SElw+TMpKc7l78TEONdlnEr3aqal4OWF+7B+xzFMWzMGdSumx5iMVcskV60/PT3VZXs1ayYD7unxwiPtTqTWtfZoruPf7veBt/W5SKuaLz0tGUhKkrdcxTaXOQ3AWa1a+bJi15dUdcP09FS41wJ3SfvuHVXTvxsBPPEE0KABgPLrwmWZs0muf1cwxpg8jkflgyPOasGFmSeRnt5QNG1yxMV6rr+SOcbz0VOzZjLS05IQJ1GiKEX2OVW4jMdxlOA4r+eSHdNqVE8E0lNR5DSwc1paisty1asleaw3PiHW457ZcboAbWqlQKqKmdFUNV3psZCaP0bk2pBj8kbXnOLYuFjXbaUm+ty22HR/ptWsoexZ48z5Xi1wCjzSaqUgPqai2u+e8gyb2I0bPNMSU1U12AABsTFGyf1NSopHkpJ0JlalJ3bdGsnZ0tNTgbKqD8aaNZNRo0YyLAa3eaSWdZOQEIuE9FRMXH0E7/2+E8NvuQiP1bICbdpUpScuBunVK67d2FiPdXjuS9W7R/b15vRcMVvteGHSBlx1SRp639SofJX+rFNi3urVk3xeQ0ajQXo7hVXHPy0tBbVS4l1/L0uBmLi4mPJ12opFf/cmPj4W8b72++sRAID0tcuB++93TE5JjveYVc4xTDxU1XGJt/nPK8zG8RoX+PVscZYQV/X+cH4sJifHAyj2SMees4VonZYCo9u3DwDEx8WIHq/09FTA7XgkJca53qvVqp5naW7vd5NJ/J6vlpoIpIi/k+XIdnq2OL9TatVKRUp8DJBUleb09FSguvi2DAq2HSvyLk9NrWqjHxfn+j73XHfVd056YZZkgOK8jJ6+x33RLJA6ffo0Pv74Y/z444+Ij/e8ebWQlVWgeXsDg6H8AnJOi1SXuzabHYLdtRZ1ZmYBcnNdH76Zma6f5AlFZXB+fBcXl8E5TCgpMbsuU1aGynA1J6cIy/Zl4JW1s9B89SLHLFa7gNyKZZKc1peZWYD4ghKkOi1vyxRvKJru9rcgeKYdglCVltxiyXVJ8VifhJi8YtSo+HfWuVwIKdLVSTzT7Xq+8vOLUQ3i15dUz3OZmQWIcwsDndOeWmaB46557z3YR4xA9t4jAMqvC+dlTDlFqOn0d2V67Va7x/GoTN/0KW+g1Rf7kT9pKlyzMnwfw8r1my025EvMm53vWa0tM6sQJ8/m4/xUZc+D1btPY82RHDzWpi4ulLmMr33ILbbAanU9ju7c71VTdqHjOOflFsGSWYDsnKoqHFnZrmNH5ecXe6y3tNRSfs8UlTnumewSC3YezsQF1RI8rjUAsPlIpzP35aXmt1hsHr+JbdsXi1vanJ8FmaezcaTAgqRYE65wS5OcdFZOS3ebVvV8kH7WiHFeT1ZWAZCdDSG1GnILqgL7zMxCxMcYRbfrrLrFisowwiAIsNsFyeNZXFyGYiXPsaIiWeciM7PA9dmdW4zcmCIkOT1XpM6/8/TK5UtLLSjMLMB7v5f3hnhT15uAwmzkTZsFoDx4sZaZYat/MQwWM0r6vYr4mTOQ99scCDXTcDqvFPszi3D9pWmOTIHkEjMSRbbpTUlx1ftp4e5zWLI3A0v2ZuDR5ueXp7OkKuOvcr7Ke9WbynnjFsxzPPPy8oph8ZEusXNbybnNaU52EYRS1yYHxuxCpLktIxiAsjIrMjMLYMgqRC2PtXrPJi8z21AgkZ4Sc/l77KKKv/Oz8mF2mrewqMxjGTnnpbTMisoQxdv8laURcs+1u8prcd+pPKB++b+d36lFTul3fhYIBgMyMguQXFgK99C1zGwVPV6ZmQVILCpDstO04lKLy70am1eMygYp2W7n0mYXkCOy3vyCUgh2o2M5pcfCZq36Fsl2eqdkZRWgNC4GiUWljjRnZha4pNHlmADIkvmMt1htyHObt6Cg6h1uLrPAOTvefd3VyyyO52F2Zr7kd3ZmZoHoN7BW5Dw3AA0DqZ07dyIrKwtdu3Z1TLPZbFi/fj0mT56M8ePHw2KxID8/36VUKisrC7Vr1wZQXvq0bds2l/VW9upXOY8SgqCfhtvOaVHSC58geJZgeeyTx9e8a5AguM/itL7K6bWKc13XYTA4fnPvW8Llb7ugrGquR9ql161ofT7aLQhOJQmCzR7QdSFUHD+x9EqtV6isLyJjXgAwZmdXHX+3ZaT6+jAYRNolVWyy1enyHMZ4t2p9vtIhd16x6R8u3IvNJ/PxdLuLPH/0otvP5V3/2+3AAPftSHx0eNuHHafz8fSULT7nt7qdV7Hr3Fs/K3a757QSix0Wqx3xIvewnOPu9/0gMl3tZ6H7OtPr1ELfx4Zi9cUtcMRHmvyaFsA+GA4cQNq1bWBp0xbHp853TLdLPL+8bcfgYx6P560XxkMHkfzFJ7LmFQR4PLt9vQq8TXdP5/mF5QP6xs39E6hTPo5hfGkxTGdOAwCSB5f3XJg4agSK3vsQXcauAwB8ft8V6NQo3bFOX2nxlr7KwMB5mqJ1Oj0eKuet1qOby7Sq95qASetPiKZFznEUO8+Sz6fKef25fp3exc5sdgHXj/oPABz3m3uaxFIj57ycLTTjYgXzB/ps2XtWPPjw1a+V+HbFj5foeXW7/71tz+t97VQRJZBntuvzzuBxzfh6B8netoJvF/ffU/q/5DIchNxmZnr6HvdFszZS1157Lf7880/MmTPH8V+zZs3QpUsXx79jY2OxevVqxzKHDh3CqVOn0KpVKwBAq1atsG/fPmQ59ZS2atUqpKSkoFGjRqHepeCpuJrEHrpijWWVXnzGjHO+Z6okt9KoFAWJEx0TQoU7K2bzRtS64lLEizTcd2zGKZAK6vgIXvYn6M+QQM+lyjafLK8qUDlwo1K7zxb6nkmG6ZtP+Zxn2YEstP9yBWZt8vywAiDrOnVvYwgAS/Zn4sEfPRtse+s5Ts/vGl8dG3y6YGRoEqJQ/KwZAMrHpFKD18Og4D6s8dC9SJgxVf6G3a5DA8TfGUolmp1KlJ3WJ9pBjFvD+k0n8gLevhbWH8vF1ysOK1rG33szkM6KpM6vY8BWFRjtrpmv/gxULcZ0cD9qXncV4qdN8Tqf0ivYZLeLZk4CwIm8Enz33xGFa5RB4hym9noO1f7XTfQ3WSTOr6wxtLxcV8HqUj7R/TsrXKIjBTQLpFJSUtC4cWOX/5KSklCjRg00btwYqampePDBBzF8+HCsWbMGO3bswKBBg9C6dWtHINWxY0c0atQIb7zxBvbs2YMVK1ZgxIgR6N69O+Li4rwnIIwovex8zi+WNa5kflEiWXtylhcE9Qcfddqe2Iu9Ws+nYczKQrX+nr2+ORgDCKSkinlCRO5LWCxVHtMk1mXIzwuLB6J7w9Ykcwkuyfavxydnr/2+EwKAV6dvFZ/BkfnhMcmnU/llHteMtysoOKdBnZX66jpcqmdGlTbu/6IqpssgCDCq9AwwnZCRyeAj7Wp0f778h+eq/vARSKl9jqUO5ZGsYsU9+ik5FucKPau9KSG6pQCOTd//fsX8CX2RUqa8HVWg2pza4/K3Wr0fprzaDzH796Fa3xdlL2N0K/UT0/r0Xsljve9cEX5ccyxkg9EbbDYYgpg561+X6kDykPdd/k6wOGeWBJAgEfrKwg2c5r32eTNo0CDceOON6NevH5544gmkp6fj66+/dvxuMpnw/fffw2g04tFHH8Xrr7+O+++/H/369dMw1dpTPLq3oPym9tVBq8u8XsqWqz3xCGo1aySaGyLanafCF0/tohzULHbLAZWzDpdAKsAPAT9flmq8nP7dl4EP/9ornhYZq4//a4HHtJhNG5De6CKkvvR8wOnzIAgwVFyPwXjYLhvzPJaOfQExIr3fVfL7sPuqVxLAdkJdeKjap69B168YxfyqaQVBuy8HjxIpgyrdn9d2rtrtnIemPEmKufcsWunhnzbgf5Ol72vRtKhwY6mdFeAS3Immr3yLA1ZOxhXnDqPHprmuP0uVWKiYUJNbICD3mvIVVBtKghgU+ghenKuJqqHEYpNsAx2QIGU+xWx3bSbT6fCmoGxHbzVh1KBpr33uJk2a5PJ3fHw8Bg8ejMGDB0suU7duXYwdOzbYSdOU1/tGhap9siu7+qO4GKkv95b8OX7RXwCAuMV/e25WhVfUoKUTMGjpBDR44w/X6nq+OB/XYFbtC7I3/9yNyzIkiuz9/MhNqujxKWHmdBR8N056RrdrM37GVJhOHEfxK6+Lzy8ImD3pNcTbzLj7KeVVvuQ8n2sX5QIA4hbOh7Vla/H1iEwznjgOIS4ewnnnyUuMnEBK9td1+L54fH9gBbFEKpDnlsSy/qyysvtzZ4eziqG8Fa86VB+Q19eN53bQ1Dzj7vfQoaxiXN/Qs3sGufwZo8prDYAAP3qNFe3NvIlzH/dMx48Lo6/jIfcjW6qmhLdFfK1TbNsKzt/JvFKXjkEyi8z4c8spdGtTV3KZQDmnTt1xpAS36hRicwhIKSsu/8mfBo4RJLKyCyNU5WXnnnumWmCvRq6JRGIS5sx0nRDoTeTn8jEudbqVHTiDHyV2alD9gyfU3Esfe/dE8rCPELNdvDpcsrkErU/vxRXnDuOCgizRebSQWlaEWm2uRHozBe0uK6v2OR0D9ytXjYwCsfWqsk6VVhrmVzAA10eb45z5zHxyWh6ej8e959Rpz+eTSDr9LoWRKulwrton8vvJ3BKcyK1qJxXoYOVqZmi7p2TEskPqrdxt/UrSXXmIkkd87nNeg9tO5JbIryrvb7VLf6+hab++hS/mfenXss6kasT4szeVywR6Wb0oUs175cFgvMec3ilqPfyLPKs1+qp1ZLSYsWPEI9g54hFAYhiXaMFASqcsNjve+nMXZm077XjxuD/0lPS45I2gRhspqQerTWYOn9xEu1RNk//oc3/ZKNqO3H2Qsy4liyl8WVVeJ3K3JtZ+xVebFjUsXr0v6NtQU728s8oXkhF8K7ks/DkrxjOnYTxy2I8l1eO7jVSIEhIA55xeQQBitm5GWvPG8pcX7B5tpNQKokW3595tl+OH8jT4HUhJXLAFZU4954n8vvpwNh4Y79mBipiYzRsRu2qlP6lzKFZQRcs9s+rvPdLjTyrO8ZfBIPIBKxiczqCsh4TrPBtP5EvMpw8P7vhX+keZ16ZfAaCPdfsz9nFICALip05GzJbyqnbSPT3KOCYixy1mzWrUbnAhYtevdZnucoxFNhmfk131c5GCjioisGofAymdmrvzLBbvy8SwRft9z+xG6Ut69eFs3zNVkpErqccbRUmbLg9BrNrnd6pEHohSBYtS+x70syRxHcze7ru6igGCKo3UJUv1vK3by/Xbc9pWyUGUnb0zdzcOZnpvvCy7FouPGaVy+Gu1aIJa17SEIS9X3oac7DxTgK0nw7N3NYcgPYdSez4N0zkfwbWvDh9Efjbk5yHuzzlASYnnj2oJwiGxO7dZCvCerXn7Tahx/10wZGYibv5cpLVqipg1q30v6GTGFt+9blZSUsvD13u18j5MebUvUns9VzHN+/bTOrT1nUiFlLyt/A2qRboU8Ws9/qrcWrMzBzDxlzflLeSrfZbbPhiys5D03dcSc4dO7NJ/Ua1fL9S87cagrD956Aei0yXPaFkZat7YAe0/fyco6QlHDKR0Kr+0qnheqmqfFKU19cwWH1UB1OzBKtCcWFltT3zVw1a4TYlAypCfB9PuXZ4/qHS8lK7F7qhOJnOBYL/7FB4HtXp+CqbNx3Ox4pBExoPT/haWWjFs0X63Xvv8vy7+2HEWa44oyPBwYjzuX3fyz03d6rim/OZPOzw91KF37vnT7bI0iJRQl3hpW2OAr1ut/Ndq/3sc1Z/tgZS335CfTh/cn4UGaF9lWFZfP5kZqP7U4zCdOokajz3g8ptB8o/AeaxO5jMpt9iCLmPX4du/diLxl5+RMHM6TG6N95U1I5FZhRQiNVUku8eOHJXX9YzJb6L56arMZn/aQUkdr2o9n4ExU7qEsnxh30c10KqsMXt3S6/baY/9LjE1ij2j3dbltAtxfy9EzK4dqLd2mX/b08PzXWUMpMKA0s4mfD5N3FboqwGoWGDiUSXHpSGBzBtF7W7PvXHpUU3hshKBVNrVLZDW6Vr/0+SD18BC5Dc1xvKQ9SgOwYPQICgPupW8RmLXr0Na6ysQN+9PxeuR1xOT4DNDQ8lh/O6/I+g7c4f8BZwFEKAG3KTRIwpxy/UN0lhxwSKVtBtG/YfcEovobwZB8KjiKLaeuP9WAAASpk4OKI2SvaQ6qvb5u2KJBZ3rRImVtAXa2YTZ7JYM722y1GQ6cRw1r78GCRMneJ1v6uaTOFtQhukbqzItqj/dPUip8lFCrfgH4I8dZ/DK7B0osfhX+0K1TDCFVfsSrQq6pJfqoKKy6YTbpuOWL5G/btH1Vmw2oLWEgGgg5SXVYtXWdfzMDgUGUmHAW26nGKVV++S2vVJF5UMrNwe1Gl/sdaOi6fAzcYGUhI1aekB0ujEnx+91yuE151hB1T4pBpHSAlULhSRWVp4r7ikYOeVS5z1uxVKYTp5w+dgZs+oI+v623WugJPc6cuyJc1M7Pz8o/bpynTu5EASUWTVqDOx+SuXcvzp+KXt7tq47Kv48MEDQTfsLgyGA+0zqvLgENmKVvgIt1QzewXM/Fu4Bb8q7AxGzdw9SX+vvkeN/Ue4Z9Fw7E4llJaJ7aDp21M/OD3wdMddf+62eJqsDB2/X7kd/7cOW3SeQ8d1YVC8JzsCs8gR2rh/o/TAuzBcvRZLa+yvOHS7fspxNe3k2ibX3DMql6/JsV2F9ErUGnL8LC8qsVRm1ge5UGNQ8UYqBVBjIK5XI6ZRq/uH29+f/igcClW49sNbr70ruVptdwB7nHqkklo2fPRNGf0bSVqOzCYX38fL9mYqDWS04qva5TZf8cKo4fnapHGwpAT4IEy1liDt2xPsmIIS8I4Kxq49hzdEcLDuQ6TLd+fjJbQMiNt/94+Q1uC9fgfxjLJoip+0PXXwAHUeuRHaxWWxOh/SiHJxf4LrvAb/zfJRUB3NA3vg/5qiyHudDUJ5cZQdF7Dr2mtml5jERy6AKdffn7tuXsX+5Xq5VNVPvs3TOS3u1v8f3xqClE/DKX2NUTJF/GSfOHTjYJT6MfWW0fT7/K3wybwTGzP7Y5/Z8lTQHm9TW4osL8c6/8ofkAIB4q7lincHZBz3lC6W8OcBzokQuj3M4fyK3BH/sOCO9Yved9DEkQKTFUgykwoCjurTYdBnjSE3bLL/xbaWDmUV4dfYO7DnrGuwUSfWIVJGO33ecwcqDXtpyyMjV1BujYEdhmf/VEC0h6hpUqmqfVC9elYd8wa5zAIAWp/dh0WeP4cHt/wQtjQAwbtZHuPzma3Dl2YNB3Y6/zDb3diXuJbYS17DzsZXxOeR9GA+nXEd/7g2n5befKcDFOaew4/fFkrMbBDs2jH4Sa799ConmUsn5lHL5OBEEefe52IExew8CxSSNCqCbZYmT472WtfS+yanaFxRq9tonYzlBpNqPeyApZ9fFupJ2T0ZmYRmW7M+UnM8zbb63rKTksLJa2VVHqtpCuR/bQNvH+PNe9LeN1B37yjv1aHdcpAqxxYLYZUtEu8kWE5SBaJ14y4BJsnh5fnlZbuRyGV3fezkf7kF55d8BHwn3+yeAb6XEn8c7rahixaJV+zyP8VpHibt+v9W0wkAqDCi9EdV4hPWduR0rDmXjqSlbXB4+H/y1V3yBipt71+kC0elVifP9EeqVBlX7jHZ7QB8+v2+T7qXOe/s3ZduxCwISfvkZff4ep6hkqfIBOfr3T1CrKBdfzP/K62In89TpVazzfh8loSGsXe7y4nc7dk9umu/4t9xTIq9+vDr756sKrAADlo3piQf7PyaxvIBYpw4UahflOP0WYOKMboGUG7ltpGrce3uACVGJ4PgfH/O5BtUeNRy9LGq3CzhboKDth5tQf+a4lNhKzCEpgI/CR37aKN3xi1oUpk+tEtagBNoBrDT5k49R4+H7HFWh3a9o94GM//RWeqEK6X2xSZTISe3+hYVZMAh2rDkiXjVX1kpEODIQgpxrUq00sDHpBKlASupvOQMX6zhjPBgYSIUDkftw+ffP4o5Nf4vP7uvGlXFfZxSW5wC7l3Icyiz2vmAo7x+pNji+qhApzBU3Kmx15r79NRJtJ7wxHjoIo8Ju120CkPpqXzy9cjranNrjc373o+A6aLG0fTIHEz2dV4ql+zNhswswy2ij43cj+ABZ7QIeGLdO8vfHty70mOYrqQGXSKlYtc9rDmZREZKHfYRmZ8Wr/yZO/BFJwz+SnRavAsgsid20MbBtmM0wFIiMryMISBnQH0nuA58q/Ph5a8mPSDonnmFiEETaSLlUNfY8P6uUDEnhjdt+GGBwuR4KShWUtPtZm8BrcCHVAYCM9RfIrCVgPHkCcbNnQrCKPd+8t5EK1HkjP0WMrTydISuElHg6BVJIlDChvLpc3FLxMaASTxxz+fuMvxkBKowjZTOaFG/2IX9rYbhlWPkjdtkS2fMuO5Dp8i698NOPsG3kY7h97yq/tg1AtETKs4OY6AqMlIrROgEkzrWTOc+HRv28s3j3t09hr1bd67KqJ8YHn7ebgm5dA02Ls0AeA0bBHlAVDZtNwIT/DuOmS2ogOc73LXf37hWo9cknirdjd3pTysqlcqt6YJQxiKwSJ/JL8fofu9DkvBQcyS6GRFmmOEH5OFLiHVj6PvMncktkv/iVtZHSsHK8zBd88ufDkfTNSMyS+L36G68AAMruud+vZFidty03YyAIObhp17SE6dRJZO49AqFmmmN6zLYtSJw0QfZ6yp/FnsfzhXWzkDNwD6z/rfH4rfxacKvyFaJrI2bXTpe/448fRfvDmxx/3/zNKizvdx0SY5V/fDo43XgB9SUi87yPX3MM9WsmypoXKD/3BosF9rJCAA1cN+nRi6SbAAOr87/+HE/ekocJV90ne5nyAXn9f08GpftzHx/WjbJPeJ1fNrmBlJffpEukpKsWX3n2IGbgVlnblk6T8urAAFDj4fuQtXkX7HXrSa3Y4bXfd2FMRhEuqfj7gnGjAQDv/jsWNryuKL3liRMkq/a5JkG959VemRmx4YQlUmFAbk5SalkR4v75W/1uxeXkbldM9vkcVPCAdZnTbEbC+B8Qc1D5AMWB8tU9vBwf/LkLH/21T9a8z26Y49c2nHuFk/M68hiA0K+tell/RXL2nisU7TXOo+2RVrleCtpwyO+1T6hYl3/XjnPgfvm5I3hgx7/KPk5kzhuzc7us+ZI/fl/+tp1sPJZb9UeQq+96Yzp1EgAQu861Oqmh2EcJO1xLKbylrOZ+kTHlUFG1r2IVcQvno8bNHVHjSPCeY87Bfo3773L5rdU9HT3mP5Yjs6qu5LNfWWcscu4Jb3MUmW147XfxYy3GYCnvrMmw2LPUwWdKFF6LYu/HhlknROZUdbOuy0quU8OMnRDyp0TKb75KY2GQdS6NFc8nF2YzTHv3BC0/zlBWhrRWTWE6ekRiDvcNe/nI8wi0gY3Hc0XX+s8++e0awwUDqXDndFH/+usgVO/2EBpP/C4EG5aqkuGjTZQgwGqz45cNx73P5ybx+2+Q+tbrqHnL9UoTWpEuifXLCDoNQmD5x5Uv15Uy6/PL6a2uUKQnR+nOJqQ25BZI6ehFa4C8oKXLrmVoc0J6wMJgEX1/uhYj+yT3aF+SexpfzfsSNx4Sr97m8wNJhfg0/p9Ffi1X5txxh2gbKZ1ySbZTRpIft4hBqMoiqN7jMcTu2IabP3rFaYbQHQVjMMbu8/kx6crlGIZw38V6rfQokfIrOd4vioAy4vy4Z8JhYHM5Zm6V7iTL27vKGsJAyuZUyi713vb37Ffv/jDSrr8G8TOmukxPMotlfPh3zk2nTyFmj+f7UzAYPI6x18vK8zMPL07fJj5vBGIgFQ5k3onNKnpBu/iv2SpvX0YC5D68BQEbjufiZJ6yOtSxG3x1TOCd1IM3rV0rwOa9bVCgVd68PfT9fchuOenZ3sM5jpITFHk0JpX5wpdbcqS4zZMfHwBf//kZZk32o0qDnwyC5+3wzbKDwBnX9jGy2kgp/O3yjCM+11m1AplXVpA/ulzWHoISqdjV/ylbQGJbmyRyU2WnzG297m2k4gpF2mtVcA68VCV3vAyFXD7cfbVPlUlOGymlNhzxzMjyGEfKIyGBb9ug+P3h9PRQMXMrkDZSZaLty4LA6XgPX+x92BYpdomqasEokZNby8TXeUzt+yKSB7/tMi2uou1U7I6qgKRh1nF0PCrdo6WanK98l/tEPBcx2MnRNQZSuuXcxqHi37KDlcC2PMtLL3OSKtImp2qfAQZZ+1JmtVflIkrUew6U6fgxGM9431+jIOhqLAgpzm2kZFVBM7j/qZ+dNAjqjCOl9j4ZIOCd+XtcPkpavfsyardogviF86rmq7hgvF43bj++tnwiFo99EallRSp0mSuvjVSw2f35yA7gZnOvyuaTxLa2nJIOdJQyALruxUpJebtUqXfVujx5VuF1/iO4z5wvllQNsRCskhqDAMSWFOPB7f+gRqnn2IjKS6Scc8Q80+xrbc73+5HsYszbcQam9etgKC5vm/LJ/JGO3w9kFHn0uCfG4h4Lanw5e3uuS7WRSv1ulOrpyCysKuUUTZNBXgAXc+ggkr772ud8T26aJzpd7WvbozTKxzXpvuveM4/1852hFgZSYcDrZef7mlYhAfLXKOd2NhkNstc5aX1F/XKxm1eq1z6xaXJ7MRNhtNvD4uZ3aSPllNyLcyR6E6scV6ZiOdkdKYgcC7GXRbxVfCBpSeEQrYq4b/cyAEDSyC8c0/x5rfVZPR2Nsk/gyU3zUFTm+XGjqITP6Vjev3OJH6lRh8FXICWz+3MAGPTveNHp/ii12PDjmmM4kSPeRurCAol6/DKv0VKnHHw1SpjKq0PLa2sTrAyRDcdy8PjEjajv/jxR+hEnkbykz4b5lzAvpm4SaXvihbde+5zPqfN1cHH2Sdw1Zgi+mP8VJsx432M5RyCl5LRUzhtgZxMPT9iAXZ+ORtrdndGi5aUYuHQCHt1eVU33x9VH8cFC390AKb2mgv0k95bJJhVI1VCr91E/6CmTUi7PYEpatLS/k8JAKgwoLpL39WIL4KL3taT7i8h40r0tVEUgJZO9Mq0B5rgofpC5dH8e2DhS5xdmY+qUgbhj9wpZ8/v70LVLJNJ5pHrnddc+ewKwWJx+88+JnGLc8f0aTFrveq6vOhlY26VQvXzct6KkFEnqN6mgtHZhDm44tBHw0u7OZLfhazmDQ4okaevJPBzPKXGZ2G/1NOmFg11S4tIRQWC99vVcPxvnFWSpkSr8tO44vvvvCD7+W7zThwd3iHfznFdqRbGPHPycYjOOZ1cFaEanziYqecu0MIpcGb9tPY2Ry+RfE0rIvcuO55biUFYxFo3v5fqDy86JlTq6b088dzv58+FOy/jIAQ8ViapmcTbXtmbNVpQPkdAk07UbcKD8/aGUmhl3D2+vGoj7xbUzPX5fvC9TeacaGhdJeS2RCmVnE15UdTgUmvbHwc7s9XYbepQ667gEPhgYSJFvMu7PrGILMgvLKh6vVQskf+U5PouSQMrx/JHRRac3Xh9kPm76QHvtG/zPGFx7fAdGzgo8x9Vqs2PV4WzR/VE47BQemjYS1bs95Pjbn7ZgxjOn0eCaK/HsgnEYtfyw4uWDTe0Xvtfgzumc3LFvVcX2Xf0z9gVMnDEY91aUYqntualb0fXH9Zi07rjvmUPB5Rvb/xLPSrEyxzrzZc9Z/7rg7TdzO3JEOi1wJtaVvnsGU3LWOUXbDWaXwUofb/FuQYSvjyY958a7l/J67RjDG+/Z9QpSFPiySj5iK+dNFu3AQAOqjCPl5VtBqmv4ULYVDRPO962vNlJK3rN2ASjxqCsa3hhIhYPK9haadQ/t+2F+Kr8ML0zfJquNVIySQKry/wMukfJf+ThS/i+fYPX+4eWs/dGtaHVaugHrmNVH0X/WDtHfXLs/l5fguOVV1b78yTVLHPUlauRlofeaGeIzCAIaZxxxDErpzFv1DAME1T/Amp3xr/GyP7rsWSGa+mrm8pKKW/ev9V64FcCAvLE2C+7u/ZDovKHmV2cTMiRYSmE6oEYX4r7T5DzHuUKzX88ir0vIWF8wQxE1161Gu0YgOB+2jTKPY+zMj1yeAx6dTXhsNvAdksqI23oyT3KZwLo/d+9Aw/fK/hn3orJtBCnwUGO9NoPyEinZ7z6FJ8ZQsUiwS6SCsfY4q3NtlaotpPbvJTa7bGuP5gS0vB4xkNIpQeLfSpdVW+WDzr1qA+A8Hon0w9AAiRIpiQeNo+5toIFUAA+yQLs/98a9bvGvU9+WmLPc79vPlC8ncjykqvbJ5c8Rdj6uF+WeQcxm1y66H9v6F/7+sQ9+cKpeqJUmGUdlzeftKCr+UJSYP97mvf2Ykus11uJaAnLzwfWiVYw04Zw7LNqxhMgy3hoqV1ykC3/si7QObZX30qeREHRgWbWs1OGTOq6BfuT5aAfnrE7+OQjOddXl9C5aWor8Lvch3qKsp1d3jbJP4NYDazH355eltyXzwLuXZHnL5EyRKO3ZecazYwrA973vu/tzwFBY4Di2cp4lFxTKG5pDjtjlS3HhgZ2+ZwyAnO7Pz8ocZF01Ut8wKn49hKp0N95mwfC/RntMNx4/BmOOSCCk4BlSUhaEIRg0xkBKZ7aezMP7v6yC5VxVY2ev16jog1/d3CJjfm7VmisSc+v+NS7zVL5IfL6IJKr2+eoNSrwHCfn7qegBVFSExF9+cvypxoC8gYq3mhG7cjni7eUPIbEXifMx9KvL4QC7eV/xw3OoeftNLtOe3fA7AOCWg+v9SE9AyfFcn8Q1oORFJ7dqny/xVjMEAMdzSnAoq0jWMlIfa+s/vhdD/vrG8bdJQR3PYFdpcUmzIMi7Z2Ucx0tyyzs8iJ/j2ebDc3XS65M1gKwK12FENxnwMWBx5T3z6Na/sOq7Z/DYhKG+V+l20BuuXYInNs8PKJliPMaR8kyJvBV5OcG3HliLRHOpsnSJ/EuuurnnkH5pXVR/9IHypIW4amWNh+5FzzceR8NMP6oXy63a52WfKrs/v2eM/CFTZD8Hvcwn9Ysg6Lt6q5jLJDLiqr34rPgCOvhG0hIDKZ159teteP/J6zDomRuRXFaMtOI8GEpVrr+s8KJPmPSzxzSxEilAZq99BoPHg2X1ER/FvSIPsJWHsjDbrat2qfZDLh/lPh6aKYPfRuL4MY6/y6v2qfOgOHbwJEwHlVdJ+mz+CNToeg9e+WuM5Dwu40j58eD251svt8R77pK/JYHlPZ0prEbhq61GiB/2Ri/teeKtZtjtArr+uB6P/uQ50K7SAOeJLQsUpy8UXHbDbvd49ijptc8vgoCfRNqLVQbPoYpvPAYqV0qNYxK0679q307miryrKrb72vJJAIDrls5xWlT+calWJi/DQZnQXAFNMw57ZNgYvey7AAA2m+hgqb7cWJFpFbe0vMMUtTOk5PJWRT2YpHrtA6BZjoZqVV69XK+qPjbdjpMAAwwGA0yH5FWP99r9eQTmKjGQ0rGrT+zCpq+747rbr1a0nKoV0QQBMPsuIq+TnwFAxnNKEGAQBHy06HuXySfzxINFm13A5hN5OFfoWRVqxNJDGLpov6MIP6/EItl+yGvOnlui4xa6jtVgsttVO6Jt2zdFWvu2MB5W1gPXvbuXAwAeWfO75DyBVu3zp7OJpQe896KmJBgKh1w7JcFYYmmx5L0YZ7O4tGlTk6K1Bv2l5lwiJW+JlLfUG2A5ftoUfLvyiOTvwQquPboODuG3Q/C6qpbYCaedyynybA9qcPx/YMe6/6qpuPq41PNdHUVm/zozUb3dHICUga/BdOSwX+kJpoF/7pJ1JpWe780n8nA6X17JXetTe/HKil8UrR8AsosCrO5XcV+vPZqDDcdyZe+j3PmS33nTv2QFvbOMivSr8LwMdYZmKDCQ0rF2FS+NuLxczdIwbuaHSBpbFfRIvRQTrc4PKO/Vn0yFnnXDpZb4betp9Jy2FRtPeg6QWfnwKKyoc1vgpe6topvXo7qHgJN5pZiw9hgKSq3ILCzDU5M3y1+fiNj18qsdyGV1aXsgMZPXak7Kt+lrxPuAHpoqPHBTzeLjBPmSVpyHh7Yv9qiS4/V1JZJes018H+KtFsW7J7ezGV3l9zlX45VZtS/hN+nu2pV2uJP40zjR6UpKiJRmTImdV11kwkq2Q5W9AvGpPnau684lqFGizgDHM6YMVGU9ldz3KLfELcNOqpc3pXeZgmvCAACCgMSfxcdNU/pU9PYR78/z+Z99EmOsBajntK04nS8v0Em2lKL/qqmKt7H8YODDJxSWWdHnt+3oNWObrPmVHOKkMd/5mSp1Kb4uIjA4UiJG6wRQ8CRYSlEaEw8YDCiz2hEfoyxubnNyNzorbNvi8wNFouOGLJGcTJfF5PRs5a3QSWLsEjmMgh0vV5R0HcwsgsFgkGworKWDmVVVXyTbA3kLpIJQIqR1KdMV55xzdOV/SE6e+jaaZhzB1ccDazT93K9bRKfH2SxBH/dDF3x0f65J7mRpKTr9NxcHUhuHbJNen4siz6OAqwIqEPAZkPE8/fG3D0TPdeLkieKrDNUYcqpFuHqIlKWofyz13Guft2vnyZ8CG4KkxGp3ZNx6bNe9FFooHz9OgKAo0LDZBRSUWVFbQbouzj2Ds/P/AB7oqmApae5nQah8IsnssCYSq+95wxKpMOLeUxAA0ZeYYDCgbt457PnyIYydVT6ad8eRK/Hx3/LrLDfIPolZv0hXsZH6AJJTtU/sJjvjlhNVJ/8cum1ZiPiKrsO9PYbk3LPOpS0Gm4/qG+6DCjvt68bjeShSo9cZgwGHs4qRV6peDzZfLa2qLih1fuwinXo4OkYMwpgl3ku5vAV12n6aNM04AgC4c69rr3AGCLh97yq0O7Ydz66fg5QypxIvkeNnlehEJc6mvERKrgsrqtnqgUtAoMIO+zMEhPtwC8mfD0fPSUMxf0I/xR/sRrsNsRLtQ70Jp+8Kg2BH8qDXEf+re9Upb0Uolf8UP55tTu0VX7TYvxJjZw9v+xv3VFR9VsrfIUXc99OfatVSbTqrlxbi/IwTfqVLKbkfvGF0+aqi25aFotMX7TnnV1iq5Dnz0oxtuPXb1YrXc/4LTylNliT3rRhQMai4Gs/wcHoYysQSKR2rm+970EZjtki3pUJ5D0kAcOuBdY7Jc7afwdu3ycuFbeqSk18l0K5Z5S739/jeSDGXoG7+OXx+Qw+vXyJy7u3KB5BdEDzrnfsckLeq7ZAAdfL3MgvNeOSnDSqsSRnBa49u4V0i5evakwrqlKTwtn1r8MX8rxx/Nz9T1XGIwf3YellxvIKxxZRomHUc7y4RrxKkBeecOmN2FuL+WeTyeyheqe69hMYt/hsAUEtmdTPn58v8Cf1Qp8B71Saxx0mg+6noLlLYzbl7KXWnQxuR9NsPAICMbk8o2bJq5AY45xVk4bMFowAA85tcB7tR2RhCaj2f/GlZte2U+PXXY/M89Ng8T/Q3QPm1FIzOJjQb0zJEhol0/Q2UNx+Qur0e3PGvy9+VAUP5OFLyt73pRJ78mUOovERKhfVEYDVAlkjp2L1+5rJpmf0pp0RKjsqxNzoe2VK+mIIHt9jLsXKaP/dwMG78I9mB5cT6+wHgbVeC0c27v+s0VHRKEgrzd56VPe9VJ1yr+t2/a5lf24y3WqSbsUm1y5BxC9yktJt50WplAs4r8K8tgbe2MDXvuAkJ03/1a72SrL5Lh0zeuixWeI1dnnnU5zzibaSUVe3TUs0SqWrLUm2kqj4jFD2XVKhmnFoW2HPU13tFbu65nPeT+x4t3O07o1QNagSL4fTpG8xeCm84vAlN7uiI1if3ePzWf5X4s02A9lXclXK/mn09J20KhtyIRAykIpGvjwMfvyeaSyVv+xi7FT9NH+xlQFEflbIE/8p0vL2oDIbybs9HLZfuCa/y4erP48w5GBAEISzaVUodLW/DdQUjkApZ7pOcgT0lzv6kDfKr0aj1yRtns4hWswS0z7H7dP5IrPv2f+jiR5C4ZdTjLsGc8zEXq8aVVpKPJzfN9S+hABIn/eRzHtEBwCvTpGBbYh9PcintpCQoAu5sQoJTsOHtI9ajBFCFj69AP1DlZE6EO62fJ3JdkJ+J9se2a50Mr5pkHkPCwX2YOP1dn/M6jruKhz9UJYHNzh50+bt55d8S11JWkfcB5iMdq/aFESU3kb/1UNsf3YZfpw7CiWriTR07H1iHGw97jntTyd82UtIP+4oqeT5WLN3tecX65XZTes4zl9DkNh5QOHUS4DGIpZePF1OAA/KK8+9YxdvMsBkTZM+vxxw/bx9plQPyaq3Eake827R2FaVuL//nX+nRKysnY0nD8iEb5HQn7T4Ugtq8BlIKPjJn//Ka32nQWaGTC/lHwHf358o2rO4d4M87r4nEwKO+uAeMPtuK6uJuV0eqzPG8lAZwI+d+7k9yNJFqVja2p5JjkV6Ug5zEakqTFHwGg+T3g9Vtutz9Vas3T62xRCpMfDJ/JHqvmSFrXm8P9Xk7z2Lj8VzJ3wcunQAAqCfRYP1eH7nUFbWCvSROgJJ8YFPFDRpwA0UZN3b8b9OQ3qwRTGdcB/k1QIDRbsN3s4fi2WVTVHn/3/zJG3h6g/SYUIGqfJAN+ftbl+mlttAWwXvLob5t/xrEODXcd573tn1rFG4nOB8q1dy6T0+0+D8OSR2nNo8xgvTYZFef2IVPFn7tMT0YuZEbjuWqvs5g5oALBuWdaXgLpORqkH1S0fye40hJp+F4nufYOVfMrurNLnH0SLw65Hkkyfx4C/SjPWQf/X6WSF1x1nenOnqUsHE9anS5HTHbtoRsm0nmEq9PDTnHb/nBLJd1bB3xWFA6DLg0KzQdbISSAQYICp/cG0Y/iZkBZNoElVQnY37ehq1OuXaAZrXZYbaGXzVBBlJh4tHti3zPJMP7C/fiv8MiHVRU8PUS9dVGICUvy6/xHaQ0P3sQ1xzfIZoqqZSK3dSD/xmD9ke3SixQ/phL/vA90Z+NgoBOhzfhzn2r0Pvfn1X7zBj8z1iV1uRJ6jy+Psez5E7p/ij5ePF2PTXNOII+q8XHDDIqbCMlp1qiGh9druOlKbPqu2dcJ0ik54Yjm/3eht6o/aFbNz8Dq797WtEyJo++fKvS9ND2xT6XFwTgotwz8jYmERwY7TYkfjNK9LfdKzZ5TGs/eojj3ykfvotG+7ei++YF8tIgIVgBkvNHtWptpLysZpBEZyqV19pFuWewcHxvPLj9H/lpkUyIvNnk7PXFD96J2LWrUbPzDQElSYldXz3sUU1LqQFzXNuFGnVeuhbSDo5kPN/8aSPV6vR+0elK15P87kAkjvtB0TJSDPBdcyhQHUeuxE2jV+HAuUJV1hcqDKT0RpUugrXTabqPm1aijZG3B8To3z8Rrw7o+H/X32485Nkb3q0H1uHXqW/7dXyNgt21JEIH7xF/c4AOZQXe3bASRh/VBe/btVSV7Ti6Z/U6T+CUHHZf9cZ1cBkFRTBrsY2ZNcT3TG68lUjds3elrHXI3af4P+eITr/zr8lI+eAd0d8ekHkPJAQQxHsTcFVlo5+fEX6WSMVbvd9XQ/7+FpdnHnXpXVMtSeYSJFhKI6qqnj9CvfeCILiMkxhOynvt0+Z6SfrhW8QvlO4BUjUSVV2XHfDeaZH7d51NAMqsduw8pc+eC6WwjVQESs04DdSV/j2YL4EYi69unf0tGvbS/bnbOmsX5SjcundGt0GEw6GNlOQ4XyLT40uDF1z5+gCNkfyYUlYdIpg9NTlTUqUlo7AMSIiV/D3EtSz94N9BdQ6e1X7WpBcrfMEKgmcg5Ue1JLkfQsbjx4F2nvv90MxvJZZQn+S9EKzOJvwdK8zLvN7aF/q6ppLMnlUl1RBntWDXVw8DAHaed6nLb76eVnoNvPQ+po/xxHHEblyPkTVaYPyGUziidYKinvh1XCZRHe+133fiIQVrv/KCVOw8I9VrqH6xRCoCxRUXwd98YX9zThzL+SoVEATFHzIGCKIvVn/SGrfES3UPiXQZBLvLC0cP1fJ99Tal5Ag/PG2kwm0r6Irex8Fy78jDX4agdJQRXO7j92iShmC0dcg+qav2Dt66P5dDyVlK+fBd1PxvaUDbU4vRbby8mHXK2h2KqV0onUkFKHzreCmR8pYx4vxMcQ5Quu5comTr8jil4wKn8cOuPOfWQ6zOAxIpiZYy/6pAhmh/a7W5EtWefwpF436Uv5AOnqvOBABpSjOAJNel3XU2d+dZWKzK3tdNz0/x+rv7mUqILQ9JvA4XoUMMpHRGrzlXvsj+IPPzIee1+3On3wyCHbW8PLRSR32heNvu7W/0cIZ8l8BIlEiJTLt8d/AGBvZ1PcfaxccBUjqOlJz7JtT3lupBSpi8XBKsZvw77kX0/e9X3L5vtdbJQYxHIyl/yL92WvTqrviDR05QrXQvqj/xiMvfqYPekNq45HP5UbdBw7/+4xORhPmZaefn/WiAgBan92HYglFIL8p1TP98/giPLuqdO6YgT28t/TEoVSB9iZ82peqPMt9VVjsc2xbE1ASZIOCdf8dpnQpVSA7ZIXEvT3yijdf1hccbzTdW7SNVVH44+LwxBInSJR8vVbtBOua3ONWRGvXHZ+iyZ4X05kWneU+1HgMpf4kFJyabOqVCYmo7feiIMak0kF8wxsAKNn2kWL3Bat0NWDk5oOVVYTDA6P6hX6qs62K/Nqvw7NqCcDHE7Nvrcx6T3YYO3e5AfMMGQMuXPH53b1PZ+pTnWFounU0ouWb8vPeNgoA/Jr4KAGhx5oDLb/VzXXtc/fqPT/3ahoPTpVMvT/7A3eFCcVVZRaSvhWp9X0TGo48j8fvRSHlvEPKmzICvRgFyxdssGPXHp1h6aVuV1ugfA6ryGOoq7GlUitbvuRiJ10VcjLIyGaPdhuZnDiDGLSNVZsUm3WEgFYWC0Z6k6sNBxi3gx/a9BV/f/3fEMc1bEAUAMW7VXeRw78D01hVzUJB0ieL1iBm2YBR+vOo+7K99sSrrqyTZRkrk4Jts4qVCStftjxinqn3uaVPyMWoQfJcBqJHuNgEMyurOroPgT62qlXoW49RGas7EBfjfIWW9mIWiCqaa21By37Q6tRfV9u8G9u92BFLe3w+iD2L/qLDPFzpVt6tMivP+JwQwXEH5Cqt2buJ08V5dAb1kioSflPcGAQBS+74Ij24JnKtwCoLL8BHedNv6FxKsZty7e7lKqQxMea996uix2XvHEVtP5qGzStsSI1W47zHcg497+5WVU9B39TRkS4yXFSaVLxxYtS8KeRtQL9Dr11d1phKLeC6krxvv/ELpLttXHJL+zV2M2xhR5Rv30VDYLW0vTP0cf/3YR/Y2vem27W/8PulVVdYlh9hHktJASk1qfcibBDvunf6NKuvy5jwvHZkoNe2PNfjzp/7oukOFbporKK1SFq5ViWVz62zif4t+UryKS97sozgnWGlmlViNmT1nXRtdB+NMKT3/os93gwEPb1uE3qumKVuftzZSXtdT9Zv7h5j7s9pXW1LXmT236Twpxks7TL132qC2ULSBrFFadf132bPCc/gICQlWtcq21BHK/LLnpkoM8aISg1n82Ip9vxm9vNtfXPsbACDNbUDecH0bMZDSGbVy+729QOQO7OsPX6+Tf/ZK5Sp53+87gtnWwscxNzj+JziSAs01DVAggVSgjV/di/bdVi5bl93Lcfu8n73OI7c3L28ZDWp675+xaH72IL6cJ6+NQjAaGoeqt0MtBdrZRPrvv6H1Kd/V5Jw1yFE2gO+S/Zke0578ZbOidVR6cMc/mDVpgOLl4nx0Kw5IXIMGAz5bMBKvr5iEJhnexxl0XZm/vUJWLVcYn+hj7sDOvVWiTUi0+/rPz7ROguYuzjmFyzKPSf4uoKL5IULX/fmF1eJDsh1fjBCwY8QjSPpCpE0lvByPiunuQ9roHQMpUpeP67+0zKo820GyO1+F6/FT/ZzTmvaW4w9vncW7CySQCrREQ6r7c6VH272KjwdBwNtLfff8VD/nNLaPeFTh1v2TXBb8tjrRLnbzJq/jSMkVrzCXO1ZhSeu7831XGZW7Fx8t+h5tFAZ+ALB9xMNILlM+FMLh7KrruHqpgoE0/QxSvH2U1ncbOFlRSVEAH7s+uz9Xs+qmDqoEyxHpmTTLxvTEuxKDQwPl9+t/h7Mr/h2ag/Hb01eHZDvuxPYuyVKG5E8+DnlatMBAilz4+5Cu4XiBen+hrDmSrb9xmCpfthIv3Z7rZ4cwMepQch4TykoUfUSoWY3FJFFdpsemuWh7crfs9Xjb3/SiHMntuOu681/Z2/QlXD54pERKtT+bCqUKkVp1y/kajbdZcfWJXS6/f/zXaJ/r2O1SBVHBsfY2jpTXXlqlvfLfFFzl9NxQdOYDCaR8XB9375E38DNFnumbT4YsqLxv3LrQbChAUneLo6V9mD1uGUjpTJhdPx583QAGQcCSfZ492PjzoPlnfC88s/53x9/13HIjI5nfXQdLfCy8snKK6HQl61BT9bIifLpwlOz5vR2PmsX5stNcGhOcqhEJlsAHCX1txUQVUuJKURuSMLX1VL7vmXyIjJBSue5bFrr8LX69VE1U1JbMzzZSQXv+qNSDqJinNs1VbV1aBPX+9K4aTs+WYNY4mbg+dGPqZRZp0z5MydFrknHUZ8ZmGF06ABhIRax4GfXdtfDE5vmYtWq/aut779+xjn9761XJKxkvpnDLkVYaaPVdNdWv7eilyqNaOX6lMXHqrMhNreLAP+ZD3ZYu0qvmkDipZ4dgEL/f/e3+PJBxpOTyNmyGB7HOJoL0fLPawmsA8USr8mdPOD0/zi/MwhfzvkSL0/tUX3et5LiIKd2XYlZwPf8wZ6jkb6USnZHpHbs/j1C9KnpFUSrQG97XbXDDkc34YNH3irdrhwFGH/NcmnPKV/LEhXkVLDFSLzGpthu+jq3e+bp+5F7XJbGha6wb6S/XiKJRRorZGtwPC/e98pVhJPa7c7Ah9zlyIrcE9VUoAfL1sR5or32yF1U4/8zNyjojiXTGrCwYJbrCDoW79/4HAHhwx7+45E31Sg8B4LL05LCv5u2LWvu351x5E5Ewy7dmiZTehPsNt/uM78bGNx/a4DHN135rXSIUipKXJhlHVFuX1Ef6G8u892ynpYCufR/LeqtydGF+hqMzATVLpFqc3o+3/x2HFD8a8IdKOOUaR6Opm4L7wX3F2UOy5zUI4gFDgrmqswm5Vfu+Xn7Y+z3r5Tfn54SvzIgGOSLDXUgRCezklsQofT+dN9e/jE4tND0n/xoJRK2SwEvt9chit+uk3kbwqJ8pGF5HjIEUhZzZqKwg1AAB9mAGUrKq9gVv8wBQrbRQtbGpvLlv9zJV1xdn00cVUp+nx8uH2ervnsZf43sDULeN1C/T38Xz6+dg1qTXRK8fvVSLJH0yGQ04mhPcIPz9f8a4/C0AeGrjn4rW8dz8qurVcns3FAC/2yQ5B1Itzhzwax2iQpiJ2XjtkpBtK1ALJvTTOglhLRp60I/2DDkGUuQiFCVi5phYxcsEM5DafbYAP645pml5ss/uu93Uy/M+ynswz+Pt+9c4/l0zTHIRfeWYXZJbnnNdFoQ2Uo2zjkX9iybcaRH01kqKDfl4Ko2yTqDZ2YOiv0m1kXLWS+YYhYIg+B24BK1KbABVDZVeH4H0JBkutVZ8VrfOygpRSrRz5ZkDePzPMUhUobMhPVMro/mBHeW95oZb1T62kYoS9XLP4KEd/+Cntl2Cuh05N5TVaPKY5ms0ckFJo2GF+s/eieyk6njJbENq0LairobZvnoCCo+XrVp8fVzIDWQspvB9JPrTa2U49aylJS2OkxZ3sHPnPaJ8fOGkF+fJ35i3wMXLdoKVKRFQgKbw+hAA1FRyrMLQJwu/9vp78vAhIUqJNgyCgHk/v6x1MkJCrYymr+Z9iX8bajMWViDC96shQgUrt23lD88BAG7ftzoo61dC7KZzr2IiZxm1VH6Ee8slZDUsffN13xhljiMVLL4+xM9XWCIpZvQfn+LPy68PeD2V2BmGttzzBhQNdhsEUm2k/PH8+A9QdudN0jN4ayMVrOsyhCU9dkHA7xNfDdn29CjrxBnU8/L7E5vnhywtFBg178kkS2nYfW0xkIoyTVXs0MBfFxYqL9Jnzrlvt+9bFfJt6iXA9FkiJWMdl2adQD8/u4H3h/PL58+fXwl4fXV9VPdU6uIoGpfNFy2ucwFwuXCf3fC71Kwh0enQRtU6/bluzUJgzULfM4oI5jhSRoleTX2pXZSraH4BBtTPO+vXtiKFae0ar7+/tuKXEKUkOOrlq/s81jO1h+YwhFndPk3bSE2ZMgVdunRBmzZt0KZNGzz66KNYtqyqMXxZWRk++OADtGvXDq1bt0bfvn2Rmemac3vq1Cn07NkTLVu2RPv27fHJJ5/AarWGeleoQrA+OBSNBxIEwe41UI2Pgx9mV43PEG1tcnydHTk5Zv+OexHtju9QJ0Fuvpr7pdffzyvKCcp2KXwJOmsLU7fAcyB1LcT4Gez4YiguRu/V04Oybnfttqjb6U848idDlfTp/MJsrZOgKdW/Ts+elZ/LcsEFF+C1117DrFmzMHPmTFx77bXo3bs39u8vH7B16NChWLJkCUaMGIFJkybh3Llz6NOnqmczm82GF154ARaLBVOnTsXw4cMxe/ZsjBo1Su3dCplo+wCWI704D+HWgejrOu5mPBL5biOl7Y0lFqDpoTSPzxt5tB5+Idp4O9qVHcOozbR/H55fPyco61YTr0WKZELIu9gJnGqBVEZGBj766CPcdtttspe5+eab0alTJ1xyySVo0KABXnnlFSQlJWHLli0oKCjAzJkzMXDgQLRv3x7NmjXD0KFDsXnzZmzZsgUAsHLlShw4cACfffYZmjZtik6dOqF///6YPHkyzGZ53bCSK60/OKV4K1Foe2KXKtvwVn0wQWHRdW+ZvVcFSzi2bwnmw1Ov17Waahfn4t0l4xUtE47XiRY0qdon6G80lVhbBNf2sNsjcnB2onATbnkFigKpvLw8vPrqq2jXrh06duyIiRMnwm63Y+TIkejcuTO2b9+OYcOG+ZUQm82GefPmobi4GK1bt8aOHTtgsVjQoUMHxzwNGzZEnTp1HIHUli1b0LhxY6Snpzvm6dixIwoLC3HggPLxJQwG7f8jcalOgz66mzn5DcSqMJ5RkVm6ykijrOMBrz+UQhU4BCt3WKlnNv7h9feLdZJOIlEi92tOiQU7zxRokBhpyRHcjXPMwf1hkbFgEAScV8BqccFSI0yG9Ih0Wn+LK/kmV9TZxBdffIHNmzeja9euWLlyJYYNG4YVK1bAaDTi559/RqtWrRQfrL179+Kxxx5DWVkZkpKS8M0336BRo0bYvXs3YmNjUa1aNZf5a9WqhYyM8rramZmZLkEUAMfflfMoUatWuHR+rV9aVTuIs/ofSMl5eeqpOsVn80b4nCdUqZUac0Zv/ojyHrKk6KF6YbDVDoP6+30lOjnZn1EU4pREr5S3Xtc6CbIYIOBODToWihYDwryTi0hgMITX97iiQGr58uUYNmwY2rdvj+7du6Nz585o2rQpXn3V/4+UBg0aYM6cOSgoKMBff/2FN998E7/8os2FnJVVoGnJviAIYZEjpkdGVY6b9EdlsD84J8x4X/a8D+9YHLyEUMjwXg+Nl1SoZhvsMzVg5eQgb4EiRaKlDLUU9hJI8tUo0VcpcLR5ZeVkxDx5DbKykjSvaSs3oFNUte/cuXNo2LAhAKBevXqIj4/Hvffe618KK8TFxeHiiy9Gs2bNMGDAAFx++eWYOHEi0tPTYbFYkJ/vWsyalZWF2rVrAygvfXLvxa/y78p5lKgcbF2r/7KKAq+eJibJS7U4d4F83NXRsLvPYFdlC3a38Wr3YFStrBAX5Ac+NlEk0Ev1QyIiNdQqiezBfLV0zYmdWichqj26fREumDtL8+/xyv/kUBRICYKAmJiqQiyj0Yj4+HhFB8kXu90Os9mMZs2aITY2FqtXVw0ge+jQIZw6dcpRhbBVq1bYt28fsrKqPkJXrVqFlJQUNGrUSNV0hUKwao8t/LGP75lUsOq7ZzSrKhTsQOqePSuCun61vbX0J6z57imtk6ELY2cN0ToJoqKhWl2k0FPVXqK0YrbjCZZo78pbD0xl4dUWU1HVPkEQ8L///c8RTJWVlaFXr16IjY11mW/27Nmy1vfFF1/ghhtuwIUXXoiioiLMnTsX69atw/jx45GamooHH3wQw4cPR/Xq1ZGSkoIhQ4agdevWjkCqY8eOaNSoEd544w28/vrryMjIwIgRI9C9e3fExcUp2TVdMABB6TUoGgb+Mwp2rZNARDpUN1+N8Y8YSIVSNPSyGYhaxblaJ4GiTL1QfkeGWcaVokDKeQwnALjlllsC2nhWVhbefPNNnDt3DqmpqWjSpAnGjx+P6667DgAwaNAgGI1G9OvXD2azGR07dsTgwYMdy5tMJnz//fd4//338eijjyIxMREPPPAA+vXrF1C6olm4jitjDODF2/bEbjy3fk5UjUSuR2wzFHrRcMxv279G6yQQqeauPStxjUpDfhDJ9Wwox1iLpkAqUEOHDvX6e3x8PAYPHuwSPLmrW7cuxo4dq2q6tBJ+w5Dpx/ezvV9L3vwwx/9licIZc/6Jwstd7LGPIl54fQurNiAvqSC8rh1RNxzepMl2rz7JHDoKL9FQGhQpvA3WTURE6gm3NqmKSqSefPJJGHzsoMFgwM8//xxQoqKVAdp/XAW6/dpFOSqlhKLFZRlHsb/2xVong0hSjF16sG4iIlLP6fxS1NA6EQooCqSaNm0q+VtlZxFmszngRBFR9Fj0Y2/c+PwPyEquoXVSQoq99oWPPquna50EIqKoYA+zd6OiQGrQoEEe06xWKyZPnozvv/8e559/Pvr3769a4qJNmJVmitK6RI3CU+tTe7H4snZaJ4NIN15bPhG/tLpL62QQEYVWmH0MKwqk3P3xxx8YNWoUSktL0adPHzz66KMu40yRMgYYNO81j43PSQvRGICnswtj8qLP6unovH+t1skgojDR8tTeoK07tN+GURBILV++HF988QVOnDiBZ555Bk8//TSSkpLUThsRUcRqmnFE6ySQzl2eeVTrJGjCxHEBiRT7fdIArZOgDmMEB1Lbtm3DZ599hq1bt+Kxxx7DhAkTkJaWFqy0RR0hAnLlkyxlWieBwhRLQ4kIAOZEygchUYQIZU964fYloCiQeuSRR5CQkIDHHnsM9erVw9y5c0Xn69GjhyqJo9ALr3wAIiIiIgqmkGZ0RnIbqTp16gAAFi9eLDmPwWBgIOUnQYjOtiJERERERBEdSP3777/BSgcRRbF4q0XrJESlG45s1joJREREVcIskDIqmXnz5s1YsmSJy7Q5c+bg5ptvRvv27fHuu+9yHKkACGA7EYpOF+ecwrCFX2udDCIiItJSJAdSo0ePxv79+x1/7927F2+//TY6dOiAnj17YsmSJfjhhx9UT2Q0Ca/Lh0gdL66bhbv3/qd1MoiIiMhNu+M7QratUHZsoQZFgdTevXvRvn17x9/z589HixYtMGTIEDz99NN4++23sWDBAtUTGU20byOl9faJiIiISC9COVyHIZIDqby8PKSnpzv+XrduHW644QbH382bN8fp06fVS120YQxDRERERFEqokuk0tPTceLECQCA2WzGrl270KpVK8fvRUVFiI2NVTWB0UbrNlJab5+IiIiIolO4fYUqCqRuuOEGfPHFF9iwYQO+/PJLJCQkoG3bto7f9+7di4suukj1REaLSBiQl4iIiIjIPxFcItW/f3+YTCY88cQTmD59OoYMGYK4uDjH7zNnzkTHjh1VT2Q0YYkQEREREUWjcKvap2gcqbS0NEyePBkFBQVISkqCyWRy+X3kyJFISkpSNYHRRID2nU1ovX0iIiIiik5CmJVIKQqkKqWmpopOr1GjRiBpIbBEioiIiIiiU7iVSCmq2kfBF16XDxERERGRShhIkb/0UBhl0EEaiIiIiCj6hNtnKAMpnWHVPiIiIiKKRqzaR37TQ2cTRERERERaCLevYAZSOmO02zXdPgM5IiIiItIES6TIb4KA5mcPaJ0KIiIiIqKQY9U+Csjl545onQQiIiIiotBjIEX+EgC8vmKSpmlgZxdEREREpA0GUkRERERERIrYGUhROAuvy5eIiIiIIkW1sye1ToIiDKR0RA+16li1j4iIiIi0kHb8kNZJUISBFLmol39O6yQQERERURSKL8jTOgmKMJDSEZYFEREREVG0SmAgRUREREREpMzpK1ppnQRFGEjpiMD2SUREREQUpU5d3lLrJCjCQIqIiIiIiDQnhFn/0QykiIiIiIhIc0J4xVEMpPSEFfuIiIiIKGoZwiuSYiClIydzS7VOAhERERGRJli1j/xmtdu1TgIRERERkSbOFpRpnQRFGEjpiCHMonAiIiIiIrVsOJardRIUYSClJ4yjiIiIiChK2cPsY5iBlI6E16VDRERERKSeBAur9pGfwqyjEiIiIiIi1dxweJPWSVCEgZSOsI0UEREREUWrOJtF6yQowkBKR1giRURERETRSjCEV2gSXqmNcAykiIiIiChacRwp8hur9hERERFRtLKHWakCAykdMZWUaJ0EIiIiIiJNMJAivxkMgtZJICIiIiLShMBAivxlCLOLh4iIiIhILQykFPjhhx/w4IMPonXr1mjfvj1eeuklHDp0yGWesrIyfPDBB2jXrh1at26Nvn37IjMz02WeU6dOoWfPnmjZsiXat2+PTz75BFarNZS7ooois03rJBARERERaYKdTSiwbt06dO/eHdOnT8eECRNgtVrx7LPPori42DHP0KFDsWTJEowYMQKTJk3CuXPn0KdPH8fvNpsNL7zwAiwWC6ZOnYrhw4dj9uzZGDVqlBa7FJDsYrPWSSAiIiIi0oSd3Z/LN378eHTt2hWXXXYZLr/8cgwfPhynTp3Czp07AQAFBQWYOXMmBg4ciPbt26NZs2YYOnQoNm/ejC1btgAAVq5ciQMHDuCzzz5D06ZN0alTJ/Tv3x+TJ0+G2RxegYnRGF4XDxERERGRWoTwKpDSVxupgoICAED16tUBADt27IDFYkGHDh0c8zRs2BB16tRxBFJbtmxB48aNkZ6e7pinY8eOKCwsxIEDBxRt32DQ9j+TSVeng4iIiIgoZOwGo+bf45X/yRET3MMhn91ux9ChQ9GmTRs0btwYAJCZmYnY2FhUq1bNZd5atWohIyPDMY9zEAXA8XflPHLVqpXqb/JVUbN6oqbbJyIiIiLSktbf40roJpD64IMPsH//fkyZMkWzNGRlFUDQsAfykpx87TZORERERKQhu8Go+fc4UF4iJSeg00Ug9eGHH2Lp0qX45ZdfcMEFFzimp6enw2KxID8/36VUKisrC7Vr13bMs23bNpf1VfbqVzmPXIIATU+cicNIEREREVGUshsMmn+PK6FpoxxBEPDhhx9i0aJF+Pnnn3HRRRe5/N6sWTPExsZi9erVjmmHDh3CqVOn0KpVKwBAq1atsG/fPmRlZTnmWbVqFVJSUtCoUaOQ7IdaYq3h1TkGEREREZFawq37c01LpD744APMnTsX3377LZKTkx1tmlJTU5GQkIDU1FQ8+OCDGD58OKpXr46UlBQMGTIErVu3dgRSHTt2RKNGjfDGG2/g9ddfR0ZGBkaMGIHu3bsjLi5Ow71TzmgMr4uHiIiIiEgt9jAbkFfTQOrXX38FADz55JMu04cNG4auXbsCAAYNGgSj0Yh+/frBbDajY8eOGDx4sGNek8mE77//Hu+//z4effRRJCYm4oEHHkC/fv1CtyMqMcTooqYlEREREVHohVkgZRCEcKmFGHyZmdo2btu68wg639RCuwQQEREREWnk22sfwiN//qh5GymDAUhP993ZBAcu0hETS6SIiIiIKEqFWxspBlJ6EhOrdQqIiIiIiDRhN4RXaBJeqY1wNVPjtU4CEREREZEmwq29EQMpHUlLTdQ6CUREREREmhBYIkX+MppMWieBiIiIiIhkYCClIxxGioiIiIiilVGwa50ERRhI6YjByNNBRERERNHJJNi0ToIi/HLXERZIEREREVG0MtpZIkV+OplfpnUSiIiIiIg0YWLVPvKXXethnImIiIiINGJiiRT5y2Rg5T4iIiIiik7sbIL8xr4miIiIiChaGcJsSF5+uusIS6SIiIiIKFrVrZGodRIUYSClI0YGUkREREQUpQxh1oc1Aykd4YC8RERERBSt8krNWidBEQZSOmJkJEVEREREUaqwxKp1EhRhIKUjrNpHRERERBQeGEjpCDubICIiIqJolRxv0joJijCQ0hHGUUREREQUrewCuz8nP5nYRoqIiIiIopQ9vMbjZSClJ2wjRURERETRSgizSIqBlI6YGEcRERERUZSyhVfNPgZSesLuz4mIiIgoWrFEivzGMIqIiIiIKDwwkNIRA9tIEREREVGUMiC86vYxkCIiIiIiIlKIgZSOCGHWdz4RERERkVqEMGvowkBKTxhIEREREVGUYtU+8ptgs2mdBCIiIiIikoGBlJ6YzVqngIiIiIhIE+FVsY+BlK4YLAykiIiIiCg61ameqHUSFGEgpSdlDKSIiIiIiMIBAyk9YdU+IiIiIopa7GyC/MSqfUREREQUrQpLLVonQREGUjoiGHg6iIiIiCg65ZdatU6CIvxy1xFb/Yu1TgIRERERkSYMYTamKgMpPTGEW6ePRERERETqOL9agtZJUISBlJ6EWRRORERERKSWdonh1V8AAykiIiIiItJcgim8amcxkCIiIiIiIu0Zwys0Ca/URjhW7CMiIiKiaFViC6+vYQZSRERERESkuSKLXeskKMJAioiIiIiINGeMMWmdBEUYSOkIO+0jIiIioqhlCK/QJLxSS0REREREEUkIszFVGUgREREREZHmBPbaR/5izT4iIiIiilYCq/YREREREREpY2eJFPlLYG8TRERERBSl2EaKiIiIiIhIMQZSREREREREigjhFUdpG0itX78eL774Ijp27IgmTZpg8eLFLr8LgoCRI0eiY8eOaNGiBZ566ikcOXLEZZ7c3FwMGDAAbdq0wVVXXYVBgwahqKgohHtBRERERESBCrdWLpoGUsXFxWjSpAkGDx4s+vvYsWMxadIkvP/++5g+fToSExPx7LPPoqyszDHPa6+9hgMHDmDChAn4/vvvsWHDBrz33nuh2gUiIiIiIlJBmMVR2gZSnTp1wiuvvIJbb73V4zdBEDBx4kT06tULnTt3xuWXX45PP/0U586dc5RcHTx4ECtWrMCQIUPQsmVLXHXVVXjnnXcwb948nD17NtS7Q0REREREfqqRGKN1EhTRbRupEydOICMjAx06dHBMS01NRcuWLbF582YAwObNm1GtWjU0b97cMU+HDh1gNBqxbds2xds0GLT9j4iIiIgoWlVLiNX8e1zJd7luw76MjAwAQK1atVym16pVC5mZmQCAzMxMpKWlufweExOD6tWrO5ZXolatVD9Tq45is1XT7RMRERERaUYQNP8eV0K3gZQWsrIKNG3kVmK2IUm7zRMRERERacdu1/x7HCgvkZIT0Om2al/t2rUBAFlZWS7Ts7KykJ6eDgBIT09Hdna2y+9WqxV5eXmO5ZUQBG3/M+Xn+Xm0iIiIiIjCnCBo/j1e+Z8cug2k6tWrh9q1a2P16tWOaYWFhdi6dStat24NAGjdujXy8/OxY8cOxzxr1qyB3W5HixYtQp7mQMXZWLWPiIiIiKKU1kVRCmlata+oqAjHjh1z/H3ixAns3r0b1atXR506ddCjRw989913uPjii1GvXj2MHDkS5513Hjp37gwAaNiwIa6//nq8++67+OCDD2CxWPDRRx/h7rvvxvnnn6/VbvmPPU4QERERUbRKSNA6BYoYBEG70G/t2rXo0aOHx/QHHngAw4cPhyAIGDVqFKZPn478/Hy0bdsWgwcPRoMGDRzz5ubm4qOPPsK///4Lo9GI2267De+88w6Sk5MVpyczU9s6mYaMDKRf2VC7BBARERERaWXRImS2aqd5wZTBAKSn+24jpWkgpTcMpIiIiIiINLJ1KzLrNAibQEq3baSiEqv2EREREVG0CrNvYQZSRERERERECjGQ0pMwi8KJiIiIiFQTZt/CDKSIiIiIiIgUYiClJ1aOI0VEREREUYolUuQvY16u1kkgIiIiIiIZGEjpiYmng4iIiIiiFEukyG9hdvEQEREREakmzL6FGUjpidajjxERERERkSwMpPTEzkCKiIiIiKIUS6TIb3a71ikgIiIiIiIZGEjpCQMpIiIiIopWLJEivzGQIiIiIiIKCwyk9ISdTRARERFRtGKJFPnLILBEioiIiIgoHDCQ0hNW7SMiIiKiaMUSKfIbAykiIiIiorDAQEpPGEgRERERUbRiiRT5jYEUEREREUUrBlLkN3baR0REREQUFhhI6QlLpIiIiIgoWoXZUEAMpHSE3Z8TERERUdQKs0IFBlJ6EmYXDxERERGRasLsW5iBlJ7YbFqngIiIiIhIGyUlWqdAEQZSehJmUTgRERERkWp++knrFCjCQEpPwqyBHRERERGRagoLtU6BIgyk9ISdTRARERFRtIqP1zoFijCQ0hEDq/YRERERUbRiIEV+YyBFRERERNGKgRT5zc42UkREREQUpRhIkd9YIkVERERE0eqCC7ROgSIMpHTEXq+e1kkgIiIiItJGq1Zap0ARBlI6IiQkap0EIiIiIiJtJIbXtzADKR0RkpO1TgIRERERkTYYSJG/7HVZtY+IiIiIolRCgtYpUISBFBERERERaS82VusUKMJAioiIiIiItGcyaZ0CRRhIERERERGR9hhIERERERERKcRAioiIiIiISKGYGK1ToAgDKSIiIiIi0p7VqnUKFGEgpTOWq9tpnQQiIiIiotBbtkzrFCjCQEpnzLfcqnUSiIiIiIhCj+NIUSCEtDStk0BEREREFHoXXqh1ChRhIKUzpY8/ifVXXIvFDa/WOimkAwVffq11EihC2FNStU4CERGRd+efr3UKFGEgpTfx8fio12d47qHBWqeEdKD00ce1TgJFiNzFyyAkJWudDBcrLm4V8DrM112Pwrf5vJRSMPwLrZMQdopeG6jJdvN+/jVk27K0jfzMWj1mHhX3fcXx7y86dhedR4iLC1Vy9Ck9XesUKMJAKsxYr2yuynrM112vynoiUdb6bSHZzi3Pfud7pthY5P0yLfiJ8YO5/XUo6d4j5Nu1XtoQRQPf8Xt5e/UasF0QPlUHbPUvcfnb2vQK/9ZzcQPk/L008ASp6HiNwHMe82bPQ0n/AbBe0UyFFGnPfMNNAa+j9LGqD7TSZ54PeH1asDRvqdm2/b3HvCnuP8Dr75Y2bWG+825kL1ml+rbttc/zmFbW5X4UfjhU9W3phe2CC5H7+wKP6YLGXWsX93/V8e+d5zcUnSdnyaqgXf+l9z4QlPUCgPn6To5/l3R7wv+MYHZ/ToESBOnfcpb8h5Inn1a+ToPB5e+8Kb8pXoc7y1XXuPxtbXCprOWyV230mGbueEPA6fGXe1Bpv/iSkGz3YPpFePXuV3zP6OV60Iq99nnI+30BYAxs4DzBR6NSsZdezprNKH71Db+3ab7zbiA21u/lfcmb5Dvwdf7Q9Sbnj7+QvWEbbPUuckwr7tXXv4SZTLA1buLfsm6ExERkr1wf8Hr+vqw99qRfrEKKgPyvv4e9WnVV1hUM9mrVkXE2D9amV3qdL++33xWvu2DENy5/F777ISxXt0P+yG9lLZ83fhJKejyjeLty5Y/9CVlrt8ieP+NkFux167pMc34WOJ9ntYOeooHvQJB5HeX9+AvKbrtD8nfn4Mn9Hewu/6cpAADblepnCNhr1EDhOx94TC95OjyDbCmWVq2RN2UGsrbtRfbWPbA1b+ExT9bWvZ7LuX3LFPd5OVhJhFCtOrLXbMLM6cuw7qJmsMP1urA0bwnbZY2R+88KCImJqm+/YNzPqq8TACzNWiBvRtWzy9rmKlgbXRaUbekNA6kwUnpfV7+XtV3SwPHv/FEySkJkyB/7k+s2LmssLy2NLkP20tUu08x33CU5f8bJLNESBNtF9V3/rlsPJf97VlYaAOD2Z0bj8NO9kf/TZNnLqOmHR1sg7qEHUearlFEksg52bm1x7/5efy+7857yf/j4OACAwo8/kfzNUFoq+ZtgMqHwo+GOv3P+XurzY8za9EqPY5O5+7DL35ZrrgXKyryuR4n8Cb8g97c/pNPkFrzYU6uhcOinPtebufMgrNe2L//D6RqQ+sjzdl/LCe6UsNc+H7bGTZBxLh9ZazYjf8wE2R/ttvMvcPzbAAFdnhqBorfeDThNtuYtkLXvqOSHtd8BqBtfH8RS8n+ZBhgMsLkFCEpIVcdy/+ASatdG7rxFKOv2hKz1mrvcpygdvkpX3JXd1xX2BpfKqrJU9Obb5Rkd7s89gwE5fy9F/vfjkTtvkWOyTeWMr7K7ukCoUUPWvOZ77kX+xKmSv5f0eBrWiveixUdmod3pHad6hkBMDGwNGrhMsl3aEIiPV7SaYJTmiAV4stntyJ/kdPytNpg7315+LCXuU6F2bZe/M3cfRtkddzv+zjh6FkUqVBUufu4Fj2lFL78GALBd2ggdrm+Jds3r44ffN8HS9irHPM7XNiyWgNPhrODzkaqur1LJE/9D7p9/AUbXkMLgrVQggjCQ0iHBrQjCVqcuMg6fRsGYCQAAy3UdFa0vZ+G/sDt9vJQ91t17sVflfJ1v8/q7vU5dZG3d4/jb32Lc3DnzvX+Qx8YiZ/EK5I2fhNzZ82BPS0P+N2M8blpLxxtQ+NlXyNq0EzlzF3msxr0UYG/tS3Cw30AI1WtIbrrs9jsd//YWEOR5eZlKaVOvBgbc3QL5S/7zPqNYIHV9J+ROm614mx6rjo1FyZNPoaTbEzB3vAG5fyxExrl8FA16D8XPvwhbffHSgsIPPi7/h4yPypJneopOd64G4C7/+/HI2n+sPOipYG3VBnYfpZ5CUhJy/1nhOq1WLZe/7RdeCIPFLLp88XMvuJxzd44A0on5nnthueFGpw1Wna/SBx9Bzsr1KOtyv2Na1s4DEGTU3Rdq1hRdp5i8H39B2WPdPTIXHGl0vo6HDBedxznNvuQ5ZT7YL22IsvsfRNkj3SRLNYp79UXez7+iYOinKBhZVXoSbzXDYopFcf8BsgMxr4xGwOD2XGjdBvnfjEHRm2+jYOinyJm/WHLx3D//8r0NiWDA2uRyr4tZru1Q/g+Re0ZwKyF1vu4BIP+HH1F2x13Im+55z1ubXglri6rMg5xFnmOwmDtVVRf0d6xC5/2zn+dZVUyO3Ll/w9KmreS5zv9+PIoHvFn+h8g1b23VBmVdH3aZJvV8AYCSZ6V/k2JrcjnsXt4JlQqGfVb+D6P4Z1TJM8/DflF95Cxbg8xDJ13vZzfuGVe5fy+R3u5Xo5E7fY7XtFlatfZYv3tNC7OX55yUzOMZipfxxdrMswTO0qKVvIUNBpdMWKnneiWx606oVcv1HCYmAiaTaCDkzr02S9aG7cjashsFn3yJonc+cPmGstc+D8WD3nP8HWMyYniXK/BQ+4Yw39S5PC1JSS5dfxu8DEybO+N3j+et7bzyqtLOGc+lDzwI28WXoHDwEJT2kFebyV6zJopefQP2lFTkj/3JI6PGvbq5+fa7gGS39rcGA2C3y9peuIuYQGry5Mm4+eab0bx5czz88MPYti007VyCIT2lPJfoePXym8J86x3lF2nFC7jsgYcUlUhY21zlkTNgsInfoM43TP7kGSju87JLgGQ7/wJknMxCxtk8wGCA/cI65Tfow4/B7PSx6ItzYCfExbl8vAmJiR6lb8J558Hc5T5YrrseWbsPo+zhxyCY3KqVVeyjvd5FsF7j+rFgvvFmWaUAlQq+Go3Srg8j/8dfkDdhMrLWb4M9tZrjd/cSMlvDRt5X6P6Q8aKo8kOiktRHtFD1kDJf3wmZew4rz8k0GlH4xSgUjvwWebPmVn3sxcai6ONPUTD6B49FSh96tGp/5GTOi3xoFHw2Ankz/3SdNuxz2M47H3m/TENZ14chpKTC1rwFchb+i6xtntUxKjl3oGBp1150HnP768rT3vVhmG++FTB75vTlTZ6OoqGfoeDr72G9vKljeulj3ZE7Zz5yp81G/nfjvO+rR+LKz53V+fqouI9zFvzjfVmpINVb8Gqz+UxSSc+XXEqArI0uQ+7UWVV/i5To5E6bjVKnD1hbM5FSVJMJhZ+P8Jhc3Ksvij74GOY770bpcy9CSKm6jxKsFR8+RiPKuj0hq3pZcf8BKPByLwtu11vB56NQ9vBjQFISSp97EVa3ajzOrNe2B57xnoayO+8WnZ43eYbX5RxEzl/unPmwNG+J3D8WlqfDrRSz7IGHkD9xKgSnZ5Bju9Nnw9bwMmQvWYXMHQdgbdnaYx6700d87p9/OT4ohcRE5FZWx/FyXQmxschZsa7q7+QULzsozdqqDXIXLkFZtydgF2tQ7lxSLFIiJfZvS8cbkPfrb6JV3guHfQ7zjTeLpqXwA8/2QTnzFwMGA4Tqvp+jpc9Kf2gXjPimav0xMeXPMpFMqbK7uiBr/TYUvfehy3T341s4ZDjM13dCzl9LUNq9Byw33uxxjVgbN0Fxr77IXrMJuX8vQ/a//yF/5LfIWrsFZY90c6mVAsBxDN1L7L1yf+dCfpV+KfZLGiB31lyXaWX33u/yt/Nz3S5yD1QS4jxL2JxLkCVLaBWUMjtnpuVP+MXlW8Be/2LY69RF6dPPAUlJKHm+l+M391o4zor7D0DBiG98VpeubAJR8PlIWDrd5JGpnrNiLfJHfYdcp7aw9gvrInv9NpT07ueYljd+EgCgpMczyNzjef6z9hxB8cB3kHXgOMru6+pRBb/4JbfSfanDJ7NESlBYMqo3ERFIzZ8/H8OGDUPv3r0xe/ZsXH755Xj22WeRlZWlddL8MvSB5rjm4hrYPvkPFHw+EoXvD3GdwWCA+e4uLpMyD51E3pQZsNeqhbxff0PRq68DgOPjp/ThxwDAUT9fSEqGkJDgUU0lu6LqlPnaDoDBgKL3PkTB199XzWAylVe7cFqupHc/FHwzxus+CUnJKLvtDscD06WUQBAgpNVCxqFTKPhiFLJXrofBW05G5bZFHupe0+BUCpCVKP0wBoDS7j1Q8P14IDYW5ru7wH7xJa45oUZj1QcI4Dvn5cABxz+tlzX2qBbprPj1t5A3eTpyFi+vSLj4w8g5typv5p8Q0mqJzmurUxeFg4d4TC9fifcXiKVdexT37o/Cj4Y5prk89JyWr8xVt7pX8TQYPEpBSp98ymNbpc/2RPaO/TDf5ppTam1zlUu1F3dFb7+H7NUbUTh4CIreGCQ6T97secjcf6z8nBoMKOnjmgNsvbJ5eYYFAKFGTeQsX4vCjz+BpWVrFL4/BJYOHWG56RYgKUkyHQ4i58BlexXVY6xtr0bh+x9XpXHydFgvLW98bGnV2iUALfh2LISYGBQM/dSlvZTHNkWuQ7Hg0rnEI2fVRlhu7ozc3/5A8XMvoOT5Xij4YhTK7rwHGcfOIXPP4fJ9l6no1ddhvrkz7NWqw56W5vGRiLiq0peSWNcXaNH7H6Fw8BBkrdsq2ei+6O3BKH36eeR/M0a8Yxj36iUijQzFPsbEgrjsf6tKi2116iL73/9Qdq9EFWu5zyORe856dTvk/rOiKiPDOa1uH9XuJf+VHQnYrmwGQaKkqOidD2Crd1H59VYRtGacy0fm0bOwOJVWSakcKL64/wCUdbkfpY91R+HHnyCvol2PX8Sea873l9vv+eMmVv3hFlSZb7kNhV+IV1vK+2U6Cj/5Ahg6FGUPPOiYXtKrDwqc3q2l93V1BNm+2kiJBoFOSh/p5lFtTqheA1nrt7lcUzAYykuK3K4J54xGoDzzI2/mn7C2buuYlj9hMsxO96Xl6nYo+uBj2C4tz7SxNWteHrBWBDpS++ReYp/9z0pkL12N4hdeEp3f0qatx7SsTTuRP+o7j+Nivrmz45tDbDkAsKdWh6XjDbCnV1W5K3mpH/J+/hWZuw8j41w+Sp5+rmqdt3T2WEf+mAmwNrpMNONPVpAkVqrolEFX3PcVZJzJRca5fOT/NBnFvfsj/5sxEGrURPGbbwOAaFs5y023IG/8ROT+vsCjSqGL+HiUPv4k7G7P9vyK76rCj4YhZ94i5E2bjYxz+VUlS07P+8xdhyDUTEPZY91d3pdiJeXmLvch41w+Cj8fUf7d4K7ymFUcF+fqs9nL1sDazLXtmaUio9KZtUVLz+BdQt602Z4ZyGEkvLrGkDBhwgQ88sgjePDB8ofkBx98gKVLl2LmzJno2VN50b7W6tRIxLcPt4AgAKXXNPU5f8aZXMD4//buPT6mM/8D+GdyE2nQSLKrrf6WIiGZXIUQqWvRRbTatGqxVLEopS4rrbYadWdbpdZqbbuWqGtpUb+W+qElrhVUko00IrGEXJDrzCSZ7++PkZMcmUhGc5v4vF+vvF7JOc+c85z5zpxzvs95nic2MDzTH5mxSaYLS88+MPQfoHzgdSNGocijPYpLHqPb2iIjPhkA4N6q9KRtbPEY0m9mV7yzSk5KuR8sgt2pk8hd8QncniodC3DnX1EorKBlUBlb5eys3GAXBnREo9277rsvY4vHgEsJ9y1TouiergLnHzMNgnRzNnXTyYyJg6t/h3KDtlUcHJA3bSYeWbEcuQuWqrpzFbdth8yYODTa8zU0OTl4ZMkC5L05Ew5Hf0L+W++gWYsWyDp+BmLnAKOZlsmCka/CMWo9so6fNcWyb+lJWczcvBe3bQcUmnmqWObmI+dvK/HIB+8h+5//RlHHTjD0/yOah9xzMavsIqPRIG/uBwAA53ffAgDkvzFdtb7Ere8OwemTFcibPQeuwf7q4xs/CY12bIX92Z9NC+6eoPMnvQGnv69E/qQ38MBEUNymHQpeLx3Ymp50DY7bt0BX0gJpY6Pqwpn/5izYnYtBo//di9w5c1FgpnW5YNxEVYuiJYr/pxVsU5KhvztDkjRthsyYONNxl7nhLhj7F2jy82Do1ccUo77Pmp4qaTTqVveQUGSk3FSSMH2fvmj0Q/nuq+YSKXPjtwq7PY3bW3aqnqQWdu+pfKZ1I0cr30WlNbKKiUJ+xN2nXQaD2deVfWKyv20w3uheerEV5yaqltOyct/7AEXepecv/d3GoXLs1Psz/q78zICFwV3Q6MD3yt+GkFDkLl9RrmG17JO3ooCOKNb6oLiiyQ2q2Kpd2L0nGn1Xfjaxsoq8SiekyIqJVa3LWbEahmf6oem40VXaH2BqKc/6+eJ9yxSMGoPG6/8JQ8/ecDh0UFmeO3c+dMNHAoBq7EhVvxuqxKHs/sZPwiOLPoC+/x9h6D8A9sePQT9wcGmBMuey9KsZ6i6VloxTc3CAbsw4OLs1QeH8xWi0c0fpZipqSr/nM5u96h9oOmUCxM4O2Ws/R9E9XS/LqaB+Jd3rDL36wOH/fkDeOxWPxdGFD4Xj9orHNha388CdLTthf+QQHDdHlW+wsEDRU21gl/Qrsg4eVT7z+iHhcFpb2hWuZBzR7X0Hgfx8uLcu8ySm5ZPQvzIchmcHwDFqA5wjTbOq6p/pB8PAwXDcuB66P78Km2v/ReM1qwCNBo5341CSYOQsXo5mY0eZri92dqZJgUqUSXTyIhfCNuUKdGPGoaRpVP/8i9A/X5okW8ymfLzyJ0+F/U+HoX/hJRSML5NUlrkmAqb//VkY1Nk05swMS3rq3Ev/0itIHxBWYY+WsuPS5Z4k9va2r2EffRT6l4dVuh/d4CGwP30Sulf+BEPv8kM6sj/9F5zfmY2CydNQ3MELdidPKOvSr9wwdYe8K+v4z7BJSUGRfyCKfP2RdyUZhcFdYZN2HY12bFWddxV3ezdZK42IdY8GMxgM8Pf3x8qVK/HMM6UtFbNnz0Z2djbWrKn6xAqZmTlVfRJZYzQawNW1SaV1cfzsH3B+2zRzWUb6fRKfKnDxbQ/b69dQ5OOH2wd/NFvm0e5dYBcXi/xJU5AfucBsmXu5uZtafbM3bjHbH1tzKwuaggIYHzcz+NpggOMX61DYszeKKxh7YJOaAudpk+Fw5BAAUytg7urSFqmS/QNAxpU0wMkJTYeFw+HA9zj9yQb8GhCCPh73aSWqgCb7Tmnrnojp594Wrbw85eRX1ZjCYDA//sJohNvvHy3d9FvvomDqdDjs+QZNx44yHd/dz0BJLJVlIspF3ebXRDTvEqje9CPOyEq+VrUDFzF1vSnzmN8m5QpcOvtDP2wEcj8q/efBqvf+bt1ski+j6SsvQjdxMnSjxijHZhsXa7o5rWCsQUVK9pG7YAl04x8s4YFeb/GAa6e5c+D099JjzczIhkhpfbLOXIA0bw7bxEumpOEBJyeojCY9Ha5epot39hcbYRg0GM07PAWbjAwAwO19B1Ds2d5sd7AHYXM1Fc0G9YduzDgUvFGF2SarQFdYDEf7ihM0c5+jytjGxaJZ+HMw9OgJ/QsvodDMWE9NRgYaf/p36F4Zbmr9d3ICNBrTd/XDxcDChco+7c6eQaOoDciPeEe5WbFJ+hWN161F489Kn9Znxv5q2rauAM0DTQmfoW9/2FxNRVFAR+SWjA0rKkKjndthdGkOp4+WI/+vb5V/KlRYiMZrPoGhR08Um+mqZ5N2Hc19TF27Mm7eqbbPmOZWFqTZo8r5RuztkXnt/j07SmJU6B+AoqDOKBg/EbaXk1DUMQhiZ19xt+biYtj9fBpFPn6qc0oJp0UfwOlD0zike2OvuXMbrm1N4wEzbtxWzh22Cf+BSzfThBxFbdvhdrRphtiSc3DOmk/RZNJflG2WLa9/7gXkrPtXueMqbvkkbp29CE16OtDY0ez4RtcWLtDc7VarHzAIOeur8KTOaLzvOU+TlYkmr/8FupGjYRhQfmzmg3i0T3fYnY9Bzt8/Ld8QUeZaUaLR1i9R3PJ/UOzra+puWGa9Eveu3XDnG3XDgCYtDfZnTpnGL5lpgHGa9x6cVq0AoI6t5vYtyKNmxpIVFODRfj1RFNRZuc5U9bpaNjYl+yqpuy78ZeSuWYdGG9ajyfQp5epjDRy+3YPilk+iuMw4yQdSXFz1p+p6PVw6+sD4xBO4813F4/nMKXtOL3HrxxOwSUlGs+FDTQtE6tX9eKXEyqWlpYmHh4f8/PPPquVLliyR8PDwOqpVLcjPFxk4UGT16t++rcREkalTRVJSKi6Tni6yebOITlf17YaFiXh6WvaaB9GtmymdOXJEvfziRZG33xbJyipdVlQkkppas/WpCTqdyFdfidy6Vbrszh0RJyeR7t1Ll505I+LlJfLNN+a3AYg4OIhERYk0by5y+PBvr5teX35ZVJRpX1988du3X5GpU0XatRPJzq65fZhjNIokJIh8/rnIwYOly69eFTl/vnbrMmaMiI9P6XdsyxbT+/722zWzP6OxZrZbkSVLTMfz8ceWve631DMnR2ToUJEdOyrfR3i4qX4REep1ixaJuLubzq019Z6tX195HR/UypWm49q+vfKyp0+LTJpkukZUp/x8kQULRM6dq3i/5r5vJdeDr78uv66wUOTPfxZZt650WZ8+pvL33EPI99+LuLhU7Rp78KCp7ObNlZetS/n5pmtEdXwmDxwQ+eMfRS5ftvy1Z86Y3vN27ar+mget88KFpn2NH1+6LCVFZOfO0m3qdCLPPGP63lLVFBaKFBdb/rqYGNO969y5pvuDZctMy41GkXnzRPbsqc5a1gqrfyJ148YNdO/eHZs3b0ZAQGnL3dKlS3Hq1Cls21bFAcCwridSVqGipzXVragINjdvmH+yVQ/UaEz1etNTrKq2SOflmeLRuLHZFshqr1tNDyKt6WO4j3rzXb3nPaiwVddKadLT7z++oDr3VZ0xrcPPZrUoKFB12bEaBgNsUlNgLNNt9b5xFTEdq7nxj5U8NSq3HWuOdy2zSU0xje+r5H8JVqTK31UR2F5KMHWHr+l7EfrN6s11FVV/ImX1Y6RcXFxga2tbbmKJzMxMuFUyIPReJff99UF9qsuDuzvOo6aPw9YOxY89US//cW1ZNRLTklmKqrrdkgG0AgA1HBuHRrUQk1r4fFWi7r+r6vdAmrnU+XtSncTNvdaPp3piWvefzd/EsbF11t/ewTThgpm6m4+rBmjsZP5YNTYWvAdWHu9aVtzy7r9q+I3vWeXfVQ2K2nlWy76o9tT9dbXqrD49d3BwgLe3N6KjS6eWNBqNiI6OVj2hIiIiIiIiqi5W/0QKAF599VXMnj0bWq0Wvr6+WL9+PQoKCvDCCxVMU0tERERERPQbNIhEasCAAcjKysLKlSuRnp6ODh06YN26dRZ37SMiIiIiIqqKBpFIAcCIESMwYkQF/7WaiIiIiIioGln9GCkiIiIiIqLaxkSKiIiIiIjIQkykiIiIiIiILMREioiIiIiIyEJMpIiIiIiIiCzERIqIiIiIiMhCTKSIiIiIiIgsxESKiIiIiIjIQkykiIiIiIiILMREioiIiIiIyEJ2dV2B+kSjqesalNahPtSFqgdj2jAxrg0PY9owMa4ND2PaMNWnuFa1DhoRkZqtChERERERUcPCrn1EREREREQWYiJFRERERERkISZSREREREREFmIiRUREREREZCEmUkRERERERBZiIkVERERERGQhJlJEREREREQWYiJFRERERERkISZSREREREREFmIiRUREREREZCEmUvVIVFQUevfuDR8fH7z00ks4f/58XVeJ7jp16hQmTJiA0NBQeHp64sCBA6r1IoKPP/4YoaGh8PX1xejRo5GcnKwqc/v2bcyYMQOBgYEICgrC22+/jby8PFWZ+Ph4/OlPf4KPjw969OiBzz77rKYP7aG1du1avPjiiwgICEDXrl0xadIkJCUlqcro9XpERkYiODgYAQEBmDJlCjIyMlRlrl27hvHjx8PPzw9du3bFkiVLUFRUpCpz4sQJDBkyBFqtFn379sVXX31V48f3sNq0aRPCwsIQGBiIwMBADB06FIcPH1bWM6bW79NPP4WnpycWLFigLGNcrc+qVavg6emp+nn22WeV9Yypdbpx4wZmzpyJ4OBg+Pr6IiwsDBcuXFDWN7j7JaF6Ye/eveLt7S3bt2+XS5cuyTvvvCNBQUGSkZFR11UjETl06JB8+OGH8v3334uHh4fs379ftX7t2rXSsWNH2b9/v8TFxcmECROkd+/eotPplDKvvfaaDB48WGJiYuTUqVPSt29fmT59urI+JydHQkJCZMaMGZKQkCB79uwRX19f2bx5c60d58NkzJgxsmPHDklISJC4uDgZN26c9OzZU/Ly8pQy7733nvTo0UOOHTsmFy5ckJdfflmGDh2qrC8qKpJBgwbJ6NGjJTY2Vg4dOiTBwcHyt7/9TSmTkpIifn5+smjRIklMTJQNGzZIhw4d5MiRI7V6vA+LH374QQ4dOiSXL1+WpKQk+fDDD8Xb21sSEhJEhDG1dufOnZNevXpJWFiYzJ8/X1nOuFqflStXysCBA+XmzZvKT2ZmprKeMbU+t2/fll69eklERIScO3dOUlJS5Mcff5QrV64oZRra/RITqXoiPDxcIiMjlb+Li4slNDRU1q5dW4e1InPuTaSMRqN069ZN1q1bpyzLzs4WrVYre/bsERGRxMRE8fDwkPPnzytlDh8+LJ6enpKWliYiIlFRUdKpUyfR6/VKmWXLlkn//v1r+pBIRDIzM8XDw0NOnjwpIqYYent7y759+5QyJXE8e/asiJgS7Pbt20t6erpSZtOmTRIYGKjEcenSpTJw4EDVvqZNmyZjxoyp4SOiEp06dZKtW7cyplYuNzdX+vXrJ0ePHpURI0YoiRTjap1WrlwpgwcPNruOMbVOy5Ytk2HDhlW4viHeL7FrXz1gMBhw8eJFhISEKMtsbGwQEhKCs2fP1mHNqCquXr2K9PR0VfyaNGkCPz8/JX5nz55F06ZN4ePjo5QJCQmBjY2N0oUzJiYGQUFBcHBwUMqEhobi8uXLuHPnTi0dzcMrJycHANCsWTMAwC+//ILCwkJVXNu0aYPHH38cMTExAEwx8/DwgJubm1ImNDQUubm5SExMVMp07dpVta/Q0FBlG1RziouLsXfvXuTn5yMgIIAxtXLz5s1Djx49VPED+F21ZleuXEFoaCj69OmDGTNm4Nq1awAYU2t18OBBaLVavPHGG+jatSuef/55bN26VVnfEO+X7Gp1b2TWrVu3UFxcDFdXV9VyV1fXcmM2qP5JT08HALPxK+nPnZGRgebNm6vW29nZoVmzZsrrMzIy0LJlS1WZkgtERkaGcoNP1c9oNGLhwoUIDAyEh4cHANN7bm9vj6ZNm6rKurq6qmJW9iIOlMassjK5ubnQ6XRwdHSskWN6mP3nP//BK6+8Ar1eDycnJ6xevRpt27ZFXFwcY2ql9u7di9jYWGzfvr3cOn5XrZOvry8WLVqE1q1bIz09HatXr8bw4cOxe/duxtRKpaam4ssvv8Srr76KCRMm4MKFC5g/fz7s7e0xZMiQBnm/xESKiB56kZGRuHTpEjZt2lTXVaFq0Lp1a+zatQs5OTn47rvvMHv2bGzcuLGuq0UP6Pr161iwYAE+//xzNGrUqK6rQ9WkR48eyu/t27eHn58fevXqhX379jHBsVIiAq1Wi+nTpwMAvLy8cOnSJWzevBlDhgyp49rVDHbtqwdcXFxga2uLzMxM1fLMzMxyLSlU/7i7uwPAfePn5uaGrKws1fqioiLcuXNHeb2bm1u5GYlK/ubnoObMmzcPhw4dwvr169GiRQtluZubGwoLC5Gdna0qn5mZWaWYVVbG2dmZNws1xMHBAX/4wx+g1WoxY8YMtG/fHv/+978ZUyt18eJFZGZm4oUXXoCXlxe8vLxw8uRJbNiwAV5eXoxrA9G0aVO0atUKKSkpjKmVcnd3R5s2bVTLnnrqKaXLZkO8X2IiVQ84ODjA29sb0dHRyjKj0Yjo6GgEBATUYc2oKlq2bAl3d3dV/HJzc3Hu3DklfgEBAcjOzsYvv/yilDl+/DiMRiN8fX0BAP7+/jh9+jQKCwuVMseOHUPr1q3Zra8GiAjmzZuH/fv3Y/369XjyySdV67VaLezt7VVxTUpKwrVr1+Dv7w/AFLOEhATVReHYsWNwdnZG27ZtlTLHjx9XbfvYsWPKNqjmGY1GGAwGxtRKdenSBbt378auXbuUH61Wi7CwMOV3xtX65eXlITU1Fe7u7oyplQoMDMTly5dVy5KTk/HEE08AaKD3S7U+vQWZtXfvXtFqtfLVV19JYmKivPvuuxIUFKSajYbqTm5ursTGxkpsbKx4eHjIF198IbGxsfLf//5XREzTeQYFBcmBAwckPj5eJk6caHY6z+eff17OnTsnp0+fln79+qmm88zOzpaQkBCZNWuWJCQkyN69e8XPz4/Tn9eQuXPnSseOHeXEiROq6XcLCgqUMu+995707NlToqOj5cKFCzJ06FCz0++OGTNG4uLi5MiRI9KlSxez0+8uWbJEEhMTZePGjZx+twYtX75cTp48KampqRIfHy/Lly8XT09P+emnn0SEMW0oys7aJ8K4WqPFixfLiRMnJDU1Vc6cOSOjR4+W4OBgZQp0xtT6nDt3Try8vGTNmjWSnJws33zzjfj5+cnXX3+tlGlo90tMpOqRDRs2SM+ePcXb21vCw8MlJiamrqtEdx0/flw8PDzK/cyePVtETFN6rlixQkJCQkSr1cqoUaMkKSlJtY1bt27J9OnTxd/fXwIDAyUiIkJyc3NVZeLi4mTYsGGi1Wrl6aef5vT3NchcPD08PGTHjh1KGZ1OJ++//7506tRJ/Pz85PXXX5ebN2+qtnP16lUZO3as+Pr6SnBwsCxevFgKCwtVZY4fPy7PPfeceHt7S58+fVT7oOr11ltvSa9evcTb21u6dOkio0aNUpIoEca0obg3kWJcrc+0adOkW7du4u3tLU8//bRMmzZN9f+GGFPrdPDgQRk0aJBotVp59tlnZcuWLar1De1+SSMiUrvPwIiIiIiIiKwbx0gRERERERFZiIkUERERERGRhZhIERERERERWYiJFBERERERkYWYSBEREREREVmIiRQREREREZGFmEgRERERERFZiIkUERERERGRhZhIERERAfD09MSBAwfquhpERGQlmEgREZHVi4iIwKRJk+q6GkRE9BBhIkVERERERGQhJlJERNSgjBw5EvPnz8fSpUvRuXNndOvWDatWrVKVSU5OxvDhw+Hj44MBAwbg6NGj5bZz/fp1TJ06FUFBQejcuTMmTpyIq1evAgB+/fVX+Pn5Yffu3Ur5b7/9Fr6+vkhMTKzZAyQionqBiRQRETU4O3fuhJOTE7Zu3YpZs2Zh9erVSrJkNBoxZcoU2NvbY9u2bYiMjMTy5ctVry8sLMRrr72GRx55BFFRUfjyyy/h5OSEsWPHwmAwoE2bNvjrX/+KyMhIXLt2DWlpaXj//fcxc+ZMtG3bti4OmYiIapldXVeAiIiounl6emLy5MkAgFatWmHjxo2Ijo5Gt27dcOzYMSQlJWHdunX4/e9/DwB48803MW7cOOX13377LYxGIxYsWACNRgMAWLRoETp16oSTJ08iNDQUw4cPx5EjRzBr1izY29vDx8cHI0eOrP2DJSKiOsFEioiIGhxPT0/V3+7u7sjMzARg6pbXokULJYkCgICAAFX5+Ph4pKSkIDAwULVcr9cjJSVF+XvhwoXo378/bGxssGfPHiXpIiKiho+JFBERNTh2durLm0ajgYhU+fX5+fnw9vYu1+UPAJo3b678Hh8fj4KCAmg0GqSnp+N3v/vdg1eaiIisChMpIiJ6qLRp0wZpaWm4efOmkvjExMSoynh7e2Pfvn1wdXWFs7Oz2e3cvn0bERERmDBhAtLT0zFz5kzs3LkTjo6ONX0IRERUD3CyCSIieqiEhISgVatWiIiIQHx8PE6fPo2PPvpIVSYsLAwuLi6YOHEiTp8+jdTUVJw4cQLz589HWloaAGDu3Ll47LHHMHHiRERERMBoNGLJkiV1cUhERFQHmEgREdFDxcbGBp988gl0Oh3Cw8MxZ84cvPnmm6oyjRs3xsaNG/H4449j8uTJGDBgAObMmQO9Xg9nZ2fs2rULR44cwdKlS2FnZwcnJycsW7YM27Ztw+HDh+voyIiIqDZpxJJO40RERERERMQnUkRERERERJZiIkVERERERGQhJlJEREREREQWYiJFRERERERkISZSREREREREFmIiRUREREREZCEmUkRERERERBZiIkVERERERGQhJlJEREREREQWYiJFRERERERkISZSREREREREFvp/AdOXbSQclVMAAAAASUVORK5CYII="
     },
     "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": [
      "/tmp/ipykernel_22219/2998122009.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        1   6.18   749.0     3668.0          2          0  747.646447   \n",
      "1        1   4.54   741.0     1031.0          2          0  123.353553   \n",
      "2        1   4.39   744.0      796.0          0          0  439.646447   \n",
      "3        1   1.27   748.0     1529.0          2          0  233.353553   \n",
      "4        0   1.16   743.0     2022.0          0          0   82.353553   \n",
      "...    ...    ...     ...        ...        ...        ...         ...   \n",
      "5995     0   4.09   745.0     1856.0          0          0  100.646447   \n",
      "5996     0   2.18   744.0      781.0          2          0  248.353553   \n",
      "5997     0   1.25   748.0     1600.0          2          0  379.646447   \n",
      "5998     0   3.17   747.0      967.0          2          0  267.646447   \n",
      "5999     1   7.24   743.0      897.0          2          0  239.353553   \n",
      "\n",
      "            CIR1        CIR2        CIR3  ...     CIR1010     CIR1011  \\\n",
      "0     367.353553  744.646447  717.646447  ...  726.646447  367.353553   \n",
      "1     470.646447  409.353553  332.353553  ...  491.353553  403.646447   \n",
      "2     447.646447  130.353553   96.353553  ...  271.646447   73.353553   \n",
      "3     239.353553  175.646447  532.646447  ...  225.353553  154.646447   \n",
      "4     219.646447  110.646447  293.353553  ...  132.353553  131.353553   \n",
      "...          ...         ...         ...  ...         ...         ...   \n",
      "5995  285.646447  184.646447  251.646447  ...  268.646447  144.353553   \n",
      "5996  136.353553  127.353553  236.646447  ...    8.353553  268.646447   \n",
      "5997  522.646447  243.646447  128.353553  ...  201.646447   88.353553   \n",
      "5998  233.646447  305.646447   91.646447  ...  207.646447  305.646447   \n",
      "5999  176.353553  228.353553   51.353553  ...  192.353553  256.353553   \n",
      "\n",
      "         CIR1012     CIR1013     CIR1014     CIR1015   RX_Level  \\\n",
      "0     802.646447  818.646447  466.646447  767.646447 -33.465374   \n",
      "1     334.353553  210.353553  102.353553    0.353553 -34.892422   \n",
      "2     125.353553  169.353553  182.353553    0.000000 -38.436975   \n",
      "3     171.646447  277.646447  317.646447    0.000000 -21.918230   \n",
      "4     102.353553  126.353553  162.646447    0.353553 -21.603535   \n",
      "...          ...         ...         ...         ...        ...   \n",
      "5995  153.353553   13.353553  194.353553    0.000000 -23.421597   \n",
      "5996  169.353553  322.646447  310.646447    0.000000 -24.201683   \n",
      "5997  305.646447  228.353553  162.353553    0.000000 -21.369582   \n",
      "5998  116.353553  169.353553   13.353553    0.000000 -29.968119   \n",
      "5999  248.646447  255.646447  200.646447    0.000000 -23.887993   \n",
      "\n",
      "      First_Path_Power_Level         SNR  Total_Distance  \n",
      "0                 -39.074609   37.700000   234035.764530  \n",
      "1                 -53.179491   95.794118   127050.760471  \n",
      "2                 -49.812107   55.384615   115175.236492  \n",
      "3                 -25.863535  200.859375   101750.988607  \n",
      "4                 -23.232193  152.723684   119190.284232  \n",
      "...                      ...         ...             ...  \n",
      "5995              -35.134911  114.500000   104449.115468  \n",
      "5996              -29.556339  286.454545   104875.931142  \n",
      "5997              -23.789663  169.866667   168382.196193  \n",
      "5998              -33.686670  187.781250   113025.354711  \n",
      "5999              -29.600876  295.633333   135295.736175  \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     1   6.18   749.0     3668.0          2          0  747.646447   \n",
      "1     1   4.54   741.0     1031.0          2          0  123.353553   \n",
      "2     1   4.39   744.0      796.0          0          0  439.646447   \n",
      "3     1   1.27   748.0     1529.0          2          0  233.353553   \n",
      "4     0   1.16   743.0     2022.0          0          0   82.353553   \n",
      "\n",
      "         CIR1        CIR2        CIR3  ...     CIR1010     CIR1011  \\\n",
      "0  367.353553  744.646447  717.646447  ...  726.646447  367.353553   \n",
      "1  470.646447  409.353553  332.353553  ...  491.353553  403.646447   \n",
      "2  447.646447  130.353553   96.353553  ...  271.646447   73.353553   \n",
      "3  239.353553  175.646447  532.646447  ...  225.353553  154.646447   \n",
      "4  219.646447  110.646447  293.353553  ...  132.353553  131.353553   \n",
      "\n",
      "      CIR1012     CIR1013     CIR1014     CIR1015   RX_Level  \\\n",
      "0  802.646447  818.646447  466.646447  767.646447 -33.465374   \n",
      "1  334.353553  210.353553  102.353553    0.353553 -34.892422   \n",
      "2  125.353553  169.353553  182.353553    0.000000 -38.436975   \n",
      "3  171.646447  277.646447  317.646447    0.000000 -21.918230   \n",
      "4  102.353553  126.353553  162.646447    0.353553 -21.603535   \n",
      "\n",
      "   First_Path_Power_Level         SNR  Total_Distance  \n",
      "0              -39.074609   37.700000   234035.764530  \n",
      "1              -53.179491   95.794118   127050.760471  \n",
      "2              -49.812107   55.384615   115175.236492  \n",
      "3              -25.863535  200.859375   101750.988607  \n",
      "4              -23.232193  152.723684   119190.284232  \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": "2024-03-19T14:49:34.451480Z",
     "start_time": "2024-03-19T14:49:17.629117Z"
    }
   },
   "id": "79c2c23691b26753",
   "execution_count": 18
  },
  {
   "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."
   ],
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   "id": "42eff9445377f73c"
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  {
   "cell_type": "code",
   "outputs": [],
   "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,
    "is_executing": true
   },
   "id": "c8745832a585d5ec",
   "execution_count": null
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  {
   "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.\n",
    "\n",
    "## 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.\n",
    "\n",
    "## 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."
   ],
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  {
   "cell_type": "code",
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   "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()\n",
    "\n",
    "# 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",
    "# 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,
    "is_executing": true
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   "id": "41091791008ff727",
   "execution_count": null
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