Beginning Machine and Deep Learning with Zeek logs/06 - Beginning Data Scaling.ipynb

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   "source": [
    "Author: Nik Alleyne <br>\n",
    "Author Blog: **https://www.securitynik.com** <br>\n",
    "Author GitHub: **github.com/securitynik** <br>\n",
    "\n",
    "Author Books: [  <br>\n",
    "\n",
    "                \"https://www.amazon.ca/Learning-Practicing-Leveraging-Practical-Detection/dp/1731254458/\", \n",
    "                \n",
    "                \"https://www.amazon.ca/Learning-Practicing-Mastering-Network-Forensics/dp/1775383024/\" \n",
    "            ] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "### Why this series?\n",
    "When teaching the SANS SEC595: Applied Data Science and Machine Learning for Cybersecurity Professionals \n",
    "**https://www.sans.org/cyber-security-courses/applied-data-science-machine-learning/** I am always asked,\n",
    "\"Will you be sharing your demo notebooks?\" or \"Can we get a copy of your demo notebooks?\" or ... well you get the point.\n",
    "My answer is always no. Not that I do not want to share, (sharing is caring :-D) , but the demo notebooks \n",
    "by themselves, would not make sense or add real value. Hence, this series! \n",
    "\n",
    "This is my supplemental work, similar to what I would do in the demos but with a lot more details and references."
   ]
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   "source": [
    "## 06 - Beginning Data Scaling"
   ]
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   "source": [
    "### The series includes the following: <br>\n",
    "01 - Beginning Numpy <br>\n",
    "02 - Beginning Tensorflow  <br>\n",
    "03 - Beginning PyTorch <br>\n",
    "04 - Beginning Pandas <br>\n",
    "05 - Beginning Matplotlib <br>\n",
    "06 - Beginning Data Scaling <br>\n",
    "07 - Beginning Principal Component Analysis (PCA) <br>\n",
    "08 - Beginning Machine Learning Anomaly Detection - Isolation Forest and Local Outlier Factor <br>\n",
    "09 - Beginning Unsupervised Machine Learning - Clustering - K-means and DBSCAN <br>\n",
    "10 - Beginning Supervise Learning - Machine Learning - Logistic Regression, Decision Trees and Metrics <br>\n",
    "11 - Beginning Linear Regression - Machine Learning <br>\n",
    "12 - Beginning Deep Learning - Anomaly Detection with AutoEncoders, Tensorflow <br>\n",
    "13 - Beginning Deep Learning - Anomaly Detection with AutoEncoders, PyTroch <br>\n",
    "14 - Beginning Deep Learning - Linear Regression, Tensorflow <br>\n",
    "15 - Beginning Deep Learning - Linear Regression, PyTorch <br>\n",
    "16 - Beginning Deep Learning - Classification, Tensorflow <br>\n",
    "17 - Beginning Deep Learning - Classification, Pytorch <br>\n",
    "18 - Beginning Deep Learning - Classification - regression - MIMO - Functional API Tensorflow <br> \n",
    "19 - Beginning Deep Learning - Convolution Networks - Tensorflow <br>\n",
    "20 - Beginning Deep Learning - Convolution Networks - PyTorch <br>\n",
    "21 - Beginning Regularization - Early Stopping, Dropout, L2 (Ridge), L1 (Lasso) <br>\n",
    "22 - Beginning Model TFServing <br>\n",
    "\n",
    "But conn.log is not the only log file within Zeek. Let's build some models for DNS and HTTP logs. <br>\n",
    "I choose unsupervised, because there are no labels coming with these data. <br>\n",
    "\n",
    "23 - Continuing Anomaly Learning - Zeek DNS Log - Machine Learning <br>\n",
    "24 - Continuing Unsupervised Learning - Zeek HTTP Log - Machine Learning <br>\n",
    "\n",
    "This was a specific ask by someone in one of my class. <br>\n",
    "25 - Beginning - Reading Executables and Building a Neural Network to make predictions on suspicious vs suspicious  <br><br>\n",
    "\n",
    "With 25 notebooks in this series, it is quite possible there are things I could have or should have done differently.  <br>\n",
    "If you find any thing, you think fits those criteria, drop me a line. <br>\n",
    "\n",
    "If you find this series beneficial, I would greatly appreciate your feedback.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's import some libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>id.orig_h</th>\n",
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       "      <th>4430183</th>\n",
       "      <td>192.168.0.21</td>\n",
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       "            id.orig_h  id.orig_p    id.resp_h  id.resp_p  duration   \n",
       "0           127.0.0.1      27762    127.0.0.1      58552  0.000000  \\\n",
       "1         192.168.0.4      27761  192.168.0.4      48798  0.000000   \n",
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       "4         192.168.0.4      48786  192.168.0.4      27761  0.000000   \n",
       "...               ...        ...          ...        ...       ...   \n",
       "4430183  192.168.0.21      55757  192.168.0.4       9200  0.000051   \n",
       "4430184   192.168.0.4      37236  192.168.0.4       9200  0.000023   \n",
       "4430185   192.168.0.4      37244  192.168.0.4       9200  0.000005   \n",
       "4430186   192.168.0.4      37246  192.168.0.4       9200  0.000005   \n",
       "4430187   192.168.0.4      37254  192.168.0.4       9200  0.000005   \n",
       "\n",
       "         orig_bytes  resp_bytes  orig_pkts  orig_ip_bytes  resp_pkts   \n",
       "0                 0           0          0              0          0  \\\n",
       "1                 0           0          0              0          0   \n",
       "2                 0           0          0              0          0   \n",
       "3                 0           0          0              0          0   \n",
       "4                 0           0          0              0          0   \n",
       "...             ...         ...        ...            ...        ...   \n",
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       "4430187           0           0          0              0          1   \n",
       "\n",
       "         resp_ip_bytes  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  \n",
       "...                ...  \n",
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's import our dataset\n",
    "df_conn = pd.read_csv(r'df_conn_cleaned.csv', index_col=0)\n",
    "df_conn"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file represents Zeek (formerly Bro) connection log - conn.log`. <br>\n",
    "Zeek is a framework used for Network Security Monitoring.  <br>\n",
    "This entire series is based on using Zeek's data.  <br>\n",
    "The majority of the notebooks use the conn.log <br>\n",
    "You can learn more about Zeek here:\n",
    "   \n",
    "    https://zeek.org/\n",
    "\n",
    "Alternatively, come hang out with us in the:\n",
    "SANS SEC595: Applied Data Science and Machine Learning for Cybersecurity Professionals\n",
    "\n",
    "        https://www.sans.org/cyber-security-courses/applied-data-science-machine-learning/ OR\n",
    "\n",
    "SEC503 SEC503: Network Monitoring and Threat Detection In-Depth\n",
    "\n",
    "        https://www.sans.org/cyber-security-courses/network-monitoring-threat-detection/\n",
    "\n",
    "\n",
    "Here are also some blog posts on using Zeek for security monitoring\n",
    "Installing Zeek: \n",
    "\n",
    "        https://www.securitynik.com/2020/06/installing-zeek-314-on-ubuntu-2004.html\n",
    "\n",
    "Detecting PowerShell Empire Usage: \n",
    "\n",
    "        https://www.securitynik.com/2022/02/powershell-empire-detection-with-zeek.html\n",
    "\n",
    "Detecting Log4J Vulnerability Exploitation: \n",
    "\n",
    "        https://www.securitynik.com/2021/12/continuing-log4shell-zeek-detection.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the IPs and source port columns\n",
    "df_conn = df_conn.drop(columns=['id.orig_h', 'id.orig_p', 'id.resp_h'])\n",
    "df_conn.head(10)"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "At this point, you may think its a good idea to go ahead with building your model. <br>\n",
    "My recommendation is you don't.  <br>\n",
    "If you will be building models using Gradient Descent (think Linear Regression or Neural Networks)  <br>\n",
    "or distance based algorithms (think unsupervised learning, i.e. clustering, nearest neighbors, etc.),  <br>\n",
    "it is likely this data should be scaled first.\n",
    "\n",
    "Let's get the basics of scaling out of the way.\n",
    "\n",
    "From a simple view <br>\n",
    "Let's say we are using feature \"id.resp_p\" which represents the responder ports.  <br>\n",
    "When working with TCP and UDP ports, we can have values from 0-65535, inclusive  <br>\n",
    "\n",
    "To learn more about TCP/UDP ports see:  <br>\n",
    "https://en.wikipedia.org/wiki/List_of_TCP_and_UDP_port_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0],\n",
       "       [   80],\n",
       "       [  443],\n",
       "       [  445],\n",
       "       [  995],\n",
       "       [65535]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's say we take a few of the common port numbers and 2 not so common numbers to make a feature\n",
    "# If you are wondering 0 and 65535 are the ones I'm considering not so common\n",
    "# https://www.stationx.net/common-ports-cheat-sheet/\n",
    "# The .T is for transposition\n",
    "# Transposing was discussed in the following notebooks:\n",
    "#   01 - Beginning Numpy\n",
    "#   02 - Beginning Tensorflow\n",
    "#   03 - Beginning PyTorch\n",
    "# Notice I add the dtype, int as ports are integers\n",
    "common_ports = np.array([0, 80, 443, 445, 995, 65535], dtype=int, ndmin=2).T\n",
    "common_ports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now if we look at the \"duration\" column in our above dataframe, \n",
    "# most of our values are very small. \n",
    "# Realistically, this feature is time in seconds\n",
    "# Let's create a sample feature for duration to match common_ports above\n",
    "# Rather than transpose like I did above, I just made it 2 dimensional\n",
    "# Notice I have the data type as float as the duration is normally float values\n",
    "duration = np.array([[1], [1], [2], [1], [2], [1]], dtype=np.float32)\n",
    "duration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.,     1.],\n",
       "       [   80.,     1.],\n",
       "       [  443.,     2.],\n",
       "       [  445.,     1.],\n",
       "       [  995.,     2.],\n",
       "       [65535.,     1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stacking the two columns to create a dataset\n",
    "# We also learned about using numpy, tensorflow and pytorch to stack\n",
    "#   01 - Beginning Numpy\n",
    "#   02 - Beginning Tensorflow\n",
    "#   03 - Beginning PyTorch\n",
    "np.set_printoptions(suppress=True)\n",
    "np.hstack((common_ports, duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65535, 1.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As can be seen above, the two values are on different scales.\n",
    "# You may also notice that the dataset was changed to float without me changing the dtype=np.float32 for duration\n",
    "# We can confirm these scales, by looking at the peak to peak. \n",
    "# Peak to Peak shows the difference between the smallest and largest port is 65535\n",
    "# and the difference between the smallest and largest duration is 1\n",
    "np.ptp(common_ports), np.ptp(duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With the basic understanding of why we need to have everything on one scale\n",
    "# let's find the easiest way to scale these values between 0 and 1.\n",
    "# Take each value and divide it by the max value\n",
    "# for example, if we take port 0/65535 we get 0\n",
    "0 / 65535"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Similarly, if we divide the largest value 65535/65535 we get 1\n",
    "65535/65535"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0003356984817273213, 0.006759746700236515, 0.0678110933089189)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This as you can see, pushes all values between 0 and 65535 to be between 0 and 1\n",
    "# For example, if we take 22, 443, 4444\n",
    "22/65535, 443/65535, 4444/65535,   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.00122072],\n",
       "       [0.00675975],\n",
       "       [0.00679026],\n",
       "       [0.01518273],\n",
       "       [1.        ]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using our intuition above to scale the common ports feature\n",
    "# We take each port (i) and divide it by the max (np.max) port value \n",
    "common_ports_scaled = np.array([ i/np.max(common_ports) for i in common_ports ], ndmin=2, dtype=np.float32)\n",
    "common_ports_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5],\n",
       "       [0.5],\n",
       "       [1. ],\n",
       "       [0.5],\n",
       "       [1. ],\n",
       "       [0.5]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Doing the same for the duration feature\n",
    "# We take each duration (i) and divide it by the max (np.max) duration value \n",
    "duration_scaled = np.array([ i/np.max(duration) for i in duration ], ndmin=2, dtype=np.float32)\n",
    "duration_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.    , 0.5   ],\n",
       "       [0.0012, 0.5   ],\n",
       "       [0.0068, 1.    ],\n",
       "       [0.0068, 0.5   ],\n",
       "       [0.0152, 1.    ],\n",
       "       [1.    , 0.5   ]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Suppressing scientific notation\n",
    "np.set_printoptions(suppress=True, precision=4)\n",
    "\n",
    "# Stacking the two matrices again.\n",
    "# As we can see now, both features (columns) are on the same scale\n",
    "# The scale is 0 to 1\n",
    "# Hence the smallest value to be had is 0 and the largest is 1. \n",
    "# We can see from the last sample below, 1s in both columns\n",
    "# This probably is the simplest of normalizations\n",
    "# Normalization is considered a per-sample transformation rather than a per-feature transformation\n",
    "np.hstack((common_ports_scaled, duration_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now leverage normalization similar to what is done in packages such as sklearn  <br>\n",
    "Keep in mind, there are many strategies available for scaling your data.  <br>\n",
    "You will have to make a decision on choice of scaling, based on the problem  <br>\n",
    "you are attempting to solve  <br>\n",
    "Do keep in mind also, scalers can be linear or nonlinear.  <br>\n",
    "\n",
    "Let's start with MinMaxScaler  <br>\n",
    "MinMax transforms the feature between 0 and 1  <br>\n",
    "Similar to what we did above but not the same way <br>\n",
    "This scaler is sensitive to outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The formula for MinMaxScaler\n",
    "# x_scaled = x - x.min() / x.max - x.min()\n",
    "# Let's write a function to perform this transformation\n",
    "def my_minmax_scaler(x: np.array) -> np.ndarray:\n",
    "    ''' Returns the a numpy array scaled between 0 and 1 \n",
    "            Parameter: \n",
    "                x (numpy array): A numpy array\n",
    "            \n",
    "            Returns:\n",
    "                A numpy ndarray\n",
    "    '''\n",
    "    return np.array([ (i - np.min(x) ) / (np.max(x) - np.min(x)) for i in x ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Returns the a numpy array scaled between 0 and 1 \n",
      "            Parameter: \n",
      "                x (numpy array): A numpy array\n",
      "            \n",
      "            Returns:\n",
      "                A numpy ndarray\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "# Reviewing the docstring\n",
    "print(my_minmax_scaler.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.    ],\n",
       "       [0.0012],\n",
       "       [0.0068],\n",
       "       [0.0068],\n",
       "       [0.0152],\n",
       "       [1.    ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run our scaler against the common ports\n",
    "my_minmax_scaler(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test our scaler against skleran MinMax Scaler\n",
    "# First import the necessary library\n",
    "# Referemce:\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.    ],\n",
       "       [0.0012],\n",
       "       [0.0068],\n",
       "       [0.0068],\n",
       "       [0.0152],\n",
       "       [1.    ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run sklearn's MinMax scaler against our data\n",
    "# Once completed, we see the same results as our manual process\n",
    "MinMaxScaler().fit_transform(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do the same thing with our standard scaler <br>\n",
    "In this case we want to have a mean of 0 and standard deviation of 1 <br>\n",
    "the formula is x - x.mean() / x.std <br>\n",
    "Keep in mind, this works best if our data is normally distributed  <br>\n",
    "If the data is not normally distributed, standard scaler may not be the best option  <br>\n",
    "Additionally, you may hear the term normalize or standardize your data used loosely  <br>\n",
    "The better term maybe to say scale your data by choosing a scaling strategy, either via normalization or standardization.  <br>\n",
    "Basically normalization and standardization are two different things."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our own standard scaler function\n",
    "def my_standard_scaler(x: np.array) -> np.ndarray:\n",
    "    ''' Returns a numpy array standardized with a mean of  0 and standard deviation of 1 \n",
    "            Parameter: \n",
    "                x (numpy array): A numpy array\n",
    "            \n",
    "            Returns:\n",
    "                A numpy ndarray    \n",
    "    '''\n",
    "    return np.array([ (i - np.mean(x)) / ( np.std(x)) for i in x ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4633],\n",
       "       [-0.46  ],\n",
       "       [-0.4451],\n",
       "       [-0.445 ],\n",
       "       [-0.4224],\n",
       "       [ 2.2359]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test our standard scaler function against the common ports\n",
    "my_standard_scaler(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validating our results, using skleran's StandardScaler\n",
    "# Reference:\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4633],\n",
       "       [-0.46  ],\n",
       "       [-0.4451],\n",
       "       [-0.445 ],\n",
       "       [-0.4224],\n",
       "       [ 2.2359]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# standard scaler validates our function works.\n",
    "StandardScaler(with_mean=True, with_std=True).fit_transform(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Leveraging Max Absolute Scaler, where each value is scaled by its maximum absolute value\n",
    "# This does not shift or center the data\n",
    "# This scaling is x_scaled = x / max(|x|)\n",
    "# This is similar to what we did before x / max(x), difference between now we are looking at max(|x|) absolute value\n",
    "\n",
    "def my_maxabs_scaler(x: np.array) -> np.ndarray:\n",
    "    ''' Returns a numpy array that is scaled by the absolute of the max value \n",
    "                Parameter: \n",
    "                x (numpy array): A numpy array\n",
    "            \n",
    "            Returns:\n",
    "                A numpy ndarray\n",
    "    '''\n",
    "    return np.array([ i / np.max(abs(x)) for i in x ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 20)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How does the absolute value come into play?\n",
    "x_neg , y_pos = -10, -20\n",
    "\n",
    "# The absolute value is the positive version of these numbers\n",
    "# Hence the reason I mention above about the similarity to what was done before\n",
    "np.abs(x_neg), abs(y_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.    ],\n",
       "       [0.0012],\n",
       "       [0.0068],\n",
       "       [0.0068],\n",
       "       [0.0152],\n",
       "       [1.    ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use our Max Absolute Scaler to scale the data\n",
    "my_maxabs_scaler(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validating our function works as expected\n",
    "# by leveraging sklearn's MaxAbsScaler\n",
    "# Reference:\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html\n",
    "from sklearn.preprocessing import MaxAbsScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.    ],\n",
       "       [0.0012],\n",
       "       [0.0068],\n",
       "       [0.0068],\n",
       "       [0.0152],\n",
       "       [1.    ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Once again, validating our function by comparing it with sklearn's MaxAbsScaler\n",
    "MaxAbsScaler().fit_transform(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One more!  <br>\n",
    "Let's look at RobustScaler <br>\n",
    "This methods uses statistics that are robust to outliers <br>\n",
    "It scales the data according to the quantile range <br>\n",
    "References: <br>\n",
    "https://aihints.com/how-to-find-interquartile-range-in-python-using-numpy/\n",
    "\n",
    "https://numpy.org/doc/stable/reference/generated/numpy.percentile.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The formula for Robust Scaler\n",
    "# x_scale = xi - x.median() / x_75th_percentile - x_25th_percentile\n",
    "\n",
    "def my_robust_scaler(x: np.array) -> np.ndarray:\n",
    "    ''' Returns a numpy array based on robust scaler \n",
    "            Parameter: \n",
    "                x (numpy array): A numpy array\n",
    "            \n",
    "            Returns:\n",
    "                A numpy ndarray    \n",
    "    '''\n",
    "    return np.array([ (i - np.median(x) ) / ( np.percentile(a=x, q=75) - np.percentile(a=x, q=25) ) for i in x ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6465],\n",
       "       [-0.53  ],\n",
       "       [-0.0015],\n",
       "       [ 0.0015],\n",
       "       [ 0.8023],\n",
       "       [94.7812]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test our Robust Scaler\n",
    "my_robust_scaler(common_ports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validating our function works as expected\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6465],\n",
       "       [-0.53  ],\n",
       "       [-0.0015],\n",
       "       [ 0.0015],\n",
       "       [ 0.8023],\n",
       "       [94.7812]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare our scaler\n",
    "RobustScaler().fit_transform(common_ports)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome, we have a better understanding now of how to create our own scalers, use them against our data, then validate against sklearn's built in functions.  <br>\n",
    "With that understanding, let's scale our data using these different scalers built into sklearn.  <br>\n",
    "While it is nice that we were able to build our own scalers, we are better off taking advantage of the existing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>192.168.0.10</td>\n",
       "      <td>52830</td>\n",
       "      <td>89.187.183.77</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.424980</td>\n",
       "      <td>227</td>\n",
       "      <td>791633</td>\n",
       "      <td>176</td>\n",
       "      <td>9387</td>\n",
       "      <td>174</td>\n",
       "      <td>367737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>192.168.0.10</td>\n",
       "      <td>52832</td>\n",
       "      <td>89.187.183.77</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.272567</td>\n",
       "      <td>227</td>\n",
       "      <td>406607</td>\n",
       "      <td>171</td>\n",
       "      <td>9127</td>\n",
       "      <td>71</td>\n",
       "      <td>169939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id.orig_h  id.orig_p      id.resp_h  id.resp_p  duration  orig_bytes   \n",
       "0     127.0.0.1      27762      127.0.0.1      58552  0.000000           0  \\\n",
       "1   192.168.0.4      27761    192.168.0.4      48798  0.000000           0   \n",
       "2   192.168.0.4      27761    192.168.0.4      48804  0.000000           0   \n",
       "3     127.0.0.1      27762      127.0.0.1      58568  0.000000           0   \n",
       "4   192.168.0.4      48786    192.168.0.4      27761  0.000000           0   \n",
       "5  192.168.0.10      52824  89.187.183.77       8888  0.053276         208   \n",
       "6  192.168.0.10      52826  89.187.183.77       8888  0.383646         227   \n",
       "7  192.168.0.10      52828  89.187.183.77       8888  0.054294         208   \n",
       "8  192.168.0.10      52830  89.187.183.77       8888  0.424980         227   \n",
       "9  192.168.0.10      52832  89.187.183.77       8888  0.272567         227   \n",
       "\n",
       "   resp_bytes  orig_pkts  orig_ip_bytes  resp_pkts  resp_ip_bytes  \n",
       "0           0          0              0          0              0  \n",
       "1           0          0              0          0              0  \n",
       "2           0          0              0          0              0  \n",
       "3           0          0              0          0              0  \n",
       "4           0          0              0          0              0  \n",
       "5         976          6            528          6           1296  \n",
       "6      692369        202          10739        117         240893  \n",
       "7         977          6            528          6           1297  \n",
       "8      791633        176           9387        174         367737  \n",
       "9      406607        171           9127         71         169939  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's review the first 10 records from our dataset\n",
    "df_conn.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id.orig_p</th>\n",
       "      <th>id.resp_p</th>\n",
       "      <th>duration</th>\n",
       "      <th>orig_bytes</th>\n",
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       "      <th>2</th>\n",
       "      <td>27761</td>\n",
       "      <td>48804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>27762</td>\n",
       "      <td>58568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>48786</td>\n",
       "      <td>27761</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>5</th>\n",
       "      <td>52824</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.053276</td>\n",
       "      <td>208</td>\n",
       "      <td>976</td>\n",
       "      <td>6</td>\n",
       "      <td>528</td>\n",
       "      <td>6</td>\n",
       "      <td>1296</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52826</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.383646</td>\n",
       "      <td>227</td>\n",
       "      <td>692369</td>\n",
       "      <td>202</td>\n",
       "      <td>10739</td>\n",
       "      <td>117</td>\n",
       "      <td>240893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52828</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.054294</td>\n",
       "      <td>208</td>\n",
       "      <td>977</td>\n",
       "      <td>6</td>\n",
       "      <td>528</td>\n",
       "      <td>6</td>\n",
       "      <td>1297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52830</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.424980</td>\n",
       "      <td>227</td>\n",
       "      <td>791633</td>\n",
       "      <td>176</td>\n",
       "      <td>9387</td>\n",
       "      <td>174</td>\n",
       "      <td>367737</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52832</td>\n",
       "      <td>8888</td>\n",
       "      <td>0.272567</td>\n",
       "      <td>227</td>\n",
       "      <td>406607</td>\n",
       "      <td>171</td>\n",
       "      <td>9127</td>\n",
       "      <td>71</td>\n",
       "      <td>169939</td>\n",
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      ],
      "text/plain": [
       "   id.orig_p  id.resp_p  duration  orig_bytes  resp_bytes  orig_pkts   \n",
       "0      27762      58552  0.000000           0           0          0  \\\n",
       "1      27761      48798  0.000000           0           0          0   \n",
       "2      27761      48804  0.000000           0           0          0   \n",
       "3      27762      58568  0.000000           0           0          0   \n",
       "4      48786      27761  0.000000           0           0          0   \n",
       "5      52824       8888  0.053276         208         976          6   \n",
       "6      52826       8888  0.383646         227      692369        202   \n",
       "7      52828       8888  0.054294         208         977          6   \n",
       "8      52830       8888  0.424980         227      791633        176   \n",
       "9      52832       8888  0.272567         227      406607        171   \n",
       "\n",
       "   orig_ip_bytes  resp_pkts  resp_ip_bytes  \n",
       "0              0          0              0  \n",
       "1              0          0              0  \n",
       "2              0          0              0  \n",
       "3              0          0              0  \n",
       "4              0          0              0  \n",
       "5            528          6           1296  \n",
       "6          10739        117         240893  \n",
       "7            528          6           1297  \n",
       "8           9387        174         367737  \n",
       "9           9127         71         169939  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's drop the source and destination IP addresses\n",
    "df_conn = df_conn.drop(columns=['id.orig_h', 'id.resp_h'], inplace=False)\n",
    "df_conn.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4236, 0.9108, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.4236, 0.7591, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.4236, 0.7592, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       ...,\n",
       "       [0.5683, 0.1431, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.5683, 0.1431, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.5685, 0.1431, 0.    , ..., 0.    , 0.    , 0.    ]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As we can see our Zeek conn.log data is on different scales\n",
    "# In machine learning you fit and transform on the training data. \n",
    "# However, you only transform the test data\n",
    "# We don't have a test dataset at this time but when we build our models\n",
    "# we will see this in practice\n",
    "# Applying MinMax Scaling to our dataset\n",
    "df_conn_minmax = MinMaxScaler().fit_transform(df_conn)\n",
    "df_conn_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
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       "      <td>0.6173</td>\n",
       "      <td>0.6282</td>\n",
       "      <td>0.6706</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.1523</td>\n",
       "      <td>0.0957</td>\n",
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       "      <td>0.1431</td>\n",
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       "      <td>0.1431</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count    mean     std  min     25%     50%     75%  max\n",
       "0  4430183.0  0.6375  0.1009  0.0  0.6173  0.6282  0.6706  1.0\n",
       "1  4430183.0  0.1523  0.0957  0.0  0.1431  0.1431  0.1431  1.0\n",
       "2  4430183.0  0.0000  0.0013  0.0  0.0000  0.0000  0.0000  1.0\n",
       "3  4430183.0  0.0000  0.0005  0.0  0.0000  0.0000  0.0000  1.0\n",
       "4  4430183.0  0.0000  0.0016  0.0  0.0000  0.0000  0.0000  1.0\n",
       "5  4430183.0  0.0000  0.0006  0.0  0.0000  0.0000  0.0000  1.0\n",
       "6  4430183.0  0.0000  0.0005  0.0  0.0000  0.0000  0.0000  1.0\n",
       "7  4430183.0  0.0000  0.0008  0.0  0.0000  0.0000  0.0000  1.0\n",
       "8  4430183.0  0.0000  0.0009  0.0  0.0000  0.0000  0.0000  1.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To confirm our data has been scaled, let's first look at the statistics\n",
    "# First, I take the scaled numpy array above and turn it into a dataframe\n",
    "# Next, I described the dataframe, then transpose it\n",
    "# Once transposed, because the values are in scientific notation, I then round it to 4 decimal places\n",
    "# We now have a better view of the scaled data\n",
    "# We can see the largest value is 1 and the smallest is 0. \n",
    "# This means, the mean std, 25, 50 and 75 percentile are all between 0 and 1\n",
    "np.round(pd.DataFrame(df_conn_minmax).describe().T,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting visual with the scaled data\n",
    "# Create a function to do view the scaled vs unscaled data, \n",
    "# This makes our lives easier, allowing us to see what the transformation looks like\n",
    "# Who wants to change these values below constantly?!\n",
    "# I do not! Setting up a function to help with the heavy lifting\n",
    "\n",
    "def plot_unscaled_vs_scaled(unscaled_data: np.array, scaled_data: np.array, title='') -> None:\n",
    "    ''' Plots subplots of normal vs scaled data \n",
    "            Parameter: \n",
    "                unscaled_data (numpy array): A numpy array\n",
    "                scaled_data (numpy array): A numpy array\n",
    "                title (string): A String\n",
    "\n",
    "            Returns:\n",
    "                None       \n",
    "    '''\n",
    "    plt.style.use('ggplot')\n",
    "    fjg, ax = plt.subplots(nrows=4, ncols=2, figsize=(12,10), sharex=False, sharey=False, squeeze=True)\n",
    "    plt.suptitle(title)\n",
    "    ax[0][0].hist(unscaled_data)\n",
    "    ax[0][0].set_title('Unscaled data - dataset', fontsize=10)\n",
    "    ax[0][1].hist(scaled_data)\n",
    "    ax[0][1].set_title('Scaled data - dataset', fontsize=10)\n",
    "\n",
    "    ax[1][0].set_title('Unscaled data - orig and resp bytes', fontsize=10)\n",
    "    ax[1][0].scatter(x=unscaled_data['orig_bytes'], y=unscaled_data['resp_bytes'])\n",
    "    ax[1][1].set_title('Scaled data - orig and resp bytes', fontsize=10)\n",
    "    ax[1][1].scatter(x=scaled_data[:, 2], y=scaled_data[:, 3])\n",
    "\n",
    "    ax[2][0].set_title('Unscaled data - orig_pkts vs resp_pkts', fontsize=10)\n",
    "    ax[2][0].scatter(x=unscaled_data['orig_pkts'], y=unscaled_data['resp_pkts'])\n",
    "    ax[2][1].set_title('Scaled data - orig_pkts vs resp_pkts', fontsize=10)\n",
    "    ax[2][1].scatter(x=scaled_data[:, 4], y=scaled_data[:, 6])\n",
    "\n",
    "    ax[3][0].set_title('Scaled data - orig_ip_bytes vs resp_ip_bytes', fontsize=10)\n",
    "    ax[3][0].scatter(x=unscaled_data['orig_ip_bytes'], y=unscaled_data['resp_ip_bytes'])\n",
    "    ax[3][1].set_title('Scaled data', fontsize=10)\n",
    "    ax[3][1].scatter(x=scaled_data[:, 5], y=scaled_data[:, 7])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# With our function developed, plot the normal data vs the MinMax scaled data.\n",
    "# As we see below, while the data has been scaled, between 0 and \n",
    "plot_unscaled_vs_scaled(unscaled_data=df_conn, scaled_data=df_conn_minmax, \\\n",
    "                        title='Unscaled vs MinMax Scaled Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.1205,  7.9239, -0.0196, ..., -0.0014, -0.0055, -0.0071],\n",
       "       [-2.1206,  6.3388, -0.0196, ..., -0.0014, -0.0055, -0.0071],\n",
       "       [-2.1206,  6.3398, -0.0196, ..., -0.0014, -0.0055, -0.0071],\n",
       "       ...,\n",
       "       [-0.6862, -0.0962, -0.0196, ..., -0.0014, -0.0032, -0.007 ],\n",
       "       [-0.6859, -0.0962, -0.0196, ..., -0.0014, -0.0032, -0.007 ],\n",
       "       [-0.6847, -0.0962, -0.0196, ..., -0.0014, -0.0032, -0.007 ]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leveraging Standard Scaler\n",
    "# The values returned in a numpy array\n",
    "df_conn_std_scaled = StandardScaler(with_mean=True, with_std=True).fit_transform(df_conn)\n",
    "df_conn_std_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-6.320</td>\n",
       "      <td>-1.591</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.201</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.092</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.327</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.593</td>\n",
       "      <td>8.856</td>\n",
       "      <td>795.211</td>\n",
       "      <td>1902.052</td>\n",
       "      <td>643.224</td>\n",
       "      <td>1625.552</td>\n",
       "      <td>1840.866</td>\n",
       "      <td>1309.378</td>\n",
       "      <td>1141.691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3            4   \n",
       "count  4430183.000  4430183.000  4430183.000  4430183.000  4430183.000  \\\n",
       "mean        -0.000        0.000       -0.000        0.000        0.000   \n",
       "std          1.000        1.000        1.000        1.000        1.000   \n",
       "min         -6.320       -1.591       -0.020       -0.001       -0.004   \n",
       "25%         -0.201       -0.096       -0.020       -0.001       -0.004   \n",
       "50%         -0.092       -0.096       -0.020       -0.001       -0.004   \n",
       "75%          0.327       -0.096       -0.020       -0.001       -0.004   \n",
       "max          3.593        8.856      795.211     1902.052      643.224   \n",
       "\n",
       "                 5            6            7            8  \n",
       "count  4430183.000  4430183.000  4430183.000  4430183.000  \n",
       "mean         0.000       -0.000       -0.000       -0.000  \n",
       "std          1.000        1.000        1.000        1.000  \n",
       "min         -0.006       -0.001       -0.006       -0.007  \n",
       "25%         -0.006       -0.001       -0.006       -0.007  \n",
       "50%         -0.006       -0.001       -0.006       -0.007  \n",
       "75%         -0.006       -0.001       -0.006       -0.007  \n",
       "max       1625.552     1840.866     1309.378     1141.691  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the statistics before plotting\n",
    "# Rather than transpose, let's look at the dataframe's statistics in its normal form\n",
    "# Let's round up to 3 decimal places this time\n",
    "np.round(pd.DataFrame(df_conn_std_scaled).describe(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the standard scaled data vs the unscaled data\n",
    "plot_unscaled_vs_scaled(unscaled_data=df_conn, scaled_data=df_conn_std_scaled, \\\n",
    "                        title='Unscaled vs Standard Scaled Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.4236, 0.9108, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.4236, 0.7591, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.4236, 0.7592, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       ...,\n",
       "       [0.5683, 0.1431, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.5683, 0.1431, 0.    , ..., 0.    , 0.    , 0.    ],\n",
       "       [0.5685, 0.1431, 0.    , ..., 0.    , 0.    , 0.    ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at MaxAbsScaler\n",
    "df_conn_max_abs_scaled = MaxAbsScaler().fit_transform(df_conn)\n",
    "df_conn_max_abs_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "      <td>4430183.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.638</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.101</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.617</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.628</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.671</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0            1            2            3            4   \n",
       "count  4430183.000  4430183.000  4430183.000  4430183.000  4430183.000  \\\n",
       "mean         0.638        0.152        0.000        0.000        0.000   \n",
       "std          0.101        0.096        0.001        0.001        0.002   \n",
       "min          0.000        0.000        0.000        0.000        0.000   \n",
       "25%          0.617        0.143        0.000        0.000        0.000   \n",
       "50%          0.628        0.143        0.000        0.000        0.000   \n",
       "75%          0.671        0.143        0.000        0.000        0.000   \n",
       "max          1.000        1.000        1.000        1.000        1.000   \n",
       "\n",
       "                 5            6            7            8  \n",
       "count  4430183.000  4430183.000  4430183.000  4430183.000  \n",
       "mean         0.000        0.000        0.000        0.000  \n",
       "std          0.001        0.001        0.001        0.001  \n",
       "min          0.000        0.000        0.000        0.000  \n",
       "25%          0.000        0.000        0.000        0.000  \n",
       "50%          0.000        0.000        0.000        0.000  \n",
       "75%          0.000        0.000        0.000        0.000  \n",
       "max          1.000        1.000        1.000        1.000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the statistics \n",
    "np.round(pd.DataFrame(df_conn_max_abs_scaled).describe(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oadOmeuKJJ/TZZ5+pePHicnNz09SpUzM9ST8AAPkVSSkAANKRPFn0oUOHVLJkSbttN27c0J9//plm0iMzKleurMqVK2vgwIHatGmT6tSpoy+++ELDhg1TmTJlZLVatX//flWrVi3V/devX6+LFy/q+++/1xNPPGFbHxUVleG5y5Qpo59++kl16tRJdyLp5D44cuSISpcubVt//vz5FE/pS8sTTzyh4sWLa+7cuXrssce0du1avffeeynKFSpUSG3btlXbtm118+ZNPf/88xo+fLgGDBiQavIvNaGhoapTp47Wr1+vnj17pjnB99323RdffKE1a9Zo+PDhGjFihHr06KHFixfbtl+/fl2LFy9W27Zt1apVqxT79+nTR7NmzUqRlDpy5IhKlSplWz569KisVqvCw8Nt61xcXFS/fn3Vr19f48aN00cffaT33ntP69at01NPPZWpfknm5uamKlWq6MiRI7pw4YKCgoLk4+NjS9ikZcGCBXryySc1ZcoUu/WxsbEqWrRomvslv2bc3NwyrGtYWJiOHDmSYv2hQ4fS3S+zkicpT06O7dmzR4cPH9a3335r93TF25/Olyy1KyIlaeHChfL09NSqVavk4eFhWz916tQsqTMAAHkZt+8BAJCO+vXry93dXZ9//nmKqya++uorJSYm6plnnrnr4169elWJiYl26ypXriwXFxfbLW7NmzeXi4uLPvzwwxTnTr7iKvkqmduvwLp586Y+++yzDOvQpk0bJSUlaejQoSm2JSYm2uYceuqpp+Tm5qaJEyfanWf8+PEZN/T/uLi4qFWrVlq6dKlmzJihxMREu1v3JOnixYt2y+7u7rr//vtljFFCQkKmzyVJw4YN06BBg2zzM6XmbvouKipKb731llq2bKl3331XY8aM0ZIlS2xPyJOkRYsW6fr16+rdu7datWqV4qdJkyZauHChLb7JJk+ebLc8ceJESbK9ri5dupSiPslJyjuPdbsjR47oxIkTKdbHxsZq8+bNKlKkiAIDA+Xi4qLmzZtr6dKl+t///pei/O2vtdv7Sro1L1nyfFBpCQoKUt26dfXll1/qzJkzKbafP3/e9v/GjRvrt99+09atW+22p3U1392YPXu2vvnmG9WqVUv169eXlPprwBijCRMmpNi/UKFCkmR7XyQrUKCALBaLkpKSbOuio6P1ww8//OM6AwCQ13GlFAAA6QgKCtIHH3yggQMH6oknnlDTpk1VsGBBbdq0SXPmzNHTTz+t55577q6Pu3btWr322mtq3bq1ypUrp8TERM2YMUMFChRQy5YtJd26xei9997T0KFD9fjjj+v555+Xh4eHtm3bppCQEI0YMUK1a9dWkSJF1KVLF/Xp00cWi0UzZsxIkTxITWRkpHr06KERI0Zo586devrpp+Xm5qYjR45o/vz5mjBhglq1aqXAwED169dPI0aMUJMmTdS4cWPt2LFDP/74Y7pXyNypbdu2mjhxogYNGqTKlSurYsWKdtuffvppBQcHq06dOipWrJgOHDigSZMm6dlnn7WbAygzIiMjbfMCpSWzfWeM0UsvvSQvLy99/vnnkqQePXpo4cKF6tu3r5566imFhIRo1qxZCggIUO3atVM9X9OmTfX1119r+fLlev75523ro6Ki1LRpUzVq1EibN2/WzJkz1aFDB9utjR9++KF+/fVXPfvsswoLC1NMTIw+++wzlShRQo899lia7du1a5c6dOigZ555Ro8//rj8/f116tQpffvttzp9+rTGjx9vS8p89NFHWr16tSIjI/XKK6+oYsWKOnPmjObPn6///ve/8vPzU5MmTfThhx/qxRdfVO3atbVnzx7NmjXL7uq5tEyePFmPPfaYKleurO7du6t06dI6d+6cNm/erJMnT2rXrl2SpP79+2vGjBlq1KiR+vbtq0KFCumrr75SWFiYdu/eneF5ki1YsEDe3t66efOmTp06pVWrVmnjxo2qWrWq5s+fbytXoUIFlSlTRv369dOpU6fk4+OjhQsXpnoFYI0aNSTduuKtYcOGKlCggNq1a6dnn31W48aNU6NGjdShQwfFxMRo8uTJKlu27F3VGQCAfMkJT/wDACDXmTlzpnn00UdNoUKFjIeHh6lQoYIZMmSI3SPujTFm3bp1RpKZP3++3fqoqCgjyUydOtUYY8zx48fNSy+9ZMqUKWM8PT2Nv7+/efLJJ81PP/2U4tz/+c9/TPXq1Y2Hh4cpUqSIiYyMNGvWrLFt37hxo3n00UeNl5eXCQkJMf379zerVq0yksy6dets5bp06WLCwsJSHP+rr74yNWrUMF5eXqZw4cKmcuXKpn///ub06dO2MklJSWbIkCGmePHixsvLy9StW9fs3bvXhIWFmS5dumSqD61WqylZsqSRZIYNG5Zi+5dffmmeeOIJExAQYDw8PEyZMmXMW2+9Za5cuZLucZP7dvTo0emW69KliylUqJDdusz03YQJE4wks3DhQrt9T5w4YXx8fEzjxo3NuXPnjKurq3nhhRfSPP+NGzdMwYIFTYsWLYwxxgwaNMhIMvv37zetWrUyhQsXNkWKFDGvvfaa+fvvv237/fzzz6ZZs2YmJCTEuLu7m5CQENO+fXtz+PDhdNt77tw58/HHH5vIyEhTvHhx4+rqaooUKWLq1atnFixYkKL8H3/8YTp37mwCAwONh4eHKV26tOndu7eJj483xhgTFxdn/v3vf9teA3Xq1DGbN282kZGRJjIy0nacO1/ryY4dO2Y6d+5sgoODjZubm7nvvvtMkyZNUtRl9+7dJjIy0nh6epr77rvPDB061EyZMsVIMlFRUem2OblPk388PT1NiRIlTJMmTcx//vOfFO9XY4zZv3+/eeqpp4y3t7cpWrSo6d69u9m1a1eKNiQmJprXX3/dBAYGGovFYm4fRk+ZMsVERETYfjdMnTrVVhcAAJA2izGZ+CoVAAAAAAAAyELMKQUAAAAAAACHIykFAAAAAAAAhyMpBQAAAAAAAIcjKQUAAAAAAACHIykFAAAAAAAAhyMpBQAAAAAAAIcjKQUAAAAAAACHIykFAAAAAAAAhyMpBQAAAAAAAIcjKQUAAAAAAACHIykFAAAAAAAAhyMpBQAAAAAAAIcjKQUAAAAAAACHIykFIMcbPHiwqlWr9o+OER0dLYvFop07d97VfuHh4Ro/fvw/Orejde3aVc2bN3d2NQAAQDrq1q2rN9544x8dY9q0afLz87urfe51TORsuXFMBiBjJKWAfCytwdC9DHBwS27sO2cNTnNjXwEA8ofz58+rZ8+eCg0NlYeHh4KDg9WwYUNt3LjR2VVzitz4hZezxhm5sa8AZ3J1dgUAAAAAICdp2bKlbt68qW+//ValS5fWuXPn9PPPP+vixYvOrhoA5ClcKQUgQ8nf+IwZM0bFixdXQECAevfurYSEBFuZzz77TBEREfL09FSxYsXUqlUr2zar1apRo0apbNmy8vDwUGhoqIYPH27b/vbbb6tcuXIqWLCgSpcurffff9/u2Kn55ptvVLFiRXl6eqpChQr67LPP7LZv3bpV1atXl6enpx566CHt2LEjw3bGxMToueeek5eXl0qVKqVZs2alKDNu3DhVrlxZhQoVUsmSJdWrVy9du3ZNkrR+/Xq9+OKLunLliiwWiywWiwYPHixJmjFjhh566CEVLlxYwcHB6tChg2JiYjKsU0aSkpL05ptvys/PTwEBAerfv7+MMXZlVq5cqccee8xWpkmTJjp27Jhte6lSpSRJ1atXl8ViUd26dSVJ27ZtU4MGDVS0aFH5+voqMjJS27dvt+1njNHgwYNt3yKHhISoT58+tu3x8fHq16+f7rvvPhUqVEiPPPKI1q9fn2FfAQDgTLGxsdqwYYNGjhypJ598UmFhYapZs6YGDBigpk2b2pXr0aOHihUrJk9PTz3wwANatmyZJOnixYtq37697rvvPhUsWFCVK1fWnDlz0j1vep+byaZNm6bQ0FAVLFhQLVq0yFSSLKMxUVJSkl5++WWVKlVKXl5eKl++vCZMmGDbPnjwYH377bdavHix7TM7uV73MobLDGeOyS5fvqyOHTsqMDBQXl5eioiI0NSpU23b//zzT7Vp00Z+fn7y9/dXs2bNFB0dnWFfAUgdV0oByJR169apePHiWrdunY4ePaq2bduqWrVq6t69u/73v/+pT58+mjFjhmrXrq1Lly5pw4YNtn0HDBigr7/+Wp988okee+wxnTlzRgcPHrRtL1y4sKZNm6aQkBDt2bNH3bt3V+HChdW/f/9U6zJr1ix98MEHmjRpkqpXr64dO3aoe/fuKlSokLp06aJr166pSZMmatCggWbOnKmoqCj17ds3wzZ27dpVp0+f1rp16+Tm5qY+ffqkSBy5uLjo008/ValSpXT8+HH16tVL/fv312effabatWtr/Pjx+uCDD3To0CFJkre3tyQpISFBQ4cOVfny5RUTE6M333xTXbt21YoVK+46FrcbO3aspk2bpv/85z+qWLGixo4dq0WLFqlevXq2MtevX9ebb76pKlWq6Nq1a/rggw/UokUL7dy5Uy4uLtq6datq1qypn376SZUqVZK7u7sk6a+//lKXLl00ceJEGWM0duxYNW7cWEeOHFHhwoW1cOFCffLJJ/ruu+9UqVIlnT17Vrt27bKd97XXXtP+/fv13XffKSQkRIsWLVKjRo20Z8+edPsKAABn8vb2lre3t3744Qc9+uij8vDwSFHGarXqmWee0V9//aWZM2eqTJky2r9/vwoUKCBJiouLU40aNfT222/Lx8dHy5cv1wsvvKAyZcqoZs2aqZ43vc/NiIgIbdmyRS+//LJGjBih5s2ba+XKlRo0aFC6bcnMmMhqtapEiRKaP3++AgICtGnTJr3yyisqXry42rRpo379+unAgQO6evWqLTnj7+8v6e7HcJnlzDHZ+++/r/379+vHH39U0aJFdfToUf3999+2fRs2bKhatWppw4YNcnV11bBhw9SoUSPt3r073b4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      "text/plain": [
       "<Figure size 1200x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the MaxAbs scaled data\n",
    "plot_unscaled_vs_scaled(unscaled_data=df_conn, scaled_data=df_conn_max_abs_scaled, \\\n",
    "                        title='Unscaled vs MaxAbs Scaled Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   -3.8396, 49352.    ,     0.    , ...,     0.    ,     0.    ,\n",
       "            0.    ],\n",
       "       [   -3.8399, 39598.    ,     0.    , ...,     0.    ,     0.    ,\n",
       "            0.    ],\n",
       "       [   -3.8399, 39604.    ,     0.    , ...,     0.    ,     0.    ,\n",
       "            0.    ],\n",
       "       ...,\n",
       "       [   -1.1243,     0.    ,     0.    , ...,     0.    ,     1.    ,\n",
       "           40.    ],\n",
       "       [   -1.1237,     0.    ,     0.    , ...,     0.    ,     1.    ,\n",
       "           40.    ],\n",
       "       [   -1.1214,     0.    ,     0.    , ...,     0.    ,     1.    ,\n",
       "           40.    ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the final scaler at this time,\n",
    "# Robust Scaler\n",
    "df_conn_robust_scaled = RobustScaler().fit_transform(df_conn)\n",
    "df_conn_robust_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4430183.0</td>\n",
       "      <td>4430183.0</td>\n",
       "      <td>4430183.0</td>\n",
       "      <td>4.430183e+06</td>\n",
       "      <td>4.430183e+06</td>\n",
       "      <td>4430183.0</td>\n",
       "      <td>4430183.0</td>\n",
       "      <td>4430183.0</td>\n",
       "      <td>4430183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.2</td>\n",
       "      <td>592.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>9.537000e+02</td>\n",
       "      <td>1.400830e+04</td>\n",
       "      <td>1.8</td>\n",
       "      <td>324.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3585.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.9</td>\n",
       "      <td>6153.5</td>\n",
       "      <td>41.9</td>\n",
       "      <td>9.896742e+05</td>\n",
       "      <td>3.261770e+06</td>\n",
       "      <td>292.8</td>\n",
       "      <td>230312.8</td>\n",
       "      <td>436.4</td>\n",
       "      <td>507029.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-11.8</td>\n",
       "      <td>-9200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.0</td>\n",
       "      <td>55086.0</td>\n",
       "      <td>33306.4</td>\n",
       "      <td>1.882413e+09</td>\n",
       "      <td>2.098064e+09</td>\n",
       "      <td>476032.0</td>\n",
       "      <td>423975367.0</td>\n",
       "      <td>571383.0</td>\n",
       "      <td>578874775.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0          1          2             3             4          5   \n",
       "count  4430183.0  4430183.0  4430183.0  4.430183e+06  4.430183e+06  4430183.0  \\\n",
       "mean         0.2      592.3        0.8  9.537000e+02  1.400830e+04        1.8   \n",
       "std          1.9     6153.5       41.9  9.896742e+05  3.261770e+06      292.8   \n",
       "min        -11.8    -9200.0        0.0  0.000000e+00  0.000000e+00        0.0   \n",
       "25%         -0.2        0.0        0.0  0.000000e+00  0.000000e+00        0.0   \n",
       "50%          0.0        0.0        0.0  0.000000e+00  0.000000e+00        0.0   \n",
       "75%          0.8        0.0        0.0  0.000000e+00  0.000000e+00        0.0   \n",
       "max          7.0    55086.0    33306.4  1.882413e+09  2.098064e+09   476032.0   \n",
       "\n",
       "                 6          7            8  \n",
       "count    4430183.0  4430183.0    4430183.0  \n",
       "mean         324.1        2.4       3585.8  \n",
       "std       230312.8      436.4     507029.5  \n",
       "min            0.0        0.0          0.0  \n",
       "25%            0.0        0.0          0.0  \n",
       "50%            0.0        0.0          0.0  \n",
       "75%            0.0        0.0          0.0  \n",
       "max    423975367.0   571383.0  578874775.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the statistics\n",
    "# This time, rounding to 1\n",
    "np.round(pd.DataFrame(df_conn_robust_scaled).describe(), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tcuXKKSAgQDfffLMWLVokSTpz5owee+wx3XDDDQoKClLt2rU1b968XD/34sWLGjhwoG644QaVKFFCt99+u1atWmVXZ+bMmapUqZKCgoLUtm3bfCXJ8oqp8ooHRowYoY8//lhff/21LT7Kate1xID5cfbsWXXr1k2lSpVSUFCQHnzwQe3bt89uHMLCwrRw4ULddNNN8vf316FDh7Ld0fXnn3+qS5cuKlGihMqXL6833ngjz8cmDxw4oDZt2qhcuXIKDg7Wbbfdpu+++86uTkxMjF577TX94x//UMmSJVWpUiV98MEHdnWuJZaNiYnRqFGj1K1bN4WEhOipp56SJP3nP//R3XffrcDAQFWsWFH9+/fXX3/9ZTsutzi+cePGeuaZZ/TMM88oNDRUZcqU0SuvvCJjTJ7tySmm+umnn+Tr66vjx4/b1X/uued099135xpT53Wd//bbb2rVqpVKlSqlEiVKqFatWlqyZEmebQWuiwFwTeLj482AAQOy7Z8xY4YJDQ21bXfv3t2EhISYp59+2uzatct88803JigoyHzwwQfGGGM2btxovL29zdy5c01SUpLZtGmTmTp1qu34QYMGmVKlSpmZM2ea/fv3m9WrV5sPP/zQVj5q1CizZs0ak5iYaBYuXGjKlStnxo0bZysfPny4qVOnjm17zpw5pnz58uaLL74wBw8eNF988YUpXbq0mTlzpjHGmD///NNERESYzp07mx07dphvvvnGVKlSxUgymzdvznE8HnzwQVOnTh2zbt0689///tc0atTIBAYGmjfeeMNW54033jA//PCDSUxMNN9//72JjY01ffr0McYYc/HiRTNlyhQTEhJijh07Zo4dO2b+/PNPY4wx06dPN0uWLDEHDhww69atMw0bNjQPPvhgnnOUl8mTJ5uQkBAzb948s3v3bjNo0CDj6+tr9u7da4wxJjEx0UgyMTExtvE6evRotjmeM2eOCQgIMB999JHZs2ePGTlypAkJCbEb9ytdunTJDBs2zGzcuNEcPHjQzJkzxwQFBZnPPvvMVievaye/436l7t27m+DgYPP444+bHTt2mB07dphLly6ZmjVrmn/84x9m27ZtZufOnaZz584mNjbWXLx40aSlpZnQ0FAzcOBAs3//frNz504zc+ZM89tvvxljMq+zEiVKmPvuu89s3rzZ/Pjjj6ZatWqmc+fOObZj5cqVRpKpWbOmWb58udm2bZtp2bKliYmJMZcuXTLGGNOrVy/TokULu+Nat25tunXrZowxZsOGDUaS+e6778yxY8fMmTNnbHOS23U+YcIEU7FiRfPTTz+ZpKQks3r1ajN37twc2woAcI60tDQTHBxsnnvuOZOamuqwTkZGhrnjjjtMrVq1zPLly82BAwfMN998Y5YsWWKMMebIkSNmwoQJZvPmzebAgQPmzTffNN7e3mb9+vW2c1wZxz355JOmUaNG5qeffjL79+83EyZMMP7+/raY4OeffzZeXl5m3LhxZs+ePWbq1KkmLCzMLh64Un5iqrzigT///NN07NjRNG/e3BYfXbx40RiTdwx4rVq3bm1q1qxpfvrpJ7NlyxbTrFkzU61aNdvv5hkzZhhfX1/TqFEjs2bNGrN7927z119/me7du5s2bdrYjWl0dLT57rvvzPbt203btm1NyZIlHcbPWbZs2WLee+89s337drN3714zdOhQExAQYIs3jDEmOjralC5d2rz99ttm3759ZuzYscbLy8vs3r073+PuSHR0tAkJCTETJ040+/fvt/0pUaKEeeONN8zevXvNmjVrTFxcnOnRo4cxJu84Pj4+3gQHB5sBAwaY3bt32+b38ljuSvmJqWrUqGHGjx9v27506ZIpU6aM+eijj3KNqfO6zh966CHTtGlTs23bNtvP1Y8//phjW4GCQFIqn3799VczduxY89RTT5kOHTrY/VLLrzVr1piBAweaLl26mD59+pivv/66EFoKZ7mapFR0dLRJT0+37evQoYN59NFHjTHGfPHFFyYkJMScP38+27nOnz9v/P397ZJQeZkwYYKpX7++bfvKpFTVqlWz/eN71KhRpmHDhsYYY95//30THh5u/v77b1v5u+++m+sv8j179hhJZsOGDbZ9u3btMpJyTY58/vnnJjw83LZ95djlZOPGjUaS7RfstYqKijJjxoyx23fbbbeZvn37GmP+l5SaMmWKXZ0r23n77bebfv362dW58847c01KOdKvXz/Trl0723Ze1861jnv37t1NuXLlbEGtMcbMnj3bxMbGGqvVatt38eJFExgYaJYtW2bOnDljJJlVq1Y5POfw4cONt7e3OXLkiG3ft99+a7y8vMyxY8ccHpOVlPr0009t+86cOWMCAwNtwfj69euNt7e3OXr0qDHGmBMnThgfHx9bO7Lm6MprM6/r/NlnnzX33XefXX8BFDziJ1yLBQsWmFKlSpmAgADTqFEjM2TIELN161Zb+bJly4yXl5fZs2dPvs/50EMPmX/+85+27cvjuN9++814e3ub33//3e6YJk2amCFDhhhjjHnssceyfUny6KOP5hq3XEtMZYzjeODyZE9OrowBr8XevXuNJLNmzRrbvtOnT5vAwEAzf/58Y0xmHCTJbNmyxe7Yy9t5/vx54+vraz7//HNbeXJysgkKCso1KeVIrVq1zFtvvWXbjo6ONl27drVtW61WU7ZsWfPuu+8aY6593KOjo83DDz9st69nz57mqaeestu3evVq4+XlZf7+++9c43hjMq+zmjVr2sUbL730kqlZs2aO7chPTDVu3Di7c3zxxRcmODjYXLhwwRjjOKbOz3Veu3ZtM2LEiBzbBhQGHt/Lp4sXLyomJkY9e/a8puM3b96st956S02bNtWkSZP05JNPavHixXaPCsFz1apVS97e3rbt8uXL2x4/a9q0qaKjo1WlShU9/vjj+uSTT5SSkiJJ2rVrly5evKgmTZrkeO7PPvtMd955pyIjIxUcHKyhQ4fq0KFDDuv+9ddfOnDggHr27Kng4GDbn9GjR+vAgQO2z8x6lCpLw4YNc+3frl275OPjo/r169v23Xjjjdkecfvuu+/UpEkT3XDDDSpZsqQef/xxnTlzxtbfnPzyyy9q1aqVKlWqpJIlSyo+Pl6Scuzna6+9Ztc/R/XOnz+vo0eP6s4777Tbf+edd2rXrl12+2699dZc27dnzx41aNDAbt+V2468/fbbql+/viIiIhQcHKwPPvggW1tzu3byO+6O1K5d226dhK1bt2r//v0qWbKkbdxKly6t1NRUHThwQKVLl1aPHj3UrFkztWrVSlOnTtWxY8fszlmpUiXdcMMNtu2GDRvKarVqz549ubbl8uurdOnSio2Ntc1BgwYNVKtWLX388ceSpDlz5ig6Olr33HNPjufLz3Xeo0cPbdmyRbGxserfv7+WL1+e55gBuHrET7gW7dq109GjR7Vw4UI1b95cq1atUr169WyLaG/ZskUVKlRQjRo1HB6fkZGhUaNGqXbt2ipdurSCg4O1bNmyHOOG7du3KyMjQzVq1LD7vfHjjz/axUe333673XH5iY/yE1PlJx5w5GpiQEl68MEHbX2rVatWjm328fGx62t4eLjd72ZJ8vPz0y233JLjZx08eFBpaWl28VBoaKhtyYCcXLhwQQMHDlTNmjUVFham4OBg7dq1K1u/Lv9si8WiyMhIu/joamPZLFfGfFu3btXMmTPtrotmzZrJarUqMTEx1zg+yx133CGLxWLXln379ikjIyPHduQVU/Xo0UP79+/Xzz//LCnzkcqOHTuqRIkSOZ4zP9d5//79NXr0aN15550aPny4tm3blq9xA64HC53nU1xcnOLi4nIsT0tL07x587RmzRqlpKSoYsWK6tKli+1/+D/99JNuu+02PfDAA5KkcuXK6eGHH9bXX3+tZs2a2f2PCu4hJCTE4Xo5ycnJ2RZ69PX1tdu2WCyyWq2SpJIlS2rTpk1atWqVli9frmHDhmnEiBHauHFjnotpr1u3Tl26dNHIkSPVrFkzhYaG6tNPP9WkSZMc1s9av+nDDz/MFlhdnvgoDElJSWrZsqX69OmjMWPGqHTp0vrPf/6jnj176tKlSwoKCnJ43F9//aVmzZqpWbNm+uSTTxQREaFDhw6pWbNmunTpksNjnn76aXXs2NG2HRUVdV1tz+0X/LX69NNPNXDgQE2aNEkNGzZUyZIlNWHCBK1fv96uXm7XzvW4sk8XLlxQ/fr1Ha5JFRERIUmaMWOG+vfvr6VLl+qzzz7T0KFDtWLFCt1xxx3X3Z7cPPnkk3r77bc1ePBgzZgxQ0888USu/8/Mz3Ver149JSYm6ttvv9V3332njh076v7778+2fhWA60P8hGsVEBCgpk2bqmnTpnrllVf05JNPavjw4erRo0ee8dGECRM0depUTZkyxbaW5XPPPZdj3HDhwgV5e3vrl19+yRYPBQcHF1ifHMlvPHClq40Bpcy1Fv/++29J2eOLqxUYGFgoP38DBw7UihUrNHHiRFWrVk2BgYFq3759trlzZnzUu3dv9e/fP1vdSpUqyc/PL8c4vjDffle2bFm1atVKM2bMUOXKlfXtt99mWwPtSvm5zp988kk1a9ZMixcv1vLlyzV27FhNmjRJzz77bGF1BSApVVCmT5+u33//Xc8995xKlSqlDRs26LXXXrMtUJyWliZ/f3+7Y/z8/HTmzBmdOnVKZcuWdVHLca1iY2Md3l2xadOmHL+5y4mPj4/uv/9+3X///Ro+fLjCwsL0ww8/qEWLFgoMDNT333+vJ598Mttxa9euVXR0tF5++WXbvqyFpx0pV66coqKidPDgQXXp0sVhnZo1a2r27NlKTU21fcOU9S1MTm688Ualp6frl19+0W233SYp8+6h5ORkW51ffvlFVqtVkyZNkpdX5k2a8+fPtzuPn59ftm+Ndu/erTNnzuj1119XxYoVJUn//e9/c21P6dKlVbp06VzrhISEKCoqSmvWrLHdeSVlLrqdn7ucLhcbG6uNGzeqW7dutn0bN27M9Zg1a9aoUaNG6tu3r21f1rdU+ZWfcc+vevXq6bPPPlPZsmUVEhKSY72sf2AOGTJEDRs21Ny5c21JqUOHDuno0aO2JODPP/8sLy+vPL8V/fnnn1WpUiVJmYur7t27VzVr1rSVd+3aVYMGDdKbb76pnTt3qnv37rayrLu9Lr9u8nOdS5nXwKOPPqpHH31U7du3V/PmzfXHH3/kee0AKDjET8ivm266SV999ZWkzLtkjhw5or179zqMudasWaM2bdqoa9eukjJfbLJ3717ddNNNDs8dFxenjIwMnTx5UnfffbfDOjVr1syWKMorPspPTJWfeMBRfHS1MaAkuztvcmtzenq61q9fr0aNGknKXDh+z549OY6fI1WqVJGvr682btxo+x1/7tw57d27N9e7ndesWaMePXqobdu2kjITKZe/dCU/riWWzUm9evW0c+dOVatWLcc6OcXxjzzyiCQ5vG6qV6+e6xfC+YmpnnzyST322GOqUKGCqlatanf3v6NrJj/XuSRVrFhRTz/9tJ5++mkNGTJEH374IUkpFCoe3ysAp0+f1qpVq/T888+rZs2aioyMVOvWrXXjjTdq5cqVkqS6detqw4YN2r59u6xWq44ePWp7Q8i1/AMSrtenTx/t3btX/fv317Zt27Rnzx5NnjxZ8+bN0z//+c98n2fRokV68803tWXLFv3222+aNWuWrFarYmNjFRAQoJdeekmDBg3SrFmzdODAAf3888+aPn26JKl69eo6dOiQPv30Ux04cEBvvvmmEhIScv28kSNHauzYsXrzzTe1d+9ebd++XTNmzNDkyZMlSZ07d5bFYlGvXr20c+dOLVmyRBMnTsz1nLGxsWrevLl69+6t9evX65dfftGTTz5p901mtWrVlJaWprfeeksHDx7U7Nmz9d5779mdJyYmRhcuXND333+v06dPKyUlxfYtVNZxCxcu1KhRo/I9vrl58cUXNW7cOH322Wfas2ePBg8erC1btmjAgAFXdZ5nn31W06dP18cff6x9+/Zp9OjR2rZtW67fIFavXl3//e9/tWzZMu3du1evvPJKnomsK+Vn3POrS5cuKlOmjNq0aaPVq1crMTFRq1atUv/+/XXkyBElJiZqyJAhWrdunX777TctX75c+/bts0seBQQEqHv37tq6datWr16t/v37q2PHjoqMjMz1s1999VV9//332rFjh3r06KEyZcrYvb2nVKlSeuSRR/Tiiy/qgQceUIUKFWxlZcuWVWBgoJYuXaoTJ07Y7l7M6zrP+lndvXu39u7dq88//1yRkZGF+q0mAHvET3DkzJkzuu+++zRnzhxt27ZNiYmJ+vzzzzV+/Hi1adNGkhQfH6977rlH7dq104oVK2x3vmY91lm9enWtWLFCa9eu1a5du9S7d2+dOHEix8+sUaOGunTpom7duunLL79UYmKiNmzYoLFjx2rx4sWSZLtTeOLEidq3b5+mTZuW52Ok+Ymp8hMPxMTE2GLN06dPKy0t7ZpiwPyoXr262rRpo169euk///mPtm7dqq5du+qGG26wjX9+lCxZUt27d9eLL76olStX6tdff1XPnj3l5eWVZ3z05ZdfasuWLdq6das6d+581XdAXUssm5OXXnpJa9eu1TPPPKMtW7Zo3759+vrrr/XMM89Iyj2Oz3Lo0CG98MIL2rNnj+bNm6e33norz1gzPzFVs2bNFBISotGjR+uJJ56wO95RTJ2f6/y5557TsmXLlJiYqE2bNmnlypV2sR5QKFy9qJU7unKhzl9++cV06NDBdO3a1e5Pp06dzOTJk40xmQvwzZ4923Tu3Nk8+uijpkePHmb+/PmmQ4cOtrcdwP1s2LDBNG3a1ERERJjQ0FBz++23m4SEBLs6jhanHDBggImPjzfGZC6WGB8fb0qVKmUCAwPNLbfcYvcGtoyMDDN69GgTHR1tfH19TaVKlcxrr71mK3/xxRdNeHi4CQ4ONo8++qh544037BY2vHKhc2OM+eSTT0zdunWNn5+fKVWqlLnnnnvMl19+aStft26dqVOnjvHz8zN169Y1X3zxRZ6LQx47dsw89NBDxt/f31SqVMnMmjXLREdH2y24PXnyZFO+fHkTGBhomjVrZmbNmmUkmbNnz9rqPP300yY8PNxIMsOHDzfGGDN37lwTExNj/P39TcOGDc3ChQvzbE9+ZGRkmBEjRpgbbrjB+Pr6mjp16phvv/3WVp7TItqOFo989dVXTZkyZUxwcLD5xz/+Yfr372/uuOOOHD87NTXV9OjRw4SGhpqwsDDTp08fM3jwYLu5yuvaMSZ/436lnBZMPXbsmOnWrZspU6aM8ff3N1WqVDG9evUy586dM8ePHzcPP/ywKV++vPHz8zPR0dFm2LBhJiMjwxjzv+vsnXfeMVFRUSYgIMC0b9/e/PHHHzm2I2uh82+++cbUqlXL+Pn5mQYNGtgtZpvl+++/N5Jsi6xe7sMPPzQVK1Y0Xl5edmOT23X+wQcfmLp165oSJUqYkJAQ06RJE7Np06Yc2wrg+hE/IT9SU1PN4MGDTb169UxoaKgJCgoysbGxZujQoSYlJcVW78yZM+aJJ54w4eHhJiAgwNx8881m0aJFtrI2bdqY4OBgU7ZsWTN06FDTrVs3u999V76wJusteDExMcbX19eUL1/etG3b1mzbts1WZ/r06aZChQomMDDQtGrVykycODHPF7TkFVPlJx44efKkadq0qQkODjaSzMqVK40xeceA1+qPP/4wjz/+uAkNDbXFbJf/vOX0Ypor44vz58+bzp07m6CgIBMZGWkmT55sGjRoYAYPHpzjZycmJpp7773XBAYGmooVK5pp06ZlmytHcU6dOnVscaMx1xbL5hQ/ZcX7wcHBpkSJEuaWW26xvSgnrzg+Pj7e9O3b1zz99NMmJCTElCpVyvzrX//K9UUrVxNTvfLKK3YvhLmco5g6r+v8mWeeMVWrVjX+/v4mIiLCPP744+b06dM5thUoCBZjjHFRPsxtdezYUQMHDrQ94rN27Vq9+eabmjx5su2xpCwBAQF237xbrVYlJycrJCRE27dv19ixY/Xvf/8718dlALifpk2bKjIyUrNnz3Z1U5xixIgR+uqrr7Rly5ZCOf/s2bP1/PPP6+jRo3YLtANwH8RPQPH2119/6YYbbtCkSZOu+eUH7qZx48aqW7eupkyZUijn79mzp06dOqWFCxcWyvkBZ2BNqQIQExMjq9Wqc+fO5Xl7o5eXl229kjVr1qhGjRoEVICbS0lJ0XvvvadmzZrJ29tb8+bN03fffacVK1a4umluLyUlRceOHdPrr7+u3r17k5ACPAjxE+DZNm/erN27d6tBgwY6d+6cXn31VUm6qscA4di5c+e0fft2zZ07l4QU3B5JqXxKTU3V8ePHbdsnT55UUlKSgoODFRUVpbvuukvTpk1Tt27dVLlyZZ0/f17bt29XdHS06tWrp/Pnz+vnn39WrVq1lJaWppUrV2rdunUaOXKkC3sFoCBYLBYtWbJEY8aMUWpqqmJjY/XFF1/o/vvvd3XT3N748eM1ZswY3XPPPRoyZIirmwPgKhE/AcXbxIkTtWfPHvn5+al+/fpavXq1ypQp4+pmub02bdpow4YNevrpp9W0aVNXNwe4Ljy+l0+//vqrwwAoPj5e/fr1U3p6ur788kv9+OOP+uOPPxQSEqLq1aurY8eOqlSpks6fP69x48bp0KFDkjIXVOzUqZOqV6/u7K4AAAA4BfETAADIDUkpAAAAAAAAOJ1X3lUAAAAAAACAgkVSCgAAAAAAAE5HUgoAAAAAAABOR1IKAAAAAAAATufj6ga4g7Nnzyo9Pb1Qzh0REaFTp04VyrmLMvpdvNDv4qe49p1+eyYfHx+VKlXK1c1wS4UVQ3n6NXetGJecMTaOMS6OMS45Y2wcY1yyy2/8RFIqH9LT05WWllbg57VYLLbzF6eXINJv+l0cFNd+S8W37/S7ePUb+VMYMRTXnGOMS84YG8cYF8cYl5wxNo4xLteHx/dcjIsWAADg6hA/AQDgGbhTygVMaoqsCXOkrRt0TEYZskh1GsirbVdZAoJc3TwAAIAih/gJAADPQ1LKyUxqiqxjB0nHDkvGKCOrYNUSWXdvk9eQ8QRWAAAAlyF+AgDAM/H4npNZE+bYAir7Aqt0/EhmOQAAAGyInwAA8EwkpZxt64bsAVUWqzWzHAAAAP9D/AQAgEciKeVExhgpPY830LBiPwAAgI0xRspIz71SRgbxEwAAboiklBNZLBYpNTX3Sql/214pCQAAUNxZLBbJO49lUL29iZ8AAHBDbrPQeUJCgjZs2KDff/9dfn5+qlGjhrp27aqoqKhcj1u3bp0+++wznTp1SpGRkerSpYvq1avnpFY7kFe8RDwFAABgr04DadWSzEf1ruTllVkOAADcjtvcKbVz5041a9ZMY8aM0dChQ5WRkaHRo0crNZc7j/bs2aOpU6fqvvvu07hx43TbbbdpwoQJOnTokBNb/j/GGMk/IPdK/gHcfg4AAHAZr7ZdpcgKmQkouwIvKbJCZjkAAHA7bpOUevnll9W4cWNVrFhRMTEx6tevn06fPq2DBw/meMySJUtUt25dtW7dWhUqVFCnTp1UpUoVLV261Ikt/x+LxSL5+OZeyceX288BAAAuYwkIkteQ8VLjFlJ4WXmHR0jhZaXGLeQ1ZLwsAUGubiIAALgGbvP43pVSUlIkScHBwTnW2bt3r1q2bGm3r06dOtq4cWOhti1X3H4OAABw1SwBQfJ+7ClZOvdWZGSkjh8/zt3lAAC4ObdMSlmtVs2cOVOxsbGqVKlSjvWSk5MVGhpqty80NFTJyckO66elpSkt7X9vx7NYLAoMDLT9vSB4P/K4MnZvk44fsU9M/f/t596PPO7xd0pl9c/T+3kl+k2/i4vi2nf6Xbz6DdfiegMAwDO4ZVJq+vTpOnz4sF599dUCPW9CQoIWLFhg265cubLGjRuniIiIAv2c9Nff16nh/ZX+2wHJSLJIPtFVFTHyTfmEF+xnFWWRkZGuboJL0O/ipbj2Wyq+faffAAAAQP64XVJq+vTp2rRpk0aOHKnw8PBc64aFhencuXN2+86dO6ewsDCH9du2bWv3uF/Wt3CnTp1Senr69TX8/5nUFGW89qJ07LCUdcu5kdJ/O6Bjg3vL+18TPH5dBIvFUixvu6ff9Lu4KK59p9+e228fH58C/4IKAAAAbpSUMsboo48+0oYNGzRixAiVLVs2z2Nq1Kih7du366GHHrLt27Ztm6pXr+6wvq+vr3x9HS9EXlCBdsaXs+0TUlmsVun4EWV8OVvejz1VIJ9V1BljPPYfMLmh38VLce23VHz7Tr8BAACA/HGbt+9Nnz5dq1ev1oABAxQYGKjk5GQlJyfr0qVLtjrTpk3T3LlzbdstWrTQ1q1b9c033+j333/X/PnzdeDAATVv3twVXci0dUP2hFQWqzWzHAAAAAAAwMO5zZ1Sy5cvlySNGDHCbn/fvn3VuHFjSdLp06ftFr6MjY1V//799emnn2revHkqX768XnzxxVwXRy9MxhgpI4/HADMyZIxhAU8AAAAAAODR3CYpNX/+/DzrXJmwkqSGDRuqYcOGhdCiq2exWCTvPIbc25uEFAAAAAAA8Hhu8/iex6jTQPLKYdi9vDLLAQAAAAAAPBxJKSfzattViqyQPTHl5SVFVsgsBwAAAAAA8HBu8/iep7AEBMlryHhZE+ZIWzfIW0YZskh1GsirbVdZAoJc3UQAAAAAAIBCR1LKBSwBQfJ+7ClZOvdWZGSkjh8/zmu0AQAAAABAscLjey7GouYAAAAAAKA4IinlYlar1dVNgJvi7joAAAAAgDvj8T0XsCafkZk6Ujp6SL8bSRZJUZVkGTBcXmHhrm4eijCTmmJbj0wZ6ZK3D+uRAQAAAADcEndKOZk1+YzMkF7SkSTJapWMNfO/R5JkhvSSNfmMq5uIIsqkpsg6dpC0crF05qSU/Efmf1ctkXXsIJnUFFc3EQAAAACAfCMp5WRm6kgpPd1xYXp6ZjnggDVhjnTssHTlY3tWq3T8SGY5AAAAAABugqSUsx09dH3lKL62bsiekMpitWaWAwAAAADgJkhKOZHV+v+P6uVeicXPkY0xJnMNqdxkZLD4OQAAAADAbZCUciIvr/wNd37rofiwWCyZi5rnxts7sx4AAAAAAG6At+85m7e3lJGRSzlTghzUaSCtWuL4bjsvr8xyAACusHPnTi1cuFCJiYk6e/asBg4cqAYNcv6d8euvv2rkyOxrXH7wwQcKCwsrxJYCAIDihgyIExljpBIlpfPJOVcqESxjDHe8IBuvtl1l3b1NOn7EPjHl5SVFVpBX266uaxwAoMi6ePGiYmJidN9992nixIn5Pm7KlCkKCgqybYeEhBRG8wAAQDFGUsqJLBaL5OuXeyVfPxJScMgSECSvIeMz37K3dUPmHXfe3lKdBvJq21WWgKC8TwIAKHbi4uIUFxd31ceFhoaqRIkShdAiAACATCSlnK1OA2nlEsk4eATLwiNYyJ0lIEjejz0lPfYUd9QBAArVoEGDlJaWpooVK6pDhw668cYbXd0kAADgYUhKOZnlwXYyPy2V0h0kpby9ZHmwnfMbBaco6DfjkZACABSGUqVKqVevXqpatarS0tL0/fffa+TIkRozZoyqVKni8Ji0tDSlpaXZti0WiwIDA21/L0hZ5+P3oD3GJWeMjWOMi2OMS84YG8cYl+tDUsrJzLdfSOnpjgszMjLLH3vKuY1CoTGpKXaP2x3185P15vqy8LgdAKCIioqKUlRUlG07NjZWJ06c0OLFi/Xss886PCYhIUELFiywbVeuXFnjxo1TREREobUzMjKy0M7tzhiXnDE2jjEujjEuOWNsHGNcrg1JKWfbuiHnMmMyy0lKeQSTmiLr2EHSscOZcyspQ5JWLZHZvU1eQ8aTmAIAuIVq1app9+7dOZa3bdtWLVu2tG1nfVt86tQppef0Zdw1slgsioyM1PHjxwv8LmR3xrjkjLFxjHFxjHHJGWPjGOPimI+PT76+nCIp5UTGGCkjj8AsI4O1gjyENWGOXULqfwVW6fgRWRPmZK4PBQBAEZeUlKRSpUrlWO7r6ytfX1+HZYUVoBtjCP4dYFxyxtg4xrg4xrjkjLFxjHG5Nl6ubkBxYrFYJO888oDe3iSkPMXWDdkTUlms1tzvmgMAoICkpqYqKSlJSUlJkqSTJ08qKSlJp0+fliTNnTtX06ZNs9VfvHixNm7cqOPHj+vQoUOaOXOmduzYoWbNmrmi+QAAwINxp5Sz1WkgrVqSmZS4khdv3/MU3BUHACgqDhw4oJEjR9q2Z82aJUmKj49Xv379dPbsWVuCSpLS09M1a9Ys/fHHH/L391d0dLReeeUV3XzzzU5vOwAA8GwkpZzMq21XWXdvk44fsU9MeXlJkRXk1bar6xqHAsNdcQCAoqJWrVqaP39+juX9+vWz227Tpo3atGlT2M0CAADg8T1nswQEyfL8SCmqUmYiyuKV+d+oSrI8P5KFrz1JnQaZc+sId8UBAAAAAIo5t7lTaufOnVq4cKESExN19uxZDRw4UA0a5PyP+l9//dXuVvUsH3zwgcLCwgqxpbkzqSkybwy3XwDbSDp6SOaN4TK8kc1jcFccAAAAAAA5c5uk1MWLFxUTE6P77rtPEydOzPdxU6ZMUVDQ/5I8ISEhhdG8fOONbMWHJSBIXkPGZ8751g1SRoa8/f1lvbm+LA93IfkIAAAAACjW3CYpFRcXp7i4uKs+LjQ0VCVKlCiEFl2j/LyRjaSUx7AEBGUmGf9/TqOionTs2DFeFQoAAAAAKPbcJil1rQYNGqS0tDRVrFhRHTp00I033uiytvBGtuKNOQUAAAAA4H88NilVqlQp9erVS1WrVlVaWpq+//57jRw5UmPGjFGVKlUcHpOWlqa0tDTbtsViUWBgoO3v18tiscjq5Z17JS8veeW0OLaHyBrL4pakod/0u7gorn2n38Wr3wAAALh+HpuUioqKUlRUlG07NjZWJ06c0OLFi/Xss886PCYhIUELFiywbVeuXFnjxo1TREREgbXrWMkQpf9xKsdyn5IhKl++fIF9XlEWGRnp6ia4BP0uXoprv6Xi23f6DQAAAOSPxyalHKlWrZp2796dY3nbtm3VsmVL23bWt76nTp1Senoej93lU/rx3/MsP3bsWIF8VlFlsVgUGRmp48ePF6u1leg3/S4uimvf6bfn9tvHx6dAv6ACAABApmKVlEpKSlKpUqVyLPf19ZWvr6/DsoIItI0x0qVLuVe6dElWq7VYPAZhjPHYf8Dkhn4XL8W131Lx7Tv9BgAAAPLHbZJSqampOn78uG375MmTSkpKUnBwsMqUKaO5c+fqjz/+0DPPPCNJWrx4scqWLauKFSvq0qVL+uGHH7Rjxw4NHTrUVV0AAAAAAADA/3ObpNSBAwc0cuRI2/asWbMkSfHx8erXr5/Onj2r06dP28rT09M1a9Ys/fHHH/L391d0dLReeeUV3XzzzU5vexaLxSL5+UsX/865kp9/sbhLCgAAAAAAFG9uk5SqVauW5s+fn2N5v3797LbbtGmjNm3aFHazrt7t8dJPS3MvBwAAAAAA8HBerm5AcePVoYcUWcFxYWSFzHIAAAAAAAAP5zZ3SnkKS0CQvF6eKGvCHGnrBnkZq6wWL6lOA3m17SpLQJCrmwgAAAAAAFDoSEq5gCUgSN6PPSVL594e/xptAAAAAAAAR3h8z8VY1BwAAAAAABRHJKUAAAAAAADgdCSlAAAAAAAA4HQkpQAAAAAAAOB0JKUAAAAAAADgdCSlXIy37gEAAAAAgOLIx9UNKI5MaoqsCXOkrRt0TEYZskh1GsirbVdZAoJc3TwAAAAAAIBCR1LKyUxqiqxjB0nHDkvGKCOrYNUSWXdvk9eQ8SSmAAAAAACAx+PxPSezJsyxJaTsC6zS8SOZ5QAAAAAAAB6OpJSzbd2QPSGVxWrNLAcAAAAAAPBwJKWcyBgjZaTnXikjg8XPAQAAAACAxyMp5UQWi0XyzmMZL2/vzHoAAAAAAAAejKSUs9VpIHnlMOxeXpnlAAAAAAAAHo637zmZV9uusu7aIh07kr2wXJS82nZ1epsAAIDn2rlzpxYuXKjExESdPXtWAwcOVIMGuX8J9uuvv2rWrFk6fPiwwsPD1a5dOzVu3Ng5DQYAAMUGd0q5Qk5LRrGUFAAAKGAXL15UTEyMevbsma/6J0+e1Ouvv65atWpp/Pjxeuihh/Tee+9py5YthdtQAABQ7HCnlJNZE+ZIxx3cJSVJJ36XNWGOvB97yrmNAgAAHisuLk5xcXH5rr98+XKVLVtW3bp1kyRVqFBBu3fv1uLFi1W3bt1CaiUAACiOuFPK2bb8nHOZMbmXAwAAFLJ9+/apdu3advvq1KmjvXv3uqhFAADAU3GnlBMZY6QLf+Ze6cKfMsbwBj4AAOASycnJCg0NtdsXGhqqv//+W5cuXZKfn1+2Y9LS0pSWlmbbtlgsCgwMtP29IGWdj1jJHuOSM8bGMcbFMcYlZ4yNY4zL9SEp5UQWi0VKT8u9UnoaFzMAAHArCQkJWrBggW27cuXKGjdunCIiIgrtMyMjIwvt3O6McckZY+MY4+IY45IzxsYxxuXakJRyImOM5OMnXUrNuZKvH3dKAQAAlwkLC9O5c+fs9p07d06BgYEO75KSpLZt26ply5a27aw45tSpU0pPTy/Q9lksFkVGRur48eOZsRUkMS65YWwcY1wcY1xyxtg4xrg45uPjk68vp0hKOZHFYpGCS0p/5JKUKlGShBQAAHCZ6tWra/PmzXb7tm3bpho1auR4jK+vr3x9fR2WFVaAbowh+HeAcckZY+MY4+IY45IzxsYxxuXauE1SaufOnVq4cKESExN19uxZDRw4UA0aNMj1mF9//VWzZs3S4cOHFR4ernbt2qlx48bOaXBO6t4urVwiGWv2MotXZjkAAEABSU1N1fHjx23bJ0+eVFJSkoKDg1WmTBnNnTtXf/zxh5555hlJ0gMPPKBly5Zpzpw5uvfee7Vjxw6tW7dOgwcPdlUXAACAh3KbpNTFixcVExOj++67TxMnTsyz/smTJ/X666+radOmevbZZ7Vjxw699957CgsLc+nrjL3adpV19zbp+BHJelliystLiqwgr7ZdXdY2AADgeQ4cOKCRI0fatmfNmiVJio+PV79+/XT27FmdPn3aVl62bFkNHjxYH3/8sZYsWaLw8HA9/fTTLo2fAACAZ3KbpFRcXJzi4uLyXX/58uUqW7asunXrJkmqUKGCdu/ercWLF7s0qLIEBMlryHhZE+ZIWzfIW0YZskh1GsirbVdZAoJc1jYAAOB5atWqpfnz5+dY3q9fP4fHjB8/vjCbBQAA4D5Jqau1b98+1a5d225fnTp1NHPmTNc06DKWgCB5P/aULJ17q1y5cjpx4gTPngIAAAAAgGLFY5NSycnJCg0NtdsXGhqqv//+W5cuXXL49pi0tDSlpaXZti0WiwIDA21/LygmNUXWL2fLbN2g4/9/p5SlTgN5PfJ4sbhTKmssi9uC7vSbfhcXxbXv9Lt49RsAAADXz2OTUtciISFBCxYssG1XrlxZ48aNy9drDPPLmvKXTrw6QOZwkmSsyvj//WbVEln271S5STPkFVSiwD6vKIuMjHR1E1yCfhcvxbXfUvHtO/0GAAAA8sdjk1JhYWE6d+6c3b5z584pMDDQ4V1SktS2bVu1bNnStp31re+pU6eUnp5eIO3KmPu+zOFE6crH9axWpR9J0tH3Jsr7sacK5LOKKovFosjISB0/frxYPbZIv+l3cVFc+06/PbffPj4+BfoFFQAAADJ5bFKqevXq2rx5s92+bdu2qUaNGjke4+vrK19fX4dlBRVom60bsiekslitMlvWy3TqVSCfVdQZYzz2HzC5od/FS3Htt1R8+06/AQAAgPzxcnUD8is1NVVJSUlKSkqSJJ08eVJJSUm2VxjPnTtX06ZNs9V/4IEHdPLkSc2ZM0e///67li1bpnXr1umhhx5yRfMl/X9iKyOPO64yMgjqAQAAAACAx3ObO6UOHDigkSNH2rZnzZolSYqPj1e/fv109uxZW4JKksqWLavBgwfr448/1pIlSxQeHq6nn35adevWdXbTbSwWi+Sdx5B7e7NYLAAAAAAA8Hhuk5SqVauW5s+fn2N5v379HB4zfvz4wmzW1atVT/ppae7lAAAAAAAAHs5tHt8rNrhJCgAAAAAAFAMkpZzt1025l+/IoxwAAAAAAMADkJRyIhY6BwAAAAAAyERSyolY6BwAAAAAACATSSlnq9NAyinpZLFklgMAAAAAAHg4klJOZnmwneTt7bjQ2zuzHAAAAAAAwMORlHIy8+0XUkaG48IMa2Y5AAAAAACAhyMp5Wxb1ks5LWRurJnlAAAAAAAAHo6klBMZY6QLf+Ze6a8/efseAAAAAADweCSlnMhisUjpl3KvlHaJt+8BAAAAAACPR1LKiYwxko9v7pV8fLlTCgAAAAAAeDySUk5ksVik4JK5VwouyZ1SAAAAAADA45GUcrabb829vHYe5QAAAAAAAB6ApFRRw5N7AAAAAACgGCAp5Ww7/nt95QAAAMUc628CAOAZfFzdgOLEGCNdOJ97pQvnZYxhXSkAAIDLmNQUWRPmSFs36JiMMmSR6jSQV9uusgQEubp5AADgGpCUciKLxSJdSsu90qU0ElIAAACXMakpso4dJB07LBmjjKyCVUtk3b1NXkPGk5gCAMAN8fieE2Xeap7X7eaGW9IBAAAuY02YY0tI2RdYpeNHMssBAIDbISnlbHndBcVdUgAAAPa2bsiekMpitWaWAwAAt0NSyoksFku+klI8vgcAAJDJGCNlpOdeKSODO80BAHBDrCnlbHkFTARUAACggC1dulTffPONkpOTFR0drX/84x+qVq2aw7qrVq3SO++8Y7fP19dXn3zyiTOamo3FYpG88whZvb35Ug8AADdEUsqJjDH5Skrx9j0AAFBQ1q5dq1mzZqlXr16qXr26Fi9erDFjxmjKlCkKDQ11eExgYKCmTp3q5Jbmok4DaeUSyVizl1m8MssBAIDb4fE9AAAAD7Zo0SI1adJE9957rypUqKBevXrJz89PK1euzPEYi8WisLAwuz+uZHmwneSdQ9jq7ZVZDgAA3A53SqFQcLcXAACul56eroMHD+rhhx+27fPy8lLt2rW1d+/eHI9LTU1V3759ZYxR5cqV9dhjj6lixYpOaLFj5tsvpPQc1pXKyMgsf+wp5zYKAABcN7dLSrn9mggFWK+oMakpma9k3rohc0FSbx+pTgN5te0qS0CQq5sHAECxc/78eVmt1mx3OoWFheno0aMOj4mKilKfPn0UHR2tlJQULVy4UEOHDtXkyZMVHh7u8Ji0tDSlpaXZti0WiwIDA21/v265vV3PGGnrBlk6977+z3FjWePsrnFkYWJsHGNcHGNccsbYOMa4XB+3Skq5+5oIxhjJ119Ku5hzJT9/t7zLyKSmyDp2kHTssP26WauWyLp7m7yGjCcxBQCAG6hRo4Zq1Khht/38889rxYoV6tSpk8NjEhIStGDBAtt25cqVNW7cOEVERFx3e4wxOiajjFzqeMsoMjLS7eKnwhAZGenqJhRZjI1jjItjjEvOGBvHGJdr41ZJqcvXRJCkXr16adOmTVq5cqXdbemXy1oToSiwWCxSyRDpj1M5VwoOccuAypowJ3tCSpKsVun4EVkT5sib2+oBAHCqkJAQeXl5KTk52W5/cnJyvuMjHx8fVa5cWcePH8+xTtu2bdWyZUvbdlYsc+rUKaXn9NjdVchQ7rFRhiy5tq84sFgsioyM1PHjxzO/CIUNY+MY4+IY45IzxsYxxsUxHx+ffH055TZJKU9ZE0F1b8/97TF1b3d+mwrC1g05v1nQas0sJykFAIBT+fj4qEqVKtqxY4caNMh8Q53VatWOHTvUvHnzfJ3DarXq0KFDiouLy7GOr6+vfH19HZYVSIBep4G0aklmTHElr8y37xX1fwg460548/9vckZ2jI1jjItjjEvOGBvHGJdr4zZJKWesiVDo6yFI8n7kcWXs3iYdP2IfWHl5SZEV5P3I4253p5QxRsrI7aZ62cotFkuxfeaWftPv4qK49p1+F69+u5OWLVvq7bffVpUqVVStWjUtWbJEFy9eVOPGjSVJ06ZNU+nSpdW5c2dJ0oIFC1S9enVFRkbqr7/+0sKFC3Xq1Ck1adLEZX3wattV1lziJ6+2XV3Wttyw3iYAALlzm6TUtbjaNREKcz2Ey1mnzta52e/q7/U/Selpko+vAm+/R6GP95FXUIkC/SxnOernl/taD35+ioqKsttXXJ+5pd/FS3Htt1R8+06/UdQ0atRI58+f1/z585WcnKyYmBj961//sn3Rd/r0abuk4oULF/T+++8rOTlZJUqUUJUqVTR69GhVqFDBRT2QLAFB8hoy3pbg8ZbJfKSvCCd4WG8TAIC8uU1SyhlrIhT2egh2WneRV5uuKleunE6cOKFUY5R67rx07nzBfo6TWG+un+tt9dbat+rYsWOSiu8zt/SbfhcXxbXv9Ntz+53fNRGKsubNm+f4uN6IESPstnv06KEePXoUfqOukiUgSN6PPSVL595ucc2x3iYAAHlzm6SUM9ZEKPT1EHJokyc8e2pp21Uml9vqLQ93ydZHT+j3taDfxUtx7bdUfPtOv4HC5xaPi7LeJgAAeXKbpJTkGWsiSFLG8SPS6y9Jf/0p22pYJUpKg8fJO9J1t8Zfjytvq1dGhuTtXaRvqwcAACgMmett5nGXfUaG0xY/BwCgqHKrpJQnrImQcfyI9Erf7AV//Sm90lcZo95x68SU92NPSY89RZAFAACKLYvFkrmoeW68vYmVAADFnlslpSQPWBPh9ZfyLp/yiXPaUogIsgAAQLFWp0Gu622qTgPntwkAgCLGy9UNKHb++vP6ygEAAFDkebXtKkVWyExA2RVkrrfp1baraxoGAEAR4nZ3SrmzjIyMfNfz9vYu5NYAAACgsLDeJgAAeSMp5UTe3t7KT1qKhBQAAID7Y71NAAByx+N7zlai5PWVAwAAwO2QkAIAIDuSUs42eNz1lQMAAAAAAHgAklJO5h1ZQRr1TvY7okqUlEa9k1kOAAAAAADg4VhTygW8IytIUz6RxWJR2bJldfLkSRljXN0sAAAAAAAAp+FOKRfzuvI1wQAAACjy+EIRAIDrx51SLmBSU2yvBz4qI6ssvB4YAACgiLs8hlNGuuTtQwwHAMB1ICnlZCY1RdbXBkrHjkiSrFkFPyySddcWef1rIkENAABAEWNSU2QdO0g6dli6/C6pVUtk3b1NXkPGE8MBAHCVeHbMyayfz7QlpLI5dkTWBTOd2RwAAADkgzVhTvaElCRZrdLxI5nlAADgqpCUcrYNP+Zevj6PcgAAADjf1g3ZE1JZrNbMcgAAcFVISjmRMUa6eDH3ShcvsnAmAABAEWKMyVxDKjcZGcRwAABcJZJSwGUIJgEAwJUsFkvmoua58fbOrAcAAPKNhc6d6P/Yu/O4qKr+D+CfmWEHAUUUEAFRUDMEzNSs58E0c03zZ+aCqSmaS6X1+FQuTy5pbqlZ2qYUmXsW5ZZLKlq4YO644IIILigqgwv7zPn9MXFz2GaAYTY+79eLl8695545y71zz3zn3nNlMhlgZw/k5ZSdyM6eAxoj45N0iIiISKfQNkDcNs2tesXJ5Zr1REREVCG8UsrY2kZUbT0ZlPQknb1bgbu3AeU9zb9x26Ce8x5Ebrapi0hERERmQN5nMODlqwlAaa2QA16+mvVERERUIQxKGVvnXlVbTwbFJ+kQERGRPmQOTpBPmg906A541APcPTT/dugO+aT5vLqaiIioEnj7nrF9PV/3+mmfGacspN+TdAaOMm6ZiIiIyCzJHJygGDgKGDgKQghOuUBERFRFvFLK2G6kVm09GQyfpENERESVxYAUERFR1TEoZURqtbrsq3KKCKFJR9WOT9IhIiIiIiIiMh0GpYxILpcDugIcMpkmHRlHaJuSE5YW4ZN0iIiILAavbCYiIrI8nFPK6OQAyrsSqvyAFOcvMCx5n8FQnz8FpF/TfsQzn6RDRERk9kRutuahJCcTNLfkK2yA0DaQ9xlc4YnHOcYiIiIyPgaljE2tYw6jv9c/PjAy5ICLtBU9Seef9lUBCgXbl4iIyMyJ3GyoP/6v5oelx6+SitsG9flTej0Rj2MsIiIi02JQyoj0vaxc9UHUPwOjFq2AC4nAreuVHnBR+fgkHSIiIsshcrNx78v5UP2+GcjNKZlArQbSr0H98w9QDHqj3HzUc94DbqZVaIxVNJ6TyWQ6xw2Pr1cVFkJhY1NiG7VaDZlMptf4g+MUMiXuf0RUHSwuKLV9+3Zs3rwZSqUS/v7+GD58OJo0aVJm+oMHD2L9+vXIyMiAl5cXIiMj0apVKyOW+B/q3Fz9Et69/c//928vI7O/B1yxqzQBFTIInmiJiCwTvyyVz5LHT0XUyrsQi6cBN1LxSGdiNbB3K1R7twKh7YAOLwFqAfj5AQ8fAs7OwLrlpT/1WK0GbqRCvXYF8NpYoLAQKMgHNsYAR/4ACvL+SWtnD7i4Ai2fBnpHQubkDPEgC9i6AThxGMjLBbIfSslVRf+p5aa5OvvvdQIA5AqgbQdgwAjA3hEKhQIqlQqy/FyIX1brfTVX8R9BVQUFkNvYSOuEEJDL5XoF1UpvWjXkcrn0PkUP6FEoFFJ+RWmKl6t4MO7xNOZ8DD8eiKxpyruaEPaONbJNdDHnfbkirKUeZbH2+lWEqdvCooJSBw4cwMqVKzFy5EgEBQVh69atmD17Nj799FO4ubmVSJ+UlIQlS5Zg0KBBaNWqFf78808sWLAA8+bNg5+fn/ErYGdn2PzUas0JgkEpIiKqgXjrlX4sfvyEvwNS/329chufPKT5q6gDv2v+ypOfB9zLAOK2AXHboPdU6w+ySi5Tq4CDuzV/+CeAVWqee7ZAvW87MP0zKLx8pWPhRuJRqPLzNdNB3P/nPYrPZiq9trUDarkCYe3KPW7UyrsQS2YA168CQvwTXHtM8WUqmQzwbggEBANJpzTHqAxAfgGQ80iTj0wGODoDdraaiprRMSxys6H+MQZI2Afk/R2ItLMH2kZA3m+YyctnDGVeTbhnC9Rxv2n2HRtbs+kzU7KW85G11KMs1l6/ijCntrCox7xt2bIFnTp1wvPPPw9fX1+MHDkSdnZ22Lt3b6npt23bhrCwMPTq1Qu+vr4YMGAAAgMDsX17GVcfVTd9r5SqCJWKT5shIqIaR/qytHer5gpj5T3Nv3HboJ7zHkRutqmLaDYsfvwEVD4gZc1UhcD/xkKVfk2zz+/dCtXtm4DyrlZAqlwF+cC9O8DerWUeN2rlXYhJI4FrKdqBCV2E0FyJduD3f47RzHvAoweaH1aF0Pz76IFmuRkdw5r5yiZq7ljIzQGEWvOXlwPs3w717Ik14jNGHbuqZEBKWqkCsjLNps9MyVrOR9ZSj7JYe/0qwtzawmKCUoWFhUhOTkZISIi0TC6XIyQkBBcuXCh1mwsXLmilB4DQ0FBcvHixWstappxS5j2oKoWClx0SEVGNU+aXpcdubycrGT9R+ea+X3bgQF9ClHnciCUzNLcwGosZHMOaz5drZSeoKZ8xJxP026/MoM9MyVrOR9ZSj7JYe/0qwtzawmJu37t//z7UajXc3d21lru7u+PGjRulbqNUKktclu7m5galUllq+oKCAhQUFEivZTIZHB0dpf9X2aThVc/jcXI5ZGFtLS4oVVReSyt3VbHerHdNUVPrznobud7lfVn6+/Z2WTmTXNcUxhg/AdU7hiqas4jK8OiBYfIp67gpbd6t6mbgY7jCn1MnE/RKY+mfMeW1ixBCM++Zvqzoc7dS+4s1nI/0qUfkaAAWOtapxn6yuDGgme2zFhOUMobY2Fhs3LhRet2oUSPMmzcPnp6eBsk/zSC5/E0uh03DANQfPRFyJ2dD5mw0Xl5epi6CSbDeNUtNrTdQc+vOelc/IQRuovR5bYooIODl5WU5A0QLV51jqLQeraucB+mn+HGjVqtx3USzRFTHMazP55QQAjcgSszDVZxcqK3mM6asdrlhZ1fu52xx1va5q+/+Yg3nI33rUb9+fQCWN9YxVj9ZQruY4z5rMUEpV1dXyOXyEr/SKZXKEr/+FXF3d0dWlvY99VlZWWWm79OnD3r27Cm9LuqEjIwMFBrgsmWbFZtRGPVSlfOBgyNkz74A0WcwbmXdB7LuVz1PI5LJZPDy8kJ6enqNmg+L9Wa9a4qaWnfW27j1VqH8gZIKMqSnpxvkvWxsbAz2A5WxGWP8BFTvGMpg4yfSqdTjRoYyZls3QVkqqaKfU2odny8AoJbJDVY+U9HVLuonn9JM4q/n1YqG7DNTquj+YszzUXXSpx63bt2y2LFOdfaTpY0BjbXP6jt+spiglI2NDQIDA5GYmIg2bdoA0Px6k5iYiK5du5a6TXBwME6fPo0ePXpIy06dOoWgoKBS09va2sLW1rbUdWaxc8nlQP0GkE9eIM2IbxblqqSixxLXNKx3zVJT6w3U3Lqz3kYS2qbsL0tyORDapkb2Q3HGGD8BFjCGsmbOtTRPs6vqbY5lHTc+fppJzo2pmo5hvT+nQtsAe7boTGMt+3ZZ7SLrMxji/Ckg/Zru/csKP3crtL9Yw/moAvWwyLGOEfrJYtrFzPZZi5noHAB69uyJ3bt3Iy4uDteuXcOKFSuQl5eHDh06AACWLl2KNWvWSOm7d++OkydPYvPmzbh+/To2bNiAy5cvlzkIMzuOzkAdT8DdA/CoB3TorhWQIiIiqqnkfQYDXr6awZPWCjng5atZTwBq4PipJpErgA/mlX4sVIRMVuZxIxs/DbAx4u/YZnAMy/sMBrx9y05QQz5jZA5OkE+aD3Torvku4lYbUChKJjSDPjMlazkfWUs9ymLt9asIc2sLmbCIUN4/tm/fjk2bNkGpVCIgIACvv/669Mvd9OnT4enpiXHjxknpDx48iHXr1iEjIwPe3t6IjIxEq1atKvSeGRkZWpN3VpVqZK/yE9g7AG07QN5vGGQOThBCmPU9yBUlk8ng7e2NmzdvWkYk2UBYb9a7pqipdWe9jV9vkZuteULMyQTNhLwKBRDaBvI+gw36A46tra3F3r5XxBTjJ8CwYyid46eaxtMbsvc+htzdAyI3G+KX1ZAnHoUqLw9QFwL3s3TnYWsH1HIFwtqVe9yolXc1T+G7flX/p/zJZIB3Q6BRMHD+lOYYlQkgv0BzZZcQmjSOzoCdLSBk1XYMV+ZzSuRmQ70xBji8D8jL0yy0swfaRkhjdEtX0XYRQgB5OUb53DWlSu8vVtAuuuph6WOd6uonS2wXY+yz+o6fLC4oZQqGDkoVkclkmjkSPlsPuYOD1nJrZokHrSGw3qx3TVFT6856m7be1fkDjjUEpUylOsZQ0vipPM0igPP7gMH/AerV01zpk5MD+PkBDx8Czs5AQQFgZwfY2gKFhZpfiFUqzTq1WrPewUGzTqHQBFGE+OdKkb/3N4VCAdXf82bJ5HKIggLI7ez+uc0lL0+zjVwO5OcDNjaQ29pCJpNBrVZD3LwJ1K4N+d9PKyy6/UOhUEClUkEul0v7tqqwEDK5HPJSrooqOhaLP1VRVVAA+d9XOhXlXZRnZY4btVoNuVwu1a/o6YgKhULKryjN44q/V/E01XkMV/VzqmgbaxujG6JdrK1NALZLkdLqYS7nfEMwZD9ZertU1z6r7/jJYuaUMiWbarxk2XvrX8jIyKi2/M1ZdbarOWO9a5aaWm+g5tad9bY+1ly36lZdbaff+OmD0hdXQ4BRaz4te3vtlY+//jvwpCUgQL98S3ltqG1qCh7LpWO7lI7tUja2TenYLtr0bQ9eKUVEREREREREREZnUROdW5ucnBy8//77yMnJMXVRjIr1Zr1rgppab6Dm1p31rln1JtPhPlc6tkvZ2DalY7uUju1SNrZN6dguVcOglAkJIXDlyhWLvO+0Klhv1rsmqKn1Bmpu3VnvmlVvMh3uc6Vju5SNbVM6tkvp2C5lY9uUju1SNQxKERERERERERGR0TEoRURERERERERERseglAnZ2trilVdeqXFPRWG9We+aoKbWG6i5dWe9a1a9yXS4z5WO7VI2tk3p2C6lY7uUjW1TOrZL1fDpe0REREREREREZHS8UoqIiIiIiIiIiIyOQSkiIiIiIiIiIjI6BqWIiIiIiIiIiMjoGJQiIiIiIiIiIiKjszF1AazJ9u3bsXnzZiiVSvj7+2P48OFo0qRJmekPHjyI9evXIyMjA15eXoiMjESrVq2k9UIIbNiwAbt378ajR4/QrFkzREVFwdvb2xjV0VtF6v37779j//79SEtLAwAEBgZi4MCBWumXLVuGffv2aW0XGhqKKVOmVF8lKqkidY+Li8MXX3yhtczW1harV6+WXltjn0+fPh1nz54tsTw8PByTJk0CYBl9fvbsWWzatAlXrlxBZmYmJk6ciDZt2pS7zZkzZ7By5UqkpaXBw8MDffv2RYcOHbTSVPRzw9gqWu/Dhw9j586dSElJQWFhIXx9fdGvXz+EhYVJaTZs2ICNGzdqbefj44NPP/20mmpRcRWt95kzZzBjxowSy7/55hu4u7tLr62tv0s7dgHA19cXixYtAmAZ/U2WxdyPo6rQdbzk5+dj5cqVOHDgAAoKChAaGoqoqCitz5k7d+5g+fLlOHPmDBwcHBAREYFBgwZBoVBIafQ5P5mSrs8ifcZLDx8+xLfffoujR49CJpOhbdu2eP311+Hg4CCluXr1KqKjo3H58mW4urqia9eu6N27t1ZZdI3XjU1X2+gzprLGtomNjUVCQgKuX78OOzs7BAcHY/DgwfDx8ZHSGPP4MZfPKX3apbSx+gsvvIBRo0ZJr62tXXbu3ImdO3ciIyMDgGbc8sorryA8PBxAzdxXTEqQQcTHx4uBAweKPXv2iLS0NPHVV1+JYcOGCaVSWWr68+fPi/79+4tff/1VpKWlibVr14oBAwaIq1evSmliY2PF0KFDRUJCgkhJSRHz5s0T48aNE3l5ecaqlk4VrfeSJUvE9u3bxZUrV8S1a9fEsmXLxNChQ8Xdu3elNEuXLhWzZ88WmZmZ0t+DBw+MVSW9VbTue/fuFUOGDNGqV2ZmplYaa+zzBw8eaNU3NTVV9O/fX+zdu1dKYwl9fuzYMbF27Vpx+PBh0a9fP3H48OFy09+6dUsMHjxYfP/99yItLU389ttvon///uL48eNSmoq2pSlUtN7fffed+OWXX8TFixfFjRs3xOrVq8WAAQNEcnKylGb9+vXi3Xff1ervrKys6q5KhVS03omJiaJfv37i+vXrWvVSqVRSGmvs70ePHmnV986dO+L1118X69evl9JYQn+T5bCE46gqdB0v33zzjRg9erQ4ffq0uHz5spg8ebKYOnWqtF6lUol3331XzJw5U1y5ckUcO3ZMDB8+XKxevVpKo8/5ydR0fRbpM16aPXu2mDhxorhw4YI4d+6ceOutt8Snn34qrX/06JGIiooSS5YsEampqeLPP/8UkZGRYteuXVIafcbrxqarbfQZU1lj28yaNUvs3btXpKamiitXroiPP/5YjBkzRuTk5EhpjHX8mNPnlD7tMm3aNPHVV19p7TOPHj2S1ltjuxw5ckQcPXpU3LhxQ1y/fl2sWbNGDBgwQKSmpgohaua+Ykq8fc9AtmzZgk6dOuH555+Hr68vRo4cCTs7O+zdu7fU9Nu2bUNYWBh69eoFX19fDBgwAIGBgdi+fTsAzS9A27Ztw//93//h6aefhr+/P958801kZmbiyJEjxqxauSpa77fffhtdunRBQEAAGjRogNGjR0MIgdOnT2uls7Gxgbu7u/Tn4uJijOpUSEXrDgAymUyrXo9H2621z11cXLTqe+rUKdjb26Ndu3Za6cy9z8PDwzFgwACdV0cV2blzJ+rVq4chQ4bA19cXXbt2Rbt27bB161YpTWX2IWOraL2HDRuG3r17o0mTJvD29sagQYPg7e2No0ePaqWTy+Va/e3q6lodxa+0ita7iJubm1a95PJ/TrPW2N9OTk5a9b18+TIePXqE559/Xiudufc3WQ5LOI6qqqzjJTs7G3v27MHQoUPx5JNPIjAwEGPHjkVSUhIuXLgAADh58iSuXbuGt956CwEBAQgPD0f//v2xY8cOFBYWAtDv/GRq5X0W6TNeunbtGk6cOIHRo0cjKCgIzZo1w/Dhw3HgwAHcu3cPAPDnn3+isLAQY8eORcOGDfHss8+iW7du2LJli/ReusbrpqDP53R5YyprbZspU6agQ4cOaNiwIQICAjBu3DjcuXMHycnJAIx7/JjT55Sudilib2+vtc84OTlJ66yxXVq3bo1WrVrB29sbPj4+GDhwIBwcHHDx4sUau6+YEoNSBlBYWIjk5GSEhIRIy+RyOUJCQqQdt7gLFy5opQc0l9ZevHgRAHD79m0olUq0bNlSWu/k5IQmTZqUmaexVabexeXl5aGwsLBEAOLs2bOIiorC+PHjsXz5cjx48MCgZa+qytY9NzcXY8eOxZgxYzB//nzpNkag5vT5nj170L59e61LxAHz7/OKunjxYqnHeFE7GaItLYFarUZOTk6JYzw9PR1vvPEG3nzzTXz22We4c+eOiUpoWO+99x5GjRqFjz76COfPn5eW15T+3rNnD0JCQuDp6am13Fr7m4yrphxHZR0vycnJUKlUWvVv0KAB6tatK9X/woUL8PPz0/rRKywsDDk5OdKYQ9f5ydzpM166cOECnJ2d0bhxYylNSEgIZDIZLl26JKVp3rw5bGz+mc0kNDQUN27cwMOHD6U05Y3XzVV5Y6qa0jbZ2dkAII0/jHX8mPvnVPF2KfLHH39gxIgR+M9//oM1a9YgLy9PWmft7aJWqxEfH4+8vDwEBwdzXzEBzillAPfv34dardbaKQHA3d0dN27cKHUbpVIJNzc3rWVubm5QKpXS+qJlZaUxtcrUu7jVq1ejTp06WgdjWFgY2rZti3r16iE9PR1r167Fxx9/jNmzZ2tddWBKlam7j48PxowZA39/f2RnZ2PTpk2YOnUqFi1aBA8PjxrR55cuXUJaWhrGjBmjtdwS+ryiyjrGc3JykJ+fj4cPH1b5+LEEmzdvRm5uLp555hlpWVBQEMaOHQsfHx9kZmZi48aN+PDDD7Fw4UI4OjqasLSVV7t2bYwcORKNGzdGQUEBdu/ejRkzZmD27NkIDAw0yOelubt37x5OnDiBt99+W2u5NfY3mUZNOI7KO16USiVsbGzg7OystU3x8WPx9ik6Fz2eprzzk52dXbXUzVD0GS8plcoSV2QqFAq4uLhopalXr55WmqK2UyqVUlpzHpeVRteYqia0jVqtRkxMDJo2bQo/Pz8AMNrxY87ju9LaBQCee+451K1bF3Xq1MHVq1exevVq3LhxAxMnTgRgve2SmpqKKVOmoKCgAA4ODpg4cSJ8fX2RkpJS4/cVY2NQikzml19+QXx8PKZPn641AHr22Wel//v5+cHf3x9vvfUWzpw5UyLabEmCg4MRHBys9fqdd97Brl27MGDAABOWzHj27NkDPz+/EpP3WWuf13R//vknNm7ciP/+979aJ+WiSSQBwN/fX/oSdvDgQXTs2NEURa0yHx8frUlDmzZtilu3bmHr1q146623TFgy49m3bx+cnZ1L3FJijf1NVF3KO17MPVhE5oFjKiA6OhppaWmYOXOmqYtiVspqlxdeeEH6v5+fH2rXro2ZM2ciPT0dXl5exi6m0fj4+GDBggXIzs7GoUOHsGzZslIfWkPVzzIvQTAzrq6u0i8PjystglrE3d0dWVlZWsuysrKk9EX/lpfG1CpT7yKbNm3CL7/8gqlTp8Lf37/ctPXr10etWrWQnp5exRIbTlXqXsTGxgaNGjWS6mXtfZ6bm4v4+Hi9voSaY59XVFnHuKOjI+zs7AyyD5mz+Ph4fPXVV3jnnXe0brEojbOzM3x8fCy6v0vTpEkTqU7W3t9CCOzduxf/+te/tG73KI219jdVP2s/jkrz+PHi7u6OwsJCPHr0SCtN8fFj8fYpOhc9nqa885O502e85O7ujvv372utV6lUePjwYbltVfRaV1tZ0v5WfExl7W0THR2NY8eOYdq0afDw8JCWG+v4MdfPqbLapTRFPx4/vs9YY7vY2NjAy8sLgYGBGDRoEAICArBt27Yav6+YAoNSBmBjY4PAwEAkJiZKy9RqNRITE7WujHlccHBwicm9T506haCgIABAvXr14O7urpUmOzsbly5dKjNPY6tMvQHg119/xU8//YTJkydr3c9elrt37+Lhw4eoXbu2QcptCJWt++PUajVSU1OlellznwPAoUOHUFhYiH/9618638cc+7yigoKCSj3Gi9rJEPuQufrzzz/xxRdfYPz48Xo9Gjo3N1f6wmVNUlJSpH3Ymvsb0Mxfkp6erlfQ2Vr7m6qftR9HpXn8eAkMDIRCodA6t9y4cQN37tyR6h8cHIzU1FStL0KnTp2Co6MjfH19Aeg+P5k7fcZLwcHBePTokdZkzomJiRBCSF+4g4ODce7cOWlSYkDTDj4+PtJ8O7rG65ag+JjKWttGCIHo6GgkJCTgww8/LHH7obGOH3P7nNLVLqVJSUkBAK19xtrapTRqtRoFBQU1dl8xJQalDKRnz57YvXs34uLicO3aNaxYsQJ5eXno0KEDAGDp0qVYs2aNlL579+44efIkNm/ejOvXr2PDhg24fPkyunbtCkDzlLbu3bvj559/xl9//YXU1FQsXboUtWvXxtNPP22KKpaqovX+5ZdfsH79eowZMwb16tWDUqmEUqlEbm4uAM3g64cffsCFCxdw+/ZtnD59GvPnz4eXlxdCQ0NNUcUyVbTuGzduxMmTJ3Hr1i0kJyfjs88+Q0ZGBjp16gTAevu8yJ49e/D000+jVq1aWsstpc9zc3ORkpIinahv376NlJQUaQLaNWvWYOnSpVL6F198Ebdv38aqVatw/fp17NixAwcPHkSPHj2kNLra0hxUtN5//vknli1bhiFDhiAoKEg6xosm1gSAlStX4uzZs7h9+zaSkpKwYMECyOVyPPfcc0atW3kqWu+tW7fiyJEjSE9PR2pqKmJiYpCYmIguXbpIaayxv4vs2bMHQUFBWnNUFLGE/ibLYQnHUVWUd7w4OTmhY8eOWLlyJRITE5GcnIwvvvhCa3qA0NBQ+Pr6YunSpUhJScGJEyewbt06dOnSBba2tgD0Oz+ZWnmfRfqMl3x9fREWFoavv/4aly5dwvnz5/Htt9+iffv2qFOnDgDNPDo2Njb46quvkJaWhgMHDuC3335Dz549pXLoGq+bQnlto8+YylrbJjo6Gn/88QfGjx8PR0dHafyRn58PAEY9fszpc0pXu6Snp2Pjxo1ITk7G7du38ddff2HZsmVo3ry5dDeLNbbLmjVrpM/a1NRU6fW//vWvGruvmJJMCCFMXQhrsX37dmzatAlKpRIBAQF4/fXXpV8Lpk+fDk9PT4wbN05Kf/DgQaxbtw4ZGRnw9vZGZGSk1lUFQghs2LABv//+O7Kzs9GsWTOMGDFCa94Sc1CReo8bNw4ZGRkl8njllVfw6quvIj8/HwsWLMCVK1fw6NEj1KlTBy1btkT//v3N8lf1itQ9JiYGCQkJUCqVcHZ2RmBgIAYMGIBGjRpJ+VljnwOaXxcmTJiAqVOnlriVy1L6/MyZM6XeZx4REYFx48Zh2bJlyMjIwPTp07W2+f7773Ht2jV4eHigb9++JU4y5bWlOahovadPn46zZ8+WmR4APv30U5w7dw4PHjyAq6srmjVrhgEDBpjVvAUVrfevv/6K33//Hffu3YO9vT38/f3Rt29fPPnkk1rbW1t/A5orFEaNGoVhw4ZpzUtRxBL6myyLuR9HVaHreMnPz8fKlSsRHx+PwsJChIaGIioqSut8mZGRgRUrVuDMmTOwt7dHREQEIiMjoVAopDT6nJ9MSddnkT7jpYcPHyI6OhpHjx6FTCZD27ZtMXz4cK2n/169ehXR0dG4fPkyatWqha5du+Lll1/Wek9d43VjK69tRo4cqdeYyhrb5tVXXy11+dixY6V925jHj7l8Tulqlzt37uDzzz9HWloa8vLy4OHhgTZt2uD//u//4OTkJKW3tnb58ssvkZiYiMzMTDg5OcHf3x+9e/eWvqfUxH3FlBiUIiIiIiIiIiIio+Pte0REREREREREZHQMShERERERERERkdExKEVEREREREREREbHoBQRERERERERERkdg1JERERERERERGR0DEoREREREREREZHRMShFRERERERERERGZ2PqAhARERFVxdmzZ7Fp0yZcuXIFmZmZmDhxItq0aVOhPA4cOIDY2FjcvHkTrq6u6Nq1K3r16lVNJSYiIiIigFdKERERkYXLy8tDQEAARowYUantjx8/js8//xydO3fGwoULERUVha1bt2L79u0GLikRERERPY5XShEREZFFCw8PR3h4eJnrCwoKsHbtWsTHxyM7OxsNGzZEZGQkWrRoAQDYv38/nn76abz44osAgPr16+Pll1/Gr7/+ii5dukAmkxmlHkREREQ1Da+UIiIiIqsWHR2NixcvYsKECViwYAHatWuHjz/+GDdv3gSgCVrZ2tpqbWNnZ4e7d+8iIyPDFEUmIiIiqhEYlCIiIiKrdefOHcTFxeGdd95B8+bN4eXlhV69eqFZs2bYu3cvACAsLAwJCQk4ffo01Go1bty4gS1btgAAlEqlCUtPREREZN14+x4RERFZrdTUVKjVaowfP15reWFhIVxcXAAAnTp1Qnp6OubOnQuVSgVHR0d0794dP/74I2/dIyIiIqpGDEoRERGR1crNzYVcLse8efMgl2tfIO7g4AAAkMlkGDx4MAYNGgSlUglXV1ecPn0agGZ+KSIiIiKqHgxKERERkdUKCAiAWq1GVlYWmjdvXm5auVyOOnXqAADi4+MRHBwMV1dXYxSTiIiIqEZiUIqIiIgsWm5uLtLT06XXt2/fRkpKClxcXODj44PnnnsOS5cuxZAhQ9CoUSPcv38fp0+fhr+/P1q1aoX79+/j0KFDaNGiBQoKCrB3714cPHgQM2bMMGGtiIiIiKyfTAghTF0IIiIioso6c+ZMqQGkiIgIjBs3DoWFhfj555+xb98+3Lt3D66urggKCsKrr74KPz8/3L9/H/PmzUNqaioAIDg4GAMGDEBQUJCxq0JERERUozAoRURERERERERERifXnYSIiIiIiIiIiMiwGJQiIiIiIiIiIiKjY1CKiIiIiIiIiIiMjkEpIiIiIiIiIiIyOgaliIiIiIiIiIjI6BiUIiIiIiIiIiIio2NQioiIiIiIiIiIjI5BKSIiIiIiIiIiMjoGpYiIiIiIiIiIyOgYlCIiIiIiIiIiIqNjUIqIiIiIiIiIiIyOQSkiIiIiIiIiIjI6BqWIiIiIiIiIiMjoGJQiIiIiIiIiIiKjY1CKiIiIiIiIiIiMjkEpIiIiIiIiIiIyOgaliIiIiIiIiIjI6BiUIrJQ06dPR1hYWJXySElJgUwmw4kTJyq0XUBAAD799NMqvbc5iomJgbu7u1Hfs0OHDpgwYYJR37Omkslk+OWXX0xdDCIiqkaGOK9WZjxQ2TGVJRg2bBhefvllo74nz9nGERcXB5lMBqVSaeqiUA3GoBRRMWUNZkwRsLAWltJ2/fv3x4ULF0xdDC2mGAjWVNYabCUiMhcZGRkYM2YM/Pz8YG9vDy8vL3Tp0gXx8fGmLppJWMo5fsmSJYiJiTF1MbTwnG0c1hxsJfNhY+oCEBGZg4KCAjg6OsLR0dHURbE4+fn5sLOzM3UxiIjIzPXt2xf5+fn4/vvvERgYiFu3bmH37t24e/euqYtGpVCpVJDJZHBzczN1USxSQUEBbG1tTV0MIrPHK6WIKqno161PPvkE3t7e8PDwwLhx41BQUCCl+eKLLxAUFAQHBwfUr18fr7zyirROrVZj/vz5aNKkCezt7eHn54fZs2dL699//30EBwfDyckJgYGB+N///qeVd2lWrFiB5s2bw8HBAc2aNcMXX3yhtT4hIQHh4eFwcHBA69atcfz4cZ31vH37Nl566SU4OjqiUaNGWL16dYk0ixYtQkhICJydndGwYUOMHTsWDx8+BKC5LPj1119HVlYWZDIZZDIZpk+fDgD44Ycf0Lp1a9SqVQteXl4YNGgQbt++rbNMuqjVasycORO+vr6wt7dHWFgYtm/fLq0v+tVn/fr1iIiIgIODA1avXl3qFV2zZs1CvXr1UKtWLURFReGDDz7Q+7bJon1kxowZ8PT0hKurK0aPHo38/Pwyt9m6dSvc3NywevVqTJ8+Hd9//z1+/fVXqe3i4uKQn5+PN998E97e3nBwcIC/vz/mzJlTan47d+6Eg4NDicuyx48fj44dOwIArl69ipdeegm1a9eGs7MzWrRogW3btpVZxoCAAHz00UcYMmQIXF1dMWrUKADAn3/+iX/9619wdHREw4YN8fbbb+PRo0fSduUdDx06dMCbb76JN998E25ubqhbty7+97//QQihs50fL9PAgQPh7OyMBg0aYNmyZeVuM23aNHh7e+PUqVPo0KEDrl69infeeUdq68q0DRERlU6pVOKPP/7AvHnz8Pzzz8Pf3x9t2rTBpEmT0KtXL610b7zxBurXrw8HBwc8+eST2LJlCwDg7t27GDhwIBo0aAAnJyeEhIRg7dq15b5vXl4eJk6ciAYNGsDZ2Rlt27ZFXFycVpqYmBj4+fnByckJffr00StIpmtMpVKpMGLECDRq1AiOjo5o2rQplixZIq0v6xwPVG4MqI/MzEwMGTIEtWvXhpOTE7p164aLFy9qtYO7uzs2bdqEJ554Avb29khNTS1xRdeDBw8QGRkJZ2dneHt7Y/HixRW6bdIcztmTJ09G27ZtSywPDQ3FzJkzAWjGsG3atIGzszPc3d3x7LPP4urVq6XmV9bYEih/fK5rTCeTyfDll1+iW7ducHR0RGBgIDZu3FhuWxUv07p169C+fXvpeNq3b1+Z22RnZ6Nbt2549tlnoVQq0ahRIwBAeHg4ZDIZOnToUOG2IdJJEJGWiIgIMX78+BLLv/vuO+Hm5ia9Hjp0qHB1dRWjR48W586dE5s3bxZOTk7im2++EUIIceTIEaFQKMSaNWtESkqKOHbsmFiyZIm0/XvvvSdq164tYmJixKVLl8Qff/whli9fLq3/6KOPRHx8vLhy5YrYtGmTqF+/vpg3b560ftq0aSI0NFR6vWrVKuHt7S1++uknkZycLH766SdRp04dERMTI4QQ4sGDB8LT01MMGjRIJCYmis2bN4vAwEABQBw/frzM9ujWrZsIDQ0VBw8eFH/99Zdo3769cHR0FIsXL5bSLF68WOzZs0dcuXJF7N69WzRt2lSMGTNGCCFEXl6e+PTTT4Wrq6u4efOmuHnzpnjw4IEQQojo6Gixbds2cfnyZXHw4EHxzDPPiG7duunsI10WLVokXF1dxdq1a8X58+fFe++9J2xtbcWFCxeEEEJcuXJFABABAQFSe924caNEH69atUo4ODiIb7/9ViQlJYkZM2YIV1dXrXYvz9ChQ4WLi4vo37+/SExMFFu2bBGenp5i8uTJUprH97fVq1eLWrVqic2bNwshNH326quviq5du0ptl5eXJxYsWCAaNmwo9u/fL1JSUsQff/wh1qxZU2oZCgsLRf369cWKFSvKXNajRw/RuXNncerUKXH58mWxefNmsW/fvjLr5e/vL1xdXcUnn3wiLl26JP05OzuLxYsXiwsXLoj4+HgRHh4uhg0bJoTQfTxEREQIFxcXMX78eHH+/HmxatUqreNJF39/f1GrVi0xZ84ckZSUJD777DOhUCjEzp07pTQARGxsrFCr1eLNN98UAQEB4uLFi0IIIe7evSt8fX3FzJkzpbauTNsQEVHpCgoKhIuLi5gwYYLIzc0tNY1KpRLt2rUTLVq0EDt37pQ+d7dt2yaEEOLatWtiwYIF4vjx4+Ly5cvSZ/3hw4elPIqP46KiokT79u3F/v37xaVLl8SCBQuEvb29NCY4dOiQkMvlYt68eSIpKUksWbJEuLu7a40HitNnTJWfny8+/PBDceTIEZGcnCyd19avXy/lUdo5XgjdY8DK6tWrl2jevLnYv3+/OHHihOjSpYto0qSJyM/PF0Joxrq2traiffv2Ij4+Xpw/f148evRIDB06VPTu3VurTf39/cXvv/8uTp8+Lfr06SNq1apV6vi5NOZwzk5MTBQAxKVLl0osu3jxoigoKBBubm5i4sSJ4tKlS+Ls2bMiJiZGXL16tdT8yhpb6hqf6xrTARAeHh5i+fLlIikpSUydOlUoFApx9uxZne1cVCZfX1+xceNGcfbsWREVFSVq1aol7ty5I4QQYu/evQKAyMzMFJmZmaJ9+/bixRdfFI8ePRJCCJGQkCAAiN9//13cvHlT3L17t8JtQ6QLg1JExVQkKOXv7y8KCwulZf369RP9+/cXQgjx008/CVdXV3H//v0Sed2/f1/Y29trBaF0WbBggXjqqaek18WDUo0bNy4RmPjoo4/EM888I4QQ4uuvvxYeHh4iJydHWv/ll1+WG5RKSkoSAERCQoK07Ny5cwKAVlCquB9//FF4eHhIr4u3XVmOHDkiAEhBq8ry8fERs2fP1lr29NNPi7Fjxwoh/jlJf/rpp1ppipezbdu2Yty4cVppnn322QoFperUqSOd2IXQtLmLi4tQqVRCiH/2t6VLlwo3NzcRFxdXIo/HB4JCCPHWW2+Jjh07CrVarVc5xo8fLzp27Ci93rFjh7C3txeZmZlCCCFCQkLE9OnT9cpLCM1g8uWXX9ZaNmLECDFq1CitZX/88YeQy+UiJyen3ONBCE07NG/eXKtO77//vmjevLneZeratavWsv79+2sFOQGIH3/8UQwaNEg0b95cXLt2rUQexffrirYNEWk7c+aMmDNnjhg1apTo16+fVvBAX8ePHxeTJ08Wr732mhg+fLhYsGCBuHXrVjWUlqrbxo0bRe3atYWDg4No3769mDRpkjh58qS0fseOHUIul4ukpCS98+zRo4f4z3/+I71+fBx39epVoVAoxPXr17W26dSpk5g0aZIQQoiBAweK7t27a63v379/ueOWyoyphBBi3Lhxom/fvtLr0s7xpSk+BqyMCxcuCAAiPj5eWnbnzh3h6OgoNmzYIITQjIMAiBMnTmht+3g579+/L2xtbcWPP/4orVcqlcLJyalCQSlzOGeHhoaKmTNnSq8nTZok2rZtK4TQBL4AlBiXlaWssaWu8bmuMR0AMXr0aK1lbdu2lX781adMc+fOlZYVFBQIX19fKchZFJQ6d+6caNmypejbt68UHH08j8f364q2DZEuvH2PqApatGgBhUIhvfb29pZuP+vcuTP8/f0RGBiI1157DatXr0Z2djYA4Ny5c8jLy0OnTp3KzHv9+vV49tln4eXlBRcXF0ydOhWpqamlpn306BEuX76MESNGwMXFRfqbNWsWLl++LL1ny5Yt4eDgIG33zDPPlFu/c+fOwcbGBk899ZS0rFmzZiVucfv999/RqVMnNGjQALVq1cJrr72Gu3fvSvUty9GjR/HSSy/Bz88PtWrVQkREBACUWc+PP/5Yq36lpbt//z5u3LiBZ599Vmv5s88+i3Pnzmkta926dbnlS0pKQps2bbSWFX+tS2hoKJycnKTXzzzzDB4+fIi0tDRp2caNG/HOO+9g165dUhuUZ9iwYThx4gSaNm2Kt99+Gzt37iw3fWRkJOLi4nDjxg0AwOrVq9GjRw+pH99++23MmjULzz77LKZNm4ZTp07pLEPxtjt58iRiYmK0+qdLly5Qq9W4cuVKucdDkXbt2kmX4AOatrp48SJUKpXO8hSlL/66eJ+/8847OHz4MPbv348GDRrozLMybUNE/8jLy0NAQABGjBhRqe1v376NBQsWoEWLFpg/fz6mTJmCBw8eYOHChQYuKRlD3759cePGDWzatAldu3ZFXFwcWrVqJU2ifeLECfj6+iI4OLjU7VUqFT766COEhISgTp06cHFxwY4dO8ocN5w+fRoqlQrBwcFa56d9+/ZpjY+K38alz/hInzHVsmXL8NRTT8HT0xMuLi745ptvyizr4yoyBgSAbt26SXVr0aJFmWW2sbHRqquHhweaNm2qda60s7NDy5Yty3yv5ORkFBQUaI2H3Nzc0LRpU531epw5nLMjIyOxZs0aAIAQAmvXrkVkZCQAoE6dOhg2bBi6dOmCl156CUuWLMHNmzd1luHx8ZE+43N9xnT6tFV5Ht/exsYGrVu3LrF9586d0aRJE6xfv17nPKGVbRuisjAoRVSMq6srsrKySixXKpUlJnosPnmhTCaDWq0GANSqVQvHjh3D2rVr4e3tjQ8//BChoaFQKpU6J9M+ePAgIiMj0b17d2zZsgXHjx/HlClTypyLqGj+puXLl+PEiRPSX2JiIg4dOqR33SsjJSUFPXv2RMuWLfHTTz/h6NGj0rwA5c2d9OjRI3Tp0gWurq5YvXo1jhw5gtjY2HK3Gz16tFb9fHx8qlR2Z2fnKm1vKOHh4fD09MS3336r1xxKrVq1wpUrV/DRRx8hJycHr776qtb8TMU9/fTTaNy4MdatW4ecnBzExsZKgy4AiIqKQnJyMl577TWcPn0arVu3xueff15uGYq33cOHD/HGG29o9c/Jkydx8eJFNG7cuNzjwZg6d+6M69evY8eOHXqlr0zbENE/wsPDMWDAgDID+gUFBVi5ciXeeOMNvPbaa5g8eTLOnDkjrU9OToZarcaAAQPg5eWFwMBAvPTSS0hJSUFhYaGxqkEG5ODggM6dO+N///sfDhw4gGHDhmHatGkAoHN8tGDBAixZsgTvv/8+9u7dixMnTqBLly7ljo8UCgWOHj2qdX46d+6c1vxO1WHdunWYOHEiRowYgZ07d+LEiRN4/fXXyx0bARUfAwKaOYuK6lbVeQ8dHR21fiAypeo+Zw8cOBBJSUk4duwYDhw4gLS0NPTv319a/9133+HgwYNo37491q9fj+DgYJ3j6sfHR/qMzys6pqsuPXr0wP79+3H27Fm90lembYjKwqAUUTFNmzbFsWPHSiw/duxYmb/clcXGxgYvvPAC5s+fj1OnTiElJQV79uxBUFAQHB0dsXv37lK3O3DgAPz9/TFlyhS0bt0aQUFB5U4eWL9+ffj4+CA5ORlNmjTR+iuaoLB58+Y4deoUcnNzpe10nTyaNWuGwsJCHD16VFqWlJSkFUg4evQo1Go1Fi5ciHbt2iE4OFi6IqeInZ1diatdzp8/j7t372Lu3Ln417/+hWbNmumc5LxOnTpadbOxKfkAUVdXV/j4+JR4vHR8fDyeeOKJcvMvrmnTpjhy5IjWsuKvdTl58iRycnKk14cOHYKLiwsaNmwoLWvcuDH27t2LX3/9FW+99ZbW9qW1HaCpZ//+/bF8+XKsX78eP/30E+7du1dmOSIjI7F69Wps3rwZcrkcPXr00FrfsGFDjB49Gj///DP+85//YPny5RWqZ6tWrXD27NkS+1+TJk2kX9zKOh6KHD58WCvPQ4cOISgoSOtqxPIU358PHTqE5s2bay3r1asX1qxZg6ioKKxbt05rXVltXdW2IaKyRUdH4+LFi5gwYQIWLFiAdu3a4eOPP5Z+dQ8MDJQmgFar1cjOzsb+/fsREhJS6jmALM8TTzwhPRSjZcuWuHbtGi5cuFBq2vj4ePTu3RuDBw9GaGgoAgMDy0wLaIKiKpUKt2/fLnFu8vLyAqAZH5V2/imPPmOq+Ph4tG/fHmPHjkV4eDiaNGkiXR1TpLTzTkXHgADQoEEDqV7+/v5llrmwsFCrrnfv3kVSUlKFxkeBgYGwtbXVGg9lZWWV2w+lMYdztq+vLyIiIrB69WqsXr0anTt3Rr169bTShIeHY9KkSThw4ACefPJJ6coqfegzPgd0j+n0aavyPL590bi++PZz587F0KFD0alTJ63AVNEYrrS2rkrbED2OZ3OiYsaMGYOlS5fi7bffRlRUFOzt7bF161asXbsWmzdv1jufLVu2IDk5Gf/+979Ru3ZtbNu2DWq1Gk2bNoWDgwPef/99vPfee7Czs8Ozzz6LjIwMnDlzBiNGjEBQUBBSU1Oxbt06PP3009i6dat0FVFZZsyYgbfffhtubm7o2rUr8vLy8NdffyEzMxPvvvsuBg0ahClTpmDkyJGYNGkSUlJS8Mknn5SbZ9OmTdG1a1e88cYb+PLLL2FjY4MJEyZo/ZLZpEkTFBQU4PPPP8dLL72E+Ph4fPXVV1r5BAQE4OHDh9i9e7d0O5ufnx/s7Ozw+eefY/To0UhMTMRHH32kd/uW57///S+mTZuGxo0bIywsDN999x1OnDhR6pMDy/PWW29h5MiRaN26tfRL0KlTpxAYGKh3Hvn5+RgxYgSmTp2KlJQUTJs2DW+++Sbkcu3fBIKDg7F371506NABNjY2+PTTTwFo2m7Hjh1ISkqCh4cH3Nzc8Pnnn8Pb2xvh4eGQy+X48ccf4eXlVeK2ysdFRkZi+vTpmD17Nl555RXY29tL6yZMmIBu3bohODgYmZmZ2Lt3b4UGO4DmSUHt2rXDm2++iaioKDg7O+Ps2bPYtWsXli5dWu7xUCQ1NRXvvvsu3njjDRw7dgyff/55hW7RiY+Px/z58/Hyyy9j165d+PHHH7F169YS6fr06YMffvgBr732GmxsbKRfJAMCArB//34MGDAA9vb2qFu3rkHahohKd+fOHcTFxeGLL75AnTp1AGi+hJ48eRJ79+7FoEGDUK9ePUydOhWLFy/GN998A7VajeDgYEyaNMnEpaeKunv3Lvr164fhw4ejZcuWqFWrFv766y/Mnz8fvXv3BgBERETg3//+N/r27YtFixahSZMmOH/+PGQyGbp27YqgoCBs3LgRBw4cQO3atbFo0SLcunWrzKBKcHAwIiMjMWTIECxcuBDh4eHIyMjA7t270bJlS/To0QNvv/02nn32WXzyySfo3bs3duzYofXE3tLoM6YKCgrCypUrsWPHDjRq1Ag//PADjhw5ohWMKO0cX5kxoD6CgoLQu3dvjBw5El9//TVq1aqFDz74AA0aNJDaXx+1atXC0KFD8d///hd16tRBvXr1MG3aNMjl8gpdYWUu5+zIyEhMmzYN+fn5WLx4sbT8ypUr+Oabb9CrVy/4+PggKSkJFy9exJAhQ/SuI6B7fL5o0SKdY7off/wRrVu3xnPPPYfVq1cjISEB0dHRepdh2bJlCAoKQvPmzbF48WJkZmZi+PDhJdJ98sknUKlU6NixI+Li4tCsWTPUq1cPjo6O2L59O3x9feHg4IB79+4ZpG2IJKae1IrIHCUkJIjOnTsLT09P4ebmJtq2bStiY2O10pQ2OeX48eNFRESEEEIzyXNERISoXbu2cHR0FC1btpSeuCKE5gkzs2bNEv7+/sLW1lb4+fmJjz/+WFr/3//+V3h4eEhPb1u8eLHWpJvFJzoXQvP0trCwMGFnZydq164t/v3vf4uff/5ZWn/w4EERGhoq7OzsRFhYmPjpp590Tsp58+ZN0aNHD2Fvby/8/PzEypUrS0wuuWjRIuHt7S0cHR1Fly5dxMqVK6UneRQZPXq08PDwEADEtGnThBBCrFmzRgQEBAh7e3vxzDPPiE2bNuksjz5UKpWYPn26aNCggbC1tRWhoaHit99+k9aXNmmjEKVPyD5z5kxRt25d4eLiIoYPHy7efvtt0a5dO73KUbSPfPjhh1Jfjhw5UuupQ8Un1j979qyoV6+eePfdd4UQQty+fVt07txZuLi4CABi79694ptvvhFhYWHC2dlZuLq6ik6dOoljx47pLE+bNm0EALFnzx6t5W+++aZo3LixsLe3F56enuK1116TnspSmtImFxXin+PGxcVFODs7i5YtW0oTzus6HiIiIsTYsWPF6NGjhaurq6hdu7aYPHmy3pO5+/v7ixkzZoh+/foJJycn4eXlpfV0PyH+eZJPkfXr1wsHBwfx008/CSE0x0fLli2Fvb29KDo9VrRtiKhsxSc6P3r0qOjXr58YPHiw1t+AAQPEokWLhBBCZGZmirffflv88MMPIjk5WZw5c0ZMmzZNzJw5U+/PBzIPubm54oMPPhCtWrUSbm5uwsnJSTRt2lRMnTpVZGdnS+nu3r0rXn/9deHh4SEcHBzEk08+KbZs2SKt6927t3BxcRH16tUTU6dOFUOGDNEajxU/rxY9BS8gIEDY2toKb29v0adPH3Hq1CkpTXR0tPD19RWOjo7ipZdeEp988onOB7ToGlPl5uaKYcOGCTc3N+Hu7i7GjBkjPvjgA62xW2nneCF0jwEr6969e+K1114Tbm5u0pit6CmEQpT9YJriY9779++LQYMGSefbRYsWiTZt2ogPPvhAr3KY0zk7MzNT2NvbCycnJ60H7aSnp4uXX35ZeHt7Czs7O+Hv7y8+/PBD6UE1xZU1thSi/PG5rjEdALFs2TLRuXNnYW9vLwICArTGT+UpKtOaNWtEmzZthJ2dnXjiiSe0xoGPP32vyFtvvSW8vb2lBw4sX75cNGzYUMjlchEREVHhtiHSRSaEHhOYEBERAM38Bl5eXvjhhx90ph02bBiUSiV++eWX6i+YhevQoQPCwsKkK8QqKiAgABMmTMCECRMMWi4iMpxXX30VEydOlOaXOnDgAD777DMsWrSoxNWjDg4OcHd3x7p163Dy5EnMmTNHWnf37l2MGTMGs2bNqvBt9URkeI8ePUKDBg2wcOFCvR5qwHO2/mQyGWJjY/Hyyy9XeNuUlBQ0atQIx48fR1hYmMHLRmQovH2PiKgM2dnZ+Oqrr9ClSxcoFAqsXbsWv//+O3bt2mXqohERWbyAgACo1WpkZWWVeYtNfn5+iVuCigJY/F2VyDSOHz+O8+fPo02bNsjKysLMmTMBoEK3ARIRFeFE50REZZDJZNi2bRv+/e9/46mnnsLmzZvx008/4YUXXgAArcf7Fv/7448/TFx66/HHH3+U29ZEZL5yc3ORkpKClJQUAMDt27eRkpKCO3fuwMfHB8899xyWLl2Kw4cP4/bt27h06RJiY2OlB460atUKly9fxsaNG3Hz5k0kJyfjiy++gKenp9bcPERkXJ988glCQ0Pxwgsv4NGjR/jjjz9Qt25dnrON6OOPPy6znbt162bq4hHpjbfvERFV0qVLl8pc16BBA52Ptib95OTk4Pr162Wub9KkiRFLQ0QVcebMGcyYMaPE8oiICIwbNw6FhYX4+eefsW/fPty7dw+urq4ICgrCq6++Cj8/PwCaCZE3bdqEGzduwN7eXpq8ukGDBsauDhHpwHO28dy7d6/MJy87OjryM5IsBoNSRERERERERERkdLx9j4iIiIiIiIiIjI5BKSIiIiIiIiIiMjoGpYiIiIiIiIiIyOgYlCIiIiIiIiIiIqOzMXUBLEFmZiYKCwurJW9PT09kZGRUS96kP/aDeWA/mA/2hXlgP5gHGxsb1K5d29TFsEjVNYbisWHZ2H+Wi31n2dh/ls3S+k/f8RODUnooLCxEQUGBwfOVyWRS/nwIoumwH8wD+8F8sC/MA/uBrEF1jKF4bFg29p/lYt9ZNvafZbPm/uPte0REFsbaTkRERJaIn8VERERVxyuliIgsgMjNhjp2FXAyAVAVAgobILQN5H0GQ+bgZOriERHVCPwsJiIiMiwGpYiIzJzIzYZ6znvAzTTg8V/m47ZBff4U5JPm88sQEVE142cxERGR4fH2PSIiM6eOXVXySxAAqNVA+jXNeiIiqlb8LCYiIjI8BqWIiMzdyYSSX4KKqNWa9UREVL34WUxERGRwDEoREZkxIYRm3pLyqFSccJeIqBrxs5iIiKh6MChFRGTGZDKZZiLd8igU0mNiiYjI8PhZTEREVD0YlCIiMnehbQB5GR/XcrlmPRERVS9+FhMRERkcg1JERGZO3mcw4OVb8suQXA54+WrWExFRteJnMRERkeExKEVEZOZkDk6QT5oPdOgOeNQD3D00/3bozkeQE1G1++WXX/Dqq68iJibG1EUxKX4WExERGZ6Om+OJiMgcyBycoBg4Chg4CkIIzltCREZx6dIl7Nq1C/7+/qYuilngZzEREZFh8UopIiILwy9BRGQMubm5+Pzzz/HGG2/A2dnZ1MUxO/wsJiIiqjpeKUVEREREJaxYsQLh4eFo2bIlfv7553LTFhQUoKCgQHotk8ng6Ogo/d+QivJjUMgysf8sF/vOsrH/LJs19x+DUkRERESkJT4+HleuXMGcOXP0Sh8bG4uNGzdKrxs1aoR58+bB09OzuooILy+vasubqh/7z3Kx7ywb+8+yWWP/MShFRERERJI7d+4gJiYGU6dOhZ2dnV7b9OnTBz179pReF/2Sm5GRgcLCQoOWTyaTwcvLC+np6RBCGDRvqn7sP8vFvrNs7D/LZon9Z2Njo9ePUwxKEREREZEkOTkZWVlZeP/996VlarUa586dw/bt27FmzRrI5drTktra2sLW1rbU/Kpr8CyEsJiBOZXE/rNc7DvLxv6zbNbYfwxKEREREZEkJCQEn3zyidayL7/8Ej4+Pujdu3eJgBQRERFRZTEoRUREREQSR0dH+Pn5aS2zt7dHrVq1SiwnIiIiqgr+1EVEREREREREREbHK6WIiIiIqFzTp083dRGIiIjICvFKKSIiIiIiIiIiMjoGpYiIiIiIiIiIyOgYlCIiIiIiIiIiIqNjUIqIiIiIiIiIiIyOQSkiIiIiIiIiIjI6BqWIiIiIiIiIiMjoGJQiIiIiIiIiIiKjY1CKiIiIiIiIiIiMjkEpIiIiIiIiIiIyOgaliIiIiIiIiIjI6BiUIiIiIiIiIiIio2NQioiIiIiIiIiIjI5BKSIiIiIiIiIiMjoGpYiIiIiIiIiIyOgYlCIiIiIiIiIiIqNjUIqIiIiIiIiIiIyOQSkiIiIiIiIiIjI6BqWIiIiIiIiIiMjoGJQiIiIiIiIiIiKjY1CKiIiIiIiIiIiMjkEpIiIiIiIiIiIyOgaliIiIiIiIiIjI6BiUIiIiIiIiIiIio7MxdQGIiIiIyHzs3LkTO3fuREZGBgDA19cXr7zyCsLDw01cMiIiIrI2VQpK/fLLL1izZg26d++OYcOGAQDy8/OxcuVKHDhwAAUFBQgNDUVUVBTc3d2l7e7cuYPly5fjzJkzcHBwQEREBAYNGgSFQiGlOXPmDFauXIm0tDR4eHigb9++6NChg9b7b9++HZs3b4ZSqYS/vz+GDx+OJk2aSOv1KQvVXEIIyGQyUxeDiIjIrNSpUweDBg2Ct7c3hBDYt28f5s+fj/nz56Nhw4amLh4RERFZkUrfvnfp0iXs2rUL/v7+Wsu///57HD16FO+++y5mzJiBzMxMLFy4UFqvVqsxZ84cFBYWYtasWRg3bhzi4uKwfv16Kc3t27cxd+5ctGjRAvPnz0ePHj3w1Vdf4cSJE1KaAwcOYOXKlXjllVcwb948+Pv7Y/bs2cjKytK7LFTziNxsqNZ+A9UHUVC/9zpUH0RBteZrqLMfmbpoREREZqF169Zo1aoVvL294ePjg4EDB8LBwQEXL140ddGIiIjIylQqKJWbm4vPP/8cb7zxBpydnaXl2dnZ2LNnD4YOHYonn3wSgYGBGDt2LJKSknDhwgUAwMmTJ3Ht2jW89dZbCAgIQHh4OPr3748dO3agsLAQgOay8Xr16mHIkCHw9fVF165d0a5dO2zdulV6ry1btqBTp054/vnn4evri5EjR8LOzg579+7VuyxUs4jcbKjnvAfs3QrcvQ0o7wF3b0PEbcOt/7wOkZtt6iISERGZFbVajfj4eOTl5SE4ONjUxSEiIiIrU6nb91asWIHw8HC0bNkSP//8s7Q8OTkZKpUKISEh0rIGDRqgbt26uHDhAoKDg3HhwgX4+flp3UIXFhaGFStWIC0tDY0aNcLFixe18gCA0NBQxMTEAAAKCwuRnJyMl19+WVovl8sREhIiBZz0KQvVLOrYVcDNNECIYivUKLyWAlnsKsgHjDRN4YiIiMxIamoqpkyZgoKCAjg4OGDixInw9fUtM31BQQEKCgqk1zKZDI6OjtL/DakoP96Cb5nYf5aLfWfZ2H+WzZr7r8JBqfj4eFy5cgVz5swpsU6pVMLGxkbr6ikAcHNzg1KplNIUn9PJzc1NWlf0b9Gyx9Pk5OQgPz8fDx8+hFqtLpGPu7s7bty4oXdZijPmgOrxPK1xxzJLJxNKBqSKqNUQJxMgGzjKuGUiCY8H88G+MA/sBzIlHx8fLFiwANnZ2Th06BCWLVuGGTNmlBmYio2NxcaNG6XXjRo1wrx58+Dp6VltZfTy8qq2vKn6sf8sF/vOsrH/LJs19l+FglJ37txBTEwMpk6dCjs7u+oqk8mYYkAFWOeOZW6EELgJAVU5aRRCDS8vL34BNDEeD+aDfWEe2A9kCjY2NtK+FxgYiMuXL2Pbtm0YNar0H2/69OmDnj17Sq+LzqUZGRnS9AyGIpPJ4OXlhfT0dIiyfmwis8X+s1zsO8vG/rNslth/NjY2esVSKhSUSk5ORlZWFt5//31pmVqtxrlz57B9+3ZMmTIFhYWFePTokdYVSllZWdJVTe7u7rh06ZJWvkWTkz+e5vEJy4vSODo6ws7ODq6urpDL5SWueHr8Kix3d3edZSnOmAOqovwtbceyZCqUH2xSQYb09HQjlYaK4/FgPtgX5oH9YD70HVRZM7VarXU1eXG2trawtbUtdV117b9CCB4bFoz9Z7nYd5aN/WfZrLH/KhSUCgkJwSeffKK17Msvv4SPjw969+6NunXrQqFQ4PTp02jXrh0A4MaNG7hz5440h1NwcDB+/vlnZGVlSbfonTp1Co6OjtIl4UFBQTh+/LjW+5w6dUrKw8bGBoGBgUhMTESbNm0AaAZLiYmJ6Nq1KwDNr3q6ylKcKQZURXlb245llkLbAHHbALW65Dq5HLKwtuwHM8DjwXywL8wD+4GMbc2aNQgLC0PdunWRm5uLP//8E2fPnsWUKVNMXTQiIiKyMhUKSjk6OsLPz09rmb29PWrVqiUt79ixI1auXAkXFxc4OTnh22+/RXBwsBQICg0Nha+vL5YuXYrIyEgolUqsW7cOXbp0kQJCL774Inbs2IFVq1bh+eefR2JiIg4ePIgPPvhAet+ePXti2bJlCAwMRJMmTbBt2zbk5eWhQ4cOAAAnJyedZaGaRd5nMNTnTwHp17QDU3I5bBoGQPQZbLrCERERmYmsrCwsW7YMmZmZcHJygr+/P6ZMmYKWLVuaumhERERkZSr19L3yDB06FDKZDAsXLkRhYSFCQ0MRFRUlrZfL5fjggw+wYsUKTJ06Ffb29oiIiED//v2lNPXq1cMHH3yA77//Htu2bYOHhwdGjx6NsLAwKU379u1x//59bNiwAUqlEgEBAZg8ebLWrXm6ykI1i8zBCfJJ8zVP4TuZAKhUgEIBWVhb1B89Ebey7vNqBCIiqvHGjBlj6iIQERFRDSET/BauU0ZGRrnzKFSWTCaDt7c3bt68yWCICQghIJPJ2A9mgv1gPtgX5oH9YD5sbW1r/JxSlVUdYygeG5aN/We52HeWjf1n2Syx//QdP8mNUBYis8Sn7BERERERERGZDoNSRERERERERERkdAxKERERERERERGR0TEoRURERERERERERsegFBERERERERERGR2DUkREREREREREZHQMShEREZHFs5THIxMRERHRP2xMXQAiIiKiyhC52VDHrgJOJgCqQkBhA4S2gbzPYMgcnExdPCIiIiLSgUEpIiIisjgiNxvqOe8BN9OAx6+SitsG9flTkE+az8AUERERkZnj7XtERERkcdSxq0oGpABArQbSr2nWExEREZFZY1CKiIiILM/JhJIBqSJqtWY9EREREZk1BqWIiIjIogghNHNIlUel4uTnRERERGaOQSkiIiKyKDKZTDOpeXkUCk06IiIiIjJbDEoRERGR5QltA8jLGMbI5Zr1RERERGTWGJQiIiIiiyPvMxjw8i0ZmJLLAS9fzXoisgi81ZaIqObSce07ERERkfmROThBPmm+5il7JxMAlQpQKIDQNpD3GQyZg5Opi0hE5RC52VD9/MPfx2+h5pZcHr9ERDUOg1JERERkkWQOTlAMHAUMHAUhBOeQIrIQ6uxHUH38X+BmmvZTNOO2QX3+FOST5jMwRURUQ/D2PSIiIrJ4DEgRWY6slV+UDEgBgFoNpF/TXAFJREQ1AoNSRERERERkNDmH95cMSBVRqzW39BERUY3AoBQRERERERmFEEIzh1R5VCpOfk5EVEMwKEVEREREREYhk8k0k5qXR6HgLblERDUEg1JERERERGQ0jm3/DcjL+BoilwOhbYxbICIiMhkGpYiIiIiIyGjchowFvHxLBqbkcsDLF/I+g01TMCIiMjod184SERERUU0SGxuLhIQEXL9+HXZ2dggODsbgwYPh4+Nj6qKRlZA7OUMxeQFUP/+gmdRcpQIUCiC0DeR9BkPm4GTqIhIRkZEwKEVEREREkrNnz6JLly5o3LgxVCoV1q5di1mzZmHRokVwcHAwdfHISsgcnKAYOAoYOApCCM4hRURUQzEoRURERESSKVOmaL0eN24coqKikJycjCeeeMJEpSJrxoAUEVHNxaAUEREREZUpOzsbAODi4lJmmoKCAhQUFEivZTIZHB0dpf8bUlF+DGRYJvaf5WLfWTb2n2Wz5v5jUIrIiIQQAKzzw4SIiKyPWq1GTEwMmjZtCj8/vzLTxcbGYuPGjdLrRo0aYd68efD09Ky2snl5eVVb3lT92H+Wi31n2dh/ls0a+49BKaJqJnKzof4xBkjYB+TlaRba2QNtIyDvN4yTeRIRkdmKjo5GWloaZs6cWW66Pn36oGfPntLroh9fMjIyUFhYaNAyyWQyeHl5IT09XfqxhywH+89yse8sG/vPslli/9nY2Oj14xSDUkTVSORmQ/3xRODmNe0VeTnA/u1QX0iEfMonDEwREZHZiY6OxrFjxzBjxgx4eHiUm9bW1ha2tralrquuwbMQwmIG5lQS+89yse8sG/vPsllj/8lNXQAia6aOXVUyIPW49GuaNERERGZCCIHo6GgkJCTgww8/RL169UxdJCIiIrJSDEoRVaeTCYZJQ0REZCTR0dH4448/MH78eDg6OkKpVEKpVCI/P9/URSMiIiIrw9v3iCpICKHXROVCCEClxzwahYV650lERFTddu7cCQCYPn261vKxY8eiQ4cOxi8QERERWS0GpYj0IHKzNbfZnUzQBJoUNkBoG8j7DC5zPiiZTKZJp4uNDQNSRERkNjZs2GDqIhAREVENwdv3iHQQudlQz3kP2LsVuHsbUN7T/Bu3Deo570HkZpe9cWgb3W+gTxoiIiIiIiIiK8OgFJEOmsnK04DiTzlQq3VOVC7vMxjw9i07cy9fTRoiIiIiIiKiGoZBKSJdTiaUDEgVUavLnahc5uAE+eRPgIiugIMjIJNr/uwdgX93hXzKJ2Xe/kdERERERERkzTinFFE59JqsXKUqd6JymYMTFIPHAoPHavIDOIcUERERERER1XgMShGVQ6/JyhUKvYNMDEYRERERERERafD2PSJdQtsA8jIOFbmcE5UTERERERERVQKDUkQ6yPsMBrx8Swam5HJOVE5ERERERERUSRW6fS82NhYJCQm4fv067OzsEBwcjMGDB8PHx0dKk5+fj5UrV+LAgQMoKChAaGgooqKi4O7uLqW5c+cOli9fjjNnzsDBwQEREREYNGgQFAqFlObMmTNYuXIl0tLS4OHhgb59+6JDhw5a5dm+fTs2b94MpVIJf39/DB8+HE2aNKlQWYh0kTk4QT5pvuYpeycTAJUKUCiA0DaQ9xnMicqJiIiIiIiIKqFCV0qdPXsWXbp0wezZszF16lSoVCrMmjULubm5Uprvv/8eR48exbvvvosZM2YgMzMTCxculNar1WrMmTMHhYWFmDVrFsaNG4e4uDisX79eSnP79m3MnTsXLVq0wPz589GjRw989dVXOHHihJTmwIEDWLlyJV555RXMmzcP/v7+mD17NrKysvQuC5G+ZA5OUAwcBcXcFZDP/xaKuSugGDiKASkiIiIiIiKiSqpQUGrKlCno0KEDGjZsiICAAIwbNw537txBcnIyACA7Oxt79uzB0KFD8eSTTyIwMBBjx45FUlISLly4AAA4efIkrl27hrfeegsBAQEIDw9H//79sWPHDhQWap5ytnPnTtSrVw9DhgyBr68vunbtinbt2mHr1q1SWbZs2YJOnTrh+eefh6+vL0aOHAk7Ozvs3btX77IQVQYnKyciIiIiIiKquio9fS87OxsA4OLiAgBITk6GSqVCSEiIlKZBgwaoW7cuLly4gODgYFy4cAF+fn5at9CFhYVhxYoVSEtLQ6NGjXDx4kWtPAAgNDQUMTExAIDCwkIkJyfj5ZdfltbL5XKEhIRIASd9ylJcQUEBCgoKpNcymQyOjo7S/w2tKE8GOUyL/WAe2A/mg31hHtgPRERERGTtKh2UUqvViImJQdOmTeHn5wcAUCqVsLGxgbOzs1ZaNzc3KJVKKU3xOZ3c3NykdUX/Fi17PE1OTg7y8/Px8OFDqNXqEvm4u7vjxo0bepeluNjYWGzcuFF63ahRI8ybNw+enp7ltkVVeXl5VWv+pB/2g3lgP5gP9oV5YD8QERERkbWqdFAqOjoaaWlpmDlzpiHLY1J9+vRBz549pddFv05nZGRItxYakkwmg5eXF9LT0yGEMHj+pB/2g3lgP5gP9oV5YD+YDxsbm2r/gYqIiIioJqpUUCo6OhrHjh3DjBkz4OHhIS13d3dHYWEhHj16pHWFUlZWlnRVk7u7Oy5duqSVX9Hk5I+neXzC8qI0jo6OsLOzg6urK+RyeYkrnh6/CkufshRna2sLW1vbUtdV5xcCIQS/cJgB9oN5YD+YD/aFeWA/EBEREZG1qtBE50IIREdHIyEhAR9++CHq1auntT4wMBAKhQKnT5+Wlt24cQN37tyR5nAKDg5GamqqVtDp1KlTcHR0hK+vLwAgKChIK4+iNEV52NjYIDAwEImJidJ6tVqNxMREKY0+ZSEiIiIiIiIiItOo0JVS0dHR+PPPP/Hee+/B0dFRulLJyckJdnZ2cHJyQseOHbFy5Uq4uLjAyckJ3377LYKDg6VAUGhoKHx9fbF06VJERkZCqVRi3bp16NKli3SV0osvvogdO3Zg1apVeP7555GYmIiDBw/igw8+kMrSs2dPLFu2DIGBgWjSpAm2bduGvLw8dOjQQSqTrrIQEREREREREZFpVCgotXPnTgDA9OnTtZaPHTtWCgYNHToUMpkMCxcuRGFhIUJDQxEVFSWllcvl+OCDD7BixQpMnToV9vb2iIiIQP/+/aU09erVwwcffIDvv/8e27Ztg4eHB0aPHo2wsDApTfv27XH//n1s2LABSqUSAQEBmDx5statebrKQkREREREREREpiETnKhCp4yMDBQUFBg8X5lMBm9vb9y8eZPzhZgQ+8E8sB/MB/vCPLAfzIetrS0nOq+k6hhD8diwbOw/y8W+s2zsP8tmif2n7/ipQnNKkfWzlB2ciIiIiIiIiCxbpZ6+R9ZF5GZDHbsKOJkAqAoBhQ0Q2gbyPoMhc3AydfGIiIiIiIiIyAoxKFXDidxsqOe8B9xMAx6/SipuG9TnT0E+aT4DU0RERERERERkcLx9r4ZTx64qGZACALUaSL+mWU9EREREREREZGAMStV0JxNKBqSKqNWa9UREREREREREBsagVA0mhNDMIVUelYqTnxMRERERERGRwXFOqRpMJpNpJjUvj0KhSUdEREQ1xtmzZ7Fp0yZcuXIFmZmZmDhxItq0aWPqYhEREZGV4ZVSNV1oG0Bexm4gl2vWExERUY2Sl5eHgIAAjBgxwtRFISIiIivGK6VqOHmfwVCfPwWkX9PMISWtkANevpD3GWy6whEREZFJhIeHIzw83NTFICIiIivHoFQNJ3NwgnzSfM1T9k4mACoVoFAAoW0g7zMYMgcnUxeRiIiIzFxBQQEKCgqk1zKZDI6OjtL/DakoP04vYJnYf5aLfWfZ2H+WzZr7j0EpgszBCYqBo4CBoyCEsModnYiIiKpPbGwsNm7cKL1u1KgR5s2bB09Pz2p7Ty8vr2rLm6of+89yse8sG/vPsllj/zEoRVoYkCIiIqKK6tOnD3r27Cm9LhpPZGRkoLBQx5N+K0gmk8HLywvp6el8QrAFYv9ZLvadZWP/WTZL7D8bGxu9fpxiUIqIiIiIqsTW1ha2tralrquuwbMQwmIG5lQS+89yse8sG/vPsllj//Hpe0REREREREREZHS8UoqIiIiItOTm5iI9PV16ffv2baSkpMDFxQV169Y1YcmIiIjImjAoRURERERaLl++jBkzZkivV65cCQCIiIjAuHHjTFUsIiIisjIMShERERGRlhYtWmDDhg2mLgYRERFZOc4pRURERERERERERsegFBERERERERERGR2DUkREREREREREZHQMShERERERERERkdExKEVEREREVkkIYeoiEBERUTn49D0iIiIishoiNxvq2FXAyQRAVQgobIDQNpD3GQyZg5Opi0dERESPYVCKiIiIiKyCyM2Ges57wM004PGrpOK2QX3+FOST5jMwRUREZEZ4+x4RERERWQV17KqSASkAUKuB9Gua9URERGQ2GJQig+P8DURERGQSJxNKBqSKqNWa9URERGQ2ePseGQTnbyAiIiJTEkJoxiDlUakghIBMJjNOoYiIiKhcDEpRlYncbKg/ngjcvKa9Ys8WqM+dgHzyJwxMERERUbWSyWSaH8XKo1AwIEVERGRGePselUlVqOPXxr+pf4wpGZAqcvMa1BtjDFYmIiIiojKFtgHkZQxv5XLNeiIiIjIbvFLKjJni8nLV1UvA3PeBwgLNawCwsQU+mAeFf5PSN0rYV36mh/cBg8catJxERERExcn7DIb6/Ckg/ZpmDilphRzw8oW8z2DTFY6IiIhKYFDKzEhzM504DKhVBpmbSd/glurqJWDWuyVXFBYAs96FauqiEoEpIQSQl1d+xnl5nL+BiIiIqp3MwQnySfMfm+dSBSgUnOeSiIjITDEoZSaEEBBZ9yCmvQVkP9ReuXcr1OdPQT5pvt6DqUpNPD73/fIznfs+8OVPer0/ERERkTEV/QAmc3CCYuAoYOAo/ihGRERk5hiUMiGRm43Mrxag8MBeID8PeHi/9McYCwHcTIM6dpVmkKW1quRgS+RmQz3nPeBmmnZ+cdvKD279fctemUpZL5PJADt7IC+n7O3s7DkgJCIiIoPT9SMcxx9ERETmjUEpExG52VDPnICHGel6biA0A66Bo8odgMHeUbPuRmrJPNRqzcTjpQS39J3UXFVYCIVNsd2mbQSwf3vZG7WN0CtvIiIiIn1V+kc4IiIiMhsMSpmI+vulgL4BqSIqFdQ5jyDmvl9yALZnC9R7twG2NkB+ftl5CDUQtw0qQOtWPoWNjWZScx1KBKQAyPsNg/pComZS0eK8fCHvN0yPnImIiIj0p/75h5LjIUDzI1x66T/CERERkXkp45m5VJ1Ebjbw158V31Auh/hlteYqqFJv81OXH5AqolZr5qma856mLEV0XeJexnqZgxPkUz4BOvYEPOoBbnU0/3bsCfmUT/grJRERERmcOJlQ+ngI0Ix1TiYYt0BERERUYbxSygTUP/9QuQ3zcoG4bYYphBAlf0V0dQOylGVv4+pW5ipOKkpERETGolarNU/WK49KxTEJERGRmWNQyhROHK7cdo8eGLYcRb8i/h1IgkzHhXMyhV6DOw7+iIiIyNAen1MzHQJQ3i1/A4WCY5IqUKvVkMt5UwUREVUvBqWMTAihmZzcXDz+K6JCx+7AwR0RERGZgDSp+d8PctE5D6ZcDoS2qfZyWRu18i7EkhnSVBEqmQzw8YNs/DTI3T1MXbwSeCUcEZHlY1DK2PJygPtKU5fiH48HmkLbaG4PVKtLpuPgjoiIiEykzCcLl0Ym1zxopc9graBF0f+FEBBCVPoqIEMHQswlsKJW3oWYNBJ4/InMQgDXUiAmjYR6znKzCEyV9xRqzmNKRGR5akRQavv27di8eTOUSiX8/f0xfPhwNGnSxCRlUa9bYZL3LVWxQJO8z2Coz5/SPEXv8cCU/J/BHREREdUM5jR+wpH9+qd1cAQaBUH9v7GaoEpermZco1YDhQVSMhWguQronRk6gy1agZDCAsDGtkqBkLICK7KXIyF3dK5wfoYgFk/TDkg9rrBQs37GUuMWCtpBO+mKueJPXdy7FerzpyCfNB+wd9Q7yPd4oLKsbQwRNCwtD3MLbppDcNQY7WSsvI2ptOB7Zbc3ZFmq47303b4onbndhmzsfa6i7WUqVh+UOnDgAFauXImRI0ciKCgIW7duxezZs/Hpp5/Cza3siburTfzvxn/P0pQSaJI5OEE+af5jgyQVoFDw1yciIqIaxpzGT0II4MF9/TfIeQTE79Yv7Y1UiP++DlXbDpAPHl3qWEfkZkM9e6LmR7vH7dkC9dkTFX7ScJmBlT1bIOJ+g8q9DhDW1vhjrxtpVVtvQGUF7VBYWLLdAM3rG6lQj48EarmWGzSU8j5xCHj44O8gox3gUktqdwBVvhqr1Dq0aKVZeeaYQa7yqupVY+Zw1Zkx2knn+1ngd50SgfLcXEAGwN5Br6C5IdtBV17G2k+ldMcPAlmZ0kUWKrncpLchG3ufq3B7mcGxIBOirGfpWofJkyejcePGGDFiBADNpI1jxoxBt27d8PLLL+uVR0ZGBgoKCnQn1EEIAfWo3lXOp2wywM7u718BZQCE5kPJ2QVwrgVkPwTUQu9Ak6kjpsYik8ng7e2NmzdvwsoPB7PGfjAf7AvzwH4wH7a2tvD09DR1MYzKEOMnwHBjKNXIXlXOQydvX8gnlwwwqX74Ati/vezt/t0VitfG6v02qrXfAHu3lgysPK7ox8NJ843y5UCtVkO88bLOdLKvf6nyVQe6PtvKDNrJ5QBkgFrnjGL/pC/WhsXnJitZODng5QMIALeul3x/PfukzDroWU59lNtOeuRXme0NfV4yRjvp9X5GPt6qSq92K6VORf1348plqD7+r0HaQVebyt6ZobnKspr3U53HNgDY2EBm5NuQDbnP6XP8Vbi9qvlY0Hf8ZD7XslWDwsJCJCcnIyQkRFoml8sREhKCCxcuGL081f6lwsMT8qUboPj6F8i/+lnz79INUMz/Doppn0Ex71vI538LxdwVUAwcpXNHqwkBKSIiItJmbuMnVX6+cd7o5jXNr8bFHd5X/na61hd3MkH3F3C1GkgvozzVQN8xnzHGhurYVaV/2Var9Q9IFaUv1oZS3mURauDmNc1VcaW9v559UmYd9CynPsptJz3yq+r2hmCMdtLr/Yx8vFWVXu1WTp3UP/9gsHbQ1aZiyQyj7Kc6j21Acxvykhn6VMtgjL3PVbi9zORYsOqg1P3796FWq+Hu7q613N3dHUqlskT6goICZGdnS385OTnSOplMVuU/fX6BqjSZDLKwtpDL5ZDJZCX+Lfor/pp/MmmAZeoy8I/9YE5/7Avz+GM/mMdfTVPR8RNQvWMoXLpUjbUt5mRCyb7Pzyt/m7/X670vqfQMrKjVJcpTXX9yuRyQK8ovj1xhsHFkee2lV9BOX8XasMp569knFX6fSvR1ue+hR36V3R4w3HnJGO1kyDYzlz+9262UOgGAMGA76GzTv5/kWdn30rfP9G6TG6nm01eVOe5R/vFnkPYy4LGgL6ufU6oiYmNjsXHjRul1o0aNMG/ePINdsl9td+LLZLDxa4T6oydC7mSayTGtgZeXl6mLQGA/mBP2hXlgP5AlqM4xVFrUS1XOQ19yCHh5ef3z5U0IXPt7RoQyyQBvb2+9B+A37Oyg7/U+imLlqU53O3VH9q7NZa53eqEHPLy9DfZ+pX22CSFwE0Lv9tFHURsCMEjeuvqksnWoSF/r8x7l5VfV7Q1xXjJGO1X0/Yx5vFVWRduteJ2EEFAYqB30KouOOJEh9tP69esjXd82EUD9+vWNMvl5de1zZR1/hmwvYx8LVh2UcnV1hVwuL/GrnlKpLPHrHwD06dMHPXv2lF4XdUJGRgYKy3oaSQXYrNiMwqoMrFq2gXzIWIjffoI4fkgTxVQoIAtrC9FnMG5l3QeyKjARKAHQ9LOXlxfS09M5b4sJsR/MB/vCPLAfzIeNjU2NmlOqouMnoHrHUFUeP1WAGjKkp6drL7SzB3JzSt/g7/UltinvPZ58Cojbpv2k4zKoSitPNRG9BwMnjwK3b5RcWc8Heb0icfPmzSq/j67PNhV0fAlSKPS/2gzabagz7wrmV16a6si3Iu+hK7/KbG/o85Ix2qki72fM460qKtJuj9epqP8M2Q46y6IjqG+I/fTWrVv6t4kMuHXrln5pDcCQba3P8Weo9jLUsaDv+Mmqg1I2NjYIDAxEYmIi2rRpA0AzkWNiYiK6du1aIr2trS1sbW1LzcvkXwjadoAi6l0AgGzASGDAyBITkZu8jBZOCME2NAPsB/PBvjAP7AcytoqOnwAzH0NVRGibkuVtE1H+ROdtIypUR1mfwRDnT2nmLSovMCWXl16e6mLvCPn/Fv3zNKaCAsD2n6d4wd7RoGUp87MttE3ZQTu5HHiuM3D5PHAtRfebFG/D8vLWh759UtH3qUxf62onXflVYXuDnZeM0U76vp+xj7eq0LfdyqiTLLQNhKHaQVeb+vhpbuGr7v1U3zbx8TNuH1fDPlfu8WeI9jLBsWDVc0oBQM+ePbF7927ExcXh2rVrWLFiBfLy8tChQwdTF01/3r6QDx5dYrE5X1pKRERElssqxk8V5e2rCb4UI+83DPD2LXubV4ZV6G1kDk6QT5oPdOgO1Kn791Plir/p309AKqU81Unm4ATFwFFQzF0B+Scxej8cx5DkfQYDXr4l26WoTV4ZBvn7czVfdmXlfJUppQ2lvMsaQ8vkmr4u7/316JMy66BnOfWhs5105FfV7Q3BGO2k1/uZ6HirLL3arZw6yf/vNYO1g642lY2fZpT9VEpX3hVANjaa8hiRsfe5CreXmRwLMmER4eCq2b59OzZt2gSlUomAgAC8/vrrCAoK0nt7Qz3OuIh+jzWWAQ4OQNsIyF8ZZhGPJ7VUMhkfu24O2A/mg31hHtgP5kPfRxpbm6qOnwDDjqH0Gz9VgoOjzvGWyM3+5woilUpzC9nfVxBVdYymznkE8cvqasnbHOnz2aZPe2ulKSwA8nI1twk5OAI2NmW2obTdiUPAwweabW3tAOdaQFhb6ctYVfu71Dq0aKX5zpx4zCB9XdX9sqLbV8d5qdQyPNlK05dnDNNOOt/PAo837f2/UHOLsQyAvQNgY1tqnR7vP3XOI4O1g642NdZ+KqU7fhDIyvznKqC/r9iSjZ8GubtHhepmCIba5/Q9/ircXtV4LOg7fqoRQamqMnRQqohMJtPMkfDmR5C3bAkhBORyubST8Uoo4+AXP/PAfjAf7AvzwH4wHzU1KGUI1TGGksZPADBiMhASohlQ29kBajXkjo4AAHVBAWQ2NlpjK7VaDZlMVqXxVvHpEwypOvM2FxX9bNOnTR5PU5E2LEpb3jaG6JPS8jB0X1c1P322r+7zkjHayVh5G5O++39Z/WfIdtCVlzH208fTqdVqo0xqrq+q1L8yx19F28vQ9B0/WfWcUoZiY1N9zeS99S9kZGRUW/6kv+rsZ9If+8F8sC/MA/vB9NgHlVddbafX+MnOrlremwyDx5XlYt9ZNvafZbOk/tO3rLxSioiIiIiIiIiIjM58rmWrgXJycvD+++8jJ6ecxwxTtWM/mAf2g/lgX5gH9gNR6XhsWDb2n+Vi31k29p9ls+b+Y1DKhIQQuHLlCucKMTH2g3lgP5gP9oV5YD8QlY7HhmVj/1ku9p1lY/9ZNmvuPwaliIiIiIiIiIjI6BiUIiIiIiIiIiIio2NQyoRsbW3xyiuvwNbW1tRFqdHYD+aB/WA+2Bfmgf1AVDoeG5aN/We52HeWjf1n2ay5//j0PSIiIiIiIiIiMjpeKUVEREREREREREbHoBQRERERERERERkdg1JERERERERERGR0DEoREREREREREZHR2Zi6ADXV9u3bsXnzZiiVSvj7+2P48OFo0qSJqYtlls6ePYtNmzbhypUryMzMxMSJE9GmTRtpvRACGzZswO7du/Ho0SM0a9YMUVFR8Pb2ltI8fPgQ3377LY4ePQqZTIa2bdvi9ddfh4ODg5Tm6tWriI6OxuXLl+Hq6oquXbuid+/eWmU5ePAg1q9fj4yMDHh5eSEyMhKtWrWqUFksVWxsLBISEnD9+nXY2dkhODgYgwcPho+Pj5QmPz8fK1euxIEDB1BQUIDQ0FBERUXB3d1dSnPnzh0sX74cZ86cgYODAyIiIjBo0CAoFAopzZkzZ7By5UqkpaXBw8MDffv2RYcOHbTKo+sY0qcslmrnzp3YuXMnMjIyAAC+vr545ZVXEB4eDoD9YCq//PIL1qxZg+7du2PYsGEA2BdElVXRcZKu8zMZV0X6Ly4uDl988YXWMltbW6xevdoYRaXH6Bpzl0af8xMZR0X778yZM5gxY0aJ5d988w3HBkamz/es0ljLuY9XSpnAgQMHsHLlSrzyyiuYN28e/P39MXv2bGRlZZm6aGYpLy8PAQEBGDFiRKnrf/31V/z2228YOXIkPv74Y9jb22P27NnIz8+X0nz22WdIS0vD1KlT8cEHH+DcuXP4+uuvpfXZ2dmYNWsW6tati7lz52Lw4MH48ccf8fvvv0tpkpKSsGTJEnTs2BHz5s3D008/jQULFiA1NbVCZbFUZ8+eRZcuXTB79mxMnToVKpUKs2bNQm5urpTm+++/x9GjR/Huu+9ixowZyMzMxMKFC6X1arUac+bMQWFhIWbNmoVx48YhLi4O69evl9Lcvn0bc+fORYsWLTB//nz06NEDX331FU6cOCGl0ecY0lUWS1anTh0MGjQIc+fOxZw5c/Dkk09i/vz5SEtLA8B+MIVLly5h165d8Pf311rOviCquIqOk/Q5P5PxVGac6+joiG+++Ub6W7ZsmRFLTEV0jbmL0+f8RMZT0f4r8umnn2odf66urtVUQiqLPt+zirOqc58go5s0aZJYsWKF9FqlUolRo0aJ2NhY0xXKQvTr108cPnxYeq1Wq8XIkSPFr7/+Ki179OiRGDRokPjzzz+FEEKkpaWJfv36iUuXLklpjh8/Ll599VVx9+5dIYQQO3bsEMOGDRMFBQVSmlWrVonx48dLrxctWiTmzJmjVZ7JkyeLr7/+Wu+yWJOsrCzRr18/cebMGSGEpq4DBgwQBw8elNJcu3ZN9OvXTyQlJQkhhDh27Jh49dVXRWZmppRmx44dYsiQIVLb//DDD+Ldd9/Veq/FixeLWbNmSa91HUP6lMXaDBs2TOzevZv9YAI5OTni7bffFidPnhTTpk0T3333nRCCxwRRZVV0nKTr/EzGVdH+27t3rxg6dKhxCkd6Kz7mLo0+5ycyDX36LzExUfTr1088fPjQSKUifRX/nlUaazr38UopIyssLERycjJCQkKkZXK5HCEhIbhw4YIJS2aZbt++DaVSiZYtW0rLnJyc0KRJE6k9L1y4AGdnZzRu3FhKExISAplMhkuXLklpmjdvDhubf+5oDQ0NxY0bN/Dw4UMpzeP9VpTm4sWLepfFmmRnZwMAXFxcAADJyclQqVRabdSgQQPUrVtXqy/8/Py0LgkOCwtDTk6OdJXPxYsXS23nojz0OYb0KYu1UKvViI+PR15eHoKDg9kPJrBixQqEh4drHfsAjwmiyqjMOEnX+ZmMp7Lj3NzcXIwdOxZjxozRuvKXzJuu8xNZhvfeew+jRo3CRx99hPPnz5u6OISS37NKY03nPs4pZWT379+HWq0ucZ+uu7s7bty4YZpCWTClUgkAcHNz01ru5uYmrVMqlSUuQ1UoFHBxcdFKU69ePa00RX2kVCqltLreR1dZrIVarUZMTAyaNm0KPz8/AJr629jYwNnZWStt8TYqvu8XtdfjaUprw5ycHOTn5+Phw4c6jyF9ymLpUlNTMWXKFBQUFMDBwQETJ06Er68vUlJS2A9GFB8fjytXrmDOnDkl1vGYIKq4yoyTdJ2fyXgq038+Pj4YM2YM/P39kZ2djU2bNmHq1KlYtGgRPDw8jFBqqixd5yc7OzsTlYz0Ubt2bYwcORKNGzdGQUEBdu/ejRkzZmD27NkIDAw0dfFqrNK+Z5XGms59DEoRUYVFR0cjLS0NM2fONHVRaiwfHx8sWLAA2dnZOHToEJYtW1bqZJVUfe7cuYOYmBhMnTqVA28iokoKDg5GcHCw1ut33nkHu3btwoABA0xYMiLr5uPjozWRdtOmTXHr1i1s3boVb731lglLVrPVxO9ZvH3PyFxdXSGXy0tEMEv7tZx0K2qz4pNnZmVlSevc3d1x//59rfUqlQoPHz7USlNanzz+Hu7u7jrfR1dZrEF0dDSOHTuGadOmaf2C6e7ujsLCQjx69EgrffE2Kt7ORe2lq50dHR1hZ2en1zGkT1ksnY2NDby8vBAYGIhBgwYhICAA27ZtYz8YUXJyMrKysvD+++9jwIABGDBgAM6ePYvffvsNAwYMgJubG/uCqIIqM07SdX4m4zHEONfGxgaNGjVCenq64QtIBqXr/ESWp0mTJjz2TKis71mlsaZzH4NSRmZjY4PAwEAkJiZKy9RqNRITE7V+JSL91KtXD+7u7jh9+rS0LDs7G5cuXZLaMzg4GI8ePUJycrKUJjExEUII6fHEwcHBOHfuHAoLC6U0p06dgo+Pj3Qvb3BwsNb7FKUJCgrSuyyWTAiB6OhoJCQk4MMPPyxxu2NgYCAUCoVW/W/cuIE7d+5o9UVqaqrWB+ipU6fg6OgIX19fAEBQUFCp7VyUhz7HkD5lsTZqtRoFBQXsByMKCQnBJ598gvnz50t/jRs3xnPPPSf9n31BVDGVGSfpOj+T8RhinKtWq5GamoratWtXVzHJQHSdn8jypKSk8NgzAV3fs0pjTec+BqVMoGfPnti9ezfi4uJw7do1rFixAnl5eejQoYOpi2aWcnNzkZKSgpSUFACaCcVTUlJw584dyGQydO/eHT///DP++usvpKamYunSpahduzaefvppAICvry/CwsLw9ddf49KlSzh//jy+/fZbtG/fHnXq1AEAPPfcc7CxscFXX32FtLQ0HDhwAL/99ht69uwplaN79+44efIkNm/ejOvXr2PDhg24fPkyunbtCgB6lcWSRUdH448//sD48ePh6OgIpVIJpVKJ/Px8AJpJ3Tt27IiVK1ciMTERycnJ+OKLL7Quyw8NDYWvry+WLl2KlJQUnDhxAuvWrUOXLl1ga2sLAHjxxRdx+/ZtrFq1CtevX8eOHTtw8OBB9OjRQyqLrmNIn7JYsjVr1uDs2bO4ffs2UlNTpdf/+te/2A9G5OjoCD8/P60/e3t71KpVC35+fuwLokrStT8vXboUa9askdLrOj+TcVW0/zZu3IiTJ0/i1q1bSE5OxmeffYaMjAx06tTJRDWoucobcwOa8cfSpUul9Pqcn8h4Ktp/W7duxZEjR5Ceno7U1FTExMQgMTERXbp0MUXxazRd37MA6z73yYQQwtSFqIm2b9+OTZs2QalUIiAgAK+//rpFRjWN4cyZM6XOlRMREYFx48ZBCIENGzbg999/R3Z2Npo1a4YRI0Zo3SP98OFDREdH4+jRo5DJZGjbti2GDx8OBwcHKc3Vq1cRHR2Ny5cvo1atWujatStefvllrfc8ePAg1q1bh4yMDHh7eyMyMhKtWrWS1utTFkv16quvlrp87Nix0kAzPz8fK1euRHx8PAoLCxEaGoqoqCity0gzMjKwYsUKnDlzBvb29oiIiEBkZCQUCoWU5syZM/j+++9x7do1eHh4oG/fviWCtrqOIX3KYqm+/PJLJCYmIjMzE05OTvD390fv3r2lp7+xH0xn+vTpCAgIwLBhwwCwL4gqq7z9efr06fD09MS4ceOk9LrOz2RcFem/mJgYJCQkQKlUwtnZGYGBgRgwYAAaNWpkyirUSLrG3MuWLUNGRgamT5+utY2u8xMZR0X779dff8Xvv/+Oe/fuwd7eHv7+/ujbty+efPJJI5ec9PmeZc3nPgaliIiIiIiIiIjI6Hj7HhERERERERERGR2DUkREREREREREZHQMShERERERERERkdExKEVEREREREREREbHoBQRERERERERERkdg1JERERERERERGR0DEoREREREREREZHR2Zi6AEREREREREREZDxnz57Fpk2bcOXKFWRmZmLixIlo06ZNhfI4cOAAYmNjcfPmTbi6uqJr167o1atXhfLglVJERERERERERDVIXl4eAgICMGLEiEptf/z4cXz++efo3LkzFi5ciKioKGzduhXbt2+vUD68UoqIiIiIiIiIqAYJDw9HeHh4mesLCgqwdu1axMfHIzs7Gw0bNkRkZCRatGgBANi/fz+efvppvPjiiwCA+vXr4+WXX8avv/6KLl26QCaT6VUOXilFRERERERERESS6OhoXLx4ERMmTMCCBQvQrl07fPzxx7h58yYATdDK1tZWaxs7OzvcvXsXGRkZer8Pg1JERERERERERAQAuHPnDuLi4vDOO++gefPm8PLyQq9evdCsWTPs3bsXABAWFoaEhAScPn0aarUaN27cwJYtWwAASqVS7/fi7XtERERERERERAQASE1NhVqtxvjx47WWFxYWwsXFBQDQqVMnpKenY+7cuVCpVHB0dET37t3x448/6n3rHsCgFBERERERERER/S03NxdyuRzz5s2DXK59g52DgwMAQCaTYfDgwRg0aBCUSiVcXV1x+vRpAJr5pfTFoBQREREREREREQEAAgICoFarkZWVhebNm5ebVi6Xo06dOgCA+Ph4BAcHw9XVVe/3YlCKiIiIiIiIiKgGyc3NRXp6uvT69u3bSElJgYuLC3x8fPDcc89h6dKlGDJkCBo1aoT79+/j9OnT8Pf3R6tWrXD//n0cOnQILVq0QEFBAfbu3YuDBw9ixowZFSqHTAghDF05IiIiIiIiIiIyT2fOnCk1gBQREYFx48ahsLAQP//8M/bt24d79+7B1dUVQUFBePXVV+Hn54f79+9j3rx5SE1NBQAEBwdjwIABCAoKqlA5GJQiIiIiIiIiIiKjk+tOQkREREREREREZFgMShERERERERERkdExKEVEREREREREREbHoBQRERERERERERkdg1JERERERERERGR0DEoREREREREREZHRMShFRERERERERERGx6AUEREREREREREZHYNSRERERERERERkdAxKERERERERERGR0TEoRURERERERERERsegFBERERERERERGR2DUkREREREREREZHQMShERERERERERkdExKEVEREREREREREbHoBQRERERERERERkdg1JERERERERERGR0DEoR6aFDhw6YMGFClfKIiYmBu7t7hbZJSUmBTCbDiRMnqvTe5mjYsGF4+eWXDZZfQEAAPv30U4PkZc3tbokMcfw9TiaT4ZdffjFYfkRERNaKY2Aiqm4MSpHFy8jIwJgxY+Dn5wd7e3t4eXmhS5cuiI+PN3XRTMLQwZ7qsmTJEsTExBgsvyNHjmDUqFEGy88QGPwwjJ9//hkfffSRqYuhxZBBUCIiosrgGFibpYyBiUibjakLQFRVffv2RX5+Pr7//nsEBgbi1q1b2L17N+7evWvqolEpVCoVZDIZ3NzcDJqvp6enQfMjjfz8fNjZ2Zm0DHXq1DHp+xMREZkjjoGJyBrwSimyaEqlEn/88QfmzZuH559/Hv7+/mjTpg0mTZqEXr16aaV74403UL9+fTg4OODJJ5/Eli1bAAB3797FwIED0aBBAzg5OSEkJARr164t933z8vIwceJENGjQAM7Ozmjbti3i4uK00sTExMDPzw9OTk7o06ePXgOEhIQEhIeHw8HBAa1bt8bx48e11qtUKowYMQKNGjWCo6MjmjZtiiVLlkjrp0+fju+//x6//vorZDIZZDKZVK73338fwcHBcHJyQmBgIP73v/+hoKBAZ5l0yczMxJAhQ1C7dm04OTmhW7duuHjxolY7uLu7Y9OmTXjiiSdgb2+P1NTUEr9mPXjwAJGRkXB2doa3tzcWL15coUvGi1+5IpPJ8OWXX6Jbt25wdHREYGAgNm7cWKG6nT9/Hu3bt5f2mX379gEAhBBo0qQJPvnkE630J06cgEwmw6VLlxAQEAAA6NOnD2QymfQaAH799Ve0atUKDg4OCAwMxIwZM1BYWCjlPX36dOlXTx8fH7z99tullu/ChQuQyWQ4f/681vLFixejcePGADT9ExkZCU9PTzg6OiIoKAjfffddmXXu0KED3nzzTUyYMAF169ZFly5dAACJiYno1q0bXFxcUL9+fbz22mu4c+eOfYjFOAAARURJREFUtN3GjRsREhICR0dHeHh44IUXXsCjR48A/PPL5YwZM+Dp6QlXV1eMHj0a+fn5urpAKtPj+0FAQAA++ugjDBw4EM7OzmjQoAGWLVumV15Fbt68Wea+0bFjR7z55pta6TMyMmBnZ4fdu3ejQ4cOuHr1Kt555x3pOCvy559/4l//+hccHR3RsGFDvP3221I7AMAXX3yBoKAgODg4oH79+njllVcqVG4iIiKAY2BzGAMTkYEIIgtWUFAgXFxcxIQJE0Rubm6paVQqlWjXrp1o0aKF2Llzp7h8+bLYvHmz2LZtmxBCiGvXrokFCxaI48ePi8uXL4vPPvtMKBQKcfjwYSmPiIgIMX78eOl1VFSUaN++vdi/f7+4dOmSWLBggbC3txcXLlwQQghx6NAhIZfLxbx580RSUpJYsmSJcHd3F25ubmXW5cGDB8LT01MMGjRIJCYmis2bN4vAwEABQBw/flwIIUR+fr748MMPxZEjR0RycrJYtWqVcHJyEuvXr5fyePXVV0XXrl3FzZs3xc2bN0VeXp4QQoiPPvpIxMfHiytXrohNmzaJ+vXri3nz5lW26SW9evUSzZs3F/v37xcnTpwQXbp0EU2aNBH5+flCCCG+++47YWtrK9q3by/i4+PF+fPnxaNHj8TQoUNF7969tdrU399f/P777+L06dOiT58+olatWlrtXh5/f3+xePFi6TUA4eHhIZYvXy6SkpLE1KlThUKhEGfPntWZ15UrVwQA4evrKzZu3CjOnj0roqKiRK1atcSdO3eEEELMnj1bPPHEE1rbvf322+Lf//63EEKI27dvCwDiu+++Ezdv3hS3b98WQgixf/9+4erqKmJiYsTly5fFzp07RUBAgJg+fboQQogff/xRuLq6im3btomrV6+Kw4cPi2+++abMsrZu3VpMnTpVa9lTTz0lLRs3bpwICwsTR44cEVeuXBG7du0SmzZtKjO/iIgI4eLiIv773/+K8+fPi/Pnz4vMzEzh6ekpJk2aJM6dOyeOHTsmOnfuLJ5//nkhhBA3btwQNjY2YtGiReLKlSvi1KlTYtmyZeLBgwdCCCGGDh0qXFxcRP/+/UViYqLYsmWL8PT0FJMnT9bZF0Vlenw/8Pf3F7Vq1RJz5swRSUlJ0jG7c+dOvfLTtW+sXr1a1K5dW+szZdGiRSIgIECo1Wpx9+5d4evrK2bOnCkdZ0IIcenSJeHs7CwWL14sLly4IOLj40V4eLgYNmyYEEKII0eOCIVCIdasWSNSUlLEsWPHxJIlS/QqMxER0eM4Bjb9GJiIDINBKT2dOXNGzJkzR4waNUr069dP68NaX8ePHxeTJ08Wr732mhg+fLhYsGCBuHXrVjWUtmbZuHGjqF27tnBwcBDt27cXkyZNEidPnpTW79ixQ8jlcpGUlKR3nj169BD/+c9/pNePn5CvXr0qFAqFuH79utY2nTp1EpMmTRJCCDFw4EDRvXt3rfX9+/cv94T89ddfCw8PD5GTkyMt+/LLL7VOyKUZN26c6Nu3r/S6eLCnLAsWLBBPPfWUznTluXDhggAg4uPjpWV37twRjo6OYsOGDUIITVAKgDhx4oTWto+X8/79+8LW1lb8+OOP0nqlUimcnJyqFJQaPXq0Vpq2bduKMWPG6MyrKCg1d+5caVlBQYHw9fWVBjHXr1/XGrjl5+eLunXripiYGK0yxMbGauXdqVMn8fHHH2st++GHH4S3t7cQQoiFCxeK4OBgKainy+LFi0Xjxo2l10lJSQKAOHfunBBCiJdeekm8/vrreuUlhGZfDw8P11r20UcfiRdffFFrWVpamgAgkpKSxNGjRwUAkZKSUmqeQ4cOFXXq1BGPHj2Sln355ZfCxcVFqFQqvcpUPCjVtWtXrTT9+/cX3bp105mXELr3jZycHFG7dm1poCuEEC1btpQCh0VleHx/E0KIESNGiFGjRmkt++OPP4RcLhc5OTnip59+Eq6uruL+/ft6lZOIiKg8HAObbgxMRIbD2/f0lJeXh4CAAIwYMaJS29++fRsLFixAixYtMH/+fEyZMgUPHjzAwoULDVzSmqdv3764ceMGNm3ahK5duyIuLg6tWrWSJtE+ceIEfH19ERwcXOr2KpUKH330EUJCQlCnTh24uLhgx44dSE1NLTX96dOnoVKpEBwcDBcXF+lv3759uHz5MgDg3LlzaNu2rdZ2zzzzTLn1OHfuHFq2bAkHB4dyt1m2bBmeeuopeHp6wsXFBd98802ZZX3c+vXr8eyzz8LLywsuLi6YOnVqudsV3arl4uKCFi1alFlmGxsbrbp6eHigadOmOHfunLTMzs4OLVu2LPO9kpOTUVBQgDZt2kjL3Nzc0LRpU531Kk/x9nvmmWe0ylWR7W1sbNC6dWtpex8fH/To0QPffvstAGDz5s3Iy8tDv379ys3z5MmTmDlzpta+M3LkSNy8eRPZ2dno168fcnJyEBgYiJEjRyI2Nla6ta80AwYMQEpKCg4dOgQAWL16NVq1aoVmzZoBAMaMGYN169YhLCwM7733Hg4cOKCz3k899VSJMu/du1erzEX5X758GaGhoejUqRNCQkLQr18/LF++HJmZmVp5hIaGwsnJSXr9zDPP4OHDh0hLS9NZntIYsm+Lb+/g4IDXXntN6ttjx44hMTERw4YNKzfPkydPIiYmRqudunTpArVajStXrqBz587w9/dHYGAgXnvtNaxevRrZ2dl6l5mIiOhxHANXzxiYiIyLE53rKTw8HOHh4WWuLygowNq1axEfH4/s7Gw0bNgQkZGR0pf55ORkqNVqDBgwAHK5Jhb40ksvYcGCBSgsLISNDbuiKhwcHNC5c2d07twZ//vf/xAVFYVp06Zh2LBhcHR0LHfbBQsWYMmSJfj0008REhICZ2dnTJgwocz5bh4+fAiFQoGjR49CoVBorXNxcTFYnUqzbt06TJw4EQsXLsQzzzyDWrVqYcGCBTh8+HC52x08eBCRkZGYMWMGunTpAjc3N6xbt67coOiKFSuQk5MDALC1ta1SuR0dHbXm3bEWUVFReO2117B48WJ899136N+/v1bgpTQPHz7EjBkz8H//938l1jk4OKBhw4ZISkrC77//jl27dmHs2LFYsGAB9u3bV2o/eHl5oWPHjlizZg3atWuHNWvWYMyYMdL6bt264erVq9i2bRt27dqFTp06Ydy4cSXmw3qcs7NziTK/9NJLmDdvXom03t7eUCgU2LVrFw4cOICdO3fi888/x5QpU3D48GE0atSo3PYwV1FRUQgLC8O1a9fw3XffoWPHjvD39y93m4cPH+KNN94odQ4wPz8/2NnZ4dixY4iLi8POnTvx4YcfYvr06Thy5EiFH5VNREQEcAxcHWNgIjIuXillINHR0bh48SImTJiABQsWoF27dvj4449x8+ZNAEBgYKA04Z5arUZ2djb279+PkJAQBqSqwRNPPCFNLtyyZUtcu3YNFy5cKDVtfHw8evfujcGDByM0NBSBgYFlpgU0AUqVSoXbt2+jSZMmWn9eXl4AgObNm5c4SRZdyVKW5s2b49SpU8jNzS1zm/j4eLRv3x5jx45FeHg4mjRpIv0yVcTOzg4qlUpr2YEDB+Dv748pU6agdevWCAoKwtWrV8stT4MGDaR6lfVlvHnz5igsLNSq6927d5GUlIQnnnii3PwfFxgYCFtbWxw5ckRalpWVVW4/6KN4+x06dAjNmzev1PaFhYU4evSo1vbdu3eHs7MzvvzyS2zfvh3Dhw/X2t7W1rZEX7Rq1QpJSUkl9p0mTZpIAWtHR0e89NJL+OyzzxAXF4eDBw/i9OnTZZYzMjIS69evx8GDB5GcnIwBAwZorff09MTQoUOxatUqfPrpp/jmm2/0boOiMp85cwYBAQElylwUwJLJZHj22WcxY8YMHD9+HHZ2doiNjZXyOHnypBTkBDRt6+LigoYNG1aoLI9vX/x1Zfu2tO1DQkLQunVrLF++HGvWrCnRt6UdZ61atcLZs2dL7duiJxja2NjghRdewPz583Hq1CmkpKRgz549epebiIioPBwDV30MTETGxaCUAdy5cwdxcXF455130Lx5c3h5eaFXr15o1qwZ9u7dCwCoV68epk6dirVr12LQoEEYNmwY7t27h3feecfEpbdsd+/eRceOHbFq1SqcOnUKV65cwY8//oj58+ejd+/eAICIiIj/b+/uw6Oqz/yPf85MEpKAEOWhAVISI0RdxYC9GrlcdwFthVVazCpUIVVUwBWqta4iCKvS5TGivdZKqy2hmLKKDyWKSEGr0FbgZ1oVaILKQ0CCQEmACUIIZOZ8f39MM5shTxNIZuZM3q/r8ooz55szN3PPge/c53vuo3/913/Vrbfeqvfee0979uzR73//e61du1aSNGDAgMAqj88++0z33Xef/v73vzf5mllZWRo/frzuvPNOrVy5Unv27FFxcbHmz5+vd955R5L04IMPau3atVq0aJF27typ559/PvB6TRk3bpwsy9KkSZO0fft2rVmzpsFqlgEDBuivf/2r1q1bpx07dui//uu/ggo5kv/OZNu2bdMXX3yhyspK1dbWasCAAdq3b59WrFih3bt367nnngsqGJyrAQMGaPTo0Zo0aZI+/PBDbd26VXl5eerbt2/g/Q/FBRdcoLvuukuPPvqo1q9fr9LSUt17771yuVzntcLq9ddf19KlS7Vjxw49+eSTKi4ubnBXteYsXrxYRUVF+vzzzzV16lQdO3YsqDjhdrs1YcIEzZgxQwMGDGiw1DwjI0Pvv/++Dh06FLic7YknnlBhYaFmz56t0tJSffbZZ1qxYoVmzZolyX/HmoKCApWUlKisrEzLly9XUlJSs6t0/v3f/11ff/217r//fg0fPlx9+vQJbHviiSf01ltvadeuXSotLdXq1atbVbyRpKlTp+ro0aO644479Je//EW7d+/WunXrdPfdd8vn8+mjjz7SvHnz9Ne//lX79u3TypUrVVFREfQ6Z86c0b333hv4bD/55JP60Y9+FCjEtdbGjRuVn5+vHTt2aPHixXr99df14x//OOTfD+WzMXHiRC1YsEDGGOXm5gZty8jI0J/+9Cd99dVXgbsQPvbYY9q0aZN+9KMfacuWLdq5c6feeuutwH5Xr16t5557Tlu2bNGXX36pwsJC2bZ93pepAgA6HubAkZ0DA2hDkW5q5URnNzr/+OOPzZgxY0xeXl7Qf7fffrt59tlnjTHGHDt2zDz44IPmt7/9rSkrKzOlpaXmySefND/96U+NbduR+qM4Xk1NjZk+fbq5+uqrTbdu3UxycrK59NJLzaxZs0x1dXVg3JEjR8zdd99tunfvbhITE82VV15pVq9eHdg2evRo06VLF9OrVy8za9Ysc+eddwY1Sjy70XLdHUAyMjJMfHy86d27t8nNzTXbtm0LjCkoKDBpaWkmKSnJfO973zOLFi1qtsmjMcZs3rzZZGdnm4SEBDNo0CDzu9/9LqjJY01NjZkwYYLp1q2bSUlJMffff7+ZPn26yc7ODuzj8OHD5rvf/a7p0qWLkWTWr19vjDHm0UcfNd27dw/cBe1nP/tZi/GE4ujRo+aHP/yh6datm0lKSjIjRowI3IHFGH+j88Ze5+xmlMePHzfjxo0zycnJJjU11Tz77LMmJyfHTJ8+PaQ4Gmt0vnjxYvPd737XdOrUyWRkZAQ1rm5OXaPzl19+2eTk5JiEhATzT//0T+aDDz5oMHb37t1GksnPz2+wbdWqVaZ///4mLi7OpKenB55fu3atufbaa01SUpLp2rWrycnJCdxhr6ioyFxzzTWma9eupnPnzmbIkCHmD3/4Q4sxjx071kgyS5cuDXr+v//7v83ll19ukpKSzEUXXWRGjx5tysrKmtzP2Z/1Ojt27DC5ubkmJSXFJCUlmcsuu8w89NBDxrZts337djNixAjTs2dP06lTJ5OVlWV+/vOfB363LtdPPPFE4DM4adKkJu8W1FJM6enpZvbs2WbMmDGBz0tr7mIX6mfj66+/NsnJyWbKlCkNtm3evNlcddVVplOnTqb+P6XFxcWB469z587mqquuMnPnzjXG+JueDx061Fx44YUmKSnJXHXVVSF/JgEAqI85cOTnwADahmWMMZEqiDnV2LFj9cgjjwSaMm/atEnPPfecnn322QZn/RMTE5WSkqIVK1Zo69atmj9/fmDbkSNHdP/992vOnDlNNiAEOqqTJ0+qb9++euaZZ87pBgOWZamoqEi33HJL2wdXz5///GfdcMMNKi8v1ze+8Y12fS2nmjBhgjwej95888022V9GRoYeeughPfTQQ22yv6bs3btXl1xyif7yl7/o6quvbtfXAgAAADoimhm1gYyMDNm2raqqqiYvizlz5kyDy5DqCljUBQHp008/1eeff66cnBxVVVXppz/9qSS16jLAcDp9+rQqKir01FNPacyYMRSkYkhtba2OHDmiWbNmaciQIRSkAAAAgHZCT6kQ1dTUaO/evdq7d68k6fDhw9q7d68qKyvVp08fXXfddXr++ef10Ucf6fDhw9q1a5eKior0ySefSPI3wN29e7feeOMNHTx4UGVlZfrFL36hnj17OvbuVEBbW7RokbKzs/Wd73xHJ0+e1J///Gf16NFDf/7zn4NuPXz2f601b968Jvf1b//2byHt45VXXlF6ero8Ho/y8/NbHQP89u3b12xuW3vL5v/93/9tcl91d0NtycaNG9W7d2/95S9/0QsvvHAufywAAAAAIeDyvRCVlpZq9uzZDZ4fOnSopk6dKq/Xq5UrV+qPf/yjjh49qq5du2rAgAEaO3as+vXrJ8n/RWfVqlU6cOCAOnXqFGgW2Ldv33D/cQBHOXXqlL766qsmt/fv379V+zt69KiOHj3a6LakpCSOyTDyer2BYn9jMjIyWnWH0q+//rrJJq3x8fHNNowHAAAAEF4UpQAAAAAAABB2XL4HAAAAAACAsIv5RudHjx7V8uXLtWXLFp0+fVqpqamaMmWKLrnkkkiHBgAAAAAA0GHF9OV7J06c0GOPPaYrrrhCN954o7p27aqDBw/qG9/4hlJTUyMdHgAAAAAAQIcV0yul3nrrLXXv3l1TpkwJPNerV68IRgQAAAAAAAApxldK/eQnP1F2draOHj2q7du366KLLtKNN96o73znO42Or62tVW1tbeCxZVlKSkrSsWPH5PV62zw+y7LUo0cPVVZWKobTEHPIm3ORO2cib84VK7mLi4vThRdeGOkwHKm95lA9e/ZURUVFm+8XrUcuogN5iA7kIXqQi8gLdf4U0yulDh8+rPfee08333yzcnNztXv3bv3mN79RXFychg0b1mB8UVGR3njjjcDjiy++WAsXLmz3iWiPHj3adf9oH+TNucidM5E35yJ3HZfX6w064dcWLMsK7NvJxc5YQC6iA3mIDuQhepALZ4npopRt27rkkks0btw4Sf4i0759+/Tee+81WpTKzc3VqFGjAo/rPswVFRXttlIqNTVVhw4dCjpYjDGB10b0aSpviH7kzpnIm3PFSu7i4uLUs2fPSIcBADgPfMcColNMF6UuvPBCpaWlBT2Xlpamjz76qNHx8fHxio+Pb3Rbe06mjTGyT52UXbRc2los+bySO07KzpErN09WYnK7vTbOnTHG0V+yOjJy50zkzbnIHQAgEkxNNd+xgCgX00WpSy+9VAcOHAh67sCBA1F3ttPUVMueP006WC7Vn7RvWCP7821yzcjnL00AAAAACBHfsQBncEU6gPZ08803a+fOnVq5cqUOHTqkDz/8UO+//75GjBgR6dCC2Ct/2/AvS0mybenQfn91HwAAAAAQErtoOd+xAAeI6aJU//799cgjj2jjxo36z//8T/3ud7/TXXfdpX/5l3+JdGhBzNbihn9Z1rFt/3JTAAAAAEBo+I4FOEJMX74nSd/61rf0rW99K9JhNMkYI/l8zQ/y+WjMBwAAAAAh8H/HauFGVXzHAqJCTK+UcgLLsiS3u/lBbjd/WQIAAABACPzfsVpYf8F3LCAqUJSKAlZ2juRqIhUul5SdE96AAAAAAMDJ+I4FOAJFqSjg+vcfSqlpDf/SdLmk1DS5cvMiExgAAAAAOJArN4/vWIADxHxPKSewEpPlmpHvvwPE1mJ/jym3W8rOkSs3j1uVAgAAAEAr8B0LcAaKUlHCSkyW+47J0h2TabgHAAAAAOeJ71hA9OPyvSjEX5ZA7DBN3YoYAAAAYcN3LCA6sVIKANqYqamut1Tc67/7C0vFAQAAACAIRSkAaEOmplr2/GnSwXKp/iqpDWtkf75Nrhn5FKYAAAAAQFy+BwBtyi5a3rAgJUm2LR3a798OAAAAAKAoBQBtamtxw4JUHdv2bwcAAAAAUJQCgLZijPH3kGqOz0fzcwAAAAAQRSkAaDOWZfmbmjfH7ebuLwAAAAAgilIA0LaycyRXE3+1ulz+7QAAAAAAilIA0JZcuXlSalrDwpTLJaWm+bcDAAAAANTCdSYAgNawEpPlmpHvv8ve1mLJ55Pcbik7R67cPFmJyZEOEQAAAACiAkUpAGhjVmKy3HdMlu6YLGMMPaQAAAAAoBFcvgcA7YiCFAAAAAA0jqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMKOohQAAAAAAADCLi7SAQAAAHQkb775pl5++WXddNNNmjBhgiTpzJkzKiws1KZNm1RbW6vs7GxNnDhRKSkpgd+rrKzUr3/9a5WWlioxMVFDhw7VuHHj5Ha7A2NKS0tVWFio8vJyde/eXbfeequGDRsW9Ppr167V22+/LY/Ho/T0dN1zzz3q379/YHsosaDjMsbIsqxIhwEAiBGslAIAAAiTXbt26b333lN6enrQ8y+99JI+/vhjPfzww5o9e7aOHTumZ555JrDdtm3Nnz9fXq9Xc+bM0dSpU7Vhwwa9+uqrgTGHDx/WggULdMUVVyg/P18333yzXnjhBW3ZsiUwZtOmTSosLNRtt92mhQsXKj09XXPnzlVVVVXIsaDjMTXV8r3yK/mmT5Q97W75pk+U75VfydRURzo0AIDDdZii1JtvvqmxY8dq2bJlkQ4FAAB0QDU1Nfr5z3+u++67T507dw48X11drQ8++EB33XWXrrzySmVmZmrKlCn64osvtGPHDknS1q1btX//fj3wwAPKyMjQ4MGD9YMf/EDr1q2T1+uVJL377rvq1auX7rzzTqWlpWnkyJEaMmSI3nnnncBrrV69WjfccIOGDx+utLQ0TZo0SQkJCVq/fn3IsaBjMTXVsudPk9a/Ix05LHmO+n9uWCPfvEdlV5+MdIgAAAfrEEWpps5KAgAAhMuSJUs0ePBgXXXVVUHPl5WVyefzaeDAgYHn+vbtqx49egQKQTt27FC/fv2CLqEbNGiQTp06pfLycknSzp07g/YhSdnZ2YF9eL1elZWVBY1xuVwaOHBgYEwosaBjsYuWSwfLJWPO2mBLh/ar6re/jExgAICYEPM9peqflVy5cmWkwwEAAB3Qxo0btWfPHs2fP7/BNo/Ho7i4uKDVU5LUrVs3eTyewJizezp169YtsK3uZ91z9cecOnVKZ86c0YkTJ2TbdoP9pKSk6MCBAyHH0pja2lrV1tYGHluWpaSkpMD/t6W6/dHXKEy2FjcsSNWxbZ366E9yjc4Lb0wIwjERHchD9CAXzhLzRan6ZyVbKkqFc0JVf58cLM5C3pyL3DkTeXMucudXWVmpZcuWadasWUpISIh0OO2iqKhIb7zxRuDxxRdfrIULF6pnz57t9pqpqanttm/4GWN0UEa+5gZ5a/WNb3yjwx/n0YBjIjqQh+hBLpwhpotSzZ2VbEwkJlQSB4tTkTfnInfORN6cq6PnrqysTFVVVXrssccCz9m2rc8++0xr167VzJkz5fV6dfLkyaAVSlVVVYFVTSkpKdq1a1fQfuuak9cfU79hed2YpKQkJSQkqGvXrnK5XA1WPNVfhZWSktJiLI3Jzc3VqFGjAo/rChQVFRWBnldtxbIspaam6tChQzJNreBBm/GphWKTO05///vfyUUEcUxEB/IQPchFdIiLiwuplhKzRalzOSsZzglV3f45WJyHvDkXuXMm8uZcsZK7UCdVTRk4cKAWLVoU9Nwvf/lL9enTR6NHj1aPHj3kdrv1t7/9TUOGDJEkHThwQJWVlcrKypIkZWVlaeXKlaqqqgpcordt2zYlJSUpLS1NkjRgwAB9+umnQa+zbdu2wD7i4uKUmZmpkpIS5eTkSPIXx0pKSjRy5EhJUmZmZouxNCY+Pl7x8fGNbmuv3BtjHP25cozsHGnDGn8PqbO5XEoaMlQ15CIqcExEB/IQPciFM8RsUaqls5Ivv/yyXK7gPu+RmFDV7ZuDxXnIm3ORO2cib87V0XOXlJSkfv36BT3XqVMnXXDBBYHnr7/+ehUWFqpLly5KTk7W0qVLlZWVFSgEZWdnKy0tTc8//7zGjx8vj8ejFStWaMSIEYG5y4033qh169Zp+fLlGj58uEpKSrR582ZNnz498LqjRo3S4sWLlZmZqf79+2vNmjU6ffq0hg0bJklKTk5uMRZ0LK7cPNmfb5MO7Q8uTLlcUmqauv3wftVUHY9cgAAAR4vZolRLZyXPLkhFI2MM1+cDANAB3HXXXbIsS88884y8Xq+ys7M1ceLEwHaXy6Xp06dryZIlmjVrljp16qShQ4fqBz/4QWBMr169NH36dL300ktas2aNunfvrv/4j//QoEGDAmOuvfZaHT9+XK+99po8Ho8yMjL0+OOPB12a11Is6FisxGS5ZuT778K3tVjy+SS3W8rOkfvffyhXcmeJohQA4BxZpgOdunzqqaeUkZGhCRMmtOr3KioqghqgtxXLstS7d28dPHgwcAbZ1FTX+0ffK7njpOwcuXLzZCUmt3kMaL3G8gZnIHfORN6cK1ZyFx8f3+79JWNVe8yhYuVz5VT1T5qSi+hAHqIDeYge5CI6hDp/itmVUk5kaqplz3tEOrg/eMMHq2V/tkWuxxdRmAIAAEDEsIofANCWOlRR6qmnnop0CM2yX1/WsCBV5+B+2W8skztvSlhjAgAAAAAAaA/R31ipIyn+Y/PbP2phOwAAAAAAgENQlIoSxhjp9OnmB50+zTWxAAAAAAAgJlCUAgAAAAAAQNhRlIoSlmVJCZ2aH5TQieaSAAAAAAAgJlCUiibXDD2/7QAAAAAAACGKdIugDnX3vWjnGjNB9o4S6VAjd+BLTZNrzISwxwQAAAAAAGKHqamWXbRc2los+bySO07KzpErN09WYnJYY6EoFUWsxGS5Zi76vw+H1yvFRe7DAQAAAAAAYoepqZY9f5p0sFyqv0pqwxrZn2+Ta0Z+WGsPFKWijJWYLPcdk2Vun+R/TA8pAAAAAADQBuyi5Q0LUpJk29Kh/bKLlst9x+SwxUNRKopE0xI6AAAAAAAQY7YWNyxI1bFt/3aKUh1PtC2hAwAAAAAAscMY418A0xyfT8aYsF21xd33okQoS+gAAAAAAADOhWVZ/iuymuN2h7WNEEWpaBHKEjoAAAAAAIBzlZ0juZooBblc/u1hRFEqCrRmCR0AAAAAAMC5cOXmSalpDQtTLpeUmubfHkb0lIoC0biEDgAAAAAAxBYrMVmuGfn1brLmk9zuiN1kjaJUtMjOkTas8V+qd7YILKEDAAAAAACxx0pMlvuOydIdk8Pa1LwxXL4XJaJtCR0AAAAAAIhtkb4ii5VSUSLaltABAAAAAAC0J4pSUSSaltABAAAAAAC0Jy7fi1IUpAAAAAAAQCyjKAUAAAAAAICwoygFAAAAAACAsKMoBQAAAAAAgLCjKAUAAAAAAICwoygFAAAAAACAsKMoBQAAAAAAgLCjKAUAAAAgqhljZIyJdBgAgDYWF+kAAAAAAOBspqZa9uvLpOI/SqdP+59M6CRdM1SuMRNkJSZHND4AwPmjKAUAAAAgqpiaatnzHpEO7g/ecPqU9Ke1sneUyDVzEYUpAHA4Lt8DAAAAEFXsouUNC1L1HdrvHwMAcDSKUgAAAACiy9bithkDAIhqFKUAAAAAtKvWNCk3xkg+b8sDvV6anwOAw9FTCgAAAECbMzXV/kvsthb7i0zuOCk7R67cvGZ7QVmW5R/bkrg4/1gAgGOxUipKcJYHAAAAscLUVMueP01a/4505LDkOer/uWGN7PnTZGqqm99Bdk7LLxLKGABAVGOlVASZmmode+FpeTetb9XZIwAAACCa+RuVl0tnn3i17UCTcvcdk5v8fVdunuzPtjTd7Dw1Ta7cvLYLGAAQEayUihBTUy3fvEd1YvXr53b2CAAAAIhWW4sbFqTq2HaLTcqtxGS5Hl8kDR0pJSZJlsv/X6ck6V9HyjVzESdxASAGsFIqQs737BEAAAAQjUJqVO7zyRjTbE8oKzFZ7rwpUt6UQKsLekgBQGxhpVSknOfZIwAAACAahdSo3O1uVYHJsiwKUgAQgyhKRUBrzh4BAAAAjpOdI7ma+KrhctGkHAAgiaJURLTH2SMAAAAgWrhy86TUtIaFKZeLJuUAgICY7ilVVFSk4uJiffXVV0pISFBWVpby8vLUp0+fSIfmPzu0YY3/Ur2zcfYIAICYEsqc5MyZMyosLNSmTZtUW1ur7OxsTZw4USkpKYExlZWV+vWvf63S0lIlJiZq6NChGjdunNxud2BMaWmpCgsLVV5eru7du+vWW2/VsGHDguJZu3at3n77bXk8HqWnp+uee+5R//79WxUL0BwrMVmuGfn+PqpbiyWfT3K7udM0ACBITK+U2r59u0aMGKG5c+dq1qxZ8vl8mjNnjmpqaiIdGmePAADoQEKZk7z00kv6+OOP9fDDD2v27Nk6duyYnnnmmcB227Y1f/58eb1ezZkzR1OnTtWGDRv06quvBsYcPnxYCxYs0BVXXKH8/HzdfPPNeuGFF7Rly5bAmE2bNqmwsFC33XabFi5cqPT0dM2dO1dVVVUhxwKEwkpMlvuOyXIvWCJX/lK5FyyR+47JFKQAAAExXZSaOXOmhg0bpm9+85vKyMjQ1KlTVVlZqbKyskiH5v9H+vGn1WXUWKl7Lymlu//nsJvkmpHPP9YAAMSQluYk1dXV+uCDD3TXXXfpyiuvVGZmpqZMmaIvvvhCO3bskCRt3bpV+/fv1wMPPKCMjAwNHjxYP/jBD7Ru3Tp5vf5ele+++6569eqlO++8U2lpaRo5cqSGDBmid955JxDL6tWrdcMNN2j48OFKS0vTpEmTlJCQoPXr14ccC9BatKUAADQmpi/fO1t1dbUkqUuXLhGOxM9KTNaF9z2imu+Pl23b/GMNAEAHcfacpKysTD6fTwMHDgyM6du3r3r06KEdO3YoKytLO3bsUL9+/YIuoRs0aJCWLFmi8vJyXXzxxdq5c2fQPiQpOztby5YtkyR5vV6VlZXplltuCWx3uVwaOHBgoOAUSixnq62tVW1tbeCxZVlKSkoK/H9bqtsf86bIIxfRgTxEB/IQPciFs3SYopRt21q2bJkuvfRS9evXr9Ex4ZxQ1d+nZVlyNXV3EkQd/pJzLnLnTOTNuchd4xqbk3g8HsXFxalz585BY7t16yaPxxMYc3ZPp27dugW21f2se67+mFOnTunMmTM6ceKEbNtusJ+UlBQdOHAg5FjOVlRUpDfeeCPw+OKLL9bChQvVs2fPZt+L85Gamtpu+0brkIvoQB6iA3mIHuTCGTpMUaqgoEDl5eX66U9/2uSYSEyoJA4WpyJvzkXunIm8ORe5CxbKnMRpcnNzNWrUqMDjukJkRUVF4NLCtmJZllJTU3Xo0CEZY9p032gdchEdyEN0IA/Rg1xEh7i4uJBqKR2iKFVQUKBPPvlEs2fPVvfu3ZscF84JVd3+OVich7w5F7lzJvLmXLGSu1AnVaFoak6SkpIir9erkydPBq1QqqqqCqxqSklJ0a5du4L2V9ecvP6Y+g3L68YkJSUpISFBXbt2lcvlarDiqf4qrFBiOVt8fLzi4+Mb3dZeuTfGOPpzFUvIRXQgD9GBPEQPcuEMMV2UMsZo6dKlKi4u1lNPPaVevXo1Oz4SE6q6fXOwOA95cy5y50zkzbnIXctzkszMTLndbv3tb3/TkCFDJEkHDhxQZWVloIdTVlaWVq5cqaqqqsAletu2bVNSUpLS0tIkSQMGDNCnn34atO9t27YF9hEXF6fMzEyVlJQoJydHkv9ywpKSEo0cOTLkWAAAANpCTBelCgoK9OGHH2ratGlKSkoKnBVMTk5WQkJCZIMDAAAdRktzkuTkZF1//fUqLCxUly5dlJycrKVLlyorKytQCMrOzlZaWpqef/55jR8/Xh6PRytWrNCIESMCJ9VuvPFGrVu3TsuXL9fw4cNVUlKizZs3a/r06YFYRo0apcWLFyszM1P9+/fXmjVrdPr0aQ0bNiwQU0uxAAAAtAXLxPCpy7Fjxzb6/JQpUwITr1BUVFQENUBvK5ZlqXfv3jp48GCHP4PsJOTNucidM5E354qV3MXHx5/35XuhzEnOnDmjwsJCbdy4UV6vV9nZ2Zo4cWLQJXMVFRVasmSJSktL1alTJw0dOlTjx4+X2+0OjCktLdVLL72k/fv3q3v37rr11lsbzHvWrl2rVatWyePxKCMjQ3fffbcGDBgQ2B5KLKFojzlUrHyuYgG5iA7kITqQh+hBLqJDqPOnmC5KtRWKUqiPvDkXuXMm8uZcsZK7tihKdVQUpWIbuYgO5CE6kIfoQS6iQ6jzJ1cYYgEAAABiFl96AAA4NzHdUwoAAHQMxpjAXXOBcDA11bKLlktbiyWfV3LHSdk5cuXmyUpMjnR4AAA4AkUpAADgSBQFECmmplr2/GnSwXKp/iqpDWtkf75Nrhn5fAYBAAgBl+8BAADHCRQF1r8jHTkseY76f25YI3v+NJma6kiHiBhmFy1vWJCSJNuWDu33bwcAAC2iKAUAAByHogAiamtxw89eHdv2bwcAAC2iKAUAAJyHogAixBjjv1y0OT4fzc8BAAgBRSkAAOAoFAUQSZZl+fuXNcftpvE+AAAhoCgFAAAchaIAIi47R3I1MY12ufzbAQBAiyhKAQAA56EogAhy5eZJqWkNP4Mul5Sa5t8OAABaRFEKAAA4DkUBRJKVmCzXjHxp2E1S915SSnf/z2E3yTUjX1ZicqRDBADAEVpY+w4AABB96ooCdtFyf1Nzn09yu6XsHLly8ygKoN1Zicly3zFZumOyjDFcLgoAwDmgKAUAAByJogCiBZ89AADODZfvAQAAx6MoAAAA4DwUpQAAAAAAABB2FKUAAAAAAAAQdhSlAAAAAAAAEHYUpQAAAAAAABB2FKUAAAAAAAAQdhSlAAAAAAAAEHYUpQAAAAAAABB2FKUAAAAAAAAQdhSlAAAAAAAAEHYUpQAAAAAAABB2FKWigDEm0iEAAAAAAACEVVykA+ioTE217KLl+upvf5VdWyu53VJ2jly5ebISkyMdHgAAAAAAQLuiKBUBtueIzJMPSNUnFLRGav07sj/fJteMfApTAAAAHYgxRpZlRToMAADCiqJUmJmaapknfyRVn2xko5EO7JNdtFzuOyaHPzgAAACETd3KeW0tlnxeyR3HynkAQIdCT6kws19f1nhBqr4t/y8ssQAAACAyTE217PnTpPXvSEcOS56j/p8frJY9faJsz5FIhwgAQLujKBVuH21oecyJr2l+DgAA4FA+r7fFMXbRculguX+l/NlOnpB58gGZmup2iA4AgOjB5XthZIyRTte0PNBbG3M9BeiTAAAAwiFScw7fl7ukBY9J3lr/Y0mKi5emL5Q7vX/DX9ha3HhBqk71CVo6AABiHkWpaBQXHxNFHPokAACAcAjMObZ8JNm+NptzhDof8325S5rzcMMN3lppzsPyzXo2qDBljPHPjVqy5SOJohQAIIZRlIpGlhUbBan50xouS9+whjsMAgCANuM7Vik99aBUfSJ4wzne1ficTqoteKz5nS54TPrl7wIPLcvy77clth0TJyoBAGgKPaXCKOQ+UWfOtG8gYdBknwTblg7t928HAAA4B3b1SfleflG+RydI0+5tWJCS/HOQg+VNzjkam5c12Xx8wxrZ86c13ePpH5fsNamx7dk5zf+OJLndFKQAADGNolQ0MrbzG5031yfBtv3bAQAAWsl3aL++uuM7Mh+s9heN1MycyZigOYepqZbvlV/JN32i7Gl3yzd9onyv/Er2Kf+dke2i5dKBfa06qRZKU/PGxrly86TOXZr+BcsKrXAFAICDcfleB9Tey8BD6pPg87EcHQAAtIrtOSLzX1OabxB+tn/MOXT6VOOtBT5YLbP+HfniE6Qzp5t5cVva9L7MWZfxuePi/E3NW+COC552W4nJsp76ucyTDzRc6WVZUu9v+gtXAADEMIpSHUQ4m46H1CeB5egAAKCVzM+ebF1BSpJcLlmWJd/K3zbeWkDyP9dcQapOzSnZ8x6V6/Gng+dPltV8XE3MeVwp3WUWLqnXpN2W3G5uDAMA6DAoSoWRZVnNLTAPYs+Y1GYTkog0Hc/OkTas8U+uzuZysRwdAAC03oF9rf+d0zXyPXavdOxI6wtajfn7V7KLlstd/654XbtJVZ6mf6drtyY3WYnJ/n3dMZlV5ACADoeeUtGqrqnmvEebbqoZokg0HXfl5kmpaf4CVNAGl5SaxnJ0AADQKnZjJ7pCcfJr6WiFZM7x9xsGEtynyhjJamFKbblD6hdKQQoA0NFQlAoj2xdKx4H6v2D77xrz6N3yvfKrcy9ORaDpuJWYLNeMfGnYTVL3XlJKd//PYTe1z8osAAAQ06KqYFPXp0q0LQAA4Hxw+V4YudxundM5uppT0gerZW8plmv2c60q6ESy6TjL0QEAQFuxjx2JdAj/5+wiE20LAAA4Jx2iKLV27Vq9/fbb8ng8Sk9P1z333KP+/fuHPY5Wr5Q629HDsl96Xu77poX8K9Fy9o6CFAAAzhIt86eAx+6J3GvX10iRyZWbJ/vzbdKh/cGFKdoWAADQrJi/fG/Tpk0qLCzUbbfdpoULFyo9PV1z585VVVVV+IPxtrBiKRR//VC+6RNbdzlfdk7D3k51OHsHAADOElXzJ0m+Sd+PyOs20ESRibYFAACcm5hfKbV69WrdcMMNGj58uCRp0qRJ+uSTT7R+/Xrdcsst4Q2m+vwalgfUNUEP8c55nL0DAACtEVXzp3DplOg/gVg3V0pI8BeWaqol20hud7N3RqZtAQAArRfTRSmv16uysrKgyZPL5dLAgQO1Y8eO8AfUlq9Z7855QbckbkTd2Tu7aLm/qbnP1+LECgAAdExRN38Kh4t6yr2wQJKCG5j/Q2uLTBSkAAAITUwXpY4fPy7btpWSkhL0fEpKig4cONBgfG1trWprawOPLctSUlJS4P/PW8G8899Hff+4c5417r4Wh1pJneUad5807j7O3p2nuveO99B5yJ0zkTfnInfO1Nr5k9S+cyjve++d1++3yLJkDR7S7OeVz3DjOMajA3mIDuQhepALZ4npolRrFRUV6Y033gg8vvjii7Vw4UL17NmzTfZf3iZ7CeaWUWpqKgdcBKSmpkY6BJwjcudM5M25yF3sa885VPmrz533PvwsSeaspyzF9btY3/iPR+RK7txGr9PxcIxHB/IQHchD9CAXzhDTRamuXbvK5XLJ4/EEPe/xeBqc/ZOk3NxcjRo1KvC4rtBTUVEhbxs0KY9b8ra8E7933vupzydLhw4datN9onmWZSk1NVWHDh0KLPGHM5A7ZyJvzhUruYuLi2uzE1RO0Nr5k9S+c6i2mD9ZN3xP1r/dKvP738l8+v/8q83dblmDrpHJzdPfq45LVcfP6zU6olg5xp2OPEQH8hA9yEV0CHX+FNNFqbi4OGVmZqqkpEQ5Of47zNm2rZKSEo0cObLB+Pj4eMXHxze6r6j8MP/jznlRGVsHYIzhvXcocudM5M25yJ2ztHb+JEX5HOr6UXLdPkmSZN0+Sbp9UoNWBhGP0eE4xqMDeYgO5CF6kAtniOmilCSNGjVKixcvVmZmpvr37681a9bo9OnTGjZsWKRDC12nJOlMjVT/gOLOeQAAoJ3ExPzJckm9G58r0fYAAIDoEPNFqWuvvVbHjx/Xa6+9Jo/Ho4yMDD3++ONNLj9vb+5fr5Jv0vdDG5yULD2+SK6Ui7hzHgAACBtHz58SOkldukqDrmGuBABAlLMM69laVFFREXRHmbZiWZa/R8K8AqlzZykuzl9wkuT+x8+zcee8yLMsS71799bBgwdZDuow5M6ZyJtzxUru4uPjO1RPqbbUHnOowPxJkha/IVe9ywYty2KuFEaxcow7HXmIDuQhepCL6BDq/CnmV0q1hbi49nuber/zV1VUVLTb/tF+2vNzgfZF7pyJvDmX03Pn9Pgjqb3eO+ZP0YVjJDqQh+hAHqIHuYisUN9/VkoBAAAAAAAg7FyRDqAjO3XqlB577DGdOnUq0qGgFcibc5E7ZyJvzkXu0B74XEUPchEdyEN0IA/Rg1w4C0WpCDLGaM+ePVzn6jDkzbnInTORN+cid2gPfK6iB7mIDuQhOpCH6EEunIWiFAAAAAAAAMKOohQAAAAAAADCjqJUBMXHx+u2225TfL1bGSP6kTfnInfORN6ci9yhPfC5ih7kIjqQh+hAHqIHuXAW7r4HAAAAAACAsGOlFAAAAAAAAMKOohQAAAAAAADCjqIUAAAAAAAAwo6iFAAAAAAAAMIuLtIBdFRr167V22+/LY/Ho/T0dN1zzz3q379/pMNCM7Zv365Vq1Zpz549OnbsmB555BHl5OREOiy0oKioSMXFxfrqq6+UkJCgrKws5eXlqU+fPpEODS1499139e6776qiokKSlJaWpttuu02DBw+OcGRojTfffFMvv/yybrrpJk2YMCHS4SAGMIcKXUtzF2OMXnvtNb3//vs6efKkLrvsMk2cOFG9e/cOjDlx4oSWLl2qjz/+WJZl6ZprrtHdd9+txMTEwJgvv/xSBQUF2r17t7p27aqRI0dq9OjRQbFs3rxZr776qioqKpSamqrx48fr6quvblUsThXKXOTMmTMqLCzUpk2bVFtbq+zsbE2cOFEpKSmBMZWVlfr1r3+t0tJSJSYmaujQoRo3bpzcbndgTGlpqQoLC1VeXq7u3bvr1ltv1bBhw4LiaekYCiUWJ2ppXkEOIqOxeQK56FhYKRUBmzZtUmFhoW677TYtXLhQ6enpmjt3rqqqqiIdGppx+vRpZWRk6N577410KGiF7du3a8SIEZo7d65mzZoln8+nOXPmqKamJtKhoQUXXXSRxo0bpwULFmj+/Pm68sorlZ+fr/Ly8kiHhhDt2rVL7733ntLT0yMdCmIEc6jWaWnu8tZbb+n3v/+9Jk2apHnz5qlTp06aO3euzpw5Exjz3HPPqby8XLNmzdL06dP12Wef6cUXXwxsr66u1pw5c9SjRw8tWLBAeXl5ev311/WHP/whMOaLL77Q//zP/+j666/XwoUL9e1vf1tPP/209u3b16pYnCqUuchLL72kjz/+WA8//LBmz56tY8eO6Zlnnglst21b8+fPl9fr1Zw5czR16lRt2LBBr776amDM4cOHtWDBAl1xxRXKz8/XzTffrBdeeEFbtmwJjAnlGGopFqdqaV5BDsKvqXkCuehgDMJuxowZZsmSJYHHPp/PTJ482RQVFUUuKLTKmDFjzEcffRTpMHAOqqqqzJgxY0xpaWmkQ8E5mDBhgnn//fcjHQZCcOrUKfPggw+arVu3mieffNL85je/iXRIiAHMoc7d2XMX27bNpEmTzFtvvRV47uTJk2bcuHHmww8/NMYYU15ebsaMGWN27doVGPPpp5+asWPHmiNHjhhjjFm3bp2ZMGGCqa2tDYxZvny5+fGPfxx4/Oyzz5r58+cHxfP444+bF198MeRYYsnZc5GTJ0+a22+/3WzevDkwZv/+/WbMmDHmiy++MMYY88knn5ixY8eaY8eOBcasW7fO3HnnnYH3/re//a15+OGHg17rZz/7mZkzZ07gcUvHUCixxJK6eQU5CL+m5gnkouNhpVSYeb1elZWVaeDAgYHnXC6XBg4cqB07dkQwMqBjqK6uliR16dIlwpGgNWzb1saNG3X69GllZWVFOhyEYMmSJRo8eLCuuuqqSIeCGMEcqm0dPnxYHo8n6BhNTk5W//79A+/njh071LlzZ11yySWBMQMHDpRlWdq1a1dgzOWXX664uP/rCpKdna0DBw7oxIkTgTH181Y3ZufOnSHHEkvOnouUlZXJ5/MFvUd9+/ZVjx49gnLRr1+/oEuGBg0apFOnTgVW+uzcubPR97luH6EcQ6HEEgvOnleQg/Brap5ALjoeekqF2fHjx2XbdoNrUFNSUnTgwIHIBAV0ELZta9myZbr00kvVr1+/SIeDEOzbt08zZ85UbW2tEhMT9cgjjygtLS3SYaEFGzdu1J49ezR//vxIh4IYwhyqbXk8HklSt27dgp7v1q1bYJvH41HXrl2DtrvdbnXp0iVoTK9evYLG1OXI4/EExrb0Oi3FEisam4t4PB7FxcWpc+fOQWPPfo/O/uzXvV/1xzT2Hp46dUpnzpzRiRMnWjyGQonFyZqaV+zdu5cchFFz8wSOh46HlVIAOoyCggKVl5froYceinQoCFGfPn309NNPa968ebrxxhu1ePFi7d+/P9JhoRmVlZVatmyZHnzwQSUkJEQ6HACIKsxFIot5ReQxT8DZWCkVZl27dpXL5WpQWW2s2gug7RQUFOiTTz7R7Nmz1b1790iHgxDFxcUpNTVVkpSZmandu3drzZo1mjx5coQjQ1PKyspUVVWlxx57LPCcbdv67LPPtHbtWr388styuTgnhtZjDtW26t6zqqoqXXjhhYHnq6qqlJGRERhz/PjxoN/z+Xw6ceJE4PdTUlIazUn910hJSWnQjL6qqipoe0uxxIKm5iIpKSnyer06efJk0IqMs9+juksm62+v21b3s7H3OSkpSQkJCSEdQ6HE4mRNzSuuvfZachAmLc0TZs6cSS46GGaFYRYXF6fMzEyVlJQEnrNtWyUlJfRJAdqBMUYFBQUqLi7WE0880eASAziLbduqra2NdBhoxsCBA7Vo0SLl5+cH/rvkkkt03XXXKT8/n4IUzhlzqLbVq1cvpaSk6G9/+1vguerqau3atSvwfmZlZenkyZMqKysLjCkpKZExJnDL9KysLH322Wfyer2BMdu2bVOfPn0CPZOysrKCXqduzIABA0KOxclamotkZmbK7XYH/fkPHDigysrKoFzs27cv6Ev2tm3blJSUFLisfcCAAY2+z3X7COUYCiWWWFI3ryAH4dPSPOGSSy4hFx0MK6UiYNSoUVq8eLEyMzPVv39/rVmzRqdPn9awYcMiHRqaUVNTo0OHDgUeHz58WHv37lWXLl3Uo0ePCEaG5hQUFOjDDz/UtGnTlJSUFDgbkpyczJLhKPfyyy9r0KBB6tGjh2pqavThhx9q+/btmjlzZqRDQzOSkpIa9Gzr1KmTLrjgAnq54bwxh2qdluYuN910k1auXKnevXurV69eWrFihS688EJ9+9vfliSlpaVp0KBBevHFFzVp0iR5vV4tXbpU1157rS666CJJ0nXXXafXX39dL7zwgkaPHq3y8nL9/ve/11133RV43ZtuuklPPfWU3n77bV199dXauHGjdu/eHVj1allWi7E4WUtzkeTkZF1//fUqLCxUly5dlJycrKVLlyorKyvwxTc7O1tpaWl6/vnnNX78eHk8Hq1YsUIjRoxQfHy8JOnGG2/UunXrtHz5cg0fPlwlJSXavHmzpk+fHoilpWMolFicqrl5BTkIn1DmCeSiY7GMMSbSQXREa9eu1apVq+TxeJSRkaG77747cLYI0am0tFSzZ89u8PzQoUM1derUCESEUIwdO7bR56dMmcKXmCj3y1/+UiUlJTp27JiSk5OVnp6u0aNHczc3B3rqqaeUkZGhCRMmRDoUxADmUKFrae5ijNFrr72mP/zhD6qurtZll12me++9V3369AmMPXHihAoKCvTxxx/Lsixdc801uueee5SYmBgY8+WXX6qgoEC7d+/WBRdcoJEjR+qWW24Jes3NmzdrxYoVqqioUO/evTV+/HhdffXVge2hxOJUocxFzpw5o8LCQm3cuFFer1fZ2dmaOHFi0CVCFRUVWrJkiUpLS9WpUycNHTpU48ePl9vtDowpLS3VSy+9pP3796t79+669dZbG8x3WjqGQonFiVqaV5CDyDl7nkAuOhaKUgAAAAAAAAg7GjsAAAAAAAAg7ChKAQAAAAAAIOwoSgEAAAAAACDsKEoBAAAAAAAg7ChKAQAAAAAAIOwoSgEAAAAAACDsKEoBAAAAAAAg7OIiHQAAAMD52L59u1atWqU9e/bo2LFjeuSRR5STk9OqfWzZskWvv/66ysvLFR8fr8svv1x33nmnevXq1U5RAwAAgJVSAADA0U6fPq2MjAzde++95/T7hw8f1tNPP60rrrhC+fn5mjlzpr7++ms988wzbRwpAAAA6mOlFAAAcLTBgwdr8ODBTW6vra3VK6+8oo0bN6q6ulrf/OY3NX78eF1xxRWSpLKyMtm2rdtvv10ul/983fe+9z09/fTT8nq9iotjugQAANAeWCkFAABiWkFBgXbu3KmHHnpITz/9tIYMGaJ58+bp4MGDkqTMzExZlqUNGzbItm1VV1frT3/6kwYOHEhBCgAAoB1RlAIAADGrsrJSGzZs0E9+8hNdfvnlSk1N1fe//31ddtllWr9+vSSpV69emjVrll555RWNGzdOEyZM0NGjR/WTn/wkwtEDAADENk7/AQCAmLVv3z7Ztq0f//jHQc97vV516dJFkuTxePTiiy9q6NCh+ud//medOnVKr732mp599lnNmjVLlmVFInQAAICYR1EKAADErJqaGrlcLi1cuDDQL6pOYmKiJGnt2rVKTk5WXl5eYNsDDzyg+++/Xzt37lRWVlZYYwYAAOgoKEoBAICYlZGRIdu2VVVVpcsvv7zRMWfOnGmwGqqugGWMafcYAQAAOip6SgEAAEerqanR3r17tXfvXknS4cOHtXfvXlVWVqpPnz667rrr9Pzzz+ujjz7S4cOHtWvXLhUVFemTTz6RJF199dXavXu33njjDR08eFBlZWX6xS9+oZ49e+riiy+O4J8MAAAgtlmGU4AAAMDBSktLNXv27AbPDx06VFOnTpXX69XKlSv1xz/+UUePHlXXrl01YMAAjR07Vv369ZMkbdy4UatWrdKBAwfUqVMnZWVlafz48erbt2+4/zgAAAAdBkUpAAAAAAAAhB2X7wEAAAAAACDsKEoBAAAAAAAg7ChKAQAAAAAAIOwoSgEAAAAAACDsKEoBAAAAAAAg7ChKAQAAAAAAIOwoSgEAAAAAACDsKEoBAAAAAAAg7ChKAQAAAAAAIOwoSgEAAAAAACDsKEoBAAAAAAAg7ChKAQAAAAAAIOz+P4tyWq/vtx4yAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Robust scaled data\n",
    "plot_unscaled_vs_scaled(unscaled_data=df_conn, scaled_data=df_conn_robust_scaled, \\\n",
    "                        title='Unscaled vs Robust Scaled Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this notebook, we took a looked at different scaling strategies\n",
    "# That's it for this notebook!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference and other good reads: <br>\n",
    "https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html <br>\n",
    "https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-scaler <br>\n",
    "http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html <br>\n",
    "https://stackabuse.com/feature-scaling-data-with-scikit-learn-for-machine-learning-in-python/ <br>\n",
    "https://machinelearningknowledge.ai/sklearn-feature-scaling-with-standardscaler-minmaxscaler-robustscaler-and-maxabsscaler/ <br>\n",
    "https://medium.com/analytics-vidhya/feature-scaling-normalization-standardization-and-scaling-c920ed3637e7 <br>\n",
    "https://scikit-learn.org/stable/modules/preprocessing.html <br>\n",
    "https://sebastianraschka.com/Articles/2014_about_feature_scaling.html <br>\n",
    "https://www.iana.org/assignments/service-names-port-numbers/service-names-port-numbers.xhtml <br>\n",
    "https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html <br>\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing. <br>"
   ]
  }
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