ALMASWA_ToN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd985fe6-3ce9-4648-a841-37f7f0434aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import time\n",
    "import psutil\n",
    "import os\n",
    "import time\n",
    "import tracemalloc\n",
    "import psutil\n",
    "import sys\n",
    "from pympler import asizeof\n",
    "import matplotlib.pyplot as plt\n",
    "from river import imblearn\n",
    "from scipy.stats import ttest_rel\n",
    "import itertools\n",
    "\n",
    "from river import compose, preprocessing, metrics, stream, linear_model, evaluate\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from ALMASWA import ALMASWAClassifier\n",
    "\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b49c779d-0d63-4461-be17-8c0e96b0c2e8",
   "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>IPV4_SRC_ADDR</th>\n",
       "      <th>L4_SRC_PORT</th>\n",
       "      <th>IPV4_DST_ADDR</th>\n",
       "      <th>L4_DST_PORT</th>\n",
       "      <th>PROTOCOL</th>\n",
       "      <th>L7_PROTO</th>\n",
       "      <th>IN_BYTES</th>\n",
       "      <th>OUT_BYTES</th>\n",
       "      <th>IN_PKTS</th>\n",
       "      <th>OUT_PKTS</th>\n",
       "      <th>TCP_FLAGS</th>\n",
       "      <th>FLOW_DURATION_MILLISECONDS</th>\n",
       "      <th>Label</th>\n",
       "      <th>Attack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192.168.1.195</td>\n",
       "      <td>63318</td>\n",
       "      <td>52.139.250.253</td>\n",
       "      <td>443</td>\n",
       "      <td>6</td>\n",
       "      <td>91.00</td>\n",
       "      <td>181</td>\n",
       "      <td>165</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>192.168.1.79</td>\n",
       "      <td>57442</td>\n",
       "      <td>192.168.1.255</td>\n",
       "      <td>15600</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192.168.1.79</td>\n",
       "      <td>57452</td>\n",
       "      <td>239.255.255.250</td>\n",
       "      <td>15600</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192.168.1.193</td>\n",
       "      <td>138</td>\n",
       "      <td>192.168.1.255</td>\n",
       "      <td>138</td>\n",
       "      <td>17</td>\n",
       "      <td>10.16</td>\n",
       "      <td>472</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.168.1.79</td>\n",
       "      <td>51989</td>\n",
       "      <td>192.168.1.255</td>\n",
       "      <td>15600</td>\n",
       "      <td>17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IPV4_SRC_ADDR  L4_SRC_PORT    IPV4_DST_ADDR  L4_DST_PORT  PROTOCOL  \\\n",
       "0  192.168.1.195        63318   52.139.250.253          443         6   \n",
       "1   192.168.1.79        57442    192.168.1.255        15600        17   \n",
       "2   192.168.1.79        57452  239.255.255.250        15600        17   \n",
       "3  192.168.1.193          138    192.168.1.255          138        17   \n",
       "4   192.168.1.79        51989    192.168.1.255        15600        17   \n",
       "\n",
       "   L7_PROTO  IN_BYTES  OUT_BYTES  IN_PKTS  OUT_PKTS  TCP_FLAGS  \\\n",
       "0     91.00       181        165        2         1         24   \n",
       "1      0.00        63          0        1         0          0   \n",
       "2      0.00        63          0        1         0          0   \n",
       "3     10.16       472          0        2         0          0   \n",
       "4      0.00        63          0        1         0          0   \n",
       "\n",
       "   FLOW_DURATION_MILLISECONDS  Label  Attack  \n",
       "0                         327      0  Benign  \n",
       "1                           0      0  Benign  \n",
       "2                           0      0  Benign  \n",
       "3                           0      0  Benign  \n",
       "4                           0      0  Benign  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2a26d8c-4aed-46c1-abea-2b3e0117ab13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'injection': 468539, 'ddos': 326345, 'Benign': 270279, 'password': 156299, 'xss': 99944, 'scanning': 21467, 'dos': 17717, 'backdoor': 17247, 'mitm': 1295, 'ransomware': 142})\n"
     ]
    }
   ],
   "source": [
    "print('Original dataset shape  %s' % Counter(data['Attack']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474b1eba-f47c-45e8-a447-bdfbb3fb5459",
   "metadata": {},
   "source": [
    "## Experiment 1: Comparing ALMA with ALMASWA on the complete dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "91232c56-2804-44da-b8d2-2e06d736dcb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9472\n",
      "Recall: 0.9727\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9653\n",
      "Recall: 0.9905\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1590c94-e57b-4022-804d-ad010a1403df",
   "metadata": {},
   "source": [
    "## Experiment 2: Comparing ALMA with ALMASWA on different attack classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3e1c6d-bc12-4d42-aa59-9e21faed1bfc",
   "metadata": {},
   "source": [
    "### DDoS attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8df893c-b9fc-4cb6-9db5-82a7e1b0ac5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'Benign': 179647, 'ddos': 149621})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.8097\n",
      "Recall: 0.9485\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.8850\n",
      "Recall: 0.9340\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT-DDoS.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6365faf6-40e4-4a46-bc52-894a04e8cd19",
   "metadata": {},
   "source": [
    "### Injection attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a21abe44-8bb6-477e-8b90-4349311b4567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'injection': 449764, 'Benign': 179647, 'password': 130639, 'xss': 99944})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9563\n",
      "Recall: 0.9806\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9665\n",
      "Recall: 0.9749\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT-Injection.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760e7b51-a1b8-46a8-b208-6f4e5e4630be",
   "metadata": {},
   "source": [
    "### Malware attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f62d435-1c12-468d-bbab-0f498260487c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'Benign': 179647, 'backdoor': 17247, 'ransomware': 142})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.5788\n",
      "Recall: 0.7517\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.7101\n",
      "Recall: 0.9921\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT-Malware.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca37a458-176f-4e70-83c8-f32bdadfe199",
   "metadata": {},
   "source": [
    "### DoS attack class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd61bf7e-3fc4-4675-aa5b-0c2b16074e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({'Benign': 179647, 'dos': 7533})\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.4955\n",
      "Recall: 0.6276\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.4679\n",
      "Recall: 0.9976\n"
     ]
    }
   ],
   "source": [
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT-DoS.csv\")\n",
    "print('Original dataset shape  %s' % Counter(data['Attack']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'Recall': metrics.Recall()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    for name, model in classifiers.items():\n",
    "        y_pred = model.predict_one(x)\n",
    "        model.learn_one(x, true_y)\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['Recall'].update(true_y, y_pred)\n",
    "\n",
    "# Display the results\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"Recall: {metric['Recall'].get():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baae0067-0100-4818-9f1a-44320d9d5675",
   "metadata": {},
   "source": [
    "## Checking for features to use for introduction "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3780cb74-7531-45f6-815b-17bab14af008",
   "metadata": {},
   "source": [
    "### Statistical characteristic of each feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a01c4391-6baa-4380-8f59-7bb4659df149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Distribution Summary:\n",
      "                                count          mean            std   min  \\\n",
      "L4_SRC_PORT                 1379274.0  41290.725338   17678.855330   0.0   \n",
      "L4_DST_PORT                 1379274.0   7091.114285   16736.675853   0.0   \n",
      "PROTOCOL                    1379274.0      6.955538       3.174542   1.0   \n",
      "L7_PROTO                    1379274.0     32.086140      40.882531   0.0   \n",
      "IN_BYTES                    1379274.0   1466.229136  177963.170840  28.0   \n",
      "OUT_BYTES                   1379274.0   2745.434165  110558.948502   0.0   \n",
      "IN_PKTS                     1379274.0     18.008291    2885.740826   1.0   \n",
      "OUT_PKTS                    1379274.0     10.711574    1454.466233   0.0   \n",
      "TCP_FLAGS                   1379274.0     21.215929       8.196017   0.0   \n",
      "FLOW_DURATION_MILLISECONDS  1379274.0   6633.697090  119731.985639   0.0   \n",
      "\n",
      "                                25%      50%      75%         max    Skewness  \\\n",
      "L4_SRC_PORT                 36620.0  45524.0  53810.0     65535.0   -1.343249   \n",
      "L4_DST_PORT                    80.0     80.0    443.0     65534.0    2.172874   \n",
      "PROTOCOL                        6.0      6.0      6.0        58.0    3.147979   \n",
      "L7_PROTO                        7.0      7.0     91.0       251.0    1.121687   \n",
      "IN_BYTES                      112.0    458.0    554.0  79415904.0  315.755743   \n",
      "OUT_BYTES                      60.0    696.0   2239.0  70165175.0  431.076772   \n",
      "IN_PKTS                         2.0      4.0      5.0   1221043.0  293.597536   \n",
      "OUT_PKTS                        1.0      4.0      6.0   1349068.0  647.198527   \n",
      "TCP_FLAGS                      18.0     26.0     27.0       219.0   -1.340979   \n",
      "FLOW_DURATION_MILLISECONDS      0.0     94.0    382.0   4294849.0   26.223876   \n",
      "\n",
      "                                 Kurtosis  \n",
      "L4_SRC_PORT                      0.843466  \n",
      "L4_DST_PORT                      3.006906  \n",
      "PROTOCOL                        12.308024  \n",
      "L7_PROTO                        -0.341012  \n",
      "IN_BYTES                    110425.498287  \n",
      "OUT_BYTES                   227685.614508  \n",
      "IN_PKTS                      94453.214795  \n",
      "OUT_PKTS                    551858.989075  \n",
      "TCP_FLAGS                        3.926922  \n",
      "FLOW_DURATION_MILLISECONDS     738.465688  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "\n",
    "# Compute statistical summaries\n",
    "summary_stats = X.describe().T  # Compute mean, std, min, 25%, 50%, 75%, max\n",
    "summary_stats['Skewness'] = X.skew()  # Add skewness\n",
    "summary_stats['Kurtosis'] = X.kurt()  # Add kurtosis\n",
    "\n",
    "# Print the numerical summaries\n",
    "print(\"Feature Distribution Summary:\")\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e4fd45-9cf1-4e79-aed8-1f88460637cf",
   "metadata": {},
   "source": [
    "### Statistical comparison of original and drift feature using the Kolmogorov-Smirnov test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "61c96976-338e-40a3-95b9-f8da6424d83e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drift Analysis Results (Kolmogorov-Smirnov Test):\n",
      "{'IN_BYTES': {'KS Statistic': 0.4946392087431504, 'P-Value': 0.0}, 'OUT_BYTES': {'KS Statistic': 0.4627992697607582, 'P-Value': 0.0}}\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import ks_2samp\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "\n",
    "# Copy the dataset to avoid altering the original\n",
    "X_drifted = X.copy()\n",
    "\n",
    "# Parameters for noise\n",
    "noise_mean = 0\n",
    "noise_std = 0.2\n",
    "\n",
    "# Introduce Gaussian noise into IN_BYTES and OUT_BYTES\n",
    "X_drifted[\"IN_BYTES\"] = X[\"IN_BYTES\"] + np.random.normal(noise_mean, noise_std * X[\"IN_BYTES\"].std(), size=len(X))\n",
    "X_drifted[\"OUT_BYTES\"] = X[\"OUT_BYTES\"] + np.random.normal(noise_mean, noise_std * X[\"OUT_BYTES\"].std(), size=len(X))\n",
    "\n",
    "# Statistical comparison using the Kolmogorov-Smirnov test\n",
    "results = {}\n",
    "for feature in [\"IN_BYTES\", \"OUT_BYTES\"]:\n",
    "    stat, p_value = ks_2samp(X[feature], X_drifted[feature])\n",
    "    results[feature] = {\"KS Statistic\": stat, \"P-Value\": p_value}\n",
    "\n",
    "print(\"Drift Analysis Results (Kolmogorov-Smirnov Test):\")\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "155680f7-2b92-444e-bfdd-3321c4abd6b6",
   "metadata": {},
   "source": [
    "## Experiment 3: Detection Accuracy Under Gradual Concept Drift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "936905a9-c326-43bb-8e1a-8653c47a3c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:   5%|▋             | 10018/200000 [01:11<31:57, 99.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 10000:\n",
      "ALMA - Accuracy: 0.9057, Recall: 0.9362\n",
      "ALMASWA - Accuracy: 0.7425, Recall: 0.9553\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  10%|█▍            | 20011/200000 [03:03<35:33, 84.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 20000:\n",
      "ALMA - Accuracy: 0.9403, Recall: 0.9691\n",
      "ALMASWA - Accuracy: 0.9766, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  15%|██            | 30011/200000 [04:59<34:11, 82.87it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 30000:\n",
      "ALMA - Accuracy: 0.9442, Recall: 0.9704\n",
      "ALMASWA - Accuracy: 0.9315, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  20%|██▊           | 40010/200000 [06:56<31:53, 83.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 40000:\n",
      "ALMA - Accuracy: 0.9693, Recall: 0.9839\n",
      "ALMASWA - Accuracy: 0.9477, Recall: 1.0000\n",
      "\n",
      "Drift step 1: Increasing noise_std to 0.10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  25%|███▌          | 50016/200000 [08:53<30:19, 82.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 50000:\n",
      "ALMA - Accuracy: 0.9344, Recall: 0.9644\n",
      "ALMASWA - Accuracy: 0.9856, Recall: 0.9999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  30%|████▏         | 60008/200000 [10:49<30:25, 76.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 60000:\n",
      "ALMA - Accuracy: 0.9464, Recall: 0.9721\n",
      "ALMASWA - Accuracy: 0.9812, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  35%|████▉         | 70014/200000 [12:48<31:33, 68.63it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 70000:\n",
      "ALMA - Accuracy: 0.9497, Recall: 0.9733\n",
      "ALMASWA - Accuracy: 0.9906, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  40%|█████▌        | 80018/200000 [14:43<21:44, 92.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 80000:\n",
      "ALMA - Accuracy: 0.9562, Recall: 0.9772\n",
      "ALMASWA - Accuracy: 0.9851, Recall: 1.0000\n",
      "\n",
      "Drift step 2: Increasing noise_std to 0.20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  45%|██████▎       | 90019/200000 [16:20<18:58, 96.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 90000:\n",
      "ALMA - Accuracy: 0.9188, Recall: 0.9372\n",
      "ALMASWA - Accuracy: 0.9873, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  50%|██████▌      | 100016/200000 [17:58<17:02, 97.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 100000:\n",
      "ALMA - Accuracy: 0.8872, Recall: 0.9121\n",
      "ALMASWA - Accuracy: 0.9827, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  55%|██████▌     | 110021/200000 [19:34<14:26, 103.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 110000:\n",
      "ALMA - Accuracy: 0.8702, Recall: 0.8947\n",
      "ALMASWA - Accuracy: 0.9839, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  60%|███████▏    | 120019/200000 [23:50<13:06, 101.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 120000:\n",
      "ALMA - Accuracy: 0.8816, Recall: 0.9016\n",
      "ALMASWA - Accuracy: 0.9740, Recall: 1.0000\n",
      "\n",
      "Drift step 3: Increasing noise_std to 0.30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  65%|████████▍    | 130016/200000 [25:30<12:11, 95.67it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 130000:\n",
      "ALMA - Accuracy: 0.8290, Recall: 0.8482\n",
      "ALMASWA - Accuracy: 0.9837, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  70%|████████▍   | 140011/200000 [27:06<09:56, 100.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 140000:\n",
      "ALMA - Accuracy: 0.8410, Recall: 0.8590\n",
      "ALMASWA - Accuracy: 0.9711, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  75%|█████████▊   | 150013/200000 [28:44<08:20, 99.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 150000:\n",
      "ALMA - Accuracy: 0.7768, Recall: 0.7975\n",
      "ALMASWA - Accuracy: 0.9738, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  80%|█████████▌  | 160020/200000 [30:21<06:30, 102.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 160000:\n",
      "ALMA - Accuracy: 0.8176, Recall: 0.8303\n",
      "ALMASWA - Accuracy: 0.9776, Recall: 1.0000\n",
      "\n",
      "Drift step 4: Increasing noise_std to 0.40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  85%|███████████  | 170016/200000 [32:02<05:34, 89.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 170000:\n",
      "ALMA - Accuracy: 0.7403, Recall: 0.7490\n",
      "ALMASWA - Accuracy: 0.9748, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  90%|██████████▊ | 180012/200000 [33:34<03:08, 106.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 180000:\n",
      "ALMA - Accuracy: 0.7506, Recall: 0.7594\n",
      "ALMASWA - Accuracy: 0.9732, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  95%|███████████▍| 190014/200000 [35:05<01:31, 109.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 190000:\n",
      "ALMA - Accuracy: 0.6853, Recall: 0.6904\n",
      "ALMASWA - Accuracy: 0.9809, Recall: 1.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream: 100%|█████████████| 200000/200000 [36:36<00:00, 91.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 200000:\n",
      "ALMA - Accuracy: 0.7048, Recall: 0.7163\n",
      "ALMASWA - Accuracy: 0.9357, Recall: 1.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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6kDKvadOmcc8993DTTTfx9NNPExgYiMViYeLEiflG25VU48aNcXFxYdu2bUXOk56ezt69e3O3G6B27dr06NGDn376ieeee461a9dy5MgR3nzzzdx5cj6Lt99+m3bt2hW67OrVq+d7XlGO3y5nm0REpOwo8SciIsUyffp0AgMD+fDDDwu8NmvWLGbPns0nn3xCtWrVCAsLY8eOHRddXlhYGOvWrSMzMxNXV9dC58lpfH7mzJl80w8fPlzsuLdv384///zDN998w4gRI3KnL1q0KN98oaGhAJeMG+COO+5gzJgx/PDDD5w7dw5XV9fcspsXc/3117NmzRp+/vnnQsuCHjp0iJUrV9K3b998B3bDhg1jwoQJzJw5k6CgIJKSkvKVzwwICMDb2xur1Urfvn0vGUdFUBm3SURERESkKshJwPXp04cPPviAsWPH5h5vubq6XvL3fXGOJ3/55RdCQ0OZNWtWvgtGc0YPXikvLy/69OnDX3/9xeHDh2nQoEGBeX766SfS09O5/vrr800fNmwYDz/8MHv37mXGjBl4enoyZMiQ3NfDwsIA8PHxqTTHOpVxm0REKjKV+hQRkUs6d+4cs2bN4vrrr+e2224rcBs9ejTJycn89ttvgFH/f+vWrcyePbvAsnKuCLz11luJi4srdKRczjwNGjTAYrGwYsWKfK9/9NFHxY4958rEnGXmPJ4yZUq++QICAujZsydfffUVR44cKTSeHP7+/gwaNIhp06Yxffp0Bg4ciL+//yVjefDBBwkMDOTpp5/OLS2aIy0tjZEjR2K323nppZfyvdaiRQtat27NjBkzmDFjBiEhIfTs2TPfNt56663MnDmz0APkU6dOXTI2Z1MZt0lEREREpKro3bs3nTt3ZvLkyaSlpREYGEjv3r359NNPOXHiRIH58/6+L87xZGHHeevWrWPNmjWltg0vvPACdrude+65h3PnzuV7LSoqimeeeYaQkBAefPDBfK/deuutWCwWfvjhB37++Weuv/56vLy8cl/v2LEjYWFhvPPOO5w9e7bAeivisU5l3CYRkYpMI/5EROSSfvvtN5KTk7nhhhsKfb1Lly4EBAQwffp0hg0bxtNPP80vv/zC7bffzr333kvHjh1JSEjgt99+45NPPqFt27aMGDGCb7/9ljFjxrB+/Xp69OhBSkoKixcv5uGHH+bGG2/E19eX22+/nffffx+TyURYWBh//PHHZfV2a968OWFhYTz11FNER0fj4+PDzJkzc3sR5PW///2P7t2706FDB/7zn//QqFEjDh06xNy5c9myZUu+eUeMGMFtt90GwKuvvlqsWPz8/Pjll1+47rrr6NChA/fffz/h4eHExMQwdepU9u/fz5QpU+jWrVuB9w4bNoyXXnoJDw8P7rvvvgIlTd944w2WLl1KREQEDzzwAOHh4SQkJBAZGcnixYtJSEgo5ifmPCrjNomIiIiIVBVPP/00t99+O1OnTuWhhx7iww8/pHv37rRu3ZoHHniA0NBQTp48yZo1azh27Bhbt27Nfd+ljievv/56Zs2axc0338x1111HVFQUn3zyCeHh4YUmnkqiZ8+evPPOO4wZM4Y2bdpwzz33EBISwp49e/j888+x2WzMmzcvt1JNjsDAQPr06cOkSZNITk4uUB3GbDbzxRdfMGjQIFq2bMnIkSOpU6cO0dHRLF26FB8fH37//fdS2YbyUhm3SUSkIlPiT0RELmn69Ol4eHjQr1+/Ql83m81cd911TJ8+nfj4ePz8/Fi5ciXjx49n9uzZfPPNNwQGBnLttddSt25dwLhCc968ebz22mt8//33zJw5Ez8/v9wDwRzvv/8+mZmZfPLJJ7i7uzN06FDefvvtYjV7B6OUzO+//85jjz3GxIkT8fDw4Oabb2b06NG0bds237xt27Zl7dq1vPjii3z88cekpaXRoEEDhg4dWmC5Q4YMoWbNmthstiITooXp0aMH27Zt4/XXX+fnn3/mxIkT+Pr60q1bN7766iu6d+9e6PuGDRvGCy+8QGpqaqFlRYOCgli/fj2vvPIKs2bN4qOPPsLPz4+WLVvm6ydRkVTGbRIRERERqSpuueWW3FFgORfybdy4kQkTJjB16lTi4+MJDAykffv2+aqeVK9e/ZLHk/fccw8xMTF8+umnLFiwgPDwcKZNm8bPP//MsmXLSm0bnnzySTp16sS7777L5MmTSUxMJCQkhNtvv53nn3++0BKgYBy/LV68GG9vbwYPHlzg9d69e7NmzRpeffVVPvjgA86ePUtwcDAREREFRhBWFJVxm0REKiqT/cL6ZSIiInJJWVlZ1K5dmyFDhvDll186OhwRERERERERERER9fgTEREpiTlz5nDq1ClGjBjh6FBEREREREREREREAI34ExERuSzr1q1j27ZtvPrqq/j7+xMZGenokEREREREREREREQAjfgTERG5LB9//DGjRo0iMDCQb7/91tHhiIiIiIiIiIiIiOTSiD8RERERERERERERERGRSkAj/kREREREREREREREREQqASX+RERERERERERERERERCoBF0cH4IxsNhvHjx/H29sbk8nk6HBEREREREQuKaeLg4+Pj45jLkHHfCIiIiIiUpHY7XaSk5OpXbs2ZvPFx/Qp8VeI48ePU69ePUeHISIiIiIictkSExPx8fFxdBhOTcd8IiIiIiJSER09epS6detedB4l/grh7e0NGB+gDpjLX2ZmJgsXLqR///64uro6OpwqS/vB8bQPnIP2g3PQfnAO2g/OQfvB8Zx1HyQlJSmZVUw65nMcZ/3+VDXaD85B+8E5aD84nvaBc9B+cA7aD87BGfdDzvFezrHMxSjxV4icUi8+Pj46CHSAzMxMPD098fHxcZovVVWk/eB42gfOQfvBOWg/OAftB+eg/eB42gcVn475HEffH+eg/eActB+cg/aD42kfOAftB+eg/eAcnHk/FKdVwcULgYqIiIiIiIiIiIiIiIhIhaDEn4iIiIiIiIiIiIiIiEgl4NDE34oVKxgyZAi1a9fGZDIxZ86cS75n2bJldOjQAXd3dxo3bszUqVMLzPPhhx/SsGFDPDw8iIiIYP369aUfvIiIiIiIiIiIiIiIiIgTcWjiLyUlhbZt2/Lhhx8Wa/6oqCiuu+46+vTpw5YtW3jiiSe4//77WbBgQe48M2bMYMyYMYwfP57IyEjatm3LgAEDiI2NLavNEBEREREREREREREREXE4F0eufNCgQQwaNKjY83/yySc0atSId999F4AWLVqwatUq3nvvPQYMGADApEmTeOCBBxg5cmTue+bOnctXX33F2LFjS38jRERERERERERERERERJyAQxN/l2vNmjX07ds337QBAwbwxBNPAJCRkcGmTZsYN25c7utms5m+ffuyZs2aIpebnp5Oenp67vOkpCQAMjMzyczMLMUtkOLI+cz12TuW9oPjaR84B+0H56D94By0H5yD9oPjOes+cLZ4REREREREpPxVqMRfTEwMQUFB+aYFBQWRlJTEuXPnOH36NFartdB59uzZU+RyJ06cyIQJEwpMX7hwIZ6enqUTvFy2RYsWOToEQfvBGWgfOAftB+eg/eActB+cg/aD4znbPkhNTXV0CCIiIiIiIuJgFSrxV1bGjRvHmDFjcp8nJSVRr149+vfvj4+PjwMjq5oyMzNZtGgR/fr1w9XV1dHhVFnaD46nfeActB+cg/aDc9B+cA7aD47nrPsgp3KJiIiIiIiIVF0VKvEXHBzMyZMn8007efIkPj4+VKtWDYvFgsViKXSe4ODgIpfr7u6Ou7t7gemurq5OdSBf1ejzdw7aD46nfeActB+cg/aDc9B+cA7aD47nbPvAmWIRERERERERxzA7OoDL0bVrV5YsWZJv2qJFi+jatSsAbm5udOzYMd88NpuNJUuW5M4jIiIiIiIiIiIiIiIiUhk5NPF39uxZtmzZwpYtWwCIiopiy5YtHDlyBDBKcI4YMSJ3/oceeoiDBw/yzDPPsGfPHj766CN++uknnnzyydx5xowZw+eff84333zD7t27GTVqFCkpKYwcObJct01ERERERERERERERESkPDm01OfGjRvp06dP7vOcPnt33303U6dO5cSJE7lJQIBGjRoxd+5cnnzySaZMmULdunX54osvGDBgQO48w4YN49SpU7z00kvExMTQrl075s+fT1BQUPltmIiIiIiIiIiIiIiIiEg5c2jir3fv3tjt9iJfnzp1aqHv2bx580WXO3r0aEaPHn2l4YmIiIiIiIiIiIiIiIhUGBWqx5+IiIiIiIiIiIiIiIiIFE6JPxEREREREREREREREZFKQIk/ERERERERERERERERkUpAiT8RkSu1dCIsf6vw15a/ZbwuIiIiUgWtWLGCIUOGULt2bUwmE3PmzLnke5YtW0aHDh1wd3encePGhfZ+//DDD2nYsCEeHh5ERESwfv360g++qnPm37iKrWQUW8kotpJz5vgUW8kotpJRbCXjzLGBc8en2ErGmWO7TEr8iYhcKbMFlr5W8D+G5W8Z080Wx8QlIiIi4mApKSm0bduWDz/8sFjzR0VFcd1119GnTx+2bNnCE088wf3338+CBQty55kxYwZjxoxh/PjxREZG0rZtWwYMGEBsbGxZbUbV5My/cRVbySi2klFsJefM8Sm2klFsJaPYSsaZYwPnjk+xlYwzx3aZXBwdgIhIhdfrGeN+6Wvnn+f8h9Dn+fOvi4iIiFQxgwYNYtCgQcWe/5NPPqFRo0a8++67ALRo0YJVq1bx3nvvMWDAAAAmTZrEAw88wMiRI3PfM3fuXL766ivGjh1b+htRVTnzb1zFdsWxma1WIBzzyndgxRtOFVvucyf83BTbZXLm+PR9UGyKrUBs+i5cJmeOT7FdcWxO9324TCa73W53dBDOJikpCV9fXxITE/Hx8XF0OFVOZmYm8+bNY/Dgwbi6ujo6nCpL++EyZaTA7FGw+1cwmcFug3b/gkFvgrt3iRapfeActB+cg/aDc9B+cA7aD47nrPvA2Y9jTCYTs2fP5qabbipynp49e9KhQwcmT56cO+3rr7/miSeeIDExkYyMDDw9Pfnll1/yLefuu+/mzJkz/Prrr4UuNz09nfT09NznSUlJ1KtXj7i4OKf8rJyJedlrWFa/hx0TJuzYvGuDd0iJl2e320lMTMTX1xeTyXRlwSWfwJx8vNRiK1UFYqsDvnUAE+Rst8mU5/lF7kvynovNH7sbc+zO87EFtoTAFmC3A3bj3m4z3p932iXv88xfkvfY7ZAUXYH26ZXHVmrfB2f+LoBzx5cbm/Ftc87YnPlz0/8Nl6VCxKbvwmXT96FkKlBs1p5jsfV4ytFRkZSUhL+/f7GO9zTiT0SkpKxZELUcts2A3X9AZooxPecgect02PI9BDSHOh2hTgeo2wkCw8HiPCcJRURERJxFTEwMQUFB+aYFBQWRlJTEuXPnOH36NFartdB59uzZU+RyJ06cyIQJEwpMX7hwIZ6enqUTfCXV8tguGgMmjCSNOfk4JB+/omXWAki94tBylWZspe18bNGQHO3gaPLLjS12J8TudHA0+VWMfVo6sZXm98GZPzdw7vhyTq07Z2zO/Lnp/4aScO7YDM4Zm/N+bqDvQ0k5e2xWkwt/JIfDvHmODofU1OL/USjxJyJyOex2iNkGW2fAjl/g7Mnzr3nUgLQzYLKA3QruPpCeBKd2G7ct04z5XDwgpG12MjA7IViz0fmrcquKpRON2tiFDZNf/hbYrNBnXPnHJSIiIpXOuHHjGDNmTO7znBF//fv314i/izm1B5fNCwGwmyyY7FasLW/FHn5ziRdptVrZvGUL7du1w2K5sj4ppl2zseycid3sgsmWdcWxlaYCsYXfgr3FjVzRaLh89zaw242TZbnTyD9PEcs1RS3DfHApNsyYsWEL7YM9tA8XH21oyj4bbDy3X2q0ocmc/UFc3qhG0645WHbNqhj7tBRiK63vgzN/F8C548uJzYYFM1f+b1xpqgifW2nFVmX/b3DC2PRduHz6PpRMRYjNhgWLPYvrvXc5zYi/4lLiT0SkOM4che0/G6P7TuW5mrxaLWh1K1gzIXLq+XrPObWpuz0ODbpC9Kbzt7REOLrOuOUup2aeRGBHqN0B3GuU91aWr5yGuZA/+Ze3rreIiIhUKcHBwZw8eTLftJMnT+Lj40O1atWwWCxYLJZC5wkODi5yue7u7ri7uxeY7urq6lTlWp2K3Q4//Quwg39TTKM3wPK3sCx9DQKbl7jHiT0zk5NRJswtBuNyJZ/98rdg50zo8zym7N/fVxpbqSkqtqAWzhHbwaVYe47lj+RwrvfehWXFG9Cgm3PEtmtWxdqnVxhbqXwfnPm74OzxZcdW4PvgRLE58+dWmrFV6f8bnCg2fRecIz59H5wjtgu/DxZLEYMXytHlHLco8SciUpRzZ2D3b7DtJzi08vx0izs0GwRt74Cwa2H15IINaPM2qnWvDte8YDy32yHh4Pkk4LGNxgjCc6dh/2Ljls3Ftz6dTMGY10ZB/c7GKEE3r3LZ9HLhzM18RURExCG6du3KvAvK6CxatIiuXbsC4ObmRseOHVmyZElujz+bzcaSJUsYPXp0eYdbuf1yL5w5DGYX+NfPxrTCfr85QmG/GRXbZcVm6/YkzJuHrcdTxoksJ4rNmT83xXaZnDk+fR8Um2IrEJu+C5fJmeNTbFccm9N9Hy6TEn8icp5KL0JWhpF82zYD9v4J1vTzrzXsAW2GQviN4OF7frrNWniiKue5zXp+mskEfmHGrc3Q8+s8uSM7GRhp3Mf9gynxCHU4AkvWZ7/XbPQHrNPh/MjAgBZgcfJ/yrMyIDUOUuIg5ZRxn5rz+BT4NTH+81z6OmCHJgOgwdVGMrRaTUdHLyIiIlfg7Nmz7N+/P/d5VFQUW7ZsoVatWtSvX59x48YRHR3Nt99+C8BDDz3EBx98wDPPPMO9997LX3/9xU8//cTcuXNzlzFmzBjuvvtuOnXqROfOnZk8eTIpKSmMHDmy3Lev0spIgX2LjMc9/g9qNjz/WmG/ccvb5fz+Lm8VJbbMzPPTnS22vBTbxTlzbDnrd9b49H0oGcVWMhUlNn0XLo8zx6fYSsaZvw+XycnPFotIucpberHbk+enV/bSi3Y7HNtgJPt2zIJzCedfC2gObYZB69uhRr3C33+xZGhxrgJxcctO5nU4Py0tkayjG/nnrx9o7pOK+fhmo8HtyR3GLdI4OYarJ4S0y58MrFH/fL/AskjmWrMgNT5P8i5PUi83wZfntfTEYi44u4/JvgXGDcC7NgS1hKBwCMy+928KLgVLdYmIiIjz2bhxI3369Ml9ntNn7+6772bq1KmcOHGCI0eO5L7eqFEj5s6dy5NPPsmUKVOoW7cuX3zxBQMGDMidZ9iwYZw6dYqXXnqJmJgY2rVrx/z58wkKCiq/DavsVr0HGcngWx+ufqLg646+0vlKf3+XJcVWMoqtZJw5NnDu+BRbySi2klFsJePMsYFzx6fYSsaZY7tMSvyJyHl5hlWbE6PxSgvHvOJtWPlm5Sy9GH/AKOO5bQacjjo/vXqQkehrMxSC25xPopUnD1/sDXuyL/gsTQYPxuzqCknHz48IjN4ExzdDehIc+du45fD0P58EPH0Itv1oTM+7//Imc202Y3RdbuKuqGRe9vS8idHiMlnAyx+8AsDTz7j3CgAvP2Ob9s4zykjZssCvsTFKMPGIkexMPg77F51fltnFGCUYFG6MgAxqadx86zlmX5U3jcwVEZEKpHfv3tjt9iJfnzp1aqHv2bx580WXO3r0aJX2LCsJUbD6f8bjAa+Bm6dj4xERERERkcuixJ+I5NfrGTi6HkvkVPrmTKseCPH7YeUkCGxh3Hzrg9nsyEhLJiUeds4ykn3HNpyf7uoFLYYYyb5GvZyzfKZPbePW4nrjuc0G8fvOJwKjN0HMDiNRl3fUHBhJvp2zoWF3iFoJp3Ybibf1n8GyiWC3XWYwpuwEXhHJvJzHnv7GPB41Cv97Wf6WkfTLSSznTUhGPAixu+HkTojdBSd3QexOSEs04j+1G5h5flnuPtl/n3mSgYHhUK3GZW6bkzMXUVe8so/MLQ1KmoqIiFzagueMcvehvY3fxyIiIiIiUqE44ZltEXGo2D0QtSL/tLOxRqIsL1dPCGhm9JgLbG4kWAKag29d5xt1lXnO6Ne37Sdj5Jgty5huMkNoH2h7BzS/Dty8HBvn5TKbs/dBM2h3pzEtMy1Pv8DsW3x2X53YXcYtR8qp/MvzqJGdsPPPk9DzLzyZ51nLSKBcieI2863f5fx77HZIij6fBDy5y0gMxv1jjH48us645eVTJzsZGA5BrYzH/k2NEqtFceYEUWGfUWGfpRSkpKmIiMjF7Vt0vhLDoLec73e9iIiIiIhckhJ/InKeNRNm/8e4whewmlyw2LOg1W3GSKpTe4wRWHH/QGaqUWry+AVlmNy8jURUzsjAgObGvXdI+Z44sNng8GqjzOWu34ykUI6QttDmDmh1K3hXsl4wrh5Qt5Nxy3HutLGfpt0GdqtRdvPmTy9I8PmBxbV8Yy1JM1+TyUgu+9aFpv3PT8/KMBKcsbuy+yBmJzkTjxqJwqToosuFBrU83z8wp1xoefa7tNuN5HRGCmSczb5PgcyU849zp6eefxzY0oglZ8Rmz2eU9LuUvEnT2F0wYCJs/k5JUxEREYCsdPjzWeNxxEPGb3oREREREalwlPgTkfNWvA0ntgJg7TKaP9I7c733Liwr3jBOit/6hTGfNcvoiRe7yxgheGq3cR+/DzKSIXqjccvLw/f86MCAFucTg14BxUsIFncEVuxu2PojbP8Fko6dn8e3XnbfvmFGDFVJtZpwbKOR9LO4gTXD2H9tbndsXKXZMNfFLTuJFw6tbzs/PS0xu1xonmTgyV2Qnqdc6I5CyoUGtYQmA4x+l+kpQAfMK9+BFW9A9yeh7XDjbz5vUi4zNX/iLuNsdqLuYgm97BtF9z66pJwyres+hdR4aP8vqN1BV+hfKCMV9vxhXBAARunbnbONx10eUdJPRERk7UeQcMDod93rWUdHIyIiIiIiJaTEn4gYojcZCTSA8JuxXfsyzJuHrcdTWCwXlMezuIB/E+MWfuP5ZWRlGCcLYndnjw7MTgwmHDQSMEfXGre8qtUqODowoIVRWjKvS5XoC7sWPukOMdvPv+buCy1vNJJ99btVzJ6EpeHCMpA5z6HyJzs8fI1SoReWC008lp0EzNM/MG5voeVCLX9P5gYgN4226j3jVhZcPY2Ss25e4FbduM+dlv3czdN4fHQ9HFhijOC0W41k5sYvjVtAC2h/l/G3Xz2gbGKtCOx2OLIGtkyHnb8aFyYUZv2nkHYGuo8B/8blGqKIXMCZSy2LVGZJx2H528bjvhPAw8ex8YiIiIiISIkp8SciRpnBWQ8CdqP/2dCpkJl5/vWLlV7My8XtfBIv3/LTjNGAeUcHxu6C04fgXIIxAidnFE4Or8A8owObQ6OeRpw5CasuD8OcUbD7N8BkJEAAzK7QpD+0GQpNBxqlL6uy4vbRq0pMJqhRz7g1HXB+elaG8Xea2z8wu4dg0jEKjJ2zuBeSjMv73AtcvfIk8S685U3q5Xlc3OT08reMv/mc/brsDaPkZ2A4xB8wvmcLn4fF442Ri+3vgib9yr+cq6OcPmyM/N36vfHvTI4aDYx+mGmJxqgGs4vR89OWZSQHt/4ALW+GHv9njPoUkfKnXpwijrHwRaMqQb0I48IhERERERGpsJT4ExFYPMFIeHiHwD1zC5/nSpJDrh4Q3Nq45ZWRavQLzDs68NRuOHMEUmIhKhaiVuR/j1t148RfzklBAOxQtzO0HQYtbwHPWiWPtbIpSR+9qsrFzUj2BLUEssugZp9otpksmO1WIyHU+zlj1KujFJbM7T0WTGZjevcxRg/EzdPgeCTsnWvcvAKME3nt7yqYnK8M0pONfp5bvofDq85Pd6sOLW+CtndC/a6w8h0j6XfhCFi/Jsa/gztmGrdm10HP/4M6HR22SSJVUt6LUzLPwVX3G4l59eIUKTuHVsOOXwATDHqr6lbJEBERERGpJJT4E6nqDi6HdR8bj2/4oHyTZm6eULudccsr/Syc2ps9OjCnbOhuSIo2eqXl1fs5o1ddrdDyirpiKc0+elVNdkLI2nMsfySHn+936eLh2M+uOMncq+4zbrG7jQTgthmQcgrWfGDc6nSEdv+CVrdCtRrlvgmlxmaDQyuNZN/u34w+iwCYILSXkexrcb0xohIuPQL2qvshNcHo/ZeTMA27Fno+DQ26lvvmiVQ5drtxQZCLO/jWh1WTjBso6SdSVqxZMO9p43HHewr+LhcRERERkQpHiT+RqiwtEeY8bDzudC806evYeHK4V4e6HY1bXmmJRhmiyG+Mkp62TKNso5J+UtryJIhs3Z4sut+lI1xOMjewBQx4Dfq+DPsWwubpsG+B0dMzehMseA5aDDGSgI16VZwr/OMPGMm+bTMg8ej56X6Noe1waHuHMerxQsVJml73LvQeZ/Rx3DbDKKl6YAk06A49n4LQ3sa/OyJSOrLS4dAq49+of+bnL8+by2SMuBaR0rfxK6PEebWacO1Ljo5GRERERERKgRJ/IlXZn2Mh6RjUbAT9XnV0NJe27lMj6XdhiT7QKAApXXkTRCXpd+lsLK7Q/DrjdjbWSGhtnm6Mqt3+s3HzrQ/thhs98Go2dHTEBaUl0iBuKZZvPoBj689Pd/eFVrcYycu6nS6elCtu0jSgKdz8MfR+FlZNNsoMHl4F362COp2MBGDTgUoAOoOlE42ecIX9H7D8rezv8kX2uzhGckx2om8BHFhq9BXLYXGDht2NHpz7FmZPtMOMu2D4Dw4JV6TSSomDpf81Hl/zgsrli4iIiIhUEkr8ScnoRFvFt/t32Pq90Rfs5k+MUXbO7FIl+vI+F7lSlblEavVA6PYodB1t9ADcPA22z4TEI7D8TePWsIfRC7DFDUZJXkexWY2kwNbvcdkzl3ZZacZ0k9kowdluuNGLz9WjbNZfsyEMmWyU+vz7fdg0FaI3wg93QFBrowdgixuM/w/FMcxFjMLN+3+GOJ7NBie2nB/Vd3xz/terB0GT/kZCPbS30YczZ/+5eRmjk/fOgyWvaESSSGlaMsGoqBHcGjqOdHQ0IiIiIiJSSpT4k5LRibaK7ewp+P0J4/HVj0P9Lg4Np1iKU6JPRIrPZDJ6/dXpCANehz1zYfN3Rt/PQyuN29ynjNF07e+CuleV3wi32D3GhQnbfoLkE0a4QJJHHbyufgBLu+HgHVw+sQD41oFBb0CPMbDmQ9jwBZzcDj/fA/5NjRKErW4Di35Wlbu8F4AkHYeOdxs9GldPUU84R0s/CweXGqP69i2Esyfzv167g5Hoa9ofgtueLzV84YU+WRmw8WuI3wcr33V8n1WRyiI6EiK/Mx4PfkcXsYiIiIiIVCI6QyUlU9hIq8JGZInzsdvh98chNQ6CWhm9rCqCyjwCS8TRXKtB69uM25mjsPUHo7zl6UNGed3Ib4wEV7t/Gf3zyiLplpoAO2YavfuOR56fXq0mtL6dzFZDWRoZzeAu12FxdS399RdH9UDoN8G4YGL9Z8aopLh/YPaDsPR16P6kUSrVxd0x8VV26clGf8eEAxB/EOL3Zz8+YLy+6WvjBsao0MjvYP9i8KkN3rWNe58Q8KkD3iHGzcXNcdtTGSVEnR/Vd2gVWDPOv+ZWHcL6QJMBxug+76DCl3HhhT4ubsbFCd/fbuzXs6fKfjtEKjubDeY9DdihzbCKcRFgBWS12VkflUBschqB3h50blQLi1llwkVERESk7CnxJyWXN/m3bCLYbUr6VQRbvoe9c8HsCjd/qhPUIpJfjXrGv+M9noLDq40E4K5fjQTX4vFGqb3GfaH9v6DpoCtLnFgzjcTMlu9h759gy+6naHYxEgPt7jSSBC5uRq9F0/HS2cYr5VkLeo+FLg/Dxi/h7w/gzGH44wnjIpirH4cOIxxbJrWiyjxnJI8SDhiJvfgD55N9F44Yuxi7zShfm3jk4vN5BVyQGMxzy5lW0lLYVaEsujUTjq4zRvX9swDi9uZ/vWZD49+Jpv2hwdXF+81R2GfStD807gf7F116n4rIpW39wShd7VYd+r3i6Ggqpfk7TjDh912cSEzLnRbi68H4IeEMbBXiwMhEREREpCpQ4k+uTK9njFEOdptxFbaSfs7tzBH481nj8TXPQ3Arx8YjIs7LbIZGPYzboLeM8olbphsn+fctMG6eftB6qJEEDG5d/ERHzHbY8gNs/wlS8ozeCW4Nbe+E1rdD9YDy29aS8vAxRvl1ftAYFbn6f5B8HOY/CyvfMfooXnUfuHs7OtIrU9oJrKwMI1GaO3ovO8GXcBASjwH2ot/r6Q9+YVArzLjPebzrV+Mzt7gZo8y6jobwG43yn0nHjf2SdBySTkBStFFC1pph/P2lnIITW4tep7tPdiIwe7SgT0jBZKGnX8FSuJW1LHpqgpGw/2e+cZ+WeP41kwUadIOmA4ykvX+T0isRPOD17NKh2ett3Ld0litS1aQlGhfygPFvU3mWzq4i5u84wahpkQX+N4tJTGPUtEg+vquDkn8iIiIiUqaU+JMrs/wtck/Q2W1G37ghkx0YkBTJZoM5D0NGMtSLgG6POToiEakoPHyM3mkd74a4fbB5Gmz9Ec7GwLqPjVtIW6geZJT5g8ITHY37wsfdjf54ObwCjDJjbYdX3IsR3DyhyyjodK8xenHVJONCi8XjYdV7xsjAiP8YZUsrorwJrG5Pnp9+sQSWzQqJR7OTegfzJ/jOHAH7RfqyuvuCXyj4Nc5O8DU2ntcKg2o1Cs6//C0j6ZdTdSAnLg/foi9IstshNf7iicGk45CeZNxOJcGpPUXHbHE3Tp5fmBhseYsRS1oiXDseVk92jrLol5PMtdshdtf5UX3H1hu/+XJUq2WM0G06AMKuKXwflYaAptD5P0aJ3fnPwaheYHFQ2V+RimzZG8YFD35NIGKUo6OpdKw2OxN+31XoJSx2jJ7FE37fRb/wYJX9FBEREZEyo8SflFzOiTVM5Cb/Nn1tXBHf+1lHRiaFWfcJHFoJrl5w8yfGCT8Rkcvl38Toc3fNi3DgL9j8nVGmM2fElCk7SRR/wLgQZM4jsHOmMSp8/2JjHosbNB1o9AxsfG3lOXnv4g6dRkL7u2D7L7DyXYjfB8teh7/fh873Q5dHKsZoxrzylPY2W61AOOaV78CKN6Dro0ZvqI1fnR+1F7/f6A+Zt7/bhVw9s5N6eRN82Um+wkbPFaWw/sKF9SG+kMkEXv7GLaRN0ctPTzaSgbmJwZxEYXaCMOkEpMSCNd0YxXjmcOHLWfOBcQNw84LtPxtJcncfI0GZe8t5XsO4v/B112qlM4LuUqMRez4D+xYZo+v+WWAkcfMKamUk+poOhDody+83Ra9njIsO4vYaf3MRD5bPekUqi9jdsO5T4/GgN9XntAysj0rIV97zQnbgRGIa66MS6BrmV36BiYiIiEiVosSflEzOiaGuj8Ka940TvS4ekJlinOA0mVT205mc2guLXzYeD/gv1Ap1aDgiUglYXIy+W037Q0q8kcjYPO38aL5tPxq3HHYb1O5g9O1rdavRJ6+ysrhCu+HQZmh2Ccp34eQOY/Tf2k+M5GC3R41RYc7MmgnJMcYtoBk06Y9lxRvcgAkTdqNX7Jr3jVthLG7G/zeFJfi8Q0ongWWzFj56Lue57SIjC4vD3RsCvI3RZkXJyjBGvxaVGEw6nr8vXUaK0TOzJMwu4OGLi7sPvdLsWE5/YYywy5c89C08aejhA27eRhnfwpKjC18wEtR+TYz7rHPn1+viAY16ZZfw7G/0AnWEajXhmhdg7hij1Hzr2yv3vyUipcluh3lPGyOum19vXHgjpS42ueikX0nmExEREREpCSX+pGRyTrSF9jFO+PnUNk5kLnnFaBKfee7Sy5DyYc2E2Q8aoxEa94WOIx0dkYhUNl5+0OUh43ZiK2yeDus/Pf/61U8YCb+AZg4L0SHMFmh1C7S82Rg5tfwtOB5plCrc8IUx4rH7E1CzYfnGZbcbfdqSjxtJvaTs++TsUpfJ2beUOArrt2fKmWbLNC78qdkgT0nOMCPZ59cYfOuW/Uiwi/UWLK8LkFzcoEZ941aYnIulcvoPXnW/8TeRlph9S8rzOBHSE/M/z3ndbgVbFqTGY0qNpwbAoUOXGazJSAC6ZycDfesbsS19ndx9Hb/PuPepm53cHwgNexglbZ1Bh7thw5cQu9OI+7p3HB2RSMWwa45R/cPFAwa85uhoKqX1UQl8svxAseYN9PYo42hEREREpCpT4k9KJudE2845xr1PHaN82aZvjDJXKiPpPFa+C8c3G2XDbni/dEZYiIgUJaStURoQjBFhtkyjtGFVS/rlZTJBs0FGAuXgUljxDhxebZTHjvzW6HHo5gXVA4vXc+1iMlIunsxLPmFMv1gZzrzMLsboPO8QI/kUtxcbZszYIOIh6P/fylOqtSxcWIo053n1oMtLTNrtxr5NN5KAWWfj2bhqCZ1aN8Ul82yepGFS4UnDtDPZ+9x+/rXEfCsw7upFZPfrGwhBLZ3zN4PFBQZOhG9vgI1fGr01g8IdHZWIc8tIgQUvGI+vfqL8Lzip5CKPnOa9Rf+wcl9csea3mE14e+hUjIiIiIiUHf3alCuTFG3c+9QG1+yrR2fcBav/B+3/bYwCkEJZbXbWRyUQm5xGoLcHnRvVKv0G79GRxklGgOvedf6yciJS8RWV6ACVgDaZIOwa43ZoNax8x+iTuPX78/OcPWn8e50j5/PrNQ4So4tO5iVlJ/TSEwuutyie/kZCzycEvIPBu7Zx71P7/HNPP6M0ZHYc1p5j+SM5nOu9d2FZ8YbxelXfr0Upaf/BwphM4F7duPnUxl4zk5O+8dhbDQbXYiZeM9MKJgYjv4Nds40Ery3LqAzQ86nL205HCO1llCrc8wfMHwsjfnXOJKWIs1g5CZKOGaN8uz/h6GgqjW3HzvDeon9YuvcUAC5mE0Ovqker2j48P3sHUNi4eeM48JaP/+al68P5V0R9TPr3S0RERERKmRJ/cmWSjhv3vnWM++bXQ6OeELUCFr0IQ791XGxObP6OE0z4fVe+xu8hvh6MHxLOwFYhpbOSzHNGiU+7FVreAq1vK53liogUpTQTHZVdw6uN27FNRgJw7zxj+oYv4OByaNQDolYaZRfdvGD5G7B8YvGW7eqVncwLyZPYu+B59SBwcS/e8vLsV1u3J2HePGw9nsJisWi/XkxZ9x+8XK4exq16oPF8+VtG0q+iJun7/xf2LYSo5cb3p/l1jo5IxDklHIS//2c8Hvg6uFZzbDyVwM7jiby3aB+Ld58EjBF8t3aow6PXNKFeLaMsci0vt0KP98b0a8q87SdYuvcUL8zZwer9cbxxaxt8q2n0vIiIiIiUHiX+5MokHjPufbITfyYTDHwDPukOu341Tlo26uG4+JzQ/B0nGDUtssDVnzGJaYyaFsnHd3UoneTfklcg7h+oHpx/9IiISFlxtkRHRVC3Iwz/AWJ2GKWZd84ykn05fdbAKNEGxqis6sHZo/IKSebljNjz8CndGPPu18zM89O1Xy/OGfoPFqUyJOlrNYKuj8Cq92DB88ZoxeIms0WqkvnPGaV+Q/sYF2lKie2NSWby4n/4c0cMAGYT3NS+Do9d04SG/l755h3YKoR+4cGFVni5tUNdvlodxZvz9/Dnjhi2HUvk/Tvb06F+TUdsllQS5VJRSERERCoMJf7kyuSM+MtJ/IHRE6bjSKPvyvxx8OBy9fzLZrXZmfD7rkJLvtgBEzDh9130Cw++sh/pUStg7UfG4xs/AM9aJV+WiEhxOXOiw9kFt4Lbv4be4+CjLsZobbMFhn1/vvymp79RdrO8ab9WPpUlSd/j/2DL93A6CtZ+rBKGIhf6ZyH886dx4cigt1QSt4T2x55lypJ9/LHtOHa78TFe36Y2j1/bhMaB1Yt8n8VsomuYX4HpZrOJ+3uEclXDWjz6w2aOJKQy9JM1PDWgGf/pEYpZyRq5TOVSUUhEREQqFAecPZJKJbfHX5380/s8Dx6+cHI7RKrcZ471UQn5foxfyA6cSExjfVRCyVeSlghzHjYedxwJTfqVfFkiIlK+ds0xkn4WNyP5ErMNarczyjM6IuknlVOfcUUnbXs9c/FkrzNx94a+LxuPV7wDyScdGo6IU8lKh/nPGo+7jIKApo6NpwI6FJfCmBlb6P/ecn7faiT9BrcOZv7jPXl/ePuLJv2Ko229GvzxWHeubxNCls3OG3/u4Z6pG4g7m15KWyBVQU5FoQvPM+RUFJq/44SDIhMRERFH0hkkKTlrFiQbZU5ye/zl8PKD3s8Zj/96Fc6dKdfQnFVsctFJv5LMV6j54yDxKNRsaPS/ERGRiiFv+cUXTxn3S18zpotI4drcAbU7QEYy/PWKo6MRcR5rPjT6+1UPgp4anX05jiak8swvW7l20nJmbY7GZod+4UHMfaw7H/2rI82CvUttXT4errw/vD1v3NIaD1czK/45xaApK1m9P67U1iGV16UqCoFRUchqK2wOERERqcyU+JOSO3syuxSZC3gFFHz9qvvAvxmkxuukZbZAb49Sna+A3X/AlumACW7+FNyv7CpUEREpJ0X1XFPyT+TizGYY9KbxePN0OL7ZsfGIOIPEaFjxtvG43yul3/u1kjp+5hzPzd5On3eW8dPGY1htdvo0C+C30Vfz+YhOtKztWybrNZlM3NG5Pr+N7k7ToOqcSk7nri/X8c6CvWRZbWWyTqnYzmVY2XU8if8t2Vf2FYVERESkQlKPPym5nP5+3iGF9/CzuMLA12HarbD+U+h4T5UvMROXfPGyLSYg2NdoxH3Zzp6C3x83Hl/9ONTvcvnLEBERx6gsPddEHKFeZ2h9O2z/Gf4cC/fOVy8zqdoWvQiZqVAvAtoMc3Q0Tu9kUhofLd3PD+uPkpGdaOvRxJ8n+zWlQ/2a5RZH0yBvfn2kO6/8sZMf1h/lg6X7WXswninD21OnRrVyi0Ocg9Vm5/iZcxyMS+HgqbNExaVw8JTx+PhFkn2FuaKKQiIiIlIhKfEnJZd0zLi/sL9fXo37QtOB8M98WPAc3PVL+cTmhGZFHuOpn7fmPjdBgZIcdmD8kHAsl9vQ3W43kn6pcRDYEvo8d6XhiohIebpYT7WierGJyHl9J8CeuXB0LeycBa1udXREIo5xaBXsmAmYYPDbSoJfxKnkdD5edoDp6w6TnmUk/LqE1mJMv2YluxCzFFRzszDxljZ0C/PnuVnb2Xj4NIOnrOSt29owoGWwQ2KS86w2O+ujEohNTiPQ27hg97KP3S9wJjWTqGSYtTmawwlpHDyVQlRcClHxKWRkFT3i07eaKwHebuyPTbnkOkpcUUhEREQqLCX+pORyRvz51L74fP1fg/1LYP8i+GchNO1f9rE5me/XHeH5Odux22FYp3r0ahrAq3N3FSjL4e3hQocGJbiqdOsPsHcumF3hlk/Bxb2UIhcRERGpAHzrwNVPwLLXYeFL0HQQuHk6OiqR8mXNgnnZF4t0GgkhbR0bj5NKSMng0+UH+GbNIdIyjcRKpwY1GdO/Kd3C/B0cnWFI29q0rVuDR3+IZOuxRB78bhP3dGvIuMHNcXcppNqOlLn5O04w4ff8x/Ahvh6MHxLOwFYhF31vepaVI/GpHMhO6h08dZaDccbjhJQMwAV27CzwPjeLmQZ+njTy9yI0oDqhAV6EZj+u5eWG1Wan+5t/EZOYVmifv5wYHZXIFhEREcdR4k9KLjHauPe9yIg/AP/G0OUh+Pt9Y9RfaG9wcSvz8JzF16ujmPD7LgDu7tqA8UNaYjabGNAqOPdqwRrVXHlt7m7+iT3LkzO28O29EcW/cvDMUfjzWeNxn3EQ3LqMtkRERETEiXV7FDZ/B4lHjd+dvZ91dEQi5WvjlxC7E6rVhGtedHQ0TudMagafrzzI1NWHSMkwSmi3rVeD/+vXlB5N/DE52ejI+n6e/PxQN95esIfPV0Yx9e9DbDiUwPvD2xMaUHl7uZfFqLorNX/HCUZNiyyQXItJTGPUtEg+vqsDA1oGE5OURtSpFA5cUJ7z2OlUbEVl5gBfNzvhdf0IC6xOI//zCb66NT0vuu0Ws4nxQ8IZNS2y0IpCAI9e09jhn5+IiIiUPyX+pOSSshN/Fyv1maPn07D1R4jfBxs+h66PlG1sTuLjZQd4c/4eAB7sGcrYQc1zDygtZhNdw/xy561TsxpD3l/N6v3xfLh0P49d2+TSK7DZ4NeHIT0J6naGbo+XyXaIiIiIOD03T+g3AX65F1a9B+3/Bb51HR2VSPlIiYOlrxmPr3kRPDXCJ0fiuUy+WhXFV6uiSE7PAqBVHR/G9GtKn2aBTpfwy8vNxczz14XTLcyf//t5KzuPJ3H9+6v4702tuKVD5fv37UpG1ZUVq83Oy7/tKjSpljNt9PebcbWYOJdZdGnO6u4u2SP3vAj1r06jnOSerxvLlyxk8OBOuLq6XnZ8A1uF8PFdHQp8bq4WE5lWOwt2nmR45/pO/XcuIiIipU+JPym5y0n8efjCtS/Bb4/Csjeh9VCoHlC28TmQ3W5n8uJ9TFmyD4DHrm3Ck32bXPTHduNAb/57Uyv+7+etTF78D50b1aJLqF+R8wOw/jOIWgGunnDzJ2DRV1pERESqsJa3wPrP4cgaWPwy3PqFoyMSKR9LJkBaIgS3gY73ODqacnOx0WFn07OYujqKz1YcJCnNSPg1D/bmyX5N6R8eVKESIX2aBzLvsR48MWMzaw8mMOanrazeH88rN7bEy71yHAMWZ1TdxZJ/drud9CwbKelZpGZYOZdpJTXDSmr289RMK+cysh9nWEnNfnwuw0pKRtGvJadlkWEtOqEHkGWzk2WzYzGbqF/Lk1B/r9zynI38vQgL8CLA273Qv7nMzMySfFz5DGwVQr/w4HzfhVpebgz5YBXL/znFL5uOcXunele8HhEREak4KscvRHGM3B5/xUj8AbT7F2z4Ak5shaX/hSFTyi42B7Lb7bwxfw+fLj8IwDMDm/Fw78bFeu+tHeuy5mA8v2w6xmM/bGbe4z3wr15Ev75T/8Di8cbj/q+CX1hphC8iIiJScZlMMPAN+Kw3bP8ZrnoA6kc4OiqRshW9CSK/Mx4PfhvMVaMHXFGjw8YOas6JxDQ+XX6A06lGUqVJYHWe6NuUQa2CMVfQsofBvh5Mv78LH/y1nylL/mFm5DE2Hz3NB8M7EF7bx9HhXRGrzc6E3y8+qu7JGVuYuekY5zJt5xNzmVZS0o2k3blM60XLaZa15we34O5uDXFzMTtk/RdWFAJ4sm9T3py/h1f/2EXPpgEE+Xg4JDYREREpf0r8SclYsyD5hPH4Uj3+cpgtxomYrwfBpm+g030Q0qbsYnQAm83OK3/sYurfhwB46fpw7u3e6LKW8cqNLdl69Az7svv9fTOyc8GDU2smzP4PZKVB2LXGZykiIiIiULsdtL/L6Pc3/1m4/y8wO+ZErEiZs9lg3tOAHdrcAfW7lPoqKlLPtROJaTz+45bc56H+XjzetwnXt6nt8JhLg8Vs4vG+TYgIrcUTP27h4KkUbvpoNS9e14K7ujSoUKMY85q3/US+BG5hzmXaWLQ7tljLc3cx4+lmwdPNJfveQrULnud/reD0am4WvNxc2BuTxGN5/qaK0qqOr8OSfkV5oEcj5u84wdZjiTw/ezufj+hUYf9GRERE5PIo8Sclc/Yk2G1gdgGvyyjZ2aCbUYJp5yyYPw7u+cO4MrsSsNrsPD97Oz9uOIrJBK/d1Jo7I+pf9nI83Vz48F8duOGDVazcF8fHyw/wSJ8LRgyunATHNxslVG/8oNJ8hiIiIiKl4tqXYOcc4/fS1h+Mfn8ildHW740Rf27eRo/LUuasPdeKGh2Ww2IyMfHW1tzSvg4uFudKxpSGLqF+zHu8B0//vJUle2J58dedrN4fz5u3tsHX8/L7xJW3TKuNTYdPs3RvLMv2nGLvyeRive/2TnXpFuZHNdeiEnjG89JM8jYOrM7EP/cQk5hW6N+cCWM0ZudGztdX08Vi5q3b2nL9+ytZvDuWX7cc56b2xbxwW0RERCo0Jf6kZHL6+3nXvvxSMv1egb3z4PAq2PUrtLyp1MMrb1lWG0//so3Zm6Mxm+Dt29pya8eSN1tvGuTNKze04pmZ25i0yOj3d1XD7AOJ6EhY8Zbx+LpJ4FO7FLZAREREpBKpHgi9noZFLxm9z8JvAHdvR0clUrrOnYFF2aX/ez0D3sGluvgr7blWFLvdToYV4s+mk27L4Gx2D7az6VlGf7b0849TMqzGfXoWKRlZpKRbiUk6d8nRYVa7nXo1PStl0i9HLS83vri7E1+tPsQbf+5m/s4Ytkcn8r/h7ejYwPmSULHJaSzbe4ple2NZ+U8cyelZua+Z4KKJ3By3tK9boJxlWbOYTYwfEs6oaZEF4sxJL44fEu60I0qbBXvz2DVNeHfRP7z8+066NfYj0FslP0VERCo7Jf6kZHISfyVJOtWoB1c/AcvfgIUvQtMB4FqtVMMrTxlZNp6YsZl522NwMZuYfEc7rm9z5cm42zsZ/f5mb47m0e+Nfn+13Kww+yGwZUHLm6HVraWwBSIiIiKVUMRDsPFrOB0FK9+Fvi87OiKR0rXsDUiNA/+mxt97KSpOz7XnZm3nXIaVtCwbKelZBRJ4KenWPAm77OfZj212F1i/vFRjvlBs8sWTg5WByWTivu6NuKphTR79YTOH41MZ+ula/q9/Ux7qGebQfoZWm52tx86wbE8sS/eeYnt0Yr7Xa3m50btpAL2bB3J1mB/Xv7/KaUfVDWwVwsd3dSgw+jXYwaNfi+uh3mHM3xnDzuNJvDRnJx/f1UElP0VERCo5Jf6kZBKzE3/F7e93oasfN/quJB6Bvz8wrsiugNIyrYz+PpLFu2Nxs5j54M729G9ZOlfamkwm/ntTK7YeO8PBUyn8309b+Cp4Nqa4vVA9yBjtpx/rIiIiIoVzcYcBr8OPw2HNh9Dhbqh1eb2XRZzWyV2w/jPj8aA3wcWtVBe/PirhkqPqElIzefKnrVe0Hi83C57uLlR3d8HL3SjTWN3dKN1oTHPBy81i3GfPcyzhHO8u+ueSy65Ko5ra1K3BH4925/nZO/ht63Hemr+XNQfimTS0HQHe7uUWx+mUDFbsO8XSPbEs/+cUp1MzL4jTlz7NAunTPJA2dXzzJSadfVTdwFYh9AsPdrp+l8XhajHz9m1tueGDVczfGcPc7SdK5WJlERERcV5K/EnJJB037ktaZtLN0yj5OfM+WDXJ6LtSwUpWnsuw8p/vNrJyXxzuLmY+/XdHejcLLNV1eLm78OGdHbjpw9Wk7VuG6fBHxgs3fACezle+RURERMSpNBsEob3h4DJY+ALcMd3REYlcObsd/nwG7FZofj2EXVPqqyjuaLkmgdVp4OdFdfc8CTw3I0HndUHiLieh526BVcuWcNN1g3B3v/yEpdVm5/v1R5x2dJijeHu4MuWOdnRv7M9Lv+1g5b44Bk1ZyXvD2tKjSUCZrNNut7PzeBLL9hqj+jYfOY0tz07x9nChZ9MA+jQLpFfTgIsmISvCqDqL2VTupUZLS3htHx7u05j/LdnHS7/upGuoH37Vyy8pLCIiIuVLiT8pmaRjxr1PyfvY0epWWP85HF0Li1+GWz4rldDKw9n0LO6buoF1UQl4uln44u5OdAvzL5N1tQjx4dWBDei2aDQAp5oOJ6Bp/zJZl1ReVpu9Ql6dKiIickVMJhgwET7pDnv+gIPLIbSXo6MSuTI7Z8OhleDiYYxqLQPFHS33yo2tLjsRkpmZiYeFEpehrOg918qSyWRi6FX1aF+/BqO/38zek8mM+Go9o3qF8WS/priWQs/D5LRMVu2LY+neWJbtPUVscnq+15sHe9O7WSDXNA+kQ/0al9VnsSKPqqsIRvdpzMKdMeyJSWb8bzv54M4Ojg5JREREyogSf1IyVzriD4wTMYPegM/6wLYZcNX9UK9z6cRXhhLPZXLP1+vZfOQM3u4uTL33qjJvnn573AeYTHEcsQVw3+Eh/JyaQQ3P0i3nI5XX/B0nClw5G+JEV86KiIiUqaBw6HQvbPgc5o+DB1eARYdBUkFlpBijVwG6Pwk1G5TJajo3qoWnm4XUDGuhrzt6VF1FGB3mSE2CvPl19NW88scuvl93hI+WHWBdVAJT7mhH3ZqeWG121kUlsCnOhF9UAl0bBxaZXLPb7eyPPcvSvbEs3XOKDYcSyMozrM/TzcLVjf3p0yyQ3s0CqF2j2hXFXpFH1Tk7Nxej5OdNH63mj20nuL5NDANblU6rEhEREXEuOuKVkrnSHn85arc3ynxungZ/Pgv3L7ny2MrQ6ZQM/v3VOnZEJ+FbzZXv7utMm7o1ynale+Zh2jIdOybe9nyCfWfgqZ+38fmIjmrILZc0f8cJRk2LLFAGKSYxjVHTIvn4rg5V/sSIiIhUAX2eg+0/Q+xOiPwGrrrP0RGJlMzKSZAUDTXqG33Ty8ivW6IvmvQDx4+q0+iwi/NwtfD6za25OsyfsTO3senwaQZPWcnwzvX5bevx7ISphW/3bSxwUeC5DCtrDsbx1x4j2Rd95ly+ZYf6e+WO6ruqUU3cXSwO2EIpidZ1fXmwZygfLTvAC3N2ENGoFjW9dFGxiIhIZaPEn1w+axacjTEe+1xh4g/gmpdg569wPBK2/Qgtb7/yZZaBU8np3PXFOvaeTMbPy41p90fQIsSnbFeaEge/PwaAqdujPNjy3yz46G8W7z7Jl6uiuL9HaNmuXyo0q83OhN93Fdr7xI5x0mbC77voFx6sEyQiIlK5edYykn9/PgN//Rda3QLVajo6KpHLE38A/v6f8XjA6+B6ZSOrirIjOpFxs7YDMKhVMFuOnnHaUXUaHXZp17UJoU1dX0b/sJmtR8/w6YqDBeaJSUzjoWmRDO1Ul5NJ6aw5GE9Gli33dTcXM11D/ejTLIDezQJp6O9Vnpsgpeyxa5uwcNdJ9see5ZU/dvHesHaODklERERKmRJ/cvnOngS7Dcwu4BV45cvzDoJeT8Oil4xef40HXvkyS1lMYhp3frGWg6dSCPR25/sHImgc6F22K7Xb4ffHIeUUBIZDn+dp5erBi9e34MVfd/Lm/D10aliLdvVqlG0cUmEt/+dUvpM0F7IDJxLTWB+VoBMmIiJS+XW6FzZ+Baf2wPK3YOBER0ckcnkWPAfWDAi7BppfXyariD+bzoPfbSI9y0afZgG5PcA0qq5iq1fLkx8f6EKn/y4ipZCRnDkXCv608VjutDo1qtGneQB9mgXSNcwPTzedPqosPFwtvH1bG279+G9mb47m+jYhXNsiyNFhiYiISCnSLze5fEnZZT69a4P5ypuDAxDxEGz8Gk5HYf57MtCxdJZbCo4mpPKvL9ZxJCGVOjWqMf3+iPK5wnHbDNjzB5hd4eZPwdUDgLu6NGDNwXjmbY9h9PeRzH2sB77VXMs+HnF6Z9Oz2HAogbUH41l7MIFtx84U632xyUUnB0VERCoNi6sxSmraLbD+M+g4EgKaOjoqkeL5ZwH8M9+4+HLgm0a/9FKWabXxyPeRRJ85RyN/Lybf0T43waeLxCq+LUfPFJr0u9C/IupxT7dGNA6srtYSlVj7+jW5v0con604yHOzt7OwYS2dVxAREalESilrI1VKUin198vLxd04EQOY132MZ3ps6S37CkTFpTDs0zUcSUilgZ8nMx7sUj5Jv8RjMO9p43HvsRDSJvclk8nEG7e2oX4tT46dPsczv2zFbi+smKNUdslpmSzdG8vEP3dz44eraTthISO/3sCnyw+y9egZivtnEejtUbaBioiIOIvG10LTQWDLMkZPiVQEWekwf6zxuMuoMktYvz5vN2sPJuDlZuGzf3dUEqCSKe7Ffp0b+dEkyFtJvypgTL+mhPp7cTIpnf/+scvR4YiIiEgpUuJPLl9iduLPp3bpLrfZIAjtg8maQcvoH0p32SWw72QyQz9dw/HENMICvJjxn67UrelZ9iu22WDOw5CeBHWvgqufKDCLj4crH9zZHleLiQU7T/LN34fKPi5xuAsTfe1eWZQv0We12alfy5OhneoyaWhbVjzThxBfDy52yB7ia5RrEhERqTIGvGZUVNi/CP5Z6OhoRC5tzQeQcBCqB0PPZ8pkFbMij/H16kMAvDu0HU2CyritgZS74l7sp4sCqw4PVwtv3dYGkwl+3nSMZXud4wJsERERuXIq9SmXL+m4ce9TiiP+wChXM3Ai9o+vpnbiJrKiVkDTa0t3HcW083gi//5yPQkpGTQP9mba/RH4V3cvn5Vv+ByiloOrp1Hi01L417RN3Ro8N7gFE37fxevz9tCxQS1a1/UtnxilXCSnZbHztIntC/5hw6HTbI9OxHbBKL76tTzpElqLLqF+RIT6UadGtXyvjx8SzqhpkZg437sjrxevC1ePFhERqVr8wqDLQ/D3+8aov9De4OLm6KhECpcYDSveMR73ewU8fEp9FduPJTJu1nYAHr2mMQNbBZf6OsTxOjeqRYivBzGJaYUeF5iAYF0UWOV0aliLe7o15OvVhxg3azsLn+yJt4dG+4qIiFR0SvzJ5UvKbvhd2ok/gMAW2Drei2Xj51gWPQ9hvYpMfJWVLUfPMOLLdSSlZdG6ji/f3tuZml7ldDLo1D+w6CXjcb9XjBNTF3FPt4asPRjPgp0neeT7SP54rDs++pFeYSWnZbLx0OnsHn3x2Yk+C3Aod54Gfp50aeRHl7BaRDTyo/YFib4LDWwVwsd3dWDC77s4kViwvE9Mkvr7iYhIFdTzadjyA8TvMy666vqIoyMSKdzCFyAzFep1gTZDS33xcWfTefC7jaRn2bimeSBP9lXfy8rKYjYVeVFgzmWA44foosCq6OkBzViyO5YjCam8Pm8PE29p7eiQRERE5Ao5vNTnhx9+SMOGDfHw8CAiIoL169cXOW9mZiavvPIKYWFheHh40LZtW+bPn59vnpdffhmTyZTv1rx587LejKolZ8Rfafb4y8PW8xkyLF6YTu2GTV+XyTqKsuFQAnd9YST9OjaoyfQHIsov6WfNgtkPQlYahPaBq+6/5FtMJhNv3dqWujWrcSQhlXEzt6vfnwNZbXbWHIjn1y3RrDkQj/XC4XkXSE7LZOmeWCbO282NH6wyevRN3cCnKw6y9Zgxus/f3c7tHevw3rC2/D32GpY/3Yc3b2vDze3rXjLpl2NgqxBWPXsNPzzQhSl3tOOHB7ow4caWALwxfw/7TiZf8baLiIhUKB6+cG32xVbL3oSUOMfGI1KYqJWwcxaYzDD4baNCSinKtNp4ZHokxxPTaOTvxXvD2mFW0qdSy7koMNg3fznPYF8PPr6rAwNbhTgoMnEkTzcX3ry1DQA/rD/C6v36P1FERKSic+iIvxkzZjBmzBg++eQTIiIimDx5MgMGDGDv3r0EBgYWmP+FF15g2rRpfP755zRv3pwFCxZw88038/fff9O+ffvc+Vq2bMnixYtzn7u4aGBjqSqrHn85qtVkT8ittDn2LSx9HVrdCp5lX27k7/1x3PfNRs5lWukSWosv774KL/dy/NtZNQmORxonom78sNgH9r6ernxwZwdu/+Rv5m4/QZd1fvy7S4MyDlYuNH/HiQKj6kJ8PRg/JDz3ADopLZONhxJYezCBtQfj2VFI6c6Gfp5EZI/o61jPl82r/2Lw4Ja4ul7ZSE6L2UTXML/c511Ca7F0TyzL9p7iiRlbmP3w1bi5OPxaEBERkfLT/i5jtF/MdvjrvzBksqMjEjnPmgV/Zvfz6zgSQtqU+ipem7ubdVEJeLlZ+OzfHfGtpsohVcHAViH0Cw9mzf5YFq5cR/8eEXRtHKiRflVc1zDjPMJ3aw/z7MxtLHiiZ/meDxEREZFS5dCzvJMmTeKBBx5g5MiRhIeH88knn+Dp6clXX31V6Pzfffcdzz33HIMHDyY0NJRRo0YxePBg3n333Xzzubi4EBwcnHvz9/cvj82pGqxZcDbGeFwWpT6zHfLvgz2gOZxLgOVvltl6cizdG8s9UzdwLtNKz6YBfH1P5/L9kXt88/ntHPzOZY+mbFevBs8ONEa2vvrHLnYeTyztCOUi5u84wahpkQVKacYkpvHQtEge+HYjN3ywinYTFnLv1I18tuIg27JH9DX08+SOq+oxeVg71oy7hmV5RvSFXHAlbmkyRou2oaanKzuPJzF58T9lti4RERGnZLbAwOzfX5HfGAlAEWex4QuI3QXVasI1L5T64mduOsbUvw8BMGlYO5oEeZf6OsR5WcwmIhrVoqO/nYhGtZT0EwDGDmpOnRrVOHb6HG/O3+PocEREROQKOOzynYyMDDZt2sS4ceNyp5nNZvr27cuaNWsKfU96ejoeHvlPhFerVo1Vq1blm7Zv3z5q166Nh4cHXbt2ZeLEidSvX7/IWNLT00lPT899npSUBBilRTMzMy972yq1pGhc7TbsZhey3GtCGXw+mZmZ2E0W0q95BY8ZQ7Gv/5ystv+GgGalvi6AhbtO8sRP28i02rm2eQBThrXFxWQjM9NWJusrICsNl1kPYrJlYWt+A9bmN5Xocx0RUZc1B+JYsucUj0yPZNZDXfD2KPlXPOdvX9+Bi7Pa7Lz8204KK+qZM23RrpO50xrU8iSiUU06N6pF54Y1CyT38n7eZb0Palaz8OoN4Yz+cSufLD9Aj8a16NSgZpmsqyLTd8E5aD84B+0H56D9UIrqdMbS4kbMu3/F9uezWP81p1hVF5x1HzhbPFJCZ08ZlU8Arnmx1KufbDt2hnGzjUT3Y9c0ZkDL4FJdvohUTF7uRsnPu75cx7drDjO4dQhdQv0u/UYRERFxOg5L/MXFxWG1WgkKCso3PSgoiD17Cr+yaMCAAUyaNImePXsSFhbGkiVLmDVrFlarNXeeiIgIpk6dSrNmzThx4gQTJkygR48e7NixA2/vwq9inDhxIhMmTCgwfeHChXh6el7BVlY+NVP20RM451KDRX/Ov+T8V2LBP2l09u1ISOImTv/wIGvCni71vhaRcSa+22fGhol2fjau8z3BkoUnSnUdF2p2YhZ2k5l/gm8CoGX0DzSO20uaiy9HEmzYvnqAvSG3lGjZ11aHSDcLh+JTuf+TxYxoYrvij2zRokVXtoBKIs0KCWkQn24iIR0S0k3Ep8GJVBNx6Zf+kPvVttI92E4N9yQgCaIPszkaNhdj3WW9DzoHmFl/yszo79bzTFsrHpYyXV2Fpe+Cc9B+cA7aD85B+6F0VDP15FrTPCyHV7Pxh1c4UeOqYr/X2fZBamqqo0OQ0rBkAqQnQnAb6HhPqS467mw6D363iYwsG9c2D+SJvk1LdfkiUrF1b+LP8M71+GH9UZ6duY35j/ekmpsOEEVERCqaClWwe8qUKTzwwAM0b94ck8lEWFgYI0eOzFcadNCgQbmP27RpQ0REBA0aNOCnn37ivvvuK3S548aNY8yYMbnPk5KSqFevHv3798fHx6fsNqgCMu2aA/+AR1BjBg8eXCbryMzMZNGiRfTr1w/Xsy2wf3o1gck7uK6JBXvTgaW2npmR0Xy71hipdXO7EF6/qSUulrKvfmteuQvLijdo2qQp9vpdsWw2EqiuzfrRdOcvWHuOJaxHyT/bxu3PcOeXG4iMN3Pz1a2446q6JVpOvv1whf3lSpPVZmfj4dPEJqcT6O1OpwY1S6U0zbkMK8fOnCP6zDmiT5/j6OlzRJ9J49hpY9rp1Cu7gn7Q1e0Y0ibkst5TXvugR1omQz5cQ/SZNDZaG/D6kJZltq6KyFm/C1WN9oNz0H5wDtoPZaDWcVj1Llcl/ErW0GfB5eLltp11H+RULpEKZOlEo+xsr+x+fsc2webvjMe128Hyt6DPuCLffjkyrTYemW6Upw/19+K9O9phVolHEbnAuMEtWLb3FIfjU3l7wV5eGhLu6JBERETkMjks8efv74/FYuHkyZP5pp88eZLg4MJLjQQEBDBnzhzS0tKIj4+ndu3ajB07ltDQ0CLXU6NGDZo2bcr+/fuLnMfd3R13d/cC011dXZ3qQN4ppBj7y+xbB3MZfzaurq64BjaFro/AqvdwWfISNOsPLgX31eX6bu1hXpyzE4Dhnevz2k2tyu+g95pxYLFgWfoaePgCdghph2XnL9DneSy9nuFKrqfrHBbA0wOaMfHPPfx33h46NfKjRUjJE9jO9D2Yv+MEE37fla+XXoivB+OHhDOw1cWTammZVo6fOcex0+c4ejrVuE8w7o+dTiXubMYl11/D05V6NT2pW7Ma9WoZ9ylpWby5YO8l3xtSw6vEn2NZ74Narq5MGtqOOz5fy8+boukbHqyST4Vwpu9CVab94By0H5yD9kMp6vl/sPUHTIlHcN34GfT4v2K9zdn2gTPFIsVktsDS14zHPZ6CeU8Zj4NaQeS30Of5UlvVa3N3sy4qgeruLnw2oiM+Hvp7EZGCfDxcmXhLa+75egNf/x3F4NbBdGpYuiWHRUREpGw5LPHn5uZGx44dWbJkCTfddBMANpuNJUuWMHr06Iu+18PDgzp16pCZmcnMmTMZOnRokfOePXuWAwcO8O9//7s0w6+6kqKNe5865bfOHv8HW76HhIOw7lO4+rErWtwXKw/y37m7AbinW0PGDwnHVMolRC+pdgcj6ZeWaDw/scU4qM+50vcKPdAjlLUH41m69xSPfB/J76O74+VeoQb4FjB/xwlGTYss0EsvJjGNUdMief/O9rSu45ubyDuakH2f/fxkUnqhy83L28MlN7FXt6Yn9Wqdv69ToxrehZwcsdrsfLv2MDGJaYX2+TMBwb4edG7k3AdKEaF+/KdnKJ8uP8i4WdvpUL8mAd5XnmQXERGpENy8oN8EmPUArHgX2t4JPpc3Ul+kRHJ+/y99DY5vgeORYHGDkztK9fhg5qZjTP37EACThralcWDhbTBERAB6Nwvkto51+WXTMZ75ZRvzHu+Bh6tKfoqIiFQUDs0EjBkzhrvvvptOnTrRuXNnJk+eTEpKCiNHjgRgxIgR1KlTh4kTJwKwbt06oqOjadeuHdHR0bz88svYbDaeeeb8wdBTTz3FkCFDaNCgAcePH2f8+PFYLBaGDx/ukG2sdByR+HP3hmvHw68PG6Vu2t4B1QMv+Tarzc76qARik9MI9DYSL58sP8Db2aOzRvUO45kBzco36ZccA/PHws7Z+adb3ErtoB7AbDbx7tB2DJ6ykoOnUnhhzg4mDW1b/gnOUmK12Znw+65CE2s500Z/f+lueZ5uFurlSejlJPhyRvD5Vrv8q54tZhPjh4QzalokpjzxgJH0Axg/JLxUypGWtTH9mrJ87yn2xCQzduY2vri7U4X9mxEREblsrW+H9Z/BsQ2w5BW4+WNHRyRVRa9nIPMcrJpkPLdmlGrSb9uxM4ybvR2Ax65tQn9VdhCRYnjxunBW/HOKg3EpvLfoH8YNbuHokERERKSYHJr4GzZsGKdOneKll14iJiaGdu3aMX/+fIKCggA4cuQIZvP5nmtpaWm88MILHDx4kOrVqzN48GC+++47atSokTvPsWPHGD58OPHx8QQEBNC9e3fWrl1LQEBAeW9e5ZSYnfjzLcfEH0Db4bDhczi+2TgRc+MHF529sJKQ1d0tnE23AvBk36Y8dm3j8ktq2Kyw8Ssj9vQkMJmhTic4tt5I+lkzjKRmKSb/anm58f6d7bnjs7XM3hxN11A/hl5Vr9SWX57WRyXk25dFcbWYaODnZSTyChm5V9PTtUz2+cBWIXx8V4cCf3PBxSxD6izcXSxMvqMdN7y/miV7Yvlxw1GGd67v6LBERETKh8kEA9+EL66Brd/DVfdD3Y6OjkqqolK8KDDubDoPfreJjCwbfVsE8sS1TUpluSJS+fl6uvL6za25/9uNfL7yIANbBdO+fk1HhyUiIiLF4PDaf6NHjy6ytOeyZcvyPe/Vqxe7du266PJ+/PHH0gpNCpN03Lj3qV2+6zWbYdBb8GU/2DwNrroParcvdNaiSkLmJP1uaV+Hx/uW4wHviW3wxxMQvcl4Xru9cdv41fkreZe/db63Rykm/65qWIsx/ZoaDbl/20G7+jVoGlSxyvqcSDzHZysOFGved25ry43tyzkpnW1gqxD6hQcXGGVaEUb65dU82IdnBjbjv3N38+ofu+ga6kdDfy9HhyUiIlI+6nY0Ljjb+gPMfxbuW2QkBEXKmin7glezS6ldFJhptfHw9EhOJKYRGuDFpGHtyq+vuYhUCn3Dg7ipXW3mbDnOM79s44/HuuPuopKfIiIizs586VlEslmz4GyM8dinbvmvv15naD0UsMOfY8FesPDjxUpC5lhzMB6r7WJzlJL0s7Dgefist5H0c/M2kpdNB+ZP+oFx3+d5I/m3/K1SDWNUrzB6Ng0gLdM48E/NyCrV5ZeVvTHJ/N9PW+nx5lKW7j1VrPcE+niUcVQXZzGb6Brmx43t6tA1zK/CJf1y3Ht1I7qG+pGaYeXJn7aQZbU5OiQREZHyc+14cPUySn5u/9nR0UhVsPwtWPmOcTzwUnypHRe8Nnc366MSqO7uwmf/7oRPIf2qRUQuZfyQlvhXd2df7Fn+t2Sfo8MRERGRYlDiT4rvbAzYbWB2BS8HlU7t+zK4esLRtbBzVu7kM6kZbDp8mrcX7L1kScgTiWmsj0oo2zj3zIMPI2DNB2C3QviNMHo9RDxoJCwL69mRk/yzWUs1FLPZxKShbQn0dmd/7Fle+nVnqS6/NNntdtYdjOfeqRsYMHkFMyOPkWWzE9GoplGms4j3mYAQX2OEnVw5s9nEO0Pb4u3uwuYjZ/h4WfFGXIqIiFQKPiHQY4zxeNF4yEhxbDxSueVU/ijliwJ/3niUqX8fAuC9Ye1oHFi9lAIWkaqmppcb/72pJQCfLD/I9mOJDo5IRERELkWJPym+nP5+PiFG6c1ylpZpZe85H/5pcj8Ap38dyx0fLqH9Kwtp98oibv34bz5ZXrwERWzypfvFlUjiMfjxX/DjcEg6Br714c6fYei358uj9hlXdNmeXs8Yr5cy/+ru/G94e8wm+GXTMWZuOlbq67gSVpud+TtOcPNHfzPss7X8tScWkwkGtw5mziNXM+PBbky8pTVAgeRfzvPxQ8Ir7Ag7Z1SnRjVeyT64m7JkH9uOnXFsQCIiIuWp62ioUR+Sj8OqyY6OpsL78MMPadiwIR4eHkRERLB+/foi583MzOSVV14hLCwMDw8P2rZty/z58/PN8/LLL2MymfLdmjdvXtabUTZs1lK/KHDr0TM8P2cHAI9f24R+4UGlEamIVGEDW4VwXZsQrDY7T/+ylYwsVYURERFxZg7v8ScVSFJO4u98DzWrzV6qPc1sNjvHTp9jzxkTcWuPcCThHAfjUjh4KoXjieew28GdDixx96duZiwRx6ez1norYIz4quXlxs7jSZdcT6B3KZeEtGbB+s+Mq3Izzhq9Obo+Ar2eBTfn6I/WJdSPJ/o2ZdKif3hhzg7a1vOlcaBj+/2lZVqZFRnN5ysPEhVnXE3v5mLm9o51ub9HKI3y9JYb2CqEj+/qwITfd+Ub1Rns68H4IeEMbBVS7vFXdje1q8Pi3bHM3XaCJ2ZsYe6jPajmpn4OIiJSBbh6QP//wk8j4O//QYd/G4lAuWwzZsxgzJgxfPLJJ0RERDB58mQGDBjA3r17CQwMLDD/Cy+8wLRp0/j8889p3rw5CxYs4Oabb+bvv/+mffvzPb5btmzJ4sWLc5+7uFTQQ9uLXfRXgh5/p5LTeWjaJjKybPRtEcjj15Zjb3MRqdReuaElaw7EsycmmQ+X7ufJfk0dHZKIiIgUoYIeHYlDXJD4m7/jRIEkTEgxkzCnUzI4GHeWg6dSiMpO7EXFpRAVn5J95ZgFdu8p8D5vDxdCA2qw2G0090S/zGMec7lu2FPUbdgUTzcXrDY73d/8i5jEtEL7/JkwEkWlWhIyOhJ+fxxithnP63aGIZMhqGXpraOUPNKnMeui4lm9P55Hpm9mziNXOySRk5iaybR1h/l69SHizqYD4FvNlRFdGzCia0MCvN0Lfd/AViH0Cw8u1WSzFM1kMvHaTa3YEJXAwVMpvPHnbibc2MrRYYmIiJSPFjdAwx5waCUseglun+roiCqkSZMm8cADDzBy5EgAPvnkE+bOnctXX33F2LFjC8z/3Xff8fzzzzN48GAARo0axeLFi3n33XeZNm1a7nwuLi4EBweXz0ZUEJlWG498H8mJxDRCA7yYNKwdZv1OFpFS4lfdnQk3tOTRHzbz4dL9DGgZTHhtH0eHJSIiIoVQ4k+KL+m4ce9Tm/k7TjBqWmSB5FpMYhqjpkXy8V0d6N0skEPxKfmSewfjzhIVl8KZ1MwiV+NqMeHnZqN1wyBCA6sT5l+dRgFeNPL3ws/LDZPJBPZuMHURlsOrabrtHWj6FQAWs4nxQ8IZNS0SE+SLr9RLQqYlwV//hQ2fG70PPXyh7wTocLdDSqEWh8VsYvKw9gyaspK9J5OZ8PtO3ri1TbmtP/rMOb5aFcUP64+QmmGULapToxr3dW/EsKvq4eV+6X+SLGYTXcP8yjpUyVbD0423b2/L3V+t55s1h7mmRRC9mjqox6eIiEh5Mplg4ET4tCfsnA2d/wMNujk6qgolIyODTZs2MW7c+VFtZrOZvn37smbNmkLfk56ejodH/uoc1apVY9WqVfmm7du3j9q1a+Ph4UHXrl2ZOHEi9esXPSozPT2d9PT03OdJSUaVkMzMTDIziz42qUhe+WM366MS8HK38NHwdlSz4JTblhOTM8ZWlWg/OIeKth8GtPCnX4tAFu2O5amft/DLgxG4Wpzz/MflqGj7oTLSPnAO2g/OQfvBOTjjfricWJT4k+JLNPrC2XzqMOH3XYWOqMuZ9sj0SKyFzZBHbV8PQgOq08jfSOqFBngR6l+dwOouLJj/J4MHt8PV1bXwN5tMMPAN40TMjplw1QPQoCtQDiUh7XbY9SvMHwvJJ4xprW+HAa9D9YLlipxNgLc7U+5ox11fruPHDUfpGubHje3qXPqNV2BPTBKfLT/Ib1uPk2Uz/jCaB3vzUK8wrmsTUikOFCqzXk0DuLtrA75Zc5inf97Kgid6UtPLzdFhiYiIlL3g1sZFXZu+hj+fhf8sA7PKXhdXXFwcVquVoKD8PeaCgoLYs6dgdQ+AAQMGMGnSJHr27ElYWBhLlixh1qxZWK3ne91FREQwdepUmjVrxokTJ5gwYQI9evRgx44deHsXXsp+4sSJTJgwocD0hQsX4unpeQVb6RzWxZr4/oDxtzm8YQZ7Nyxnr4NjupRFixY5OgRB+8FZVKT90NMTVrtY2HUimae/XED/upc4+VOBVKT9UFlpHzgH7QfnoP3gHJxpP6SmphZ7XiX+pPiyR/ztO+eTL6FWmJykn4+HC6EB1bOTel408jceN/TzKrLEZLEz1yFtoMMIiPwG5j8LDyzLHWlXZiUhTx+GeU/DvgXG81qhcN27EHbNlS23nF3d2J9Hr2nC/5bs47lZ22ldx5fQgOqlug673c6ag/F8tuIgy/aeyp3eLcyPB3uF0bOJvzF6UyqEsYNasGp/HAdOpfD8nO18eGcH7T8REakarnkBdswyyrpvmW78/pQyM2XKFB544AGaN2+OyWQiLCyMkSNH8tVXX+XOM2jQoNzHbdq0ISIiggYNGvDTTz9x3333FbrccePGMWbMmNznSUlJ1KtXj/79++PjU7FL1W07lsjTX24AbDzaJ5THrmns6JAuKjMzk0WLFtGvX7+iL/SUMqf94Bwq6n6o1vA4T83cwcLjLjxyY1eaBJXu+YTyVlH3Q2WifeActB+cg/aDc3DG/ZBTtaQ4lPiT4stO/MXgB2RdcvbXb27F8M71yzY5cM2LRumlE1uzT8T8O/elUi0Jac2ENR/C8jchMxXMrtD9SegxBlyrlc46ytnj1zZhfVQ8aw8m8Mj3m5n9cDc8XK/8Cnarzc78HTF8uuIA244lAmA2waDWITzYM5Q2dWtc8Tqk/FVzs/DesHbc8tHfzNsew5wt0dzcvq6jwxIRESl7Xv7Q+1lY8BwseQXCbwJLxfz9V978/f2xWCycPHky3/STJ08W2Z8vICCAOXPmkJaWRnx8PLVr12bs2LGEhoYWuZ4aNWrQtGlT9u/fX+Q87u7uuLsX7CPt6urqNAfyJXEqOZ1HfthKRpaNvi2CeLJf8wrT16+if/aVhfaDc6ho++HWTvX5c2csS/bEMm7OTmaO6oZLJajkU9H2Q2WkfeActB+cg/aDc3Cm/XA5cVT8/5WlfFiz4GwMAF4BDYr1lkb+1ct+RFD1AOj1rPF4yQSj715pO7oePu0Fi8cbSb8G3WHUarjm+Qqb9AMjMTrljvb4ebmx+0QSr/6x64qWl5Zp5bu1h7nm3WU88n0k244l4u5i5t9dGrD0qd58eGcHJf0quDZ1a/D4tU0AeGnOTqLPnHNwRCIiIuXkqgfArzGknIIVbzs6mgrDzc2Njh07smTJktxpNpuNJUuW0LVr14u+18PDgzp16pCVlcXMmTO58cYbi5z37NmzHDhwgJCQKyzpX8FkZNl4ZHokMUlphAZ48d6wthUm6SciFZvJZOK1m1vj7eHC1mOJfLEqytEhiYiISB5K/EnxnI0Buw3MrrRv0YRA74JXy+YwASG+RmnNctH5P2VzIubcafj9CfiyP8TuhGq14MaP4J4/IKBZ6a3HgYJ8PHhvWDtMJpi+7gh/bDt+2cs4nZLB/5bs4+o3/uLFOTs4HJ9KDU9XHr+2CX+PvYZXb2pFAz+vMoheHGFU7zDa169BcnoW//fTFmy2ytPPQUREpEgubjBgovF47ceQcMCx8VQgY8aM4fPPP+ebb75h9+7djBo1ipSUFEaOHAnAiBEjGDduXO7869atY9asWRw8eJCVK1cycOBAbDYbzzzzTO48Tz31FMuXL+fQoUP8/fff3HzzzVgsFoYPH17u2+dI/527i/WHEqju7sJn/+6Et4dzXIksIlVDsK8HL14XDsCkRf+wP/asgyMSERGRHEr8SfEkRhv3PiHYMFHdo/AqsTnXl44fEn7l/fSK68ITMfFXeCLGboftv8AHV8GmrwE7tLsLRm+E9v+CStbXrGfTAB7uHQbA2JnbORSXUqz3HU1I5eXfdtLtjb+YtOgf4lMyqFuzGhNuaMnfY6/hyX5N8atedIJYKiYXi5n3hrbD083C2oMJfKkrO0VEpKqI3mT0d7ZlYlk8Pv9ry9+CpRMdE5eTGzZsGO+88w4vvfQS7dq1Y8uWLcyfP5+goCAAjhw5wokTJ3LnT0tL44UXXiA8PJybb76ZOnXqsGrVKmrUqJE7z7Fjxxg+fDjNmjVj6NCh+Pn5sXbtWgICAsp78xzmp41H+XbNYQDeG9aOxoEVu7+WiFRMt3eqS8+mAWRk2Xjml61YdWGoiIiIU1CPPymepJzEX13eWbCXg6dS8HA14+3uyqmz6bmzBft6MH5IOANblXOZnab9oXE/2L8IFr4Aw38o2XLiD8Dc/4ODS43n/k3h+vegYffSi9UJPdm3KRuiTrP+UAKjf4jkpwe7EnkogU1xJvyiEujaODA3kbvzeCKfrTjIH9tO5P6ob1nbhwd7hTG4VXClqOsvF9fQ34sXrw9n3KztvL1gLz2a+tM82MfRYYmIiJQtswUSDoLJjHnffALC2gCDs5N+r0Gf5x0dodMaPXo0o0ePLvS1ZcuW5Xveq1cvdu26eAn6H3/8sbRCq5C2HD3DC7N3APBE3yb0Cw9ycEQiUlWZTCYm3tKaAe+tIPLIGb5eHcX9PYruySoiIiLlQ4k/KZ7sxF8Mtfh0xUEAJg9rT7/wINZHJRCbnEagt1Hes9xG+l1owOtGwm7vPNi/BBpfW/z3ZmXA31Ng+dtgTQeLO/R8Gq5+DFwq/6g1F4uZKcPbMXjKSnZEJ9Hpv4tJzbACFr7dt5FgXw/u6FSPTUdOs3JfXO77ejTx58GeYVzd2K/s+zmKU7njqnos3nWSJXtieeLHLfw6+mrcXSyODktERKTs9MouNbn0NQBaRU/HvAJY+ZaR9Ov1TNHvFSklsclpPPTdJjKsNvqFB/HYNU0cHZKIVHF1alRj3ODmPD97B+8s3EvfFkE09Fe7DxEREUfS0BwpniSj99ufR4wT+yOvbsjAVsFYzCa6hvlxY7s6dA3zc1zSDyCgqdHvD2DBc2DNLN77Dq2GT7rDX/81kn6hfeDhNdDr6SqR9MsR4luNOyPqA2Qn/c6LSUxj8pJ9rNwXh9kEN7StzR+Pdue7+yLo3sRfSb8qyGQy8catbfDzcmNPTDKTFv7j6JBERETKXq9noPsYAHzSorEo6SflKCPLxiPTI4lJSiMswItJQ9tiduTxl4hItjs716dbmB9pmTaemblNveBFREQcTIk/KRbbmWMAHMqsQdu6vowb1MLBERWh1zPg6Qen9sDGry4+b2oCzHkEpg6GuL3gFQC3fgn/ng1+YeUTrxOx2uzMioy+6Dyebhb++r/e/G94e1rV8S2nyMRZBXi7M/GW1gB8tvIgaw/GOzgiERGRctB3PHaTcTGc3eyipJ+Um1f/2MWGQ6fxdnfhsxGd8PZwdXRIIiKAcWHom7e2wdPNwvqoBL5be9jRIYmIiFRpSvxJsRw/egCARNcAPrizA24uTvqnU60mXPOC8Xjpa5BSSCLCboct38MHnWDLNGNax5EwegO0vg2q6Oi19VEJnEhMu+g8qRnWS84jVUv/lsEM61QPux3+76etJKUVc6StiIhIRbX8LUx2K1aTCyZbltHjT6SM/bThaO6J9PeGtSMsoLqDIxIRya9eLU+eHdgcgDfn7+FoQqqDIxIREam6nDR7I85k4c4YXM4apT7vuLYL9Wp5OjiiS0g6AV6BkJYIy17P/9q8Z2ByG5gzClLjIbAl3LcIhkw2koZVWGxy8RJ6xZ1Pqo4Xh4RTr1Y1os+cY8JvuxwdjoiISNlZ/hYsfQ1rz7H80e4rrD3HGhebKfknZWjL0TO8MGcHAE/2bUrf8CAHRyQiUrh/d2lA50a1SM2w8uzMbdjtKvkpIiLiCEr8yUUdTUhl7M+bCOQMAF3atXFsQMVhcYWUWOPxxq/g5E7ITIOvr4P1n0LiEXCpBv1egQeXQ73Ojo3XSQR6e5TqfFJ1VHd34b2h7TCbYGbkMf7cfsLRIYmIiJS+7KQffZ7H1uMpAOO+z/NK/kmZiU1O46HvNpFhtdE/PIhHr2ns6JBERIpkNpt469Y2eLia+ftAPN+vP+LokERERKokJf6kSBlZNkb/sBmPtDjMJjt2s6vRB8/Z9XrGOAEDYLfBnIdhUnM4vMqY1mQAPLIOrn7cSBIKAJ0b1SLE14OiCp2agBBfDzo3qlWeYUkF0alhLUb1Nnpjjpu9ndgkjQwVEZFKxmY1fmNe2NMv57enzeqYuKTSysiy8cj0SGKS0ggL8OLdoW0xm6tmWwIRqTga+nvxVP9mAEyct4foM+ccHJGIiEjVo8SfFOmNP/ew9egZGnskAWDyCQFzBfmT6fUMdHnYeHxiC5w7DW7VYei3cOcMqNnAoeE5I4vZxPgh4QAFkn85z8cPCceikw1ShMevbUrL2j6cSc3k6V9U1kVERCqZPuMKJv1y9HrGeF2kFL36xy42HDqNt7sLn43ohLeHLloUkYph5NWN6FC/BmfTsxg7cxtZVhtrDsTz65Zo1hyIx2rTsaKIiEhZqiBZHClv83fE8NXqKACe7uJlTPSp68CISmDgRDBZjMcmM4zZDeE3gkmJq6IMbBXCx3d1INg3fznPYF8PPr6rAwNbhTgoMqkI3FzMTB7WDjcXM8v/OcW0dSrrIiIiIlISP204yndrD2MyweQ72hEWUN3RIYmIFJvFbOKt29ri5mJm5b44Ov53McM/X8vjP25h+Odr6f7mX8zfoRYRIiIiZUWJPyngSHwqT/+yFYD/9AyltU+K8YJPbQdGVQLL3wK7FSxuRsnPdZ84OqIKYWCrEFY9ew3T7u3EiCZWpt3biVXPXqOknxRLkyBvxg5sDsBrc3dx4NRZB0ckIiIi4vysNnvuaJjv1hzi+dnbAXiyb1OubRHk4OhERC5f48DqXNfaOI+QeC4z32sxiWmMmhap5J+IiEgZcXF0AOJc0rOsPPJ9JMlpWXRsUJOnBzSDhd8YL/rWcWxwl2P5W7D0tfN9WHKeQ9ElmiSXxWwiolEt4nfbiWhUS+U95bLc060hf+2JZdX+OMbM2MIvo7rhatF1JiIiIiKFmb/jBBN+38WJxPw9ktvW9WV0n8YOikpE5MrkXNBQGDtGS5EJv++iX3iwzjmIiIiUMp2JlXwmztvD9uhEani68v7w9sbJ+qRo40WfCpL4uzDpB9l9V543pi9/y7HxiVRyZrOJt29vg4+HC1uPJfLBX/sdHZKIiIiIU5q/4wSjpkUWSPoBbDuWyMJdMQ6ISkTkyq2PSiAmqeC/bTnswInENNZHJZRfUCIiIlWEEn+Sa972E0z9+xAA7w1tR+0a1YwXKlriz2bNn/TLkZP8s1kdE5dIFRLiW43/3twagA+W7mfzkdMOjkhERETEuVhtdib8vgv7ReaZ8PsurLaLzSEi4pxik4tO+pVkPhERESk+Jf4EgMPxKTz7yzYAHuoVRp/mgedfTDpu3FeUHn99xhVdzrPXM8brIlLmbmhbmxva1sZqs/PkjC2kZmQ5OiQRERERp7E+KqHQkX45NBpGRCqyQG+PUp1PREREik+JPyEt08rD0yNJTs+iU4OaPNW/6fkXrZmQnF1exreuYwIUkQrr1RtbEeLrwaH4VF6bu9vR4YiIiIg4DY2GEZHKrHOjWoT4enCx7n0hvh50blSr3GISERGpKpT4E16bu5udx5Oo5eXG+3e2x8WS588iOQawg9kVPP0dFqOIVEy+nq68c3tbAKavO8LSPbEOjkhERETEOWg0jIhUZhazifFDwgGKTP61quOL+WKZQRERESkRJf6quN+3Hue7tYcBmDS0LSG+1fLPkNvfrzaY9eciIpfv6sb+3Ht1IwCe/mUb8WfTHRyRiIiIiONdajSMCY2GEZGKbWCrED6+qwPBvvkvYPDxcAFg0a6TjP9tJzb1MhURESlVLo4OQBwnKi6FcbO2A/BInzB6NwssOFNu4q9OOUYmIpXNMwObsXLfKfbFnmXcrO18+u+OmEy6tFNERESqrpzRMKOmRWLC6OmXI+dX0vgh4Vg0HEZEKrCBrULoFx7M+qgEYpPTCPQ2LmiYseEoz8/ZzrdrDpOaYeWNW1rnr0AlIiIiJab/UauotEwrj0yP5Gx6Fp0b1eLJvk0LnzExO/Hnq8SfiJSch6uF94a1w9ViYuGuk/yy6ZijQxIRERFxuKJGwwT7evDxXR0Y2CrEQZGJiJQei9lE1zA/bmxXh65hfljMJu6MqM+koW2xmE38sukYj/+4hYwsm6NDFRERqRQ04q+KeuWPXew6kYSflxvvD29f9FVVSceNe5/a5ReciFRKrer48mS/prw1fy8Tft9Fl1A/6tXydHRYIiIiIg5V1GgYjfQTkcru5vZ1qebqwqM/RDJ3+wnSMq18+K8OeLhaHB2aiIhIhaYRf1XQr1ui+X7dEUwmeG9YO4J8LtIsPrfUZ93yCU5EKrUHe4bRqUFNzqZnMeanLVjVy0FERESk0NEwIiJVwcBWwXw+ohPuLmaW7Inl3qkbSEnPcnRYIiIiFZoSf1XMgVNneS67r9/oPo3p2TTg4m/ITfxpxJ+IXDmL2cR7w9rh5WZhw6HTfLbioKNDEhEREREREQfq3SyQb+7tjJebhb8PxDPiq/Uknst0dFgiIiIVlhJ/VUhOX7+UDCtdQmvxRFF9/fLKKfWpHn8iUkrq1fJk/A0tAZi0aC/bjp1hzYF4ft0SzZoD8RoFKCIiIiIiUsV0CfVj2v0R+Hi4sOnwaf71xVoSUjIcHZaIiEiFpB5/VciE33eyJyYZ/+ru/O+O9pcuH2PNhOQY47GPEn8iUnpu71iXxbtOsnDXSW756G+y8iT7Qnw9GD8knIGtQhwYoYiIiIiIiJSn9vVr8uN/uvLvL9exIzqJYZ+uYfr9EQRerEWNiIiIFKARf1XEnM3R/LD+KCYTTLmjXfF+NCXHAHawuIGnf5nHKCJVh8lkol94EEC+pB9ATGIao6ZFMn/HCUeEJiIiIiIiIg4SXtuHGQ92JcjHnX2xZ7n90zUcO53q6LBEREQqFCX+qoD9sWd5brbR1++xa5pwdeNiJvFy+vt5h4BZfyoiUnqsNjuTFv1T6Gs5acAJv+9S2U8REREREZEqpnFgdX5+sBt1a1bjcHwqQz9ZQ1RciqPDEhERqTCUzankzmUYff1SM6xc3diPx65tUvw35yT+VOZTRErZ+qgETiSmFfm6HTiRmMb6qITyC0pEREREREScQn0/T35+qCuhAV4cT0zj9k/WsDcm2dFhiYiIVAhK/FVy43/bwd6TyQR4uzN5WDH6+uWVmJ3481XiT0RKV2xy0Um/kswnIiIiIiIilUuIbzVm/KcrzYO9iTubzh2frWH7sURHhyUiIuL0lPirxGZuOsZPG49hzu7rF+DtfnkLSDpu3PvULv3gRKRKC/QuXnP24s4nIiIiIiIilU+Atzs//qcLbevV4HRqJnd+vpaNh1QZRkRE5GKU+Kuk9p1M5oU5OwB4om9TuoUVs69fXknHjHufuqUYmYgIdG5UixBfDy42BjnE14POjWqVW0wiIiIiIiLifGp4ujHtvs50blSL5PQs/v3lelbvj3N0WCIiIk5Lib9KKDUji4enR3Iu00qPJv480qdxyRakEX8iUkYsZhPjh4QDFJn8G9ap3uWVJxYREREREZFKydvDlW9GdqZn0wDOZVoZOXUDi3eddHRYIiIiTkmJv0roxTk72Rd7lkBvd94b1q7kJ87V409EytDAViF8fFcHgn3zl/Os5moB4Ou/DxEVl+KI0ERERERERMTJVHOz8PmIjvQPDyIjy8ZD0zbxx7bjjg5LRETE6bg4OgApXT9tPMrMSKOv3/+Gt8e/+mX29cthzYSz2VdO+SjxJyJlY2CrEPqFB7M+KoHY5DQCvT1oU9eXu75cx+YjZ7j/mw3MfuRqfDxcHR2qiIiIiIiIOJi7i4UP/9WBp37eyq9bjvPYD5tJzbAytFM9R4cmIiLiNDTirxLZG5PMS78aff3+r38zuoT6lXxhyScAO1jcwPP/2bvzuCrL/P/j73MOywEFFJBFRUFcEZfUJJeszN1o3zRbnLJvTk5OzvxmsizGZiZna51psmnaJi1tmzbLMh0zcyF3cUdQBFkEEhBkP78/DpAmKsuB+5zD6/l4OAdu7nOd9+EatJvPfX2uJuwPCAANZDGbNCI6SNcN7qIR0UFq5+2hl2cMVZi/VYdPFOuhd7arqtpmdEwAAAAAgBPwtJj1zK2DNW14hKpt0m/e36X/bDxidCwAAJwGhT83UVxWqZ8v3arSimqN6d1Js6+Ibt6Atfv7+YVLZv5vAqB1hfhb9cpdw2T1NGvtgRP688r9RkcCAAAAADgJi9mkp24YoJmjIiVJT3y8Ry+tPWxsKAAAnAQVHTdgs9m04KMkHT5RrDB/q569dZDMTd3Xr1ZBuv0xoGvzAwJAEwzoGqC/3jxIkvSvdSl6f2u6wYkAAAAAAM7CZDLpiWti9IuxPSVJf165X09/dUA2Gx1jAABtG4U/N/DulmP67/YMWcwm/X36JQpq6r5+Z6pd8effufljAUATxQ/qXHcR9+iHu7X16A8GJwIAAAAAOAuTyaRfTeij30zqI0n6+5pk/WHFPop/AIA2jcKfi9uXWagnPt4jSfrVhN66NDLQMQMXZtgf/bs4ZjwAaKKHx/XWxP6hKq+q1v+9tVXHT542OhIAAAAAwIn8/MqeWnhtf0nSq+tT9eh/d7NXPACgzaLw58JOlVXqwaXbVFZZrav6dNIDY5q5r9+ZKPwBcBJms0nP3DpYfcP8lHuqTPe/tUWny6uMjgUAAAAAcCJ3j4zUX24eKLNJeifxmH717g5VVlUbHQsAgFZH4c9F2Ww2PfrhbqXkFis8wKqnbx3c/H39zlRQU/gLoPAHwHjtvD30yl3DFNjOS0kZhfr1+ztp3QIAAAAAOMutwyL0/O2XyMNs0kc7juvBt7eprJIbRwEAbQuFPxf1TuIxfbLzuCxmk/4x/RIFtvNy7Auwxx8AJxMR6KvFM4bK02LSil2Z+vuaZKMjAQAAAACcTPygzlo8Y6i8LGZ9uSdb9/9nK11jAABtCoU/F7TneIF+96l9X7/fTOyjod0dtK9frcpy6VS2/WP/ro4dGwCaYXhUoH5/Xawk6ZlVB7UyKdPgRAAAAAAAZzMuJlSv3XOpfDwt+ubgCd3zeqJOlVUaHQsAgFZB4c8FVFXbtPFwnj7ekaE1+7L18yVbVV5Zrav7hmjW5T0c/4KnsiTZJIuX5Bvk+PEBoBluH95N94yMlCQ9vHyn9h4vNDYQAAAAAMDpjO4VrP/cO1x+3h7anJqvO/69WQUlFUbHAgCgxVH4c3IrkzI1+s9rNO2VTZq7bId+9uYWHc0/rUBfLz196yDH7utXq3Z/P//Okpn/iwBwPgum9tPlvYJ1uqJKs/6zRbmnyoyOBAAAAABwMpdGBmrprDh18PXUzmMndfsrm7h+BAC4Pao6TmxlUqZmL9mmzILSc76WX1KuTSl5LfPChbWFvy4tMz4ANJOHxax/TBuiqOB2yjh5WrNrVkIDAAAAAHCmgV07aPn9IxTc3lv7Mgt168sblVXP79oAAHAXFP6cVFW1TQs/3Svbeb5ukrTw072qqj7fGc1QeNz+SOEPgBML8PXUK3cNk5/VQ98f+UGPf5Qkm60F/k4EAAAAALi0PmF+eu+BEeocYFXKiWLd8vIGHcsvUVW1TZtT87U116TNqfkt83s2AABaGYU/J5WYml/vSr9aNkmZBaVKTM13/IsXntHqEwCcWM+Q9vr7tEtkNknLtxzT698dMToSAAAAAMAJRQW307sPjFD3IF8dyz+t+L+vV9xTX2vGa1v0n0MWzXhti0b/eY1WJmUaHRUAgGah8Oekcooa1nKgoec1Sm3hL6Cr48cGAAe7sk+IHp3ST5L0hxV7te7gCYMTAQAAAACcUdeOvnrv/0Yo3N+qk6crlHuq/KyvZxWUavaSbRT/AAAujcKfkwrxszr0vEYpYMUfANdy7+go3Ty0q6pt0py3tynlxCmjIwEAAAAAnFBQe29VnWebiNqjLba9DgAArYDCn5MaHhWo8ACrTOf5uklSeIBVw6MCHf/i7PEHwMWYTCb98YZYDenWQYWllbrvzS0qOF1hdCwAAAAAgJNJTM1XTlHZeb/eotvrAADQCij8OSmL2aSE+BhJOqf4V/t5QnyMLObzlQabqLJcOpVt/5jCHwAX4u1h0eI7hyo8wKqU3GL94p3tqqyqNjoWAAAAAMCJGLq9DgAArcDwwt+LL76oyMhIWa1WxcXFKTEx8bznVlRU6Mknn1R0dLSsVqsGDRqklStXNmtMZzYpNlwvzRiisICz23mGBVj10owhmhQb7vgXPZUlySZZvCTfIMePDwAtKMTPqlfuGiarp1nrDp7Qoi/2Gx0JAAAAAOBEGrptjr/Vs4WTAADQMgwt/C1fvlzz5s1TQkKCtm3bpkGDBmnixInKycmp9/wFCxbo5Zdf1t///nft3btXDzzwgG644QZt3769yWM6u0mx4Vr/27F6Z9Zlev72wXpn1mVa/9uxLVP0k87e389seF0YABottkuAnr5lsCTp1fWpevf7Y8YGAgAAAAA4jYttr1Pr8Y93a0NybqtkAgDAkQyt7DzzzDOaNWuWZs6cqZiYGC1evFi+vr567bXX6j3/rbfe0qOPPqopU6aoR48emj17tqZMmaKnn366yWO6AovZpBHRQbpucBeNiA5yfHvPMxXWFv5o8wnAdU0dGK6Hru4lSXrso93acoS9GQAAAAAADdteJ9DXS+k/lGr6vzdr/oe7VVjKHvIAANfhYdQLl5eXa+vWrZo/f37dMbPZrHHjxmnjxo31PqesrExW69nL8X18fLR+/fomj1k7blnZj5v6FhYWSrK3Fq2oaFv/sJt/SJNFUrVfuKoMeu+13/O29r13NsyD8ZiD5nlwTKQOZBboy705+r+3turDB+LUuYNPo8dhHpwD8+AcmAfnwDwYz1nnwNnyAADgrGq311n46V5lFvy4l19YgFUJ8TEa3auT/vTFPi3ZlKZ3EtO09kCOnrpxgK7qE2JgagAAGsawwl9ubq6qqqoUGhp61vHQ0FDt31//nkwTJ07UM888ozFjxig6OlqrV6/Whx9+qKqqqiaPKUmLFi3SwoULzzn+1VdfydfXt7FvzaXFpm9UtKTDOae19/PPDc2yatUqQ18fdsyD8ZiDpru6vZTka1FGcbmmv7ROc2Or5G1p2ljMg3NgHpwD8+AcmAfjOdsclJSUGB0BAACXMSk2XONjwrQxOUdffbtZEy6P04ieIXWdtv5w/QBNHdBZv/1gl9LySzTz9e9105CueuKaGAX4sv8fAMB5GVb4a4rnn39es2bNUt++fWUymRQdHa2ZM2c2u43n/PnzNW/evLrPCwsLFRERoQkTJsjf37+5sV2K5f3l0gmpxyVjFDlsiiEZKioqtGrVKo0fP16envyHlFGYB+MxB44x/PLTunHxJmUUV2j1qc564bZBMjeiZTLz4ByYB+fAPDgH5sF4zjoHtZ1LAABAw1jMJsVFBSpvn01xUYHnbK8zIjpIK395uf725UG9viFVH2xL17pDJ/TH62M1oX+YQakBALgwwwp/wcHBslgsys7OPut4dna2wsLq/4ezU6dO+uijj1RaWqq8vDx17txZjzzyiHr06NHkMSXJ29tb3t7e5xz39PR0qgv5VlGUKUmydIyQxeD33ia//06IeTAec9A8kZ089a87h2naK5v05d4c/XPdET08vnejx2EenAPz4ByYB+fAPBjP2ebAmbIAAOAufL089ER8jKYODNP/e3+XUk4U6/63tip+UGctvLa/Att5GR0RAICzmI16YS8vLw0dOlSrV6+uO1ZdXa3Vq1drxIgRF3yu1WpVly5dVFlZqQ8++EDXXXdds8dEjcIM+6N/F2NzAIADDYsM1B+vHyBJen71Ia3YlWlwIgAAAACAKxnaPVCfP3S5HrgiWmaT9OnO4xr/zDf6bNdx2Ww2o+MBAFDHsMKfJM2bN0+vvPKK3nzzTe3bt0+zZ89WcXGxZs6cKUm66667NH/+/LrzN2/erA8//FApKSn69ttvNWnSJFVXV+s3v/lNg8fEBVSWS6dy7B9T+APgZm69NEI/GxUlSfrVezuUlFFgcCIAAAAAgCuxelr0yOS++u/PR6lPqJ/yiss15+3temDJVuUUlRodDwAASQbv8XfbbbfpxIkTeuKJJ5SVlaXBgwdr5cqVCg0NlSSlpaXJbP6xNllaWqoFCxYoJSVF7du315QpU/TWW2+pQ4cODR4TF1CUKckmWbykdsFGpwEAh3t0Sl8lnzildQdP6P7/bNHHc0ark9+5rZ4BAAAAADifQREd9MkvRunF/x3WP/+XrC/3ZGtTSr4S4mN0wyVdZDI1fF95V1RVbVNiar5yikoV4mfV8Hr2RwQAGMfQwp8kzZkzR3PmzKn3a2vXrj3r8yuuuEJ79+5t1pi4gMLj9kf/zpKb/wcKgLbJw2LW36ddohte/E4pucX6v7e26J37L5O3h8XoaAAAAAAAF+LtYdG88b01qX+Y/t/7O7XneKHmvbtTn+3K1B9viFV4gI/REVvEyqRMLfx0rzILflzhGB5gVUJ8jCbFhhuYDABQy9BWn3Aydfv7dTU2BwC0oAAfT71y9zD5WT20Le2kHvtvEvsxAAAAAACaJKazvz56cJT+38Q+8rKYtWZ/jiY8s07LEtPc7lpzZVKmZi/ZdlbRT5KyCko1e8k2rUzKNCgZAOBMFP7wo7rCX2djcwBAC4vu1F4vTh8is0l6f2u6Xl2fanQkAADc1osvvqjIyEhZrVbFxcUpMTHxvOdWVFToySefVHR0tKxWqwYNGqSVK1c2a0wAAFqap8WsB6/qqRUPjdbgiA4qKqvUIx/u1p2vJupYfonR8RyiqtqmhZ/uVX2lzNpjCz/dq6pq9yp2AoArovCHHxXUFP4CuhibAwBawZjenfTY1BhJ0lOf79P/DuQYnAgAAPezfPlyzZs3TwkJCdq2bZsGDRqkiRMnKien/n93FyxYoJdffll///vftXfvXj3wwAO64YYbtH379iaPCQBAa+kV6qcPZo/UY1P6ydvDrPXJuZr43Dr9Z+MRVbt4QSwxNe+clX5nsknKLChVYmp+64UCANSLwh9+VLfij8IfgLbhZ6Mideuwrqq2SQ+9vV3JOaeMjgQAgFt55plnNGvWLM2cOVMxMTFavHixfH199dprr9V7/ltvvaVHH31UU6ZMUY8ePTR79mxNmTJFTz/9dJPHBACgNVnMJs0a00MrfzlGwyMDVVJepSc+3qPbX9mkI7nFRsdrlGP5JVr+fZoeeme77v/P1gY9J6fo/MVBAEDr8DA6AJwIhT8AbYzJZNLvr49VyolibTn6g2b9Z4s++vkoBfh6Gh0NAACXV15erq1bt2r+/Pl1x8xms8aNG6eNGzfW+5yysjJZrdazjvn4+Gj9+vVNHrN23LKysrrPCwsLJdlbi1ZUVDT+zaHJar/ffN+NxTw4B+bBObTUPHQN8NJbM4dqaeIx/W3VISWm5mvS8+s0b1wv3XVZN1nMJoe+niPkFZdrU0q+NqbkacPhfB374XSjxwjy9Wj095KfBefAPDgH5sE5OOM8NCYLhT/8qPC4/ZE9/gC0Id4eFi2+c6iu+8d3Ss0t1px3tun1ey6Vh4VF8QAANEdubq6qqqoUGhp61vHQ0FDt37+/3udMnDhRzzzzjMaMGaPo6GitXr1aH374oaqqqpo8piQtWrRICxcuPOf4V199JV9f38a+NTjAqlWrjI4AMQ/OgnlwDi01D0GSft1fWpZi1sEC6akvDujtb/drWnSVwgz+J6i0SjpcaNLBAvuf4yVnFyPNsqlbe6l3gE09/W1aetisgnJJqq9oaVMHL+nE3k36fF/T8vCz4ByYB+fAPDgHZ5qHkpKG7xlL4Q92leXSqZo9MQK6GpsFAFpZcHtv/euuobr5pY369lCu/vj5PiXE9zc6FgAAbc7zzz+vWbNmqW/fvjKZTIqOjtbMmTOb3cZz/vz5mjdvXt3nhYWFioiI0IQJE+Tv79/c2GiEiooKrVq1SuPHj5enJ10WjMI8OAfmwTm01jzMsNn07tYMLVp5QEdOVelvSZ566Kpo3Tc6stVuPC2rrNaOYye1MSVfG1PytSu9QJU/2XuwT2h7jegRqBHRQbq0e0f5WX/89XHMnmz9YtlOSfY9/c5kkkl/uHGQJvYPVWPxs+AcmAfnwDw4B2ech9quJQ1B4Q92RZmSbJLFW/INMjoNALS6/p0D9Oxtg/TAkm16/bsj6hvmp9su7WZ0LAAAXFZwcLAsFouys7PPOp6dna2wsLB6n9OpUyd99NFHKi0tVV5enjp37qxHHnlEPXr0aPKYkuTt7S1vb+9zjnt6ejrNhXxbw/feOTAPzoF5cA6tMQ8zRkRpbL8wPfrf3Vp74ISe/jpZX+07ob/cPFD9wh1/I0p1tU17Mwv1XXKuvjucp+9T83W6ouqscyICfTQqOlgjewZrZHSQgtuf++9lrWsGd5WHh0ULP92rzIKz9/K7f0wPXTO4eYsJ+FlwDsyDc2AenIMzzUNjclD4g92ZbT5NztdjHABaw6TYcD08rree/fqgFnyUpO5B7VRZWamtuSYFpeZrRM8Qp9yHAQAAZ+Tl5aWhQ4dq9erVuv766yVJ1dXVWr16tebMmXPB51qtVnXp0kUVFRX64IMPdOuttzZ7TAAAnEHnDj56/Z5L9eG2DC38dI92ZxTo2n+s14NX9dTPr+wpLw/76r+qapsSU/OVU1SqED+rhkcFXvR61GazKTW3WN8dztOG5FxtTMnTyZKz94QKbu+lEdHBGhUdpFE9gxUR2Lh+o5NiwzU+Jqwu2//25+ijHcf17aFcPWKzycTvFQHAcBT+YFeYYX/072JsDgAw2ENX99TB7CKt2J2p6a9skr3riUX/ObRF4QFWJcTHaFJsuNExAQBwCfPmzdPdd9+tYcOGafjw4XruuedUXFysmTNnSpLuuusudenSRYsWLZIkbd68WRkZGRo8eLAyMjL0u9/9TtXV1frNb37T4DEBAHB2JpNJNw3tqst7BWvBR0n6am+2nvv6kFYmZemvNw9SxsmSc1bVne96NLuw1L6iLzlPGw7nnrMSr723h+KiAjWyZ7BG9QxSn1C/ZhfnLGaTRkTbO4aN6dVJq/Zma29mob7el6PxMY1v9QkAcCwKf7CrLfwFUPgD0LaZTCZN6B+qFbsz9ZOtDpRVUKrZS7bppRlDKP4BANAAt912m06cOKEnnnhCWVlZGjx4sFauXKnQUPsvBdPS0mQ2/7ivUWlpqRYsWKCUlBS1b99eU6ZM0VtvvaUOHTo0eEwAAFxFiL9VL985VJ/tylTCJ3u0P6tI1724/pxrUenH69G/3TJI7bw9tOFwrr5LztXhE8VnnedlMWtI9w517TsHdQ1o0T0EO7bz0l0jI/XS2sN6fvVBjesXwqo/ADAYhT/YFdSu+OtsbA4AMFhVtU1/+mJ/vV+zSTJJWvjpXo2PCaPtJwAADTBnzpzztuFcu3btWZ9fccUV2rt3b7PGBADAlZhMJsUP6qyR0UF64uMkrdidVe95tbXAX7238yfPlwZ0CdDIaPuKvmHdA+XjZWnh1Ge7b3SU3vjuiJIyCvW/Azka25ebcQDASBT+YEerTwCQJCWm5p/TGuVMNkmZBaVKTM2va20CAAAAAEBzBLX31ozLIs9b+DtT5wCrxsWEamR0sEb0CFKAr2crJDy/oPbeumtEd728LkXPf31IV/Vh1R8AGKnl1nnDtVD4AwBJUk7R+Yt+TTkPAAAAAICGaOh15m8n99WT18VqUmyY4UW/WrPG9JDV06yd6QX65uAJo+MAQJtG4Q92hcftj7T6BNDGhfhZG3RefnF5CycBAAAAALQlDb0ebeh5rSm4vbdmxHWXJD2/+pBstno2KgQAtAoKf5Aqy6VTOfaPA7oamwUADDY8KlDhAVZdrCnJwk/36omPk3SqrLJVcgEAAAAA3NvFrkdNksIDrBoeFdiasRrs/it6yNvDrO1pJ/XtoVyj4wBAm0XhD1JRpiSbZPGWfNmvCkDbZjGblBAfI0nnXGyZav6Mqtnb7z8bj2ris+u0jjYmZ6mqtmnj4Tx9vCNDGw/nqaqaOz0BAAAA4GIudj0qSQnxMbKYnXP/vBA/q+5g1R8AGI7CH87Y36+zxMa7AKBJseF6acYQhQWc3T4lLMCql2YM0dJZl2npfXGKCPRRxsnTuuu1RP36vZ0qKKkwKLHzWJmUqdF/XqNpr2zS3GU7NO2VTRr95zVamZRpdDQAAAAAcHoXux6dFBtuULKG+b8resjLw6ytR3/QhsN5RscBgDbJw+gAcAJ1+/t1MTYHADiRSbHhGh8Tpo3JOfrq282acHmcRvQMqbuzclTPYH35yzH665cH9MaGI3p/a7q+OXhCv6/ZYL0tWpmUqdlLtumn93RmFZRq9pJtLnGRCgAAAABGq70eTUzNV05RqUL87O09nXWl35lC/a2aPryb3thwRM9/fUgjo4NkYqEBALQqVvxBKki3PwZQ+AOAM1nMJsVFBWposE1x9Vxk+Xp5KCG+v95/YIR6dGqnE0VlemDJVj24dJtOFJUZlNoYVdU2Lfx07zlFP0l1xxZ+upe2nwAAAADQABazSSOig3Td4C4aER3kEkW/Wg9cES0vi1mJR/K1KSXf6DgA0OZQ+MMZK/46G5sDAFzU0O6B+vyhy/XzK6NlMZu0Ynemxj/7jf67Pb3N7GmQmJqvzILS837dJimzoFSJqVz0AQAAAIA7Cwuw6rZLIyRJz68+aHAaAGh7KPzhjD3+WPEHAE1l9bToN5P66uMHR6lfuL9OllTo4eU7de+bW5RZcNroeC3uaF5xg87LKTp/cRAAAAAA4B5mXxktT4tJm1LytTmFvf4AoDVR+AOFPwBwoNguAfpkzij9ekJveVnMWrM/RxOeWae3N6e55eq/1NxiJXycpIRP9jTo/BA/68VPAgAAAAC4tM4dfHTrMPuqvxfWHDI4DQC0LRT+IBXUFP7Y4w8AHMLTYtacsb204qHRGhzRQUVllXr0v7s1/ZXNDV4Z58xsNpu+S87VvW98r7FPr9WbG4+qrLJaHhfYc8IkKTzAviE9AAAAAMD91a76+y45T1uOsO0DALQWCn9tXWW5VJxj/5gVfwDgUL1C/fTB7JFaMLWfrJ5mbUzJ08Tn1unf36aoqtr1Vv+VVlRp+fdpmvTct7rj35u1en+ObDbp6r4hWnpfnP4+7RKZZC/y/ZRNUkJ8jEttSA8AAAAAaLquHX1189CukqTnV7PqDwBai4fRAWCwouP2R4u35BtkbBYAcEMWs0n3Xd5D42NC9cgHu7UxJU9/WLFPK3Zn6i83DVSvUD+jI15UTmGplmw6qiWb05RfXC5J8vWy6JahXXX3yEj16NS+7tyXZgzRwk/3KrPg7L38wv2tmhAT1qq5AQAAAADG+vmVPfXelnR9eyhXW4/+oKHdOxodCQDcHoW/tq6wpvDn31kysQoDAFpK96B2entWnN5JPKanPt+n7WknNfWF9frF2J564MpoeVqcbxF+UkaBXlufqk93HVdFlX2FYpcOPrpnZKRuvTRCAT6e5zxnUmy4xseEKTE1XzlFpWrv7aFfLtuuzMJSfZGUpakDw1v7bQAAAAAADBIR6Ksbh3TRu1vS9cLqQ3rzZ8ONjgQAbo/CX1tXt79fV2NzAEAbYDKZND2um67q20mP/TdJa/bn6OlVB/VFUpb+cvNAxXYJMDqiqqptWrU3S6+tP6LEM/ZgGNa9o342OkoTYkLlcZEipcVs0ojoH1eR33t5Dz339SG9sPqQJseGyUy7TwAAAABoMx68qqc+2Jahbw6e0I5jJzU4ooPRkQDArTnf8gK0rsKawp9/Z2NzAEAbEh7go1fvHqbnbhusDr6e2ptZqOte/E5/WblfpRVVhmQqLK3Qv79N0RV//Z8eWLJNiUfy5WE26frBnfXxg6P0/uyRmjIg/KJFv/rMHBklP28PHcgu0pd7slogPQAAAADAWXUPaqfrB3eRJL3AXn8A0OIo/LV1dYW/LsbmAIA2xmQy6fpLumjVw1do6oBwVVXb9M+1hzX1hW+19Wj+xQdwkCO5xfrdJ3s04qnV+sOKfUr/4bQ6+npqzlU99d0jY/Xc7ZdoUDPvxgzw9dTMUZGS7Bu6V1fbmh8cAAAAAOAy5oztKbNJWrM/R7vSTxodBwDcGoW/tu7MPf4AAK2uk5+3XrxjiBbPGKpOft46fKJYNy/eqN99skfFZZUt8po2m00bknN135vf66qn1+qNDUdUXF6l3qHt9acbB2jj/Kv164l9FOpvddhr/mx0lNp7e2h/VpFW7ct22LgAAAAAAOcXFXzmqr9kg9MAgHuj8NfWFaTbH9njDwAMNSk2TF8/fIVuHtpVNpv0xoYjmvjcOq0/lOuw1yitqNK73x/T5Oe/1fR/b9bX+3Jks0lX9emkJffG6ctfjtHtw7vJ6mlx2GvW6uDrpXtGRkqyt3ax2Vj1BwAAAABtyYM1q/6+3petpIwCo+MAgNui8NfWseIPAJxGgK+n/nbLIL35s+Hq0sFH6T+c1oxXN+u37+9SwemKJo+bU1iqZ746oFF/WqPffLBL+7OK5ONp0Z2XddfqX12h12cO1+hewTKZTA58N+e6d3SU2nlZtOd4ob7el9OirwUAAAAAcC7RndorfpD9d5Ds9QcALYfCX1tWWS4V1/zi1Z8VfwDgLK7o3UlfPjxGd43oLklavuWYJjz7jb7e+2OLzKpqmzYeztPHOzK08XCequrZNy8po0Dz3t2hUX9eoxfWJCuvuFydA6yaP7mvNs2/Wr+/PlbRndq32vvq2M5Ld9Ws+nt+9UFW/QEAAABAG/OLsT1lMklf7c3W3uOFRscBALfkYXQAGKioZrWfh1XyDTQ2CwDgLO29PfTkdbGaOiBcj3y4W6m5xbrvP1t07aDOurxXsJ5ZdVCZBaV154cHWJUQH6PxMWFatTdbr32XqsTU/LqvD+3eUT8bFaWJ/UPlYTHuvp9Zl/fQmxuOKCmjUP87kKOxfUMNywIAAAAAaF09Q/w0dUC4PtuVqb+vOaSXZgw1OhIAuB0Kf23ZmW0+W7i9GwCgaeJ6BOmLuZfr2VUH9cq3Kfpk53F9svP4OedlFZTqgSXbFNTOS3nF5ZIkD7NJUweGa+aoKA2O6NDKyesX2M5Ld47orpe/SdHzXx/SVX1CWrzFKAAAAADAeTx0dS+t2J2pL5KytD+rUNFBPkZHAgC3QqvPtqwgw/7o38XYHACAC7J6WjR/Sj+9/8BIeZjrL5LVNs3MKy5XgI+HHrwqWut/O1bP336J0xT9as26vId8PC3amV6gtQdPGB0HAAAAANCKeof6aUpsuCTp72uSDU4DAO6Hwl9bVlhb+OtsbA4AQIOUVVarsp69/H7qhdsv0f+b2FdhAdZWSNV4we29NeOybpKk578+xF5/AAAAANDG/OLqnpKkz3dn6lDOKYPTAIB7ofDXlhWy4g8AXElOUenFT5J08nRFCydpvvvHRMvbw6wdx07q20O5RscBAAAAALSivmH+mtQ/TDab9OLaFKPjAIBbofDXlp25xx8AwOmF+DVsBV9DzzNSJz9v3RHXXZL0/GpW/QEAAABAW1O36i8pS9mnDQ4DAG6Ewl9bVpBufwzoamwOAECDDI8KVHiAVfXv8ieZJIUHWDU8KrA1YzXZA1f0kLeHWVuP/qDvkvOMjgMAAAAAaEX9OwdofEyobDbpy3R+TQ0AjsLfqG0ZK/4AwKVYzCYlxMdI0jnFv9rPE+JjZDGfrzToXEL8rZo2vGavv9UHWfUHAAAAAG3M3Kt7SZK25ZqUmltscBoAcA8U/tqqyjKpOMf+sT8r/gDAVUyKDddLM4YoLODsdp5hAVa9NGOIJsWGG5SsaWZfGS0vD7O+P/KDNqaw6g8AAAAA2pLYLgEa26eTbDLppW/Y6w8AHMHD6AAwSFGm/dHDKvm6Rks4AIDdpNhwjY8JU2JqvnKKShXiZ2/v6Sor/c4U6m/V7ZdG6D8bj+r5rw9pZHSw0ZEAAAAAAK1ozlU9tObACX2yK0tzxxUrMrid0ZEAwKWx4q+tKsiwP/p3lkyu94tiAGjrLGaTRkQH6brBXTQiOsgli361Zl8ZLS+LWZtT87WJVX8AAAAA0KYM6BKgmA7Vqqq26cX/JRsdBwBcHoW/tqpuf78uxuYAALR54QE+uvVSe9vp578+ZHAaAAAAAEBrm9i1WpL04fYMpeWVGJwGAFwbhb+2qjDd/kjhDwDgBGZf2VOeFpM2puQpMTXf6DgAAAAAgFYU6Sdd3jOIVX8A4AAU/tqquhV/nY3NAQCApC4dfHTz0AhJ0gurWfUHAAAAAG3NL66KliR9sC1dx/JZ9QcATUXhr62q3eMvgBV/AADn8PMro+VhNml9cq62HmXVHwAAAAC0JZd066DLewWrstqmf649bHQcAHBZFP7aqsKawh+tPgEATiIi0Fc3D63Z6281rV0AAAAAoK156OpekqT3tx5TxsnTBqcBANdE4a+tovAHAHBCD17VUxazSesOntD2tB+MjgMAAAAAaEWXRgZqZHSQKqpsemktN4QCQFNQ+GuLKsuk4hP2jyn8AQCcSESgr268xP5v0/Ps9QcAAAAAbU7tqr93v09XZgGr/gCgsSj8tUWFx+2PHlbJN9DYLAAA/MScsfZVf2sPnNCOYyeNjgMAAAAAaEWX9QhSXFSgyquq9RJ7/QFAo1H4a4tqC3/+nSWTydgsAAD8RPegdrp+sH3V399Z9QcAAAAAbc7ccfZVf8sSjymroNTgNADgWij8tUXs7wcAcHJzxvaU2SSt3p+j3ekFRscBAAAAALSiET2CdGlkR5VXVWvxN6z6A4DGoPDXFlH4AwA4uajgdrpuMHv9AQAAAEBbZDKZNPfq3pKkdxLTlFPIqj8AaCgKf21RQU3hL4DCHwDAedWu+vt6X7b2HC80Og4AAAAAoBWN6hmkod07qqyyWi+vSzE6DgC4jEYX/iIjI/Xkk08qLS2tJfKgNZy5xx8AAE4qulN7xQ+y/1v14lou8gAAAACgLTGZTHroavtef0s3H9WJojKDEwGAa2h04e+Xv/ylPvzwQ/Xo0UPjx4/XsmXLVFbGX7oupa7VZ1djcwAAcBG/GNtTJpO0al+OMoqNTgMAAAAAaE1jegVrcEQHlVZU65VvuSEUABqiSYW/HTt2KDExUf369dMvfvELhYeHa86cOdq2bVtLZISj1RX+WPEHAHBuPUP8NHVAuCTpy3Q6lAMAAABAW2IymTR3nH3V31sbjyr3FAtQAOBimvwbtCFDhuiFF17Q8ePHlZCQoH//+9+69NJLNXjwYL322muy2WyOzAlHqSyTik/YPw5gxR8AwPk9dHUvmUzSznyzDmQVGR0HAAAAANCKruzdSQO7Buh0RRWr/gCgAZpc+KuoqNC7776ra6+9Vr/61a80bNgw/fvf/9ZNN92kRx99VHfccYcjc8JRavf387BKPh2NzQIAQAP0DvXTpJhQSdI/v+EiDwAAAADaEpPJpLlX/7jqL7+43OBETVNVbdPGw3n6eEeGNh7OU1U1C2cAtAyPxj5h27Ztev311/XOO+/IbDbrrrvu0rPPPqu+ffvWnXPDDTfo0ksvdWhQOEht4c+/s2QyGZsFAIAGevDKHvpiT7a+2JOtQ9lF6hXqZ3QkAAAAAEArGds3RLFd/JWUUah/f5ui30zqe/EnOZGVSZla+OleZRaU1h0LD7AqIT5Gk2LDDUwGwB01esXfpZdeqkOHDumll15SRkaG/va3v51V9JOkqKgo3X777Q4LCQeq29+vi7E5AABohD5hfhoYWC2bTXphTbLRcQAAAAAArchkMumhsfZVf29uOKIfXGjV38qkTM1esu2sop8kZRWUavaSbVqZlGlQMgDuqtGFv5SUFK1cuVK33HKLPD096z2nXbt2ev311xs03osvvqjIyEhZrVbFxcUpMTHxguc/99xz6tOnj3x8fBQREaGHH35YpaU//qX5u9/9TiaT6aw/Py1MtmkU/gAALmpS12pJ0me7jis5h73+AAAAAKAtGR8TqphwfxWXV+m171KNjtMgVdU2Lfx0r+pr6ll7bOGne2n7CcChGl34y8nJ0ebNm885vnnzZm3ZsqVRYy1fvlzz5s1TQkKCtm3bpkGDBmnixInKycmp9/y3335bjzzyiBISErRv3z69+uqrWr58uR599NGzzuvfv78yMzPr/qxfv75RudxaQU3hL4DCHwDAtXRpJ43vFyKbTfoHq/4AAAAAoE0xmUx6qGavvze+O6KCkgqDE11cYmr+OSv9zmSTlFlQqsTU/NYLBcDtNbrw9+CDD+rYsWPnHM/IyNCDDz7YqLGeeeYZzZo1SzNnzlRMTIwWL14sX19fvfbaa/Wev2HDBo0aNUrTp09XZGSkJkyYoGnTpp2zStDDw0NhYWF1f4KDgxuVy62duccfAAAu5sEre0iSPtl5XCknThmcBgAAAADQmibEhKpvmJ+Kyir1qgus+ks+0bBuNTlF5y8OAkBjeTT2CXv37tWQIUPOOX7JJZdo7969DR6nvLxcW7du1fz58+uOmc1mjRs3Ths3bqz3OSNHjtSSJUuUmJio4cOHKyUlRZ9//rnuvPPOs847dOiQOnfuLKvVqhEjRmjRokXq1q3bebOUlZWprKys7vPCwkJJUkVFhSoqnP/OkcbwKDgmk6TKdmGyOel7q/2eu9v33tUwD8ZjDpwD8+Acar//vTv5aGyfTlpz4IRe+Pqg/nrzAIOTtS38PDgH5sF4zjoHzpYHAADA0cxm+6q/ny/dpte/S9W9o6MU4FP/dlRGsdls2nA4T0s3H9XKpKwGPSfEz9rCqQC0JY0u/Hl7eys7O1s9evQ463hmZqY8PBo+XG5urqqqqhQaGnrW8dDQUO3fv7/e50yfPl25ubkaPXq0bDabKisr9cADD5zV6jMuLk5vvPGG+vTpo8zMTC1cuFCXX365kpKS5OfnV++4ixYt0sKFC885/tVXX8nX17fB78kVTMw9Iqukb3emqPDQ50bHuaBVq1YZHQFiHpwBc+AcmAfnsGrVKg3xktbIQx/vPK7+pmMK8TE6VdvDz4NzYB6M52xzUFJSYnQEAACAFjepf5h6h7bXwexTeuO7I5o7rpfRkSRJPxSX64Nt6Xp7c5pScovrjntaTKqoqn8PP5OksACrhkcFtlJKAG1Bowt/EyZM0Pz58/Xxxx8rICBAknTy5Ek9+uijGj9+vMMDnmnt2rV66qmn9M9//lNxcXFKTk7W3Llz9fvf/16PP/64JGny5Ml15w8cOFBxcXHq3r273n33Xd177731jjt//nzNmzev7vPCwkJFRERowoQJ8vf3b9H31Koqy+S53b6acfSU2yRf5/wHpaKiQqtWrdL48ePl6elcd+y0JcyD8ZgD58A8OIefzsOWsm1aezBX+0zddM+UWKPjtRn8PDgH5sF4zjoHtZ1LmmrXrl0NPnfgwIHNei0AAICmMptN+sXYXvrFO9v16voUzRwdKX+rMf9NZrPZtC3tBy3dlKbPdmeqvLJaktTOy6IbhnTR9OHdlZZfrNlLttnPr2eMhPgYWcymVkwNwN01uvD3t7/9TWPGjFH37t11ySWXSJJ27Nih0NBQvfXWWw0eJzg4WBaLRdnZ2Wcdz87OVlhYWL3Pefzxx3XnnXfqvvvukyQNGDBAxcXFuv/++/XYY4/JbD53y8IOHTqod+/eSk5OPm8Wb29veXt7n3Pc09PTqS7km60o3f7oYZWnf4hkcu5/UNzu+++imAfjMQfOgXlwDrXz8MvxfbT2YK4+3pmpueN6q3tQO6OjtSn8PDgH5sF4zjYHzc0yePBgmUwm2WznuSO95msmk0lVVVXNei0AAIDmmDIgXM+vPqTknFP6z4YjmjO2dVf9FZVW6KMdx7V001Htz/pxH7+YcH/NuKy7rh3cWe297b96j+nsr5dmDNHCT/cqs+DsvfwevyZGk2LDWzU7APfX6MJfly5dtGvXLi1dulQ7d+6Uj4+PZs6cqWnTpjXqQtPLy0tDhw7V6tWrdf3110uSqqurtXr1as2ZM6fe55SUlJxT3LNYLJJ03ovTU6dO6fDhw+fsA9gmFWbYH/27OH3RDwCACxkc0UFX9O6kbw6e0Iv/S9Zfbh5kdCQAcHmpqalGRwAAAGgQi9mkX4ztqbnLdujf61N1z6ioukJbS0rKKNDSzWn6eEeGSsrtN0J5e5h17aDOuuOy7hrUNUCmen7vOik2XONjwpSYmq+colIt2XRU3x/5QYdPnGrxzADanib9bdiuXTvdf//9zX7xefPm6e6779awYcM0fPhwPffccyouLtbMmTMlSXfddZe6dOmiRYsWSZLi4+P1zDPP6JJLLqlr9fn4448rPj6+rgD461//WvHx8erevbuOHz+uhIQEWSwWTZs2rdl5XV7hcfujf2djcwAA4ABzx/XSNwdP6MNtGfrF2F6KCHSvfXkBoLV1797d6AgAAAANds3Aznp+9SGlnCjWmxuO6MGrerbI65wur9Jnu45r6eY07Th2su54dKd2uiOuu24a0lUBvhdfEGMxmzQiOkiSFOpv1e3/2qQPt2XoNxP7Nuj5ANBQTb4NYu/evUpLS1N5eflZx6+99toGj3HbbbfpxIkTeuKJJ5SVlaXBgwdr5cqVCg0NlSSlpaWdtcJvwYIFMplMWrBggTIyMtSpUyfFx8frj3/8Y9056enpmjZtmvLy8tSpUyeNHj1amzZtUqdOnZr6Vt1HQU2rz4CuxuYAAMABhnTrqMt7BevbQ7n659pkLbqR/aYAoDk++eSTBp/bmOs+AACAllC76u/h5Tv1729TdM/ISLVz4Kq/5JwiLd2cpg+2pquwtFKS5GkxaVJsuO6I66a4qMB6V/c1RFxUoPqG+Wl/VpHe3XJMs8b0cFhuAGj034QpKSm64YYbtHv37rP2f6j9S66xez3MmTPnvK09165de3ZYDw8lJCQoISHhvOMtW7asUa/fprDiDwDgZuZe3UvfHsrVe1vS9eBVPdW1I6v+AKCpardguBj2+AMAAM4ifmBnvbA6Wam5xXpr01E9cEV0s8Yrr6zWl3uytHTzUW1Kya873rWjj6bHddOtwyIU3N67ubFlMpk0c1SkfvvBbr258Yh+NjpKFjNbMwFwDPPFTznb3LlzFRUVpZycHPn6+mrPnj1at26dhg0bdk6hDk7mzD3+AABwA8MiAzWqZ5Aqq23659rDRscBAJdWXV3doD+NLfq9+OKLioyMlNVqVVxcnBITEy94/nPPPac+ffrIx8dHERERevjhh1VaWlr39d/97ncymUxn/enbt2+T3jMAAHBtHhZzXYvPV9alqKS8sknjHMsv0Z9X7tfIP63WL97Zrk0p+TKbpPExoXpj5qVa9/+u0s+v7OmQol+t6wZ3UQdfT6X/cFqr92U7bFwAaHThb+PGjXryyScVHBwss9kss9ms0aNHa9GiRXrooYdaIiMchcIfAMANzb26tyTpvS3HlHHytMFpAABnWr58uebNm6eEhARt27ZNgwYN0sSJE5WTk1Pv+W+//bYeeeQRJSQkaN++fXr11Ve1fPlyPfroo2ed179/f2VmZtb9Wb9+fWu8HQAA4ISuH9xZ3QJ9lVdcrqWb0hr8vMqqaq3am627X0vUmL/+Ty+tPazcU+UK8fPWQ1f30vrfjtUrdw3TlX1CZG6B1XhWT4umDe8mSXpjwxGHjw+g7Wp0q8+qqir5+flJkoKDg3X8+HH16dNH3bt314EDBxweEA5UUFP4C6DwBwBwH8OjAjWiR5A2puRp8drD+v31sUZHarKqapsSU/OVU1SqED+rhkcF0u4FgGGKi4v1zTff1Lu3e0Nv+nzmmWc0a9YszZw5U5K0ePFirVixQq+99poeeeSRc87fsGGDRo0apenTp0uSIiMjNW3aNG3evPms8zw8PBQWFtaUtwUAANyMh8WsOVf11G8+2KXF3ySrd2h7nTxdcd5rqqyCUi3//piWfZ+mzIIfuwpc3itYd8R119X9QuRpafR6mSaZcVl3/WtdijYcztOBrCL1CfNrldcF4N4aXfiLjY3Vzp07FRUVpbi4OP3lL3+Rl5eX/vWvf6lHDzYhdVoVpVJJrv1jVvwBANzMQ1f30saUPC3//ph+flW0wgN8jI7UaCuTMrXw071nXXiGB1iVEB+jSbHhBiYD0BZt375dU6ZMUUlJiYqLixUYGKjc3Fz5+voqJCSkQYW/8vJybd26VfPnz687ZjabNW7cOG3cuLHe54wcOVJLlixRYmKihg8frpSUFH3++ee68847zzrv0KFD6ty5s6xWq0aMGKFFixapW7du581SVlamsrKyus8LCwslSRUVFaqoqLjoe4Hj1H6/+b4bi3lwDsyDc2AejOeIObhmQIj+vNJLecXluvv17+uOh/l7a8GUvhrfL0QbUvL1duIxrTlwQlXVNklSR19P3TSki24f1lXdg2r2jK+uUkV16+xnHNLOQ+P7hWjlnmy9tj5Ff7guplVetz78LDgH5sE5OOM8NCZLowt/CxYsUHFxsSTpySef1DXXXKPLL79cQUFBWr58eWOHQ2spOm5/9PCRfDoamwUAAAcbER2k4VGBSkzN1+K1h7XwOtda9bcyKVOzl2yT7SfHswpKNXvJNr00YwjFPwCt6uGHH1Z8fLwWL16sgIAAbdq0SZ6enpoxY4bmzp3boDFyc3NVVVWl0NDQs46HhoZq//799T5n+vTpys3N1ejRo2Wz2VRZWakHHnjgrFafcXFxeuONN9SnTx9lZmZq4cKFuvzyy5WUlFTXneanFi1apIULF55z/KuvvpKvr2+D3g8ca9WqVUZHgJgHZ8E8OAfmwXjNmYOdeSblFZsl/WR1X2Gp5izbIT8Pqajyx69F+9k0MrRag4Mq5VGVrD2bk7Wnya/ePL0lrZSHPtx2TIN0RO08DQpSg58F58A8OAdnmoeSkpIGn9vowt/EiRPrPu7Zs6f279+v/Px8dezYUSYTraicVmFN4c+/s8Q8AQDc0C+v7qXp/96sd74/pp9f1VOh/lajIzVIVbVNCz/de07RT5Jssl+2Lvx0r8bHhNH2E0Cr2bFjh15++WWZzWZZLBaVlZWpR48e+stf/qK7775bN954Y4u87tq1a/XUU0/pn//8p+Li4pScnKy5c+fq97//vR5//HFJ0uTJk+vOHzhwoOLi4tS9e3e9++67uvfee+sdd/78+Zo3b17d54WFhYqIiNCECRPk7+/fIu8F9auoqNCqVas0fvx4eXoa/JvNNox5cA7Mg3NgHozX3DmoqrZp0dPrJJXV81X7NVRRpdTOy6Ibh3TRtGFd1Su0ffNCO5DNZtOqf27SvqwinQzqp1tGRxmSg58F58A8OAdnnIfariUN0ajCX0VFhXx8fLRjxw7Fxv54J31gYGBjhoER2N8PAODmRkQH6dLIjvr+yA9a/M1hJcT3NzpSgySm5p/V3vOnbJIyC0qVmJqvEdFBrRcMQJvm6ekps9m+t01ISIjS0tLUr18/BQQE6NixYw0aIzg4WBaLRdnZ2Wcdz87OPu/+fI8//rjuvPNO3XfffZKkAQMGqLi4WPfff78ee+yxukxn6tChg3r37q3k5OTzZvH29pa3t3e979NZLuTbGr73zoF5cA7Mg3NgHozX1DnYcjhPWYX1Ff3O9uIdQ3Rln5CmRGtxM0dH6Tfv79LSzem6f0xPebTSHoP14WfBOTAPzsGZ5qExORr1N4inp6e6deumqqrW6XEMByqsKfyxvx8AwE2ZTCY9dHUvSdLbm9OUU3j+YpqzsNlsWnswp0Hn5hQ5//sB4D4uueQSff+9fX+cK664Qk888YSWLl2qX/7yl2fdBHohXl5eGjp0qFavXl13rLq6WqtXr9aIESPqfU5JSck5xT2LxSLJ/ndmfU6dOqXDhw8rPJyWyAAAtEUNvVYqOO08e3X91LWDOiuwnZcyTp7W1/sado0IAOfT6FsHHnvsMT366KPKz89viTxoKXWtPin8AQDc1+iewRrSrYPKKqv18roUo+OcV0VVtT7clq5Jz32rl79pWM4QP9doXQrAPTz11FN1hbQ//vGP6tixo2bPnq0TJ07o5ZdfbvA48+bN0yuvvKI333xT+/bt0+zZs1VcXKyZM2dKku666y7Nnz+/7vz4+Hi99NJLWrZsmVJTU7Vq1So9/vjjio+PrysA/vrXv9Y333yjI0eOaMOGDbrhhhtksVg0bdo0B34HAACAq2jotZIzX1NZPS2aNjxCkvTGhlSD0wBwdY3e4+8f//iHkpOT1blzZ3Xv3l3t2rU76+vbtm1zWDg4UN2Kv87G5gAAoAWZTCbNHddbd7+WqKWbj+qBK6LVye/c1m5GOVVWqWWJaXptfaqO17T39PU0y2Qyqbi8/o4KJklhAVYNj6K1OoDWM2zYsLqPQ0JCtHLlyiaNc9ttt+nEiRN64oknlJWVpcGDB2vlypUKDQ2VJKWlpZ21wm/BggUymUxasGCBMjIy1KlTJ8XHx+uPf/xj3Tnp6emaNm2a8vLy1KlTJ40ePVqbNm1Sp06dmvhuAQCAKxseFajwAKuyCkrr3TvdVa6pZlzWXYu/SdGmlHztyyxUv3D2IQbQNI0u/F1//fUtEAMtrrbwF9DV2BwAALSwMb2CNSiig3YeO6lXvk3Ro1P6GR1JOUWlenPDEb218agKSyslScHtvTVzVKRmxHXXxpRczV5iv3mqvgvVhPgYWcymVkwMoK1LTU1VZWWlevXqddbxQ4cOydPTU5GRkQ0ea86cOZozZ069X1u7du1Zn3t4eCghIUEJCQnnHW/ZsmUNfm0AAOD+LGaTEuJjNHvJNpl09jVV7VWUK1xThQf4aFJsmFbsytSbG47oTzcNNDoSABfV6MLfhS7A4MQKWPEHAGgbTCaTfnl1L81843u9tfGo7h/TQ8HtjVn1l3LilF75NlUfbEtXeWW1JKlHcDvdP6aHrr+ki6ye9rZ1k2LD9dKMIVr46V5lFvy4P4WPp0XP3jZIk2LZtwpA67rnnnv0s5/97JzC3+bNm/Xvf//7nIIdAACAkc53TRUWYFVCfIzLXFPNHBmpFbsy9d/tGfrtpL7q2M7L6EgAXFCjC39wQRWlUkmu/WP2+AMAtAFX9umkgV0DtCu9QK98m6L5k1t31d/2tB+0+JvD+mpvtmw1t5te0q2DHrgiWuP7hcpcz52mk2LDNT4mTImp+fr+SJ6eWXVIldXVuqxHUKtmBwBJ2r59u0aNGnXO8csuu+y8q/cAAACMdOY1VU5RqUL87O09nX2l35mGdu+o2C7+Ssoo1LLvj2n2ldFGRwLggswXP+UnTzCbZbFYzvsHTqjouP3Rw0fy6WhsFgAAWoHJZNLcq+2rVN7aeFT5xeUt/prV1Tat3petW1/eqBv+uUFf7rEX/cb1C9F7D4zQh7NHamL/sHqLfrUsZpNGRAfpF2N7KSbcXxVVNr2/Nb3FswPAT5lMJhUVFZ1zvKCgQFVV9e9JCgAAYLTaa6rrBnfRiOgglyr6Sfb/BrtnZJQk6a2NR1RZVW1wIgCuqNEr/v773/+e9XlFRYW2b9+uN998UwsXLnRYMDhQYU3hz7+zZHKtf+wAAGiqsX1D6u6U/Pe3KfrNpL4t8jrlldX6eEeG/rUuRYdyTkmSPC0mXT+4i+4f00O9Qv0aPabJZNIdl3XTY/9N0tub03Tv6CiZ+DccQCsaM2aMFi1apHfeeafuBs+qqiotWrRIo0ePNjgdAACA+7pmYLgWfb5PxwtKtWpvtiYPcI02pQCcR6MLf9ddd905x26++Wb1799fy5cv17333uuQYHCg2v39AmjzCQBoO0wmkx4a20v3v7VVb244olmX93Do/ghFpRV6JzFNr60/oqxC+x4S7b09dEdcN80cFaWwAGuzxr9ucBc9tWKfUnKLtTElTyOjgx0RGwAa5M9//rPGjBmjPn366PLLL5ckffvttyosLNSaNWsMTgcAAOC+rJ4WTY/rpr+vSdbrG45Q+APQaI1u9Xk+l112mVavXu2o4eBIhTWFP/b3AwC0MeNjQhUT7q/i8iq9uj7VIWPmFJbqT1/s18hFa/TU5/uVVViqED9vPTK5rzbMH6v5U/o1u+gn2YuI119i/7f77c1pzR4PABojJiZGu3bt0q233qqcnBwVFRXprrvu0v79+xUbG2t0PAAAALd2R1x3eZhNSkzN157jBUbHAeBiGr3irz6nT5/WCy+8oC5dKCw5JQp/AIA2ymQy6aGre+mBJVv1xoYjuu/yKHXwbdqqv+ScU3plXYr+uz1D5TX7LER3aqf/GxOt6y7pLG8Px+91PD2um5ZuTtOXe7J0oqhMnfy8Hf4aAHA+nTt31lNPPWV0DAAAgDYnLMCqyQPC9enO43rjuyP66y2DjI4EwIU0esVfx44dFRgYWPenY8eO8vPz02uvvaa//vWvLZERzXXmHn8AALQxE2JC1TfMT6fKKvXad0ca/fytR/M16z9bNO6Zb7R8yzGVV1Xr0siO+vddw7Tq4St066URLVL0k6T+nQM0OKKDKqpsem/rsRZ5DQA4n2+//VYzZszQyJEjlZFhv5nwrbfe0vr16w1OBgAA4P7uGRkpSfp453HlnSozNgwAl9LoFX/PPvusTCZT3edms1mdOnVSXFycOnbs6NBwcJCCdPtjQFdjcwAAYACz2b7q7+dLt+n171J17+goBfh4XvA51dU2rd6fo5e/OawtR3+oOz4hJlT/d0UPDe0e2NKx69wR1007jp3U25vT9MCYaJnNpos/CQCa6YMPPtCdd96pO+64Q9u2bVNZmf2XTQUFBXrqqaf0+eefG5wQAADAvQ3p1kGDugZoZ3qBln1/TA9e1dPoSABcRKMLf/fcc08LxECLYsUfAKCNm9Q/TH1C/XQgu0ivrk/RiB7ByikqVYifVcOjAmWpKaaVVVbp4+3H9fK6wzp8oliS5GUx68YhXXTf5T3UM6R9q2e/ZmBn/f6zvUr/4bTWHTqhK/uEtHoGAG3PH/7wBy1evFh33XWXli1bVnd81KhR+sMf/mBgMgAAgLbBZDLpnlGRenj5Tr218ajuH9NDnpZGN/AD0AY1uvD3+uuvq3379rrlllvOOv7ee++ppKREd999t8PCwQEqSqWSXPvH7PEHAGijzGaTfnF1T815e7v+vjpZL6xOrvtaeIBV/29iH+UUlem19anKKbKvavGzemjGZd01c2SkQvytRkWXj5dFNw3tqte/O6Klm9Mo/AFoFQcOHNCYMWPOOR4QEKCTJ0+2fiAAAIA2aMqAcP1xxX5lFZbqyz1ZumYgCzsAXFyjbxFYtGiRgoODzzkeEhLCxu/OqKhmtZ+Hj+RDK1YAQNtlln1Vn+0nxzMLSjXv3Z360xf7lVNUpjB/qx6b0k8bHhmr307qa2jRr9Ydcd0kSWv25yiz4LTBaQC0BWFhYUpOTj7n+Pr169WjRw8DEgEAALQ93h6WuuvBN5qwZz2AtqnRhb+0tDRFRUWdc7x79+5KS0tzSCg4UEGG/TGgi2RiTyAAQNtUVW3T71fsveA5HmaT/nLzQK37zVWaNaaH/KwX3gewNfUM8VNcVKCqqm1a/v0xo+MAaANmzZqluXPnavPmzTKZTDp+/LiWLl2qX/3qV5o9e7bR8QAAANqMO+K6ydNi0pajP2h3eoHRcQC4gEYX/kJCQrRr165zju/cuVNBQUEOCQUHYn8/AACUmJqvzILSC55TWW1TREdfeXk4554J02vu8lyWeEyVVdUGpwHg7h555BFNnz5dV199tU6dOqUxY8bovvvu0+zZs3XfffcZHQ8AAKDNCPG3auqAcEnSGxuOGBsGgEto9G+2pk2bpoceekj/+9//VFVVpaqqKq1Zs0Zz587V7bff3hIZ0RyF6fZH/67G5gAAwEA5RRcu+jX2PCNMig1TYDsvZRWWas3+HKPjAHBzJpNJjz32mPLz85WUlKRNmzbpxIkTCggIqLcDDAAAAFrOPaPs//316c7jyj1VZnAaAM6u0YW/3//+94qLi9PVV18tHx8f+fj4aMKECRo7dix7/DkjVvwBAKAQv4bt09fQ84zg7WHRLcPsN/Is3Ux7dQAto6ysTPPnz9ewYcM0atQoff7554qJidGePXvUp08fPf/883r44YeNjgkAANCmDI7ooMERHVReVa13uB4EcBGNLvx5eXlp+fLlOnDggJYuXaoPP/xQhw8f1muvvSYvL6+WyIjmOHOPPwAA2qjhUYEKD7DqfLvdmiSFB1g1PCqwNWM12vTh9naf6w6d0LH8EoPTAHBHTzzxhF566SVFRkYqNTVVt9xyi+6//349++yzevrpp5Wamqrf/va3RscEAABoc2aOipQkvbXpqCrY/gHABTR5E5tevXrplltu0TXXXKPu3bs7MhMcqbCm8OdP4Q8A0HZZzCYlxMdI0jnFv9rPE+JjZDGfrzToHLoHtdPlvYJls0nvJHKXJwDHe++99/Sf//xH77//vr766itVVVWpsrJSO3fu1O233y6LxWJ0RAAAgDZpcmy4Ovl5K6eoTF8kZRkdB4ATa3Th76abbtKf//znc47/5S9/0S233OKQUHAgCn8AAEiSJsWG66UZQxQWcHY7z7AAq16aMUSTYsMNStY4d8TZb7h6d8sxlVdylycAx0pPT9fQoUMlSbGxsfL29tbDDz8sk8m5b4wAAABwd14eZs2ouR5847tUg9O0nqpqmzYeztPHOzK08XCeqqptRkcCnJ5HY5+wbt06/e53vzvn+OTJk/X00087IhMcpaJUKsmzf8wefwAAaFJsuMbHhCkxNV85RaUK8bO393T2lX5nurpfiEJq7vL8am+WrhnIv/EAHKeqquqsLRw8PDzUvn17AxMBAACg1vS4bvrH/w5pW9pJ7Tx2UoMiOhgdqUWtTMrUwk/3KrOgtO5YeIBVCfExLnPzLmCERhf+Tp06Ve9efp6eniosLHRIKDhI7Wo/T1/Jp6OxWQAAcBIWs0kjooOMjtFknhazbr80Qi+sSdbSTWkU/gA4lM1m0z333CNvb29JUmlpqR544AG1a9furPM+/PBDI+IBAAC0aZ38vBU/sLM+3J6hNzcc0TO3DTY6UotZmZSp2Uu26afr+7IKSjV7yTaX6twDtLZGt/ocMGCAli9ffs7xZcuWKSYmxiGh4CCFx+2P/p0lWvMAAOA2bhveTWaTtDElT8k5p4yOA8CN3H333QoJCVFAQIACAgI0Y8YMde7cue7z2j8AAAAwxt0jIyVJn+46rpyi0guf7KKqqm1a+Onec4p+kuqOLfx0L20/gfNo9Iq/xx9/XDfeeKMOHz6ssWPHSpJWr16tt99+W++//77DA6IZ6gp/7O8HAIA76dLBR2P7hujrfTl6JzFNj1/DzVcAHOP11183OgIAAAAuYFBEBw3p1kHb0k7qnc3HNHdcL6MjOVxiav5Z7T1/yiYps6BUian5Lt3RB2gpjV7xFx8fr48++kjJycn6+c9/rl/96lfKyMjQmjVr1LNnz5bIiKYqTLc/UvgDAMDt3FGzqfv7W9NVWlFlcBoAAAAAQGu5Z1SUJGnJ5qMqr6w2OI3jNXQlo7uueASaq9GFP0maOnWqvvvuOxUXFyslJUW33nqrfv3rX2vQoEGOzofmOLPVJwAAcCtjendSlw4+Kjhdoc93ZxodBwAAAADQSibHhinU31snisr0RZL7XQ+G+Fkdeh7Q1jSp8CdJ69at0913363OnTvr6aef1tixY7Vp0yZHZkNzFWTYHwNY8QcAgLuxmE2aHtdNkrR0c5rBaQAAAAAArcXTYtaMmi4wr393xNgwLWB4VKA6+nqe9+smSeEBVg2PCmy9UIALaVThLysrS3/605/Uq1cv3XLLLfL391dZWZk++ugj/elPf9Kll17aUjnRFIU1hT9afQIA4JZuGdZVHmaTth79QfsyC42OY5iqaps2Hs7TxzsytPFwHhu8AwAAAHB70+K6ycti1o5jJ7U97Qej4zhUam6xTpdfeEuLhPgYWcymVkoEuJYGF/7i4+PVp08f7dq1S88995yOHz+uv//97y2ZDc1F4Q8AALcW4mfVhP6hkqS32+iqv5VJmRr95zWa9somzV22Q9Ne2aTRf16jlW7Y7gYAAAAAagW391b8IPsWT29uOGJsGAcqLK3Q/W9tUWlltXp2aq8w/3Pbef755oGaFBtuQDrANTS48PfFF1/o3nvv1cKFCzV16lRZLJaWzIXmqiiVSvLsH7PHHwAAbuuOmvYu/92eoeKySoPTtK6VSZmavWSbMgvO3tA9q6BUs5dso/gHAAAAwK3dMzJSkrRid6ZyCksvfLILqK626eFlO5RyoljhAVa9c/9l+u6RsXpn1mV6/vbBigpuJ0mqrKLLC3AhDS78rV+/XkVFRRo6dKji4uL0j3/8Q7m5uS2ZDc1Ru9rP01fy6WhsFgAA0GJG9AhSVHA7nSqr1Cc7jxsdp9VUVdu08NO9qu9yr/bYwk/30vYTAAAAgNsa0DVAw7p3VEWVTUvcoAvMs18f1Or9OfLyMOvlO4eqk5+3LGaTRkQH6brBXXTLsK6SpC+4yRO4oAYX/i677DK98soryszM1P/93/9p2bJl6ty5s6qrq7Vq1SoVFRW1ZE40VmHNL/78O0smeh0DAOCuzGaTpg/vJqlttftMTM0/Z6XfmWySMgtKlZia33qhAAAAAKCV3TMqUpL09uajKqu88L54zuyL3Zn6+5pkSdKfbhyggV07nHPO5Jr2nhsP5+lkSXlrxgNcSoMLf7XatWunn/3sZ1q/fr12796tX/3qV/rTn/6kkJAQXXvttS2REU3B/n4AALQZNw3tKi8Ps3ZnFGhX+kmj47SKnKKGtbFp6HkAAAAA4Iom9g9TmL9VuafKtWKXa66EO5BVpF+9t1OSdO/oKN04pGu950UFt1PfMD9VVtu0am92a0YEXEqjC39n6tOnj/7yl78oPT1d77zzjqMywREo/AEA0GYEtvPSlNgwSdLSTW1j1V+I37kbvDfnPAAAAABwRZ4Ws+4cYd/7/fXvjshmc63tDk6WlGvWf7aopLxKI6ODNH9y3wueP6nm2ndlUlZrxANcUrMKf7UsFouuv/56ffLJJ44YDo5QUFP4C6DwBwBAW3DHZfYLvU92HlfB6QqD07S84VGBCvP3Pu/XTZLCA6waHhXYeqEAAAAAwAC3XxpR1wVmW9pJo+M0WGVVtX7xznal5Zeoa0cf/WP6EHlYLlyyqG33+e2hXBWVuv+1L9AUDin8wQmduccfAABwe8O6d1Tv0PY6XVGlj7ZnGB2nxVnMJg2K6HDBcxLiY2Qxs9cxAAAAAPcW1N5b1w2y/x74jQ1HjA3TCH/98oC+PZQrH0+L/nXnMAW287roc3qHtleP4HYqr6rWmv05rZAScD0U/txVYbr90b/+fsgAAMC9mEwm3RFnX/W3dPNRl2vv0lh7jxfq6332i7yOvp5nfc3P6qGXZgzRpJo7QQEAAADA3d09MlKS9MXuTGUVOP9e5x/vyNDL61IkSX+9ZaBiOvs36Hkmk4l2n8BFUPhzV6z4AwCgzblhSBf5eFp0MPuUthz9weg4Laaq2qZHPtylqmqbpgwI05YF4/XOrMt026URkqSuHXwo+gEAAABoU2K7BGh4ZKAqq21auvmo0XEuKCmjQL/9YJckafaV0bpmYON+h13b7nPtgRM6XV7l8HyAq6Pw544qTkslefaP2eMPAIA2w9/qqWtr2ru8vTnN4DQt5/XvUrUrvUB+Vg/9Lr6/LGaTRkQH6ZFJfWUxm7Qvq0gpJ04ZHRMAAAAAWtU9oyIl2a8HSyucsyCWd6pM//fWVpVWVOuK3p306wl9Gj1GbBd/de3oo9MVVfrmIO0+gZ+i8OeOalf7efpK1g6GRgEAAK3rjsu6SZJW7M5UfnG5wWkc71h+iZ7+6qAk6bEp/RTib637Wsd2XhrVM1iS9PnuTEPyAQAAAIBRJsSEqnOAVXnF5fpsl/NdE1VUVevBt7cp4+RpRQb56oXbL2nSvuwmk0mTa9p9fkG7T+AcFP7cUWGG/dG/i2Rq/F+cAADAdQ3s2kGxXfxVXlmtD7amGx3HoWw2mx77KEmnK6oUFxVY19rzTNcMsLd8ccaLXAAAAABoSR4Ws+4cESnJ3inF2fZ+/+OKfdqUkq92Xha9ctcwBfxkv/bGqN3eYfW+HJVVOufqRsAoFP7cEfv7AQDQpt0R112S9HZimqqrnetCrzk+3nFc6w6ekJeHWYtuHCBTPTc4TegfKg+zSfuzipScU2RASgAAAAAwzu2XRsjbw6w9xwu11Yn2fn93yzG9seGIJOmZ2warV6hfs8a7JKKDQv29daqsUusP5TogIeA+KPy5o4Kau/sDuhqbAwAAGOLaQZ3V3ttDqbnF2piSZ3Qch8gvLteTn+2VJM29upd6dGpf73kdfL10eS97u88Vu2j5AgAAAKBt6djOSzdc0kWS9HpNoc1o29N+0IL/JkmyX89N7B/W7DHNZpMm9afdJ1AfCn/uiBV/AAC0ae28Peou9JZuPmpwGsf4w2d7lV9crr5hfrp/TI8Lnjt1oP2/gVbsPt4a0QAAAADAqdw9MlKStDIpS5kFpw3NklNUqgeWbFV5VbXGx4Rq7tW9HDZ2bbvPVXuzVVFV7bBxAVdH4c8dnbnHHwAAaJOmx3WTJH21J1s5RaUGp2medQdP6MPtGTKZpD/dNFCelgv/J+z4mFB5Wkw6mH1KB7Np9wkAAACgbekX7q/LegSqqtqmJZuMuxm0vLJas5dsU3ZhmXqGtNcztw6S2Xzulg1NNTwqUEHtvFRwukKb3KTbDeAIFP7cEYU/AADavH7h/hravaMqq216b0u60XGarKS8Uo/+d7ck6Z6RkRoc0eGizwnw8dSYXp0kSZ/tymzJeAAAAADglO4ZGSVJentzmkorqgzJkPDJHm09+oP8rB76151D5Wf1dOj4FrNJE/qHSqLdJ3AmCn/uqKCm8BdA4Q8AgLZs+nD7qr+3N6epqtpmcJqmeXbVQaX/cFpdOvjo1xP6NPh51wyyt3xZseu4bDbXfO8AAAAA0FTj+oWoSwcf/VBSoU92tv42CEs3H9U7iWkymaQXbr/kvPu0N9fkmnafX+3JctnrXsDRKPy5m4rT0ul8+8fs8QcAQJs2dWC4Anw8lXHytNYdPGF0nEbbnV6gV9enSpL+cEOs2nl7NPi54/qFysvDrMMninWAdp8AAAAA2hgPi1l3jeguSXrjuyOtekPk90fy9btP9kiSfj2hj67qG9JirzUiOkgBPp7KPVWuLUfyW+x1AFdC4c/dFNbcveHZTrJ2MDQKAAAwltXTopuHdpVkv9vSlVRUVeu3H+xStU26dlBnXdWncReKflZPXdG7pt3nTtp9AgAAAGh7brs0QlZPs/ZmFur7Iz+0ymtmFpzW7CXbVFFl09QB4fr5ldEt+nqeFrPG9aPdJ3AmCn/upm5/v86SyXEbpQIAANc0Pc7e7nPN/hxlnDxtcJqGe3V9qvZmFqqDr6eeiI9p0hjXDKxp97k7k3afAAAAANqcDr5euuES+82gb2xIbfHXK62o0gNvbVXuqTL1DfPTX28ZKFMr/I56cmyYJGllUpaqafcJUPhzO7Ur/mjzCQAAJEV3aq8RPYJUbZOWJ6YZHadBjuQW69lVByVJC6bGKLi9d5PGubpfqLw9zErNLdbezEJHRgQAAAAAl3DPyEhJ0pd7slv0ZlCbzabH/puknekF6uDrqX/dOUy+Xg3frqE5RvcKVjsvi7IKS7Uj/WSrvCbgzCj8uZuCdPtjQFdjcwAAAKdxx2X2VX/Lvj+miqpqg9NcmM1m02Mf7VZZZbVG9wzWTUO6NHms9t4edS1CV+yi3ScAAACAtqdPmJ9GRgepqtqmJZtabguINzYc0Qfb0mU2Sf+YNkTdgnxb7LV+yupp0diadp8rafcJUPhzO6z4AwAAPzEhJkzB7b2UU1Sm1ftyjI5zQe9vTdd3yXmyepr1xxtim90WZirtPgEAAAC0cbWr/t5JTFNpRZXDx9+QnKs/rNgnSXp0Sj+N7hXs8Ne4mNp2n18kce0HUPhzN3V7/DX97ngAAOBevDzMumVYhCRp6eaWu8OzuU4UldVdLD48rre6B7Vr9phj+4bI6mnW0bwSJWXQ7hMAAABA23N1v1B17eijkyUV+nhHhkPHPpZfogff3qaqaptuuKSL7h0d5dDxG+rKPp1k9TTrWP5p7TnOtR/aNsMLfy+++KIiIyNltVoVFxenxMTEC57/3HPPqU+fPvLx8VFERIQefvhhlZaWNmtMt0LhDwAA1GPapd1kMknfHsrV0bxio+PU68nP9qrgdIX6d/Z32MViO28PXd3X3vLls93HHTImAAAAALgSi9mku0dESpJe/+6Iw1bEnS6v0v1vbdUPJRWK7eKvRTcOaHbXlqby9fLQFb07SaLdJ2Bo4W/58uWaN2+eEhIStG3bNg0aNEgTJ05UTk79LajefvttPfLII0pISNC+ffv06quvavny5Xr00UebPKbbKagp/AVQ+AMAAD/qFuSrMb3sF0FvJ6YZnOZca/Zn69Odx2Uxm/TnmwbKw+K4/0yta/e5i5YvAAAAANqmW4dFyMfTov1ZRdqUkt/s8Ww2m37zwS7tyyxUUDsvvXznMFk9LQ5I2nRTBtiv/b5IYo93tG2GFv6eeeYZzZo1SzNnzlRMTIwWL14sX19fvfbaa/Wev2HDBo0aNUrTp09XZGSkJkyYoGnTpp21oq+xY7qVitPS6Zq/tNnjDwAA/MQdcd0kSe9tSVdZpeP3dWiqU2WVWvDfJEnSvaOjFNslwKHjX9UnRD6eFqX/cFq70gscOjYAAAAAuIIAX0/dOMS+WOSNDanNHu/ldSn6dOdxeZhN+ucdQ9Slg0+zx2yusX1D5GUx6/CJYh3KLjI6DmAYwwp/5eXl2rp1q8aNG/djGLNZ48aN08aNG+t9zsiRI7V169a6Ql9KSoo+//xzTZkypcljupXCmvZVnu0kawdDowAAAOcztm+Iwvytyi8u15d7so2OU+dvXx7Q8YJSdQv01cPjejt8fB8vi67uFyJJ+mwX7T4BAAAAtE33jIyUJK3am61j+SVNHmftgRz9eeV+SVJCfIziegQ5Il6z+Vk9NbpXsCTp8920+0Tb5WHUC+fm5qqqqkqhoaFnHQ8NDdX+/fvrfc706dOVm5ur0aNHy2azqbKyUg888EBdq8+mjClJZWVlKisrq/u8sNC++WdFRYUqKiqa9P6MYMo/Kg9JNv9wVVZWGh2nyWq/5670vXdHzIPxmAPnwDw4B+bBcW4d2kUv/O+wlmw8oskxnRr13JaYh+3HTurNjUckSQvj+8nDVK2KimqHjV9rcv8QfbYrU5/tytT/G9/TsH0nHIGfB+M56xw4Wx4AAAA4l16hfhrdM1jrk3O1ZNNRzZ/Sr9FjHMkt1kPvbJfNJt02LEIzLuveAkmbblJsmNbsz9EXSZmaO66X0XEAQxhW+GuKtWvX6qmnntI///lPxcXFKTk5WXPnztXvf/97Pf74400ed9GiRVq4cOE5x7/66iv5+vo2J3KrishbryGSTpR7a+Pnnxsdp9lWrVpldASIeXAGzIFzYB6cA/PQfEFlkkkWJR75Qa+9/7nCmvCfOo6ah8pq6W+7LLLZTBreqVqFBzfr84MOGfoc5VWSt9mizIJSvfTuF4r0a5nXaU38PBjP2eagpKTpd20DAACgbbhnZKTWJ+fqncQ0zR3XS75eDS8RnCqr1P1vbVFhaaUu6dZBT17f3+luqhzfL1QWs0n7s4p0JLdYkcHtjI4EtDrDCn/BwcGyWCzKzj67zVR2drbCwsLqfc7jjz+uO++8U/fdd58kacCAASouLtb999+vxx57rEljStL8+fM1b968us8LCwsVERGhCRMmyN/fv6lvsdWZvzsgpUnBUQPr2p+6ooqKCq1atUrjx4+Xp6en0XHaLObBeMyBc2AenAPz4FjflmzX1/tP6LhvD/1sSt8GP8/R8/Di2hRlnk5WYDtP/eO+Uero69XsMS9kXekufborSyf9ozVlcp8Wfa2WxM+D8Zx1Dmo7lwAAAADnc1XfEHUL9FVafok+2n5c02v2gr+Y6mqbfvXuDh3MPqUQP28tnjFU3h6WFk7beB3beWlEjyCtT87VF0lZmn1ltNGRgFZnWOHPy8tLQ4cO1erVq3X99ddLkqqrq7V69WrNmTOn3ueUlJTIbD57W0KLxf6Xi81ma9KYkuTt7S1vb+9zjnt6ejrVhfxFncqUJJk7RMjsSrnPw+W+/26KeTAec+AcmAfnwDw4xowRkfp6/wn9d/txPTI5Rj5ejbtYc8Q8JOec0j/XpkiSEuL7KySg5e/CvHZwV326K0tfJGXr8Wv6y2x2rjtTG4ufB+M52xw4UxYAAAA4J4vZpLtGdNcfVuzTGxtSNW14RINW7f3jf8n6ck+2vCxmLb5zqEL9ra2QtmkmxYZpfXKuViZlUvhDm2S++CktZ968eXrllVf05ptvat++fZo9e7aKi4s1c+ZMSdJdd92l+fPn150fHx+vl156ScuWLVNqaqpWrVqlxx9/XPHx8XUFwIuN6dYKj9sf/TsbmwMAADi1Mb06qWtHHxWWVuqzXcdb/fWrq2169MPdKq+q1pV9OunaQa3z3y6X9wqWn7eHsgpLtS3th1Z5TQAAAABwNrcMi5Cvl0UHs09p4+G8i56/am+2nlll35fhyev6a0i3ji0dsVkm9A+VySTtTC9QxsnTRscBWp2he/zddtttOnHihJ544gllZWVp8ODBWrlypUJDQyVJaWlpZ63wW7BggUwmkxYsWKCMjAx16tRJ8fHx+uMf/9jgMd1aQYb9MaCrsTkAAIBTM5tNmh7XTX9ZeUBvJ6bplmERrfr6y74/psQj+fL1sugP18e22p4QVk+LxseE6sPtGfpsV6aGRQa2yusCAAAAgDMJ8PHUTUO66q1NR/X6hiMa2TP4vOcm55zSw8t3SJLuvKy7bh/esNagRgrxs+rSyEAlpuZrZVKW7h0dZXQkoFUZuuJPkubMmaOjR4+qrKxMmzdvVlxcXN3X1q5dqzfeeKPucw8PDyUkJCg5OVmnT59WWlqaXnzxRXXo0KHBY7q1wprCHyv+AADARdwyNEKeFpO2p53UnuMFrfa62YWlWvTFPknSryf0UdeOvq322pI0dWC4JOnz3Zmqrra16msDbdWLL76oyMhIWa1WxcXFKTEx8YLnP/fcc+rTp498fHwUERGhhx9+WKWlpc0aEwAAAGe7e2R3SdLX+7J1LL+k3nMKTlfo/v9s0amySg2PCtQT8TGtGbFZJseGSZJWJmUanARofYYX/uAg5SXS6Xz7x/5djM0CAACcXic/b03sb78QentzWqu9bsLHe1RUWqlBER1098jIVnvdWpf36iQ/q4dyisr0/ZH8Vn99oK1Zvny55s2bp4SEBG3btk2DBg3SxIkTlZOTU+/5b7/9th555BElJCRo3759evXVV7V8+XI9+uijTR4TAAAA5+oZ4qfLewXLZpP+s/HIOV+vqrbpl8u2KyW3WOEBVv3zjiHytLhOOWFSTeFvy9EflFNYepGzAffiOj+puLCimjsXPNtJ1gBjswAAAJcwPc7eouWj7Rk6VVbZ4q+3MilLK/dkycNs0p9uHCCLuXVafJ7Jy8NcV/BcsZs7P4GW9swzz2jWrFmaOXOmYmJitHjxYvn6+uq1116r9/wNGzZo1KhRmj59uiIjIzVhwgRNmzbtrBV9jR0TAAAA9Zs5KlKSfTuG4p9cEz676qD+d+CEvD3M+tedwxTc3tuAhE0XHuCjwREdZLNJX+7JMjoO0Koo/LmLgnT7Y0AXqZX2yQEAAK5tRI8g9Qhup+LyKn28I6NFX6uwtEJPfJwkSfq/K3qoX7h/i77ehfzY7jNLVbT7BFpMeXm5tm7dqnHjxtUdM5vNGjdunDZu3Fjvc0aOHKmtW7fWFfpSUlL0+eefa8qUKU0eEwAAAPW7sneIugf5qqi0Us+tTtbWXJM2p+brs53H9Y//JUuS/nTTAA3o6poLTWrbfX6RROEPbYuH0QHgIIXH7Y/s7wcAABrIZDJpelw3/WHFPi3ZlKbpw7vJ1EI3EP35i/3KKSpTVHA7/WJsrxZ5jYYaFR2sAB9P5Z4qU2JqvkZEBxmaB3BXubm5qqqqUmho6FnHQ0NDtX///nqfM336dOXm5mr06NGy2WyqrKzUAw88UNfqsyljSlJZWZnKysrqPi8sLJQkVVRUqKKioknvD01T+/3m+24s5sE5MA/OgXkwHnNgrGHdO+hoXone2JgmyaL/HNqi2qvCn43srmtiQ112bsb1DdaiL6TNqfnKPlmswHZeRke6KH4enIMzzkNjslD4cxeFNSv+/LsamwMAALiUm4d21V++PKB9mYXaceykLunW0eGv8f2RfC2t2Udw0Y0DZPW0OPw1GsPLw6xJ/cO0fMsxfbbrOIU/wImsXbtWTz31lP75z38qLi5OycnJmjt3rn7/+9/r8ccfb/K4ixYt0sKFC885/tVXX8nX17c5kdFEq1atMjoCxDw4C+bBOTAPxmMOWt/OPJM+OFjbFPDHm0BtNf9bnZuizz8/bEAyx+nia1FGifTsu6s1ItR1Or7w8+AcnGkeSkpKGnwuhT93wYo/AADQBB18vXTNwHB9uC1Db29Oc3jhr6yySo98sEuSdPulEbqsh3MU2aYODNfyLce0MilLC6/tLw8X2qQecBXBwcGyWCzKzs4+63h2drbCwsLqfc7jjz+uO++8U/fdd58kacCAASouLtb999+vxx57rEljStL8+fM1b968us8LCwsVERGhCRMmyN/fuNbDbVFFRYVWrVql8ePHy9PT0+g4bRbz4ByYB+fAPBiPOTBGVbVNi55eJ6nsPGeY9GW2rx65Y4wh+7M7Sqpvip5bnaxMS6imTBlidJyL4ufBOTjjPNR2LWkICn/uoqBmX56ALsbmAAAALueOuO76cFuGPt11XAumxijA13H/Ufvi/w7r8IliBbf31vzJ/Rw2bnONiA5SR19P5RWXa3Nqvkb1DDY6EuB2vLy8NHToUK1evVrXX3+9JKm6ulqrV6/WnDlz6n1OSUmJzOazC/EWi32VsM1ma9KYkuTt7S1vb+9zjnt6ejrNhXxbw/feOTAPzoF5cA7Mg/GYg9a15XCesgrPV/Szyywo0/b0IpfuknLNoC56bnWyNqTkqaRSCvBxjf+P8fPgHJxpHhqTg1ub3UXdij8KfwAAoHGGdOugvmF+Kq2o1ofb0x027sHsIr201r4h/JPX9XdoQbG5PC1mTarZ6P2zXZkGpwHc17x58/TKK6/ozTff1L59+zR79mwVFxdr5syZkqS77rpL8+fPrzs/Pj5eL730kpYtW6bU1FStWrVKjz/+uOLj4+sKgBcbEwAAABeXU1Tq0POcVc+Q9uoV0l4VVTat2Z998ScAboAVf+6isGbFH4U/AADQSCaTSXfEddPjH+/R0s1pumdkpEym5rVyqaq26bcf7FJFlU3j+oVqcuz5W/AZ5ZqBnfVO4jGtTMrUk9f1lyftPgGHu+2223TixAk98cQTysrK0uDBg7Vy5UqFhoZKktLS0s5a4bdgwQKZTCYtWLBAGRkZ6tSpk+Lj4/XHP/6xwWMCAADg4kL8rA49z5lNjg3ToTXJ+nx3lm64pKvRcYAWR+HPHZSXSKfz7R+zxx8AAGiC6y/pokVf7FdyziklpuYrrpl78S3ZdFTb006qvbeHfn99/2YXEltCXFSggtp5Ka+4XBsP52lM705GRwLc0pw5c87bhnPt2rVnfe7h4aGEhAQlJCQ0eUwAAABc3PCoQIUHWJVVUCpbPV83SQoLsGp4VGBrR3O4SbHhemFNstYdPKHiskq186YsAvfGbc3uoKimPZVnO8kaYGwWAADgkvysnrpusP0GoqWb05o11vGTp/WXlfslSb+d1EfhAT7NztcSPM5o97mCdp8AAAAA2hCL2aSE+BhJ9iLfmWo/T4iPkcXsfDdxNla/cD91D/JVWWW1/ncgx+g4QIuj8OcOCmr24gnoIjnh3fQAAMA1TB/eXZK0MilLeacuvMn7+dhsNj3+UZKKy6s0tHtH3RHX3ZERHW7qwHBJ0so9WaqoqjY4DQAAAAC0nkmx4XppxhCFBZzdzjMswKqXZgzRpNhwg5I5lslkqrvp84ukLIPTAC2Pwp87KDxuf6TNJwAAaIYBXQM0qGuAyquq9f7W9CaNsWJ3plbvz5GnxaQ/3ThAZie/OzQuKkjB7b1VcLpC65NzjY4DAAAAAK1qUmy41v92rJb8bJju6lWlJT8bpvW/Hes2Rb9ak2vez//256i0osrgNEDLovDnDgprfjHnz8akAACgeWpX6L2dmKbq6vp2eji/kyXl+t0neyRJP7+yp3qF+jk8n6NZzCZNGUC7TwAAAABtl8VsUlxUoIYG2xQXFegW7T1/alDXAHUOsKqkvErrDp4wOg7Qoij8uQNW/AEAAAe5ZlC4/Lw9dDSvRN8dbtwKuKc+36fcU+XqGdJeP78quoUSOt7UAfY7P7/ck6XyStp9AgAAAIC7sbf7rNnqgXafcHMU/txBQYb9MaCLsTkAAIDL8/Xy0I1D7P9NsXRTWoOft+Fwrt7dYu9C8KcbB8jbw9Ii+VrCsMhAhfh5q6i0UuuTufMTAAAAANzR5JpuL6v2ZXPTJ9wahT93ULfij8IfAABovuk17T5X7ctWdmHpRc8vrajSox/uliTNuKybhkUGtmg+R7O3+7Tf+fnZTtp9AgAAAIA7GtqtozrV3PTZ2A43gCuh8OcO6vb4o/AHAACar0+Yny6N7KiqapuWf3/souc/v/qQjuSVKMzfqt9M6tsKCR3vmoH2wt+qvdls9A4AAAAAbshsNmli/1BJ0srdtPuE+6Lw5+rKS6TTP9g/Zo8/AADgIHfUrPpblpimqmrbec/be7xQ/1qXIkl68rr+8rd6tko+RxvSraPC/K0qKqvUt4e48xMAAAAA3NHkmn3+vtqbpcoq2n3CPVH4c3W1bT692kvWAGOzAAAAtzEpNkwdfT11vKBUaw/k1HtOVbVNj3y4S1XVNk2ODdOE/mGtnNJxzGe0+1yx67jBaQAAAAAALSEuKlAdfT31Q0mFElPzjY4DtAgKf66uMMP+6N9ZMpmMzQIAANyG1dOim4d2lSQt3ZxW7zmvf5eqXekF8rN6aOG1/VszXou4ZhDtPgEAAADAnXlYzBofY2/3+UUS7T7hnij8ubq6wh/7+wEAAMeaNrybJOl/B3KU/kPJWV87ll+ip786KEl6dEo/hfhbWz2fo10S0UFdOviouLxKaw+cMDoOAAAAAKAF1Lb7/HJPlqovsLUF4Koo/Lk6Cn8AAKCF9OjUXqN6Bslmk5YlHqs7brPZ9NhHSTpdUaW4qEDdNizCwJSOYzKZNGWAvV3pit2ZBqcBAAAAALSEkT2D5Gf1UE5Rmbal/WB0HMDhKPy5uoKawl8AhT8AAOB4d8R1lyQt+z5N3yXnamuuSc98nax1B0/Iy8OsRTcOkNnsPu3Gpw7sLElavS9bp8tp9wkAAAAA7sbbw6Jx/Wj3CfdF4c/VFR63P/p3NjYHAABwS+NjQuVv9VDuqXLd8+Y2/eeQRYvXpUqSJvUPVY9O7Q1O6FiDugaoa0cflZRXae2BHKPjAAAAAABawKRYe7eXlUlZstlo9wn3QuHP1dW1+uxqbA4AAOCWVu/LVmFpZb1f+3RnplYmuVdLTJPJpKkD7fs9fLbLvd4bAAAAAMDuit6d5OtlUcbJ09qVXmB0HMChKPy5urrCHyv+AACAY1VV27Tw070XPGfhp3tV5WaboV8zoKbd5/5slZTXX/QEAAAAALguq6dFV/UJkUS7T7gfCn+urLxEOl2z+Sh7/AEAAAdLTM1XZkHpeb9uk5RZUKrE1PzWC9UKYrv4q1ugr0orqrVmP+0+AQAAAMAd/djuM5N2n3ArFP5cWe3+fl7tJW9/Y7MAAAC3k1N0/qJfU85zFSaTSdfUtPtcQbtPAAAAAHBLV/UNkZeHWUfySrQ/q8joOIDDUPhzZYXp9kf/LpLJZGwWAADgdkL8rA49z5XU7vO3Zn+OTpXR7hMAAAAA3E17bw+N6dVJEu0+4V4o/Lmy2hV/7O8HAABawPCoQIUHWHW+24tMksIDrBoeFdiasVpFTLi/ooLbqayyWqv3ZRsdBwAAAADQAiaf0e4TcBcU/lxZQYb9kf39AABAC7CYTUqIj5Gkc4p/tZ8nxMfIYna/zgMmk0lTB9DuEwAAAADc2bh+ofK0mHQw+5SSc04ZHQdwCAp/rqywpvDnT+EPAAC0jEmx4XppxhCFBZzdzjMswKqXZgzRpNhwg5K1vGsG2d/b2oMnVFRaYXAaAAAAAICjBfh6amR0sCRW/cF9UPhzZXWFP1p9AgCAljMpNlzrfztWS342THf1qtKSnw3T+t+OdeuinyT1CfVTdKd2Kq+s1te0+wQAAAAAt1Tb7pN9/uAuKPy5sro9/roamwMAALg9i9mkuKhADQ22KS4q0C3be/6UyWTS1IH2G6xo9wkAAAAA7ml8TKjMJmnP8UKl5ZUYHQdoNgp/rowVfwAAAC3qmoH2VY3rDuaq4DTtPgEAAADA3QS191ZcVJAkaeUebvqE66Pw56rKS6TTP9g/DmCPPwAAgJbQO9RPvUPbq7yqWl/vpd0nAAAAALijyQNo9wn3QeHPVdW2+fRqL3n7G5sFAADAjU0dYO+u8Nmu4wYnAQAAAAC0hIn97YW/7WknlVlw2uA0QPNQ+HNVhen2R/8uksn999gBAAAwytSB9gvAbw/lqqCEdp8AAAAA4G5C/a0a2r2jJOlLVv3BxVH4c1W1K/7Y3w8AAKBF9QzxU98wP1VW2/TlXi4AAQAAAMAdTY6l3SfcA4U/V1WQYX9kfz8AAIAWd83AcEnSil1s9A4AAAAA7mhSTeHv+yP5OlFUZnAaoOko/LmqwprCnz+FPwAAgJY2ZYC98Pddcq5+KC43OA0AAAAAwNG6dvTVwK4BqrZJX9HtBS6Mwp+rovAHAADQanp0aq+YcH97u889XAACAAAAgDuqXfW3knafcGEU/lxV3R5/FP4AAABaw9Tadp+7afcJAAAAAO5ocqz9um/j4TydLKHbC1wThT9XVZBuf2SPPwAAgFZRu8/fhsN5yjvFfg8AAAAA4G6igtupb5ifKqttWrU32+g4QJNQ+HNF5cVS6Un7x/6dDY0CAADQVnQPaqcBXQJUVW3TStp9AgAAAIBbot0nXB2FP1dU2+bTy0+yBhibBQAAoA2pa/e5i3afAAAAAOCOatt9fnsoV0WlFQanARqPwp8rKsywP7LaDwAAoFVNHWC/ANyUkqcTRbT7BAAAAAB30zu0vXp0aqfyqmqt2Z9jdByg0Sj8uaKCmsIf+/sBAAC0qohAXw2K6KBqm2j3CQAAAABuyGQyaTLtPuHCKPy5otpWn6z4AwAAaHXX1Kz6+2zncYOTAAAAAABaQm27z/8dyFFJeaXBaYDGofDnigrT7Y/+XY3NAQAA0AZNHmC/8zPxSL5yCksNTgMAAAAAcLT+nf3VtaOPSiuq9c2BE0bHARqFwp8rYsUfAACAYbp29NUl3TrIZpO+oO0LAAAAALidM9t9ct0HV0PhzxWxxx8AAIChrhlovwFrxa5Mg5MAAAAAAFrCpJp2n2v256isssrgNEDDUfhzRYU1hT9/Cn8AAABGmFLT7vP7o/nKKqDdJwAAAAC4m0siOijU31unyiq1/lCu0XGABqPw52rKi6XSk/aPKfwBAAAYIjzAR8O6d5TNJn2+m1V/AAAAAOBuzGaTJvWn3SdcD4U/V1O7v5+Xn2T1NzYLAABAGzZ1oL3tywoKfwAAAADgliYPsF/3rdqbrYqqaoPTAA1D4c/VFKTbH9nfDwAAwFBTBoTLZJK2Hv1Bx0+eNjoOAAAAAMDBLo0MVHB7LxWcrtCmlDyj4wANQuHP1dSu+PPvbGwOAACANi7U36pLIwMl0e4TAAAAANyRxWzS+Bh7u8/Pd9PuE66Bwp+rKcywP1L4AwAAMNw1Ne0+P9tF4Q8AAAAA3NHkWHvhb9XeLFVV2wxOA1wchT9XU1f462psDgAAAGhSbJjMJmnHsZM6ll9idBwAAAAAgIONiA5SgI+nck+V6/sj+UbHabCqaps2Hs7TxzsytPFwHkXLNsTD6ABoJFp9AgAAOI0QP6viooK0MSVPXyRl6v4x0UZHAgAAAAA4kKfFrHH9QvXBtnStTMrSZT2CjI50USuTMrXw073KLCitOxYeYFVCfIwmxYYbmAytgRV/rqagZsVfQBdjcwAAAECSNLWm3ecK2n0CAAAAgFuqbfe5MilL1U6+cm5lUqZmL9l2VtFPkrIKSjV7yTatTOLa1d1R+HM1da0+KfwBAAA4g9p2nzvTC5SWR7tPAAAAAHA3o3sFq52XRVmFpdqRftLoOOdVVW3Twk/3qr7SZO2xhZ/upe2nm6Pw50rKi6XSk/aPKfwBAAA4heD23hoRbW/1smK369w5yX4PAAAAANAwVk+Lru4XKsm+6s9ZJabmn7PS70w2SZkFpUpMdZ29CtF4TlH4e/HFFxUZGSmr1aq4uDglJiae99wrr7xSJpPpnD9Tp06tO+eee+455+uTJk1qjbfSsmr39/Pyk6z+xmYBAABAnWsG2vdfXrH7uMFJGmZlUqZG/3mNpr2ySXOX7dC0VzZp9J/X0PIFAAAAAM6jtt3nF0mZstmc68bJwtIKLdl0VP/v/Z0NOj+n6PzFQbg+wwt/y5cv17x585SQkKBt27Zp0KBBmjhxonJycuo9/8MPP1RmZmbdn6SkJFksFt1yyy1nnTdp0qSzznvnnXda4+20rIJ0+yP7+wEAADiVif3DZDGblJRRqCO5xUbHuSD2ewAAAACAxruiTydZPc06ln9ae44XGh1HNptNW4/m69fv7VTcH1drwUdJSv/hdIOeG+JnbeF0MJLhhb9nnnlGs2bN0syZMxUTE6PFixfL19dXr732Wr3nBwYGKiwsrO7PqlWr5Ovre07hz9vb+6zzOnbs2Bpvp2XVrvjz72xsDgAAAJwlsJ2XRrpAu0/2ewAAAACApvH18tCVvUMk2Vf9GSW/uFz//jZFE55dp5te2qj3t6brdEWVeoW012NT+ynEz1um8zzXJCk8wKrhUYGtGRmtzNDCX3l5ubZu3apx48bVHTObzRo3bpw2btzYoDFeffVV3X777WrXrt1Zx9euXauQkBD16dNHs2fPVl5enkOzG6Iww/7I/n4AAABO55qB4ZKkz3Y5b+GP/R4AAAAAUKLc8QAAZ7hJREFUoOkmD6ht95nVqu0+q6ttWn8oV3Pe3qbLnlqtP6zYp0M5p+TjadEtQ7vqg9kj9dXDYzTr8h568rr+knTe4l9CfIws5vN9Fe7Aw8gXz83NVVVVlUJDQ886Hhoaqv3791/0+YmJiUpKStKrr7561vFJkybpxhtvVFRUlA4fPqxHH31UkydP1saNG2WxWM4Zp6ysTGVlZXWfFxbal+lWVFSooqKiKW+tRZhPHpNFUlX7MFU7US5Hq/2eO9P3vi1iHozHHDgH5sE5MA/OgXm4sLG9g+VhNmlfZqEOHD+pHp3aXfxJTdDUedibWah/rDnYoHMzTxarooI9pc/HWX8WnC0PAAAA4G7G9g2Rl8WslBPFOpRzSr1D/Vr09bILS/XelmNavuWYjuX/2MZzQJcA3T48QtcO6iw/q+dZz5kUG66XZgzRwk/3nnXjZzsvi56+dZAmxYa3aGYYz9DCX3O9+uqrGjBggIYPH37W8dtvv73u4wEDBmjgwIGKjo7W2rVrdfXVV58zzqJFi7Rw4cJzjn/11Vfy9fV1fPAmuuzwDoVK2nU0X2mff250nBa3atUqoyNAzIMzYA6cA/PgHJgH58A8nF8vf7P2nTTr+f+u08SuLXv3Z0PmobRK2pZr0sZss9KKG35HZ8qeHfo8fXtz4rUJzvazUFJSYnQEAAAAwK35WT01ulew1uzP0Re7s1qk8Fdlk1bvz9H7245rzf4c1e7E4Gf10PWDu+i2SyMU2yXggmNMig3X+JgwJabm65uDOVr8TYo8LSZd1TfE4XnhfAwt/AUHB8tisSg7O/us49nZ2QoLC7vgc4uLi7Vs2TI9+eSTF32dHj16KDg4WMnJyfUW/ubPn6958+bVfV5YWKiIiAhNmDBB/v7Oc6ezxyt/kgqlASMnKjZ6rNFxWkxFRYVWrVql8ePHy9PT8+JPQItgHozHHDgH5sE5MA/OgXm4uNNhGXrkv3uUXBag56eMbJHXuNg82Gw27c4o1Ltb0/XZriwVl1dJkjwtJl3dp5M2H/lBJ0sq6t3nzyQpLMBbc24bQ+uXC3DWn4XaziUAAAAAWs6k2DB74S8pU3PH9XLYuGl5JXon8Yje3mpRwaYddceHRwbqtksjNGVAuHy8zu1oeD4Ws0kjooM0PCpQH20/rqzCUq3el6MpA1jx5+4MLfx5eXlp6NChWr16ta6//npJUnV1tVavXq05c+Zc8LnvvfeeysrKNGPGjIu+Tnp6uvLy8hQeXv//ob29veXt7X3OcU9PT6e6kK/d488jsLvkTLlaiNN9/9so5sF4zIFzYB6cA/PgHJiH85s8oIse/2SvDuac0pH8UvVqwbYvP52HgtMV+nhHht5JPKZ9mT8WgKKC2+n2SyN009CuCm7vrZVJmZq9ZJtMUr3Fv4T4/rJ6e7VYbnfibD8LzpQFAAAAcFcTYkL1qNmk/VlFOpJbrMjgpm/zUFZZpa/2ZGv598e0Pjm35qhJge08dfPQCN06LEI9Q9o3K6/FbNLNQ7vqH/9L1rtbjlH4awMMb/U5b9483X333Ro2bJiGDx+u5557TsXFxZo5c6Yk6a677lKXLl20aNGis5736quv6vrrr1dQUNBZx0+dOqWFCxfqpptuUlhYmA4fPqzf/OY36tmzpyZOnNhq78vhyk5JpQX2j/07G5sFAAAA9Qrw9dSYXp20en+OVuzO1C9beL8Hm82mrUd/0DuJx7Ri93GVVlRLkrw8zJoSG6bbh3dTXFSgTKYfV++db78HSUq4Nob9HgAAAADgAjr4emlEdJC+PZSrL5KyNPvK6EaPcSi7SMu+P6YPt6XrhxL7Xt0mkzQqOki9zDn69bRxaudz7mKlpqot/K07eEKZBacVHuDjsLHhfMxGB7jtttv0t7/9TU888YQGDx6sHTt2aOXKlQoNDZUkpaWlKTMz86znHDhwQOvXr9e99957zngWi0W7du3Stddeq969e+vee+/V0KFD9e2339a7qs9lFB63P3r7S1bnaT8KAACAs00daC+crdiVeZEzm664Qnp9w1FNeHadbl68UR9sS1dpRbV6h7bXE9fEKPHRq/Xc7Zfosh5BZxX9ak2KDdf6347VO7Mu0/O3D9YlER0kSZknS885F2iuF198UZGRkbJarYqLi1NiYuJ5z73yyitlMpnO+TN16tS6c+65555zvj5p0qTWeCsAAACAJHu7T0n6Iqnh130l5ZV6b8sx3fTSBo1/dp1eXZ+qH0oqFOZv1UNje2rd/7tKr989VIODbPLycGzpJjK4neKiAlVtkz7cluHQseF8DF/xJ0lz5sw5b2vPtWvXnnOsT58+stnqa0wk+fj46Msvv3RkPOdQ0+aT1X4AAADObVxMqLwsZh3KOaUDWUXqE+aYVX82m00bU/L09qaj+iLJoirbAUmS1dOsawZ21rTh3TSkW4d6C331qd3vQZJ8vTw06z9b9N7WdM2b0FveHg3fNwK4kOXLl2vevHlavHix4uLi9Nxzz2nixIk6cOCAQkJCzjn/ww8/VHl5ed3neXl5GjRokG655Zazzps0aZJef/31us9d+iZPAAAAuJwJMWFa8FGSdqUX6JOdGbLZpBA/q4ZHBZ6zX/ru9AIt+z5Nn+w4rqKySkn267GxfUM0bXiExvTqJA+LvdBXUVHRYplvHRahzan5enfLMc2+Ilpm9nV3W05R+EMD1BX+uhibAwAAABfkb/XUmN6d9PW+bK3YdVx9wvo0a7wTRWX6YFu6ln9/TKm5xTVHTeoX5qfpl3XXdYM7y9/avL3drurz/9u777AorvUP4N/dpSNFOggIWLCAXdHELgq2aGIs2JJoTDQab2Ju9Jpi4erVmMTkmmvUGBT9mdhiSTQGO9hQbNhQRERBpdgAQeru+f2xYeNmkaILO+D38zz7yM6cmXl3DqP7+s6c4whna1OkZxdgb1w6BrTgzWakH4sXL8aECRM0UzksX74cv//+O1atWoV//etfOu3t7Oy03m/YsAEWFhY6hT9TU1O4uLhUXeBERERERGVwtDJFA8c6uJaRg6nrYzXLXW3MMHtgM7zU0AG/nr2NDSdTcOnOX3Owe9pZYHh7Dwxt6w4na7Nqjbmfvytm/3YJN+8/RsyNB+joY1/+RlQjsfAnZQcXAHIF0G36X0N9ljzxF7UIUCmBHjMNFx8RERERlWpgS1fsu5yOnRdS8WHvxhV+Cq+ESiVw5No9rI9Jxt64dBSr1KNdWJooMKCFK9wLbuDdoR1hYmKil3iNFHIMb+eBJQeuYX1MMgt/pBeFhYU4ffo0Zs78K2eRy+UIDAxEdHR0hfYRFhaGESNGwNLSUmt5ZGQknJycULduXfTs2RPz5s3Tmf+diIiIiKiqRFxMxbWMHJ3lqVn5mLjuDIwVMhQp1XmciUKOYD8XjGjvgY4+9gZ70s7cRIGBLd2wPiYZm06lsPBXi7HwJ2VyBXBwvvrnrFvqP23c1UW/g/OBHp8aLjYiIiIieqpeTZ1hYiTH9bu5uJL2CE1dKzZHc1pWPjafSsHGUym49TBPs7ylhy1C2ntgYEs3mMgFdu26UeliYnmGtffAdwev4ei1+7hxLxdeDpblb0RUhnv37kGpVGrmby/h7OyMK1eulLt9TEwMLl68iLCwMK3lwcHBeO211+Dt7Y3ExER88skn6Nu3L6Kjo6FQlD5MbUFBAQoKCjTvs7PVd10XFRVV6XBKpKvkfPO8Gxb7QRrYD9LAfjA89oE0sB8qTqkSmPPbpTLbFCkFGjqqn+4b1NIVdS3UN20qlcVQKsvYror74bVWLlgfk4xdF1LxWV9fWJmxRFQaKV4PlYmFvSpl3aar/zw4H7BroP459TwQ/7u66FeynoiIiIgkpY6pEXr4OmL3pXSsiEpEjyZOT53voVipQtTVu1gfk4wDVzLw58N9sDIzwmut62FEB0+twmFVJR7udS3QrbEjIuPvYsPJFPyrb5MqOQ5RRYWFhcHf3x8dOnTQWj5ixAjNz/7+/mjRogUaNGiAyMhI9OrVq9R9LViwAHPnztVZvmfPHlhYWOg3cKqQvXv3GjoEAvtBKtgP0sB+MDz2gTSwH8qXkCVDWnb586IHOTyC08NLiI4su0hYmqrqByEAF3MF0vJU+GL9XrzkLKrkOLWFlK6Hx48fV7gtC39S92TxD2DRj4iIiKiGcK+rLiZsj72D7bHqYdtL5nsI9nPFrYePselkCjaduoW07HzNdu296mJEe0/083eFuUn5yaQ+hXTwRGT8XfxyOgXTejeGiZG8Wo9PtYuDgwMUCgXS09O1lqenp5c7P19ubi42bNiA0NDQco/j4+MDBwcHXLt27amFv5kzZ2LatGma99nZ2fDw8ECfPn1gbV2xJ3JJP4qKirB371707t0bxsbPNz8pPTv2gzSwH6SB/WB47ANpYD9U3I7zqUDchXLbNfBrhX4tXCu17+rohzTbG1gYcRXxRXaY1y+gSo5R00nxeigZtaQiWPirCbpNBw7+B4AA5MYs+hERERFJXMTFVKw6kqSzPO3P+R6auVrjclo2xJ83V9a1MMZrbdwxor0HGjlbVXO0f+nZxAlOVqbIeFSAvXHp6F/JJJXoSSYmJmjbti3279+PwYMHAwBUKhX279+PKVOmlLnt5s2bUVBQgNGjR5d7nFu3buH+/ftwdX3676upqSlMTU11lhsbG0smkX/R8NxLA/tBGtgP0sB+MDz2gTSwH8rnaluxaRFcbS2f+VxWZT8MaeuJr/YkIDYlCzce5Bs0B5U6KV0PlYmDt/DWBFGLAAhAYQKoiv58T0RERERSpFQJzN0Rh9IGTClZFpeqLvp18rHHkpDWOP5JL3w+oJnBEy5jhRzD23sAANbHJBs0Fqodpk2bhpUrV2LNmjW4fPkyJk2ahNzcXLz11lsAgLFjx2LmzJk624WFhWHw4MGwt7fXWp6Tk4OPP/4Yx48fx40bN7B//34MGjQIDRs2RFBQULV8JiIiIiJ6sXXwtoOrjRmeNuu6DOrRXjp421VnWBXmaGWKnk2cAACbT98ycDRUFVj4k7qoRephPnt8Cnx+V/3nwfks/hERERFJVEzSA6Rm5Zfb7pthLbH+nY54paUbTI2qd0jPsgxr5wGZDDhy7R5u3s81dDhUww0fPhxfffUVZs2ahVatWiE2NhYRERFwdnYGACQnJyM1NVVrm/j4eBw5cgTjx4/X2Z9CocD58+fxyiuvoHHjxhg/fjzatm2Lw4cPl/pEHxERERGRvinkMswe2AwAdIp/Je9nD2ymM7+7lAxrp77hc+uZWyhSqgwcDekbh/qUsieLfiXDe/59zj8O+0lEREQkKRmPyi/6AYBcokmgh50FujZyRNTVu9hwMgUzgpsYOiSq4aZMmfLUoT0jIyN1lvn6+kKI0p6ZBczNzbF79259hkdEREREVGnBfq5YNroN5u6I07rx0+WJed2lrLuvIxytTHH3UQEOXslAn+Zlz8FNNQsLf1KmUmoX/UqUvFcpqz8mIiIiIiqTk5WZXtsZQkgHT0RdvYvNp1LwYWBjmBhxoBAiIiIiIqInBfu5onczF8QkPUDGo3w4WamH95Tyk34ljBRyvNamHlZEXcemU7dY+KtlWPiTsh66c11o8Ek/IiIiIkkqme8hLSu/1Hn+ZFDfBSrV+R4AoFdTJ83dn/svp6Ovv7TvViUiIiIiIjIEhVyGTg3sy28oQUPbemBF1HUcjM9ARnY+nKyle3MqVQ5v3SUiIiIi0qPaMN+DsUKOYe3cAQA/xyQbOBoiIiIiIiLSt4ZOddC2fl0oVQJbz942dDikRyz8ERERERHpWcl8Dy422ndMutiYYdnoNpKf7wEARrT3BAAcTriHlAePDRwNERERERER6dvwdh4AgE2nUp46zzbVPBzqk4iIiIioCtTk+R4AwMPOAl0aOeBwwj1sOJmMj4OaGDokIiIiIiIi0qN+LVwxZ8clXL+bizPJD9G2vnSnpKCK4xN/RERERERVpGS+h0Gt6qFTA/saU/QrMbKD+qm/TaduoUipMnA0REREREREpE91TI3Q/8853TedvGXgaEhfWPgjIiIiIqJSBTZzhkMdU9x9VID9lzMMHQ4RERERERHp2bD26uE+d56/g9yCYgNHQ/rAwh8REREREZXKWCHHsHbuAICfY5INHA0RERERERHpW7v6deHtYIncQiV2XUg1dDikByz8ERERERHRU41orx7u83DCXaQ8eGzgaIiIiIiIiEifZDIZhv55w+fmUxzuszZg4Y+IiIiIiJ7K094CXRo5QAhg48kUQ4dDREREREREejakjTvkMiDmxgNcv5tj6HDoObHwR0REREREZQrpoH7qb9OpFBQpVQaOhoiIiIiIiPTJ2doM3X2dAAC/nOZTfzUdC39ERERERFSmwKbOcKhjgoxHBThwJcPQ4RAREREREZGelczvvuXMLRTzhs8ajYU/IiIiIiIqk4mRHK+39QAArI9JNnA0REREREREpG89mzjDztIE6dkFOJxwz9Dh0HNg4Y+IiIiIiMo1or268Bd19S5uPXxs4GiIiIiIiIhIn0yM5Hi1dT0AnN+9pmPhj4iIiIiIyuXlYImXG9pDCGATk0AiIiIiIqJaZ1g79Q2f+y6n435OgYGjoWfFwh8REREREVVISAdPAMDGUymc84GIiIiIiKiW8XWxQksPWxSrBLadvW3ocOgZsfBHREREREQV0qeZC+z/nPPhYPxdQ4dDREREREREejasnTsAYNOpFAghDBwNPQsW/oiIiIiIqEJMjOR4/c8k8OcTNw0cDREREREREenbwJZuMDWS42p6Ds7fyjJ0OPQMWPgjIiIiIqIKG9FePdxn5NW7uJ2ZZ+BoiIiIiIiISJ+szYzRz98VgPqpP6p5WPgjIiIiIqIK83awxEsN7CEEsPEkk0AiIiIiIqLaZuifI738FnsHeYVKA0dDlcXCHxERERERVUpIB/VTf5tOpqBYqTJwNERERERERKRPHb3t4WFnjkcFxdh9Kc3Q4VAlsfBHRERERESV0qe5M+wsTZCWnY/I+LuGDoeIiIiIiIj0SC6XYWhbDwAc7rMmYuGPiIiIiIgqxdRIgdfbqod+WR+TbOBoiIiIiIiISN+GtHWHTAYcS7yPlAePDR0OVQILf0REREREVGkj2qvv/jwYn4E7mXkGjoaIiIiIiIj0qZ6tOTo3dAAAbD59y8DRUGWw8EdERERERJXm41gHHX3soBIc+oWIiIiIiKg2GtZOfcPnL6dSoFQJA0dDFcXCHxERERERPZOQDp4AgI0nmQQSERERERHVNr2bOcPG3Bh3svJx9No9Q4dDFcTCHxERERERPZNgPxfUtTBGalY+oq5mGDocIiIiIiIi0iMzYwUGt3IDwJFeahIW/oiIiIiI6JmYGinwelt3AMDPJ5gEEhERERER1TbD/pzffc+ldGQ+LjRwNFQRLPwREREREdEzG/HncJ8HrqQjNSvPwNEQERERERGRPjV3s0FzN2sUKlX4NfaOocOhCmDhj4iIiIiInlkDxzoI8LaDSgCbTt4ydDhERERERESkZ8PaqZ/643CfNQMLf0RERERE9FxGBqif+tt4MhlKlTBwNERERERERKRPg1q5wUQhx6U72bh4O8vQ4VA5WPgjIiIiIqLnEtTcBbYWxriTlY9DV+8aOhwiIiIiIiLSI1sLE/Rp7gwA+OU0R3qROhb+iIiIiIjouZgZKzCkjTsA4OeYZANHQ0RERERERPpWMtzntrO3kV+kNHA0VBYW/oiIiIiI6LmFdFAngQeuZCAtK9/A0RAREREREZE+vdzQAW42ZsjKK8K+y+mGDofKwMIfERERERE9t4ZOVujgZQelSmAzJ3wnIiIiIiKqVRRyGV5vqx7pZdMpDvcpZSz8ERERERGRXoQEqJ/623AyBUqVMHA0REREREREpE+vt1XnfIcT7uJOZp6Bo6GnYeGPiIiIiIj0oq+fK2zMjXE7Mw+HE+4aOhwiIiIiIiLSI097C3TysYcQwC+n+dSfVLHwR0REREREemFmrMCQNuqhX9bHJBs4GiIiIiIiItK3Ye3VOd/m0ylQcaQXSWLhj4iIiIiI9Cakg3rol32XM5CRnW/gaIiIiIiIiEifgpu7wsrUCCkP8nA86b6hw6FSsPBHRERERER608jZCu296kKpEth0KsXQ4RAREREREZEemZso8EorNwDA5lO1b7hPpUrgRNIDnL4nw4mkBzVy/noW/oiIiIiISK9COngCANbHcOgXIiIiIiKi2mZYO/VIL7supCI7v8jA0ehPxMVUdP7iAEavOoW1CQqMXnUKnb84gIiLqYYOrVJY+CMiIiIiIr3q5+8KazMj3M7Mw+Fr9wwdDhEREREREelRC3cb+DpboaBYhR3n7hg6HL2IuJiKSevOIDVLe8qKtKx8TFp3pkYV/1j4IyIiIiIivTIzVuC1NuoJ39efSDZwNERERERERKRPMpkMQ9upc75NtWC4T6VKYO6OOJQ2Xk3Jsrk74mrMsJ8s/BERERERkd6VDPe573I6MrLzy2lNRERERERENcmrrevBSC7DuZRMxKc9MnQ4zyUm6YHOk35PEgBSs/IRk/Sg+oJ6Diz8ERERERGR3vm6WKFt/booVglsPl3z7wAlIiIiIiKiv9jXMUVgU2cAwOZTKQaO5vlkPKrYzaoVbWdoLPwREREREVGVKHnqb8PJZKhqyJAoREREREREVDHD2quH+9x29jYKi1UGjubZ2VuaVKidk5VZFUeiHyz8ERERERFRlRjQwhXWZkZIeZCHo4n3DB0OERERERER6VHXRo5wsjLF/dxCHLiSYehwnklhsQprjt0os40MgKuNGTp421VLTM+LhT8iIiIiIqoSZsYKvNZGfQfo+phkA0dDRERERERE+mSkkGNIW3XOVxOH+8wvUmLiutPYezkDRnIZAHWR70kl72cPbAaF/O9rpYmFPyIiIiIiqjIjOngAAPZcSsfdRwUGjub5KVUCJ5Ie4PQ9GU4kPYCSQ5gSEREREdELbOifhb+D8RlIz64Zc+AB6qLfO/93GgeuZMDUSI7Vb7XH8tFt4GKjPZyni40Zlo1ug2A/VwNFWnlGhg6AiIiIiIhqryYu1mjjaYszyZnYfDoF73VvaOiQnlnExVTM3RGH1Kx8AAqsTTgFVxszzB7YrEYlgURERERERPri41gH7b3q4uSNh9hy5laNyPnyCpV4e+1JHL12H+bGCoS92Q4vNXAAAPRu5oLoaxnYc/gE+nQJQKeGTjXmSb8SfOKPiIiIiIiqVEgHTwDAhpgUqGroE3IRF1Mxad2ZP4t+f0nLysekdWcQcTHVQJEREREREREZ1tB26pFeNp+6BSGknfPlFhTjzdUxOHrtPixNFFgzroOm6AcACrkMAd52aOsgEOBtV+OKfgCf+HsuSqUSRUVFhg6j1ikqKoKRkRHy8/OhVCoNHQ4RERERPacBLdwQujMOyQ8e41jifXRu5FD+RhKiVAnM3RGH0tJXAfWcD3N3xKF3M5camRRS6ZjvVQ3me9JQXFxs6BCIiIioFunv74q5v11C0r1cnLr5EO297AwdUqke5RfhrdUncermQ9QxNcKace3Rtr40Y30eLPw9AyEE0tLSkJmZaehQaiUhBFxcXJCSkgKZjP9xYihCCFhZWUn+Dg0iIiKSPnMTBV5tXQ9ro29ifUxyjSv8xSQ90HnS70kCQGpWPmKSHqBTA/vqC4yqBPO9qsV8TxqEEHB2dkZRURGMjY0NHQ4RERHVcJamRhjQwg0bT6Vg08kUSRb+svOL8MaqGJxNzoSVmRH+b3wAWnnYGjqsKsHC3zMoSQKdnJxgYWHBZEXPVCoVcnJyUKdOHcjlHI3WEIQQyMnJQUFBATIyMuDu7m7okIiIiKiGG9HeE2ujb2L3pTTcfVQARytTQ4dUYRmPKjZBfUXbkbQx36tazPekobi4GDdu3EB6ejq8vLz4e05ERETPbVh7d2w8lYLfL6Ri9ivNUcdUOuWnrMdFGLPqBM7fyoKthTHWjQ+AXz0bQ4dVZaRz5msIpVKpSQLt7Xk3b1VQqVQoLCyEmZkZE0EDMjU1RX5+PrKzs6FUKqFQKAwdEhEREdVgzdys0crDFrEpmdhy5hYmdmtg6JAqLDe/YkPiOVmZVXEkVNWY71U95nvSoFKpYGdnh8zMTBQXF/OpPyIiInpubTzrwsfREtfv5mLX+VQMa+9h6JAAAA9zCzHqxxOIS82GnaUJ1o0PQDM3a0OHVaUk8S176dKl8PLygpmZGQICAhATE/PUtt27d4dMJtN59e/fX9NGCIFZs2bB1dUV5ubmCAwMREJCgl5iLZnjwcLCQi/7I5IyExMTAODcJkRERKQXIzt4AgA2xCRDpZL+cOJCCKw5dgOzf7tUZjsZAFcbM3Twlt5wNlQ5zPfoRWJkpL4XnHMtEhERkT7IZDIMa6cu9m06lWLgaNTu5RQgZOVxxKVmw6GOCdZP6Fjri36ABAp/GzduxLRp0zB79mycOXMGLVu2RFBQEDIyMkptv3XrVqSmpmpeFy9ehEKhwNChQzVtFi1ahCVLlmD58uU4ceIELC0tERQUhPx8/Q29w2Ew6EXA33MiIiLSpwEtXWFlaoQb9x/j+PX7hg6nTNn5RXjvpzOY/dslFKkEWrjbQAZ1ke9JJe9nD2wGhZzfnWoLfg8mIiIiIqq811rXg0Iuw6mbD5F4N8egsWQ8ykfID8dxJe0RHK1MseGdjvB1sTJoTNXF4IW/xYsXY8KECXjrrbfQrFkzLF++HBYWFli1alWp7e3s7ODi4qJ57d27FxYWFprCnxAC3377LT777DMMGjQILVq0wNq1a3Hnzh1s3769Gj8ZVTUvLy98++23VX6cGzduQCaTITY2VrPs6NGj8Pf3h7GxMQYPHozIyEjIZDJkZmZWeTxERERENZWFiREGt64HAPg5JtnA0Tzd+VuZGLDkCP64mAZjhQyzBjTDr5NfxrLRbeBioz2cp4uNGZaNboNgP1cDRUtUezHnIyIiIqpZnKzN0MPXEQCw+dQtg8WRnp2PET8cR0JGDlyszbDxnY5o6PRiFP0AAxf+CgsLcfr0aQQGBmqWyeVyBAYGIjo6ukL7CAsLw4gRI2BpaQkASEpKQlpamtY+bWxsEBAQUOF9VgelSiA68T5+jb2N6MT7UFbTUEfR0dFQKBRaQ6OWKC3ZeVJ4eDhkMhmaNm2qs27z5s2QyWTw8vLSWZeXlwc7Ozs4ODigoKCgQnFmZ2fjs88+Q5MmTWBmZgYXFxcEBgZi69atEKJ6h4Xy8PBAamoq/Pz8NMumTZuGVq1aISkpCeHh4XjppZeQmpoKG5vaOyEoERERkT6E/Dnc5+5LabifU7HvhtVFCIHwo0kYsuwYkh88hntdc/wy8SWM6+wNmUyGYD9XHJnRE+vGtcPYRkqsG9cOR2b0ZNGPSsWcr2zZ2dn49NNPmfMRERER1TJD/xzuc8uZWyhWqqr9+Hcy8zB8RTSu381FPVtzbHy3I3wc61R7HIZkZMiD37t3D0qlEs7OzlrLnZ2dceXKlXK3j4mJwcWLFxEWFqZZlpaWptnH3/dZsu7vCgoKtJKT7OxsAOr5Hf4+t1lRURGEEFCpVFCpnu2XNuJiGkJ3XkZa9l9Dj7pYm2HWgKYI9nN5pn1W1I8//ogpU6Zg1apVuHXrFtzc3DTrSj7P0z6bSqWCpaUlMjIycPToUXTq1Elrv56enlr7KbF582Y0b94cQghs3boVw4cPLzPGhw8fIigoCDk5OQgNDUX79u1hZGSEqKgoTJ8+Hd27d4etrS0AaPqiKslkMjg5OQH467MlJibinXfe0Tp/Tk5OEEI8c5JaWFiomVNPCko+hxACRUVFUCgUBo7oxVPy9w/nWDQs9oM0sB+kgf0gDTW9Hxo5mqOFuzXO38rGppPJeLuzl6FDAgBk5xVh5vZL2BOnnnKgd1MnLHy1OazNjXXOdRt3K9x3EGjjbgWVshgqiUyPVVN/J2qjiIupmLsjDqlZf+V8rjZmmD2wWZUXisPCwvD+++8jLCwMd+7c0cpZKqIk54uOjtbK+cLCwjQ5399t2bJFk/Nt37693JwvKysL/fv3R1ZWFubNm6eT8/Xs2VOT81UHhUIBFxftXDwxMRETJ06Eu7u7Ztnf21SW1HI+IiIioqrQs4kTHOqY4O6jAkTG30VgM+fyN9KTlAePMfLH40h5kAf3uuZYP6EjPOxevPm7DVr4e15hYWHw9/dHhw4dnms/CxYswNy5c3WW79mzR2dSdyMjI7i4uCAnJweFhYWVPtb++Pv457Yr+HtpKD07H5N/PouvXm2CXr72ld5vReTk5GDTpk04cOAAUlJSsGLFCnz00Uda6wEgNzdXU/x8Un5+PhQKBYYMGYIffvgBzZs3BwDcvn0bUVFRmDRpErZs2aKz7cqVK/Haa69BCIGVK1eib9++ZcY5ffp0pKSk4OTJk3B1/SspHj58OPr37w+VSoXs7GyoVCrk5+drjrd06VL89NNPuHnzJmxtbREcHIy5c+eiTh11NT85ORnTp0/H8ePHUVRUBE9PT8ydOxd9+vRBZmYmPv74Yxw8eBC5ublwc3PDtGnTMGrUKCQnJ6Nly5Y4dOgQbGxs0LJlSwDA22+/jbfffhtLly6Fp6cnBg4ciBs3bmjuAI2OjkZoaChiY2NhZ2eHAQMGYNasWZqnU1u0aIExY8YgMTERu3btwoABA/D9999XvEOrSX5+Pg4dOoTi4mJDh/LC2rt3r6FDILAfpIL9IA3sB2moyf3QzESG81BgVVQ8XLPiYOjp1G7mAGuuKnC/QAaFTGBQfRW62tzBkYN3ytxOan3w+PFjQ4dAUBf9Jq07o5PzpWXlY9K6M1U6NGxOTg42btyIU6dOIS0tDeHh4fjkk08qtQ8jIyOMHDkSq1at0hT+bt26hcjISHz44YdYv369zjZhYWEYPXo0hBAICwsrt/AXGhqKGzdu4OrVq1qFycaNGyMkJARmZmalbrd48WKsXr0a169fh52dHQYOHIhFixZpcr6bN29iypQpOHLkCAoLC+Hl5YUvv/wS/fr1w8OHDzFlyhTs2bMHOTk5cHd3xyeffIK33noLN27cgLe3N86ePQtbW1t4e3sDAMaNG4dx48Zh9erV8PLyQo8ePfDw4UNNUfLIkSOYOXMmTp06BQcHB7z66qtYsGCBJufz8vLC+PHjkZCQgO3bt+O1115DeHh4pfqDiIiIqKYxVsjxaut6WHk4CZtOpVRb4S/5/mOErDyO25l5qG9vgfUTOsLN1rxaji01Bi38OTg4QKFQID09XWt5enp6uXfS5ebmYsOGDQgNDdVaXrJdenq6VtEoPT0drVq1KnVfM2fOxLRp0zTvs7Oz4eHhgT59+sDa2lqrbX5+PlJSUlCnTh1NMiKEQF5R+bf5KlUCi/Yn6SSAACAAyAB8uT8Jgf7uUMjL/98Pc2NFpSad/+WXX9CkSRO0bdsWb775JqZNm4Y5c+Zo9lGSLFlaWup8bgAwMzODTCbDu+++i549e2Lp0qWwsLDAd999h6CgIHh6ekIul2ttm5iYiJMnT2L79u0QQuDTTz/Fw4cPUb9+/VJjVKlU2LZtG15//XU0btxY5/M9uW+5XA4zMzPNspJYvL29cf36dUyZMgXz58/H0qVLAaj7WalUIioqCpaWloiLi4O1tTWsra3x6aef4tq1a9i1axccHBxw7do15OXlwdraWuu8NG3aFLdv30bTpk0xd+5cDBs2DDY2Njhx4gQAwMrKCtbW1khMTMTQoUPx73//G+Hh4bh79y6mTp2KTz/9VDN/pVwux//+9z98/vnn+Pe//63z+QxNCIH79+/DzMwMXbt2fWryTVWnqKgIe/fuRe/evWFsbGzocF5Y7AdpYD9IA/tBGmpDP3QrKMaOL6NwN18J+6Yd0dHHziBxCCGw5ngyvou5iiKlgHtdc/x3WAu0cC97KD+p9kFpN+/R86tovgeoc77Zv10qM+eb81scXm7oUCU536ZNm9CkSRP4+vpi9OjR+OCDDzBz5sxK7QNQF7y6d++O//73v7CwsEB4eDiCg4N1RtYB1DlfdHS0ZojODz/8EDdv3iwz59u6dStGjhxZ6tOIJflXaeRyOZYsWaLJ+d577z1Mnz5dcwPl5MmTUVhYiEOHDmlyvpL9ff7554iLi8Mff/yhlfP9Xcmwn76+vggNDcXw4cO1cr4nP3dwcDDmzZuHVatW4e7du5gyZQqmTJmC1atXa9p99dVXmDVrFmbPnv3Uz0VERERU2wxt54GVh5Nw4EoG7j4qgKOVaZUeL+leLkJ+OI607Hz4OFji5wkddeZnf5EYtPBnYmKCtm3bYv/+/Rg8eDAAdRKwf/9+TJkypcxtN2/ejIKCAowePVprube3N1xcXLB//35NoS87OxsnTpzApEmTSt2XqakpTE11f/GMjY11EnmlUgmZTAa5XA65XD1F4uPCYvjNef67fQWAtOwCtAzdV6H2caFBsDCp+PCLq1evxujRoyGXy9GvXz+MHz8ehw8fRvfu3QFA83me/GxPKlnWtm1b+Pj4YOvWrRgzZgzWrFmDxYsX4/r161rtAPUcEX379oW9vfopxqCgIKxZswZz5swpNcZ79+7h4cOHmqJfaXE86ck2H374oWa5j48P5s2bh4kTJ2LZsmUAgJSUFAwZMkTzxF7Dhg017VNSUtC6dWvN06M+Pj46n1sul8PY2Bhubm6QyWSwtbXVJKp/P3dffPEFRo0apYnJ19cXS5YsQbdu3bB8+XJNEa1nz5745z//WeZnNJSSYU1lMlmp1wJVH55/aWA/SAP7QRrYD9JQk/vB1tgYg1vVw08nkrHpzB108a2+oV9KZD0uwse/nMeeOPVNiMHNXfDF6y1gY17xcyq1PpBSLLVJXpESzWbt1su+1DlfPvzn7KlQe3XOV/G0veTJOwAIDg5GVlYWoqKiNDlfRbVu3Ro+Pj745ZdfMGbMGISHh2vlfE9atWoV+vbti7p16wJQ53yrV68uM+fLzMxEkyZNKhUTAHzwwQean728vDQ5X0nhLzk5GUOGDIG/vz8A7bwuOTkZrVu3Rrt27TTbl6Zk2E+ZTAYbG5un3pS8YMECjBo1ShNTo0aNNDnfsmXLtHK+J0faISIiInoRNHa2QisPW8SmZGL72duY0NWn/I2e0bWMHIxceRwZjwrQ0KkOfp4QACerF7foBwBlV1WqwbRp07By5UqsWbMGly9fxqRJk5Cbm4u33noLADB27FjMnDlTZ7uwsDAMHjxYU1AqIZPJ8MEHH2DevHn47bffcOHCBYwdOxZubm6a4uKLKD4+HjExMQgJCQGgHr5l+PDhWvMjVkbJcCdRUVHIzc1Fv379dNoolUqsWbNGqzg7evRohIeHP3VevueZxH3fvn3o1asX6tWrBysrK4wZMwb379/XDHk0depUzJs3Dy+//DJmz56N8+fPa7adNGkSNmzYgFatWmH69Ok4duzYM8cBAOfOnUN4eDjq1KmjeQUFBUGlUiEpKUnTriTpJCIiInrRhHRQzxW2+2Ia7ucUlNNav2JTMtH/u8PYE5cOE4Ucc19pjmWj21Sq6EcVt3TpUnh5ecHMzAwBAQGIiYl5atvu3btDJpPpvPr3769pI4TArFmz4OrqCnNzcwQGBiIhIaE6PoqkMedjzkdEREQkJcPaeQAANp1Kea7vgGW5mv4II36IRsajAjRxscKGdzq+8EU/QAJz/A0fPhx3797FrFmzkJaWhlatWiEiIkIzhEhycrLOU1/x8fE4cuQI9uwp/S7J6dOnIzc3F++88w4yMzPRuXNnREREVNlQhebGCsSFBpXbLibpAd5cfbLcduFvtUcH7/KHOzI3rvjTfmFhYSguLtYaSkUIAVNTU/zvf//TzEtXUaNGjcL06dMxZ84cjBkzBkZGur9Ku3fvxu3bt3Xmd1Aqldi/fz969+6ts42joyNsbW1x9erVSsVz48YNDBgwAJMmTcL8+fNhZ2eHI0eOYPz48SgsLISFhQXefvttBAUF4ffff8eePXuwYMECfP3113j//ffRt29f3Lx5E7t27cLevXvRq1cvTJ48GV999VWl4iiRk5ODd999F1OnTtVZ5+npqfm5ZO4HIiIioheNXz0btHC3wflbWdh6pmrvAC0hhMCqozew8I/LKFIKeNpZYOnINvAvZ2hPenYbN27EtGnTsHz5cgQEBODbb79FUFAQ4uPj4eTkpNN+69atWnOp379/Hy1btsTQoUM1yxYtWoQlS5ZgzZo18Pb2xueff46goCDExcVVSc5X0XwPYM5Xorycz8bGBleuXKlUPMz5iIiIiGqWgS1dEbrzEhIychCbkonWnnX1uv+4O9kYHXYCD3IL0czVGuveDoCdpYlej1FTGfyJPwCYMmUKbt68iYKCApw4cQIBAQGadZGRkTqTX/v6+kIIUWoSAaif+gsNDUVaWhry8/Oxb98+NG7cuMril8lksDAxKvfVpZEjXG3M8LTZFWQAXG3M0KWRY4X2V9F5GoqLi7F27Vp8/fXXiI2N1bzOnTsHNze3UidnL4+dnR1eeeUVREVFYdy4caW2CQsLw4gRI7SOGRsbixEjRjz1rlO5XI7hw4fjl19+wZ07d3TW5+TkoLi4WGf56dOnoVKp8PXXX6Njx45o3Lhxqdt7eHhg4sSJ2Lp1Kz766COsXLlSs87R0RFvvPEG1q1bh2+//RY//PBDRU+HjjZt2iAuLg4NGzbUeZmY8C8fIiIiIuCvp/7WxyRX2R2gJbIeF+Gd/zuNf++MQ5FSoJ+/C3ZO7cyiXxVbvHgxJkyYgLfeegvNmjXD8uXLYWFhoZn3+u/s7Ozg4uKiee3duxcWFhaawp8QAt9++y0+++wzDBo0CC1atMDatWtx584dbN++vUo+Q0XzPeZ8Fc/5XnvtNfz888/M+YiIiIhqMSszY/TzdwUAbDp1S6/7vng7CyN/PI4HuYXwr2eDnyew6PckSRT+XhQKuQyzBzYDAJ1EsOT97IHNKjTJe2Xs3LkTDx8+xPjx4+Hn56f1GjJkiE5CFh8fr5O4FRUV6ew3PDwc9+7dK3Vuhrt372LHjh144403dI45duxYbN++HQ8ePCg13nnz5qFevXro1KkT1q5di7i4OCQkJGDVqlVo3bo1cnJydLZp2LAhioqK8N133+H69ev4v//7PyxfvlyrzQcffIDdu3cjKSkJZ86cwcGDB9G0aVMAwKxZs/Drr7/i2rVruHTpEnbu3KlZ9yxmzJiBY8eOYcqUKYiNjUVCQgJ+/fXXcueuJCIiInqRvNLSDZYmCly/l4sTSaV/N9SH2JRM9FtyGHv/HNozdFBzLB3ZBtZmHNqzKhUWFuL06dMIDAzULJPL5QgMDER0dHSF9lFSWCp5aiopKQlpaWla+7SxsUFAQECF91mVmPNVLOf7/PPP4eHhgYCAAOZ8RERERLVYyXCfO87dQV6hUi/7PJeSiZErjyPzcRFaedhi3dsBsLVg0e9JBh/q80UT7OeKZaPbYO6OOKRm5WuWu9iYYfbAZgj2c9X7McPCwhAYGFjq0C5DhgzBokWLcP78eVhbWwMARowYodMuJSVFZ5m5uTnMzc1LPebatWthaWmJXr166azr1asXzM3NsW7dulKHRbGzs8OePXuwbNkyzJs3Dzdv3kTdunXh7++PL7/8stTP0bJlSyxevBhffPEFZs6cia5du2LBggUYO3aspo1SqcTkyZNx69YtWFtbIzg4GN988w0AwMTEBDNnzsSNGzdgbm6OLl26YMOGDaV+topo0aIFoqKi8Omnn6JLly4QQqBBgwY6Q+AQERERvcgsTY0wqHU9/HwiGetjktHRx778jSpBCIGwI0lY+McVFKsE6turh/b0q8en/KrDvXv3oFQqNdM4lHB2dq7QMI8xMTG4ePGiVtEqLS1Ns4+/77NkXWkKCgpQUPDXXJLZ2dkAgKKiIq2CV1FREYQQUKlUT52jrjx9mjlj6cjWCN15GWnZ2jnf5/2bok8z52fe99P8+OOP6NWrF6ysrHT2/eqrr2LRokWIjY0tM+e7efOmZtuSP01NTWFqaqp5X/Jkrkqlwpo1a2BpaYkePXroHLNHjx4wNzfH//3f/+H999/XWieEQN26dXH06FEsWrRIK+fz8/PDF198ofU5SvrD398fX3/9tSbn69KlC+bPn48333xT01/FxcVaOV9QUBAWL14MlUoFY2NjrZyvc+fO+Pnnn7X6+u/9/vd1Ty7z8/PDwYMH8dlnn2nlfMOGDdPaR0n8UlPSl0IIFBUVQaGo+LCypD8lf/+UVnin6sN+MDz2gTSwH6ShtvVDG3creNqZI/lBHnaeu4XBrdzK36gMZ5MzMW7tGeQUFKONpy1+HNMGFkb6P19S7IfKxCITVT2mTg2UnZ0NGxsbZGVlaRKjEvn5+UhKSoK3t/dzzR+hVAnEJD1AxqN8OFmZoYO3nd7v+qypVCoVsrOzYW1trTO/I1UflUqFe/fu4d69e/Dx8amyOTLp6YqKirBr1y7069cPxsZ8IsFQ2A/SwH6QBvaDNNS2frh4OwsDvjsCE4UcJz7phbp6Gp4l83Eh/rn5PPZdTgcA9Pd3xYIh/np5yk+qfVBWHmMId+7cQb169XDs2DF06tRJs3z69OmIiorCiRMnytz+3XffRXR0NM6fP69ZduzYMbz88su4c+cOXF3/umly2LBhkMlk2LhxY6n7mjNnDubOnauz/Oeff4aFhYXmvZGREVxcXODh4fHcwzUqVQJnUrJxL7cQDpYmaONhzZyPJKWwsBApKSlIS0srdXhVIiIiIn3Yc0uG31MUaGitwvvNn/2GqMRsYMVlBQpUMjSwEni3qRKmL9C9S48fP8bIkSMrlO/xiT8DUchl6NRAv3c0ExERERHVNH71bOBfzwYXbmdhy5lbeLuLz3Pv80zyQ7z/81nczsyDiUKOzwc0xeiO9Ss8Xxrph4ODAxQKBdLT07WWp6enw8XFpcxtc3NzsWHDBoSGhmotL9kuPT1dq/CXnp6OVq1aPXV/M2fOxLRp0zTvs7Oz4eHhgT59+mglzfn5+UhJSUGdOnX0cuNbL1s+Xfp3Qgg8evQIVlZWvCYNSAiB+/fvw8zMDF27duWNngZSVFSEvXv3onfv3pK6keRFw34wPPaBNLAfpKE29kPrrHzs+voQrmXL0bxjV9S3syh/o785kfQA//q/MyhQqdDJxw7LR7WChUnVlbek2A8lo5ZUBAt/RERERERkUCEdPHFh2wWsj0nG+M7ez1wM4NCe0mJiYoK2bdti//79GDx4MAD1qBL79+8vdx60zZs3o6CgAKNHj9Za7u3tDRcXF+zfv19T6MvOzsaJEycwadKkp+6vZMjKvzM2NtZK5JVKJWQyGeRyOUcfqSIlw16WnGcyjCf74e/XAVU/9oE0sB8Mj30gDewHaahN/eDpYIyujRwRdfUufj2Xho/6+FZq+yMJ9/D2/51BfpEKXRo5YOXYdjAzrp5H/aTUD5WJg9+yiYiIiIjIoF5p5QYLEwUS7+YiJunBM+0j83EhJqw9hXm/X0axSqB/C1fsfL8zi34GNm3aNKxcuRJr1qzB5cuXMWnSJOTm5uKtt94CAIwdOxYzZ87U2S4sLAyDBw+Gvb32KCkymQwffPAB5s2bh99++w0XLlzA2LFj4ebmpikuEhERERGRtAxr5wEA+OX0LShVFZ99LjI+A+PWnER+kQo9mzhVa9GvJuMTf0REREREZFB1TI0wqJUb1sekYH1MMgJ8Kjck/umbDzF1/Z9DexrJMWtAM4wK8OQwghIwfPhw3L17F7NmzUJaWhpatWqFiIgIODs7AwCSk5N1nvqKj4/HkSNHsGfPnlL3OX36dOTm5uKdd95BZmYmOnfujIiICA5VSEREREQkUYHNnGBrYYzUrHwcTriL7r5O5W6zLy4d7/10BoVKFXo3c8b/RraGqRGLfhXBwh8RERERERlcSAdPrI9Jwa6LaZidW4i6liblbiOEwI+Hk/BFhHpoTy97C/yPQ3tKzpQpU546tGdkZKTOMl9fXwjx9LuAZTIZQkNDdeb/IyIiIiIiaTI1UmBwq3oIP3YDm0/dKrfwF3ExDe+vP4MipUBfPxcsCWkNYwUHsKwonikiIiIiIjI4/3o2aO5mjcJiFbaevV1u+4e5hXh7zSnM36Ue2nNAC1fs4NCeREREREREklQy3OeeuDQ8yC18arvfz6di8s/qot/Alm74jkW/SuPZIiIiIiIig5PJZAjp4AkAWB+TXOYTX6dvPkT/JYex/0oGTIzkmP+qH74LaQ0rM2lMuk5ERERERETamrlZw6+eNYqUAr/Gln6z56+xt/H++jNQqgRebV0P3wxrCSMW/SqNZ4yIiIiIiCRhUCs3WJgocC0jB6duPtRZr1IJrIhKxPAV0biTlQ9vB0tse+8ljAqoz/n8iIiIiIiIJG74n0/9bTyZonOz55bTt/DhxlioBDC0rTu+Gsqi37PiHH9ERERERCQJVmbGeKWlGzacTMGSfQl4vZ07nKzM0MHbDtl5Rfho8zkcuJIBAHilpRv+85o/6pgypSEiIiIiIqoJXmlZD//+/TKupD3C+phkWJoawcnKDDfu5+CTbRchhHr+9/mD/SCX8+bOZ8VyKdEzePPNNzF48OBqOZaXlxe+/fZbzfu0tDT07t0blpaWsLW1BaAeGmv79u3VEg8RERFRVfKytwQAHL52D//YEIuQlcfRYf4+9Pg6Egf+HNpzwWv++O+IViz6EVGVYc5HREREpH82FsZo8ee87J9su6jJ+WZuVRf9xnaqz6KfHrDw94KJjo6GQqFA//79ddbduHEDMpkMsbGxpW4bHh4OmUyGpk2b6qzbvHkzZDIZvLy8dNbl5eXBzs4ODg4OKCgo0Fl/7tw5vPLKK3BycoKZmRl8fHwwbtw4ZGSo7+Z2dXXFwoULtbb517/+BZlMhsjISK3l3bt3x5gxY7SWBQUFQaFQ4OTJk6V+rr8TQuCHH35AQEAA6tSpA1tbW7Rr1w7ffvstHj9+XKF96NPJkyfxzjvvaN5/8803SE1NRWxsLK5evQoASE1NRd++fas9NiIiIiJ9iriYii8irugsv59biMzHRXC2MsX2915GSAdPDu1J9BRSz/ksLCzQokULjBgxgjnfn5jzERER0Ysi4mJqqdM6lOjkY8+inx6w8FfdDi4AohaVvi5qkXp9FQoLC8P777+PQ4cO4c6dO5Xe3tLSEhkZGYiOjtbZr6enZ6nbbNmyBc2bN0eTJk107lC8e/cuevXqBTs7O+zevRuXL19GWFgYXFxckJubC0Cd2P092Tt48CA8PDy0lufn5+P48ePo2bOnZllycjKOHTuGKVOmYNWqVRX6jGPGjMEHH3yAQYMG4eDBg4iNjcXnn3+OX3/9FXv27KnQPvTJ0dERFhYWmveJiYlo27YtGjVqBCcnJwCAi4sLTE1Nn/kYhYWFzx0nERER0fNQqgTm7oiDKKONTCaDr4tVtcVE9EyY82mt+3vOd+nSJSxduhRubm7M+f7EnI+IiIheBCU539PIAITujINSVVZWSBXBwl91kyuAg/N1E8GoRerlckWVHTonJwcbN27EpEmT0L9/f4SHh1d6H0ZGRhg5cqRWQnXr1i1ERkZi5MiRpW4TFhaG0aNHY/To0QgLC9Nad/ToUWRlZeHHH39E69at4e3tjR49euA///kPvL29AQA9evTA0aNHUVxcDAB49OgRzp49ixkzZmglgdHR0SgoKECPHj00y1avXo0BAwZg0qRJWL9+PfLy8sr8fJs2bcJPP/2E9evX45NPPkH79u3h5eWFQYMG4cCBA1r7flJERAQ6d+4MW1tb2NvbY8CAAUhMTNSsLywsxJQpU+Dq6gozMzPUr18fCxaoE34hBObMmQNPT0+YmprCzc0NU6dO1Wz75LAvXl5e2LJlC9auXQuZTIY333wTgO6wLykpKRg2bBhsbW1hZ2eHQYMG4caNG5r1JcPWzJ8/H25ubvD19S3zvBARERFVtZikB0jNyi+zTVp2PmKSHlRTRETPiDmf1rrScr4uXbpg8eLFzPn+xJyPiIiIXgTl5XwCQGoWcz59YOFPH4QACnMr9uo0Gej6sTrhOzBPvezAPPX7rh+r11d0X6Jyle9NmzahSZMm8PX1xejRo7Fq1SqISu4DAMaNG4dNmzZphkAJDw9HcHAwnJ2dddomJiYiOjoaw4YNw7Bhw3D48GHcvHlTs97FxQXFxcXYtm3bU2Pp0aMHcnJyNMO2HD58GI0bN8aQIUNw4sQJ5Oer/7I4ePAgvLy8NEPPCCGwevVqjB49Gk2aNEHDhg3xyy+/lPnZfvrpJ/j6+mLQoEE662QyGWxsbErdLjc3F9OmTcOpU6ewf/9+yOVyvPrqq1CpVACAJUuW4LfffsOmTZsQHx+Pn376SRPnli1b8M0332DFihVISEjA9u3b4e/vX+pxTp48ieDgYAwbNgypqan473//q9OmqKgIQUFBsLKywuHDh3H06FHUqVMHwcHBWnd57t+/H/Hx8di7dy927txZ5nkhIiIiqmoZj8ou+lW2HZHeVCbfY87HnI85HxEREVGpmPNVHyNDB1ArFD0G/uNW+e0Ofal+Pe19eT65A5hYVrh5yV2YABAcHIysrCxERUWhe/fuFT8mgNatW8PHxwe//PILxowZg/DwcCxevBjXr1/Xabtq1Sr07dsXdevWBaCee2H16tWYM2cOAKBjx4745JNPMHLkSEycOBEdOnRAjx49MHjwYFhbWwMAGjVqhHr16iEyMhKdOnVCZGQkunXrBhcXF3h6eiI6Oho9evRAZGSk1t2Z+/btw+PHjxEUFAQAmrtP/z4fxJMSEhKe6U7IIUOG6HxuR0dHxMXFwc/PD8nJyWjUqBE6d+4MmUyG+vXra9omJyfDxcUFgYGBMDY2hqenJzp06FDqcRwdHWFqagpzc3O4uLiU2mbjxo1QqVT48ccfNXPfrF69Gra2toiMjESfPn0AqIfw+fHHH2FiYlLpz0tERESkb05WZnptR6Q3z5rvAcz5oJvztW/fHi+99BImTJgAV1dXAMz5nsScj4iIiGor5nzVh0/8vSDi4+MRExODkJAQAOrhW4YPH64zDEtFjRs3DqtXr0ZUVBRyc3PRr18/nTZKpRJr1qzRJJ6AOhELDw/X3BUJAPPnz0daWhqWL1+O5s2bY8WKFejQoQMuXLigafPknA+RkZGaxLVbt26IjIxEXl4eTpw4oZUErlq1CsOHD4eRkbq+HRISgqNHj2oNx/J3z3I3LKBOHkNCQuDj4wNra2vNnZ3JyckA1MOsxMbGwtfXF1OnTtWaN2Lo0KHIy8uDj48PJkyYgG3btmmGuHkW586dw7Vr12BlZYU6deqgTp06sLOzQ35+vtZn9/f3ZwJIREREktHB2w6uNmZ42jTuMgCuNmbo4G1XnWER1Rg1KedbvXo1mjVrxpzvGTHnIyIiopqIOV/14RN/+mBsob4TszKOfKO+01NhAigL1UO+dP6w8setoLCwMBQXF8PN7a87VYUQMDU1xf/+97+nDmfyNKNGjcL06dMxZ84cjBkzRpNoPWn37t24ffs2hg8frrVcqVRi//796N27t2aZvb09hg4diqFDh2LevHlo3bo1vv76a6xduxaAeuiXf/zjH7h//z7Onj2Lbt26AVAngStWrEDXrl1RWFiomeT9wYMH2LZtG4qKirBs2TKtY69atQrz588v9XM1btwYV65cqdS5AICBAweifv36WLlyJdzc3KBSqeDn56cZZqVNmzZISkrCH3/8gX379mHYsGEIDAzEL7/8Ag8PD8THx2Pfvn3Yu3cv3nvvPXz55ZeIioqCsbFxpWPJyclB27Zt8dNPP+msc3R01PxsaVnxO4eJiIiIqppCLsPsgc0wad0ZyKCe36FESWI4e2AzKORPSxOJqsiz5HsAc76n5HxDhgzBjBkz0KNHD3z11VdYs2YNAOZ8lcGcj4iIiGoi5nzVh0/86YNMph5+paKv6KXqBLDHp8Dnd9V/HvpSvbwy+5FV7AIoLi7G2rVr8fXXXyM2NlbzOnfuHNzc3LB+/fpKf2Q7Ozu88soriIqKwrhx40ptExYWhhEjRmgdMzY2FiNGjCjzrlMTExN4eXkhNzdXs6xHjx7Izc3F4sWL0ahRIzg5OQEAunbtipiYGPzxxx+a4WEA9bwN7u7uOHfunNaxv/76a4SHh0OpVJZ67JEjR+Lq1av49ddfddYJIZCVlaWz/P79+4iPj8dnn32GXr16oWnTpnj48KFOO2trawwfPhwrV67Exo0bsWXLFjx4oJ6o1NzcHAMHDsSSJUsQGRmJ6OhorbtfK6NNmzZISEiAk5MTGjZsqPWqbLJPREREVJ2C/VyxbHQbuNhoD+3iYmOGZaPbINjP1UCR0Qutsvkec74K5Xw+Pj7M+ZjzERER0QuGOV/14BN/1S1qkXpS9x6fAt2mq5eV/HlwvvZ7Pdm5cycePnyI8ePH6yQBQ4YMQVhYGCZOnKhZFh8fr7OP5s2b6ywLDw/H999/D3t7e511d+/exY4dO/Dbb7/Bz89Pa93YsWPx6quv4sGDBzh27Bg2bNiAESNGoHHjxhBC4LfffsPevXu1EkUfHx94enriu+++w6hRozTLPTw84Obmhh9++EEzpA2gTkBff/11nWN7eHhg5syZiIiIQP/+/XXiHjZsGLZt24aQkBB89tln6NOnDxwdHXHhwgV88803eP/99zF48GCtberWrQt7e3v88MMPcHV1RXJyMv71r39ptVm8eDFcXV3RunVryOVybN68GS4uLrC1tdUkpQEBAbCwsMC6detgbm6uNSdEZYwaNQpffvklBg0ahNDQULi7u+PmzZvYunUrpk+fDnd392faLxEREVF1CPZzRe9mLohJeoCMR/lwslIP9cK7PqnGYM5XZs6nVCrxyy+/4I8//sDq1as12zDnqzjmfERERFSTMeereiz8VTeVUjsBLFHyXlX6XYnPIywsDIGBgaXe+TdkyBAsWrQI58+fh7W1NQBgxIgROu1SUlJ0lpmbm8Pc3LzUY65duxaWlpbo1auXzrpevXrB3Nwc69atw4ABA2BhYYGPPvoIKSkpMDU1RaNGjbBkyRKdCdl79OiBNWvW6ExM361bN4SHh2vmejh9+jTOnTuHlStX6hzbxsYGvXr1QlhYWKlJoEwmw88//4wffvhBMzyMkZERGjVqhLFjx2omjX+SXC7Hhg0bMHXqVPj5+cHX1xdLlizRitPKygqLFi1CQkICFAoF2rdvj127dkEul8PW1hYLFy7EtGnToFQq4e/vjx07dpSaXFeEhYUFDh06hBkzZuC1117Do0ePUK9ePfTq1UvTx0RERERSppDL0KnBs30XIjI45nzl5nw+Pj744YcfmPMx5yMiIqIXFHO+qiUTzzqzdS2WnZ0NGxsbZGVl6Xxpzs/PR1JSEry9vWFmZvaUPdDzUKlUyM7OhrW1NeRyjkZrKCqVCvfu3cO9e/fg4+PD33cDKCoqwq5du9CvX79nmvuD9IP9IA3sB2lgP0gD+8HwpNoHZeUxpO1p54r5XtVjvicNzPekQar/nrxo2A+Gxz6QBvaDNLAfpEGK/VCZfI/fsomIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6IiIiIiIiIiIiIiIiIagEW/oiIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6ekRDC0CEQVTn+nhMRERHRi4jfg4mIiIiIqKZi4a+SjI2NAQCPHz82cCREVa+wsBDAX7/3RERERES1GfM9epEUFxcDABQKhYEjISIiIiJ9MjJ0ADWNQqGAra0tMjIyAAAWFhaQyWQGjqp2UalUKCwsRH5+PuRy1qYNQQiBnJwc3Lt3D46OjkwEiYiIiOiFwHyv6jHfk4bi4mI8ePAAlpaWMDLifw0RERER1Sb8dvcMXFxcAECTDJJ+CSGQl5cHc3NzJtkGJITAw4cP0bx5c0OHQkRERERUbZjvVS3me9JQcrOnj48P+4GIiIiolmHh7xnIZDK4urrCyckJRUVFhg6n1ikqKsKhQ4fQtWtXDjFpYAkJCUwCiYiIiOiFwnyvajHfk4bi4mLs37+ffUBERERUC7Hw9xwUCgWHQKwCCoUCxcXFMDMzYxJiQPxPDiIiIiJ6kTHfqxrM96SB+R4RERFR7cUB9YmIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6IiIiIiIiIiIiIiIiIagHO8VcKIQQAIDs728CRvJiKiorw+PFjZGdnc84HA2I/GB77QBrYD9LAfpAG9oM0sB8MT6p9wPyl4pjzGY5Ur58XDftBGtgP0sB+MDz2gTSwH6SB/SANUuyHktylJJcpCwt/pXj06BEAwMPDw8CREBERERERkb4x5yMiIiIiopro0aNHsLGxKbONTFSkPPiCUalUuHPnDqysrCCTyQwdzgsnOzsbHh4eSElJgbW1taHDeWGxHwyPfSAN7AdpYD9IA/tBGtgPhifVPihJ7aytrZnHlIM5n+FI9fp50bAfpIH9IA3sB8NjH0gD+0Ea2A/SIMV+EELg0aNHcHNzg1xe9ix+fOKvFHK5HO7u7oYO44VnbW0tmYvqRcZ+MDz2gTSwH6SB/SAN7AdpYD8YHvug5mLOZ3i8fqSB/SAN7AdpYD8YHvtAGtgP0sB+kAap9UN5T/qVKLssSEREREREREREREREREQ1Agt/RERERERERERERERERLUAC38kOaamppg9ezZMTU0NHcoLjf1geOwDaWA/SAP7QRrYD9LAfjA89gHRs+P1Iw3sB2lgP0gD+8Hw2AfSwH6QBvaDNNT0fpCJkhngiYiIiIiIiIiIiIiIiKjG4hN/RERERERERERERERERLUAC39EREREREREREREREREtQALf0RERERERERERERERES1AAt/9NwWLFiA9u3bw8rKCk5OThg8eDDi4+O12nTv3h0ymUzrNXHiRK02ycnJ6N+/PywsLODk5ISPP/4YxcXFWm0iIyPRpk0bmJqaomHDhggPD9eJZ+nSpfDy8oKZmRkCAgIQExOj988sRXPmzNE5x02aNNGsz8/Px+TJk2Fvb486depgyJAhSE9P19oH++D5eXl56fSDTCbD5MmTAfBaqAqHDh3CwIED4ebmBplMhu3bt2utF0Jg1qxZcHV1hbm5OQIDA5GQkKDV5sGDBxg1ahSsra1ha2uL8ePHIycnR6vN+fPn0aVLF5iZmcHDwwOLFi3SiWXz5s1o0qQJzMzM4O/vj127dlU6lpqqrH4oKirCjBkz4O/vD0tLS7i5uWHs2LG4c+eO1j5Ku34WLlyo1Yb9ULbyroc333xT5xwHBwdrteH18PzK64fS/p2QyWT48ssvNW14PTyfinw/ldJ3o4rEQmQozPekgfmeNDDfMwzmfNLAnE8amPMZHvM9aWDOVw5B9JyCgoLE6tWrxcWLF0VsbKzo16+f8PT0FDk5OZo23bp1ExMmTBCpqamaV1ZWlmZ9cXGx8PPzE4GBgeLs2bNi165dwsHBQcycOVPT5vr168LCwkJMmzZNxMXFie+++04oFAoRERGhabNhwwZhYmIiVq1aJS5duiQmTJggbG1tRXp6evWcDAOaPXu2aN68udY5vnv3rmb9xIkThYeHh9i/f784deqU6Nixo3jppZc069kH+pGRkaHVB3v37hUAxMGDB4UQvBaqwq5du8Snn34qtm7dKgCIbdu2aa1fuHChsLGxEdu3bxfnzp0Tr7zyivD29hZ5eXmaNsHBwaJly5bi+PHj4vDhw6Jhw4YiJCREsz4rK0s4OzuLUaNGiYsXL4r169cLc3NzsWLFCk2bo0ePCoVCIRYtWiTi4uLEZ599JoyNjcWFCxcqFUtNVVY/ZGZmisDAQLFx40Zx5coVER0dLTp06CDatm2rtY/69euL0NBQrevjyX9L2A/lK+96eOONN0RwcLDWOX7w4IFWG14Pz6+8fnjy/KempopVq1YJmUwmEhMTNW14PTyfinw/ldJ3o/JiITIk5nvSwHxPGpjvGQZzPmlgzicNzPkMj/meNDDnKxsLf6R3GRkZAoCIiorSLOvWrZv4xz/+8dRtdu3aJeRyuUhLS9MsW7ZsmbC2thYFBQVCCCGmT58umjdvrrXd8OHDRVBQkOZ9hw4dxOTJkzXvlUqlcHNzEwsWLHjejyV5s2fPFi1btix1XWZmpjA2NhabN2/WLLt8+bIAIKKjo4UQ7IOq8o9//EM0aNBAqFQqIQSvhar29y9cKpVKuLi4iC+//FKzLDMzU5iamor169cLIYSIi4sTAMTJkyc1bf744w8hk8nE7du3hRBCfP/996Ju3bqaPhBCiBkzZghfX1/N+2HDhon+/ftrxRMQECDefffdCsdSW5T2xffvYmJiBABx8+ZNzbL69euLb7755qnbsB8q52lJ4KBBg566Da8H/avI9TBo0CDRs2dPrWW8HvTr799PpfTdqCKxEEkJ8z3DYL4nTcz3qh9zPmlgzicNzPkMj/medDDn08ahPknvsrKyAAB2dnZay3/66Sc4ODjAz88PM2fOxOPHjzXroqOj4e/vD2dnZ82yoKAgZGdn49KlS5o2gYGBWvsMCgpCdHQ0AKCwsBCnT5/WaiOXyxEYGKhpU9slJCTAzc0NPj4+GDVqFJKTkwEAp0+fRlFRkda5adKkCTw9PTXnhn2gf4WFhVi3bh3GjRsHmUymWc5rofokJSUhLS1N61zY2NggICBA63ff1tYW7dq107QJDAyEXC7HiRMnNG26du0KExMTTZugoCDEx8fj4cOHmjZl9UtFYnmRZGVlQSaTwdbWVmv5woULYW9vj9atW+PLL7/UGl6B/aAfkZGRcHJygq+vLyZNmoT79+9r1vF6qH7p6en4/fffMX78eJ11vB705+/fT6X03agisRBJCfM9w2G+Jy3M96SBOZ90MeczHOZ80sF8r/ow59NmVCV7pReWSqXCBx98gJdffhl+fn6a5SNHjkT9+vXh5uaG8+fPY8aMGYiPj8fWrVsBAGlpaVoXGADN+7S0tDLbZGdnIy8vDw8fPoRSqSy1zZUrV/T+WaUmICAA4eHh8PX1RWpqKubOnYsuXbrg4sWLSEtLg4mJic6XLWdn53LPb8m6stqwD0q3fft2ZGZm4s0339Qs47VQvUrOWWnn4snz6eTkpLXeyMgIdnZ2Wm28vb119lGyrm7duk/tlyf3UV4sL4r8/HzMmDEDISEhsLa21iyfOnUq2rRpAzs7Oxw7dgwzZ85EamoqFi9eDID9oA/BwcF47bXX4O3tjcTERHzyySfo27cvoqOjoVAoeD0YwJo1a2BlZYXXXntNazmvB/0p7fuplL4bVSQWIqlgvmc4zPekh/meNDDnkybmfIbDnE9amO9VD+Z8ulj4I72aPHkyLl68iCNHjmgtf+eddzQ/+/v7w9XVFb169UJiYiIaNGhQ3WHWSn379tX83KJFCwQEBKB+/frYtGkTzM3NDRjZiyssLAx9+/aFm5ubZhmvBXrRFRUVYdiwYRBCYNmyZVrrpk2bpvm5RYsWMDExwbvvvosFCxbA1NS0ukOtlUaMGKH52d/fHy1atECDBg0QGRmJXr16GTCyF9eqVaswatQomJmZaS3n9aA/T/t+SkSVx3zPcJjvSQ/zPaLSMeczLOZ80sJ8r3ow59PFoT5Jb6ZMmYKdO3fi4MGDcHd3L7NtQEAAAODatWsAABcXF6Snp2u1KXnv4uJSZhtra2uYm5vDwcEBCoWi1DYl+3iR2NraonHjxrh27RpcXFxQWFiIzMxMrTZPnhv2gX7dvHkT+/btw9tvv11mO14LVavk85Z1LlxcXJCRkaG1vri4GA8ePNDL9fHk+vJiqe1KEsCbN29i7969Wnd+liYgIADFxcW4ceMGAPZDVfDx8YGDg4PW30G8HqrP4cOHER8fX+6/FQCvh2f1tO+nUvpuVJFYiKSA+Z60MN8zLOZ70sGcT1qY80kPcz7DYb5XPZjzlY6FP3puQghMmTIF27Ztw4EDB3QeQy5NbGwsAMDV1RUA0KlTJ1y4cEHrH56SLwjNmjXTtNm/f7/Wfvbu3YtOnToBAExMTNC2bVutNiqVCvv379e0eZHk5OQgMTERrq6uaNu2LYyNjbXOTXx8PJKTkzXnhn2gX6tXr4aTkxP69+9fZjteC1XL29sbLi4uWuciOzsbJ06c0Prdz8zMxOnTpzVtDhw4AJVKpUnUO3XqhEOHDqGoqEjTZu/evfD19UXdunU1bcrql4rEUpuVJIAJCQnYt28f7O3ty90mNjYWcrlcMwwJ+0H/bt26hfv372v9HcTrofqEhYWhbdu2aNmyZblteT1UTnnfT6X03agisRAZEvM9aWK+Z1jM96SDOZ90MOeTJuZ8hsN8r2ox5yuHIHpOkyZNEjY2NiIyMlKkpqZqXo8fPxZCCHHt2jURGhoqTp06JZKSksSvv/4qfHx8RNeuXTX7KC4uFn5+fqJPnz4iNjZWRERECEdHRzFz5kxNm+vXrwsLCwvx8ccfi8uXL4ulS5cKhUIhIiIiNG02bNggTE1NRXh4uIiLixPvvPOOsLW1FWlpadV3Qgzko48+EpGRkSIpKUkcPXpUBAYGCgcHB5GRkSGEEGLixInC09NTHDhwQJw6dUp06tRJdOrUSbM9+0B/lEql8PT0FDNmzNBazmuhajx69EicPXtWnD17VgAQixcvFmfPnhU3b94UQgixcOFCYWtrK3799Vdx/vx5MWjQIOHt7S3y8vI0+wgODhatW7cWJ06cEEeOHBGNGjUSISEhmvWZmZnC2dlZjBkzRly8eFFs2LBBWFhYiBUrVmjaHD16VBgZGYmvvvpKXL58WcyePVsYGxuLCxcuaNpUJJaaqqx+KCwsFK+88opwd3cXsbGxWv9WFBQUCCGEOHbsmPjmm29EbGysSExMFOvWrROOjo5i7NixmmOwH8pXVj88evRI/POf/xTR0dEiKSlJ7Nu3T7Rp00Y0atRI5Ofna/bB6+H5lff3khBCZGVlCQsLC7Fs2TKd7Xk9PL/yvp8KIa3vRuXFQmRIzPekgfmedDDfq37M+aSBOZ80MOczPOZ70sCcr2ws/NFzA1Dqa/Xq1UIIIZKTk0XXrl2FnZ2dMDU1FQ0bNhQff/yxyMrK0trPjRs3RN++fYW5ublwcHAQH330kSgqKtJqc/DgQdGqVSthYmIifHx8NMd40nfffSc8PT2FiYmJ6NChgzh+/HhVfXRJGT58uHB1dRUmJiaiXr16Yvjw4eLatWua9Xl5eeK9994TdevWFRYWFuLVV18VqampWvtgH+jH7t27BQARHx+vtZzXQtU4ePBgqX8HvfHGG0IIIVQqlfj888+Fs7OzMDU1Fb169dLpm/v374uQkBBRp04dYW1tLd566y3x6NEjrTbnzp0TnTt3FqampqJevXpi4cKFOrFs2rRJNG7cWJiYmIjmzZuL33//XWt9RWKpqcrqh6SkpKf+W3Hw4EEhhBCnT58WAQEBwsbGRpiZmYmmTZuK//znP1rJiRDsh/KU1Q+PHz8Wffr0EY6OjsLY2FjUr19fTJgwQec/iHg9PL/y/l4SQogVK1YIc3NzkZmZqbM9r4fnV973UyGk9d2oIrEQGQrzPWlgvicdzPeqH3M+aWDOJw3M+QyP+Z40MOcrm0wIISrzhCARERERERERERERERERSQ/n+CMiIiIiIiIiIiIiIiKqBVj4IyIiIiIiIiIiIiIiIqoFWPgjIiIiIiIiIiIiIiIiqgVY+CMiIiIiIiIiIiIiIiKqBVj4IyIiIiIiIiIiIiIiIqoFWPgjIiIiIiIiIiIiIiIiqgVY+CMiIiIiIiIiIiIiIiKqBVj4IyIiIiIiIiIiIiIiIqoFWPgjIqJa6caNG5DJZIiNjTV0KBpXrlxBx44dYWZmhlatWhk6HCIiIiIiohqJ+R4REdHTsfBHRERV4s0334RMJsPChQu1lm/fvh0ymcxAURnW7NmzYWlpifj4eOzfv7/UNm+++SYGDx6st2N6eXnh22+/1dv+iIiIiIiImO/pYr5HRERSwcIfERFVGTMzM3zxxRd4+PChoUPRm8LCwmfeNjExEZ07d0b9+vVhb2+vx6iIiIiIiIiqF/M9bcz3iIhIKlj4IyKiKhMYGAgXFxcsWLDgqW3mzJmjMwzKt99+Cy8vL837krsi//Of/8DZ2Rm2trYIDQ1FcXExPv74Y9jZ2cHd3R2rV6/W2f+VK1fw0ksvwczMDH5+foiKitJaf/HiRfTt2xd16tSBs7MzxowZg3v37mnWd+/eHVOmTMEHH3wABwcHBAUFlfo5VCoVQkND4e7uDlNTU7Rq1QoRERGa9TKZDKdPn0ZoaChkMhnmzJlTxpn7S/fu3TF16lRMnz4ddnZ2cHFx0dpWCIE5c+bA09MTpqamcHNzw9SpUzXb3rx5Ex9++CFkMpnmztv79+8jJCQE9erVg4WFBfz9/bF+/fpKHRcAMjMz8e6778LZ2Vlzfnfu3KlZf+TIEXTp0gXm5ubw8PDA1KlTkZubq1n//fffo1GjRjAzM4OzszNef/31Cp0TIiIiIiIyPOZ7zPeY7xERSRMLf0REVGUUCgX+85//4LvvvsOtW7eea18HDhzAnTt3cOjQISxevBizZ8/GgAEDULduXZw4cQITJ07Eu+++q3Ocjz/+GB999BHOnj2LTp06YeDAgbh//z4AdSLTs2dPtG7dGqdOnUJERATS09MxbNgwrX2sWbMGJiYmOHr0KJYvX15qfP/973/x9ddf46uvvsL58+cRFBSEV155BQkJCQCA1NRUNG/eHB999BFSU1Pxz3/+s8Kffc2aNbC0tMSJEyewaNEihIaGYu/evQCALVu24JtvvsGKFSuQkJCA7du3w9/fHwCwdetWuLu7IzQ0FKmpqUhNTQUA5Ofno23btvj9999x8eJFvPPOOxgzZgxiYmIqfFyVSoW+ffvi6NGjWLduHeLi4rBw4UIoFAoA6rtdg4ODMWTIEJw/fx4bN27EkSNHMGXKFADAqVOnMHXqVISGhiI+Ph4RERHo2rVrhc8JEREREREZFvM95nvM94iIJEoQERFVgTfeeEMMGjRICCFEx44dxbhx44QQQmzbtk08+c/P7NmzRcuWLbW2/eabb0T9+vW19lW/fn2hVCo1y3x9fUWXLl0074uLi4WlpaVYv369EEKIpKQkAUAsXLhQ06aoqEi4u7uLL774QgghxL///W/Rp08frWOnpKQIACI+Pl4IIUS3bt1E69aty/28bm5uYv78+VrL2rdvL9577z3N+5YtW4rZs2eXuZ8nz1vJ8Tt37qyz3xkzZgghhPj6669F48aNRWFhYan7q1+/vvjmm2/Kjb9///7io48+qvBxd+/eLeRyueY8/d348ePFO++8o7Xs8OHDQi6Xi7y8PLFlyxZhbW0tsrOzy42NiIiIiIikhfke8z3me0RE0sUn/oiIqMp98cUXWLNmDS5fvvzM+2jevDnk8r/+2XJ2dtbc6Qio7za1t7dHRkaG1nadOnXS/GxkZIR27dpp4jh37hwOHjyIOnXqaF5NmjQBoL6DsUTbtm3LjC07Oxt37tzByy+/rLX85Zdffq7PXKJFixZa711dXTWfc+jQocjLy4OPjw8mTJiAbdu2obi4uMz9KZVK/Pvf/4a/vz/s7OxQp04d7N69G8nJyRU+bmxsLNzd3dG4ceNSj3Hu3DmEh4drndugoCCoVCokJSWhd+/eqF+/Pnx8fDBmzBj89NNPePz4caXOCxERERERGR7zvefDfI+IiPSNhT8iIqpyXbt2RVBQEGbOnKmzTi6XQwihtayoqEinnbGxsdZ7mUxW6jKVSlXhuHJycjBw4EDExsZqvRISErSGIbG0tKzwPqtCWZ/Tw8MD8fHx+P7772Fubo733nsPXbt2LfUclvjyyy/x3//+FzNmzMDBgwcRGxuLoKAgnYnsyzquubl5mTHn5OTg3Xff1Tqv586dQ0JCAho0aAArKyucOXMG69evh6urK2bNmoWWLVsiMzOzoqeFiIiIiIgkgPne82G+R0RE+sbCHxERVYuFCxdix44diI6O1lru6OiItLQ0rWQwNjZWb8c9fvy45ufi4mKcPn0aTZs2BQC0adMGly5dgpeXFxo2bKj1qkzyZ21tDTc3Nxw9elRr+dGjR9GsWTP9fJAymJubY+DAgViyZAkiIyMRHR2NCxcuAABMTEygVCp14ho0aBBGjx6Nli1bwsfHB1evXq3UMVu0aIFbt249dbs2bdogLi5O57w2bNgQJiYmANR35AYGBmLRokU4f/48bty4gQMHDjzDGSAiIiIiIkNivld1mO8REVFlsfBHRETVwt/fH6NGjcKSJUu0lnfv3h13797FokWLkJiYiKVLl+KPP/7Q23GXLl2Kbdu24cqVK5g8eTIePnyIcePGAQAmT56MBw8eICQkBCdPnkRiYiJ2796Nt956Syd5Ks/HH3+ML774Ahs3bkR8fDz+9a9/ITY2Fv/4xz/09llKEx4ejrCwMFy8eBHXr1/HunXrYG5ujvr16wMAvLy8cOjQIdy+fRv37t0DADRq1Ah79+7FsWPHcPnyZbz77rtIT0+v1HG7deuGrl27YsiQIdi7dy+SkpLwxx9/ICIiAgAwY8YMHDt2DFOmTNHcVfvrr79qJnvfuXMnlixZgtjYWNy8eRNr166FSqWCr6+vHs8OERERERFVB+Z7VYP5HhERPQsW/oiIqNqEhobqDM3StGlTfP/991i6dClatmyJmJgY/POf/9TbMRcuXIiFCxeiZcuWOHLkCH777Tc4ODgAgOauTaVSiT59+sDf3x8ffPABbG1tteaXqIipU6di2rRp+Oijj+Dv74+IiAj89ttvaNSokd4+S2lsbW2xcuVKvPzyy2jRogX27duHHTt2wN7eHoD6nN+4cQMNGjSAo6MjAOCzzz5DmzZtEBQUhO7du8PFxQWDBw+u9LG3bNmC9u3bIyQkBM2aNcP06dM1CXSLFi0QFRWFq1evokuXLmjdujVmzZoFNzc3Tdxbt25Fz5490bRpUyxfvhzr169H8+bN9XNiiIiIiIioWjHf0z/me0RE9Cxk4u8DbRMRERERERERERERERFRjcMn/oiIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6IiIiIiIiIiIiIiIiIagEW/oiIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6IiIiIiIiIiIiIiIiIagEW/oiIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6IiIiIiIiIiIiIiIiIagEW/oiIiIiIiIiIiIiIiIhqARb+iIiIiIiIiIiIiIiIiGoBFv6IiIiIiIiIiIiIiIiIaoH/B0IWJ2iAd0YyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from collections import deque\n",
    "from river import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "\n",
    "# Define feature columns and target\n",
    "feature_columns = [col for col in data.columns if col not in ['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label']]\n",
    "X = data[feature_columns]\n",
    "y = data['Label'] \n",
    "\n",
    "# Parameters for drift simulation\n",
    "total_instances = 200000\n",
    "drift_steps = 5\n",
    "instances_per_drift = total_instances // drift_steps\n",
    "initial_noise_std = 0.0\n",
    "noise_increment = 0.10\n",
    "\n",
    "def gradual_drift_stream(X, y, total_instances, drift_steps, instances_per_drift, \n",
    "                         noise_increment, feature_cols, target_col):\n",
    "    \"\"\"\n",
    "    Generator that yields data instances with gradual concept drift.\n",
    "    Noise is incrementally added to specified features every drift step.\n",
    "    \"\"\"\n",
    "    current_noise_std = initial_noise_std\n",
    "    for i in tqdm(range(total_instances), desc=\"Creating data stream\"):\n",
    "        if i > 0 and i % instances_per_drift == 0:\n",
    "            current_noise_std += noise_increment\n",
    "            print(f\"\\nDrift step {i // instances_per_drift}: Increasing noise_std to {current_noise_std:.2f}\")\n",
    "        \n",
    "        # Apply noise to specified features\n",
    "        instance = X.iloc[i].copy()\n",
    "        instance[\"IN_BYTES\"] += np.random.normal(0, current_noise_std * X[\"IN_BYTES\"].std())\n",
    "        instance[\"OUT_BYTES\"] += np.random.normal(0, current_noise_std * X[\"OUT_BYTES\"].std())\n",
    "        \n",
    "        label = y.iloc[i]\n",
    "        yield instance.to_dict(), label\n",
    "\n",
    "# Initialize classifiers\n",
    "alma = linear_model.ALMAClassifier()\n",
    "almaswa = ALMASWAClassifier()\n",
    "\n",
    "# Metrics tracking\n",
    "accuracy_alma = []\n",
    "accuracy_almaswa = []\n",
    "recall_alma = []\n",
    "recall_almaswa = []\n",
    "evaluation_points = []\n",
    "\n",
    "window_size = 10000  # Sliding window size\n",
    "slide_interval = 10000  # Evaluate every 10,000 instances\n",
    "\n",
    "# Queues to store true labels and predictions\n",
    "true_labels = deque(maxlen=window_size)\n",
    "predictions_alma = deque(maxlen=window_size)\n",
    "predictions_almaswa = deque(maxlen=window_size)\n",
    "\n",
    "# Create the data stream\n",
    "data_stream = gradual_drift_stream(\n",
    "    X=X,\n",
    "    y=y,\n",
    "    total_instances=total_instances,\n",
    "    drift_steps=drift_steps,\n",
    "    instances_per_drift=instances_per_drift,\n",
    "    noise_increment=noise_increment,\n",
    "    feature_cols=feature_columns,\n",
    "    target_col='Label'\n",
    ")\n",
    "\n",
    "# Iterate through the data stream\n",
    "for i, (x, y_true) in enumerate(data_stream):\n",
    "    # Make predictions before learning\n",
    "    y_pred_alma = alma.predict_one(x)\n",
    "    y_pred_almaswa = almaswa.predict_one(x)\n",
    "\n",
    "    # Update the queues with true labels and predictions\n",
    "    true_labels.append(y_true)\n",
    "    predictions_alma.append(y_pred_alma)\n",
    "    predictions_almaswa.append(y_pred_almaswa)\n",
    "\n",
    "    # Learn from the current instance\n",
    "    alma.learn_one(x, y_true)\n",
    "    almaswa.learn_one(x, y_true)\n",
    "\n",
    "    # Evaluate at each sliding window\n",
    "    if (i + 1) % slide_interval == 0:\n",
    "        # Initialize River's metrics\n",
    "        acc_metric_alma = metrics.Accuracy()\n",
    "        acc_metric_almaswa = metrics.Accuracy()\n",
    "        rec_metric_alma = metrics.Recall()\n",
    "        rec_metric_almaswa = metrics.Recall()\n",
    "\n",
    "        # Update metrics for ALMA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_alma):\n",
    "            acc_metric_alma.update(true, pred)\n",
    "            rec_metric_alma.update(true, pred)\n",
    "        \n",
    "        # Update metrics for ALMASWA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_almaswa):\n",
    "            acc_metric_almaswa.update(true, pred)\n",
    "            rec_metric_almaswa.update(true, pred)\n",
    "\n",
    "        # Append metrics\n",
    "        evaluation_points.append(i + 1)\n",
    "        accuracy_alma.append(acc_metric_alma.get())\n",
    "        accuracy_almaswa.append(acc_metric_almaswa.get())\n",
    "        recall_alma.append(rec_metric_alma.get())\n",
    "        recall_almaswa.append(rec_metric_almaswa.get())\n",
    "\n",
    "        # Print metrics\n",
    "        print(f\"\\nEvaluation at instance {i + 1}:\")\n",
    "        print(f\"ALMA - Accuracy: {acc_metric_alma.get():.4f}, Recall: {rec_metric_alma.get():.4f}\")\n",
    "        print(f\"ALMASWA - Accuracy: {acc_metric_almaswa.get():.4f}, Recall: {rec_metric_almaswa.get():.4f}\")\n",
    "\n",
    "# Define the plotting function\n",
    "def plot_metrics(evaluation_points, accuracy_alma, accuracy_almaswa, \n",
    "                rec_alma, rec_almaswa):\n",
    "    plt.figure(figsize=(18, 6))\n",
    "\n",
    "    # Plot Detection Accuracy\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(evaluation_points, accuracy_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, accuracy_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Accuracy Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Plot F1-Score\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(evaluation_points, recall_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, recall_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.title('Recall Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/ToN_Gradual_Drift.png')\n",
    "    plt.show()\n",
    "\n",
    "# Plot the metrics\n",
    "plot_metrics(evaluation_points, accuracy_alma, accuracy_almaswa, recall_alma, recall_almaswa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc8b2f4-ca13-4dbd-9c79-7d015229a284",
   "metadata": {},
   "source": [
    "# Experiment 4: Response to Sudden Concept Drift Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1ee5a07b-717a-403e-8129-23341fd59be4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:   5%|▋             | 10012/200000 [02:33<37:27, 84.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 10000:\n",
      "ALMA - Accuracy: 0.9057, Recall: 0.9351\n",
      "ALMASWA - Accuracy: 0.7425, Recall: 0.8433\n",
      "\n",
      "Baseline Accuracy (ALMA): 0.9057\n",
      "Baseline Recall (ALMA): 0.9351\n",
      "Baseline Accuracy (ALMASWA): 0.7425\n",
      "Baseline Recall (ALMASWA): 0.8433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  10%|█▍            | 20008/200000 [04:30<36:24, 82.41it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 20000:\n",
      "ALMA - Accuracy: 0.9403, Recall: 0.9689\n",
      "ALMASWA - Accuracy: 0.9766, Recall: 0.9879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  15%|█▉           | 30015/200000 [06:13<26:35, 106.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 30000:\n",
      "ALMA - Accuracy: 0.9442, Recall: 0.9698\n",
      "ALMASWA - Accuracy: 0.9315, Recall: 0.9642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  20%|██▌          | 40015/200000 [07:46<25:14, 105.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 40000:\n",
      "ALMA - Accuracy: 0.9693, Recall: 0.9837\n",
      "ALMASWA - Accuracy: 0.9477, Recall: 0.9730\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  25%|███▎         | 50020/200000 [09:17<22:57, 108.86it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 50000:\n",
      "ALMA - Accuracy: 0.9006, Recall: 0.9469\n",
      "ALMASWA - Accuracy: 0.9883, Recall: 0.9938\n",
      "ALMA recovered at instance 50000\n",
      "ALMASWA recovered at instance 50000\n",
      "\n",
      "Sudden drift introduced at instance 50000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  30%|███▉         | 60023/200000 [10:51<18:50, 123.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 60000:\n",
      "ALMA - Accuracy: 0.5736, Recall: 0.7205\n",
      "ALMASWA - Accuracy: 0.9713, Recall: 0.9852\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  35%|████▌        | 70020/200000 [12:09<17:00, 127.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 70000:\n",
      "ALMA - Accuracy: 0.5783, Recall: 0.7245\n",
      "ALMASWA - Accuracy: 0.9765, Recall: 0.9878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  40%|█████▏       | 80017/200000 [13:24<15:22, 130.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 80000:\n",
      "ALMA - Accuracy: 0.5686, Recall: 0.7177\n",
      "ALMASWA - Accuracy: 0.9755, Recall: 0.9874\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  45%|█████▊       | 90016/200000 [14:40<14:04, 130.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 90000:\n",
      "ALMA - Accuracy: 0.5819, Recall: 0.7288\n",
      "ALMASWA - Accuracy: 0.9797, Recall: 0.9895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  50%|██████      | 100017/200000 [15:55<12:56, 128.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 100000:\n",
      "ALMA - Accuracy: 0.5691, Recall: 0.7150\n",
      "ALMASWA - Accuracy: 0.9741, Recall: 0.9865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  55%|██████▌     | 110021/200000 [17:11<11:51, 126.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 110000:\n",
      "ALMA - Accuracy: 0.5495, Recall: 0.6964\n",
      "ALMASWA - Accuracy: 0.9702, Recall: 0.9843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  60%|███████▏    | 120023/200000 [18:27<10:10, 130.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 120000:\n",
      "ALMA - Accuracy: 0.5622, Recall: 0.7099\n",
      "ALMASWA - Accuracy: 0.9671, Recall: 0.9829\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  65%|███████▊    | 130013/200000 [19:43<09:24, 123.91it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 130000:\n",
      "ALMA - Accuracy: 0.5763, Recall: 0.7206\n",
      "ALMASWA - Accuracy: 0.9787, Recall: 0.9888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  70%|████████▍   | 140015/200000 [21:00<07:35, 131.69it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 140000:\n",
      "ALMA - Accuracy: 0.5809, Recall: 0.7281\n",
      "ALMASWA - Accuracy: 0.9690, Recall: 0.9841\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  75%|█████████   | 150042/200000 [21:50<04:03, 205.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 150000:\n",
      "ALMA - Accuracy: 0.5702, Recall: 0.7153\n",
      "ALMASWA - Accuracy: 0.9692, Recall: 0.9838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  80%|█████████▌  | 160035/200000 [22:36<03:13, 206.38it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 160000:\n",
      "ALMA - Accuracy: 0.5802, Recall: 0.7296\n",
      "ALMASWA - Accuracy: 0.9748, Recall: 0.9872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  85%|██████████▏ | 170025/200000 [23:24<02:34, 193.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 170000:\n",
      "ALMA - Accuracy: 0.5735, Recall: 0.7243\n",
      "ALMASWA - Accuracy: 0.9751, Recall: 0.9874\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  90%|██████████▊ | 180027/200000 [24:11<01:35, 209.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 180000:\n",
      "ALMA - Accuracy: 0.5846, Recall: 0.7332\n",
      "ALMASWA - Accuracy: 0.9729, Recall: 0.9862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream:  95%|███████████▍| 190022/200000 [24:56<00:46, 212.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 190000:\n",
      "ALMA - Accuracy: 0.5588, Recall: 0.7132\n",
      "ALMASWA - Accuracy: 0.9807, Recall: 0.9902\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating data stream: 100%|████████████| 200000/200000 [25:41<00:00, 129.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation at instance 200000:\n",
      "ALMA - Accuracy: 0.5729, Recall: 0.7155\n",
      "ALMASWA - Accuracy: 0.9350, Recall: 0.9663\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from collections import deque\n",
    "from river import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ------------------------------\n",
    "# Load and Prepare the Dataset\n",
    "# ------------------------------\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "\n",
    "# Define feature columns and target\n",
    "feature_columns = [col for col in data.columns if col not in ['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label']]\n",
    "X = data[feature_columns]\n",
    "y = data['Label']  # Assuming 'Label' is the target variable\n",
    "\n",
    "# ------------------------------\n",
    "# Parameters for Drift Simulation\n",
    "# ------------------------------\n",
    "\n",
    "total_instances = 200000\n",
    "drift_point = 50000  # Instance at which sudden drift occurs\n",
    "noise_before_drift = 0.0\n",
    "noise_after_drift = 0.8  # Increased noise to simulate sudden drift\n",
    "noise_increment = 0.0  # Not used in sudden drift\n",
    "\n",
    "# ------------------------------\n",
    "# Data Stream Generator with Sudden Drift\n",
    "# ------------------------------\n",
    "\n",
    "def sudden_drift_stream(X, y, total_instances, drift_point, \n",
    "                        noise_before, noise_after, feature_cols, target_col):\n",
    "    \"\"\"\n",
    "    Generator that yields data instances with a sudden concept drift.\n",
    "    Noise is added to specified features at the drift point.\n",
    "    \"\"\"\n",
    "    for i in tqdm(range(total_instances), desc=\"Creating data stream\"):\n",
    "        if i == drift_point:\n",
    "            print(f\"\\nSudden drift introduced at instance {i}\")\n",
    "            current_noise_std = noise_after\n",
    "        elif i < drift_point:\n",
    "            current_noise_std = noise_before\n",
    "        # After drift point, use increased noise\n",
    "        else:\n",
    "            current_noise_std = noise_after\n",
    "        \n",
    "        # Apply noise to specified features\n",
    "        instance = X.iloc[i].copy()\n",
    "        instance[\"IN_BYTES\"] += np.random.normal(0, current_noise_std * X[\"IN_BYTES\"].std())\n",
    "        instance[\"OUT_BYTES\"] += np.random.normal(0, current_noise_std * X[\"OUT_BYTES\"].std())\n",
    "        \n",
    "        label = y.iloc[i]\n",
    "        yield instance.to_dict(), label\n",
    "\n",
    "# Initialize classifiers\n",
    "alma = linear_model.ALMAClassifier()\n",
    "almaswa = ALMASWAClassifier()\n",
    "\n",
    "# ------------------------------\n",
    "# Metrics Tracking Initialization\n",
    "# ------------------------------\n",
    "\n",
    "accuracy_alma = []\n",
    "accuracy_almaswa = []\n",
    "recall_alma = []\n",
    "recall_almaswa = []\n",
    "evaluation_points = []\n",
    "\n",
    "window_size = 10000  # Sliding window size\n",
    "slide_interval = 10000  # Evaluate every 10,000 instances\n",
    "\n",
    "# Queues to store true labels and predictions\n",
    "true_labels = deque(maxlen=window_size)\n",
    "predictions_alma = deque(maxlen=window_size)\n",
    "predictions_almaswa = deque(maxlen=window_size)\n",
    "\n",
    "# Detection Delay Tracking\n",
    "# Define baseline metrics before drift\n",
    "baseline_accuracy = None\n",
    "baseline_recall = None\n",
    "\n",
    "# Threshold to consider recovery (95% of baseline)\n",
    "recovery_threshold = 0.95\n",
    "\n",
    "# Flags and counters\n",
    "alma_recovered = False\n",
    "almaswa_recovered = False\n",
    "alma_recovery_point = None\n",
    "almaswa_recovery_point = None\n",
    "\n",
    "# ------------------------------\n",
    "# Create the Data Stream\n",
    "# ------------------------------\n",
    "\n",
    "data_stream = sudden_drift_stream(\n",
    "    X=X,\n",
    "    y=y,\n",
    "    total_instances=total_instances,\n",
    "    drift_point=drift_point,\n",
    "    noise_before=noise_before_drift,\n",
    "    noise_after=noise_after_drift,\n",
    "    feature_cols=feature_columns,\n",
    "    target_col='Label'\n",
    ")\n",
    "\n",
    "# ------------------------------\n",
    "# Iterate Through the Data Stream\n",
    "# ------------------------------\n",
    "\n",
    "for i, (x, y_true) in enumerate(data_stream):\n",
    "    # Make predictions before learning\n",
    "    y_pred_alma = alma.predict_one(x)\n",
    "    y_pred_almaswa = almaswa.predict_one(x)\n",
    "\n",
    "    # Update the queues with true labels and predictions\n",
    "    true_labels.append(y_true)\n",
    "    predictions_alma.append(y_pred_alma)\n",
    "    predictions_almaswa.append(y_pred_almaswa)\n",
    "\n",
    "    # Learn from the current instance\n",
    "    alma.learn_one(x, y_true)\n",
    "    almaswa.learn_one(x, y_true)\n",
    "\n",
    "    # Evaluate at each sliding window\n",
    "    if (i + 1) % slide_interval == 0:\n",
    "        # Initialize River's metrics\n",
    "        acc_metric_alma = metrics.Accuracy()\n",
    "        acc_metric_almaswa = metrics.Accuracy()\n",
    "        rec_metric_alma = metrics.F1()\n",
    "        rec_metric_almaswa = metrics.F1()\n",
    "\n",
    "        # Update metrics for ALMA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_alma):\n",
    "            acc_metric_alma.update(true, pred)\n",
    "            rec_metric_alma.update(true, pred)\n",
    "        \n",
    "        # Update metrics for ALMASWA Classifier\n",
    "        for true, pred in zip(true_labels, predictions_almaswa):\n",
    "            acc_metric_almaswa.update(true, pred)\n",
    "            rec_metric_almaswa.update(true, pred)\n",
    "\n",
    "        # Append metrics\n",
    "        evaluation_points.append(i + 1)\n",
    "        accuracy_alma.append(acc_metric_alma.get())\n",
    "        accuracy_almaswa.append(acc_metric_almaswa.get())\n",
    "        recall_alma.append(rec_metric_alma.get())\n",
    "        recall_almaswa.append(rec_metric_almaswa.get())\n",
    "\n",
    "        # Print metrics\n",
    "        print(f\"\\nEvaluation at instance {i + 1}:\")\n",
    "        print(f\"ALMA - Accuracy: {acc_metric_alma.get():.4f}, Recall: {rec_metric_alma.get():.4f}\")\n",
    "        print(f\"ALMASWA - Accuracy: {acc_metric_almaswa.get():.4f}, Recall: {rec_metric_almaswa.get():.4f}\")\n",
    "\n",
    "        # Set baseline metrics before drift (first window before drift)\n",
    "        if i + 1 == window_size and i + 1 < drift_point:\n",
    "            baseline_accuracy = acc_metric_alma.get()\n",
    "            baseline_recall = rec_metric_alma.get()\n",
    "            print(f\"\\nBaseline Accuracy (ALMA): {baseline_accuracy:.4f}\")\n",
    "            print(f\"Baseline Recall (ALMA): {baseline_recall:.4f}\")\n",
    "            baseline_accuracy_almaswa = acc_metric_almaswa.get()\n",
    "            baseline_recall_almaswa = rec_metric_almaswa.get()\n",
    "            print(f\"Baseline Accuracy (ALMASWA): {baseline_accuracy_almaswa:.4f}\")\n",
    "            print(f\"Baseline Recall (ALMASWA): {baseline_recall_almaswa:.4f}\")\n",
    "\n",
    "        # After drift, check for recovery\n",
    "        if i + 1 >= drift_point:\n",
    "            # ALMA Recovery Check\n",
    "            if not alma_recovered and baseline_accuracy is not None:\n",
    "                if acc_metric_alma.get() >= recovery_threshold * baseline_accuracy:\n",
    "                    alma_recovered = True\n",
    "                    alma_recovery_point = i + 1\n",
    "                    print(f\"ALMA recovered at instance {i + 1}\")\n",
    "            \n",
    "            # ALMASWA Recovery Check\n",
    "            if not almaswa_recovered and baseline_accuracy_almaswa is not None:\n",
    "                if acc_metric_almaswa.get() >= recovery_threshold * baseline_accuracy_almaswa:\n",
    "                    almaswa_recovered = True\n",
    "                    almaswa_recovery_point = i + 1\n",
    "                    print(f\"ALMASWA recovered at instance {i + 1}\")\n",
    "\n",
    "# ------------------------------\n",
    "# Define the Plotting Function\n",
    "# ------------------------------\n",
    "\n",
    "def plot_metrics(evaluation_points, accuracy_alma, accuracy_almaswa, \n",
    "                recall_alma, recall_almaswa, drift_point, alma_recovery, almaswa_recovery):\n",
    "    plt.figure(figsize=(18, 6))\n",
    "\n",
    "    # Plot Detection Accuracy\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(evaluation_points, accuracy_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, accuracy_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.axvline(x=drift_point, color='r', linestyle='--', label='Drift Point')\n",
    "    # if alma_recovery:\n",
    "    #     plt.axvline(x=alma_recovery, color='g', linestyle=':', label='ALMA Recovery')\n",
    "    # if almaswa_recovery:\n",
    "    #     plt.axvline(x=almaswa_recovery, color='b', linestyle=':', label='ALMASWA Recovery')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Accuracy Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Plot F1-Score\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(evaluation_points, recall_alma, label='ALMA Classifier', marker='o')\n",
    "    plt.plot(evaluation_points, recall_almaswa, label='ALMASWA Classifier', marker='x')\n",
    "    plt.axvline(x=drift_point, color='r', linestyle='--', label='Drift Point')\n",
    "    # if alma_recovery:\n",
    "    #     plt.axvline(x=alma_recovery, color='g', linestyle=':', label='ALMA Recovery')\n",
    "    # if almaswa_recovery:\n",
    "    #     plt.axvline(x=almaswa_recovery, color='b', linestyle=':', label='ALMASWA Recovery')\n",
    "    plt.xlabel('Number of Instances')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.title('Recall Over Time')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/ToN_Sudden_Drift.png')\n",
    "    plt.show()\n",
    "\n",
    "# ------------------------------\n",
    "# Plot the Metrics\n",
    "# ------------------------------\n",
    "\n",
    "plot_metrics(\n",
    "    evaluation_points, \n",
    "    accuracy_alma, \n",
    "    accuracy_almaswa, \n",
    "    recall_alma, \n",
    "    recall_almaswa, \n",
    "    drift_point=drift_point, \n",
    "    alma_recovery=alma_recovery_point, \n",
    "    almaswa_recovery=almaswa_recovery_point\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adac7f7a-727a-4558-822c-c006e6a5e227",
   "metadata": {},
   "source": [
    "## Experiment 5: Paired t-test using different seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39d7a97a-7a6e-466a-94fd-7b4b3ed59215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running experiment with seed=0 ...\n",
      "  ALMA accuracy: 0.7168\n",
      "  ALMA+SWA accuracy: 0.9323\n",
      "\n",
      "Running experiment with seed=1 ...\n",
      "  ALMA accuracy: 0.7171\n",
      "  ALMA+SWA accuracy: 0.9321\n",
      "\n",
      "Running experiment with seed=2 ...\n",
      "  ALMA accuracy: 0.7177\n",
      "  ALMA+SWA accuracy: 0.9322\n",
      "\n",
      "Running experiment with seed=3 ...\n",
      "  ALMA accuracy: 0.7173\n",
      "  ALMA+SWA accuracy: 0.9323\n",
      "\n",
      "Running experiment with seed=4 ...\n",
      "  ALMA accuracy: 0.7170\n",
      "  ALMA+SWA accuracy: 0.9322\n",
      "\n",
      "=== Summary of 5 Runs ===\n",
      "ALMA Accuracies   : [0.7168162381078741, 0.7170750699280926, 0.7176703106126846, 0.7172548746659474, 0.7169576168332036]\n",
      "ALMASWA Accuracies: [0.9322824906436284, 0.9321302366317352, 0.9321991134466393, 0.9322600150513966, 0.9322433396119988]\n",
      "\n",
      "Mean ± Std for ALMA      : 0.7172 ± 0.0003\n",
      "Mean ± Std for ALMA+SWA : 0.9322 ± 0.0001\n",
      "\n",
      "Paired t-test (ALMA vs ALMA+SWA): t_stat=-1359.0457, p-value=0.000000\n",
      "=> Statistically significant difference at alpha=0.05\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from river import stream\n",
    "from river import metrics\n",
    "\n",
    "\n",
    "from scipy.stats import ttest_rel\n",
    "\n",
    "\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "\n",
    "# Separate features (X) and label (y)\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "\n",
    "\n",
    "NUM_RUNS = 5\n",
    "seeds = range(NUM_RUNS) \n",
    "\n",
    "# We'll store final accuracy for each run\n",
    "alma_accuracies = []\n",
    "almaswa_accuracies = []\n",
    "\n",
    "for seed in seeds:\n",
    "    print(f\"Running experiment with seed={seed} ...\")\n",
    "    \n",
    "    # 2a. Shuffle data with this seed\n",
    "    X, y = shuffle(X, y, random_state=seed)\n",
    "    \n",
    "    # 2b. Convert to a River stream\n",
    "    data_stream = stream.iter_pandas(X, y)\n",
    "    \n",
    "    # 2c. Instantiate models & metrics\n",
    "    model_alma = linear_model.ALMAClassifier()\n",
    "    model_almaswa = ALMASWAClassifier()\n",
    "    \n",
    "    metric_alma = metrics.Accuracy()\n",
    "    metric_almaswa = metrics.Accuracy()\n",
    "    \n",
    "    # 2d. Progressive learning/evaluation\n",
    "    for x_dict, y_true in data_stream:\n",
    "        # ALMA\n",
    "        y_pred_alma = model_alma.predict_one(x_dict)\n",
    "        model_alma.learn_one(x_dict, y_true)\n",
    "        metric_alma.update(y_true, y_pred_alma)\n",
    "        \n",
    "        # ALMA+SWA\n",
    "        y_pred_almaswa = model_almaswa.predict_one(x_dict)\n",
    "        model_almaswa.learn_one(x_dict, y_true)\n",
    "        metric_almaswa.update(y_true, y_pred_almaswa)\n",
    "        \n",
    "    # 2e. Collect final accuracies for this run\n",
    "    alma_accuracies.append(metric_alma.get())\n",
    "    almaswa_accuracies.append(metric_almaswa.get())\n",
    "    \n",
    "    print(f\"  ALMA accuracy: {metric_alma.get():.4f}\")\n",
    "    print(f\"  ALMA+SWA accuracy: {metric_almaswa.get():.4f}\")\n",
    "    print(\"\")\n",
    "\n",
    "# 3. Print summary of results\n",
    "print(\"=== Summary of 5 Runs ===\")\n",
    "print(\"ALMA Accuracies   :\", alma_accuracies)\n",
    "print(\"ALMASWA Accuracies:\", almaswa_accuracies)\n",
    "\n",
    "print(\"\\nMean ± Std for ALMA      : \"\n",
    "      f\"{np.mean(alma_accuracies):.4f} ± {np.std(alma_accuracies):.4f}\")\n",
    "print(\"Mean ± Std for ALMA+SWA : \"\n",
    "      f\"{np.mean(almaswa_accuracies):.4f} ± {np.std(almaswa_accuracies):.4f}\")\n",
    "\n",
    "# 4. (Optional) Significance test\n",
    "#    Paired t-test over the 10 runs\n",
    "t_stat, p_val = ttest_rel(alma_accuracies, almaswa_accuracies)\n",
    "print(f\"\\nPaired t-test (ALMA vs ALMA+SWA): t_stat={t_stat:.4f}, p-value={p_val:.6f}\")\n",
    "if p_val <= 0.05:\n",
    "    print(\"=> Statistically significant difference at alpha=0.05\")\n",
    "else:\n",
    "    print(\"=> No statistically significant difference at alpha=0.05\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13bb1c67-bd03-453f-bec4-e57d0757745a",
   "metadata": {},
   "source": [
    "## Experiment 7: Measuring Resource Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "56b9b5ef-51e5-400e-8a22-1cb4108f6f15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 1108995, 0: 270279})\n",
      "Processed 100000 samples. Throughput: 12660.35 samples/sec.\n",
      "Processed 200000 samples. Throughput: 13252.62 samples/sec.\n",
      "Processed 300000 samples. Throughput: 13593.37 samples/sec.\n",
      "Processed 400000 samples. Throughput: 13928.35 samples/sec.\n",
      "Processed 500000 samples. Throughput: 14142.36 samples/sec.\n",
      "Processed 600000 samples. Throughput: 14283.98 samples/sec.\n",
      "Processed 700000 samples. Throughput: 14384.18 samples/sec.\n",
      "Processed 800000 samples. Throughput: 14470.84 samples/sec.\n",
      "Processed 900000 samples. Throughput: 14536.81 samples/sec.\n",
      "Processed 1000000 samples. Throughput: 14584.67 samples/sec.\n",
      "Processed 1100000 samples. Throughput: 14639.38 samples/sec.\n",
      "Processed 1200000 samples. Throughput: 14677.16 samples/sec.\n",
      "Processed 1300000 samples. Throughput: 14703.45 samples/sec.\n",
      "\n",
      "Total samples processed: 1379274\n",
      "Total elapsed time: 93.64 seconds\n",
      "Throughput: 14729.05 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9472\n",
      "F1 Score: 0.9673\n",
      "ROCAUC Score: 0.9076\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9653\n",
      "F1 Score: 0.9787\n",
      "ROCAUC Score: 0.9262\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0195 ms\n",
      "Average CPU Usage: 0.85%\n",
      "Average Memory Usage: -0.15 KB\n",
      "Average Total Processing Time per Sample: 0.0324 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0194 ms\n",
      "Average CPU Usage: 0.85%\n",
      "Average Memory Usage: -0.15 KB\n",
      "Average Total Processing Time per Sample: 0.0324 ms\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['Latency']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('Latency (ms)', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/ToN_Latency.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf3a951-ac54-4717-be17-41e0340ca5b5",
   "metadata": {},
   "source": [
    "### CPU Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc6bb9f7-45d3-4456-8b9a-073aafe9f963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 1108995, 0: 270279})\n",
      "Processed 100000 samples. Throughput: 13199.16 samples/sec.\n",
      "Processed 200000 samples. Throughput: 14037.58 samples/sec.\n",
      "Processed 300000 samples. Throughput: 14358.16 samples/sec.\n",
      "Processed 400000 samples. Throughput: 14532.80 samples/sec.\n",
      "Processed 500000 samples. Throughput: 14631.53 samples/sec.\n",
      "Processed 600000 samples. Throughput: 14615.28 samples/sec.\n",
      "Processed 700000 samples. Throughput: 14678.83 samples/sec.\n",
      "Processed 800000 samples. Throughput: 14704.53 samples/sec.\n",
      "Processed 900000 samples. Throughput: 14747.63 samples/sec.\n",
      "Processed 1000000 samples. Throughput: 14775.03 samples/sec.\n",
      "Processed 1100000 samples. Throughput: 14807.07 samples/sec.\n",
      "Processed 1200000 samples. Throughput: 14843.10 samples/sec.\n",
      "Processed 1300000 samples. Throughput: 14869.76 samples/sec.\n",
      "\n",
      "Total samples processed: 1379274\n",
      "Total elapsed time: 92.63 seconds\n",
      "Throughput: 14890.22 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9472\n",
      "F1 Score: 0.9673\n",
      "ROCAUC Score: 0.9076\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9653\n",
      "F1 Score: 0.9787\n",
      "ROCAUC Score: 0.9262\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0193 ms\n",
      "Average CPU Usage: 0.72%\n",
      "Average Memory Usage: -0.26 KB\n",
      "Average Total Processing Time per Sample: 0.0321 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0193 ms\n",
      "Average CPU Usage: 0.72%\n",
      "Average Memory Usage: -0.26 KB\n",
      "Average Total Processing Time per Sample: 0.0321 ms\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['CPU_Usage']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('CPU Usage', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/ToN_CPU.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed01946-808a-41d2-9ab5-b0344c5ad06b",
   "metadata": {},
   "source": [
    "### Memory usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f340980-5d56-479e-b34c-a86a4f384800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 1108995, 0: 270279})\n",
      "Processed 100000 samples. Throughput: 13176.04 samples/sec.\n",
      "Processed 200000 samples. Throughput: 14098.91 samples/sec.\n",
      "Processed 300000 samples. Throughput: 14233.38 samples/sec.\n",
      "Processed 400000 samples. Throughput: 14388.86 samples/sec.\n",
      "Processed 500000 samples. Throughput: 14515.05 samples/sec.\n",
      "Processed 600000 samples. Throughput: 14605.26 samples/sec.\n",
      "Processed 700000 samples. Throughput: 14664.72 samples/sec.\n",
      "Processed 800000 samples. Throughput: 14679.87 samples/sec.\n",
      "Processed 900000 samples. Throughput: 14708.87 samples/sec.\n",
      "Processed 1000000 samples. Throughput: 14738.35 samples/sec.\n",
      "Processed 1100000 samples. Throughput: 14737.52 samples/sec.\n",
      "Processed 1200000 samples. Throughput: 14745.77 samples/sec.\n",
      "Processed 1300000 samples. Throughput: 14756.30 samples/sec.\n",
      "\n",
      "Total samples processed: 1379274\n",
      "Total elapsed time: 93.46 seconds\n",
      "Throughput: 14757.70 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9472\n",
      "F1 Score: 0.9673\n",
      "ROCAUC Score: 0.9076\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9653\n",
      "F1 Score: 0.9787\n",
      "ROCAUC Score: 0.9262\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0195 ms\n",
      "Average CPU Usage: 0.85%\n",
      "Average Memory Usage: -0.32 KB\n",
      "Average Total Processing Time per Sample: 0.0324 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0195 ms\n",
      "Average CPU Usage: 0.85%\n",
      "Average Memory Usage: -0.32 KB\n",
      "Average Total Processing Time per Sample: 0.0324 ms\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['Memory_Usage']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('Memory Usage', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/ToN_Memory.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004e567a-e0ea-4bd2-b7b4-03913e8b33ff",
   "metadata": {},
   "source": [
    "### Processing Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "858d7307-a2f8-4b25-8b36-32b75044e095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples per class: Counter({1: 1108995, 0: 270279})\n",
      "Processed 100000 samples. Throughput: 13166.61 samples/sec.\n",
      "Processed 200000 samples. Throughput: 14041.02 samples/sec.\n",
      "Processed 300000 samples. Throughput: 14299.98 samples/sec.\n",
      "Processed 400000 samples. Throughput: 14430.98 samples/sec.\n",
      "Processed 500000 samples. Throughput: 14432.90 samples/sec.\n",
      "Processed 600000 samples. Throughput: 14453.59 samples/sec.\n",
      "Processed 700000 samples. Throughput: 14510.41 samples/sec.\n",
      "Processed 800000 samples. Throughput: 14572.85 samples/sec.\n",
      "Processed 900000 samples. Throughput: 14623.08 samples/sec.\n",
      "Processed 1000000 samples. Throughput: 14653.26 samples/sec.\n",
      "Processed 1100000 samples. Throughput: 14669.24 samples/sec.\n",
      "Processed 1200000 samples. Throughput: 14700.24 samples/sec.\n",
      "Processed 1300000 samples. Throughput: 14729.88 samples/sec.\n",
      "\n",
      "Total samples processed: 1379274\n",
      "Total elapsed time: 93.50 seconds\n",
      "Throughput: 14751.79 samples/second\n",
      "\n",
      "--- ALMAClassifier ---\n",
      "Accuracy: 0.9472\n",
      "F1 Score: 0.9673\n",
      "ROCAUC Score: 0.9076\n",
      "\n",
      "--- ALMASWAClassifier ---\n",
      "Accuracy: 0.9653\n",
      "F1 Score: 0.9787\n",
      "ROCAUC Score: 0.9262\n",
      "\n",
      "--- ALMAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0195 ms\n",
      "Average CPU Usage: 0.73%\n",
      "Average Memory Usage: -0.26 KB\n",
      "Average Total Processing Time per Sample: 0.0324 ms\n",
      "\n",
      "--- ALMASWAClassifier Performance ---\n",
      "Average Latency per Sample: 0.0196 ms\n",
      "Average CPU Usage: 0.73%\n",
      "Average Memory Usage: -0.26 KB\n",
      "Average Total Processing Time per Sample: 0.0324 ms\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, linear_model, metrics, stream\n",
    "import time\n",
    "import psutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# Step 1: Initialize Classifiers and Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Define classifiers with their respective pipelines\n",
    "classifiers = {\n",
    "    'ALMAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        linear_model.ALMAClassifier()\n",
    "    ),\n",
    "    'ALMASWAClassifier': compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier()  # Ensure ALMASWAClassifier is correctly imported or defined\n",
    "    )\n",
    "}\n",
    "\n",
    "# Initialize metrics for each classifier\n",
    "metrics_dict = {\n",
    "    name: {\n",
    "        'Accuracy': metrics.Accuracy(),\n",
    "        'F1': metrics.F1(),\n",
    "        'ROCAUC': metrics.ROCAUC()\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize performance tracking dictionaries\n",
    "performance_metrics = {\n",
    "    name: {\n",
    "        'Latency': [],             # Time taken per sample (predict + learn)\n",
    "        'Total_Time': [],          # Cumulative processing time per sample\n",
    "        'CPU_Usage': [],           # CPU usage in percentage\n",
    "        'Memory_Usage': []         # Memory usage in KB (Modified from MB to KB)\n",
    "    }\n",
    "    for name in classifiers\n",
    "}\n",
    "\n",
    "# Initialize overall tracking variables\n",
    "total_samples = 0\n",
    "start_time = time.time()\n",
    "\n",
    "# Initialize psutil process for monitoring\n",
    "process = psutil.Process()\n",
    "\n",
    "# ---------------------------\n",
    "# Step 2: Load and Prepare Data\n",
    "# ---------------------------\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "print('Number of samples per class:', Counter(data['Label']))\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "# ---------------------------\n",
    "# Step 3: Training Loop with Performance Measurement\n",
    "# ---------------------------\n",
    "\n",
    "# Iterate through the data stream and update models and metrics\n",
    "for i, (x, true_y) in enumerate(stream.iter_pandas(X, y)):\n",
    "    total_samples += 1\n",
    "    sample_start_time = time.time()\n",
    "\n",
    "    # Get current CPU and memory usage before processing\n",
    "    cpu_before = process.cpu_percent(interval=None)\n",
    "    memory_before = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    for name, model in classifiers.items():\n",
    "        clf_start_time = time.time()\n",
    "\n",
    "        # Predict\n",
    "        y_pred = model.predict_one(x)\n",
    "\n",
    "        # Learn\n",
    "        model.learn_one(x, true_y)\n",
    "\n",
    "        # Calculate latency for this classifier\n",
    "        clf_latency = time.time() - clf_start_time\n",
    "\n",
    "        # Update metrics\n",
    "        metrics_dict[name]['Accuracy'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['F1'].update(true_y, y_pred)\n",
    "        metrics_dict[name]['ROCAUC'].update(true_y, y_pred)\n",
    "\n",
    "        # Record latency\n",
    "        performance_metrics[name]['Latency'].append(clf_latency)\n",
    "\n",
    "    # Get current CPU and memory usage after processing\n",
    "    cpu_after = process.cpu_percent(interval=None)\n",
    "    memory_after = process.memory_info().rss / 1024  # Convert to KB (Modified)\n",
    "\n",
    "    # Calculate CPU and memory usage during processing\n",
    "    cpu_usage = cpu_after - cpu_before\n",
    "    memory_usage = memory_after - memory_before\n",
    "\n",
    "    # Record CPU and memory usage for each classifier (approximate)\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['CPU_Usage'].append(cpu_usage / len(classifiers))\n",
    "        performance_metrics[name]['Memory_Usage'].append(memory_usage / len(classifiers))\n",
    "\n",
    "    # Calculate total processing time for this sample\n",
    "    total_time = time.time() - sample_start_time\n",
    "    for name in classifiers.keys():\n",
    "        performance_metrics[name]['Total_Time'].append(total_time / len(classifiers))\n",
    "\n",
    "    # Optional: Print progress every 100,000 samples\n",
    "    if (i + 1) % 100000 == 0:\n",
    "        elapsed_time = time.time() - start_time\n",
    "        throughput = total_samples / elapsed_time\n",
    "        print(f'Processed {i + 1} samples. Throughput: {throughput:.2f} samples/sec.')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 4: Calculate Throughput\n",
    "# ---------------------------\n",
    "\n",
    "# Calculate total elapsed time\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "\n",
    "# Calculate throughput (samples per second)\n",
    "throughput = total_samples / elapsed_time\n",
    "print(f'\\nTotal samples processed: {total_samples}')\n",
    "print(f'Total elapsed time: {elapsed_time:.2f} seconds')\n",
    "print(f'Throughput: {throughput:.2f} samples/second\\n')\n",
    "\n",
    "# ---------------------------\n",
    "# Step 5: Display Results\n",
    "# ---------------------------\n",
    "\n",
    "# Display classification metrics\n",
    "for name, metric in metrics_dict.items():\n",
    "    print(f'--- {name} ---')\n",
    "    print(f\"Accuracy: {metric['Accuracy'].get():.4f}\")\n",
    "    print(f\"F1 Score: {metric['F1'].get():.4f}\")\n",
    "    print(f\"ROCAUC Score: {metric['ROCAUC'].get():.4f}\\n\")\n",
    "\n",
    "# Display performance metrics summary\n",
    "for name, metrics_perf in performance_metrics.items():\n",
    "    avg_latency = sum(metrics_perf['Latency']) / len(metrics_perf['Latency']) if metrics_perf['Latency'] else 0\n",
    "    avg_cpu = sum(metrics_perf['CPU_Usage']) / len(metrics_perf['CPU_Usage']) if metrics_perf['CPU_Usage'] else 0\n",
    "    avg_memory = sum(metrics_perf['Memory_Usage']) / len(metrics_perf['Memory_Usage']) if metrics_perf['Memory_Usage'] else 0\n",
    "    avg_total_time = sum(metrics_perf['Total_Time']) / len(metrics_perf['Total_Time']) if metrics_perf['Total_Time'] else 0\n",
    "\n",
    "    print(f'--- {name} Performance ---')\n",
    "    print(f\"Average Latency per Sample: {avg_latency * 1000:.4f} ms\")\n",
    "    print(f\"Average CPU Usage: {avg_cpu:.2f}%\")\n",
    "    print(f\"Average Memory Usage: {avg_memory:.2f} KB\")  # Modified to KB\n",
    "    print(f\"Average Total Processing Time per Sample: {avg_total_time * 1000:.4f} ms\\n\")\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# ---------------------------\n",
    "# Step 6: (Optional) Visualization of Performance Metrics\n",
    "# ---------------------------\n",
    "\n",
    "# Enhanced Plotting Function to handle multiple classifiers side by side\n",
    "def plot_performance_side_by_side(performance_metrics, classifier_names, window_size=50000):\n",
    "    metrics_names = ['Total_Time']\n",
    "    num_classifiers = len(classifier_names)\n",
    "    \n",
    "    # Create subplots arranged side by side with shared y-axis\n",
    "    fig, axes = plt.subplots(1, num_classifiers, figsize=(15, 6), sharey=True)\n",
    "    \n",
    "    # If there's only one classifier, axes might not be a list\n",
    "    if num_classifiers == 1:\n",
    "        axes = [axes]\n",
    "    \n",
    "    for ax, classifier_name in zip(axes, classifier_names):\n",
    "        for metric in metrics_names:\n",
    "            metric_series = pd.Series(performance_metrics[classifier_name][metric])\n",
    "            rolling_metric = metric_series.rolling(window=window_size).mean()\n",
    "            ax.plot(rolling_metric, label=f'{metric} (Rolling Mean, window={window_size})')\n",
    "        \n",
    "        # Set titles and labels with bold font\n",
    "        ax.set_title(f'{classifier_name} Performance', fontweight='bold', fontsize=14)\n",
    "        ax.set_xlabel('Number of Instances', fontweight='bold', fontsize=12)\n",
    "        ax.set_ylabel('Total Processing Time (s)', fontweight='bold', fontsize=12)\n",
    "        \n",
    "        # Make ticks bold\n",
    "        ax.tick_params(axis='both', which='major', labelsize=10, width=2)\n",
    "        for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "            label.set_fontweight('bold')\n",
    "        \n",
    "        # Make legend bold using prop\n",
    "        ax.legend(prop={'weight': 'bold'})\n",
    "        ax.grid(True)\n",
    "\n",
    "    # Force y-axis tick labels to be bold on all subplots\n",
    "    for ax in axes:\n",
    "        for tick in ax.yaxis.get_ticklabels():\n",
    "            tick.set_fontweight('bold')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('/Users/promisea/SAMKNN/ALMASWA/ToN_Time.png')\n",
    "    plt.show()\n",
    "\n",
    "# Example: Plot performance metrics for both classifiers side by side\n",
    "plot_performance_side_by_side(performance_metrics, ['ALMAClassifier', 'ALMASWAClassifier'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089bef1e-4192-4d62-961c-36f24b156fa9",
   "metadata": {},
   "source": [
    "## Experiment 8: Effect of Varying SWA Frequency and SWA Start on performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389fa61a-abdd-445f-8017-c183b8107179",
   "metadata": {},
   "source": [
    "### Varying SWA Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77f2e49a-2ab0-407e-a072-9feb133bb2ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluating SWA Frequency: 10\n",
      "Final Recall for SWA Frequency 10: 0.9905\n",
      "\n",
      "Evaluating SWA Frequency: 50\n",
      "Final Recall for SWA Frequency 50: 0.9905\n",
      "\n",
      "Evaluating SWA Frequency: 100\n",
      "Final Recall for SWA Frequency 100: 0.9905\n",
      "\n",
      "Evaluating SWA Frequency: 200\n",
      "Final Recall for SWA Frequency 200: 0.9901\n",
      "\n",
      "Evaluating SWA Frequency: 500\n",
      "Final Recall for SWA Frequency 500: 0.9863\n",
      "\n",
      "Evaluating SWA Frequency: 1000\n",
      "Final Recall for SWA Frequency 1000: 0.9861\n",
      "\n",
      "Evaluating SWA Frequency: 5000\n",
      "Final Recall for SWA Frequency 5000: 0.9728\n",
      "\n",
      "Evaluating SWA Frequency: 10000\n",
      "Final Recall for SWA Frequency 10000: 0.9452\n",
      "\n",
      "=== Experiment Results ===\n",
      "SWA Frequency: 10 | Recall: 0.9905\n",
      "SWA Frequency: 50 | Recall: 0.9905\n",
      "SWA Frequency: 100 | Recall: 0.9905\n",
      "SWA Frequency: 200 | Recall: 0.9901\n",
      "SWA Frequency: 500 | Recall: 0.9863\n",
      "SWA Frequency: 1000 | Recall: 0.9861\n",
      "SWA Frequency: 5000 | Recall: 0.9728\n",
      "SWA Frequency: 10000 | Recall: 0.9452\n",
      "\n",
      "Optimal SWA Frequency: 10 with Recall: 0.9905\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "from river import compose, preprocessing, metrics, stream\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load and prepare data\n",
    "data = pd.read_csv(\"/Users/promisea/SAMKNN/NF-TON-IoT/NF-ToN-IoT.csv\")\n",
    "X = data.drop(['IPV4_SRC_ADDR', 'IPV4_DST_ADDR', 'Attack', 'Label'], axis=1)\n",
    "y = data['Label']\n",
    "X, y = shuffle(X, y, random_state=42)  # Shuffle the data to ensure randomness\n",
    "\n",
    "# Define the list of SWA frequencies to experiment with\n",
    "swa_frequencies = [10, 50, 100, 200, 500, 1000, 5000, 10000]\n",
    "\n",
    "# Initialize a dictionary to store accuracy results for each SWA frequency\n",
    "recall_results = {}\n",
    "\n",
    "# Iterate over each SWA frequency to evaluate performance\n",
    "for freq in swa_frequencies:\n",
    "    print(f\"\\nEvaluating SWA Frequency: {freq}\")\n",
    "\n",
    "    # Define the pipeline with StandardScaler and ALMASWAClassifier\n",
    "    model = compose.Pipeline(\n",
    "        preprocessing.StandardScaler(),\n",
    "        ALMASWAClassifier(swa_freq=freq)\n",
    "    )\n",
    "    \n",
    "    # Initialize the accuracy metric\n",
    "    recall = metrics.Recall()\n",
    "    \n",
    "    # Stream the data and update the model and metric\n",
    "    for xi, yi_true in stream.iter_pandas(X, y):\n",
    "        y_pred = model.predict_one(xi)  # Make a prediction\n",
    "        model.learn_one(xi, yi_true)    # Update the model with the true label\n",
    "        recall.update(yi_true, y_pred)  # Update the accuracy metric\n",
    "    \n",
    "    # Store the final accuracy for this SWA frequency\n",
    "    final_recall = recall.get()\n",
    "    recall_results[freq] = final_recall\n",
    "    print(f\"Final Recall for SWA Frequency {freq}: {final_recall:.4f}\")\n",
    "\n",
    "# Identify the SWA frequency with the highest accuracy\n",
    "best_freq = max(recall_results, key=recall_results.get)\n",
    "best_recall = recall_results[best_freq]\n",
    "\n",
    "print(\"\\n=== Experiment Results ===\")\n",
    "for freq, rec in recall_results.items():\n",
    "    print(f\"SWA Frequency: {freq} | Recall: {rec:.4f}\")\n",
    "    \n",
    "print(f\"\\nOptimal SWA Frequency: {best_freq} with Recall: {best_recall:.4f}\")\n",
    "\n",
    "# Optional: Plot the results for visualization\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(list(recall_results.keys()), list(recall_results.values()), marker='o', linestyle='-')\n",
    "plt.title('ALMASWAClassifier Recall vs. SWA Frequency')\n",
    "plt.xlabel('SWA Frequency')\n",
    "plt.ylabel('Recall')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b1ca9c6-cd1c-45a7-9fa1-29c5a1770082",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "river",
   "language": "python",
   "name": "river"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}