CF_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "342f3100-20a9-48d5-abec-59b11884c9dd",
   "metadata": {},
   "source": [
    "### Using NFStream to process all the PCAP files to have flow records, set high timeouts to not expire the flows by any of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49f6a1de-c342-4b53-8862-68b1a7784f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting NFStream v6.5.4b\n",
      "Found 165 .pcap files. Processing...\n",
      "All files processed.\n"
     ]
    }
   ],
   "source": [
    "import nfstream\n",
    "import os\n",
    "from nfstream import NFPlugin\n",
    "import csv\n",
    "\n",
    "def process_pcap_NF(pcap_directory: str):\n",
    "    \n",
    "    pcap_files = [f for f in os.listdir(pcap_directory) if f.endswith('.pcap')]\n",
    "    print(f\"Found {len(pcap_files)} .pcap files. Processing...\")\n",
    "\n",
    "    for filename in pcap_files:\n",
    "        path = os.path.join(pcap_directory, filename)\n",
    "\n",
    "        streamer = nfstream.NFStreamer(source=path,\n",
    "                                       statistical_analysis=True,\n",
    "                                       splt_analysis=255,\n",
    "                                       idle_timeout=99999999999999,\n",
    "                                       active_timeout=99999999999999,\n",
    "                                       accounting_mode=2,\n",
    "                                       udps=None,\n",
    "                                       n_dissections=20,\n",
    "                                       n_meters=1,\n",
    "        )\n",
    "\n",
    "        os.makedirs(\"VNAT/CF\", exist_ok=True)\n",
    "        output_folder = os.path.join(os.path.dirname(path), \"CF\")\n",
    "\n",
    "        output_csv = os.path.join(output_folder, os.path.splitext(os.path.basename(path))[0] + \".csv\")\n",
    "\n",
    "        streamer.to_csv(output_csv)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    pcap_directory =  \"VNAT\"\n",
    "\n",
    "    print(f\"Starting NFStream v{nfstream.__version__}\")\n",
    "    \n",
    "    process_pcap_NF(pcap_directory)\n",
    "\n",
    "    print(\"All files processed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a6914a7-ff77-406e-8ae3-809cdd128980",
   "metadata": {},
   "source": [
    "### Merge the CSV files into one large CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a6172ac-f26d-4453-aad0-4e64cb36c482",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "directory = \"VNAT/CF\"\n",
    "\n",
    "dataframes = []\n",
    "\n",
    "for filename in os.listdir(directory):\n",
    "    if filename.endswith('.csv'):  \n",
    "        df = pd.read_csv(os.path.join(directory, filename),low_memory=False)\n",
    "        \n",
    "        if 'nonvpn' in filename.lower():\n",
    "            df['label'] = 'NoVPN'\n",
    "        else:    \n",
    "            df['label'] = 'VPN'\n",
    "        df['source_file']=filename\n",
    "        dataframes.append(df)\n",
    "\n",
    "df_large = pd.concat(dataframes, ignore_index=True)\n",
    "\n",
    "df_large.to_csv(\"VNAT/CF/flows_CF.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e824246-7f2e-49b7-bd36-4fa3ce02acb7",
   "metadata": {},
   "source": [
    "### Read the large CSV in and assign categories based on the source file names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd6e8dc0-0a9a-4817-ba21-b958eaa911b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history saving thread hit an unexpected error (OperationalError('attempt to write a readonly database')).History will not be written to the database.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/26/m1ty4dhn4kd02qvztlq5pdlh0000gp/T/ipykernel_60072/3332365726.py:3: DtypeWarning: Columns (87,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(\"VNAT/CF/flows.csv\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "category\n",
       "File Transfer        16430\n",
       "Command & Control    13599\n",
       "Streaming             1764\n",
       "Chat                  1301\n",
       "VoIP                   617\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"VNAT/CF/flows_CF.csv\")\n",
    "\n",
    "\n",
    "category_mapping = {\n",
    "    'skype': 'Chat',\n",
    "    'vimeo': 'Streaming',\n",
    "    'netflix': 'Streaming',\n",
    "    'youtube': 'Streaming',\n",
    "    'ssh': 'Command & Control',\n",
    "    'rdp': 'Command & Control',\n",
    "    'sftp': 'File Transfer',\n",
    "    'rsync': 'File Transfer',\n",
    "    'scp': 'File Transfer',\n",
    "    'voip': 'VoIP'\n",
    "}\n",
    "\n",
    "def assign_category(source_file):\n",
    "    for keyword, category in category_mapping.items():\n",
    "        if keyword in source_file.lower():\n",
    "            return category\n",
    "    return 'Unknown'  \n",
    "\n",
    "df['category'] = df['source_file'].apply(assign_category)\n",
    "\n",
    "df['category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92676b68-65af-4a44-b47d-15e41f639df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('VNAT/CF/flows_extra_labelled_CF.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae2b339-f377-4c46-a851-1897f36afa75",
   "metadata": {},
   "source": [
    "###  CDF to see how the samples distribute across flow duration and packet count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cbd1e56-c3ce-45d5-a70e-3cad2657ed8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/26/m1ty4dhn4kd02qvztlq5pdlh0000gp/T/ipykernel_60072/276095087.py:7: DtypeWarning: Columns (87,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  filtered_df = pd.read_csv('VNAT/CF/flows_extra_labelled.csv')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows before filtering: 33711\n",
      "Number of rows after filtering: 33711\n",
      "CDF at 41.0 seconds: 0.9180089585001928\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CDF at 8000 packets: 0.994185874047047\n"
     ]
    },
    {
     "data": {
      "image/png": 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edqrWX5d6no+i0HXqQVOr4LUmR4OJfhnrr17tv6CdTn3PdaPDV7Xa3+58rTVJ2s/C7ugZDLos7Y+gw3n1wKAdO7Vc9ECsB6gLL7ywyMvWwKC1VUprhzQgamDRf2tfkbxojZr2+9Bh21pzo6cH0Bob3Q9OZX/T16bL1NCifan0/dB16Xug5anNIjocXmsdtGlHhxbruW10H9A+L1qDoH1btGyU9t+YPn262Zd0Pn2v9L0NRJtCtJZAQ6nu4/ZQcK2p0KHhhaXbrs1Quj59TVoTpgFAD9y6LxUHLRdtqtU+LFqLpv3cNJzZ58DxPVuwPqZlbg/h1u3SctZh2IH6nmkZaRDRYewaLnUggvbHef/9900Ha62V0lo3DSz6faTT9TNh9wfU90M/x/rZ1fCiQ+m1CVSbQvV91X1d3yOdR79jTtbZHiES6uFaCO+h4J5DXe2hr88880zAIb4zZswIuFzPYaI6DFWH1s6dO9dq3bq1GWrerFkzv+fmNRRch5EGEmgYrA5nffjhh62GDRtaMTExVnJysnXttddaf/zxh3ue1157zWrcuLF7O3Sb7WG6hR0K7rneZ5991jr77LOtChUqmOHwug4dZrtx40b3fPZ67Ft8fLxVp04d67LLLjND2T2H0BdlmLYvHaY/btw4d3nUrVvXDOP3Xc+pDgVPTEy0unfvbn355ZcnfU/Vyy+/bLZJ34MOHTqYoca+72deQ8Hz204dUt6+fXurXLlyZsh3q1atrAceeMDasWOH13yffPKJde6555r5dNs7duxohj3bDh8+bA0cONCqXLmy2QZ7WHigbVL6us877zz38vr27Wv99ttvBToNgP2Z0WV7bp9+VnT/aNCggfXUU0+5T3XgOV9hhoLrZzA/f/31lxmWrq9Zh1j369fPlJuuU7fd05YtW8yQcD1FhL6HOsxbh2Tbw/sDfQ+4XC5zqojo6Ghr1qxZ7lMD6GvTz7gup0qVKub9031WTxdhW7dundW1a1dTvrpc3Q90Xffff785HYW+17pf6L9130LpFKH/CVWwAoJN+9To6AodpQQACE/0uQEAAI5CuAEAAI5CuAEAAI5CnxsAAOAo1NwAAABHIdwAAABHCbuT+OmJv/QMtnpyp5K6Dg4AADg12otGLxysJ1f0veabhHu40WBTkIukAQCA0mfbtm3mjNH5CbtwozU2duHoacCDSU8lrqcKt0/HHu4oD3+UiTfKwx9l4o3y8BeuZZKWlmYqJ+zjeH7CLtzYTVEabIoj3CQkJJjlhtMOlxfKwx9l4o3y8EeZeKM8/IV7mUQUoEsJHYoBAICjEG4AAICjhF2zVEG5XC5T9VcYOn90dLRkZGSY54c7uzwyMzMlKirqpL3bAQAIBsJNgKFmu3btkgMHDhTpucnJyaazMsPMT5TH1q1bTbhp2LChxMbGhnqzAAAOR7jxYQeb6tWrmw5bhQkpeg6dw4cPS4UKFail8CgPLUct1507d0q9evUIfgCAYkW48aBNSXawqVq1apEO5llZWRIfH0+48SgPDTfVqlUz5xjKyckJy979AICSwxHYg93HRg/GCC67OYq+SACA4ka4CYBmk+CjTAEAJYVwAwAAHIVwAwAAHCWk4WbJkiXSt29fc4VPbbaYNWvWSZ+zePFiOeussyQuLk7OOOMMeeONN0pkWwEAQNkQ0nBz5MgRadOmjUyaNKlA82/atEkuvfRSufDCC+XHH3+Ue+65R26++WaZO3euhLsbb7zRBETf28UXX+yeZ/Xq1dKvXz+pUaOGGdHVuHFjGT58uGzYsME8vnnzZq/n6sXJzjzzTBkxYoT8/vvvXuvTUBlofa+++mqJv3YAAErNUPBLLrnE3Apq8uTJ5kRwzz33nLnfvHlzWbp0qTz//PPSu3fvgM/Rs+PqzfOqovbIKN8zEOt9PfGcDmHWW2Hpc+2/RXn+qdB1ahlMmzbNa7rWcOm2fPbZZybY6FVk3377bTn99NMlJSVFZs6cKY888oi899577m3Wq81qqElPT5dffvlFXnzxRRNCP/74Y+nevbuZR+fVi7atXbvWa32VKlVyL8e3PPSvlrGe0C9c2ftcYc9+7VSUhz/KpODlYb5bLJGcXP2OsY79tSxx5R676f3DmTmSmZ0rWa5cOXD02DKsXEuyPeZx5eZKRnauZObkup9rbua769jfQxk5sj89W6pVODbyU7/d9CvOfMtZ1rG/7mmW+7HjX4PmGb7TPOczy3BP916OPU3/cyQrR3alZUhcVqR8fmC1jtbwKI/AZWh5POA5i+f8J16B73TPJxR83tOrlZdH+jSTYCrMZ6JMnedm+fLl0qNHD69pekDXGpy8jB8/XsaNG+c3XQ/gvkO+9VIBekZdPfGcnp/F3il0py+Mo3sLf3bjQOJjIgs8ysgODYGGsesJ9G666Sbp2bOnCTY2PZfPP//5Tzl48KAJffq6zXrj481y9Ka1ZN26dZMrrrhChg0bZmp/dD16iQnlu75AofHQoUOmPI8ePWqaIvVcN+Fu/vz5od6EUoXyyL9M9ADiOn7TryM9oJubxwHWc1q269gxz/OxEwfMCK/7Ov/RnGPz6/RMl8hR17F/u6xj8x77t8iOdJFq8ScOtJbvQT7Q/QD/tudJz4mQwzkiiTGWZLgi5EiOiCs30DZHyQPfLfTaZt228BYpsm+PlFZ/7UqVs+TPoC5Tf3A7MtzoQVqbVDzpfT0w64GzXLlyfs8ZPXq0jBw50n1f561bt66pwdCaB096wNZLJ+gZhvUAr9KzcqTdU6H54l3zaE9JiC3YW6QnxtNw5vua1IIFC2Tv3r2mLAI9bk/T163Kly/vN9+9994r11xzjWme6tixoykfDV6BlmfTYKjBRpu3tPZM35+uXbu6yzYcafDTg5YGTU5m6Lzy0F/7Ow9myL4jWXI022X+/nXgqHls7c5DUiEu2vz6T8vIPlZr4DpWc5CTmyu/7jgkDaomSFZOrhxOPypp2ccO3jFREZKtyaKU2HAw+MvcfbR4gkp0ZITEx0RJ+bgoiYuOlNioSPkj9Yi0qFlRysVESVRkhERHRpr5oqMizDT9d1RUhERFREhk5Im/Oj0j2yVHMl1Ss5J+/x1bh/kTof/Xpvnj983pL45NO/7/Y033Xo+dOEWGue9eXkTAx+116bSMrGz5Ze0G6di6uURGRbmXa4vwmHBirb7TA8/v+Yjvb+uIQqzjtPKx0rVxkgST3fLiuHBTFNosozdf+kXq+2WqJ5jTHUfPLmyfYTiUZxr23I6T0e3+/PPP/cLGQw89ZEKPatGiRb7L83zNvvPpc5VeJ+qcc84xj2uNj+f6NBxpALXZzVN2merfQOUejigHZ5SHBvgdBzPkjW82ycyVf5kDnzZ/FNXmvfYv0xMHi7yCjR5s9aAbGSEnDsTHD8b6WUs9nCm1K5cT/ShHRtjTxcwTeXyeqOOP6b+37UuXRknlzUFJQ1icfaA/fmDXA77+/Wv/UWmUVMEsS9d9rL/diYOymWYfoI8f0I9NDzxNw51WxyRXKiflY6MksVyMext1+1w5Llmy5Cu58MILJC4mxv2azfJM+It0hxHz+j0CiZN/FMw+vF76dG5QJj83RVWY11qmwo02Ge3evdtrmt7XA2ygWptg0CT/22OB+/P40oP5obRDUjGxYlBCka67MLQJ6ZVXXvGadtppp8nUqVNPeVvsNlvPZjKtkVm1apX7PpecQEGZ/lcuSw5n5Eh6jpgajogol2gedvdz0H4Rrtzj97WZwqcfhPvfIlkul6kFOdGH4kRfCl2P1obY03Q5Zl7Lkr2HM02zc+WEGPO4PnZsPfp5Pv7v4+v7ZftBaZhUQVZt3S9VEmIk9XCWHDzeh8NXrUrxUi42ytQaHM1ymQNt69qVzLJa1a5kAkRs9PEag8hjB+eYyEjJzs2VirGR8t3yb+Sibl0lLjbG1N5qrYIexLXmQWsgnHzgDnQgX1dOpP5pCWF1IMepKVPhpnPnzjJ79myvaVqlrdOLix7MC9o0pOEmJzbKzB+KA702J+nweF9NmjQxf9etW1fksrI7DmuHbpu+xkDrQ9mnB3OtgdCOmJkul/y07aDsOaRBwGWaUDQw7D2cJUcyc6RCfLQJIXrTDpnpmS7ZsPuQ+XdF92OW7E7LMNP013nG8bBxTLTI94ulLPhjzxHzV8OYp6QKcdL/7DrSp1VNaZacaGoPTuVgvr28yBnVK3AwB8piuNEOrBs3bvQa6q1DvLW2Qa8erX1Etm/fLm+99ZZ5/NZbb5WXXnpJHnjgAdNBduHChfL++++b5hjkTfsXJSUlydNPPy0fffSR3+N6sdDKlSvnG9r+85//mGDTrl27Yt5anKoD6VlyJMslqYcyZfH6Pab2QUPFbzvTZMveI1K1fKz7fsW4aFO7YPp9aC1JrmWeW5zyW/6JJoUTTS06SkVrVuKjj/WR8HzMsylCa0K09sM0o0SdaE7R5gvfJha7D4Xe14CmOatGYpx7uceaRI79Nc0+x/+to210FIjWojSqVl5OS4iVSgkxEhcdviMAgdIopOHmhx9+ME0pNrvj75AhQ8x5VHbu3Gn6eNj04KpBRju3vvDCC1KnTh1zXpW8hoGHG+2069nnRWl/Gw02Wk46FPzyyy+Xu+66y9S4pKammnCoZaxDwW3a+ViXoz3T16xZIxMnTpQVK1aYsg/nYdyhogff7QeOyo4DR+WrDXtkY8phc3DVphatXVm1Zb/UqVLOdGDdnXbitAd52eLu1yGSlpFjbvnRlshK5Y4122iTSnKleBMiNEBok442Men6TbNJdKRpjtHaS6210WYUDU+x0ceaXzQgaE1PgjbZaFgRl8ybO1cu7XOJxMUdG2ILAGU63FxwwQVe4+99BTr7sD5HhyPD35w5c6RmzZpe05o2bWqao3Qo97Jly8zQ+IEDB7pHjV100UXyxBNPeD3HHm6vw7zr169vAuiUKVNoggoyDSurtx6QX3ccNIFFaydWbT0g1SvGmaacX3ekSXoBa1FOdEQ9QcOF1npoM9B1HeuZ4KEBpWmNilK1QqwJHSoxPsZd02FqPkz/jmPNsRpO9N/FdeFTPWuA6dQaRn1IABS/MtXnBpJvEDzZpSg6dOggH3zwQZ6PN2jQIN+w6XtGZL2h4LS/yv0zfpZPf46Wu5fPy3M+DTp5dVLVk3e1qJUoXRpXkyY1KkhsVJQJH7nHR5toJ3StRSkfx0cbQPjiGxAoRgfTs+X5LzfIG8s25zmPDrttXrOiGbbbuk5l07zUMKm8OYmjqndagpxeTYfeUrsBAAVBuAGCTDvmPvH52nwDzYO9m8igzg1MkxAAILgIN0AQaOfeiV9ukJcX/xHw8fpVE+S2bqfLFa1ryLy5c6TP+eF18i0AKEmEG6CIYeaj1X/JK4v/CNiZ1/bYFWfKoE713ec94WKIAFD8CDcBFLRTLcKrTLWj77hPf5Wvf0/Nd74HL24mQ89r4B6NBAAoWYQbD3YzgZ7fpbgu5xCu7Kusl7Xz5Lz97Rb556w1+c7TpXGSDOhYT3qfmXxKZ6YFAAQH4caDHnj1TL0pKSnu87wUZoSKnslXD+J6dXGus3SiPDQs7tmzx5SnfRHP0jy66Z0VW+TpOevzne+hPs1McxNDrgGg9OGbOcDFOZUdcArb9HL06FFT68OwXe/y0OCol9QoLeWi26YnzHt7+WbZeyRLvvtzX75Xc776rNqmualGYnyJbicAoPAINz704Ktn+a1evXqhO3/q/EuWLJGuXbsyEsajPLp162ZqbUJdm5V6OFPunf7jSfvMqGoV4+TvXRvJle1qm4siAgDKDsJNHrSmobD9Q3T+nJwciY+PJ9x4lEdcXFzIgo2ec+bVpZvk31+sy3MevW7SoE71pEmNiubsv42Syku0XhMAAFAmEW7gSHqhyVEf/BywliapQqxpYrqkVU1zdmAAgLPwzQ5H+Wt/ulz98jJJOeR/deyH+zSXQefUMxeEBAA4F9/ycIQD6Vky+sNf5Is1u7ymN0uuKK9c395cqwkAEB4INyjzft1xUC79z1KvaT2aV5f/DGhHLQ0AhCG++VHmvbRwo9f9Bf/oZq6iDQAIT4QblPlaG7spqk2dSvLWTZ2kUgIj1QAgnBFuUOboCfh++uug3Pzm95J6+NhlHdRN5zck2AAACDcoWzamHJIR76yW9bsPeU2/4Zz60r15jZBtFwCg9CDcoMxIy8iWPv9ZKlk5Jy6TMLBTPTPEm2s8AQBsHBFQJkLNys375dWlf7qDzSUtk2XcFWdK9Ypc6wkA4I1wg1Jrd1qGzP5lp4z79Dev6TUS48y5awAACIRwg1InN9eSDSmH5OKJX/s9dlnrmjLs/IYh2S4AQNlAuEGpke3KlVVb9st9M3+SbfuOuqfXrlxORl3STPq2qRXS7QMAlA2EG4Tc0SyXfLdprzz6ya+yeW+612O9WtSQyde3l8jIiJBtHwCgbCHcoMTPUbN4wx7ZfTBD5v+2W6IiI2Teb7v95rugaTV54bp2Uqkc560BABQO4QYlauWW/TL09e/zfPyiZtXl31e3kuqJjIICABQN4Qb5ysxxycaUw2JZx+67/+r/LJGMbJd8+vMOiY6MNLUyuZZI7vG/i9enSHZGlDy/Yam4LEtcLkt2HMxwL7tH8xqSejhT+nWoY4Z0d29WneYnAMApI9wgX4Omfic/bNl/CkuIkNTMdP/ldqon/7qq1SltGwAAgRBuwszI93+UOccvNFkQ6Vku979rVjrWVGTXrUREHPtXZk6uVEmIkYtbJptpWvkSGREhVm6u/Lpugwzu3VES4mMlOjLC9LGJi47kqt0AgGJDuAkzs1ZvN01GhZFUIVaWPHChJMQWbnfJzs6W2enrpFPD0yQmho7BAICSQbgJU7NGnCdVy8cWaN5qFeMkPiaq2LcJAIBgINyEqVqV47kuEwDAkSJDvQEAAADBRLgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOEvJwM2nSJGnQoIHEx8dLp06dZMWKFfnOP3HiRGnatKmUK1dO6tatK/fee69kZGSU2PYCAIDSLaThZvr06TJy5EgZO3asrFq1Stq0aSO9e/eWlJSUgPO/++67MmrUKDP/2rVr5bXXXjPLeOihh0p82wEAQOkU0nAzYcIEGT58uAwdOlRatGghkydPloSEBJk2bVrA+ZctWybnnXeeDBw40NT29OrVSwYMGHDS2h4AABA+okO14qysLFm5cqWMHj3aPS0yMlJ69Oghy5cvD/icc889V/73v/+ZMNOxY0f5888/Zfbs2XLDDTfkuZ7MzExzs6WlpZm/2dnZ5hZM9vKCvdzikJOdU+zbWZbKo6RQJt4oD3+UiTfKw1+4lkl2IV5vyMJNamqquFwuqVGjhtd0vb9u3bqAz9EaG33e+eefL5ZlSU5Ojtx66635NkuNHz9exo0b5zd93rx5ppaoOMyfP19KK8uKEpEIWbBggSTGlsw6S3N5hApl4o3y8EeZeKM8/IVbmaSnp5f+cFMUixcvlieffFJefvll0/l448aNcvfdd8vjjz8u//znPwM+R2uGtF+PZ82NdkTWJq3ExMSgp0rd2Xr27CkxMTFSGt377TyxLJHu3btLtYpxxbquslAeJY0y8UZ5+KNMvFEe/sK1TNKOt7yU6nCTlJQkUVFRsnv3bq/pej85OTngczTAaBPUzTffbO63atVKjhw5Irfccos8/PDDplnLV1xcnLn50h2iuHaK4lx2sETHRJfYNpaF8ihplIk3ysMfZeKN8vAXbmUSU4jXGrIOxbGxsdK+fXvTPGLLzc019zt37pxnlZRvgNGApLSZCgAAIKTNUtpcNGTIEOnQoYPpIKznsNGaGB09pQYPHiy1a9c2/WZU3759zQirdu3auZultDZHp9shBwAAhLeQhpv+/fvLnj17ZMyYMbJr1y5p27atzJkzx93JeOvWrV41NY888ohERESYv9u3b5dq1aqZYPOvf/0rhK8CAACUJiHvUHzHHXeYW14diD1FR0ebE/jpDQAAoFRefgEAACCYCDcAAMBRQt4sheBZ+nuq3PXeajmSmZPnPLkMKgMAOBzhxkG+2pAi+45knXS+uqeVkyoJJXR6YgAAShjhxoEGdKwnIy48Pc/H9czEMVG0SAIAnIlwU4otWp8iD334ixzNdhVo/gPpxy4qlhgfLXWqFM91swAAKO0IN6XY3DW7ZOfBjEI/r2lyxWLZHgAAygLCTRlw47kN5Ppz6hVo3oTYaKlVuVyxbxMAAKUV4aYMSKoQK2dUpzYGAICCoFcpAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwlJCHm0mTJkmDBg0kPj5eOnXqJCtWrMh3/gMHDsiIESOkZs2aEhcXJ02aNJHZs2eX2PYCAIDSLTqUK58+fbqMHDlSJk+ebILNxIkTpXfv3rJ+/XqpXr263/xZWVnSs2dP89jMmTOldu3asmXLFqlcuXJIth8AAJQ+IQ03EyZMkOHDh8vQoUPNfQ05n3/+uUybNk1GjRrlN79O37dvnyxbtkxiYmLMNK31AQAACHm40VqYlStXyujRo93TIiMjpUePHrJ8+fKAz/nkk0+kc+fOplnq448/lmrVqsnAgQPlwQcflKioqIDPyczMNDdbWlqa+ZudnW1uwWQvL1jLzc3NNX9drtygb2tJCHZ5OAFl4o3y8EeZeKM8/IVrmWQX4vWGLNykpqaKy+WSGjVqeE3X++vWrQv4nD///FMWLlwogwYNMv1sNm7cKLfffrt5wWPHjg34nPHjx8u4ceP8ps+bN08SEhKkOMyfPz8oy9m2TbtERcqGDetldnrgMikLglUeTkKZeKM8/FEm3igPf+FWJunp6WWjWaooNRna32bKlCmmpqZ9+/ayfft2eeaZZ/IMN1ozpP16PGtu6tatK7169ZLExMSgbp+GLN3ZtF+Q3Wx2Kr6Z9assT9kuTZo0lT4XNJKyJtjl4QSUiTfKwx9l4o3y8BeuZZJ2vOWlVIebpKQkE1B2797tNV3vJycnB3yOjpDSN9KzCap58+aya9cu08wVGxvr9xwdUaU3X7qc4topgrVsbaZTUVGRZXoHLs6yLqsoE2+Uhz/KxBvl4S/cyiSmEK81ZEPBNYhozcuCBQu8amb0vvarCeS8884zTVF2XxS1YcMGE3oCBRsAABB+QnqeG20umjp1qrz55puydu1aue222+TIkSPu0VODBw/26nCsj+toqbvvvtuEGh1Z9eSTT5oOxgAAACHvc9O/f3/Zs2ePjBkzxjQttW3bVubMmePuZLx161Z304zSvjJz586Ve++9V1q3bm3Oc6NBR0dLAQAAlIoOxXfccYe5BbJ48WK/adpk9e2335bAlgEAgLIo5JdfAAAACCbCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAAcBTCDQAACN9wM23aNMnMzCy+rQEAACjJcDN8+HA5ePCg+36tWrVk8+bNp7oNAAAAoQk3lmV53T906JDk5uYGb2sAAABOEX1uAABA+IabiIgIc8vrPgAAQKhFF7ZZqkmTJu5Ac/jwYWnXrp1ERnpnpH379gV3KwEAAIoj3Lz++uuFmR0AAKB0h5shQ4YU35YAAACUdLjxbJ5auXKlGQauTVQNGzY0zVP0vwEAAGUu3CxatEiGDRsmW7ZscQ8NtwOOnuSva9euxbGdAAAAwR8ttXHjRrnsssukQYMG8uGHH8ratWvlt99+kxkzZkidOnWkT58+8ueffxZmkQAAAKGruZk4caKcc845smDBAq/pzZo1k6uuukp69Oghzz//vLz44ovB3UoAAIDiqLlZvHix3HPPPQEf06YpfUybrQAAAMpEuNm6dau0atUqz8dbtmxp+uIAAACUiXCjJ+1LSEjI83F9LD09PRjbBQAAUDKjpbQD8a5duwI+lpqaWrStAAAACFW46d69u9/Vwe0+Nzqdc90AAIAyE242bdpUfFsCAABQ0uGmfv36wVgnAABA6ehQ/Pvvv8uAAQMkLS3N77GDBw/KwIEDOYkfAAAoO+HmmWeekbp160piYqLfY5UqVTKP6TwAAABlItx89dVX0q9fvzwf/9vf/iYLFy4MxnYBAACUzEn8qlevnufjSUlJsm3btqJtCQAAQEmHG216+uOPP/K9sGagJisAAIBSGW66du2a70Ux//Of/0iXLl2CsV0AAADFH25Gjx4tX3zxhVx77bWyYsUKM0JKb999951cc801MnfuXDMPAABAmTjPTbt27WTmzJly0003yUcffeT1WNWqVeX999+Xs846K9jbCAAAUHyXX7jsssvMlb/nzJlj+tjoJReaNGkivXr1yveimgAAAKWuWapPnz6mGapcuXJy1VVXicvlkltuuUWuvPJKE2z27t0rLVq0KL6tBQAACGa40T41mZmZ7vtPPvmk7Nu3z30/JydH1q9fH9wtBAAAKK5w43s18EBXBwcAACgz4QYAAMBR4SYiIsLcfKcBAACUydFS2gx14403SlxcnLmfkZEht956q5QvX97c9+yPAwAAUOrDzZAhQ7zuX3/99X7zDB48+NS3CgAAoCTCzeuvv17U9QAAAJQIOhQDAABHIdwAAABHIdwAAABHIdwAAABHIdwAAABHIdwAAABHIdwAAABHIdwAAABHKRXhZtKkSdKgQQOJj4+XTp06yYoVKwr0vPfee89c2+rKK68s9m0EAABlQ8jDzfTp02XkyJEyduxYWbVqlbRp00Z69+4tKSkp+T5v8+bNct9990mXLl1KbFsBAEDpF/JwM2HCBBk+fLgMHTpUWrRoIZMnT5aEhASZNm1ans9xuVwyaNAgGTdunDRq1KhEtxcAADjo2lLBlpWVJStXrpTRo0e7p0VGRkqPHj1k+fLleT7vsccek+rVq8uwYcPk66+/zncdeqVyz6uVp6Wlmb/Z2dnmFkz28oK13NzcXPPX5coN+raWhGCXhxNQJt4oD3+UiTfKw1+4lkl2IV5vSMNNamqqqYWpUaOG13S9v27duoDPWbp0qbz22mvy448/Fmgd48ePNzU8vubNm2dqiIrD/Pnzg7Kcbdu0Yi1SNmxYL7PTA5dHWRCs8nASysQb5eGPMvFGefgLtzJJT08vG+GmsA4dOiQ33HCDTJ06VZKSkgr0HK0V0j49njU3devWlV69ekliYmLQU6XubD179pSYmJhTXt43s36V5SnbpUmTptLngrLX/Bbs8nACysQb5eGPMvFGefgL1zJJO97yUurDjQaUqKgo2b17t9d0vZ+cnOw3/x9//GE6Evft29ev6SY6OlrWr18vp59+utdz4uLizM2X7hDFtVMEa9naRKeioiLL9A5cnGVdVlEm3igPf5SJN8rDX7iVSUwhXmtIOxTHxsZK+/btZcGCBV5hRe937tzZb/5mzZrJL7/8Ypqk7Nvll18uF154ofm31sgAAIDwFvJmKW0yGjJkiHTo0EE6duwoEydOlCNHjpjRU2rw4MFSu3Zt03dGz4PTsmVLr+dXrlzZ/PWdDgAAwlPIw03//v1lz549MmbMGNm1a5e0bdtW5syZ4+5kvHXrVnfzDAAAQKkPN+qOO+4wt0AWL16c73PfeOONYtoqAABQFlElAgAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHIVwAwAAHKVUhJtJkyZJgwYNJD4+Xjp16iQrVqzIc96pU6dKly5dpEqVKubWo0ePfOcHAADhJeThZvr06TJy5EgZO3asrFq1Stq0aSO9e/eWlJSUgPMvXrxYBgwYIIsWLZLly5dL3bp1pVevXrJ9+/YS33YAAFD6hDzcTJgwQYYPHy5Dhw6VFi1ayOTJkyUhIUGmTZsWcP533nlHbr/9dmnbtq00a9ZMXn31VcnNzZUFCxaU+LYDAIDSJzqUK8/KypKVK1fK6NGj3dMiIyNNU5PWyhREenq6ZGdny2mnnRbw8czMTHOzpaWlmb/6HL0Fk728YC1XQ5tyuXKDvq0lIdjl4QSUiTfKwx9l4o3y8BeuZZJdiNcb0nCTmpoqLpdLatSo4TVd769bt65Ay3jwwQelVq1aJhAFMn78eBk3bpzf9Hnz5pkaouIwf/78oCxn2zatWIuUDRvWy+z0gpVHaRSs8nASysQb5eGPMvFGefgLtzJJT08vG+HmVP373/+W9957z/TD0c7IgWitkPbp8ay5sfvpJCYmBj1V6s7Ws2dPiYmJOeXlfTPrV1mesl2aNGkqfS5oJGVNsMvDCSgTb5SHP8rEG+XhL1zLJO14y0upDzdJSUkSFRUlu3fv9pqu95OTk/N97rPPPmvCzZdffimtW7fOc764uDhz86U7RHHtFMFatjbRqaioyDK9AxdnWZdVlIk3ysMfZeKN8vAXbmUSU4jXGtIOxbGxsdK+fXuvzsB25+DOnTvn+bynn35aHn/8cZkzZ4506NChhLYWAACUBSFvltImoyFDhpiQ0rFjR5k4caIcOXLEjJ5SgwcPltq1a5u+M+qpp56SMWPGyLvvvmvOjbNr1y4zvUKFCuYGAADCW8jDTf/+/WXPnj0msGhQ0SHeWiNjdzLeunWru3lGvfLKK2aU1bXXXuu1HD1PzqOPPlri2w8AAEqXkIcbdccdd5hbINpZ2NPmzZtLaKsAAEBZFPKT+AEAAAQT4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADgK4QYAADhKqQg3kyZNkgYNGkh8fLx06tRJVqxYke/8M2bMkGbNmpn5W7VqJbNnzy6xbQUAAKVbyMPN9OnTZeTIkTJ27FhZtWqVtGnTRnr37i0pKSkB51+2bJkMGDBAhg0bJqtXr5Yrr7zS3NasWVPi2w4AAEqf6FBvwIQJE2T48OEydOhQc3/y5Mny+eefy7Rp02TUqFF+87/wwgty8cUXy/3332/uP/744zJ//nx56aWXzHN9ZWZmmpstLS3N/B3+5vcSW6580F7HkSyXrNp6QCIkSu79dl5QlplrHfvrcuVKdna2lDX2NpfFbS8ulIk3ysMfZeKN8vAXrmWSXYjXG2FZ1vFDaMnLysqShIQEmTlzpql9sQ0ZMkQOHDggH3/8sd9z6tWrZ2p67rnnHvc0rfWZNWuW/PTTT37zP/roozJu3Di/6XXveV8i4xKktIuKsOSWZrnSrHLI3iYAAEIuPT1dBg4cKAcPHpTExMTSW3OTmpoqLpdLatSo4TVd769bty7gc3bt2hVwfp0eyOjRo00Y8qy5qVu3rjx+eXNJqFBRgik7J0f2/blG+vbsKjHRMUFZZnxMlFSMD3kFW5FTttaq9ezZU2JiglMeZR1l4o3y8EeZeKM8/IVrmaQdb3kpiLJ51CyEuLg4c/N1Vft6J01+RdnhZqeskVpVKoTVDncyWhaUhzfKxBvl4Y8y8UZ5+Au3MokpxGsNaYfipKQkiYqKkt27d3tN1/vJyckBn6PTCzM/AAAILyENN7GxsdK+fXtZsGCBe1pubq6537lz54DP0eme8yutnstrfgAAEF5C3iyl/WG0A3GHDh2kY8eOMnHiRDly5Ih79NTgwYOldu3aMn78eHP/7rvvlm7duslzzz0nl156qbz33nvyww8/yJQpU0L8SgAAQGkQ8nDTv39/2bNnj4wZM8Z0Cm7btq3MmTPH3Wl469atEhl5ooLp3HPPlXfffVceeeQReeihh6Rx48ZmpFTLli1D+CoAAEBpEfJwo+644w5zC2Tx4sV+0/r162duAAAApe4MxQAAAMFEuAEAAI5CuAEAAI5CuAEAAI5CuAEAAI5CuAEAAI5CuAEAAI5CuAEAAI5SKk7iV5Isyyr0pdMLc1Xw9PR0s+xwulJrXigPf5SJN8rDH2XijfLwF65lknb8uG0fx/MTduHm0KFD5m/dunVDvSkAAKAIx/FKlSrlO0+EVZAI5CB61fEdO3ZIxYoVzYU6v//+e795zj77bK/pBb2vqVJD07Zt2yQxMTEo2+u7rmDMn988gR4ryDTP+8VZHifb/qLOn9c8BZ3OPuLsfaSw5RFoOvsI+8jJprOPnJ3vNI0r7du3lw0bNnhdczKQsKu50QKpU6eO+XdUVFTAHcN3emHv67+DtcPltY2nMn9+8wR6rCDTPO8XZ3mcbPuLOn9B94W8prOPOHsfKWx5BJrOPsI+crLp7CNRJ50WGxt70mAj4d6heMSIEQWaXtj7wVTYZRdk/vzmCfRYQaZ53i/O8ijpMmEfKdhjTt9HClsegaazj7CPnGw6+8iIk04r6HaGXbNUcdKqQm0HPHjwYFB/YZRVlIc/ysQb5eGPMvFGefijTE4urGtugi0uLk7Gjh1r/oLyCIQy8UZ5+KNMvFEe/iiTk6PmBgAAOAo1NwAAwFEINwAAwFEINwAAwFEINwAAwFEINwAAwFEINyXks88+k6ZNm0rjxo3l1VdfDfXmlApXXXWVVKlSRa699loJd3oa9QsuuEBatGghrVu3lhkzZki4O3DggHTo0EHatm0rLVu2lKlTp4Z6k0oFvWBi/fr15b777gv1ppQKDRo0MJ8Z3U8uvPBCCXebNm0y5aDfJa1atZIjR45IOGIoeAnIyckxO9qiRYvMiZf02hjLli2TqlWrSjhbvHixuQDam2++KTNnzpRwtnPnTtm9e7f5gt61a5f7+inly5eXcOVyuSQzM1MSEhLMF7QGnB9++CHsPzcPP/ywbNy40Vxb6Nlnn5Vwp+FmzZo1UqFChVBvSqnQrVs3eeKJJ6RLly6yb98+c5K/6Oiwu9ISNTclYcWKFXLmmWdK7dq1zQfwkksukXnz5km405oKvYApRGrWrGmCjUpOTpakpCTzxRTO9JoyGmyUhhz9HRbuv8V+//13WbdunfkOAXz9+uuvEhMTY4KNOu2008Iy2CjCTQEsWbJE+vbtK7Vq1ZKIiAiZNWuW3zyTJk0yvyDi4+OlU6dOJtDY9CrkGmxs+u/t27dLOJeJ0wSzPFauXGlqLfSXebiXiTZNtWnTxlzs9v777zehL5zLQ5uixo8fL04RjDLR52lthV49+p133pFwLg8Nv/oDum/fvnLWWWfJk08+KeGKcFMAWiWuX7C6UwUyffp0GTlypDkd9qpVq8y8vXv3lpSUFHEqyqR4ykNrawYPHixTpkyRsi4YZVK5cmX56aefTD+Cd9991zTdhWt5fPzxx9KkSRNzc4pg7CNLly41Pwg++eQTczD/+eefJVzLQ7tAfP311/Lyyy/L8uXLZf78+eYWlrTPDQpOi+yjjz7ymtaxY0drxIgR7vsul8uqVauWNX78eHP/m2++sa688kr343fffbf1zjvvWOFcJrZFixZZ11xzjeUkRS2PjIwMq0uXLtZbb71lOc2p7CO22267zZoxY4YVruUxatQoq06dOlb9+vWtqlWrWomJida4ceNKfNtL8z5y3333Wa+//roVruWxbNkyq1evXu7Hn376aXMLR9TcnKKsrCzzq6FHjx7uaZGRkea+JmfVsWNH0+FNm6IOHz4sX3zxhUnb4Vwm4aQg5aHfZTfeeKNcdNFFcsMNN4jTFaRMtJZGO5wrvfqxVtnriMNwLQ9tjtJRdZs3bzYdiYcPHy5jxowRpypImWhNh72P6HfrwoULTf/GcC0PbZrTWpz9+/dLbm6u+cw0b95cwlF49jQKotTUVNM/okaNGl7T9b52/FPaoeu5554zw/N0h3vggQccPeKjIGWi9EOpTQ76BaV9KnT4c+fOnSUcy+Obb74xVc46pNVuZ3/77bfNUE4nKkiZbNmyRW655RZ3R+I777wzrMsj3BSkTDQA6ykllM6rgU8P8OF8rNGmua5du5rPTK9eveSyyy6TcES4KSGXX365ueGEL7/8MtSbUGqcf/75JvjiBK3x/PHHH0O9GaWS1vJBpFGjRuYHEk7QkXSXMJqODsWnSkdv6JBV346Oel+H9IYjysQb5eGPMvFGefijTLxRHoVDuDlFsbGx5oRrCxYscE/TX+B634lNLAVBmXijPPxRJt4oD3+UiTfKo3BolioA7aimZwS16bBUrS7XEyTVq1fPDM0bMmSIOVW8VqVPnDjR9CMZOnSoOBVl4o3y8EeZeKM8/FEm3iiPIAr1cK2yQIcra1H53oYMGeKe58UXX7Tq1atnxcbGmuF63377reVklIk3ysMfZeKN8vBHmXijPIKHa0sBAABHoc8NAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMINAABwFMIN4FCbN2+WiIgIc22avCxevNjMc+DAAXP/jTfekMqVK0sohGrdN954o1x55ZUSKqEsc8CpCDdAGaQHZA0l9q1q1apy8cUXy88//+yep27durJz505p2bJlgZfbv39/2bBhgxS3Bg0amIv+hWLdhWUHQPtWo0YNueaaa+TPP/+U0sA3oAIg3ABlloYZDS96W7BggURHR8tll13mfjwqKkqSk5PN9IIqV66cVK9ePc/Hs7KyTnm7i7ruUFu/fr3s2LFDZsyYIb/++qv07dtXXC5XqDcLQACEG6CMiouLM+FFb23btpVRo0bJtm3bZM+ePXk2S82ePVuaNGligsSFF15o5smvieTRRx81y3711VelYcOGEh8fb6ZrLcHNN98s1apVk8TERLnooovkp59+8lrWp59+KmeffbZ5TlJSklx11VVm+gUXXCBbtmyRe++9110bEmjd6pVXXpHTTz9dYmNjpWnTpvL22297Pa7P1W3TZSckJEjjxo3lk08+cT+u4WPYsGFm2/U16zJeeOGFIpW3Bq+aNWtK165dZcyYMfLbb7/Jxo0b5fvvv5eePXua11ipUiXp1q2brFq1yuu5Wl5///vfTa2PlofWpn322WcB16PvX4cOHcxryszMlNzcXBk/frz7NbRp00Zmzpxp5tX3T99HVaVKFVMeWqundJ5WrVqZ52jNXo8ePeTIkSNFeu1AWUO4ARzg8OHD8r///U/OOOMMcyALRIPP1VdfbWocNPBoONFAdDJ6AP/ggw/kww8/dAelfv36SUpKinzxxReycuVKOeuss6R79+6yb98+8/jnn39uDs59+vSR1atXm5qljh07msd0OXXq1JHHHnvMXfMUyEcffSR33323/OMf/5A1a9aYcDB06FBZtGiR13zjxo2Tv/3tb6ZJTtc3aNAg93ZoMNB1aW2LhhENJQ899JC8//77cio0MNg1WYcOHZIhQ4bI0qVL5dtvvzUBS7dDp9vbcMkll8g333xj3iPdjn//+9+mZi3Qe9SlSxcTfjScaIDVYPPWW2/J5MmTTY2RhsLrr79evvrqK9P0qO+NXbOkZanhTf8OGDBAbrrpJlm7dq1putL33rKsU3rdQJlhAShzhgwZYkVFRVnly5c3N/0o16xZ01q5cqV7nk2bNpnpq1evNvdHjx5ttWjRwms5Dz74oJln//795v7rr79uVapUyf342LFjrZiYGCslJcU97euvv7YSExOtjIwMr2Wdfvrp1n//+1/z786dO1uDBg3Kc/vr169vPf/8817TfNd97rnnWsOHD/eap1+/flafPn3c93XbH3nkEff9w4cPm2lffPFFnuseMWKEdc0113iV5RVXXJHn/IsWLfIqox07dphtq127tpWZmek3v8vlsipWrGh9+umn5v7cuXOtyMhIa/369QGXb7/udevWWXXr1rXuuusuKzc31zymZZyQkGAtW7bM6znDhg2zBgwYEHD7lO4HOm3z5s15vi7Ayai5AcoobY7QmhS9rVixQnr37m1qCLTJJxD9Bd+pUyevaZ07dz7peurXr2+an2za/KQ1RVpDVKFCBfdt06ZN8scff5h5dJu0JudU6Paed955XtP0vk731Lp1a/e/y5cvb5rJtFbJNmnSJGnfvr15DbqdU6ZMka1btxZ6e7QGSJdfq1Yt07yjNSbaXLZ7924ZPny4qbHRZildv5aPvQ4tC32uNgfm5ejRo6bGRmtXtObFbqrTWrP09HTT7OVZ1lqTY5d1INp0peWvzVJayzZ16lTZv39/oV8zUFYVvKchgFJFD7TaDGXTvid6cNUD2RNPPBHU9XjSA7f2PdGmDl92nxm72aYkxMTEeN3XYKBNQeq9996T++67T5577jkT5CpWrCjPPPOMfPfdd4Vez9dff22Ci/a90eXYtElq7969JpRoENSmJF2X3fm6IGWhz9E+MdoP5/7775fatWu7y9pu5rOneT4nL9rkNX/+fFm2bJnMmzdPXnzxRXn44YfN69a+O4DTUXMDOIQe1CMjI00tQCDNmzc3NTyetI9IYWn/ml27dplRWBquPG/aqdauTdF+NnnRGo+TjTTS7dV+Kp70fosWLQq8rTr/ueeeK7fffru0a9fObGN+NR750VCgnZs9g429jrvuusv0sznzzDNN6EhNTXU/rmXx119/5TvMXd837SytNUxaI6ejspS+Vl2e1gL5lrX2t7HLUvmWp+4PWtOlfZK035POp/2YgHBAzQ1QRulIGg0ZSpscXnrpJfNLXzsMB3LrrbeaGgytGdDOxNoRWEcoFZbWMGjNhJ747umnnzbNLXowtjsR60ifsWPHmmYRDQPXXXed5OTkmJFaDz74oPs8N0uWLDGP6cHbDkWedDu1o7CGEl2njr7SzshffvllgbdVm4q0CWfu3LkmnGiA0NFNway90HXocvV1p6Wlme32rK3R0VM6wkrPjTNhwgQTTNatW2fChw7n96xteeedd0xHYB19pjVjOhJOa560E7HWRp1//vly8OBBE6i0FklrjbS2SJeltT4asHTd2vFYw2WvXr1MTZPW2OgoLA2MQFgIdacfAIWnnWD142vftAPr2Wefbc2cOTPPDsVKO7meccYZVlxcnNWlSxdr2rRpJ+1Q3KZNG7/1p6WlWXfeeadVq1Yt0+FYO8JqB+KtW7e65/nggw+stm3bWrGxsVZSUpJ19dVXux9bvny51bp1a7Md9teQ77rVyy+/bDVq1Miso0mTJtZbb73l9bg+96OPPvKapsvQZdkdcm+88UYzrXLlytZtt91mjRo1yus1FbZDsa9Vq1ZZHTp0sOLj463GjRtbM2bM8OswvXfvXmvo0KFW1apVzXwtW7a0Pvvss4CvOzs725RV8+bNrd27d5vOxRMnTrSaNm1qyqFatWpW7969ra+++sr9nMcee8xKTk62IiIizOv57bffzDw6r5axlt2LL76Y52sEnCZC/xPqgAUAABAs9LkBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAACOQrgBAADiJP8PpqUye0nMJCwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16292\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "\n",
    "# Load dataset\n",
    "filtered_df = pd.read_csv('VNAT/CF/flows_extra_labelled_CF.csv')\n",
    "print(f\"Number of rows before filtering: {df.shape[0]}\")\n",
    "\n",
    "\n",
    "print(f\"Number of rows after filtering: {filtered_df.shape[0]}\")\n",
    "data_duration = filtered_df['bidirectional_duration_ms'].dropna() / 1000  \n",
    "\n",
    "ecdf_duration = ECDF(data_duration)\n",
    "\n",
    "value_duration = 41.0  \n",
    "print(f\"CDF at {value_duration} seconds: {ecdf_duration(value_duration)}\")\n",
    "\n",
    "plt.step(ecdf_duration.x, ecdf_duration.y, label=\"ECDF\")\n",
    "plt.xscale('log') \n",
    "plt.xlabel('Bidirectional Duration (s)')\n",
    "plt.ylabel('ECDF')\n",
    "plt.title('Empirical CDF of Bidirectional Duration')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "###################################################################\n",
    "\n",
    "data_packets = filtered_df['bidirectional_packets'].dropna() \n",
    "\n",
    "ecdf_packets = ECDF(data_packets)\n",
    "\n",
    "value_packets = 8000  \n",
    "print(f\"CDF at {value_packets} packets: {ecdf_packets(value_packets)}\")\n",
    "\n",
    "plt.step(ecdf_packets.x, ecdf_packets.y, label=\"ECDF\")\n",
    "plt.xscale('log') \n",
    "plt.xlabel('Bidirectional Packets')\n",
    "plt.ylabel('ECDF')\n",
    "plt.title('Empirical CDF of Bidirectional Packets')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(filtered_df['bidirectional_duration_ms'].value_counts().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2c775d1-d08c-4c39-b63d-f743175cf36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     33711.000000\n",
       "mean        379.621809\n",
       "std        2480.214632\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.001000\n",
       "75%           0.001000\n",
       "max      142910.250000\n",
       "Name: bidirectional_duration_ms, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_duration.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75059db6-7bf1-4691-a287-93b4710db5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     33711.00\n",
      "mean       1130.29\n",
      "std       37416.79\n",
      "min           1.00\n",
      "10%           2.00\n",
      "25%           2.00\n",
      "50%           2.00\n",
      "75%           2.00\n",
      "80%           4.00\n",
      "85%           4.00\n",
      "90%          14.00\n",
      "91%          18.00\n",
      "92%          21.00\n",
      "93%          24.00\n",
      "94%          29.00\n",
      "95%          44.00\n",
      "96%          65.00\n",
      "97%          93.00\n",
      "98%         228.00\n",
      "99%        5250.00\n",
      "max     3842411.00\n",
      "Name: bidirectional_packets, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('display.float_format', '{:.2f}'.format):\n",
    "    print(data_packets.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.8, 0.85, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a34474-2b46-42ca-954e-1432fc8afb8e",
   "metadata": {},
   "source": [
    "### CDF with filter out any flows that have less than 20 packets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db6c0eb5-1110-4b0f-a873-42fb6fa2e64e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows before filtering: 33711\n",
      "Number of rows after filtering: 2923\n",
      "CDF at 41.0 seconds: 0.6387273349298666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/26/m1ty4dhn4kd02qvztlq5pdlh0000gp/T/ipykernel_60072/3264661265.py:7: DtypeWarning: Columns (87,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  filtered_df = pd.read_csv('VNAT/CF/flows_extra_labelled.csv')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CDF at 8000 packets: 0.9329456038316798\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "\n",
    "filtered_df = pd.read_csv('VNAT/CF/flows_extra_labelled_CF.csv')\n",
    "print(f\"Number of rows before filtering: {df.shape[0]}\")\n",
    "\n",
    "filtered_df = filtered_df[filtered_df['bidirectional_packets'] > 19]\n",
    "\n",
    "print(f\"Number of rows after filtering: {filtered_df.shape[0]}\")\n",
    "\n",
    "data_duration = filtered_df['bidirectional_duration_ms'].dropna() / 1000  \n",
    "\n",
    "ecdf_duration = ECDF(data_duration)\n",
    "\n",
    "value_duration = 41.0  \n",
    "print(f\"CDF at {value_duration} seconds: {ecdf_duration(value_duration)}\")\n",
    "\n",
    "plt.step(ecdf_duration.x, ecdf_duration.y, label=\"ECDF\")\n",
    "plt.xscale('log') \n",
    "plt.xlabel('Bidirectional Duration (s)')\n",
    "plt.ylabel('ECDF')\n",
    "plt.title('Empirical CDF of Bidirectional Duration')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "###################################################################\n",
    "\n",
    "data_packets = filtered_df['bidirectional_packets'].dropna()  \n",
    "\n",
    "ecdf_packets = ECDF(data_packets)\n",
    "\n",
    "value_packets = 8000  \n",
    "print(f\"CDF at {value_packets} packets: {ecdf_packets(value_packets)}\")\n",
    "\n",
    "plt.step(ecdf_packets.x, ecdf_packets.y, label=\"ECDF\")\n",
    "plt.xscale('log') \n",
    "plt.xlabel('Bidirectional Packets')\n",
    "plt.ylabel('ECDF')\n",
    "plt.title('Empirical CDF of Bidirectional Packets')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1055e6c5-6102-47cf-9148-bf8f7e822c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2923.000000\n",
       "mean       350.509459\n",
       "std       1329.255471\n",
       "min          0.014000\n",
       "25%          1.479000\n",
       "50%         21.921000\n",
       "75%         63.471500\n",
       "max      26542.718000\n",
       "Name: bidirectional_duration_ms, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_duration.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "971c5422-c961-45c6-950a-40902cda9769",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      2923.00\n",
      "mean      13008.38\n",
      "std      126478.91\n",
      "min          20.00\n",
      "10%          22.00\n",
      "25%          26.00\n",
      "50%          57.00\n",
      "75%         173.00\n",
      "80%         354.60\n",
      "85%        1966.80\n",
      "90%        5465.80\n",
      "91%        5728.10\n",
      "92%        6764.48\n",
      "93%        7657.06\n",
      "94%       11090.04\n",
      "95%       20821.10\n",
      "96%       36182.28\n",
      "97%       82131.42\n",
      "98%      166045.80\n",
      "99%      237003.60\n",
      "max     3842411.00\n",
      "Name: bidirectional_packets, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('display.float_format', '{:.2f}'.format):\n",
    "    print(data_packets.describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.8, 0.85, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2682cd1-a8f0-488f-b848-b482b0cd0028",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}