2023-08-greynoise-gptbot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Researching OpenAI's GPTBot\n",
    "\n",
    "Some exploratory data analysis into web traffic that GreyNoise has tagged as `GPTBot`, the crawler software used by OpenAI to collect data for their large language models.\n",
    "\n",
    "Full blog post here: https://blog.aaronsdevera.com/posts/20230823-detecting-and-blocking-openai-crawlers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the GreyNoise python package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: greynoise in /opt/homebrew/lib/python3.11/site-packages (2.0.1)\n",
      "Requirement already satisfied: Click>=8.0.0 in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (8.1.3)\n",
      "Requirement already satisfied: ansimarkup in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (2.0.0)\n",
      "Requirement already satisfied: cachetools in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (5.3.1)\n",
      "Requirement already satisfied: colorama in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (0.4.6)\n",
      "Requirement already satisfied: click-default-group in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (1.2.4)\n",
      "Requirement already satisfied: click-repl in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (0.3.0)\n",
      "Requirement already satisfied: dict2xml in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (1.7.3)\n",
      "Requirement already satisfied: ipaddress in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (1.0.23)\n",
      "Requirement already satisfied: jinja2 in /Users/bigcorn/Library/Python/3.11/lib/python/site-packages (from greynoise) (3.1.2)\n",
      "Requirement already satisfied: more-itertools in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (10.1.0)\n",
      "Requirement already satisfied: requests in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (2.28.2)\n",
      "Requirement already satisfied: six in /opt/homebrew/lib/python3.11/site-packages (from greynoise) (1.16.0)\n",
      "Requirement already satisfied: prompt-toolkit>=3.0.36 in /Users/bigcorn/Library/Python/3.11/lib/python/site-packages (from click-repl->greynoise) (3.0.38)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/bigcorn/Library/Python/3.11/lib/python/site-packages (from jinja2->greynoise) (2.1.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/homebrew/lib/python3.11/site-packages (from requests->greynoise) (3.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/homebrew/lib/python3.11/site-packages (from requests->greynoise) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/homebrew/lib/python3.11/site-packages (from requests->greynoise) (1.26.15)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/homebrew/lib/python3.11/site-packages (from requests->greynoise) (2022.12.7)\n",
      "Requirement already satisfied: wcwidth in /Users/bigcorn/Library/Python/3.11/lib/python/site-packages (from prompt-toolkit>=3.0.36->click-repl->greynoise) (0.2.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install greynoise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then import the client object from the package library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [],
   "source": [
    "from greynoise import GreyNoise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're also going to need `pandas` and `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get our API key from our env variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "GREYNOISE_KEY = os.environ.get('GREYNOISE_KEY')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a GreyNoise client object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "gn = GreyNoise(GREYNOISE_KEY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our GNQL query for `GPTBot` is `tags:GPTBot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "GNQL_QUERY = 'tags:GPTBot'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to make a quick function to run GNQL queries. I'm also going to make a query to turn the response into a `pandas` dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_gnql_request(query: str):\n",
    "    return gn.session.get(\n",
    "        f'{gn.api_server}/{gn.EP_GNQL}',\n",
    "        headers={'key': f'{gn.api_key}'},\n",
    "        params={'query': f'{query}'}\n",
    "    )\n",
    "\n",
    "def gnql_to_df(gnql_response):\n",
    "    if gnql_response.status_code != 200:\n",
    "        raise Exception(f'Bad status code: {gnql_response.status_code}')\n",
    "    if gnql_response.json()['message'] != 'ok':\n",
    "        raise Exception(f'Bad status: {gnql_response.json()[\"message\"]}')\n",
    "    \n",
    "    return pd.json_normalize(gnql_response.json()['data'])\n",
    "\n",
    "def get_gnql_df(query: str):\n",
    "    return gnql_to_df(make_gnql_request(query))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make a dataframe from just a GNQL query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "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>ip</th>\n",
       "      <th>bot</th>\n",
       "      <th>vpn</th>\n",
       "      <th>vpn_service</th>\n",
       "      <th>spoofable</th>\n",
       "      <th>first_seen</th>\n",
       "      <th>last_seen</th>\n",
       "      <th>seen</th>\n",
       "      <th>tags</th>\n",
       "      <th>actor</th>\n",
       "      <th>...</th>\n",
       "      <th>metadata.region</th>\n",
       "      <th>metadata.destination_countries</th>\n",
       "      <th>metadata.source_country</th>\n",
       "      <th>metadata.source_country_code</th>\n",
       "      <th>metadata.destination_country_codes</th>\n",
       "      <th>raw_data.scan</th>\n",
       "      <th>raw_data.web.paths</th>\n",
       "      <th>raw_data.ja3</th>\n",
       "      <th>raw_data.hassh</th>\n",
       "      <th>raw_data.web.useragents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.15.240.77</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[GPTBot, TLS/SSL Crawler, Web Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>[United States]</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>[US]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>[/index.php]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.15.241.14</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[GPTBot, TLS/SSL Crawler, Web Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>[United States, Georgia]</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>[US, GE]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>[/, /index.php]</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.15.242.139</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[GPTBot, TLS/SSL Crawler, Web Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>[Georgia, United States]</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>[GE, US]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.15.240.96</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[GPTBot, TLS/SSL Crawler, Web Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>[United States]</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>[US]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.15.241.13</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[GPTBot, TLS/SSL Crawler, Web Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>[United States]</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>[US]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>[/index.php]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ip    bot    vpn vpn_service  spoofable  first_seen   last_seen  \\\n",
       "0   20.15.240.77  False  False         N/A      False  2023-08-08  2023-08-08   \n",
       "1   20.15.241.14  False  False         N/A      False  2023-08-08  2023-08-08   \n",
       "2  20.15.242.139  False  False         N/A      False  2023-08-08  2023-08-08   \n",
       "3   20.15.240.96  False  False         N/A      False  2023-08-08  2023-08-08   \n",
       "4   20.15.241.13  False  False         N/A      False  2023-08-08  2023-08-08   \n",
       "\n",
       "   seen                                    tags    actor  ... metadata.region  \\\n",
       "0  True  [GPTBot, TLS/SSL Crawler, Web Crawler]  unknown  ...            Iowa   \n",
       "1  True  [GPTBot, TLS/SSL Crawler, Web Crawler]  unknown  ...            Iowa   \n",
       "2  True  [GPTBot, TLS/SSL Crawler, Web Crawler]  unknown  ...            Iowa   \n",
       "3  True  [GPTBot, TLS/SSL Crawler, Web Crawler]  unknown  ...            Iowa   \n",
       "4  True  [GPTBot, TLS/SSL Crawler, Web Crawler]  unknown  ...            Iowa   \n",
       "\n",
       "  metadata.destination_countries metadata.source_country  \\\n",
       "0                [United States]           United States   \n",
       "1       [United States, Georgia]           United States   \n",
       "2       [Georgia, United States]           United States   \n",
       "3                [United States]           United States   \n",
       "4                [United States]           United States   \n",
       "\n",
       "  metadata.source_country_code metadata.destination_country_codes  \\\n",
       "0                           US                               [US]   \n",
       "1                           US                           [US, GE]   \n",
       "2                           US                           [GE, US]   \n",
       "3                           US                               [US]   \n",
       "4                           US                               [US]   \n",
       "\n",
       "                        raw_data.scan raw_data.web.paths  \\\n",
       "0  [{'port': 443, 'protocol': 'TCP'}]       [/index.php]   \n",
       "1  [{'port': 443, 'protocol': 'TCP'}]    [/, /index.php]   \n",
       "2  [{'port': 443, 'protocol': 'TCP'}]                NaN   \n",
       "3  [{'port': 443, 'protocol': 'TCP'}]                NaN   \n",
       "4  [{'port': 443, 'protocol': 'TCP'}]       [/index.php]   \n",
       "\n",
       "                                        raw_data.ja3  raw_data.hassh  \\\n",
       "0                                                 []              []   \n",
       "1  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...              []   \n",
       "2  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...              []   \n",
       "3                                                 []              []   \n",
       "4                                                 []              []   \n",
       "\n",
       "  raw_data.web.useragents  \n",
       "0                     NaN  \n",
       "1                     NaN  \n",
       "2                     NaN  \n",
       "3                     NaN  \n",
       "4                     NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_gnql_df(GNQL_QUERY)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA on our GreyNoise GPTBot results\n",
    "\n",
    "Let's run a couple of aggregations to just checkout the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns in our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ip', 'bot', 'vpn', 'vpn_service', 'spoofable', 'first_seen',\n",
       "       'last_seen', 'seen', 'tags', 'actor', 'classification', 'cve',\n",
       "       'metadata.asn', 'metadata.city', 'metadata.country',\n",
       "       'metadata.country_code', 'metadata.organization', 'metadata.category',\n",
       "       'metadata.tor', 'metadata.rdns', 'metadata.os', 'metadata.sensor_count',\n",
       "       'metadata.sensor_hits', 'metadata.region',\n",
       "       'metadata.destination_countries', 'metadata.source_country',\n",
       "       'metadata.source_country_code', 'metadata.destination_country_codes',\n",
       "       'raw_data.scan', 'raw_data.web.paths', 'raw_data.ja3', 'raw_data.hassh',\n",
       "       'raw_data.web.useragents'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top IPs with GPTBot tags by volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(0, 0, '20.15.240.67'),\n",
       "  Text(1, 0, '20.15.240.107'),\n",
       "  Text(2, 0, '20.15.240.187'),\n",
       "  Text(3, 0, '20.15.240.83'),\n",
       "  Text(4, 0, '40.83.2.78'),\n",
       "  Text(5, 0, '20.15.240.75'),\n",
       "  Text(6, 0, '20.15.240.89'),\n",
       "  Text(7, 0, '20.15.240.92'),\n",
       "  Text(8, 0, '20.15.242.132'),\n",
       "  Text(9, 0, '40.83.2.71')])"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['ip','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'metadata.sensor_hits':'volume'})\\\n",
    "    .groupby('ip')\\\n",
    "    .sum('volume')\\\n",
    "    .sort_values('volume',ascending=False)\\\n",
    "    .head(10)\\\n",
    "    .plot(kind='bar',\n",
    "          title=f'Top 10 IPs with \"{GNQL_QUERY}\" by volume',\n",
    "          xlabel='IP',\n",
    "          ylabel='Volume'\n",
    "    )\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top ASNs with GPTBot tags by volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0]), [Text(0, 0, 'Microsoft Corporation')])"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['metadata.organization','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'metadata.organization':'aso','metadata.sensor_hits':'volume'})\\\n",
    "    .groupby('aso')\\\n",
    "    .sum('volume')\\\n",
    "    .sort_values('volume',ascending=False)\\\n",
    "    .head(10)\\\n",
    "    .plot(kind='bar',\n",
    "          title=f'Top ASOs with \"{GNQL_QUERY}\" by volume',\n",
    "          xlabel='ASO',\n",
    "          ylabel='Volume'\n",
    "    )\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's all Microsoft."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do we have any user agents to work with?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "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>raw_data.web.useragents</th>\n",
       "      <th>metadata.sensor_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               raw_data.web.useragents  metadata.sensor_hits\n",
       "13   [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    53\n",
       "36   [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    62\n",
       "58   [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    63\n",
       "62   [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    36\n",
       "96   [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    58\n",
       "116  [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    69\n",
       "121  [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    74\n",
       "142  [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...                    35"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['raw_data.web.useragents'].notna()][['raw_data.web.useragents','metadata.sensor_hits']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a better look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 53},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 62},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 58},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 69},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 74},\n",
       " {'raw_data.web.useragents': ['Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)'],\n",
       "  'metadata.sensor_hits': 35}]"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['raw_data.web.useragents'].notna()][['raw_data.web.useragents','metadata.sensor_hits']].to_dict('records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They appear to be the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so how much traffic did GreyNoise observe that self-identified as `GPTBot` via the User-Agent header? And how much traffic did GreyNoise observe that had no `GPTBot` header, but was tagged as `GPTBot`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_useragent</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <th>139</th>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         has_useragent  volume\n",
       "0                                                  NaN      33\n",
       "1                                                  NaN      61\n",
       "2                                                  NaN      49\n",
       "3                                                  NaN      45\n",
       "4                                                  NaN      41\n",
       "..                                                 ...     ...\n",
       "139                                                NaN      43\n",
       "140                                                NaN      63\n",
       "141                                                NaN      59\n",
       "142  [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...      35\n",
       "143                                                NaN      57\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['raw_data.web.useragents','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'raw_data.web.useragents':'has_useragent','metadata.sensor_hits':'volume'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just turn the `has_useragent` column into a boolean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>has_useragent</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>139</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>140</th>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         has_useragent  volume\n",
       "0                                                  NaN      33\n",
       "1                                                  NaN      61\n",
       "2                                                  NaN      49\n",
       "3                                                  NaN      45\n",
       "4                                                  NaN      41\n",
       "..                                                 ...     ...\n",
       "139                                                NaN      43\n",
       "140                                                NaN      63\n",
       "141                                                NaN      59\n",
       "142  [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...      35\n",
       "143                                                NaN      57\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 535,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_useragent_traffic = df[['raw_data.web.useragents','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'raw_data.web.useragents':'has_useragent','metadata.sensor_hits':'volume'})\n",
    "df_useragent_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_useragent_traffic['has_useragent'] = df[['raw_data.web.useragents','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'raw_data.web.useragents':'has_useragent','metadata.sensor_hits':'volume'})[['has_useragent']].notna()['has_useragent']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ugly. But we should have that dataframe now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>has_useragent</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>139</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>False</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>True</td>\n",
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       "      <th>143</th>\n",
       "      <td>False</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     has_useragent  volume\n",
       "0            False      33\n",
       "1            False      61\n",
       "2            False      49\n",
       "3            False      45\n",
       "4            False      41\n",
       "..             ...     ...\n",
       "139          False      43\n",
       "140          False      63\n",
       "141          False      59\n",
       "142           True      35\n",
       "143          False      57\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_useragent_traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now group by that `has_useragent` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'False'), Text(1, 0, 'True')])"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_useragent_traffic\\\n",
    "    .groupby('has_useragent')\\\n",
    "    .sum('volume')\\\n",
    "    .sort_values('volume',ascending=False)\\\n",
    "    .plot(kind='bar',\n",
    "          title=f'Self-identifying traffic with \"{GNQL_QUERY}\" by volume',\n",
    "          xlabel='has_useragent',\n",
    "          ylabel='Volume'\n",
    "    )\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most traffic tagged by GreyNoise as `GPTBot` did not self-identify as such via its User-Agent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JA3 fingerprint of GPTBot traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pull down the traffic with user agents and with JA3 prints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "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>raw_data.web.useragents</th>\n",
       "      <th>raw_data.ja3</th>\n",
       "      <th>metadata.sensor_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>[Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...</td>\n",
       "      <td>[]</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               raw_data.web.useragents  \\\n",
       "0                                                  NaN   \n",
       "1                                                  NaN   \n",
       "2                                                  NaN   \n",
       "3                                                  NaN   \n",
       "4                                                  NaN   \n",
       "..                                                 ...   \n",
       "139                                                NaN   \n",
       "140                                                NaN   \n",
       "141                                                NaN   \n",
       "142  [Mozilla/5.0 AppleWebKit/537.36 (KHTML, like G...   \n",
       "143                                                NaN   \n",
       "\n",
       "                                          raw_data.ja3  metadata.sensor_hits  \n",
       "0                                                   []                    33  \n",
       "1    [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...                    61  \n",
       "2    [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...                    49  \n",
       "3                                                   []                    45  \n",
       "4                                                   []                    41  \n",
       "..                                                 ...                   ...  \n",
       "139  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...                    43  \n",
       "140                                                 []                    63  \n",
       "141                                                 []                    59  \n",
       "142                                                 []                    35  \n",
       "143                                                 []                    57  \n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ja3 = df[['raw_data.web.useragents','raw_data.ja3','metadata.sensor_hits']]\n",
    "df_ja3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clear out some of these lists and make it single value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df_ja3.iterrows():\n",
    "     # change the type value for the user agents\n",
    "    if type(df_ja3.loc[index]['raw_data.web.useragents']) == type([]):\n",
    "        if len(df_ja3.loc[index]['raw_data.web.useragents']) > 0:\n",
    "            df_ja3.at[index, 'raw_data.web.useragents'] = df_ja3.loc[index]['raw_data.web.useragents'][0]\n",
    "    else:\n",
    "        df_ja3.at[index, 'raw_data.web.useragents'] = None\n",
    "    # change the type value for the ja3 prints\n",
    "    if type(df_ja3.loc[index]['raw_data.ja3']) == type([]):\n",
    "        if len(df_ja3.loc[index]['raw_data.ja3']) > 0:\n",
    "            df_ja3.at[index, 'raw_data.ja3'] = df_ja3.loc[index]['raw_data.ja3'][0]\n",
    "        else:\n",
    "            df_ja3.at[index, 'raw_data.ja3'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raw_data.web.useragents      8\n",
       "raw_data.ja3                19\n",
       "metadata.sensor_hits       144\n",
       "dtype: int64"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ja3.notnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn it into records, I wanna see something..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 33},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 61},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 49},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 41},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 72},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 53},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 56},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 30},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 66},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 62},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 72},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 61},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 48},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 38},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 41},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 46},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 62},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 48},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 59},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 38},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 50},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 30},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 27},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 33},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 48},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 39},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 66},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 72},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 48},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 37},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 72},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 66},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 71},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 44},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 69},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 48},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 46},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 85},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 47},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 41},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 67},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 44},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 85},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 44},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 33},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 72},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 58},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 66},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 24},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 59},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 39},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 69},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 52},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 49},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 74},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 36},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 21},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 51},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 60},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 30},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 45},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 30},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 32},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 76},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 42},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 69},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 81},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 54},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': {'fingerprint': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "   'port': 443},\n",
       "  'metadata.sensor_hits': 43},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 63},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 59},\n",
       " {'raw_data.web.useragents': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 35},\n",
       " {'raw_data.web.useragents': None,\n",
       "  'raw_data.ja3': None,\n",
       "  'metadata.sensor_hits': 57}]"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ja3.to_dict('records')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ugh really gotta dive just to get the fingerprint value huh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "ja3_records = []\n",
    "for row in df_ja3.to_dict('records'):\n",
    "    ja3_value = row['raw_data.ja3']\n",
    "    if ja3_value is not None:\n",
    "        ja3_value = ja3_value['fingerprint']\n",
    "    record = {\n",
    "        'user_agent': row['raw_data.web.useragents'],\n",
    "        'ja3': ja3_value,\n",
    "        'volume': row['metadata.sensor_hits']\n",
    "    }\n",
    "    ja3_records.append(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'user_agent': None, 'ja3': None, 'volume': 33},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 61},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 49},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 41},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 72},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 53},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 56},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 30},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 66},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 62},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 72},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 61},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 48},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 38},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 41},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 46},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 62},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 48},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 59},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 38},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 50},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 30},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 27},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 33},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 48},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 39},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 66},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 72},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 48},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 37},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 72},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 66},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 71},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 44},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 69},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 48},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 46},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 85},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 47},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 41},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 67},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 44},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 85},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 44},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 33},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 72},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 58},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 66},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 24},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 59},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 39},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 69},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 52},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 49},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': '8d9f7747675e24454cd9b7ed35c58707',\n",
       "  'volume': 74},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 36},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 21},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 51},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 60},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 30},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 45},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 30},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 32},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 76},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 42},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 69},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 81},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 54},\n",
       " {'user_agent': None, 'ja3': '8d9f7747675e24454cd9b7ed35c58707', 'volume': 43},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 63},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 59},\n",
       " {'user_agent': 'Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)',\n",
       "  'ja3': None,\n",
       "  'volume': 35},\n",
       " {'user_agent': None, 'ja3': None, 'volume': 57}]"
      ]
     },
     "execution_count": 544,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ja3_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...</td>\n",
       "      <td>None</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
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       "      <td>None</td>\n",
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       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            user_agent  \\\n",
       "0                                                 None   \n",
       "1                                                 None   \n",
       "2                                                 None   \n",
       "3                                                 None   \n",
       "4                                                 None   \n",
       "..                                                 ...   \n",
       "139                                               None   \n",
       "140                                               None   \n",
       "141                                               None   \n",
       "142  Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "143                                               None   \n",
       "\n",
       "                                  ja3  volume  \n",
       "0                                None      33  \n",
       "1    8d9f7747675e24454cd9b7ed35c58707      61  \n",
       "2    8d9f7747675e24454cd9b7ed35c58707      49  \n",
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       "4                                None      41  \n",
       "..                                ...     ...  \n",
       "139  8d9f7747675e24454cd9b7ed35c58707      43  \n",
       "140                              None      63  \n",
       "141                              None      59  \n",
       "142                              None      35  \n",
       "143                              None      57  \n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ja3_records = pd.DataFrame(ja3_records)\n",
    "df_ja3_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
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       "      <td>8d9f7747675e24454cd9b7ed35c58707</td>\n",
       "      <td>67</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...</td>\n",
       "      <td>8d9f7747675e24454cd9b7ed35c58707</td>\n",
       "      <td>58</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...</td>\n",
       "      <td>8d9f7747675e24454cd9b7ed35c58707</td>\n",
       "      <td>69</td>\n",
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       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...</td>\n",
       "      <td>8d9f7747675e24454cd9b7ed35c58707</td>\n",
       "      <td>74</td>\n",
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       "      <td>None</td>\n",
       "      <td>8d9f7747675e24454cd9b7ed35c58707</td>\n",
       "      <td>21</td>\n",
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       "      <td>8d9f7747675e24454cd9b7ed35c58707</td>\n",
       "      <td>43</td>\n",
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       "</table>\n",
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      "text/plain": [
       "                                            user_agent  \\\n",
       "1                                                 None   \n",
       "2                                                 None   \n",
       "13   Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "24                                                None   \n",
       "34                                                None   \n",
       "36   Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "58   Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "62   Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "70                                                None   \n",
       "71                                                None   \n",
       "72                                                None   \n",
       "78                                                None   \n",
       "96   Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "116  Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "117                                               None   \n",
       "120                                               None   \n",
       "121  Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Ge...   \n",
       "125                                               None   \n",
       "139                                               None   \n",
       "\n",
       "                                  ja3  volume  \n",
       "1    8d9f7747675e24454cd9b7ed35c58707      61  \n",
       "2    8d9f7747675e24454cd9b7ed35c58707      49  \n",
       "13   8d9f7747675e24454cd9b7ed35c58707      53  \n",
       "24   8d9f7747675e24454cd9b7ed35c58707      61  \n",
       "34   8d9f7747675e24454cd9b7ed35c58707      46  \n",
       "36   8d9f7747675e24454cd9b7ed35c58707      62  \n",
       "58   8d9f7747675e24454cd9b7ed35c58707      63  \n",
       "62   8d9f7747675e24454cd9b7ed35c58707      36  \n",
       "70   8d9f7747675e24454cd9b7ed35c58707      46  \n",
       "71   8d9f7747675e24454cd9b7ed35c58707      85  \n",
       "72   8d9f7747675e24454cd9b7ed35c58707      42  \n",
       "78   8d9f7747675e24454cd9b7ed35c58707      67  \n",
       "96   8d9f7747675e24454cd9b7ed35c58707      58  \n",
       "116  8d9f7747675e24454cd9b7ed35c58707      69  \n",
       "117  8d9f7747675e24454cd9b7ed35c58707      52  \n",
       "120  8d9f7747675e24454cd9b7ed35c58707      49  \n",
       "121  8d9f7747675e24454cd9b7ed35c58707      74  \n",
       "125  8d9f7747675e24454cd9b7ed35c58707      21  \n",
       "139  8d9f7747675e24454cd9b7ed35c58707      43  "
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ja3_records[df_ja3_records['ja3'].notna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we got that JA3 alllll over this GPTBot traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is it really just super common? It could literally just be a headless browser binary on Linux.\n",
    "Let's do a GNQL query to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>actor</th>\n",
       "      <th>...</th>\n",
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       "      <th>metadata.destination_countries</th>\n",
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       "      <th>metadata.source_country_code</th>\n",
       "      <th>metadata.destination_country_codes</th>\n",
       "      <th>raw_data.scan</th>\n",
       "      <th>raw_data.web.paths</th>\n",
       "      <th>raw_data.web.useragents</th>\n",
       "      <th>raw_data.ja3</th>\n",
       "      <th>raw_data.hassh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85.239.240.253</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>True</td>\n",
       "      <td>[ENV Crawler, PHPUnit RCE, Python Requests Cli...</td>\n",
       "      <td>unknown</td>\n",
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       "      <td>Missouri</td>\n",
       "      <td>[Australia, Ukraine, India, United Kingdom, Ru...</td>\n",
       "      <td>United States</td>\n",
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       "      <td>[AU, UA, IN, GB, RU, JP, DE, TW, ES, CN, TR, U...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114.99.121.160</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2022-08-20</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>True</td>\n",
       "      <td>[TLS/SSL Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>[Canada, United States, India]</td>\n",
       "      <td>China</td>\n",
       "      <td>CN</td>\n",
       "      <td>[CA, US, IN]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '473cd7cb9faa642487833865d516...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>185.36.81.23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2018-10-24</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>True</td>\n",
       "      <td>[ENV Crawler, PHPUnit RCE, Python Requests Cli...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Vilnius</td>\n",
       "      <td>[Germany, Canada, United States, Netherlands, ...</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>LT</td>\n",
       "      <td>[DE, CA, US, NL, TR]</td>\n",
       "      <td>[{'port': 80, 'protocol': 'TCP'}, {'port': 443...</td>\n",
       "      <td>[/, /vendor/phpunit/phpunit/src/Util/PHP/eval-...</td>\n",
       "      <td>[Mozilla/5.0 (Linux; U; Android 4.4.2; en-US; ...</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162.250.191.179</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-02</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>True</td>\n",
       "      <td>[TLS/SSL Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>[Brazil, Romania, Netherlands, Turkey, Germany...</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CA</td>\n",
       "      <td>[BR, RO, NL, TR, DE, IE, FR, GB, IN, LV, KZ, C...</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135.148.237.208</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-07-27</td>\n",
       "      <td>2023-08-24</td>\n",
       "      <td>True</td>\n",
       "      <td>[ENV Crawler, TLS/SSL Crawler, Web Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>[South Korea, Japan, Australia, Brazil, Hong K...</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>[KR, JP, AU, BR, HK, DE, ZA, US, FR]</td>\n",
       "      <td>[{'port': 80, 'protocol': 'TCP'}, {'port': 443...</td>\n",
       "      <td>[/, /.env]</td>\n",
       "      <td>[Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/5...</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>89.163.145.112</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>RUS_VPN</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-05-15</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>True</td>\n",
       "      <td>[Open Proxy Scanner, Web Crawler, ZMap Client]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>North Rhine-Westphalia</td>\n",
       "      <td>[Russia, United Kingdom, Ukraine]</td>\n",
       "      <td>Germany</td>\n",
       "      <td>DE</td>\n",
       "      <td>[RU, GB, UA]</td>\n",
       "      <td>[{'port': 88, 'protocol': 'TCP'}, {'port': 90,...</td>\n",
       "      <td>[/login.rsp, /image/lgbg.jpg, /skin/default_1/...</td>\n",
       "      <td>[Chrome (AppleWebKit/537.1; Chrome50.0; Window...</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.113.60.138</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-07-27</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>True</td>\n",
       "      <td>[TLS/SSL Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>[United States, Netherlands, Germany, France]</td>\n",
       "      <td>Iran</td>\n",
       "      <td>IR</td>\n",
       "      <td>[US, NL, DE, FR]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.217.167.150</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>2023-05-29</td>\n",
       "      <td>True</td>\n",
       "      <td>[TLS/SSL Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>[Finland, Germany, Ukraine, Kazakhstan, Romani...</td>\n",
       "      <td>Iran</td>\n",
       "      <td>IR</td>\n",
       "      <td>[FI, DE, UA, KZ, RO, PL]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2.147.44.248</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>2023-05-28</td>\n",
       "      <td>True</td>\n",
       "      <td>[TLS/SSL Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Tehran</td>\n",
       "      <td>[United States]</td>\n",
       "      <td>Iran</td>\n",
       "      <td>IR</td>\n",
       "      <td>[US]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2.147.187.70</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>N/A</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-03-16</td>\n",
       "      <td>2023-05-28</td>\n",
       "      <td>True</td>\n",
       "      <td>[TLS/SSL Crawler]</td>\n",
       "      <td>unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>Alborz Province</td>\n",
       "      <td>[Germany, France, Netherlands, United States, ...</td>\n",
       "      <td>Iran</td>\n",
       "      <td>IR</td>\n",
       "      <td>[DE, FR, NL, US, CA, JP, MX, SG]</td>\n",
       "      <td>[{'port': 443, 'protocol': 'TCP'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ip    bot    vpn vpn_service  spoofable  first_seen  \\\n",
       "0     85.239.240.253  False  False         N/A      False  2023-07-30   \n",
       "1     114.99.121.160  False  False         N/A      False  2022-08-20   \n",
       "2       185.36.81.23  False  False         N/A      False  2018-10-24   \n",
       "3    162.250.191.179  False  False         N/A      False  2023-08-02   \n",
       "4    135.148.237.208  False  False         N/A      False  2023-07-27   \n",
       "..               ...    ...    ...         ...        ...         ...   \n",
       "145   89.163.145.112  False   True     RUS_VPN      False  2023-05-15   \n",
       "146     5.113.60.138  False  False         N/A      False  2019-07-27   \n",
       "147    5.217.167.150  False  False         N/A      False  2023-05-29   \n",
       "148     2.147.44.248  False  False         N/A      False  2021-03-12   \n",
       "149     2.147.187.70  False  False         N/A      False  2023-03-16   \n",
       "\n",
       "      last_seen  seen                                               tags  \\\n",
       "0    2023-08-24  True  [ENV Crawler, PHPUnit RCE, Python Requests Cli...   \n",
       "1    2023-08-24  True                                  [TLS/SSL Crawler]   \n",
       "2    2023-08-24  True  [ENV Crawler, PHPUnit RCE, Python Requests Cli...   \n",
       "3    2023-08-24  True                                  [TLS/SSL Crawler]   \n",
       "4    2023-08-24  True        [ENV Crawler, TLS/SSL Crawler, Web Crawler]   \n",
       "..          ...   ...                                                ...   \n",
       "145  2023-05-29  True     [Open Proxy Scanner, Web Crawler, ZMap Client]   \n",
       "146  2023-05-29  True                                  [TLS/SSL Crawler]   \n",
       "147  2023-05-29  True                                  [TLS/SSL Crawler]   \n",
       "148  2023-05-28  True                                  [TLS/SSL Crawler]   \n",
       "149  2023-05-28  True                                  [TLS/SSL Crawler]   \n",
       "\n",
       "       actor  ...         metadata.region  \\\n",
       "0    unknown  ...                Missouri   \n",
       "1    unknown  ...                   Anhui   \n",
       "2    unknown  ...                 Vilnius   \n",
       "3    unknown  ...                  Quebec   \n",
       "4    unknown  ...                Virginia   \n",
       "..       ...  ...                     ...   \n",
       "145  unknown  ...  North Rhine-Westphalia   \n",
       "146  unknown  ...                  Tehran   \n",
       "147  unknown  ...                  Tehran   \n",
       "148  unknown  ...                  Tehran   \n",
       "149  unknown  ...         Alborz Province   \n",
       "\n",
       "                        metadata.destination_countries  \\\n",
       "0    [Australia, Ukraine, India, United Kingdom, Ru...   \n",
       "1                       [Canada, United States, India]   \n",
       "2    [Germany, Canada, United States, Netherlands, ...   \n",
       "3    [Brazil, Romania, Netherlands, Turkey, Germany...   \n",
       "4    [South Korea, Japan, Australia, Brazil, Hong K...   \n",
       "..                                                 ...   \n",
       "145                  [Russia, United Kingdom, Ukraine]   \n",
       "146      [United States, Netherlands, Germany, France]   \n",
       "147  [Finland, Germany, Ukraine, Kazakhstan, Romani...   \n",
       "148                                    [United States]   \n",
       "149  [Germany, France, Netherlands, United States, ...   \n",
       "\n",
       "    metadata.source_country metadata.source_country_code  \\\n",
       "0             United States                           US   \n",
       "1                     China                           CN   \n",
       "2                 Lithuania                           LT   \n",
       "3                    Canada                           CA   \n",
       "4             United States                           US   \n",
       "..                      ...                          ...   \n",
       "145                 Germany                           DE   \n",
       "146                    Iran                           IR   \n",
       "147                    Iran                           IR   \n",
       "148                    Iran                           IR   \n",
       "149                    Iran                           IR   \n",
       "\n",
       "                    metadata.destination_country_codes  \\\n",
       "0    [AU, UA, IN, GB, RU, JP, DE, TW, ES, CN, TR, U...   \n",
       "1                                         [CA, US, IN]   \n",
       "2                                 [DE, CA, US, NL, TR]   \n",
       "3    [BR, RO, NL, TR, DE, IE, FR, GB, IN, LV, KZ, C...   \n",
       "4                 [KR, JP, AU, BR, HK, DE, ZA, US, FR]   \n",
       "..                                                 ...   \n",
       "145                                       [RU, GB, UA]   \n",
       "146                                   [US, NL, DE, FR]   \n",
       "147                           [FI, DE, UA, KZ, RO, PL]   \n",
       "148                                               [US]   \n",
       "149                   [DE, FR, NL, US, CA, JP, MX, SG]   \n",
       "\n",
       "                                         raw_data.scan  \\\n",
       "0    [{'port': 80, 'protocol': 'TCP'}, {'port': 443...   \n",
       "1                   [{'port': 443, 'protocol': 'TCP'}]   \n",
       "2    [{'port': 80, 'protocol': 'TCP'}, {'port': 443...   \n",
       "3                   [{'port': 443, 'protocol': 'TCP'}]   \n",
       "4    [{'port': 80, 'protocol': 'TCP'}, {'port': 443...   \n",
       "..                                                 ...   \n",
       "145  [{'port': 88, 'protocol': 'TCP'}, {'port': 90,...   \n",
       "146                 [{'port': 443, 'protocol': 'TCP'}]   \n",
       "147                 [{'port': 443, 'protocol': 'TCP'}]   \n",
       "148                 [{'port': 443, 'protocol': 'TCP'}]   \n",
       "149                 [{'port': 443, 'protocol': 'TCP'}]   \n",
       "\n",
       "                                    raw_data.web.paths  \\\n",
       "0    [/.env, /vendor/phpunit/phpunit/src/Util/PHP/e...   \n",
       "1                                                  NaN   \n",
       "2    [/, /vendor/phpunit/phpunit/src/Util/PHP/eval-...   \n",
       "3                                                  NaN   \n",
       "4                                           [/, /.env]   \n",
       "..                                                 ...   \n",
       "145  [/login.rsp, /image/lgbg.jpg, /skin/default_1/...   \n",
       "146                                                NaN   \n",
       "147                                                NaN   \n",
       "148                                                NaN   \n",
       "149                                                NaN   \n",
       "\n",
       "                               raw_data.web.useragents  \\\n",
       "0    [Mozilla/5.0 (Linux; U; Android 4.4.2; en-US; ...   \n",
       "1                                                  NaN   \n",
       "2    [Mozilla/5.0 (Linux; U; Android 4.4.2; en-US; ...   \n",
       "3                                                  NaN   \n",
       "4    [Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/5...   \n",
       "..                                                 ...   \n",
       "145  [Chrome (AppleWebKit/537.1; Chrome50.0; Window...   \n",
       "146                                                NaN   \n",
       "147                                                NaN   \n",
       "148                                                NaN   \n",
       "149                                                NaN   \n",
       "\n",
       "                                          raw_data.ja3 raw_data.hassh  \n",
       "0    [{'fingerprint': 'db687c576d7ee0017b289fc05e0f...             []  \n",
       "1    [{'fingerprint': '473cd7cb9faa642487833865d516...             []  \n",
       "2    [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "3    [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "4    [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "..                                                 ...            ...  \n",
       "145  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "146  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "147  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "148  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "149  [{'fingerprint': '8d9f7747675e24454cd9b7ed35c5...             []  \n",
       "\n",
       "[150 rows x 33 columns]"
      ]
     },
     "execution_count": 547,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GNQL_QUERY_2 = 'raw_data.ja3.fingerprint:8d9f7747675e24454cd9b7ed35c58707'\n",
    "df_ja3_query = get_gnql_df(GNQL_QUERY_2)\n",
    "df_ja3_query"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like a mixed crowd..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's grab the os info and the hits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "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>metadata.os</th>\n",
       "      <th>metadata.sensor_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Windows 7/8</td>\n",
       "      <td>610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows 2000</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linux 2.2-3.x</td>\n",
       "      <td>3371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unknown</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Windows 2000</td>\n",
       "      <td>3036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>unknown</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Windows 2000</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>unknown</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       metadata.os  metadata.sensor_hits\n",
       "0      Windows 7/8                   610\n",
       "1     Windows 2000                    88\n",
       "2          unknown                    84\n",
       "3    Linux 2.2-3.x                  3371\n",
       "4          unknown                   168\n",
       "..             ...                   ...\n",
       "145   Windows 2000                  3036\n",
       "146        unknown                    26\n",
       "147   Windows 2000                    65\n",
       "148        unknown                     6\n",
       "149        unknown                    43\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ja3_query[['metadata.os','metadata.sensor_hits']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]),\n",
       " [Text(0, 0, 'Windows 2000'),\n",
       "  Text(1, 0, 'Windows 7/8'),\n",
       "  Text(2, 0, 'Linux 2.2-3.x'),\n",
       "  Text(3, 0, 'Linux 2.2.x-3.x (Embedded)'),\n",
       "  Text(4, 0, 'unknown'),\n",
       "  Text(5, 0, 'Linux 3.11+')])"
      ]
     },
     "execution_count": 585,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uXcOECRMwevRolWkdO3bE69evERQUhObNm2PRokXo16+fWhuHDx/GkCFD0KlTJyxcuDDHG6Y6duyIFy9eICgoCB07dsSaNWswefJkafr69ethaGgIDw8PaRl+++23Ksv53X1K48aNcfXqVYwfPx63bt3C7du3MXXqVJw9e1Zl/cztOqz8AaDpgG1sbIzz588jMzMTnp6e0jr71Vdfqa3D79q4cSMcHBxy7F6xZ88exMfHZ/nDYPDgwWjSpIlad5n3afPgwYMA3nRhqF27NkxNTWFiYoLOnTvj2bNnHxTn5MmTUaRIERgZGeGLL77QmFStX78efn5+KFKkCGbNmoXx48fj2rVraNiwIe7evZvt5yclJSEpKQlWVlZq07Zu3YqwsLAcT8RkZGRg4MCB+Oabb1CtWrVs6/77779SMmtra4vx48drTHRy2+bkyZPRvXt36OvrY8qUKZg8eTIcHBxw+PBhqc6ECRMwfvx4VK9eHXPmzEHZsmXh7e2N5ORklbZSUlI0rq8mJiYA3pz8etvNmzfRqVMnNGvWDEFBQdL+8MCBA9kug6zW4dz+Hffv34927dpBoVAgKCgI/v7+6N27d5Y3h6anp+PJkyd49OgR9u/fj3HjxqFo0aKoU6eOWt3evXtLJwabNGmisc3cLHPgTaJrZmYGc3NzWFpaYsCAAWo/9JQ/QgcNGoSTJ0/i4cOH2LNnD6ZPnw5/f3+Nff6Vstp2zp8/D1NTU7Uu18rve/78+Ty3+SECAwNx/fp1TJo0CT169MDGjRvh7+8vnRzu3r07nj59in379qnMFxMTg8OHD+Prr7/Osu1OnTohOTlZ7Rj88uVL/PXXX2jfvj10dXUBvDlhNGDAANjb22Pu3Llo164dfvnlF3h7e2vcBt9XRkYGmjVrBgcHB8yePRtlypRBYGAg1qxZA19fX9SuXRuzZs1C0aJF0aNHD0RFRanE3ahRI2zYsAE9evTAokWL0KBBA4wZM0Zjbp2t97008G63iZCQEAFATJs2TaVe+/bthUKhELdu3ZLKAAgA4uzZs1LZvXv3hJGRkWjTpk22n+vm5ibMzc2zrTNv3jwBQOzcuVMIIURMTIwoUaKEACAqVaokvvvuO7Fp0yYRHx+fq+/q5+cn6tSpI71v27ataNu2rdDV1RV///23EEKIc+fOCQBix44dUr1GjRqJRo0aSe9z6tIDQKxbt04qS0lJEba2tqJdu3a5ijMryktvZcuWVbu8/vr1a7VL2lFRUcLQ0FBMmTJFKpszZ47Q1dUViYmJQgghFi1aJBwdHUWdOnXEqFGjhBBvLuNbWFiIIUOG5Dq2BQsWCABiy5YtUllycrIoX7682iVCTV0DgoKChEKhEPfu3ZPKsuvS824byi5iX375ZY6xarqsl5f2oqOjpXUfgChVqpT4/fffc/xcITRfLldeRn17ferZs6cAoPK3E0KIGjVqiFq1aknv//jjDwFALFiwQCrLyMgQX375pVqbTZs2FdWqVVPp2pKZmSnq168vnJ2dpTLl5dqGDRuqXSKeOHGiACBatWqlUv79998LAOLixYtSGQCho6Mjrl69qrYcAEhd195ut0+fPir12rRpI4oXL65Sll2XHgAq26oQb7ojdezYUSgUCulvZmJiIkJCQlTq5XYdjouLEwqFQgQEBKjMf+PGDan9J0+eqMSUU5eeK1euCABi5MiR2dYTQoh27doJQ0ND8fz5c7Vpu3btEnp6etIyz233g6zabNWqlQAgihcvLrp16ya2bdsmxo8fL/T09ET9+vVVujTlts179+4Jb29vsWzZMrFz506xYMECUbp0aaGjo6PS/eHFixfCwsJCrXtITEyMMDc3z7HbyNSpUwUAcejQIZXyly9fitKlS4sxY8YIIbLv8rFkyRJhbm4uYmNjhRBZd93p06ePmDRpkvjjjz/EunXrpOXWsWPH92rz5s2bQkdHR7Rp00Ztv65c5rGxscLAwED4+fmp/B3Gjh0rAKhsIy1bthQWFhbSfl/J3d1dABA//fSTVObo6CgAiD/++EMqS0hIEHZ2dqJGjRpq30cpq3U4L39HNzc3YWdnp3JM379/vwCgsStGeHi4yr64YsWKat1RTpw4Idq1aydWrlwpduzYIYKCgkTx4sWFkZGRSrea3CxzIYQYPXq0GDVqlPj999/Fb7/9Ju2rGzRoINLS0lTm+/XXX4WFhYVKjD179lSr966sth0/Pz9RtmxZtfrJyckCgBg9enSe23wfymNErVq1RGpqqlSu7HatzJ8yMjJEqVKlRKdOnVTmnzdvnlAoFOLOnTtZfkZmZqYoWbKkWt60ZcsWAUCEhoYKIf63HXh7e6v83ZYsWSIAiFWrVkll7x77s+pynN0xecaMGVLZ8+fPhbGxsVAoFCrdz5XHgrePcVOnThWmpqbi33//Vfms0aNHC11dXXH//v0sl8W78i3h79evn0pCqKTcsBYvXvy/DwWEu7u7WpudOnUSJiYm2fYnK1eunChVqlS2sa1YsUIAEBs2bJDKHj16JL777jthY2MjbUAGBgZiypQp2R58hBBi5syZQk9PT+qPbG1tLX799VdRq1YtMXbsWCGEEAsXLhQKhUI8ffpUmi+vCX+RIkXUYmnVqlW2O8vcUK6ckydPzrZeenq6ePLkiYiLixOurq7C399fmnby5EkBQOzdu1cIIUSHDh3E119/LYYNGybq168vhBDi4sWLAoD4888/cx2bt7e3sLOzU/veyh1AVn0Ck5KSRFxcnNTf9u0kLLd9+J89eybi4uJE//79hYWFRY71c+rHl1N7KSkp4sCBA+Kvv/4SU6ZMEW5ubmLlypU5fq4QeU/4lUmB0g8//CCKFSsmve/bt6/Q19cXycnJKvWUPwSUbT59+lQoFAoxdepUERcXp/KaPHmyACAePnwohPjfznzt2rVq8SsT83379qmUX79+XQAQQUFBUhkA0aRJE43LIauE//Tp0yr1lD/6ExISpLK89uFPS0sT48aNEx06dBC//fab2LBhg/D09BRFihRR6eubl3W4U6dOQk9PT/z000/i9u3bIjQ0VFSvXl3o6+sLACp9ZHOT8I8ZM0btB5MmCQkJWZ5QSUlJEc7OziIwMFAqy03Cn12byh+Ovr6+KuVBQUECgDhw4ECe29Tk6dOnwsbGRlSsWFEq+/PPPwUAcfjwYbV11tvbW5QvXz7L9o4dOyb09PQ0JtwTJkwQdnZ24sWLF0KIrBP+J0+eCEtLS5VkOC/97fv27avWnzy3bc6ZM0cAEOfPn8+y/U2bNqnsy5ViY2PVEv49e/YIAKJZs2bi3LlzIjIyUgwaNEhaX6dOnSrVdXR0FPb29mrbwahRowQAER0drTGerNbh3P4dHz16lGXS6uLionGfnZCQIA4cOCBCQkLEyJEjRc2aNcVff/2V5TJTunnzpjA2NhY+Pj5SWW6WeVamT5+u8T6uv//+W3h7e4sFCxaI7du3i6FDhwo9PT0xbNiwLNvKaXusXLmyWnlGRoYAIAYNGpTnNt+H8hjxyy+/qJS/ePFC6OnpiW+//VYqGzVqlDA2NlbJKWvVqiUaNGiQ4+cMHjxYGBsbS9uqEG9+uJQsWVJaP5XbwZ49e1TmTUlJEWZmZio/GPIj4X/3mOzm5qYx57OwsBDdu3eX3ru6ugpfX1+1beDgwYNqeW5O8q1Lz71792Bvb4+iRYuqlCsvId27d0+l3NnZWa2NChUq4OXLl9kOVVi0aFG8ePEi21iU09+Oxc7ODsuWLUN0dDQiIyOxaNEilChRAhMmTMDKlSuzbc/DwwPp6ekIDw9HZGQkYmNj4eHhAU9PTxw/fhzAm64sLi4usLS0zLat7JQqVUqtr26xYsVU+rZ9CCcnJ7WyzMxMzJ8/H87OzjA0NISVlRVKlCih0mUHAGrWrAkTExOV76tcBmfPnsXr16+laQ0bNsx1TPfu3UP58uXVvremPnr3799Hr169YGlpKfVNb9SoEQCoxJqdXbt2oV69etJQriVKlMCyZctyPf+HtGdgYAAvLy+0aNEC48ePx9KlSxEQEJDlULLvSznc49veXY/u3bsHOzs76dK80rujT9y6dQtCCIwfPx4lSpRQeSlHDIqNjVWZR9N6pvTudl+uXDno6OiodbPIrg1NSpcurfK+WLFiAPBB205gYCD++usvbN68GZ07d0a3bt1w8OBB2NnZYdCgQVK9vKzDv/zyC5o3b47hw4ejXLly8PT0RLVq1dCyZUsAyNNoYUIIbNq0CVWrVoWrq2u2df/44w+8fv1a42X5+fPn48mTJypdoHIjuzaV3UC6dOmiUt61a1cAQFhYWJ7b1MTS0hK9e/dGZGQkHj58CABSv/Ivv/xSbZ3dv3+/2vqqdOPGDbRp0wZVq1ZVG0nl7t27mDNnDqZPn57j32jcuHGwtLTEwIEDc/Ud3jVs2DAA/+sWlZc2b9++DR0dHbi4uGRZR3ksfndbLFGihLTdKDVr1gyLFy9GaGgoatasiYoVK2L37t2YPn06APX1VdN2UKFCBQDQ2JUqu3U4t3/HrL4PoHkbBN7cu+fl5YXWrVtj1qxZGDZsGFq3bq1y75om5cuXR+vWrXHkyBGpH3hulnlWhgwZAh0dHZW/9YkTJ9CiRQtMnz4dgwYNgr+/P+bOnYtx48Zh3rx5uHbtmsa2ctoeNd0Xp7x/Kqv7AvK6PebWu3+rIkWKwM7OTmUd6dGjB169eiV1G42MjERERESuhqTu1KkTXr16Jd1nlpSUhD179qBDhw7S+qlcb95dRwwMDFC2bFm1nPVDaDomm5uba8z5zM3NVY5bN2/exN69e9W2AS8vLwDqx9/svN/4dlpUuXJlXLhwAffv31c7yCtdunQJADRugAqFAhUqVECFChXg5+cHZ2dnbNy4Ed98802Wn1m7dm0YGRkhNDQUpUuXhrW1NSpUqAAPDw/8/PPPSElJwfHjx9GmTZsP+m7KfmXvEu/c9Py+NG3UM2bMwPjx49GnTx9MnToVlpaW0NHRweDBg1VuCNHX10fdunURGhqKW7duISYmBh4eHrCxsUFaWhpOnTqF48ePo1KlStn2L39fGRkZ+Oqrr/Ds2TOMGjUKlSpVgqmpKf777z/06tUrVzevHD9+HK1atYKnpyd+/vln2NnZQV9fH6tXr9Z4o8zHbq9+/fqws7PDxo0b8/WBNVmtR+9DuVyHDx8OHx8fjXXe/ZGQ3U1l78rqZtS8tAHk/7aTmpqKlStXYuTIkSo3derr66NZs2ZYsmQJUlNTYWBgkKd2zc3NsWPHDty/fx93796Fo6MjHB0dUb9+fZQoUQIWFha5buvEiRO4d++e2pCOmmzcuBHm5uZq61lCQgKmTZuG77//HomJidI9SUlJSRBC4O7duzAxMYG1tXWu2wT+N6zkuzceKtvJ6odYdm1mxcHBAcCb4ZdLlSolrbPr16/XOKymphuSHzx4AG9vb5ibm2PPnj1qJ64mTJiAkiVLonHjxlJSEhMTA+DNM17u3r2L0qVL4/bt21i+fDkWLFgg3QMFvEms0tLScPfuXZiZmWV7Yujt7wO8OeB/aJsfIjAwEL1798alS5dgYGAANzc36SSZMpl/X9mtw+/zd3xfbdu2Rffu3bF582ZUr14927oODg5ITU1FcnIyzMzMPuhzjY2NUbx4cZX7Wn755RfY2Nigdu3aKnVbtWqFSZMmISwsTGNuk922Y2dnhyNHjkAIobLPVT5vIqthYN9ne8wvLi4uqFWrltR3fcOGDTAwMEDHjh1znLdevXooU6YMtmzZgq5du+Kvv/7Cq1ev0KlTp3yJLavjVlYDE2R1fMrNcSszMxNfffWVxvsagbxtg/m2xTg6OuLgwYN48eKFys7yxo0b0vS3abq7/99//4WJiUm2CWOLFi3w22+/Yd26dRg3bpza9MTEROzYsQOVKlXKcazcsmXLolixYjk+ZMXAwAB16tTB8ePHUbp0aWl0Bg8PD6SkpGDjxo14/PhxjjfNZTfShrZs27YNTZo0UbvKER8fr3bTmoeHB2bNmoWDBw/CysoKlSpVgkKhQJUqVXD8+HEcP348zzsGR0dHXLlyRW1HFBkZqVLv8uXL+Pfff7F27VqVmys13QyW1XL+448/YGRkhH379qmMvLJ69eo8xZyf7b1+/fq9ry58CEdHRxw5cgQvX75UOcuvvNFdqWzZsgDeJLrKMwof4ubNmypn72/duoXMzMxP8hTDvGx/T58+RXp6usYdeFpaGjIzM6VpuV2H31a6dGnphEV8fDwiIiLQrl27XMcH/O8BWcqz5lmJjo7GkSNH0KtXL5X1FHiTeCclJWH27Nkab0R1cnJC69at1R5Yll2bAFCrVi2sWLEC//33n0q5MmHVtI/Pqc2s3LlzR6XNcuXKAXjz4yI36+zTp0/h7e2NlJQUHDp0SBoN5W3379/HrVu3pO3hbcqRT54/f47//vsPmZmZ+OGHH/DDDz+o1XVycsKgQYOyHWXn3e+TlzbLlSuHzMxMXLt2LctnvSiPxTdv3lT5PnFxcVn+EDM1NVV51sHBgwdhbGyMBg0aqNRTXhF8ezv4999/AUDjNp7dOpzbv+Pb3+dd2W2Db0tJSUFmZmau9sV37tyBkZGRdHUjN8s8Ky9evMCTJ09UtofHjx9nud8BoHG0oJy2HTc3N/z666+4fv26yo+FU6dOSdPz2uaHuHnzJpo0aSK9T0pKQnR0tNqAAT169MDQoUMRHR2NTZs2wc/PT+0qVFY6duyIhQsXIjExEb///jvKlCmjMlKjcr2JjIxU2Q5SU1MRFRWV7TqnjOHd0Xzy86qAUrly5ZCUlJQvx99869LTvHlzZGRkYMmSJSrl8+fPh0KhQLNmzVTKw8PDce7cOen9gwcPsGPHDnh7e2d7hrJ9+/ZwcXHBzJkz1e6Wz8zMRP/+/fH8+XOVBxSdOnVKbfQBADh9+jSePn2a7RBPSh4eHjh16hSOHDkiJfxWVlaoXLkyZs2aJdXJjvJBKTkN+fQp6erqqp0F3bp1q9qBGvjfD5wFCxagYcOG0k5dOfLJo0ePclwG72revDkePXqkMpTly5cvVUaUUcYJqP7yFUJg4cKFam1mtZx1dXWhUChUdqZ3797V+PTV+/fvSz9Ws5Lb9pKTkzUOEfbHH3/g+fPnKmdy0tLScOPGjRx/hH4oHx8fpKWlYcWKFVJZZmamNFSYkrW1NRo3boxffvlFY0y5fVKw0rvtL168GADU9g8fg6mpaZbb3rvDclpbW8PCwgLbt29XGf4xKSkJf/31l/TEbiD363BWxowZg/T09Dw9yCstLQ1bt25Fw4YNs7zSqbR582ZkZmZqvCxvbW2N7du3q72aNGkCIyMjbN++HWPGjMlTmwDQunVrGBoaYvXq1SpX35RdZTQ9lTenNjWta//99x9WrVoFV1dXKVH38fGBmZkZZsyYoXGkjbfbSU5ORvPmzfHff/9hz549GruFAMC0adPUltHUqVMBACNHjsT27dthamoqDQf57qtKlSooXbo0tm/fjoCAAABvTk69281C/P9wmcrvASBPbfr7+0NHRwdTpkxRu+qp3Hd6eXlBX18fixcvVtmf5mb4UOBNd6w///wTAQEBag+NevTokcrIXYmJiVi3bh3c3NzUztLntA7n9u9oZ2cHNzc3rF27ViVhP3DggFr3l/j4eI1tKdfLt/fFmta3ixcvYufOnfD29pau/OVmmb9+/VpjV+SpU6dCCKHyZOUKFSrg8ePHakM+Kp+KW6NGDbV2crM96uvr4+eff1aJLTg4GCVLlkT9+vXz3OaHWL58ucrfYdmyZUhPT1c7DnTp0gUKhQKDBg3CnTt3sh2d512dOnVCSkoK1q5di71796pdGfDy8oKBgQEWLVqksh2sXLkSCQkJ8PPzy7JtR0dH6Orqqg0l//byzS8dO3ZEeHi42ohFwJv1OafhYt+Wb2f4W7ZsiSZNmuDHH3/E3bt3Ub16dezfvx87duzA4MGDpV/rSlWrVoWPjw9++OEHGBoaSgsqp36kBgYG2LZtG5o2bYqGDRuqPGl306ZNOHfuHIYNG6b25LiNGzeiTZs2qFWrFgwMDHD9+nWsWrUKRkZGGDt2bI7fz8PDA9OnT8eDBw9UklpPT0/88ssvKFOmTI5j1JYrVw4WFhYIDg5G0aJFYWpqirp16+a5v3J+atGiBaZMmYLevXujfv36uHz5MjZu3KjxTJa7uzv09PQQGRmpMpSip6cnli1bBiDnHz3v6tu3L5YsWYIePXogIiICdnZ2WL9+vVrf8kqVKqFcuXIYPnw4/vvvP5iZmUkJ87tq1aoF4M0TEn18fKCrq4vOnTvDz88P8+bNg6+vL7p27YrY2FgsXboU5cuXl7qBKfXo0QPHjh3LtktIbtu7efMmvLy80KlTJ1SqVAk6Ojo4e/YsNmzYgDJlyqj0B//vv/9QuXJl9OzZU20s/Pzk7++POnXqYNiwYbh16xYqVaqEnTt3SpeW3z5Dt3TpUjRs2BDVqlVD3759UbZsWTx+/Bjh4eF4+PBhjv1e3xYVFYVWrVrB19cX4eHh2LBhA7p27ZrjZfT8UKtWLRw8eBDz5s2Dvb09nJycpOdmVK5cGY0aNZIOsrq6uhg+fDjGjRuHevXqoUePHsjIyMDKlSvx8OFDbNiwQWo3t+swAMycORNXrlxB3bp1oaenh5CQEOzfvx/Tpk3L0xNG9+3bh6dPn+bqYLxx40bY29trHP7VxMRE45OHQ0JCcPr06SyfSpxdmwBga2uLH3/8ERMmTICvry/8/f1x8eJFrFixAl26dNH4XXNqc+TIkbh9+zaaNm0Ke3t73L17F7/88guSk5NVfvibmZlh2bJl6N69O2rWrInOnTujRIkSuH//Pnbv3o0GDRpIJ6a6deuG06dPo0+fPrh+/brK2PtFihSRvr+m+5KU3a+++OILqZ6VlZXGZaZMpt+edu7cOXTp0gVdunRB+fLlpf7KJ06cQL9+/aTnvOSlzfLly+PHH3/E1KlT4eHhgbZt28LQ0BBnzpyBvb09goKCpOdyBAUFoUWLFmjevDnOnz+Pv//+W+2q7r1799CxY0e0atUKtra2uHr1KoKDg+Hq6ooZM2aoxVShQgUEBATgzJkzsLGxwapVq/D48WONVz1zWofz8ncMCgqCn58fGjZsiD59+uDZs2dYvHgxqlSpojLs5dGjR/HDDz+gffv2cHZ2RmpqKo4fP44///wTtWvXVkkoO3XqBGNjY9SvXx/W1ta4du0ali9fDhMTE8ycOTNPyzwmJgY1atRAly5dpGE19+3bhz179sDX1xetW7eW2gsMDMTq1avRsmVLDBw4EI6Ojjh27Bh+++03fPXVV2rPHwJy3nZKlSqFwYMHY86cOUhLS8MXX3yBkJAQHD9+HBs3btR4kjWnNj9EamoqmjZtio4dOyIyMhI///wzGjZsiFatWqnUK1GiBHx9fbF161ZYWFhkm4S/q2bNmtLfJiUlRa07T4kSJTBmzBhMnjwZvr6+aNWqlRTLF198ke2PC3Nzc3To0AGLFy+GQqFAuXLlsGvXrjz1p8+tESNGYOfOnWjRogV69eqFWrVqITk5GZcvX8a2bdtw9+5djUMIa5Tr23vfoWkklBcvXoghQ4YIe3t7oa+vL5ydncWcOXPU7kLG/48+sWHDBuHs7CwMDQ1FjRo18vRU1djYWDF06FBRvnx5YWhoKCwsLISXl5c0FOfbLl26JEaMGCFq1qwpLC0thZ6enrCzsxMdOnTI8al1SomJiUJXV1cULVpUZRShDRs2CAAqd1UrvTtKjxBC7NixQ7i4uAg9PT2Vu7mzGsUhP57wlt3wca9fvxbDhg0TdnZ2wtjYWDRo0ECEh4drjF0IIb744gsBQJw6dUoqe/jwoQAgHBwc3iu+e/fuiVatWgkTExNhZWUlBg0aJPbu3at2F/y1a9eEl5eXKFKkiLCyshJ9+/aVRgZ6+6749PR0MXDgQFGiRAlpSEWllStXSutcpUqVxOrVq6WRXt6mHCb1bZr+FrlpLy4uTvTr109UqlRJmJqaCgMDA+Hs7CwGDx4s4uLiVNpT3uX/7mgyeRmlR9PoKpq+Y1xcnOjatasoWrSoMDc3F7169RInTpwQgOqTqoUQ4vbt26JHjx7C1tZW6Ovri5IlS4oWLVqIbdu2SXWye4qi8vOvXbsm2rdvL4oWLSqKFSsmAgMD1Z6iq9w/aIIsRul5dzkqY4mKipLKbty4ITw9PYWxsbHaMoaGYTmFePPUyzp16ggLCwthbGws6tatq/KdlXK7Du/atUvUqVNHFC1aVJiYmIh69eqpDOeZ2+XQuXNnoa+vrzIqmCbKYd6GDh2abb13ZTdKT27bzMzMFIsXLxYVKlQQ+vr6wsHBQYwbN05lOL68tLlp0ybh6ekpSpQoIfT09ISVlZVo06aNiIiI0Fj/yJEjwsfHR5ibmwsjIyNRrlw50atXL5XhoJVDSWp65bTfzcuTWDXt3+/cuSM6dOggypQpI4yMjISJiYmoVauWCA4OznHkuKzaVFq1apWoUaOGMDQ0FMWKFRONGjVSGRkpIyNDTJ48WdrvN27cWFy5ckXtSbvPnj0TrVu3Fra2tsLAwEA4OTmJUaNGqY3GJ8T/9lH79u0Trq6u0j4xq+WT23U4N39HId6MMFa5cmVhaGgoXFxcxJ9//qm2z75165bo0aOHKFu2rDA2NhZGRkaiSpUqYuLEiWpPBV+4cKGoU6eOSs7w9ddfi5s3b+Z5mT9//lx8/fXXonz58sLExEQYGhqKKlWqiBkzZmS5PbRv3144ODgIfX194ejoKIYPH642qpqybm62x4yMDDFjxgzh6OgoDAwMRJUqVbIc4eV99xs5Ue6Xjx07Jvr16yeKFSsmihQpIrp165bleqAcTrNfv355/rwff/xRAMh2ZK4lS5aISpUqCX19fWFjYyP69++vNgSppmN/XFycaNeunTAxMRHFihUT3377rTTEbG6OyVltv5qO9S9evBBjxowR5cuXFwYGBsLKykrUr19f/PTTTxrXn6wohMinO0LzQKFQYMCAAWrdf4gKsu7duyM8PFytn7uchISEoE2bNvjnn3/U+ud+iEmTJmHy5MmIi4vL/dkIIiL6rO3YsQP+/v4IDQ3Ncw8CUpVvffiJ5C46OlpWyeqrV69U3mdkZGDx4sUwMzOTuhMQERFpy4oVK1C2bNk8DfdNmhW6YTmpcEhNTVUZakwTc3PzPA+/qA2XLl1CSEgIQkNDMWLECG2Hk28GDhyIV69ewd3dHSkpKfjzzz8RFhaGGTNmFIq/CxERydPmzZtx6dIl7N69GwsXLiyQoxwWNkz46aMICwtTGXZLk9WrV6NXr16fJqAP8Oeff2Lx4sXo3LmzxhFLCqsvv/wSc+fOxa5du/D69WuUL18eixcvRmBgoLZDIyKiz1iXLl1QpEgRBAQESEPf0ofRSh9+kr/nz58jIiIi2zpVqlTROOY1EREREeUfJvxERERERDLGLj2kJjMzE48ePULRokXZb46IiKiQEELgxYsXsLe3lx4ORgQw4ScNHj16BAcHB22HQURERO/hwYMHOT4MlD4vTPhJTdGiRQG82WGYmZlpORoiIiLKjcTERDg4OEjHcSIlJvykRtmNx8zMjAk/ERFRIcPuuPQudvAiIiIiIpIxJvxERERERDLGhJ+IiIiISMbYh5+IiIhylJGRgbS0NG2H8VnT19eHrq6utsOgQogJPxEREWVJCIGYmBjEx8drOxQCYGFhAVtbW96YS3nChJ+IiIiypEz2ra2tYWJiwkRTS4QQePnyJWJjYwEAdnZ2Wo6IChMm/ERERKRRRkaGlOwXL15c2+F89oyNjQEAsbGxsLa2ZvceyjXetEtEREQaKfvsm5iYaDkSUlL+LXg/BeUFE34iIiLKFrvxFBz8W9D7YMJPRERERCRjTPiJiIiIiGSMN+3mQmhoKObMmYOIiAhER0dj+/bt8Pf3l6ZndXlt9uzZGDFiBACgTJkyuHfvnsr0oKAgjB49Wnp/6dIlDBgwAGfOnEGJEiUwcOBAjBw5UmWerVu3Yvz48bh79y6cnZ0xa9YsNG/eXJouhMDEiROxYsUKxMfHo0GDBli2bBmcnZ0/dDEQEREBAMqM3v1JP+/uTL9P+nnAm2P7u8d7osKKZ/hzITk5GdWrV8fSpUs1To+OjlZ5rVq1CgqFAu3atVOpN2XKFJV6AwcOlKYlJibC29sbjo6OiIiIwJw5czBp0iQsX75cqhMWFoYuXbogICAA58+fh7+/P/z9/XHlyhWpzuzZs7Fo0SIEBwfj1KlTMDU1hY+PD16/fp3PS4WIiIiICgOe4c+FZs2aoVmzZllOt7W1VXm/Y8cONGnSBGXLllUpL1q0qFpdpY0bNyI1NRWrVq2CgYEBqlSpggsXLmDevHno168fAGDhwoXw9fWVrhpMnToVBw4cwJIlSxAcHAwhBBYsWIBx48ahdevWAIB169bBxsYGISEh6Ny583svAyIiIiIqnHiGP589fvwYu3fvRkBAgNq0mTNnonjx4qhRowbmzJmD9PR0aVp4eDg8PT1hYGAglfn4+CAyMhLPnz+X6nh5eam06ePjg/DwcABAVFQUYmJiVOqYm5ujbt26Uh1NUlJSkJiYqPIiIiIqrJYvXw57e3tkZmaqlLdu3Rp9+vQBACxbtgzlypWDgYEBKlasiPXr12fZ3tGjR6FQKFSeNnzhwgUoFArcvXsXALBmzRpYWFhg165dqFixIkxMTNC+fXu8fPkSa9euRZkyZVCsWDH88MMPyMjIkNpJSUnB8OHDUbJkSZiamqJu3bo4evRovi0LIoBn+PPd2rVrUbRoUbRt21al/IcffkDNmjVhaWmJsLAwjBkzBtHR0Zg3bx6AN08ydHJyUpnHxsZGmlasWDHExMRIZW/XiYmJkeq9PZ+mOpoEBQVh8uTJ7/Ft8+ZT9/l8H9roJ0pERPmrQ4cOGDhwII4cOYKmTZsCAJ49e4a9e/diz5492L59OwYNGoQFCxbAy8sLu3btQu/evVGqVCk0adLkvT/35cuXWLRoETZv3owXL16gbdu2aNOmDSwsLLBnzx7cuXMH7dq1Q4MGDdCpUycAQGBgIK5du4bNmzfD3t4e27dvh6+vLy5fvsz77yjfMOHPZ6tWrUK3bt1gZGSkUj506FDp/66urjAwMMC3336LoKAgGBoafuowVYwZM0YlvsTERDg4OGgxIiIiovdXrFgxNGvWDJs2bZIS/m3btsHKygpNmjSBh4cHevXqhe+//x7Am2P0yZMn8dNPP31Qwp+WliZdOQCA9u3bY/369Xj8+DGKFCkCFxcXNGnSBEeOHEGnTp1w//59rF69Gvfv34e9vT0AYPjw4di7dy9Wr16NGTNmfOCSIHqDXXry0fHjxxEZGYlvvvkmx7p169ZFenq6dCnQ1tYWjx8/VqmjfK/s959Vnbenvz2fpjqaGBoawszMTOVFRERUmHXr1g1//PEHUlJSALy5V65z587Q0dHB9evX0aBBA5X6DRo0wPXr1z/oM01MTKRkH3hzhb1MmTIoUqSISllsbCwA4PLly8jIyECFChVQpEgR6XXs2DHcvn37g2IhehsT/ny0cuVK1KpVC9WrV8+x7oULF6CjowNra2sAgLu7O0JDQ1UelX3gwAFUrFgRxYoVk+ocOnRIpZ0DBw7A3d0dAODk5ARbW1uVOomJiTh16pRUh4iI6HPQsmVLCCGwe/duPHjwAMePH0e3bt3eqy0dnTfpkhBCKnv7eK2kr6+v8l6hUGgsU95bkJSUBF1dXURERODChQvS6/r161i4cOF7xUqkCbv05EJSUhJu3bolvY+KisKFCxdgaWmJ0qVLA3iTWG/duhVz585Vmz88PBynTp1CkyZNULRoUYSHh2PIkCH4+uuvpWS+a9eumDx5MgICAjBq1ChcuXIFCxcuxPz586V2Bg0ahEaNGmHu3Lnw8/PD5s2bcfbsWWnoToVCgcGDB2PatGlwdnaGk5MTxo8fD3t7e44jTEREnxUjIyO0bdsWGzduxK1bt1CxYkXUrFkTAFC5cmWcOHECPXv2lOqfOHECLi4uGtsqUaIEgDfDcCuP2xcuXPjgGGvUqIGMjAzExsbCw8Pjg9sjygoT/lw4e/asSp8+ZX/3nj17Ys2aNQCAzZs3QwiBLl26qM1vaGiIzZs3Y9KkSUhJSYGTkxOGDBmi0m/e3Nwc+/fvx4ABA1CrVi1YWVlhwoQJ0pCcAFC/fn1s2rQJ48aNw9ixY+Hs7IyQkBBUrVpVqjNy5EgkJyejX79+iI+PR8OGDbF37161ewqIiIjkrlu3bmjRogWuXr2Kr7/+WiofMWIEOnbsiBo1asDLywt//fUX/vzzTxw8eFBjO+XLl4eDgwMmTZqE6dOn499//9V4gi+vKlSogG7duqFHjx6YO3cuatSogbi4OBw6dAiurq7w8+NAEpQ/mPDnQuPGjVUu42nSr18/leT8bTVr1sTJkydz/BxXV1ccP3482zodOnRAhw4dspyuUCgwZcoUTJkyJcfPIyIieh+FZUSzL7/8EpaWloiMjETXrl2lcn9/fyxcuBA//fQTBg0aBCcnJ6xevRqNGzfW2I6+vj5+++039O/fH66urvjiiy8wbdq0bI/HubV69WpMmzYNw4YNw3///QcrKyvUq1cPLVq0+OC2iZQUIqdMlj47iYmJMDc3R0JCQr7ewMthOYmICpfXr18jKioKTk5OvFJcQGT3N/lYx28q/HjTLhERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATERFRtpQPiiLt49+C3geH5SQiIiKNDAwMoKOjg0ePHqFEiRIwMDCAQqHQdlifJSEEUlNTERcXBx0dHRgYGGg7JCpEmPATERGRRjo6OnByckJ0dDQePXqk7XAIgImJCUqXLg0dHXbSoNxjwk9ERERZMjAwQOnSpZGeno6MjAxth/NZ09XVhZ6eHq+yUJ4x4SciIqJsKRQK6OvrQ19fX9uhENF74PUgIiIiIiIZY8JPRERERCRjTPiJiIiIiGSMCT8RERERkYwx4SciIiIikjEm/EREREREMsaEn4iIiIhIxpjwExERERHJGBN+IiIiIiIZY8JPRERERCRjTPiJiIiIiGSMCT8RERERkYwx4SciIiIikjEm/EREREREMsaEn4iIiIhIxpjwExERERHJGBN+IiIiIiIZY8JPRERERCRjTPiJiIiIiGSMCT8RERERkYwx4SciIiIikjEm/EREREREMsaEn4iIiIhIxpjwExERERHJGBN+IiIiIiIZY8JPRERERCRjTPiJiIiIiGSMCT8RERERkYwx4SciIiIikjEm/LkQGhqKli1bwt7eHgqFAiEhISrTe/XqBYVCofLy9fVVqfPs2TN069YNZmZmsLCwQEBAAJKSklTqXLp0CR4eHjAyMoKDgwNmz56tFsvWrVtRqVIlGBkZoVq1atizZ4/KdCEEJkyYADs7OxgbG8PLyws3b97MnwVBRERERIUOE/5cSE5ORvXq1bF06dIs6/j6+iI6Olp6/fbbbyrTu3XrhqtXr+LAgQPYtWsXQkND0a9fP2l6YmIivL294ejoiIiICMyZMweTJk3C8uXLpTphYWHo0qULAgICcP78efj7+8Pf3x9XrlyR6syePRuLFi1CcHAwTp06BVNTU/j4+OD169f5uESIiIiIqLBQCCGEtoMoTBQKBbZv3w5/f3+prFevXoiPj1c78690/fp1uLi44MyZM6hduzYAYO/evWjevDkePnwIe3t7LFu2DD/++CNiYmJgYGAAABg9ejRCQkJw48YNAECnTp2QnJyMXbt2SW3Xq1cPbm5uCA4OhhAC9vb2GDZsGIYPHw4ASEhIgI2NDdasWYPOnTvn6jsmJibC3NwcCQkJMDMzy+siylKZ0bvzra2P5e5MP22HQERE9F4+1vGbCj+e4c8nR48ehbW1NSpWrIj+/fvj6dOn0rTw8HBYWFhIyT4AeHl5QUdHB6dOnZLqeHp6Ssk+APj4+CAyMhLPnz+X6nh5eal8ro+PD8LDwwEAUVFRiImJUaljbm6OunXrSnU0SUlJQWJiosqLiIiIiOSBCX8+8PX1xbp163Do0CHMmjULx44dQ7NmzZCRkQEAiImJgbW1tco8enp6sLS0RExMjFTHxsZGpY7yfU513p7+9nya6mgSFBQEc3Nz6eXg4JCn709EREREBZeetgOQg7e7ylSrVg2urq4oV64cjh49iqZNm2oxstwZM2YMhg4dKr1PTExk0k9EREQkEzzD/xGULVsWVlZWuHXrFgDA1tYWsbGxKnXS09Px7Nkz2NraSnUeP36sUkf5Pqc6b09/ez5NdTQxNDSEmZmZyouIiIiI5IEJ/0fw8OFDPH36FHZ2dgAAd3d3xMfHIyIiQqpz+PBhZGZmom7dulKd0NBQpKWlSXUOHDiAihUrolixYlKdQ4cOqXzWgQMH4O7uDgBwcnKCra2tSp3ExEScOnVKqkNEREREnxcm/LmQlJSECxcu4MKFCwDe3Bx74cIF3L9/H0lJSRgxYgROnjyJu3fv4tChQ2jdujXKly8PHx8fAEDlypXh6+uLvn374vTp0zhx4gQCAwPRuXNn2NvbAwC6du0KAwMDBAQE4OrVq/j999+xcOFCla42gwYNwt69ezF37lzcuHEDkyZNwtmzZxEYGAjgzQhCgwcPxrRp07Bz505cvnwZPXr0gL29vcqoQkRERET0+WAf/lw4e/YsmjRpIr1XJuE9e/bEsmXLcOnSJaxduxbx8fGwt7eHt7c3pk6dCkNDQ2mejRs3IjAwEE2bNoWOjg7atWuHRYsWSdPNzc2xf/9+DBgwALVq1YKVlRUmTJigMlZ//fr1sWnTJowbNw5jx46Fs7MzQkJCULVqVanOyJEjkZycjH79+iE+Ph4NGzbE3r17YWRk9DEXEREREREVUByHn9RwHH4iIqLCh+PwU1bYpYeIiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhD8XQkND0bJlS9jb20OhUCAkJESalpaWhlGjRqFatWowNTWFvb09evTogUePHqm0UaZMGSgUCpXXzJkzVepcunQJHh4eMDIygoODA2bPnq0Wy9atW1GpUiUYGRmhWrVq2LNnj8p0IQQmTJgAOzs7GBsbw8vLCzdv3sy/hUFEREREhQoT/lxITk5G9erVsXTpUrVpL1++xLlz5zB+/HicO3cOf/75JyIjI9GqVSu1ulOmTEF0dLT0GjhwoDQtMTER3t7ecHR0REREBObMmYNJkyZh+fLlUp2wsDB06dIFAQEBOH/+PPz9/eHv748rV65IdWbPno1FixYhODgYp06dgqmpKXx8fPD69et8XipEREREVBgohBBC20EUJgqFAtu3b4e/v3+Wdc6cOYM6derg3r17KF26NIA3Z/gHDx6MwYMHa5xn2bJl+PHHHxETEwMDAwMAwOjRoxESEoIbN24AADp16oTk5GTs2rVLmq9evXpwc3NDcHAwhBCwt7fHsGHDMHz4cABAQkICbGxssGbNGnTu3FnjZ6ekpCAlJUV6n5iYCAcHByQkJMDMzCzXyyYnZUbvzre2Ppa7M/20HQIREdF7SUxMhLm5eb4fv6nw4xn+jyAhIQEKhQIWFhYq5TNnzkTx4sVRo0YNzJkzB+np6dK08PBweHp6Ssk+APj4+CAyMhLPnz+X6nh5eam06ePjg/DwcABAVFQUYmJiVOqYm5ujbt26Uh1NgoKCYG5uLr0cHBze+7sTERERUcHChD+fvX79GqNGjUKXLl1Ufl3/8MMP2Lx5M44cOYJvv/0WM2bMwMiRI6XpMTExsLGxUWlL+T4mJibbOm9Pf3s+TXU0GTNmDBISEqTXgwcP8vq1iYiIiKiA0tN2AHKSlpaGjh07QgiBZcuWqUwbOnSo9H9XV1cYGBjg22+/RVBQEAwNDT91qCoMDQ21HgMRERERfRw8w59PlMn+vXv3cODAgRz7ztWtWxfp6em4e/cuAMDW1haPHz9WqaN8b2trm22dt6e/PZ+mOkRERET0eWHCnw+Uyf7Nmzdx8OBBFC9ePMd5Lly4AB0dHVhbWwMA3N3dERoairS0NKnOgQMHULFiRRQrVkyqc+jQIZV2Dhw4AHd3dwCAk5MTbG1tVeokJibi1KlTUh0iIiIi+rywS08uJCUl4datW9L7qKgoXLhwAZaWlrCzs0P79u1x7tw57Nq1CxkZGVJ/eUtLSxgYGCA8PBynTp1CkyZNULRoUYSHh2PIkCH4+uuvpWS+a9eumDx5MgICAjBq1ChcuXIFCxcuxPz586XPHTRoEBo1aoS5c+fCz88PmzdvxtmzZ6WhOxUKBQYPHoxp06bB2dkZTk5OGD9+POzt7bMdVYiIiIiI5IvDcubC0aNH0aRJE7Xynj17YtKkSXByctI435EjR9C4cWOcO3cO33//PW7cuIGUlBQ4OTmhe/fuGDp0qErf+UuXLmHAgAE4c+YMrKysMHDgQIwaNUqlza1bt2LcuHG4e/cunJ2dMXv2bDRv3lyaLoTAxIkTsXz5csTHx6Nhw4b4+eefUaFChVx/3481rBeH5SQiIvp4OCwnZYUJP6lhwk9ERFT4MOGnrLAPPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREcmYrBP+48eP4+uvv4a7uzv+++8/AMD69evxzz//aDkyIiIiIqJPQ7YJ/x9//AEfHx8YGxvj/PnzSElJAQAkJCRgxowZWo6OiIiIiOjTkG3CP23aNAQHB2PFihXQ19eXyhs0aIBz587lqa3Q0FC0bNkS9vb2UCgUCAkJUZkuhMCECRNgZ2cHY2NjeHl54ebNmyp1nj17hm7dusHMzAwWFhYICAhAUlKSSp1Lly7Bw8MDRkZGcHBwwOzZs9Vi2bp1KypVqgQjIyNUq1YNe/bsyXMsRERERPT5kG3CHxkZCU9PT7Vyc3NzxMfH56mt5ORkVK9eHUuXLtU4ffbs2Vi0aBGCg4Nx6tQpmJqawsfHB69fv5bqdOvWDVevXsWBAwewa9cuhIaGol+/ftL0xMREeHt7w9HREREREZgzZw4mTZqE5cuXS3XCwsLQpUsXBAQE4Pz58/D394e/vz+uXLmSp1iIiIiI6POhEEIIbQfxMZQtWxbLly+Hl5cXihYtiosXL6Js2bJYt24dZs6ciWvXrr1XuwqFAtu3b4e/vz+AN2fU7e3tMWzYMAwfPhzAm25DNjY2WLNmDTp37ozr16/DxcUFZ86cQe3atQEAe/fuRfPmzfHw4UPY29tj2bJl+PHHHxETEwMDAwMAwOjRoxESEoIbN24AADp16oTk5GTs2rVLiqdevXpwc3NDcHBwrmLJjcTERJibmyMhIQFmZmbvtZw0KTN6d7619bHcnemn7RCIiIjey8c6flPhJ9sz/H379sWgQYNw6tQpKBQKPHr0CBs3bsTw4cPRv3//fPucqKgoxMTEwMvLSyozNzdH3bp1ER4eDgAIDw+HhYWFlOwDgJeXF3R0dHDq1Cmpjqenp5TsA4CPjw8iIyPx/Plzqc7bn6Oso/yc3MSiSUpKChITE1VeRERERCQPetoO4GMZPXo0MjMz0bRpU7x8+RKenp4wNDTE8OHDMXDgwHz7nJiYGACAjY2NSrmNjY00LSYmBtbW1irT9fT0YGlpqVLHyclJrQ3ltGLFiiEmJibHz8kpFk2CgoIwefLknL8sERERERU6sj3Dr1Ao8OOPP+LZs2e4cuUKTp48ibi4OEydOlXboRU4Y8aMQUJCgvR68OCBtkMiIiIionwi2zP8SgYGBnBxcflo7dva2gIAHj9+DDs7O6n88ePHcHNzk+rExsaqzJeeno5nz55J89va2uLx48cqdZTvc6rz9vScYtHE0NAQhoaGufq+RERERFS4yPYM/+vXrzFnzhw0b94ctWvXRs2aNVVe+cXJyQm2trY4dOiQVJaYmIhTp07B3d0dAODu7o74+HhERERIdQ4fPozMzEzUrVtXqhMaGoq0tDSpzoEDB1CxYkUUK1ZMqvP25yjrKD8nN7EQERER0edFtmf4AwICsH//frRv3x516tSBQqF477aSkpJw69Yt6X1UVBQuXLgAS0tLlC5dGoMHD8a0adPg7OwMJycnjB8/Hvb29tJIPpUrV4avry/69u2L4OBgpKWlITAwEJ07d4a9vT0AoGvXrpg8eTICAgIwatQoXLlyBQsXLsT8+fOlzx00aBAaNWqEuXPnws/PD5s3b8bZs2eloTsVCkWOsRARERHR50W2Cf+uXbuwZ88eNGjQ4IPbOnv2LJo0aSK9Hzp0KACgZ8+eWLNmDUaOHInk5GT069cP8fHxaNiwIfbu3QsjIyNpno0bNyIwMBBNmzaFjo4O2rVrh0WLFknTzc3NsX//fgwYMAC1atWClZUVJkyYoDJWf/369bFp0yaMGzcOY8eOhbOzM0JCQlC1alWpTm5iISIiIqLPh2zH4XdxccHmzZvh6uqq7VAKHY7DT0REVPhwHH7Kimz78M+dOxejRo3CvXv3tB0KEREREZHWyLZLT+3atfH69WuULVsWJiYm0NfXV5n+7NkzLUVGRERERPTpyDbh79KlC/777z/MmDEDNjY2H3TTLhERERFRYSXbhD8sLAzh4eGoXr26tkMhIiIiItIa2fbhr1SpEl69eqXtMIiIiIiItEq2Z/hnzpyJYcOGYfr06ahWrZpaH37evU6FGUc8IiIiotySbcLv6+sLAGjatKlKuRACCoUCGRkZ2giLiIiIiOiTkm3Cf+TIEW2HQERERESkdbJN+Bs1aqTtEIiIiIiItE62CX9oaGi20z09PT9RJERERERE2iPbhL9x48ZqZW+Pxc8+/ERERET0OZDtsJzPnz9XecXGxmLv3r344osvsH//fm2HR0RERET0Scj2DL+5ubla2VdffQUDAwMMHToUERERWoiKiIiIiOjTku0Z/qzY2NggMjJS22EQEREREX0Ssj3Df+nSJZX3QghER0dj5syZcHNz005QRERERESfmGwTfjc3NygUCgghVMrr1auHVatWaSkqIiIiIqJPS7YJf1RUlMp7HR0dlChRAkZGRlqKiIiIiIjo05Ntwu/o6KjtEIiIiIiItE5WCf+iRYtyXfeHH374iJEQERERERUMskr458+fn6t6CoWCCT8RERERfRZklfC/22+fiIiIiOhz91mMwy+EUButh4iIiIjocyDrhH/dunWoVq0ajI2NYWxsDFdXV6xfv17bYRERERERfTKy6tLztnnz5mH8+PEIDAxEgwYNAAD//PMPvvvuOzx58gRDhgzRcoRERERERB+fbBP+xYsXY9myZejRo4dU1qpVK1SpUgWTJk1iwk9EREREnwXZdumJjo5G/fr11crr16+P6OhoLURERERERPTpyTbhL1++PLZs2aJW/vvvv8PZ2VkLERERERERfXqy69Jz5coVVK1aFVOmTEHHjh0RGhoq9eE/ceIEDh06pPGHABERERGRHMnuDL+rqyvq1q2LJ0+e4PDhw7CyskJISAhCQkJgZWWF06dPo02bNtoOk4iIiIjok5DdGf5jx45h9erVGD58ODIzM9GuXTvMnz8fnp6e2g6NiIiIiOiTk90Zfg8PD6xatQrR0dFYvHgx7t69iyZNmqBChQqYNWsWYmJitB0iEREREdEnI7uEX8nU1BS9e/fGsWPHEBkZiQ4dOmDp0qUoXbo0WrVqpe3wiIiIiIg+Cdkm/G8rX748xo4di3HjxqFo0aLYvXu3tkMiIiIiIvokZNeH/12hoaFYtWoV/vjjD+jo6KBjx44ICAjQdlhERERERJ+ELBP+R48eYc2aNVizZg1u3bqF+vXrY9GiRejYsSNMTU21HR4RERER0Scju4S/WbNmOHjwIKysrNCjRw/06dMHFStW1HZYRERERERaIbuEX19fH9u2bUOLFi2gq6ur7XCIiIiIiLRKdgn/zp07tR0CEREREVGB8VmM0kNERERE9Lliwp9PypQpA4VCofYaMGAAAKBx48Zq07777juVNu7fvw8/Pz+YmJjA2toaI0aMQHp6ukqdo0ePombNmjA0NET58uWxZs0atViWLl2KMmXKwMjICHXr1sXp06c/2vcmIiIiooKNCX8+OXPmDKKjo6XXgQMHAAAdOnSQ6vTt21elzuzZs6VpGRkZ8PPzQ2pqKsLCwrB27VqsWbMGEyZMkOpERUXBz88PTZo0wYULFzB48GB888032Ldvn1Tn999/x9ChQzFx4kScO3cO1atXh4+PD2JjYz/BUiAiIiKigoYJfz4pUaIEbG1tpdeuXbtQrlw5NGrUSKpjYmKiUsfMzEyatn//fly7dg0bNmyAm5sbmjVrhqlTp2Lp0qVITU0FAAQHB8PJyQlz585F5cqVERgYiPbt22P+/PlSO/PmzUPfvn3Ru3dvuLi4IDg4GCYmJli1atWnWxhEREREVGAw4f8IUlNTsWHDBvTp0wcKhUIq37hxI6ysrFC1alWMGTMGL1++lKaFh4ejWrVqsLGxkcp8fHyQmJiIq1evSnW8vLxUPsvHxwfh4eHS50ZERKjU0dHRgZeXl1RHk5SUFCQmJqq8iIiIiEgeZDdKT0EQEhKC+Ph49OrVSyrr2rUrHB0dYW9vj0uXLmHUqFGIjIzEn3/+CQCIiYlRSfYBSO9jYmKyrZOYmIhXr17h+fPnyMjI0Fjnxo0bWcYbFBSEyZMnv/f3JSIiIqKCiwn/R7By5Uo0a9YM9vb2Ulm/fv2k/1erVg12dnZo2rQpbt++jXLlymkjTMmYMWMwdOhQ6X1iYiIcHBy0GBERERER5Rcm/Pns3r17OHjwoHTmPit169YFANy6dQvlypWDra2t2mg6jx8/BgDY2tpK/yrL3q5jZmYGY2Nj6OrqQldXV2MdZRuaGBoawtDQMHdfkIiIiIgKFfbhz2erV6+GtbU1/Pz8sq134cIFAICdnR0AwN3dHZcvX1YZTefAgQMwMzODi4uLVOfQoUMq7Rw4cADu7u4AAAMDA9SqVUulTmZmJg4dOiTVISIiIqLPCxP+fJSZmYnVq1ejZ8+e0NP738WT27dvY+rUqYiIiMDdu3exc+dO9OjRA56ennB1dQUAeHt7w8XFBd27d8fFixexb98+jBs3DgMGDJDOvn/33Xe4c+cORo4ciRs3buDnn3/Gli1bMGTIEOmzhg4dihUrVmDt2rW4fv06+vfvj+TkZPTu3fvTLgwiIiIiKhDYpScfHTx4EPfv30efPn1Uyg0MDHDw4EEsWLAAycnJcHBwQLt27TBu3Dipjq6uLnbt2oX+/fvD3d0dpqam6NmzJ6ZMmSLVcXJywu7duzFkyBAsXLgQpUqVwq+//gofHx+pTqdOnRAXF4cJEyYgJiYGbm5u2Lt3r9qNvERERET0eVAIIYS2g6CCJTExEebm5khISFB5VsCHKjN6d7619bHcnZl9V6yCgsuSiIje9bGO31T4sUsPEREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAl/Ppk0aRIUCoXKq1KlStL0169fY8CAAShevDiKFCmCdu3a4fHjxypt3L9/H35+fjAxMYG1tTVGjBiB9PR0lTpHjx5FzZo1YWhoiPLly2PNmjVqsSxduhRlypSBkZER6tati9OnT3+U70xEREREBR8T/nxUpUoVREdHS69//vlHmjZkyBD89ddf2Lp1K44dO4ZHjx6hbdu20vSMjAz4+fkhNTUVYWFhWLt2LdasWYMJEyZIdaKiouDn54cmTZrgwoULGDx4ML755hvs27dPqvP7779j6NChmDhxIs6dO4fq1avDx8cHsbGxn2YhEBEREVGBwoQ/H+np6cHW1lZ6WVlZAQASEhKwcuVKzJs3D19++SVq1aqF1atXIywsDCdPngQA7N+/H9euXcOGDRvg5uaGZs2aYerUqVi6dClSU1MBAMHBwXBycsLcuXNRuXJlBAYGon379pg/f74Uw7x589C3b1/07t0bLi4uCA4OhomJCVatWpVl3CkpKUhMTFR5EREREZE8MOHPRzdv3oS9vT3Kli2Lbt264f79+wCAiIgIpKWlwcvLS6pbqVIllC5dGuHh4QCA8PBwVKtWDTY2NlIdHx8fJCYm4urVq1Kdt9tQ1lG2kZqaioiICJU6Ojo68PLykupoEhQUBHNzc+nl4ODwgUuCiIiIiAoKJvz5pG7dulizZg327t2LZcuWISoqCh4eHnjx4gViYmJgYGAACwsLlXlsbGwQExMDAIiJiVFJ9pXTldOyq5OYmIhXr17hyZMnyMjI0FhH2YYmY8aMQUJCgvR68ODBey0DIiIiIip49LQdgFw0a9ZM+r+rqyvq1q0LR0dHbNmyBcbGxlqMLGeGhoYwNDTUdhhERERE9BHwDP9HYmFhgQoVKuDWrVuwtbVFamoq4uPjVeo8fvwYtra2AABbW1u1UXuU73OqY2ZmBmNjY1hZWUFXV1djHWUbRERERPR5YcL/kSQlJeH27duws7NDrVq1oK+vj0OHDknTIyMjcf/+fbi7uwMA3N3dcfnyZZXRdA4cOAAzMzO4uLhIdd5uQ1lH2YaBgQFq1aqlUiczMxOHDh2S6hARERHR54UJfz4ZPnw4jh07hrt37yIsLAxt2rSBrq4uunTpAnNzcwQEBGDo0KE4cuQIIiIi0Lt3b7i7u6NevXoAAG9vb7i4uKB79+64ePEi9u3bh3HjxmHAgAFSd5vvvvsOd+7cwciRI3Hjxg38/PPP2LJlC4YMGSLFMXToUKxYsQJr167F9evX0b9/fyQnJ6N3795aWS5EREREpF3sw59PHj58iC5duuDp06coUaIEGjZsiJMnT6JEiRIAgPnz50NHRwft2rVDSkoKfHx88PPPP0vz6+rqYteuXejfvz/c3d1hamqKnj17YsqUKVIdJycn7N69G0OGDMHChQtRqlQp/Prrr/Dx8ZHqdOrUCXFxcZgwYQJiYmLg5uaGvXv3qt3IS0RERESfB4UQQmg7CCpYEhMTYW5ujoSEBJiZmeVbu2VG7863tj6WuzP9tB1CrnBZEhHRuz7W8ZsKP3bpISIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPATEREREcmYnrYDICLSljKjd2s7hFy5O9NP2yEQEVEhxjP8REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhljwk9EREREJGNM+ImIiIiIZIwJPxERERGRjDHhJyIiIiKSMSb8REREREQyxoSfiIiIiEjGmPDnk6CgIHzxxRcoWrQorK2t4e/vj8jISJU6jRs3hkKhUHl99913KnXu378PPz8/mJiYwNraGiNGjEB6erpKnaNHj6JmzZowNDRE+fLlsWbNGrV4li5dijJlysDIyAh169bF6dOn8/07ExEREVHBx4Q/nxw7dgwDBgzAyZMnceDAAaSlpcHb2xvJyckq9fr27Yvo6GjpNXv2bGlaRkYG/Pz8kJqairCwMKxduxZr1qzBhAkTpDpRUVHw8/NDkyZNcOHCBQwePBjffPMN9u3bJ9X5/fffMXToUEycOBHnzp1D9erV4ePjg9jY2I+/IIiIiIioQNHTdgBysXfvXpX3a9asgbW1NSIiIuDp6SmVm5iYwNbWVmMb+/fvx7Vr13Dw4EHY2NjAzc0NU6dOxahRozBp0iQYGBggODgYTk5OmDt3LgCgcuXK+OeffzB//nz4+PgAAObNm4e+ffuid+/eAIDg4GDs3r0bq1atwujRoz/G1yciIiKiAopn+D+ShIQEAIClpaVK+caNG2FlZYWqVatizJgxePnypTQtPDwc1apVg42NjVTm4+ODxMREXL16Varj5eWl0qaPjw/Cw8MBAKmpqYiIiFCpo6OjAy8vL6nOu1JSUpCYmKjyIiIiIiJ54Bn+jyAzMxODBw9GgwYNULVqVam8a9eucHR0hL29PS5duoRRo0YhMjISf/75JwAgJiZGJdkHIL2PiYnJtk5iYiJevXqF58+fIyMjQ2OdGzduaIw3KCgIkydP/rAvTUREREQFEhP+j2DAgAG4cuUK/vnnH5Xyfv36Sf+vVq0a7Ozs0LRpU9y+fRvlypX71GFKxowZg6FDh0rvExMT4eDgoLV4iIiIiCj/MOHPZ4GBgdi1axdCQ0NRqlSpbOvWrVsXAHDr1i2UK1cOtra2aqPpPH78GACkfv+2trZS2dt1zMzMYGxsDF1dXejq6mqsk9W9A4aGhjA0NMz9lyQiIiKiQoN9+POJEAKBgYHYvn07Dh8+DCcnpxznuXDhAgDAzs4OAODu7o7Lly+rjKZz4MABmJmZwcXFRapz6NAhlXYOHDgAd3d3AICBgQFq1aqlUiczMxOHDh2S6hARERHR54Nn+PPJgAEDsGnTJuzYsQNFixaV+tybm5vD2NgYt2/fxqZNm9C8eXMUL14cly5dwpAhQ+Dp6QlXV1cAgLe3N1xcXNC9e3fMnj0bMTExGDduHAYMGCCdgf/uu++wZMkSjBw5En369MHhw4exZcsW7N69W4pl6NCh6NmzJ2rXro06depgwYIFSE5OlkbtISIiIqLPBxP+fLJs2TIAbx6u9bbVq1ejV69eMDAwwMGDB6Xk28HBAe3atcO4ceOkurq6uti1axf69+8Pd3d3mJqaomfPnpgyZYpUx8nJCbt378aQIUOwcOFClCpVCr/++qs0JCcAdOrUCXFxcZgwYQJiYmLg5uaGvXv3qt3IS0RERETyx4Q/nwghsp3u4OCAY8eO5diOo6Mj9uzZk22dxo0b4/z589nWCQwMRGBgYI6fR0RERETyxj78REREREQyxoSfiIiIiEjGmPATEREREckYE34iIiIiIhnjTbtERPTByozenXMlLbs700/bIRARaQXP8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY0z4iYiIiIhkjAk/EREREZGMMeEnIiIiIpIxJvxERERERDLGhJ+IiIiISMaY8BMRERERyRgTfiIiIiIiGWPCT0REREQkY3raDoCIiIj+p8zo3doOIUd3Z/ppOwQiygOe4SciIiIikjEm/EREREREMsaEn4iIiIhIxpjwExERERHJGG/aJSIiItkpDDc/A7wBmj4NnuEnIiIiIpIxJvwytnTpUpQpUwZGRkaoW7cuTp8+re2QiIiIiOgTY8IvU7///juGDh2KiRMn4ty5c6hevTp8fHwQGxur7dCIiIiI6BNiwi9T8+bNQ9++fdG7d2+4uLggODgYJiYmWLVqlbZDIyIiIqJPiDftylBqaioiIiIwZswYqUxHRwdeXl4IDw9Xq5+SkoKUlBTpfUJCAgAgMTExX+PKTHmZr+19DPn9nT8WLsv8URiWI8BlmV8Kw3IEuCzzS2FYjkD+LktlW0KIfGuT5IEJvww9efIEGRkZsLGxUSm3sbHBjRs31OoHBQVh8uTJauUODg4fLcaCynyBtiOQDy7L/MNlmT+4HPMPl2X++RjL8sWLFzA3N8//hqnQYsJPGDNmDIYOHSq9z8zMxLNnz1C8eHEoFAotRpa9xMREODg44MGDBzAzM9N2OIUWl2P+4bLMP1yW+YPLMf8UhmUphMCLFy9gb2+v7VCogGHCL0NWVlbQ1dXF48ePVcofP34MW1tbtfqGhoYwNDRUKbOwsPiYIeYrMzOzArvzLUy4HPMPl2X+4bLMH1yO+aegL0ue2SdNeNOuDBkYGKBWrVo4dOiQVJaZmYlDhw7B3d1di5ERERER0afGM/wyNXToUPTs2RO1a9dGnTp1sGDBAiQnJ6N3797aDo2IiIiIPiEm/DLVqVMnxMXFYcKECYiJiYGbmxv27t2rdiNvYWZoaIiJEyeqdUeivOFyzD9clvmHyzJ/cDnmHy5LKswUgmM3ERERERHJFvvwExERERHJGBN+IiIiIiIZY8JPRERERCRjTPiJiIiIiGSMCT8RERERkYwx4afP3rsDVXHgKiLKLe4viKgwYMJPnz2FQgEAuHr1Kl69eiW9p7zLzMzUdgiFWkZGBgDg2LFjiIqK0nI0lBtv7y+4/hNRQcWEnz5byoPzpk2b4O3tDV9fX5QoUQLdunXDjh078PjxYy1HWLApl19ERATGjh0LHx8f/PLLL1qOqnDT1dUFAEycOBE7duzQcjSUnZ07d2L06NH466+/sGnTJsTHx0NHh4dUyh5/FJK2cO9EnyUhBHR0dPDkyRP07dsXderUwaJFi7B69Wr8+++/6N+/PyZPnox///0XAHfS71Iuv9TUVHTs2BE3btyAnp4eBgwYgN27d0t16P3Ur18f8+bNw6NHjwC8OfPP5VlwJCYmIjQ0FFFRUfjpp5/w888/o1SpUujevTvCwsLw6tUrAIV/G8jMzCz030HblMeO9PR0pKenSz8Kp06diqSkJG2GRp8ZPmmXPmsTJkzAP//8g8OHD6uUr1mzBmPGjIG1tTV2796NUqVKaSnCgikzMxM6OjoIDAzEv//+i507d8LIyAijRo1C8eLFce3aNSgUCjg4OCAwMBDW1tbaDrlQef36NWrUqIGgoCD4+/tL5cnJyTA1NdVeYCR5/fo1jIyM8OzZMzx9+hRXr17FtGnTcPnyZQQGBmLOnDnQ0dGBEKLAdxPMyMiArq4u7t+/j/v37+PBgwdo2LAhHBwcAPxve6fcU/7dY2NjoaOjAysrK2nasGHDcPbsWRw+fFi6qkf0sXELps+anZ0dnj9/jvj4eACQzsz16tULN2/ehKmpKQYOHCj1raY3dHR0kJCQgNOnT6N3794wMjICAMTFxWHhwoVITU1FZmYm/v77b2zYsEHL0RZ8yvMuMTExOHXqFE6fPg0TExOMGjUKw4cPx7fffosvv/wSNWvWxE8//aTlaD9vFy9exM2bN6V13tLSEs7OzvD398fZs2exdu1arFmzBg0bNsS9e/cKfLIvhJCSzvbt26Ndu3aYNm0avLy8MG7cOKSmpkrJPq905p7y7x4cHAxra2uMHTsWwJsz/cuXL8e4ceOY7NMnxYSfPmsuLi64c+cONm3aBAAwNjZGRkYGXr9+jSJFiqBPnz6IjIxEbGysliMtWDIzM2Fubg47OzscO3YMMTExuHjxItatW4fg4GBs2rQJa9euRZ06dRAcHCz9oCLNFAoFzp07B3t7e3Tp0gX+/v4oVaoUbt68iX379uH169eoXr06hgwZggEDBmg73M9aUFAQvLy8MGvWLKSkpEjlqampAIDOnTsjPDwclpaWmDhxIl68eKGtUHNFmZiOHj0aurq6CA0NxdKlS9G1a1fs2LEDNWrUwLp16wCAZ/nfw4QJE7Br1y78+eefsLW1RZ06ddChQwd89dVXyMjI4I8o+nQE0Wdu3rx5okiRIqJTp04iMjJSZdq1a9eEvb29uHXrlpaiK1gyMzOFEEI8e/ZMCCHE7NmzhbGxsahWrZrw8PAQbm5u4tWrVyI1NVUIIcSxY8eEm5ubuH//vtZiLiwiIyPF0aNHxYMHD0RCQoKIj48X3bp1E2PGjNF2aPT/MjMzxb59+8SIESNEjRo1hLu7uwgJCVGZnp6eLoQQ4siRI8LV1VXMmDFDW+HmKCMjQwghxOvXr0VwcLDYuHGjNC0xMVEcPHhQ9O/fX5QqVUo0aNBAvHz5UluhykL//v2Fjo6OqFKlirh8+bJUrtyvEn1M7MNPBGDt2rVYsWIFHj16BD8/P3z//fe4cOECVq9eDQMDA+zatUvbIRYo/fv3h4+PD/z9/XHo0CHcuXMHrq6u+Pbbb7F3717Y2toCAL7//nvcvHkTBw4c0HLEhYd4q8/33r170bNnTxw4cACurq5ajoyU0tPTsWnTJvzyyy+4cOECWrVqhaFDh+KLL75QqXfgwAHMnz8fy5cvL9D3AY0cORJbt25FixYtsHjxYpVpjx8/xt9//420tDT07dtXSxHKg6urK3x8fJCcnIyVK1fCz88PGzZsgImJibZDo88AE376bChvPIuKikJYWBiioqLg4uKCChUqoGrVqrh+/Tq2bduGrVu34t9//0Xp0qXh5uaG4OBgWFpaajv8AiMxMRETJ07ExYsX8euvv6Js2bIAgISEBDRt2hQWFhbo3bs3wsLCsH37dhw5cgQVK1YsFDcvFiRCCKSlpaFBgwaoXr06fv31Vy5DLUtLS4O+vj6ioqIwfPhwpKSk4NWrV4iPj0dycjL8/PwwatQo6Sb19PR07N69G15eXgX2ZuvXr19j6tSpOHjwIC5fvoypU6diyJAh7L6TT5Q3RC9ZsgQLFy7E+fPnUaRIEVy4cAHdu3fHsGHD0KtXL22HSZ8BJvz0WVAmSg8fPkTTpk2hr6+Ply9fIiMjA2XKlEH9+vXx3XffwdHREZmZmbh16xYMDAxQunRpHvg0yMjIQJ8+fRAWFoaff/4ZX331FQDg2rVr+OGHHxAWFgZvb2907NgRXbt25SgfWchNAr98+XKcOHECa9eu/URRUU5q1qwJd3d3zJw5E0WLFsXJkyexbds2rFq1Ck5OTmjfvj3GjBmj7TBzJSYmBiVKlMDx48exZcsWHD9+HFZWVhg6dChatmyp7fBkISMjA6VLl0ZQUBB69Oih7XDoM8WEnz4rLVq0gKmpKebPnw97e3uEhYVhy5YtOHXqFKytrTFs2DB4enoCyF0y9rkRQiA9PR36+voAgLFjx+Lx48eYM2eOylWQx48fo3jx4tDT09NWqAXWu+tVZmYmFAqFVPbuj6Nnz55BoVCgWLFinzxWUnfv3j34+vpi8eLF8PLyUpnWo0cP/P3336hXrx7++usvLUWYe7t378aKFSuwfv16FC1aFDExMTh8+DB27tyJK1euoFy5cvjpp5/g7Oys7VALjaxObpw7dw41a9bUQkREb/CUG302njx5gidPnqBjx46wt7cH8OYBRwsWLMDYsWMRFxeHYcOGISEhAQCY7P8/5SgSaWlpUCgU0NfXl8p69uyJmzdvwtvbG3fv3pXmsbGxYbL/DuW5FSEEoqOjsXXrVty6dQs6Ojoq65oyWZg4cSK2bNkCS0tLWFhYaCNk0sDGxgYWFhZYvnw5Xr9+rTKtXbt28PX1xcKFCwEU/GEsjYyMcPbsWbRo0QKxsbGwtbVF165dMXXqVHz77beIiYnB06dPtR1moZGQkJDlEKZM9knbmPDTZ8PKygp2dnZYu3YtUlJSpLPVANCyZUvs378fMTExmDdvnpYjLViUBzBvb29Mnz5desouAFSsWBGhoaGoWrUqJkyYICX9yuVK/6NM+GfNmoUWLVpgxIgR8PT0xPbt29XqvnjxAteuXcP333+PjIwM/vgsQIyMjDBw4EBcvnwZixcvxu3bt6Vpx48fR2JionRfS0Hvxta0aVNcvHgRmZmZ6NChA44dOwYAcHZ2xsCBA7FhwwbUq1dPy1EWbMrE/scff0SxYsUwd+5cAP/727MTBRUUBXtvRJTP2rVrh1OnTmHJkiVQKBTQ09NDRkYGhBAoUqQI2rRpgzt37jBh/X/Kg9mWLVtw7NgxbNu2DefOnZOmK5fTDz/8gCdPnmDmzJkAwLP771D+SDp//jxmzZqFESNG4LfffkP9+vXx77//4uHDh9i5c6c0ZnvRokWxcuVKnDt3jg/nKYC6du2KPn36YOrUqejZsye6du2Kxo0bY9WqVRgxYgSAgnl2XxlTbGwstm/fjgsXLqB48eJYtWoV7O3tsWbNGty7d0+qz648OdPR0UF8fDw2bNgAb29vzJw5E+XKlcPOnTsBvLlSrEz6MzMzpYc7En1q7MNPn525c+di7NixqFevHubMmYPatWtLZ2O++uorlCtXDsHBwVqOsmCxsLDAqFGjcOzYMdy4cQO7d+9GlSpVVOrcuHEDHTp0gLOzMxYtWlSghyHUli+//BKurq5YsGABAGDTpk2YNm0aMjIy8OLFC7x48QIbNmxA69attRsoSZT3XNy4cQM7duxAWloaKlSogDZt2iA6OhqzZs1Ceno6DA0N4evri+bNmxf4m9S7dOmC33//HVWqVEGxYsXwww8/4N69e5gxYwZcXV2xYsUKlC9fXtthFhpbt27FypUrMWnSJBQrVgyzZ8/G2rVr0bhxYyxatAguLi4AgPXr1yM+Ph4DBw7UcsT0Wfoko/0TacHbDzN5/fq1iI+Pl96fPHlSNGnSRCgUCuHl5SW+/vpr4enpKRwcHFTqfc6UDxAaMWKEqFatmhBCiAcPHogGDRqILl26SA/XSk9Plx7gc/XqVdGmTRuxdetWPkzmHWfOnBGVKlUSZ8+elcrc3d1F8+bNxZEjR8SrV69E7969RaVKlURSUpIWIyUl5TZw8uRJUbFiRdGkSRPRuHFjoa+vL9auXavl6N5PRkaGCAkJEfXr1xfjxo0TixYtEo0bNxaBgYHCw8NDKBQK0bhxY22HWaicP39ezJ07V9puX758KQ4dOiQaNWokdHR0xIABA8S1a9eEoaGh+OWXX7QcLX2umPCTLCmTzUePHokJEyYIJycn4eXlJdq2bSu2b98u1Tty5Ijo3Lmz6Nq1q5gyZYo4ffq0liIumGJiYoRCoRBHjx6Vyn777Tehr68vBg4cqHGe/fv3i9DQ0E8VYqGRmJgotm7dKqKiooQQQpw+fVpUq1ZNei+EEJs2bRJffPGFePDggXaCJI1cXV3FsGHDhBBCHDhwQBQvXlzExMQIIYQ4e/ZsofuBlp6eLn799VdhZ2cnVqxYIYQQ4tKlS+LIkSMiICBAbNq0ScsRFj4pKSlqZc+ePROrV68WFStWFAqFQnzxxRdaiIzoDXbpIVn78ssvAQCenp5ISUlBVFQUDh8+DDc3N8ybNw9Vq1YFAKSkpMDQ0FCboRZIv/zyCy5duoSlS5eqdFPYsGEDJk6ciNGjR6Nv374Qb04eFOhuDAVNcnIybt++DVdXV2nZ7tu3D8OHD0doaCiH4Swgzp49i27duiE0NBQ2NjZwdHTE0KFDMWjQINy5cweTJk3C119/DW9vb22HmiXx/92Sdu/ejYyMDLRq1QrAm6EiR40ahXr16mHYsGEcDeo9KFOo7G6s37FjB9q0aYOzZ89ytB7SGib8JFthYWFo2bIlIiIiUKZMGQDAgwcPEBYWhpUrVyI2NhZTpkxBq1atCnyfW215+vQpTExMYGxsDOB/iUNCQgIGDRqEHTt2YOfOnfDw8NBypIWLcjmKt8bkz8jIQMOGDeHu7s6RogoA5d/myZMnaNasGY4ePYpffvkF69atQ1hYGExMTPDw4UN4eXlhxYoVBX4bSE9PR79+/bBx40aUKlUKvr6+KFeuHPT19XHhwgW4u7vjm2++0XaYsiOEgK+vLxQKBfbu3avtcOgzxoSfZOvEiRPo168fdu3aBScnJ5VpZ86cwdSpU5GSkoIdO3bAyMhIS1EWbj169MCZM2fw+++/w9XVVdvhFFqPHj3C0qVLsXHjRpXnGdCnlZmZiVOnTsHNzU36kZucnIzWrVsjLS0Np0+fxpEjR6ShKgMDAxEREYHw8HBthp0j5Y+Xx48fw8jICCtWrMD58+fx7NkzhIaGSiPHrF+/Ht26ddNytAXb21fjSpUqpTZ4wbuePHmCefPmYfjw4SoPJyT61HhKk2SrbNmyePXqFebNm4e4uDiVaV988QUmTpyIM2fOqAwzSbmjHN5vwIABePr0KQwMDLQcUeGQkZEBAAgODsaWLVuk8mvXriE5ORlLly7VVmgE4KeffkLHjh2xdOlSXLlyBUIImJqaYsWKFTAxMYGpqSlCQkLw999/45tvvkFISIg0oldBPHem3E6VV5EMDQ1hbm6O4cOHY8WKFViyZAlOnDiB0aNHw9PTU+3JwaRK2W3xwYMHaNasGdzd3TFt2rRsH05mZWWFiRMnMtknreMZfpIl5Rmt5cuXY968efD390evXr2kS9gA8OrVK9SpUwdjxoxB165dtRxx4fX06VMUL16c3aKyoFwXlcvnxYsXKFWqFNasWYM2bdoAAFJTU5GYmAgrKystR/t5S0hIwPjx4xESEoIqVaqgY8eO+PLLL+Ho6IirV69iw4YNCAkJQWxsLJo1a4Z27dqhTZs2BX7d//XXX7F161akpKQgNTUV48ePR7NmzVTqJCUloUiRIlqKsHAZMmQIrl69isaNG2Pu3LkoVqwYpk6dirZt26rcCxYZGQk7OzuYmZlpMVqiN5jwk+wtWbIEY8eORalSpdC/f3/UqFEDtra22LVrFyZOnIi4uDieoX5PBT3R0Za3++YrKZdV7969ER0dzf68Bczb6/LZs2cRGBiIR48eoX79+ujTpw8aN24sPVAuNjYWtra22gw3R+np6dDT08P69esxduxYNGvWDGXKlMGVK1ewbds2+Pv7Y8WKFTA3N9e4vpJm8fHxmDJlCooVK4bx48fjyZMnGDduHH799Vc0btwY06ZNQ7169XD79m107doVy5cvR/Xq1bUdNhETfpIH5QErLS0NDx48QFxcHBQKBerUqQPgzZm7IUOG4NChQ7C0tMSlS5dQv3599O3bFz169NBy9CRHT58+xeHDh1GzZk2UK1cOwJv18Ntvv8W4ceOkEaKo4Bk/fjyuX7+OJ0+e4N9//4VCoUCXLl3QpUsXuLi4SP37C4MyZcpg2LBh0sOeEhMTcejQIUyaNAn9+vXDgAEDtBxh4SKEwOnTp2FkZKSSyJ8/fx4jRozAsWPH0LdvXzx48AC3b9/GtWvXtBgt0f8w4SdZGTlyJHbv3o3Y2Fi4uLjgm2++Qffu3aXpd+7cQVRUFAwMDODs7Fzgz9J9ahkZGdDV1cWJEycQHx8PPz8/aRrPAuZMeZb4559/xrp16xAZGYmEhASMGDECs2bNUqlDBYvy77Jo0SLMmzcP//zzj/S06GnTpmHatGlwdXVF27Zt0bFjR5QtW1bLEefs/v37aNu2LWbNmoWmTZuqTOvTpw+uXbuGw4cPw8TEREsRys/27dsxYMAAxMTE4NKlS/xhTwUGE34q9JQH6k2bNmHEiBFYvnw5zMzMsGbNGhw/fhyHDh2Cg4ODlMySZm8n9JUrV8aXX36J4cOHq41wRJopl9+9e/dQo0YNzJw5Ex4eHvj333/Rv39//Pzzz/D399d2mKTB28Oktm7dGjY2NlixYoXULQZ4M5Z6x44dYWhoiH/++adQjEr18uVLNGzYECVLlsTGjRtV+pLv27cPI0eOxMGDB1GiRAktRik/tWrVQq1atbB8+XJth0IkYcJPslGtWjX0798f33//PYA3N+V6enpiyJAh6Nq1K89Q50D5w2nQoEE4c+YM/vnnH+jo6CAhIQGzZ8+Gvb092rdvDxsbG22HWqB169YNQghs2rQJAJCWloauXbsiMzMTf/zxB9LS0qCvr49Xr17ByMiI62QBoVz/Z86cia1bt+LMmTPQ0dFBSkoKDAwMEBUVhenTp6N79+5o3LixtsPNtb1792LUqFH46quv0LJlS3h4eODp06fo0aMHTE1NsW3bNm2HKCv79+9H69atERsbi6JFi2o7HCIJryuTLISFhaFYsWKoUaMGgDddU4yNjVGnTh1p+EOFQoFz586hQ4cOSElJ0Wa4BZIyud+/fz8mTJgAHR0d/P333+jUqRM2bdqEQYMGYfDgwdJQf6QuOjoaDx48QMuWLaUyfX19BAQE4Nq1a0hPT4e+vj6SkpLQt29fxMTEaDFaWr9+PTZv3ozXr19L3awaNWqEO3fuoFWrVnj69Kk06kpiYiJ27txZoLtoaDp/5+3tjW+++UZ6inPlypXRoEED/Pfff1ixYoUWopQ3BwcHHD16lMk+FTh62g6AKD+UKFECNWrUkM4+K8+adu3aFe3atcPjx49hY2ODb775BvXr11cZOo3+Jz09HZaWljhy5AhsbGwwceJE1K1bF3///TcOHTqEKVOmFIoRSrTF0tISjRs3Rnp6OoD/dRXx9PTEq1evcPLkSTRs2BCjR4/GiRMnYGdnp+WIP1/Pnz/H4sWLoaOjg/Pnz6NVq1Zo0KAB3N3d8ffff2PAgAEoWbIkOnbsiLS0NISHh6N9+/awsrIqsPdhKPd7Bw8exMGDB+Hk5ISOHTti4MCBaNeuHbZt2wZ9fX2YmJigSZMmKFasmJYjLlw0XSV+d9jdypUrayk6ouyxSw/Jxv3791G6dGmVspSUFLi5uWH9+vW4d+8eevfujcTERC1FWDAp721QHriCg4MxYcIEJCUl4bvvvsOIESNgZ2eHLVu2YMyYMbh27Rp/MOWBsguPr68vatasiW+//Rbly5fH6dOnpStSpB0PHz7EihUrsH//fhQvXhyNGzdG27ZtUbZsWURHR2PXrl3YsmULihcvjoYNG6J///4q20pBotyOly9fjrFjx8LR0RFRUVEwNDTEyJEj0b9/fz5RPA/evq/j4cOHMDQ0hLW1tcY6SUlJ2Lp1K+rVq8eEnwouQSRjGRkZolWrVmLs2LHCzs5OLFmyRNshFVjt2rUT58+fF0IIcfv2bXH69Glp2t27d4WLi4sICgoSQrxZrpQ306dPFy1bthReXl6ibdu22g7ns5eWlib9Pzg4WJQuXVoYGxsLPz8/sWHDBhEfHy9NT0lJkf6fmZn5SePMi8zMTFG+fHmxdu1a8eLFC5GSkiLGjBkjDAwMRM2aNcVff/0lEhMTtR1moTJ48GBRtWpVoVAoREBAgMY6e/bsEQqFQgwbNuwTR0eUezzDT7K3cOFCDBkyBDVr1sTZs2e1HU6BlJ6ejhYtWiAqKgpz585FixYtpGmXLl3C/PnzcevWLRw/flyLURZuFy9eRI0aNVCkSBFER0fD1NRU2yF91pRnxGfNmoWTJ0+iZMmSKFOmDP744w8kJSWhbt266NSpExo1alTgH8yn/C4xMTGYOXMmBg0apDK61v379zF8+HBs27YN06ZNw9ixY7UYbcGn7J4zffp0bN68GYMHD0bx4sXx7bffIiwsDPr6+jAwMICBgQEsLS0BALdv30aRIkU4qAEVWEz4SfZu3ryJzp07Y9WqVXziYTZiY2MxadIk3LlzB5MmTUK9evWQmZmJa9euITIyEq6urnB2di6w/ZcLsszMTLx8+RLt2rWDj48Phg4dqu2QCEBSUhJKliyJzZs3o1mzZgDeDGU5b948TJkyBdWrV0fDhg0xbdq0Av8DLTo6GhUrVkRqaiqWLl2KgIAA6SZeZfejw4cPo1y5cnB0dNRmqIVCQkICHB0d8fvvv8PHxwcA4O/vj4yMDJw+fRoA0KVLF8yePVv6QSgKYFcvIiUetUkWshs5xtnZGb/99huT/XdkZGQAeHP2D3hzw+mUKVNgbm6OVq1a4fjx49DR0UHVqlXh7+8PZ2dnAGCy/w5lUpWZmak2SoryvRACRYoUwd69e5nsFyA3btyAubm5tE6np6fDxMQE48aNQ5cuXfDkyRMUL168wCf7AGBra4vRo0fDzs4OkyZNwu+//460tDQpARVC4Msvv2Syn0tLliyBh4cHGjVqBODN/nLfvn0oWbIk9u7di3nz5mHRokVYuHChNA+TfSrIeOSmQkmZrL548QJAzklohQoVPnpMhY2uri7u3buHMmXKoGXLlpg6dSq2b9+O5cuXo1evXujevTs2b96M9PR0PrDsHW8n9ikpKUhKSoKOjo40WoeS8oa+devW4fr160wICpgaNWqgXLly+PvvvwFAesgWAHh4eOCbb77B8OHDAWge8lLblPvB27dvIz4+HmPHjsWJEyfg4+ODrl27onXr1oiIiADAZDSvKleujK+++kpabrNmzUKdOnWwZMkS1KhRA926dUP79u0RFxen5UiJcocJPxVKygT0u+++w5gxY/D8+fMCeUAu6GJjY2FnZ4dDhw7ByMgIYWFhsLW1xaVLl5CSkoLBgwfj2rVr2g6zwFEmAQsXLkSrVq1QtWpVjBw5EoD6j89//vkHAQEBWLly5SePk7Km/CHbvXt3LFq0CB06dMCtW7eQkJCAW7duYenSpdLD0QpqVw3lfrBXr15SNxN7e3v8+uuvCA8Px8uXL+Hh4YHu3bsjOTlZm6EWOm3btkW/fv1gaGiIzMxMNGzYEBs2bICenp70o97S0hLx8fHaDZQol9iHnwod5ePuly9fjlmzZmHdunVo0KABACAmJgaGhoawsLAokAfoguju3bvo168fzMzMMHPmTOjo6GDTpk24c+cOrl+/juPHj6uc+fzcvf1E1s2bN6NBgwYoV64cFi9ejM6dOyMoKEgtQbx16xaKFi3KG/q07O37T1JTU6W+18ePH8fw4cNx5swZVK9eHXFxcbC1tS3QN/lnZmYiIyMD+vr68PPzQ6NGjaQfnW9bs2YNNm/ejL1792ohysIpNz/w7t+/D1dXV+zcuROenp6fKDKi98eEnwqtkiVLYurUqejTpw8ePXqEX3/9FbNnz0a5cuWwfPly1K1bV9shFmhhYWFwcHCAg4MDbt26hQULFsDOzg4//vgjgDc3reno6KBo0aLSKCD0hvKGvk2bNqF58+YAgDlz5mD58uUIDw+HpaWl2pn+gnqW+HPx9vIPDg7Gvn37cPv2bbRt2xa9evWCvb09Tpw4gTNnzqBWrVqoUKECHBwcCtxN6n/88QfKly+vck/SsGHD8N9//0lPDX53vH2ue/nrwoULmDZtGlJSUvDXX39pOxyiXCk4ezGiPLh8+TLs7e2l0ROmTJmCv//+Gxs2bICZmRlmzJiBtLQ0LUdZcP32228ICAhAly5dEBAQgLi4OFSqVAlr165F//7/196dx9WY928Av047MUlJtFgSMkhkCDMmS3bKZEmN7MYyljFj7EwJmRn7EqKxlSXL2JmymySyr0XRoqRFSfv5/v7wO4eMmcc8T7pPp+v9zzTnPl6vy+k45zrf8/ne9xjk5eXBwMBAeXl4lv3iNm/ejM8++wwdOnRQ3vbDDz9AJpMhIiJCWRDDw8OVI1EsXNJSjGHMnTsXq1evRosWLeDs7AwvLy8kJSVBR0cHjo6OmDp1Kjp27AgLCwsAqrVJvaioCP7+/rCzs8O4ceOUFxHs2rUrbt68CQDQ09NDdnY2Dhw4gJEjR2LFihV87pWgnJwcnD9/HqmpqfD395c6DtEHU51XMqJ/oVatWigsLETHjh3Rtm1bPHjwAF5eXnB2dsbkyZPx4sUL5OTkSB1TZbm4uGD27Nnw8PDAw4cP0aNHD1y8eBHVq1fHunXr4OHhwZnfvyGEgKGhIfT09JCXlwcAyg+XLVu2xL59+wC8Hj1r3749QkNDJctKb2hqaiIlJQVLly7F6tWrMWvWLGRkZKBLly5o3bo1UlJSsG3bNqlj/iNNTU1s374dW7ZswYkTJ2BpaYklS5agVatWqFmzJnr16oXu3bvDysoK3377LdLT0+Hi4iJ1bLVSoUIFDB8+HIGBgRzRozKFIz1UZii+lo6MjETz5s1x9+5drFq1CmlpaViyZAlq1KgB4PVqV82aNbFp0yaJE5cd9+7dw+HDh/Hy5Uts3rwZLVu2xM6dO6WOpbKys7Px+PFjNGrUqNi4xNq1a7F27VrcuHEDs2bNwq5du/DgwQOJ05LC0aNHsXDhQpw9exaXLl1Cp06dcP78eTRt2hS3b9/GqFGj8NNPP6FTp05SR/2P4uLisHHjRvzyyy8wNjZGUlISXF1d0apVKzRu3Bh2dnaoUqWK1DHLDMXYYlpaGqpUqQIhBL/ZJLXCwk9lSnR0NOrXr4958+Zhzpw5xY7Fx8cjICAAa9asQVRUFCpVqiRRStWjeDNLSUnByZMnsXv3btSvXx/W1tbo3r27cqVKsZExLy8Purq6nP39lx4+fIgOHTogKCgITk5O2Lt3L5ycnKSORf/v/v37aNeuHS5evIhRo0ahSZMmWLZsGQDg4MGDmDRpEq5du6YcZVN1+fn5uHv3Lvz8/LBlyxa0adMGO3bsgJGRkdTRyqzu3bvDz88PlpaWUkchKlEc6aEypV69evjtt99w+PBhLFy4EOnp6QBer/7fvHkTjx8/xq+//sqy/w7FSpXiypDp6en4448/sHHjRkydOlV5Sj/F2XgUZy9h2f9wcrkcVlZWqFevHtq1a4eOHTuy7KuYBg0awM3NDa6urrhy5QqmTJkCAHj8+DFmzpwJDw8PVK5c+R8v5KcKsrOzIYSAjo4ObG1tsWzZMuzduxcZGRkwMzPDiBEjVP7voGqEEMjKysK9e/eUY3lv4+NJZR1X+EnlvXuGmLy8PPj5+WHNmjUYM2YMJk2aBOD1mVNevnwJMzMziZKqJsVZRnbs2IFJkyYhLCwMderUQV5eHrZu3YqVK1eiSZMmKj+/rOoU34ZMnDgRK1euRGJiIkxNTaWORe+4ceMGvLy8cOvWLTRo0ADa2tp4+vQpNDQ0cO7cOanj/S3F6+DOnTuxc+dOnD9/Hn379sWgQYPQokUL6OvrIzMzExs3bsTJkyd59pj/0uzZs7F9+3ZcvnwZVatWlToOUYlh4SeVpihR+fn5GDduHJydnWFsbIyWLVvi6NGj6N+/P0aMGIEFCxZAX19f6rgq5+2RnPHjxyMtLQ2BgYHFTjUYFhaG9u3bIyQkhOeTLgGpqamIiopC69atpY5Srr39HI+Pj0dUVBQMDQ1hZmaGSpUqYf369bh58ybi4+Px9ddf44svvlDJ03ACb/4dp6WloWbNmhg2bBjMzc2xZcsWZGVlwdPTE25ubrCxsYGWllaxawzQP3t3bPHRo0cYNmwYJkyYgPr160NbWxsPHjzAw4cP0a5dO9jb20uYlui/x8JPZcK2bdswePBg2NnZ4fPPP0dERITylJz+/v5wd3fHDz/8wBWZ91BcqGzjxo3w8fHBuXPnYGZmprzSaG5uLhwdHTFy5EgMHz5c6rgqiRv6yh7F78zLywvBwcG4desWzM3N8emnn8LV1RVDhgyBhoZGmRpbW7NmDS5cuIDt27crb1uyZAl8fX1hZmYGNzc3DBs2jDP8H0hR9m/cuKEcEX306BEeP36MgoIC5XuNmZkZ5HI5rl69ik8++UTq2ET/FdVaxiD6G926dcPQoUMRFRUFY2NjzJs3D2FhYTh48CCys7Ph6+vLGcu3BAYGYvPmzQDezOW3aNECeXl5GDt2LJKSkpS3x8XF4caNG2jRooVkeVWdotx7eHggPj6eZV+FxcbGQi6XQ1NTE48fP8aiRYswbtw4JCcnY+3atTAzM8OKFSuwZs2aMlH2Fa9r8fHxEEKgatWqeHud7rvvvkNUVBSaNWsGHx8fXhX7X1D8/oOCgqCnp4eGDRtizpw5mDdvHgCgX79+uHfvHiIjI3Hnzh2WfSrTuMJPKi8nJwcVKlQAAPj6+uLmzZuYMGECPvvsM9y/fx8xMTHQ0NDgBsn/9+rVK7i5uSE+Ph4tWrTA4MGD0a5dOwDA1atXMXjwYMTGxqJfv354/vw5Hj9+jKZNm2Lr1q0qOc6gCoQQePnyJWxtbTFx4kRMnDix2HE+bqohMTER5ubm8PT0xLx583D16lUEBwcX25+Sm5uLhQsXwsfHB+Hh4WXmg+4333yD9evXQ19fHwcOHECbNm3+ciatZ8+ewcTEROKkZcN/+jc7dOhQaGtrY/369TxbGakFvkORSrt48SJmzJiBZcuWITMzE56enqhWrRqWLl2KmJgYNGjQAF27dmXZf0vFihXx888/o3///oiPj8fs2bPx008/ITY2FnZ2drh06RKWLFmCx48fo2rVqhg7dizWr18PgGfl+TsymQyVK1eGu7s7li9fjrS0tGLHWfZVQ82aNREYGIiIiAg0b94cx48fR0xMDBISEgC8HvPR09PDjBkz0KRJE+VVkFVVUVERACAjIwN+fn44ceIEDA0N0a9fP6xZswZxcXHF7s+y/+E0NDQghMBPP/1U7CKDigs29uzZE0FBQYiMjOTrIqkHQaTCVq9eLbp06SIcHByErq6uGDJkiJg/f74wNTUVderUEUePHpU6osopKipS/rxmzRrRpEkToaOjI7788kuxdu1a8fLlS+XxvLw85c9yubxUc5YF7z4mDx8+FO3btxd79uwRN2/eFPfu3RMHDhwQS5cuFRERERKlpHfl5uaKhQsXClNTUyGTyYSvr6/IzMxUHn/16pUwNTUVGzdulDDlh+vSpYv44YcfRHx8vBBCCG9vb6GpqSns7OzEnj17xPPnzyVOWDadPn1aVK9eXTRu3Fhs2LCh2DG5XC5sbW2Ft7e3ROmIShZHekjlvXjxAjk5Obh58yb8/PwAAFlZWQgJCUHfvn0RHBwscULVotikGxAQgF27dsHa2hpGRkYICwtDSkoK6tWrh6FDh6Jr165SR1Vpghv6yryEhAR4e3tjw4YN6NChA0aMGIGsrCzcuXMHJ06cwK1bt6SO+I+EEMjLy8N3332Hs2fPwtjYGIMGDcLQoUORlZWFb7/9FkFBQZg1axa8vLykjlvmZGdnIzw8HPv27cOhQ4dgaWkJb29v5dnKdu3aBTs7O1hbW0uclOh/x8JPKuXtucrU1FSkpqZCV1cXBgYGysvEP3jwAJmZmYiMjETLli1hZ2cnYWLVJISAiYkJfv31VwwePBgAkJSUhICAACxYsAA1atRAq1atsGrVKhgYGEicVrVNnz4dSUlJMDAwgL29PeLi4jBz5kysWLECvXv3hqGhIXR0dKCrqyt11HJN/MOc9cWLF/Hjjz8qz7O/fPly9OrVC7Vr1y4z+y8UHzyvXLmCRo0a4dtvv0XHjh1x6dIlVK1aFfXq1ZM6Ypn17NkznD17Flu3bsWff/6JgQMHYvLkyahbt67U0YhKDAs/qRTFm6+vry8OHTqECxcuwMbGBi1atECvXr3w1VdflYk3Z6nduXMHvXv3xq+//oo+ffoUOzZp0iScOHECX3/9NaZPny5RQtXGDX1lj+J3du7cOYSEhKB69epo3rw5GjZsqFws2LdvH0aOHImvvvoK69atkzbwB3j3ooMAsH//fkyePBkA0K5dOyxatIgXG/xAisczPj4eNWrU+Mtje/fuXYwaNQoXLlxAo0aNcO3aNZ71iNQGmxOpjKKiImhoaCAiIgJeXl5wdXXFjRs3MGrUKLx8+RLLli3DH3/8IXXMMqFRo0Zo0KAB9u3bh8zMzGKn8fvss8/g6OiIsWPHAgD4mf+vuKGvbFGU/du3b6N37944evQoZsyYAU9PT/z8888IDw9Hfn4+XFxc8OTJEyxevBiAaj73FZlu376NZcuWISoqqthxZ2dnbNiwAXl5eYiOjoahoaEUMcskRcGfPHkyWrRogf379xc7bmNjAw8PD3zzzTfYtWsXyz6pFRZ+UhmKF+Off/4ZgwcPxsSJE9G4cWNMnDgRS5YsgYmJCcaOHYvnz59LnFS1Kc7s4ebmht27d2P06NG4fv06nj17hoSEBOXKpoGBAVen/8HZs2exdu1atG7dGv7+/gCgPD1s3759YWVlhSNHjkgZkf6f4tuYpUuXws3NDZcuXUJGRgYGDx6MwMBATJo0Cf7+/rh9+zYqVqyoHGNTxee+ItOdO3cwa9YszJgxA7t27UJycrLyPnXr1sWAAQMQFBSEihUrShW1TMrLy0PPnj3RpEkTTJs2DQMGDMCVK1eUxzMyMpCTk4NGjRpJmJKo5HGkh1SC4uIyQggMHz4cL168wO7du5UrrZqamoiKikKPHj2wa9cuNGvWTNrAKubvRlDOnDmDMWPG4MmTJ2jUqBHi4+NhZGSE8PBwVKxYkYX/H3BDX9nw9nN4y5YtKCgoKHbF6MTERMydOxe7du1Cnz59sGXLFqmi/msvXrzAlClTEBoaim7duqFPnz6oU6cODhw4gDVr1uDRo0dSRyyzoqOjceLECfz+++949OgRmjdvDktLS6xevRq7d+9Gjx49pI5IVKJY+ElST548gaWlZbHbNm3ahPnz52PPnj3FNuQ+ffoUdnZ2CAoKgqOjY2lHLRM2b96MmzdvIicnBxMnTkT9+vUBAHv37sWTJ09gbW2Nhg0bwsrKqsxsVpQaN/SprrfLfmRkJDZt2oT09HSsXr1aObevcPLkSRQVFaFz585l7oPu/fv3MWrUKGRkZCA5ORk6OjpYs2YNevbsKXW0MkHxWpeZmYn79++jcePGym/rrl27htDQUBw6dAhyuRyurq749ttvJU5MVPJY+ElS3bt3R9WqVTFu3Dg4ODgAANLT0/HVV18hPDwcs2bNwsCBAxEVFYXg4GBcvHgRN27ckDi1alFsRFuyZAl8fX1Rr149yGQyhIWFYcSIEVi1ahW0tbWljqnyuKGv7FH8zhYuXIgNGzYgMTER2tracHZ2xoABA+Dk5AQdHR2pY5aYyMhIxMbGwtLSEvb29lLHKRMUz5H79+9j+PDhePDgAV69egVPT09MmjQJ1tbWyg+Aubm50NXVLVMfBok+FAs/SSYjIwO+vr6IjIzEq1ev0K5dO4wePRq1a9cGACxevBiLFy+GEAI6OjqwsbHB8uXL0aRJE2mDq6DCwkI0bNgQy5Ytg5OTE1JSUnD48GH4+voiJSUF8+fPx4QJE6SOWSb069cPUVFRmDdvHpydnYsdW7duHa5fv47x48dzxldiipKWnZ2NunXrYunSpejUqRP27t2LwMBAFBUV4YsvvoCzszNatWoldVyS2BdffAETExOMGDECqampmDFjBrKzszF16lQMGTIExsbG/MaT1BoLP0nu6tWr2L59O0JDQ2FgYAB3d3cMGjQI+vr6AIDjx4/D1NQUtWrV+svX9OWd4qvqu3fvYufOnXBxcYGtrS2A1x8CHj16hK1bt8LHxwfjx4/HihUrJE6s2vLy8rBjxw6EhIQgIiICtra2mDp1Klq0aAEA8PX1xb179xAQECBxUlIU/tOnTyMoKAg+Pj4wNjYGAMTHx2PlypU4c+YMioqK4O3tzQvNlUOK1f2UlBQMGzYM69evR40aNQC8fv54e3vj559/RvXq1bF8+XLO7ZNaY+EnyeTl5UFXVxfx8fFYtGgRQkJCkJKSAmNjY9ja2sLT05MvwO+hKDqK/z59+hR169ZFQUEBNm3apLzQlkJOTg6uXr2KBg0awMjIqMzNL0uBG/pUm+I5HBsbCw8PDzx+/Bi7du1SjgUqhIWFYe3atVi+fDlPX1mOTZ8+HZcvX8bEiRPRs2fPYvuXkpOTMXToUAwaNAgeHh4SJyX6eFj4SXLW1tbo27cvRowYAVNTU2zcuBHBwcFISEiAi4sLXF1d0aZNG6ljqoyYmBgYGxujcuXKAF6/Ye3duxfBwcE4e/Yspk+fDi8vL4lTlh3c0Fc2KEr+22UtMjISS5YsQVhYGPT09PD999/Dw8PjvXtWuEm9/MjPz1fu3YiNjcXnn3+OhIQEuLm54Zdffim2ys/FDyovWPhJUufPn0f//v1x5coV5Ysw8HqDZO/evfHixQt069YNmzdvljClaunSpQvs7OywaNEi5W35+fmIiorC3r17sXHjRmhqamLBggUYMGCAhElVHzf0lR2K30OfPn0wfPhw9O7dG8Dr5/7+/fsRHByMuLg4WFlZYejQoejYsaPEiUkK06dPh5WVFUaMGKG8TfHt58yZM1GlShX89NNPcHV1ha6uroRJiUoXlztIUtWqVYMQAocPHwbw5nz8NjY26Nu3L3r06IEZM2ZIGVHlBAQEYMGCBQBel/+DBw9CR0cHn376KSZNmoRt27ahU6dOGD9+PBo3boz09HSVvKKoKlCciWfkyJEwNTXFli1bsG7dOhw6dAgODg5YvHgxUlJSIJfLoaenx7IvEblcDplMhtDQUBw8eFC5pwIAdHR00L9/fyxbtgz9+/dHamoq5s6di9GjRyMrK0vC1CQFfX192NjYAADCw8ORlJQEbW1tjB49Gnfv3kXnzp3h6emJXr164fTp09KGJSpFLPwkKSsrK3Tu3Bnbt2/HtWvXih3LzMxEzZo10aBBA2nCqaiMjAxoaGggPT0dAODq6ooePXrg4cOHqFy5Mtq1awcvLy+sXr0anTt3hqGhIYvqeyiuSJySkgIDAwOsXLkSXbt2hbu7O2JjYzFhwgR4e3ujTZs2OHr0qMRpyzfFKM7IkSPx008/wczMDABw5coVfP/99ygoKEDNmjUxefJk+Pr6olmzZtDT01OOvVH5MWvWLLRt2xaPHz+Gg4MDhg4ditOnTyMrKwvVqlXD2rVrceXKFeTl5aFLly54+fKl1JGJSocgktiDBw9Es2bNRKVKlcSUKVPE0qVLxYQJE4S2tra4du2a1PFUyqlTp4RMJhMbNmwQQghRVFQkjh07JhwcHISWlpaYPHmyyM3NFUIIIZfLRV5envJ+9H7Tpk0TnTp1EgcPHhRCFH+skpKSRLdu3cTWrVulilfuFRYWCiGEWLp0qTA3Nxc5OTnKY/b29mLChAnF7qfw8uVLIcTrfwdUPrz7uw4LCxOfffaZ0NXVFePHjxc3btxQviYKIcTdu3dLOyKRZFj4SRIZGRkiMjJSxMfHK29buXKlqF+/vrC3txddunRRllp648WLF2L8+PGiadOmwt/fXxQVFQm5XC6Sk5OFn5+fsLCwEDVq1BBLly4VQrDsvM/bb/gxMTHC3NxcyGQyMWjQIJGYmKg8xsdOdRQWFgoNDQ0RHBysvC0gIECYm5uLZ8+eCSHe/L42bdokkpKSJMlJquPtD+5bt24V1apVEzVq1BDLli0TMTExXAShcoeFn0pNQUGBEEKIM2fOCDs7O1G7dm1Rs2ZN8c033yiPCSHE48ePpYpYJsTExIj+/fsLY2PjYgWosLBQPHr0SEyaNEno6+uLtLQ0CVOqpmnTpv3lg2R+fr7w8/MTRkZGwsrKSmzbtk35LQmphj///FPo6emJRo0aiaNHjwohhLCwsBCrV68WQrwp+8eOHRMymUy8ePFCsqwknXdL/Lvf+kybNk1oa2sLS0tLkZCQUJrRiCTHGX4qNVpaWgCAwYMH4/PPP4ePjw9mzpyJ48ePw9DQEMuWLQMAWFpaAgA3muL9j0Ht2rWxc+dOuLq6YtCgQViwYAEKCwuhqamJOnXqYPHixYiOjoahoaFyEzS9xg19ZVOzZs1w6dIltG7dGt27d0f16tVRuXJljB07FsCbfydTpkzBrFmz8Mknnyj3aFD5odjroXg+KDblFxYWAgAWLlyIhIQEjB49GjVr1pQmJJFUpP28QeWFYqUlOTlZuLm5KVdQCwoKxP3798W0adOEoaGhMDExEeHh4VJGVSmKlcvVq1eLwMBAkZWVVWxcYcaMGcLW1lasWbOm2KgK/bPY2Fghk8lE165dxalTp0RmZqby2LVr18QXX3whdHR0RFZWloQpafHixcVGq3JyckRISIhwdXUVGhoaYtKkScrf3fLly4WJiYlUUUkCiufGkSNHxLhx40R0dLTy2PtGdq5evSp+++23UstHpEpY+KnU5ObmioEDB4o2bdqImzdvCiHevGC/evVK/Pnnn6J79+7i1KlTEqZUPbdu3RIymUzIZDLRpk0bMWXKFPHDDz+ICxcuiPT0dOHm5iZMTEzEjh07pI6q0rihr+yQy+Xi+fPnomnTpuLAgQPK2xRSUlLExo0bRZ06dYSRkZH49ddfhaGhodi5c6cQgpvUy5vZs2eL5s2biy5duogVK1YUG+lSPBfy8vJEmzZtxNChQ6WKSSQpXniLSs2lS5cwbNgwPHz4EMOHD8eqVav+cp/s7Gzo6+tLkE61LV68GBERETA3N4epqSni4+OxZcsWtG7dGl27dsWiRYuQkpICf39/DBs2TOq4Ku/tq65u27YN3333HbS0tPDjjz+iT58+sLS05FVZVcCSJUvg7++PrVu3Fjv3vsKTJ0+wYcMG+Pj4wMHBARcuXJAgJamCw4cPY9euXbh//z7MzMzg7u6Ovn37Ko9v3boVw4cPR3JyMgwNDSVMSiQNFn76aNzc3DBw4ED06dMHwOsrYt65cwdHjhzBqlWroKenBx8fH7i5uQHgZc7fR1FMExMTsXr1aiQnJ+PHH3+EtbU1CgsLsXbtWsTExCAxMRG7du1CZGQkmjVrJnVslfR2yQfeXGVXYfr06fj1119Ro0YNhIWFccZXRYwbNw4XL17EtGnT4OLiotwLpFBQUIDr16+jTp06MDIy+svvmcqPjIwM7N69G4cOHUJycjKaNWuGYcOGwdbWFp9++ik8PDwwb948qWMSSYKFnz6K/Px8LFiwAEOGDEHt2rVx+fJl2NvbAwCysrJw/fp1bN26FcHBwWjWrBl8fHzQunVriVOrtoyMDIwfPx6HDh3CunXrMGDAAABAXl4eioqKkJmZCVNTUxae/+DdD5aFhYXKEpmSkoINGzbw6s4qQPE8jo+Px+zZsxEbGwsXFxd0794d9erVkzoeqbDo6GgEBQXh9OnTEELgxYsXSExMxNOnT6WORiQZFn4qce8WqoiICLRq1Qrdu3fH8uXLYWVlBQBITk5GeHg4lixZgri4OERHR3OFH28ev3Xr1sHIyAh2dnaoXLkyTExMAAArV67E8ePH4ebmhn79+kFHR4cl/z0Uj+PRo0dx+PBhTJ48Wfnce9/jde3aNVy/fh2enp5SxKV/kJiYCC8vL5w4cQJ2dnawt7dHhw4d0KpVK7x69QoVK1aUOiKpoD///BM7duzAnj174O/vj27dukkdiUgyLPz0UVy/fh0rVqzAxo0bAQAHDx7E4sWLcfHiRXz77bdYuHAhdHV1IYRAVFQUZDIZrK2tJU6tOl69egUXFxeEhoaiVq1aaNq0KSwsLDB69GhoamoiICAAT548wffff//e2WZ6Y86cOTh8+DCqVauGHj16wNPTE5988gmAN8U/Pz8fjo6OaNCgATZt2iRxYnrb2wsIoaGh2LhxI9LT03H79m1UrVoVDRs2hLm5OWbNmoUqVapIG5ZUTmFhIa5cuYJWrVpJHYVIUiz89FEcO3YMLi4ucHV1xYIFC2BhYYHnz59jz5498PHxQU5ODhYtWoThw4dLHVVl5eTkIDc3FwEBAbh9+zYiIyNx/fp1ODo6onbt2ggODkZWVhZ+//139OrVS+q4Ko0b+sq2d7+RuX//PmQyGc6cOYPKlSvD0NAQnTt35rdcRER/g4WfPoqCggIEBARgxYoV6NixI3755Rdoa2ujoKAAcXFx8PPzw8qVK2Fubo6rV6+iUqVKUkdWaTk5OXj16hViY2OxYcMGZGRkIC4uDmFhYThy5Ai6du0qdUSVxw19ZR9H14iI/jss/PRRrVu3DuPHj0ffvn2xZcsW6OrqAnj9gSAiIgKXL1/GhAkTJE5Z9jx9+hS5ublITExE27ZtpY5TpnBDn+pSjO+8ePECBgYG/3jmLp7Vi4jow7HwU4lQrLzFxcXBwsKi2LHjx49j7NixsLW1xfz589GoUSOJUpZ9LDklhxv6VNODBw/wyy+/YP369VJHISJSGyz8VGKSkpLQqFEj1K9fH6NGjUK1atVQr1492NjYYN++fZg3bx7atWuHX375BRUqVJA6LhE39KmgPXv2wN3dHZGRkbCxsQGAYh9y+aGXiOjfY+GnEhMeHg4nJyfo6urC2toa1tbWePToEQwMDDBlyhQcP34cvr6+cHFxwYYNG1C1alWpIxORCmrbti0cHR0xf/58ACz5RET/KxZ+KlEhISE4dOgQGjdujNatWyM3NxcbNmxAREQEWrdujf379yMpKQl//PEHOnbsKHVcIpLY+8r85s2bsWrVKvj7+ytPtZmeno6wsDB4eHigcuXKEiQlIiq7WPipRCjetPPy8uDv74/Vq1dj2LBh+P777wG8PkPKpUuX8OLFC9y5cwdz586VODERqQLF/p89e/bgyJEjKCgowNWrV3Hv3j2YmZkhPz8fWlpaSE1NRceOHXHgwAGpIxMRlTks/PRfKyoqgqamJtLS0lC1atVip8w7duwYxo8fj06dOmHmzJl/2chLRKSQl5eH7t27Q19fH9bW1jA2NkZYWBjCw8Nx4sQJ5OfnKy/MV6VKFZ6ek4joX2Lhp/+JXC5H7dq1YWxsjIEDB8LCwgJOTk4wMjLC2bNnsXnzZjRt2hTjxo2DlpYW36iJ6L1evnxZ7HocCQkJaN++PVauXKk8gxJfP4iI/jss/PQ/yc7Oxt69e3HmzBmEhobCwMAACQkJ6Nq1K7p06YLz588jNDQU7u7uvKgREb2XosgrRgMLCgqgra0NDw8PPHz4ECEhIdDX15c6JhFRmcXCTyUmNTUVFy9eRExMDAIDA5GQkAAbGxucPn0a+fn5OHjwIHr06CF1TCIqI2JjY9G6dWscO3YMzZo1kzoOEVGZxcJPH83169cRERGB+Ph4RERE4PDhw1JHIiIV80+n3Hz58iVmzpyJ5cuXl3IqIiL1wsJPpYLn0SYq3959DSgsLISWltZ771NYWIioqCjlhbfe9+eJiOjDcfcTfTRvf5bkGzVR+aZ4DTh06BA8PT0xdepUnDx5EsCb1wq5XA7g9dV2u3XrhoCAgL/8eSIi+ve0/vNdiP47fIMmIuDNKXz9/PywatUq1KhRA/fu3UNYWBhOnjyJChUqAAA0NTUBAGZmZmjfvj3at28vZWwiIrXBkR4iIvroCgoKYGpqimXLluHrr79GcnIy3Nzc4O7ujj///BM5OTlwc3NDr169lPfX1tbmKA8RUQlg4Scioo9u9uzZOHPmDE6dOgVNTU3I5XJUqlQJbdu2haGhIeRyOeLj4+Hv74/GjRtLHZeISK1wpIeIiD6qvLw8AMCQIUOU8/pjxoxB3bp1sX37dpiYmODWrVto27YtLl26xMJPRFTCWPiJiOij0tXVxdChQyGEgJaWFoQQqF69OlasWAETExMAwKeffoqePXsiPT1d4rREROqHIz1ERCS5pKQk2NvbY/PmzejYsSNn94mIShALPxERSUJR6nNzczFu3DhER0fjzJkzUsciIlI7PA8/ERGVCsX60q1bt1BUVASZTIbnz59j0aJFCAsLg7+/f7H7ERFRyWDhJyKij05R8E+ePIkpU6YgKysLAGBkZITmzZtjyZIlsLa2hlwu5ygPEVEJ40gPERGVmsaNG6NPnz7w8fGROgoRUbnBFX4iIvpokpOTlT9v2bIFiYmJmDp1qvI2rjkREX18XOEnIqKPYuXKlTh48CBOnDgBuVyOtLQ0JCYmomnTplJHIyIqV7jCT0REH0XTpk3x6NEjJCUlQUNDA8bGxmjSpAkAQC6XS5yOiKj8YOEnIqKPokWLFjAwMICXl5fyNm7IJSIqfbzSLhERlYiioiJoamqisLAQubm5qFSpEnr27Ilz587hwoULePLkCWQyGSIiIqCrq4t58+ZBR0dH6thERGqPhZ+IiEqEpqYmAGDMmDE4d+4cdHR0lHP7jo6OcHBwQHx8PCwtLeHk5AQdHR1eUZeIqBSw8BMRUYnJzMyEEALe3t7IycmBvb091qxZg5CQEAQEBMDS0hJaWm/eelj2iYg+Pp6lh4iIPqqHDx/CyckJS5cuRe/evZWjP0REVDq4aZeIiEqcYi1JLpfDysoK3bt3h5+fHwCw7BMRlTIWfiIiKnHvjur07dsXx44dw5MnTyRKRERUfnGkh4iI/icfuvE2NDQUHTt2hFwuh4YG15uIiEoLCz8REf0rbxf8vLw86OrqAkCxIq/4OSYmBjdu3ECHDh1QuXJlyTITEZVnXGIhIqIPpij7GRkZWL9+PTp37gxnZ2fcvXu32Kq94ucVK1Zg0KBBuHLlilSRiYjKPZ6Wk4iI/rWxY8fi0aNHsLGxQWJiIlq2bInff/8dHTt2LHa/RYsWwcLCAl9++aU0QYmIiCM9RET0YRRjOiEhIejXrx+uXbuGWrVqQS6Xo2fPnrCyssLKlSuV93t3Vp8X2SIikgZHeoiI6IMoyvuGDRswevRo1KpVS3n7qFGjcPDgQeTk5CjvFxERgWfPnin/PMs+EZE0WPiJiOiDZWRkQFNTEwUFBcrbioqK0KZNGwDAuXPnAAB3796Fg4MDsrOzJclJRERvcKSHiIj+lYSEBKSlpaFJkybFxnS6deuGVq1aYd68eXByckKVKlWwa9cunoaTiEhi3LRLREQfTAgBMzMzmJmZAXg9pqMo/V9++SXOnTuHc+fO4fTp00hLS1Peh4iIpMMlFyIi+mDvK++K2zp16oSbN2+ic+fOmDNnDipVqoSioiIWfiIiiXGkh4iISkRqaipsbGygo6OD+Ph4qeMQEdH/40gPERF9kP90Wk0jIyMcOnQIOjo6AMDZfSIiFcEVfiIi+mBFRUXQ0NBQFn/Fh4DExEQYGBhAX19f4oRERPQuLr0QEdFfyOVyAEBSUhJCQkLw888/IzExEZqamsVW+WUyGaKiouDk5IQVK1agqKhIqshERPQ3WPiJiKgYIQQ0NDSQmZmJwYMHY+TIkVi/fj1q166NzZs3A3jzgQAA6tWrB1tbW2RmZkJTU1Oq2ERE9Dc40kNERMUoZu8HDhyI7OxsLFiwAKampli6dCnOnDmDU6dOKef03+c/zfoTEVHp4go/EREVo6GhgXv37uHcuXOYP38+mjRpgmrVqmH48OF48uQJDh48WOz+747xsOwTEakWFn4iIvqL8PBw1K1bF1WrVgXwetXeysoK9vb2OH36tPJ+s2fPxpQpUyRKSUREH4KFn4iI/uLLL79E165dYWpqCuDNqr2zszMuXLgAAEhMTISvry+cnJwAvP5QQEREqocz/ERE9F55eXnQ1dUtdj79uLg4fPbZZ7h8+TJmzpyJxMREnDhxgnP7REQqjIWfiIg+SGFhIeRyORwdHVGnTh3s2LEDMTExsLCwYOEnIlJhHOkhIqIPoqWlBR0dHdjZ2SEwMBCzZs2ChYUF5HI5yz4RkQrTkjoAERGVLSNHjkRqairmzZsHAMpxHyIiUk0c6SEiog+mGN3Jzs6Gvr5+sfl+IiJSTXyVJiIi5bn0Q0JCkJiY+Lf3U4zu6OvrA+DqPhFRWcBXaiIigqamJl69eoV+/fph3759AP56QS0iIiqbWPiJiAgAULFiRYwePRoHDhwA8PpDAPB6jIfTn0REZRcLPxFROfZukR8yZAhu3ryJEydOKG+TyWSQyWR4/vx5accjIqISwE27RESEiIgIyGQy3LhxA/7+/tDS0kK7du1w7do15Obm4sGDB1i1ahWcnZ2ljkpERP8SCz8RUTkXFBSESZMmITs7GzY2NigsLMT169fh4eGBTz75BBYWFtDX18f48eOljkpERP8FFn4ionLuwYMHyMzMVBZ7mUwGV1dX1KpVC35+fsXuyyvqEhGVPbzwFhFROVRUVARNTU2kpKSgfv36f7ndxcUF3t7eiIuLg4WFhfI4yz4RUdnDTbtEROWQouw3a9YMU6ZMQU5OjvJ2ABgxYgTMzc2xf/9+CVMSEVFJYOEnIiqnsrOzMXjwYJw+fRrNmzeHv7+/8piGhgaaNWuGe/fuSZiQiIhKAmf4iYjKsezsbFy6dAnBwcE4evQoateuDR8fHzg4OAB4M+LD2X0iorKLhZ+IiJCcnIyQkBD4+PggPj4eX375JQIDA1GxYkVoaPDLYCKisoyv4kRE5YRifWfPnj3w8vKCXC5XHqtevTrc3d3h4eEBS0tL1KpVC5UqVWLZJyJSA3wlJyIqJ2QyGYQQuHz5MrZt24ZOnTph3759xe7j6OiIli1bYs6cOQBQ7EMBERGVTRzpISIqZ5KTk3HmzBns378fkZGRsLW1xYwZM1CzZk2MHz8ehYWF2LNnj9QxiYiohLDwExGpubc33Co24QJAbGwsjhw5gt9//x1nz55FpUqVYGxsjCNHjqBOnTrcqEtEpCZY+ImIyom9e/fijz/+gJaWFkaOHImmTZsCAKKjo/Hs2TPExcXBzs4O9evXh1wu5/w+EZGaYOEnIlJjihX94OBgDBs2DA4ODkhMTMT9+/cxZMgQTJ06FfXq1ZM6JhERfUQs/ERE5UCbNm3g7u6O0aNHQ0tLC8HBwZgyZQqys7Mxbdo0uLm5wczMTOqYRET0EfD7WiIiNVVYWAgAePr0KTp16oSmTZtCS0sLAODq6orY2FhMnjwZU6dOxbRp06SMSkREHxFX+ImI1Fh2djZMTEyQk5ODH3/8EQsWLFCenlOxIffp06d49eoVrKysuFGXiEgNcYWfiEiNpKen4+rVq8r/19fXx44dO+Do6IglS5Zgzpw5eP78ubLUy+Vy1KhRA1ZWVgDAsk9EpIa4wk9EpEYGDhwIPT09/PbbbygoKICGhgY0NTVRVFSEVatWwcvLC1WqVMH8+fPRt29f6OrqSh2ZiIg+MhZ+IiI1k52dDX19fXh6eqJ9+/bo1asXqlWrBgBISUnBnDlz8Ntvv8Ha2hpHjx7lZl0iIjXHkR4iIjVRVFQEANDT00NhYSFSUlIwcuRIDBkyBKdOnUJWVhaqVauGtWvX4vz582jSpAnLPhFROcAVfiIiNXblyhWMGjUKt2/fxsiRIzFq1Cg0aNAAOjo6yvvwIltEROqNhZ+ISA0VFRVBJpMpi/y2bdvw3XffoUKFCsrib2JiInFKIiIqDSz8RERq5N3VesWVdhVmzJiBRYsW4cGDB7zCLhFROcHCT0RUxgkhcPHiRTg4OCj/Hyh+ik1F8ZfL5cjPz4eenh5HeYiIygm+0hMRlXGbNm3CkCFDMGvWLERHR0Mmk0Emk0EulyvLv6amJgIDA+Ho6Khc8WfZJyIqH/hqT0RUxjVt2hTdunXDuXPnMGbMGKxcuRKZmZnQ0NBQFv/nz5/jhx9+QPv27aGtrQ25XC51bCIiKiUc6SEiUhOHDh3C7t27cf/+fZiZmcHd3R19+/YFAMydOxf+/v5ISEiQOCUREZU2Fn4iIjWSkZGB3bt349ChQ0hOToadnR0cHR0xfPhwBAQEwNXVlbP7RETlDAs/EZEaio6ORlBQEM6fP4/z58/D3t4eZ86ckToWERFJgIWfiEiNXbhwAYGBgZg2bRosLCwghCh29h4iIlJ/LPxERGpOUfJZ9omIyicWfiIiIiIiNcZdW0REREREaoyFn4iIiIhIjbHwExERERGpMRZ+IiIiIiI1xsJPRERERKTGWPiJiIiIiNQYCz8RkRqKi4vDsGHDULNmTejo6KBWrVqYOHEiUlNTlfeJiYnBoEGDULNmTejp6cHc3Bx9+vTBvXv3JExOREQljYWfiEjNPHr0CPb29oiKikJQUBCio6Ph5+eH0NBQODg4IC0tDQUFBejcuTNevHiBvXv34v79+9i5cyeaNGmCjIwMqf8KRERUgnjhLSIiNdOtWzfcunULDx48QIUKFZS3JyUlwcrKCoMHD8bo0aNhZ2eH2NhY1KpVS8K0RET0sXGFn4hIjaSlpeH48eMYO3ZssbIPAKampnB3d8fOnTtRrVo1aGhoIDg4GEVFRRKlJSKi0sDCT0SkRqKioiCEgI2NzXuP29jYID09Hdra2lixYgXmzJkDQ0NDdOjQAd7e3nj06FEpJyYioo+NhZ+ISA19yLTmuHHjkJSUhO3bt8PBwQG7d+/Gp59+ij/++KMUEhIRUWlh4SciUiP16tWDTCbD3bt333v87t27MDQ0RLVq1QAAlStXRq9eveDj44Pr16/j888/x/z580szMhERfWQs/EREasTIyAidO3fGmjVrkJOTU+yYYjV/wIABkMlkf/mzMpkMDRs2RHZ2dmnFJSKiUsDCT0SkZlatWoW8vDx06dIFZ8+eRVxcHI4dO4bOnTvDzMwMPj4+uHbtGvr06YPg4GDcuXMH0dHR2LhxIzZt2oQ+ffpI/VcgIqISxNNyEhGpocePH2Pu3Lk4duwY0tLSYGpqCmdnZ8ydOxdGRkZ4/vw5vL29cfLkScTGxkImk6F27drw9PTE5MmToaHB9SAiInXBwk9EREREpMa4hENEREREpMZY+ImIiIiI1BgLPxERERGRGmPhJyIiIiJSYyz8RERERERqjIWfiIiIiEiNsfATEREREakxFn4iIiIiIjXGwk9EREREpMZY+ImIiIiI1BgLPxERERGRGvs/UykNxMY6l1QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ja3_query[['metadata.os','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'metadata.os':'os','metadata.sensor_hits':'volume'})\\\n",
    "    .groupby('os')\\\n",
    "    .sum('volume')\\\n",
    "    .sort_values('volume',ascending=False)\\\n",
    "    .head(10)\\\n",
    "    .plot(kind='bar',\n",
    "          title=f'Top OS with \"{GNQL_QUERY_2}\" by volume',\n",
    "          xlabel='OS',\n",
    "          ylabel='Volume'\n",
    "    )\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, we should do the same on GPTBot traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OS distribution of GPTBot traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'unknown'), Text(1, 0, 'Linux 2.2-3.x')])"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['metadata.os','metadata.sensor_hits']]\\\n",
    "    .rename(columns={'metadata.os':'os','metadata.sensor_hits':'volume'})\\\n",
    "    .groupby('os')\\\n",
    "    .sum('volume')\\\n",
    "    .sort_values('volume',ascending=False)\\\n",
    "    .head(10)\\\n",
    "    .plot(kind='bar',\n",
    "          title=f'Top OS with \"{GNQL_QUERY}\" by volume',\n",
    "          xlabel='OS',\n",
    "          ylabel='Volume'\n",
    "    )\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So most of the GreyNoise-tagged GPTBot traffic did not have an observed oeprating system, and what little did was `Linux 2.2-3.x`. That specific OS and OS Version was the third entry when we search on the JA3 we were looking at by volume."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By te way, how much of this GPTBot-tagged traffic have both OS and JA3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'os': 'unknown', 'volume': 33, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 61,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown',\n",
       "  'volume': 49,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 41, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 72, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 53,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 56, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 30, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 66, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 62, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 72, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 61,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 48, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 38, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 41, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 46,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'Linux 2.2-3.x',\n",
       "  'volume': 62,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 48, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 59, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 38, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 50, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 30, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 27, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 33, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 48, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 39, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 66, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 72, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 48, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 37, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 63,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 72, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 36,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 66, 'ja3': []},\n",
       " {'os': 'Linux 2.2-3.x', 'volume': 71, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 44, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 69, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 48, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 46,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown',\n",
       "  'volume': 85,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown',\n",
       "  'volume': 42,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 47, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 41, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 67,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 44, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 85, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 44, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 33, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 72, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 58,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 66, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 24, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 59, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 39, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 69,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown',\n",
       "  'volume': 52,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 49,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown',\n",
       "  'volume': 74,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 36, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 21,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 51, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 60, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 30, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 45, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 30, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 32, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 76, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 42, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 69, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 81, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 54, 'ja3': []},\n",
       " {'os': 'unknown',\n",
       "  'volume': 43,\n",
       "  'ja3': [{'fingerprint': '8d9f7747675e24454cd9b7ed35c58707', 'port': 443}]},\n",
       " {'os': 'unknown', 'volume': 63, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 59, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 35, 'ja3': []},\n",
       " {'os': 'unknown', 'volume': 57, 'ja3': []}]"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tagged_traffic_records = df[['metadata.os','metadata.sensor_hits','raw_data.ja3']]\\\n",
    "    .rename(columns={'metadata.os':'os','raw_data.ja3':'ja3','metadata.sensor_hits':'volume'})\\\n",
    "    .to_dict('records')\n",
    "df_tagged_traffic_records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [],
   "source": [
    "for record in df_tagged_traffic_records:\n",
    "    if type(record['ja3']) == type([]):\n",
    "        if len(record['ja3']) > 0:\n",
    "            record['ja3'] = True\n",
    "        else:\n",
    "            record['ja3'] = False\n",
    "    else:\n",
    "        record['ja3'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "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>os</th>\n",
       "      <th>volume</th>\n",
       "      <th>ja3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unknown</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>unknown</td>\n",
       "      <td>61</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unknown</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>unknown</td>\n",
       "      <td>41</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>unknown</td>\n",
       "      <td>43</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>unknown</td>\n",
       "      <td>63</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>unknown</td>\n",
       "      <td>59</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>unknown</td>\n",
       "      <td>35</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>unknown</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          os  volume    ja3\n",
       "0    unknown      33  False\n",
       "1    unknown      61   True\n",
       "2    unknown      49   True\n",
       "3    unknown      45  False\n",
       "4    unknown      41  False\n",
       "..       ...     ...    ...\n",
       "139  unknown      43   True\n",
       "140  unknown      63  False\n",
       "141  unknown      59  False\n",
       "142  unknown      35  False\n",
       "143  unknown      57  False\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tagged_traffic = pd.DataFrame(df_tagged_traffic_records)\n",
    "df_tagged_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'unknown'), Text(1, 0, 'Linux 2.2-3.x')])"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tagged_traffic[df_tagged_traffic['ja3'] == True]\\\n",
    "    .groupby('os')\\\n",
    "    .sum('volume')\\\n",
    "    .sort_values('volume',ascending=False)\\\n",
    "    .head(10)\\\n",
    "    .plot(kind='bar',\n",
    "          title=f'Top OS with \"{GNQL_QUERY}\" by volume',\n",
    "          xlabel='OS',\n",
    "          ylabel='Volume'\n",
    "    )\n",
    "plt.xticks(rotation=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting full list of IPs and first_seen/last_seen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try and pin some of these hosts to specific time windwos-- maybe there is a pattern for some of this traffic that either doesn't self-identify as `GPTBot`, observed operating system, doesn't have a JA3 print, or is not in the publicly disclosed IP ranges from OpenAI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For ease, let's just create some columns with booleans for each of the features listed above:\n",
    "```\n",
    "raw_data.web.useragents\n",
    "metadata.os\n",
    "raw_data.ja3\n",
    "```\n",
    "\n",
    "Kinda like pseudo-feature extraction, we will just make a column that states whether or not that row has that feature:\n",
    "```\n",
    "has_useragent\n",
    "os_not_known\n",
    "has_ja3\n",
    "is_disclosed_ip\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a column for `has_useragent`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['has_useragent'] = df['raw_data.web.useragents'].notna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make a column for `has_ja3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [],
   "source": [
    "has_ja3 = []\n",
    "for record in df.to_dict('records'):\n",
    "    if type(record['raw_data.ja3']) == type([]):\n",
    "        if len(record['raw_data.ja3']) > 0:\n",
    "            has_ja3.append(True)\n",
    "        else:\n",
    "            has_ja3.append(False)\n",
    "    else:\n",
    "        has_ja3.append(False)\n",
    "df['has_ja3'] = pd.Series(has_ja3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a column for `os_not_known`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "os_not_known = []\n",
    "for record in df.to_dict('records'):\n",
    "    if record['metadata.os'] :\n",
    "        if record['metadata.os'] == 'unknown':\n",
    "            os_not_known.append(True)\n",
    "        else:\n",
    "            os_not_known.append(False)\n",
    "    else:\n",
    "        os_not_known.append(False)\n",
    "df['os_not_known'] = pd.Series(os_not_known)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a column for `is_disclosed_ip`, based on OpenAI's list of disclosed GPTBot IP ranges: https://openai.com/gptbot-ranges.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipaddress\n",
    "\n",
    "OPENAI_RANGES = [\n",
    "    '20.15.240.64/28',\n",
    "    '20.15.240.80/28',\n",
    "    '20.15.240.96/28',\n",
    "    '20.15.240.176/28',\n",
    "    '20.15.241.0/28',\n",
    "    '20.15.242.128/28',\n",
    "    '20.15.242.144/28',\n",
    "    '20.15.242.192/28',\n",
    "    '40.83.2.64/28'\n",
    "]\n",
    "\n",
    "def check_openai_ip(ip):\n",
    "    for openai_range in OPENAI_RANGES:\n",
    "        if ipaddress.ip_address(ip) in ipaddress.ip_network(openai_range):\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_openai_ip = []\n",
    "for record in df.to_dict('records'):\n",
    "    is_openai_ip.append(check_openai_ip(record['ip']))\n",
    "df['is_openai_ip'] = pd.Series(is_openai_ip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can cleanly look at those features over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2q/v9nlyk3j2fx7wv892l6n0pvw0000gn/T/ipykernel_80788/463893595.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_features.rename(columns={'metadata.sensor_hits':'volume'},inplace=True)\n"
     ]
    },
    {
     "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>first_seen</th>\n",
       "      <th>last_seen</th>\n",
       "      <th>has_useragent</th>\n",
       "      <th>has_ja3</th>\n",
       "      <th>os_not_known</th>\n",
       "      <th>is_openai_ip</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2023-08-05</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>2023-08-08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     first_seen   last_seen  has_useragent  has_ja3  os_not_known  \\\n",
       "0    2023-08-08  2023-08-08          False    False          True   \n",
       "1    2023-08-08  2023-08-08          False     True          True   \n",
       "2    2023-08-08  2023-08-08          False     True          True   \n",
       "3    2023-08-08  2023-08-08          False    False          True   \n",
       "4    2023-08-08  2023-08-08          False    False          True   \n",
       "..          ...         ...            ...      ...           ...   \n",
       "139  2023-08-08  2023-08-08          False     True          True   \n",
       "140  2023-08-08  2023-08-08          False    False          True   \n",
       "141  2023-08-08  2023-08-08          False    False          True   \n",
       "142  2023-08-05  2023-08-08           True    False          True   \n",
       "143  2023-08-08  2023-08-08          False    False          True   \n",
       "\n",
       "     is_openai_ip  volume  \n",
       "0            True      33  \n",
       "1            True      61  \n",
       "2            True      49  \n",
       "3            True      45  \n",
       "4            True      41  \n",
       "..            ...     ...  \n",
       "139          True      43  \n",
       "140          True      63  \n",
       "141          True      59  \n",
       "142          True      35  \n",
       "143          True      57  \n",
       "\n",
       "[144 rows x 7 columns]"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = df[[\n",
    "    'first_seen',\n",
    "    'last_seen',\n",
    "    'has_useragent',\n",
    "    'has_ja3',\n",
    "    'os_not_known',\n",
    "    'is_openai_ip',\n",
    "    'metadata.sensor_hits'\n",
    "]]\n",
    "df_features.rename(columns={'metadata.sensor_hits':'volume'},inplace=True)\n",
    "df_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wait are all these timestamps the same lol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2020-05-08', '2022-03-02', '2023-08-04', '2023-08-05', '2023-08-08'}"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([x[0] for x in df_features[['first_seen']].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'2023-08-08'}"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set([x[0] for x in df_features[['last_seen']].values])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok... I would blame this on maybe sporadic coverage on detecting GPTBot traffic. This of course may be partially because GPTBot may not have always self-identified or operated off Microsoft's cloud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, let's make a reusable function to group by date and whether the selected feature is present or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_feature_histogram(df, time_column, feature_column):\n",
    "\n",
    "    df_feature = df[[time_column,feature_column,'volume']]\\\n",
    "        .rename(columns={time_column:'time',feature_column:'feature'})\\\n",
    "        .groupby(['time','feature'])\\\n",
    "        .sum('volume')\\\n",
    "        .sort_values('time',ascending=True)\\\n",
    "        .reset_index()\n",
    "    \n",
    "    df_feature['time'] = pd.to_datetime(df_feature['time'])\n",
    "    \n",
    "    df_feature_pivot = df_feature.pivot(index='time',columns='feature',values='volume')\n",
    "    \n",
    "    df_feature_pivot.plot(kind='bar',\n",
    "          title=f'feature:\"{feature_column}\" over \"{time_column}\" for {GNQL_QUERY}',\n",
    "          xlabel=time_column,\n",
    "          ylabel='volume',\n",
    "          color=['red','blue']\n",
    "    )\n",
    "    return plt.xticks(rotation=75)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPTBot-tagged traffic over time without a self-identifying user agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]),\n",
       " [Text(0, 0, '2020-05-08 00:00:00'),\n",
       "  Text(1, 0, '2022-03-02 00:00:00'),\n",
       "  Text(2, 0, '2023-08-04 00:00:00'),\n",
       "  Text(3, 0, '2023-08-05 00:00:00'),\n",
       "  Text(4, 0, '2023-08-08 00:00:00')])"
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_feature_histogram(df_features,'first_seen','has_useragent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some thoughts when looking at user agent precense in the GreyNoise-tagged GPTBot traffic:\n",
    "- Much of the traffic is from the past 30 days.\n",
    "- Large gaps in between captured traffic time periods.\n",
    "- Most traffic did not self-identify as GPTBot in the user agent.\n",
    "- 2023-08-04 and 2023-08-05: back-to-back days of consistent self-identifying GPTBot traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPTBot-tagged traffic over time without a JA3 print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]),\n",
       " [Text(0, 0, '2020-05-08 00:00:00'),\n",
       "  Text(1, 0, '2022-03-02 00:00:00'),\n",
       "  Text(2, 0, '2023-08-04 00:00:00'),\n",
       "  Text(3, 0, '2023-08-05 00:00:00'),\n",
       "  Text(4, 0, '2023-08-08 00:00:00')])"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_feature_histogram(df_features,'first_seen','has_ja3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some thoughts when looking at JA3 precense in the GreyNoise-tagged GPTBot traffic:\n",
    "- Much of the traffic is from the past 30 days.\n",
    "- Large gaps in between captured traffic time periods.\n",
    "- 2023-08-04 and 2023-08-05: back-to-back days of consistent GPTBot traffic with JA3 observed.\n",
    "- 2023-08-08 has JA3 traffic with somewhat consistent levels as 2023-08-04. and 2023-08-05."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPTBot-tagged traffic over time without known OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]),\n",
       " [Text(0, 0, '2020-05-08 00:00:00'),\n",
       "  Text(1, 0, '2022-03-02 00:00:00'),\n",
       "  Text(2, 0, '2023-08-04 00:00:00'),\n",
       "  Text(3, 0, '2023-08-05 00:00:00'),\n",
       "  Text(4, 0, '2023-08-08 00:00:00')])"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_feature_histogram(df_features,'first_seen','os_not_known')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some thoughts when looking at JA3 precense in the GreyNoise-tagged GPTBot traffic:\n",
    "- Much of the traffic is from the past 30 days.\n",
    "- Large gaps in between captured traffic time periods.\n",
    "- 2023-05-08, 2022-03-02, 2023-08-04, 2023-08-05 all have consistent levels of traffic where the observed operating system was not known.\n",
    "- large spike of traffic on 2023-08-08.\n",
    "- large proportion of traffic observed on 2023-08-08 did not have an identified operating system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPTBot-tagged traffic over time outside of disclosed OpenAI IP space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4]),\n",
       " [Text(0, 0, '2020-05-08 00:00:00'),\n",
       "  Text(1, 0, '2022-03-02 00:00:00'),\n",
       "  Text(2, 0, '2023-08-04 00:00:00'),\n",
       "  Text(3, 0, '2023-08-05 00:00:00'),\n",
       "  Text(4, 0, '2023-08-08 00:00:00')])"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_feature_histogram(df_features,'first_seen','is_openai_ip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some thoughts when looking at JA3 precense in the GreyNoise-tagged GPTBot traffic:\n",
    "- Much of the traffic is from the past 30 days.\n",
    "- Large gaps in between captured traffic time periods.\n",
    "- All traffic observed is from the IP space disclosed by OpenAI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions and Caveats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- GreyNoise data does not have continuous oberservations of traffic-tagged as GPTBot.\n",
    "- GreyNoise sensors likely do not typically attract GPTBot; that will depend on what application/software/protocol that the GreyNoise sensor is configured to mimic.\n",
    "- As a detection method, relying on GPTBot to self-identify via its user agent will result in mixed degrees of precision.\n",
    "- Despite consistent JA3 prints when a JA3 was able to be captured, most of the observed GPTBot-tagged traffic was not accompanied by a JA3 fingerprint.\n",
    "- Traffic tagged as GPTBot by GreyNoise typically did not have an observed operating system.\n",
    "- All traffic tagged by GreyNoise as GPTBot originated from the IP space that OpenAI has publicly disclosed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When present, the top user agent in the traffic tagged by GreyNoise as GPTBot was:\n",
    "```\n",
    "Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; GPTBot/1.0; +https://openai.com/gptbot)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When present, the top JA3 fingerprint in the traffic tagged by GreyNoise as GPTBot was:\n",
    "```\n",
    "8d9f7747675e24454cd9b7ed35c58707\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When present, the top operating in the traffic tagged by GreyNoise as GPTBot was:\n",
    "```\n",
    "Linux 2.2-3.x\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ASN and ASO for all the traffic tagged by GreyNoise as GPTBot was:\n",
    "```\n",
    "AS8075 Microsoft Corporation\n",
    "```"
   ]
  }
 ],
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