notebooks/pii-analysis-ms-presidio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# PII Detection on Collection - MSFT Presidio Example\n",
    "The following is an example of how to use the PIIAnalysisService with the default MSFT Presidio analyzer on a collection of text.\n",
    "\n",
    "> Note: This notebook requires all dependencies to have been installed. For more information, review readme.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[34mInstalling dependencies from lock file\u001B[39m\r\n",
      "\r\n",
      "\u001B[39;1mPackage operations\u001B[39;22m: \u001B[34m0\u001B[39m installs, \u001B[34m0\u001B[39m updates, \u001B[34m7\u001B[39m removals\r\n",
      "\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mphonenumbers\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m8.13.3\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mphonenumbers\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m8.13.3\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mphonenumbers\u001B[39m\u001B[39m (\u001B[39m\u001B[32m8.13.3\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpresidio-analyzer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpresidio-analyzer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpresidio-analyzer\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2.2.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2.2.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.16.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2022.10.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mrequests-file\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m1.5.1\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mrequests-file\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m1.5.1\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mrequests-file\u001B[39m\u001B[39m (\u001B[39m\u001B[32m1.5.1\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.4.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.4.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mRemoving...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mRemoving \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\r\n",
      "\u001B[39;1mInstalling\u001B[39;22m the current project: \u001B[36mpii-codex\u001B[39m (\u001B[39;1m0.4.2\u001B[39;22m)\u001B[1G\u001B[2K\u001B[39;1mInstalling\u001B[39;22m the current project: \u001B[36mpii-codex\u001B[39m (\u001B[32m0.4.2\u001B[39m)\r\n",
      "\u001B[34mInstalling dependencies from lock file\u001B[39m\r\n",
      "\r\n",
      "\u001B[39;1mPackage operations\u001B[39;22m: \u001B[34m7\u001B[39m installs, \u001B[34m0\u001B[39m updates, \u001B[34m0\u001B[39m removals\r\n",
      "\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mrequests-file\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m1.5.1\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mrequests-file\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m1.5.1\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mrequests-file\u001B[39m\u001B[39m (\u001B[39m\u001B[32m1.5.1\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mphonenumbers\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m8.13.3\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.4.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.4.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[4A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.4.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[3A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mphonenumbers\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m8.13.3\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.4.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[3A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[2A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[2A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2022.10.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[2A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2022.10.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[4A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2022.10.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[3A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mphonenumbers\u001B[39m\u001B[39m (\u001B[39m\u001B[32m8.13.3\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m3.16.0\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2022.10.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[3A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2022.10.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[2A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpycryptodome\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.16.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mregex\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2022.10.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mtldextract\u001B[39m\u001B[39m (\u001B[39m\u001B[32m3.4.0\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-analyzer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mPending...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[2A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-analyzer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "  \u001B[34;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[39;1m2.2.31\u001B[39;22m\u001B[39m)\u001B[39m: \u001B[34mInstalling...\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2.2.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[2A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2.2.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\u001B[1A\u001B[0J  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-analyzer\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2.2.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "  \u001B[32;1m•\u001B[39;22m \u001B[39mInstalling \u001B[39m\u001B[36mpresidio-anonymizer\u001B[39m\u001B[39m (\u001B[39m\u001B[32m2.2.31\u001B[39m\u001B[39m)\u001B[39m\r\n",
      "\r\n",
      "\u001B[39;1mInstalling\u001B[39;22m the current project: \u001B[36mpii-codex\u001B[39m (\u001B[39;1m0.4.2\u001B[39;22m)\u001B[1G\u001B[2K\u001B[39;1mInstalling\u001B[39;22m the current project: \u001B[36mpii-codex\u001B[39m (\u001B[32m0.4.2\u001B[39m)\r\n"
     ]
    }
   ],
   "source": [
    "# Install dependencies\n",
    "! poetry install\n",
    "! poetry install --extras=\"detections\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collection Risk Score:  2.238095238095238\n"
     ]
    },
    {
     "data": {
      "text/plain": "Counter({'PERSON': 1,\n         'NRP': 2,\n         'EMAIL_ADDRESS': 1,\n         'PHONE_NUMBER': 2,\n         'URL': 1,\n         'LOCATION': 1})"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pii_codex.services.analysis_service import PIIAnalysisService\n",
    "import pandas as pd\n",
    "\n",
    "pii_analysis_service = PIIAnalysisService()\n",
    "\n",
    "# Run the detection and assessments in one shot with the PIIAnalysisService\n",
    "analysis_results = pii_analysis_service.analyze_collection(\n",
    "    texts=[\n",
    "        \"How can I reach you, Jim?\",\n",
    "        \"As a democrat, I promise to uphold....\",\n",
    "        \"As a Catholic, I can tell you that....\",\n",
    "        \"Here is my contact information: Phone number 555-555-5555 and my email is example123@email.com\",\n",
    "        \"Perfect, my number if you need me is 777-777-7777. Where is the residence and what is the earliest the crew can arrive?\",\n",
    "        \"I'll be at my home at 123 Dark Data Lane, OH, 11111 after 7PM\",\n",
    "        \"Cool, I'll be there!\"\n",
    "        ],\n",
    "    collection_type=\"population\",\n",
    "    collection_name=\"PII Collection 1\"\n",
    ")\n",
    "\n",
    "print(\"Collection Risk Score: \", analysis_results.risk_score_mean)\n",
    "analysis_results.detected_pii_type_frequencies"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Sometimes text analyses aren't enough. For those handling social media records, they may be provided with metadata that can be used in determining if the post contains other elements considered to be PII. For this, we expose a data parameter. Pass in a data frame with a text column (str) and a metadata column (dict) for each social media post. You could also pass in the individual text entry instead of passing in the metadata dict as shown below."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "{'collection_name': 'PII Collection 1',\n 'collection_type': 'POPULATION',\n 'analyses': [{'analysis': [{'pii_type_detected': None,\n     'risk_level': 1,\n     'risk_level_definition': 'Non-Identifiable',\n     'cluster_membership_type': None,\n     'hipaa_category': None,\n     'dhs_category': None,\n     'nist_category': None},\n    {'pii_type_detected': 'LOCATION',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Secure Identifiers',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'LOCATION',\n     'score': 0.0,\n     'start': 0,\n     'end': 0},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 0,\n   'risk_score_mean': 2,\n   'sanitized_text': 'I attend the University of Central Florida, how about you?'},\n  {'analysis': [{'pii_type_detected': 'NRP',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'NRP',\n     'score': 0.85,\n     'start': 5,\n     'end': 13},\n    {'pii_type_detected': 'LOCATION',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Secure Identifiers',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'LOCATION',\n     'score': 0.0,\n     'start': 0,\n     'end': 0},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 1,\n   'risk_score_mean': 2.3333333333333335,\n   'sanitized_text': 'As a <REDACTED>, I promise to uphold....'},\n  {'analysis': [{'pii_type_detected': 'NRP',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'NRP',\n     'score': 0.85,\n     'start': 5,\n     'end': 13},\n    {'pii_type_detected': 'LOCATION',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Secure Identifiers',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'LOCATION',\n     'score': 0.0,\n     'start': 0,\n     'end': 0},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 2,\n   'risk_score_mean': 2.3333333333333335,\n   'sanitized_text': 'As a <REDACTED>, I can tell you that....'},\n  {'analysis': [{'pii_type_detected': 'EMAIL_ADDRESS',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Stand Alone PII',\n     'nist_category': 'Directly PII',\n     'entity_type': 'EMAIL_ADDRESS',\n     'score': 1.0,\n     'start': 72,\n     'end': 92},\n    {'pii_type_detected': 'PHONE_NUMBER',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Contact Information',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Stand Alone PII',\n     'nist_category': 'Directly PII',\n     'entity_type': 'PHONE_NUMBER',\n     'score': 0.75,\n     'start': 43,\n     'end': 55},\n    {'pii_type_detected': 'URL',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Community Interaction',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Linkable',\n     'nist_category': 'Linkable',\n     'entity_type': 'URL',\n     'score': 0.5,\n     'start': 83,\n     'end': 92},\n    {'pii_type_detected': 'LOCATION',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Secure Identifiers',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'LOCATION',\n     'score': 0.0,\n     'start': 0,\n     'end': 0},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 3,\n   'risk_score_mean': 2.6,\n   'sanitized_text': 'If anyone needs trig help, my phone number <REDACTED> and my email is <REDACTED>'},\n  {'analysis': [{'pii_type_detected': 'PHONE_NUMBER',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Contact Information',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Stand Alone PII',\n     'nist_category': 'Directly PII',\n     'entity_type': 'PHONE_NUMBER',\n     'score': 0.75,\n     'start': 22,\n     'end': 34},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 4,\n   'risk_score_mean': 3,\n   'sanitized_text': 'Oh I do! My number is <REDACTED>. Where is the residence hall?'},\n  {'analysis': [{'pii_type_detected': 'LOCATION',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Secure Identifiers',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'LOCATION',\n     'score': 0.85,\n     'start': 40,\n     'end': 42},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 5,\n   'risk_score_mean': 2.5,\n   'sanitized_text': 'The dorm is over at 123 Dark Data Lane, <REDACTED>, 11111'},\n  {'analysis': [{'pii_type_detected': None,\n     'risk_level': 1,\n     'risk_level_definition': 'Non-Identifiable',\n     'cluster_membership_type': None,\n     'hipaa_category': None,\n     'dhs_category': None,\n     'nist_category': None},\n    {'pii_type_detected': 'LOCATION',\n     'risk_level': 2,\n     'risk_level_definition': 'Semi-Identifiable',\n     'cluster_membership_type': 'Secure Identifiers',\n     'hipaa_category': 'Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Linkable',\n     'entity_type': 'LOCATION',\n     'score': 0.0,\n     'start': 0,\n     'end': 0},\n    {'pii_type_detected': 'SCREEN_NAME',\n     'risk_level': 3,\n     'risk_level_definition': 'Identifiable',\n     'cluster_membership_type': 'Personal Preferences',\n     'hipaa_category': 'Not Protected Health Information',\n     'dhs_category': 'Not Mentioned',\n     'nist_category': 'Directly PII',\n     'entity_type': 'SCREEN_NAME',\n     'score': 0.0,\n     'start': 0,\n     'end': 0}],\n   'index': 6,\n   'risk_score_mean': 2,\n   'sanitized_text': \"Cool, I'll be there!\"}],\n 'detection_count': 21,\n 'risk_scores': [2, 2.3333333333333335, 2.3333333333333335, 2.6, 3, 2.5, 2],\n 'risk_score_mean': 2.395238095238095,\n 'risk_score_mode': 2,\n 'risk_score_median': 2.3333333333333335,\n 'risk_score_standard_deviation': 0.32485824572972455,\n 'risk_score_variance': 0.1055328798185941,\n 'detected_pii_types': {'EMAIL_ADDRESS',\n  'LOCATION',\n  'NRP',\n  'PHONE_NUMBER',\n  'SCREEN_NAME',\n  'URL'},\n 'detected_pii_type_frequencies': {'LOCATION': 6,\n  'SCREEN_NAME': 7,\n  'NRP': 2,\n  'EMAIL_ADDRESS': 1,\n  'PHONE_NUMBER': 2,\n  'URL': 1}}"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "analysis_results = pii_analysis_service.analyze_collection(\n",
    "    data=pd.DataFrame.from_dict({\n",
    "        \"text\": [\n",
    "            \"I attend the University of Central Florida, how about you?\",\n",
    "            \"As a democrat, I promise to uphold....\",\n",
    "            \"As a Catholic, I can tell you that....\",\n",
    "            \"If anyone needs trig help, my phone number 555-555-5555 and my email is example123@email.com\",\n",
    "            \"Oh I do! My number is 777-777-7777. Where is the residence hall?\",\n",
    "            \"The dorm is over at 123 Dark Data Lane, OH, 11111\",\n",
    "            \"Cool, I'll be there!\"\n",
    "        ],\n",
    "        \"metadata\": [\n",
    "            {\"location\": True, \"url\": False, \"screen_name\": True},\n",
    "            {\"location\": True, \"url\": False, \"screen_name\": True},\n",
    "            {\"location\": True, \"url\": False, \"screen_name\": True},\n",
    "            {\"location\": True, \"url\": False, \"screen_name\": True},\n",
    "            {\"location\": False, \"url\": False, \"screen_name\": True}, # Not all social media posts will have location metadata\n",
    "            {\"location\": False, \"url\": False, \"screen_name\": True},\n",
    "            {\"location\": True, \"url\": False, \"screen_name\": True},\n",
    "        ]\n",
    "    }),\n",
    "    collection_type=\"population\",\n",
    "    collection_name=\"PII Collection 1\"\n",
    ")\n",
    "\n",
    "results = analysis_results.to_dict()\n",
    "results"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For those in need to store off the data but do not wish to have the raw text stored, the library leverages Microsoft Presidio to sanitize the input text, redacting the detected items from the original analysis. The redactions are entirely configurable within the `presidio_analysis.py` file."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "['I attend the University of Central Florida, how about you?',\n 'As a <REDACTED>, I promise to uphold....',\n 'As a <REDACTED>, I can tell you that....',\n 'If anyone needs trig help, my phone number <REDACTED> and my email is <REDACTED>',\n 'Oh I do! My number is <REDACTED>. Where is the residence hall?',\n 'The dorm is over at 123 Dark Data Lane, <REDACTED>, 11111',\n \"Cool, I'll be there!\"]"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanitized_texts = [result[\"sanitized_text\"] for result in results[\"analyses\"]]\n",
    "sanitized_texts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                                               0\ncollection_name                                                 PII Collection 1\ncollection_type                                                       POPULATION\ndetection_count                                                               21\nrisk_scores                    [2, 2.3333333333333335, 2.3333333333333335, 2....\nrisk_score_mean                                                         2.395238\nrisk_score_mode                                                                2\nrisk_score_median                                                       2.333333\nrisk_score_standard_deviation                                           0.324858\nrisk_score_variance                                                     0.105533\ndetected_pii_types             {SCREEN_NAME, LOCATION, URL, EMAIL_ADDRESS, NR...\ndetected_pii_type_frequencies  {'LOCATION': 6, 'SCREEN_NAME': 7, 'NRP': 2, 'E...",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>collection_name</th>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>collection_type</th>\n      <td>POPULATION</td>\n    </tr>\n    <tr>\n      <th>detection_count</th>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>risk_scores</th>\n      <td>[2, 2.3333333333333335, 2.3333333333333335, 2....</td>\n    </tr>\n    <tr>\n      <th>risk_score_mean</th>\n      <td>2.395238</td>\n    </tr>\n    <tr>\n      <th>risk_score_mode</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>risk_score_median</th>\n      <td>2.333333</td>\n    </tr>\n    <tr>\n      <th>risk_score_standard_deviation</th>\n      <td>0.324858</td>\n    </tr>\n    <tr>\n      <th>risk_score_variance</th>\n      <td>0.105533</td>\n    </tr>\n    <tr>\n      <th>detected_pii_types</th>\n      <td>{SCREEN_NAME, LOCATION, URL, EMAIL_ADDRESS, NR...</td>\n    </tr>\n    <tr>\n      <th>detected_pii_type_frequencies</th>\n      <td>{'LOCATION': 6, 'SCREEN_NAME': 7, 'NRP': 2, 'E...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peering into each and every analysis in collection\n",
    "data = results.copy()\n",
    "del data['analyses']\n",
    "\n",
    "analyses_collection_df = pd.DataFrame(\n",
    "    [data]\n",
    ")\n",
    "\n",
    "analyses_collection_df.T"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "                                            analysis  index  risk_score_mean  \\\n0  [{'pii_type_detected': None, 'risk_level': 1, ...      0         2.000000   \n1  [{'pii_type_detected': 'NRP', 'risk_level': 2,...      1         2.333333   \n2  [{'pii_type_detected': 'NRP', 'risk_level': 2,...      2         2.333333   \n3  [{'pii_type_detected': 'EMAIL_ADDRESS', 'risk_...      3         2.600000   \n4  [{'pii_type_detected': 'PHONE_NUMBER', 'risk_l...      4         3.000000   \n5  [{'pii_type_detected': 'LOCATION', 'risk_level...      5         2.500000   \n6  [{'pii_type_detected': None, 'risk_level': 1, ...      6         2.000000   \n\n                                      sanitized_text   collection_name  \n0  I attend the University of Central Florida, ho...  PII Collection 1  \n1           As a <REDACTED>, I promise to uphold....  PII Collection 1  \n2           As a <REDACTED>, I can tell you that....  PII Collection 1  \n3  If anyone needs trig help, my phone number <RE...  PII Collection 1  \n4  Oh I do! My number is <REDACTED>. Where is the...  PII Collection 1  \n5  The dorm is over at 123 Dark Data Lane, <REDAC...  PII Collection 1  \n6                               Cool, I'll be there!  PII Collection 1  ",
      "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>analysis</th>\n      <th>index</th>\n      <th>risk_score_mean</th>\n      <th>sanitized_text</th>\n      <th>collection_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[{'pii_type_detected': None, 'risk_level': 1, ...</td>\n      <td>0</td>\n      <td>2.000000</td>\n      <td>I attend the University of Central Florida, ho...</td>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[{'pii_type_detected': 'NRP', 'risk_level': 2,...</td>\n      <td>1</td>\n      <td>2.333333</td>\n      <td>As a &lt;REDACTED&gt;, I promise to uphold....</td>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[{'pii_type_detected': 'NRP', 'risk_level': 2,...</td>\n      <td>2</td>\n      <td>2.333333</td>\n      <td>As a &lt;REDACTED&gt;, I can tell you that....</td>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>[{'pii_type_detected': 'EMAIL_ADDRESS', 'risk_...</td>\n      <td>3</td>\n      <td>2.600000</td>\n      <td>If anyone needs trig help, my phone number &lt;RE...</td>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>[{'pii_type_detected': 'PHONE_NUMBER', 'risk_l...</td>\n      <td>4</td>\n      <td>3.000000</td>\n      <td>Oh I do! My number is &lt;REDACTED&gt;. Where is the...</td>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>[{'pii_type_detected': 'LOCATION', 'risk_level...</td>\n      <td>5</td>\n      <td>2.500000</td>\n      <td>The dorm is over at 123 Dark Data Lane, &lt;REDAC...</td>\n      <td>PII Collection 1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>[{'pii_type_detected': None, 'risk_level': 1, ...</td>\n      <td>6</td>\n      <td>2.000000</td>\n      <td>Cool, I'll be there!</td>\n      <td>PII Collection 1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyses = results[\"analyses\"]\n",
    "analyses_df = pd.DataFrame(\n",
    "    analyses\n",
    ")\n",
    "analyses_df[\"collection_name\"] = results[\"collection_name\"]\n",
    "analyses_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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YWGRmZlqty8jIQGxsLAAgKioKoaGhVmXy8/Oxb98+SxkiIpKIklvoy3vdf+enrbJFRZUr64C2bdsiIiICmzZtsqzbtGkT6tevjzZt2liVNZvNSE1NRVRUFDQaDaKjo7Fx40bLdpPJhBEjRli2N2nSBIsWLbKqY+jQoejXrx/effddhIWFoU6dOhg7dqxd4zcCAwORkZGBAQMGoEmTJnjyySexePFiHDx4EBcuXLC5b0FBAV5++WUsX74ctWrVqvQxvY1dCcvYsWPx73//G+vWrYO/vz9yc3ORm5uLolIda/DgwUhJSbG8f/PNN7FlyxYsWLAAx48fx6xZs3DgwAEkJSUBuJutjRs3DvPmzcNXX32FX375BYMHD0Z4eDj69evnnE9JRETuUaNGxa/n77tUHRxccdmePa3LRkaWX85Bw4cPt7rss2rVKgwbNqxMudTUVKxduxbLli3D0aNHMX78eLzyyivYsWMHgLsJTb169fDZZ5/h119/xYwZMzB16lR8+umnVvVs374dp0+fxvbt27FmzRqkpaUhLS3Nsn3WrFmIjIy06zPcvn0bgiCgZs2aNsuNHTsWvXv3trq5RYrsuiRUcr2va9euVutXr16NoUOHAgAuXLhgdbqoY8eOWLduHaZNm4apU6eicePG2Lx5s9VA3UmTJkGr1eL111/HrVu30KlTJ2zZssXj10aJiKhqeuWVV5CSkoLz588DAHbv3o3169cjKyvLUkav12P+/Pn47rvvLGf8H330UezatQsffvghunTpAoVCYXUTSFRUFLKzs/Hpp59iwIABlvW1atXC4sWLIZfL0bRpU/Tu3RuZmZkYOXIkAKBu3bpo2LBhpePX6XSYPHkyBg0aZHPs5vr163Ho0CHs37+/0nV7K7sSlpLbmmwp/Y9d4sUXX8SLL75Y4T6CIGDOnDmYM2eOPeEQEZG3KSioeNv9U7L/8XyZct0/TuLcOYdDKk9QUBB69+6NtLQ0iKKI3r17o27dulZlTp06hcLCQvTo0cNqvcFgsLp0tGTJEqxatQoXLlxAUVERDAZDmTGYLVq0sLqdNywsDL/88ovlfVJSkuXKw4MYjUYMGDAAoiiWGThc2sWLF/Hmm29Wmbtc+SwhG/z8/GAwGJCeno5cTRGkc48Q0T3FGjWWZX8CABDMAGz8nhA9NHse/+CMsg8x98rw4cMtScKSJUvKbC/4I/n65ptv8Mgjj1htK7nxY/369ZgwYQIWLFiA2NhY+Pv745133sG+ffusyt8/uFQQBIfmMylJVs6fP4/vv//e5tmVgwcP4urVq2jbtq1lnclkws6dO7F48WLo9XqHn+vjCUxYiIioWkpMTITBYIAgCEhISCizvXnz5lCpVLhw4QK6dOlSbh27d+9Gx44dMWbMGMu606dPuyTekmTl5MmT2L59+wMfPNm9e3erszjA3du6mzZtismTJ0sqWQGYsBARUTUll8tx7Ngxy/L9/P39MWHCBIwfPx5msxmdOnXC7du3sXv3bgQEBGDIkCFo3Lgx1q5di61btyIqKgoff/wx9u/fX6m7aEtbvHgxvvjiizJ31ZYwGo144YUXcOjQIXz99dcwmUyWyVVr164NpVIJ4G6S8qc//QlJSUnw9/cvM7Grn58f6tSpY3PCV2/FhMUGnU6Hl19+Gbm5uRg07y/w0Sg9HRKR3eR6A56Z/S8AwPbpYwBoPBsQkRd50GSjc+fORVBQEFJTU3HmzBnUrFkTbdu2xdSpUwEAf/nLX3D48GEMHDgQgiBg0KBBGDNmDL79Y3bpyrp+/brNMzOXLl3CV199BQBlxsds377dcjPM6dOncf36dbuOLRWCWJmRtF4uPz8fgYGBuH37ttNnuq3xx21zi75bDaWfdAYtjXpSWg+NNBqNSE9PR69evSQ1ydayvevt3kdfrMO4zLu3Ty7svhoqH9f2K58iHV575u7xVn63GnULakmunaVIqn26MnQ6Hc6ePYuoqCiPD+Y0m83Iz89HQECA1032VpU8bDtX1Gfs+f3mvy4RERF5PSYsRERE5PWYsBAREZHXY8JCREREXo8JCxEREXk9JixERETk9TgPiw2+vr64efMmtm7diptqx6d/JvKkYrUKK76/+1Rak1LFqfmJSJKYsNggCAL8/PygVqshCHyWEEmUIKBYc3feA4F5NxFJFC8JEREROVFWVhYEQcCtW7cAAGlpaahZs6ZHY6oKmLDYoNfrMWLECCxatAhGg9HT4RA5RGYwotvcpeg2dylk7MdUzQ0dOhSCIGDUqFFlto0dOxaCIGDo0KFOPebAgQPxv//9z6l1Vsa5c+cwYsQIREVFQaPRoGHDhpg5cyYMBoPN/f7yl7+gYcOG0Gg0CAoKQt++fXH8+HHL9hs3biAxMRHh4eFQqVSIiIhAUlIS8vPzXfp5mLDYUFxcjI8//hjbt2+H2WTydDhEDpGZTGiSvhNN0ndCxn5MhIiICKxfvx5FRUWWdTqdDuvWrUP9+vWdfjyNRoPg4GCn1/sgx48fh9lsxocffoijR4/i/fffx7JlyyzPQapIu3btsHr1ahw7dgxbt26FKIpITEyE6Y/vD5lMhr59++Krr77C//73P6SlpeG7774rNwl0JiYsRERUrbRt2xYRERHYtGmTZd2mTZtQv359tGnTxqqs2WxGamqq5SxFdHQ0Nm7caFUmPT0djz32GDQaDbp164Zz585Zbb//ktDp06fRt29fhISEoEaNGnjiiSfw3XffWe0TGRmJ+fPnY/jw4fD390f9+vXx0Ucf2fU5ExMTsXr1asTHx+PRRx/Fc889hwkTJlh97vK8/vrrePrppxEZGYm2bdti3rx5uHjxIi5cuAAAqFWrFkaPHo327dujQYMG6N69O8aMGYMffvjBrvjsxYSFiIgemiiK0Bq0Hnk58gzf4cOHY/Xq1Zb3q1atwrBhw8qUS01Nxdq1a7Fs2TIcPXoU48ePxyuvvIIdO3YAAC5evIj+/fujT58+yMnJwWuvvYYpU6bYPHZBQQF69eqFzMxMHD58GImJiejTp48lISixYMECtG/fHocPH8aYMWMwevRonDhxwrK9a9eudl++un37NmrXrl3p8lqtFqtXr0ZUVBQeeeSRcstcvnwZmzZtQpcuXeyKxV68S4iIiB5aobEQNVJreOTYv435DYEItGufV155BSkpKTh//jwAYPfu3Vi/fj2ysrIsZfR6PebPn4/vvvsOsbGxAIBHH30Uu3btwocffoguXbpg6dKlaNiwIRYsWAAAaNKkCX755Rf84x//qPDY0dHRiI6OtryfO3cuvvjiC3z11VdISkqyrO/VqxfGjBkDAJg8eTLef/99bN++HU2aNAEA1K9fH2FhYZX+zKdOncI///lPvPvuuw8s+69//QuTJk2CVqtFkyZNsHXrViiVSqsygwYNwpdffomioiL06dMHK1asqHQsjuAZFiIiqnaCgoLQu3dvpKWlYfXq1ejduzfq1q1rVebUqVMoLCxEjx49UKNGDctr7dq1OH36NADg2LFjiImJsdqvJLmpSEFBASZMmIBmzZqhZs2aqFGjBo4dO1bmDEurVq0sy4IgIDQ0FFevXrWsW7t2LVJTUyv1eS9duoTExES8+OKLGDly5APLv/zyyzh8+DB27NiBxx57DC+99BJ0Op1Vmffffx+HDh3Cl19+idOnTyM5OblSsTiKZ1iIiOih+Sp8UZDi/lkJzWYziouKHdp3+PDhljMaS5YsKbO9oODu5/nmm2/KXA5RqVQOHRMAJkyYgIyMDLz77rto1KgRNBoNXnjhhTJ37ygUCqv3giDAbLZ/MqXLly+jW7du6NixY6XHwQQGBiIwMBCNGzfGk08+iVq1auHrr7/G8OHDLWVCQ0MRGhqKpk2bonbt2ujcuTOmT59u11kfezBhISKihyYIAvyUfm4/rtlsRr7OsdtpExMTYTAYIAgCEhISymxv3rw5VCoVLly4UOH4jGbNmuGrr76yWrd3716bx929ezeGDh2KP/3pTwDuJkb3D9R1lkuXLqFbt26WO39kMvsvrIiiCFEUbd4OXZJI6fV6h2N9ECYsNvj6+uLSpUv47rvvUKhWPHgHIi9UrFYhLf1DyzKn5ie6Sy6X49ixY5bl+/n7+2PChAkYP348zGYzOnXqhNu3b2P37t0ICAjAkCFDMGrUKCxYsAATJ07Ea6+9hoMHDyItLc3mcRs3boxNmzahT58+EAQB06dPd+jMyeDBg/HII49UeFno0qVL6Nq1Kxo0aIB3330X165ds2wLDQ21lOnevTvWrl2LDh064MyZM9iwYQPi4+MRFBSE3377DW+99RY0Gg169OgB4O5dUXl5eXjiiSdQo0YNHD16FBMnTsRTTz2FyMhIuz9HZdmdau3cuRN9+vRBeHg4BEHA5s2bbZYvmaTn/leLFi0sZWbNmlVme9OmTe3+MM4mCAKCgoIQGBgIQRA8HQ6RYwQBuloB0NUKANiPiawEBAQgICCgwu1z587F9OnTkZqaimbNmiExMRHffPMNoqKiANwd+Pr5559j8+bNiI6OxrJlyzB//nybx3zvvfdQq1YtdOzYEX369EFCQgLatm1rd+wXLlzAlStXKtyekZGBU6dOITMzE/Xq1UNYWJjlVcJoNOLEiRMoLCwEAKjVavzwww/o1asXGjVqhIEDB8Lf3x+7du1CUFAQgLvzyixfvhydOnVCs2bNMH78eDz33HP4+uuv7f4M9hBEO+8H+/bbb7F79260a9cO/fv3xxdffIF+/fpVWP727dtWk/MUFxcjOjoab7zxBmbNmgXgbsKyceNGq/vQfXx8ygyAqkh+fj4CAwNx+/Ztmx3PEUajEenp6cgNKoIooSHKo558ydMh2KWknXv16lXmuq03W7Z3vd376It1GJd59/bJhd1XQ+WjdnZYFRLMQOg1jeTaWYqk2qcrQ6fT4ezZs4iKioJa7b7+Wx6z2Yz8/HwEBAQ4dLmDKudh27miPmPP77fdl4R69uyJnj17Vrp8ycCdEps3b8bNmzfL3O/u4+NjOUXlLfR6PcaNG4fz58+j16RB8OFlIZIgmcGIjos+BgBkv/EqAI1nAyIicoDbx7CsXLkScXFxaNCggdX6kydPIjw8HGq1GrGxsUhNTa1wimS9Xm81sKfk+QVGoxFGo/OelVJUVIRly5YBAHqOHwBBKZ2ExZnt4A4l8UotbkeefiyI1suufoKy3GjC45syAAA/jvozAOm1sxRJtU9XhtFohCiKMJvNDo29cKaSiwQl8ZBrPGw7m81miKIIo9FoNV7Inr8PtyYsly9fxrfffot169ZZrY+JiUFaWhqaNGmCK1euYPbs2ejcuTOOHDkCf3//MvWkpqZi9uzZZdZv27YNvr6+Tou39D3nwb9roC707KlPe6Snp3s6BIdkZGR4OgS7hDpwtkJnujeOJOSaBmq5a/uVXHfveMG/a2BSS6+dpawqtnXJGfGCgoIHPkjPXe7cuePpEKoFR9vZYDCgqKgIO3fuRHHxvdvQS8bOVIZbE5Y1a9agZs2aZca8lL7E1KpVK8TExKBBgwb49NNPMWLEiDL1pKSkWE1Qk5+fj4iICMTHxzt1DItWq7UsX61dBKWf/dM/e8rwJ573dAh2MRqNyMjIQI8ePSR1vX/V/s/t3kdvupcI5wUVQSV3bb/yKbp3vKu1i1CnUC25dpYiqfbpytDpdLh48SJq1Kjh8TEsoijizp078Pf3580RLvSw7azT6aDRaPD000+XGcNSWW5LWERRxKpVq/Dqq6+Wmd73fjVr1sRjjz2GU6dOlbtdpVKVO2mPQqFw6hdD6bpEGSQ16FaqX5DO/jd0NUf6hFjqbKoouL5fla6/ZFlq7SxlVbGtTSYTBEGATCbz+EDXkssTJfGQazxsO8tkMgiCUObvwZ6/Dbf96+7YsQOnTp0q94zJ/QoKCnD69GmXzZZHRERE0mJ3wlJQUICcnBzk5OQAAM6ePYucnBzLMxBSUlIwePDgMvutXLkSMTExePzxx8tsmzBhAnbs2IFz585hz549+NOf/gS5XI5BgwbZGx4RERFVQXZfEjpw4AC6detmeV8ylmTIkCFIS0vDlStXyjzA6fbt2/j888+xaNGicuv87bffMGjQINy4cQNBQUHo1KkT9u7da5mkhoiIiKo3uxOWrl27wtZcc+VNSRwYGGhzJPD69fZPvuUOGo0G//vf/7B9+3aYVLbH3RB5q2KVEv/Z9IFlmVPzE5EUcYSSDTKZDJGRkQgJCeFgLpIumQx3woJwJywIYD8mcrmsrCwIgoBbt255OpQqhd9eRERUbZQ8327UqFFlto0dOxaCIGDo0KHuD+wBjEYjJk+ejJYtW8LPzw/h4eEYPHgwLl++7OnQ3IYJiw0GgwFTpkxBWloaio3FD96ByAvJjMV48p//wZP//A9k7MdEiIiIwPr1662ec6fT6bBu3boKZ1j3tMLCQhw6dAjTp0/HoUOHsGnTJpw4cQLPPfecp0NzGyYsNhiNRrz33nvYvHkzTMX8oidpkhUXo/W6r9F63deQsR8ToW3btoiIiMCmTZss6zZt2oT69eujTZs2VmX1ej3++te/Ijg4GGq1Gp06dcL+/futyqSnp+Oxxx6DRqNBt27dcO7cuTLH3LVrFzp37gyNRoOIiAj89a9/tZqc9EECAwORkZGBAQMGoEmTJnjyySexePFiHDx4sMyNLlUVExYiInIarVZb4av0404eVLb02Q9bZR01fPhwrF692vJ+1apVZR7KCwCTJk3C559/jjVr1uDQoUNo1KgREhIS8PvvvwMALl68iP79+6NPnz7IycnBa6+9hilTpljVcfr0aSQmJuL555/Hzz//jA0bNmDXrl1ISkqylJk1axYiIyPt+gy3b9+GIAioWbOmXftJFRMWIiJymho1alT4ev5560eGBAcHV1i29CNbACAyMrLcco565ZVXsGvXLpw/fx7nz5/H7t278corr1iV0Wq1WLp0Kd555x307NkTzZs3x/Lly6HRaLBy5UoAwNKlS9GwYUMsWLAATZo0wcsvv1xmDExqaipefvlljBs3Do0bN0bHjh3xwQcfYO3atZYkrm7dumjYsGGl49fpdJg8eTIGDRrk1EfSeDO3P62ZiIjI04KCgtC7d2+kpaVBFEX07t0bdevWtSpz+vRpGI1GPPXUU5Z1CoUCHTp0wLFjxwAAx44dQ0xMjNV+sbGxVu9/+ukn/Pzzz/jPf/5jWVfy1OOzZ8+iWbNmSEpKsjrjYovRaMSAAQMgiiKWLl1q1+eWMiYsRETkNAUFFU/0I5fLrd5fvXq1wrL3TyVR3riQhzV8+HBLkrBkyRKn11+ioKAAf/nLX/DXv/61zDZ7B/mWJCvnz5/H999/X23OrgBMWIiIyIn8/PzcWrbkoXyOSExMhMFggCAISEhIKLO9YcOGUCqV2L17Nxo0aADgbsKwf/9+jBs3DgDQrFkzfPXVV1b77d271+p927Zt8euvv6JRo0YOx1py7AEDBuDkyZPYvn076tSp81D1SQ3HsBARUbUkl8tx7Ngx/Prrr2XO/gB3k6TRo0dj4sSJ2LJlC3799VeMHDkShYWFlgf5jho1CidPnsTEiRNx4sQJrFu3rsyM75MnT8aePXuQlJSEnJwcnDx5El9++aXVJaDFixeje/fuFcZqNBrxwgsv4MCBA/jPf/4Dk8mE3Nxc5ObmwmAwOKdBvBzPsNig0Whw+PBh/PDDD/Dh1PwkUcUqJTb8523LMqfmJ7rnQZdU3nrrLZjNZrz66qu4c+cO2rdvj61bt6JWrVoA7l7S+fzzzzF+/Hj885//RIcOHTB//nwMHz7cUkerVq2wY8cO/P3vf0fnzp0hiiIaNmyIgQMHWspcv34dp0+frjCOS5cuWc7ktG7d2mrb9u3b0bVrVzs/ufQwYbFBJpOhRYsWOH/+PHJlRaj4CUpEXkwmw81HIwAAguNnz4mqhPKed1fa5s2brd6r1Wp88MEH+OCDDyrc59lnn8Wzzz5rte7+W6SfeOIJbNu2rcI6Zs2ahVmzZlW4PTIy0uZz/KoDXhIiIiIir8eExQaDwYA5c+bgk08+4dT8JFkyYzHar9iI9is2cmp+IpIsXhKywWg0Yt68eQCAjiMTIVexuUh6ZMXFaL/ycwDAT4OefUBpIiLvxDMsRERE5PWYsBARkUOq+yBQqjxn9BUmLEREZBeFQgEAKCws9HAkJBUlfaWk7ziCgzKIiMgucrkcNWvWtEyt7+vrC0EQPBKL2WyGwWCATqcrM50/OY+j7SyKIgoLC3H16lXUrFmz3An6KosJCxER2S00NBSA7ecBuYMoiigqKoJGo/FY0lQdPGw716xZ09JnHMWEhYiI7CYIAsLCwhAcHAyj0eixOIxGI3bu3Imnn376oS43kG0P084KheKhzqyUYMJig1qtxp49e7B7924olJyan6TJpFTi85XzLMtEziSXy53yY/Qwxy8uLoZarWbC4kLe0M5MWGyQy+Vo3749rl69ilw5p+YnaRLlMlxr3hAAp+YnIumye4TSzp070adPH4SHh0MQhDLPXbhfVlYWBEEo88rNzbUqt2TJEkRGRkKtViMmJgY//vijvaERERFRFWV3wqLVahEdHY0lS5bYtd+JEydw5coVyys4ONiybcOGDUhOTsbMmTNx6NAhREdHIyEhweODuQwGAxYsWIAvvviCU/OTZMmMxYj+938R/e//cmp+IpIsuy8J9ezZEz179rT7QMHBwahZs2a529577z2MHDnS8nTLZcuW4ZtvvsGqVaswZcoUu4/lLEajESkpKQCAtq925dT8JEmy4mLELlkHAPj1Tz08HA0RkWPc9gvcunVr6PV6PP7445g1axaeeuopAHfPYhw8eNCSGACATCZDXFwcsrOzy61Lr9dDr9db3ufn5wO4m2A4c7R66boEs7Su/3ty1L4jSuKVWtyO9AlBtF52db8qXX/JstTaWYqk2qelhu3sHq5qZ3vqc3nCEhYWhmXLlqF9+/bQ6/VYsWIFunbtin379qFt27a4fv06TCYTQkJCrPYLCQnB8ePHy60zNTUVs2fPLrN+27Zt8PX1dVrsOp3Oshz8uwbqQrXT6na19PR0T4fgkIyMDE+HYJdQaOzeR2e6N4dByDUN1HLX9iu57t7xgn/XwKSWXjtLGdvaPdjO7uHsdrZntmSXJyxNmjRBkyZNLO87duyI06dP4/3338fHH3/sUJ0pKSlITk62vM/Pz0dERATi4+MREBDw0DGX0Gq1luWrtYug9JPOfULDn3je0yHYxWg0IiMjAz169JDUrYmr9n9u9z56071EOC+oCCq5a/uVT9G9412tXYQ6hWrJtbMUSbVPSw3b2T1c1c4lV0gqwyODMjp06IBdu3YBAOrWrQu5XI68vDyrMnl5eRXOiqdSqaBSqcqsVygUTm3I0nWJsrsvqZDqH66z/w1dzZE+IZa6RCMKru9XpesvWZZaO0sZ29o92M7u4crf2QfxyE9wTk4OwsLCAABKpRLt2rVDZmamZbvZbEZmZiZiY2M9ER4RERF5GbvPsBQUFODUqVOW92fPnkVOTg5q166N+vXrIyUlBZcuXcLatWsBAAsXLkRUVBRatGgBnU6HFStW4Pvvv8e2bdssdSQnJ2PIkCFo3749OnTogIULF0Kr1VruGiIiIqLqze6E5cCBA+jWrZvlfclYkiFDhiAtLQ1XrlzBhQsXLNsNBgP+9re/4dKlS/D19UWrVq3w3XffWdUxcOBAXLt2DTNmzEBubi5at26NLVu2lBmI625qtRoZGRnYu3cvp+YnyTIplfhqyXTLMhGRFNmdsHTt2hWiWPEgwbS0NKv3kyZNwqRJkx5Yb1JSEpKSkuwNx6Xkcjm6dOkCrVbLqflJskS5DJfbNgcgrVvziYhKk9AwUiIiIqqumLDYYDQasXTpUqSnp8NUzCnNSZpkxcVosXEbWmzcBhn7MRFJFOeat8FgMODNN98EACwaEAulks1F0iMzFqPzgtUAgP/1fNrD0RAROYZnWIiIiMjrMWEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx/t0bVCpVNi8eTMOHDgAHz4FlCTKpFAg/d2JlmUiIiliwmKDj48PevXqBQDI9eHU/CRNoo8cF55qC4BT8xORdPGSEBEREXk9Jiw2GI1GrF27FpmZmZyanyRLVlyMJt/sQJNvdnBqfiKSLF4SssFgMOC1114DACzqu5pT85MkyYzF6DZvGQDgTNcYD0dDROQYnmEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx4SFiIiIvB7v07VBpVJh3bp1OHz4MKfmJ8kyKRTYNu9NyzIRkRQxYbHBx8cHL7zwAnx9fTk1P0mW6CPHme5PAuDU/EQkXbwkRERERF7P7oRl586d6NOnD8LDwyEIAjZv3myz/KZNm9CjRw8EBQUhICAAsbGx2Lp1q1WZWbNmQRAEq1fTpk3tDc3piouLsXHjRuzevRumYpOnwyFyiFBswqOZe/Fo5l4I7MdEJFF2JyxarRbR0dFYsmRJpcrv3LkTPXr0QHp6Og4ePIhu3bqhT58+OHz4sFW5Fi1a4MqVK5bXrl277A3N6fR6Pf785z/jnXfeQbHR6OlwiBwiNxoRP20R4qctgpz9mIgkyu4xLD179kTPnj0rXX7hwoVW7+fPn48vv/wS//3vf9GmTZt7gfj4IDQ01N5wiIiIqBpw+6Bbs9mMO3fuoHbt2lbrT548ifDwcKjVasTGxiI1NRX169cvtw69Xg+9Xm95n5+fD+Du05WNTvw/yNJ1CWZpDVh0Zju4Q0m8UovbkT4hiNbLru5XpesvWZZaO0uRVPu01LCd3cNV7WxPfW5PWN59910UFBRgwIABlnUxMTFIS0tDkyZNcOXKFcyePRudO3fGkSNH4O/vX6aO1NRUzJ49u8z6bdu2wdfX12mx6nQ6y3Lw7xqoC9VOq9vV0tPTPR2CQzIyMjwdgl1CobF7H51JsCyHXNNALXdtv5Lr7h0v+HcNTGrptbOUsa3dg+3sHs5u58LCwkqXdWvCsm7dOsyePRtffvklgoODLetLX2Jq1aoVYmJi0KBBA3z66acYMWJEmXpSUlKQnJxseZ+fn4+IiAjEx8cjICDAafFqtVrL8tXaRVD6SefG5uFPPO/pEOxiNBqRkZGBHj16QCGhuUJW7f/c7n30pnuJcF5QEVRy1/Yrn6J7x7tauwh1CtWSa2cpkmqflhq2s3u4qp1LrpBUhtsSlvXr1+O1117DZ599hri4OJtla9asicceewynTp0qd7tKpYJKpSqzXqFQOLUhS9clyu6+pEKqf7jO/jd0NUf6hFjqEo0ouL5fla6/ZFlq7SxlbGv3YDu7hyt/Zx/ELT/Bn3zyCYYNG4ZPPvkEvXv3fmD5goICnD59GmFhYW6IjoiIiLyd3WdYCgoKrM58nD17Fjk5Oahduzbq16+PlJQUXLp0CWvXrgVw9zLQkCFDsGjRIsTExCA3NxcAoNFoEBgYCACYMGEC+vTpgwYNGuDy5cuYOXMm5HI5Bg0a5IzP6DClUokVK1bgp59+go+CkwKTNJkVPtg+bZRlmYhIiuw+w3LgwAG0adPGcktycnIy2rRpgxkzZgAArly5ggsXLljKf/TRRyguLsbYsWMRFhZmeb355puWMr/99hsGDRqEJk2aYMCAAahTpw727t2LoKCgh/18D0WhUGDw4MHo3r075D78oidpMvv44ETvLjjRuwvM7MdEJFF2f3t17doVoljxIMG0tDSr91lZWQ+sc/369faGQURERNWIhIaRul9xcTHS09Nx4MABTs1PkiUUm1B/9yHU332IU/MTkWTx/LANer0e/fr1AwAs6roaSqXcswEROUBuNKLXhHcAACu/W+3haIiIHMMzLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PWYsBAREZHX423NNiiVSixatAhHjx7l1PwkWWaFD3742zDLMhGRFPHbywaFQoHRo0cjPT0duT5FqHh+XyLvZfbxwdEX4gEAgvkBhYmIvBQvCREREZHXY8Jig8lkwo4dO/DLL7/AbOL/mpI0CSYzwg/9ivBDv0JgPyYiieIlIRt0Oh169OgBAFj03WooFWoPR0RkP7nBgOfGzgVQMjW/n2cDIiJyAM+wEBERkddjwkJERERejwkLEREReT0mLEREROT1mLAQERGR12PCQkRERF6PtzXboFAokJqaiuPHj0Puw6YiaTL7+CB77J8ty0REUsRvLxuUSiX+9re/3Z2aX8Gp+UmazAof/PRKHwCcmp+IpIuXhIiIiMjrMWGxwWQy4cCBAzh58iSn5ifJEkxmBP16GkG/nubU/EQkWXYnLDt37kSfPn0QHh4OQRCwefPmB+6TlZWFtm3bQqVSoVGjRkhLSytTZsmSJYiMjIRarUZMTAx+/PFHe0NzOp1Oh44dO2LixIkwGgyeDofIIXKDAc+PmIbnR0yDnP2YiCTK7oRFq9UiOjoaS5YsqVT5s2fPonfv3ujWrRtycnIwbtw4vPbaa9i6daulzIYNG5CcnIyZM2fi0KFDiI6ORkJCAq5evWpveERERFQF2T3otmfPnujZs2elyy9btgxRUVFYsGABAKBZs2bYtWsX3n//fSQkJAAA3nvvPYwcORLDhg2z7PPNN99g1apVmDJlir0hEhERURXj8ruEsrOzERcXZ7UuISEB48aNAwAYDAYcPHgQKSkplu0ymQxxcXHIzs4ut069Xg+9Xm95n5+fDwAwGo0wGo1Oi710XYJZWndYOLMd3KEkXqnF7UifEETrZVf3q9L1lyxLrZ2lSKp9WmrYzu7hqna2pz6XJyy5ubkICQmxWhcSEoL8/HwUFRXh5s2bMJlM5ZY5fvx4uXWmpqZi9uzZZdZv27YNvr6+Totdp9NZloN/10BdqHZa3a6Wnp7u6RAckpGR4ekQ7BIKjd376EyCZTnkmgZquWv7lVx373jBv2tgUkuvnaWMbe0ebGf3cHY7FxYWVrqsJOdhSUlJQXJysuV9fn4+IiIiEB8fj4CAAKcdR6vVWpav1i6C0k86M7EMf+J5T4dgF6PRiIyMDPTo0QMKhcLT4VTaqv2f272P3nQvEc4LKoJK7tp+5VN073hXaxehTqG6WrSzpwlmIOSGBnl1iiBK6H5Mfne4h9T6dEl/dnY7l1whqQyXJyyhoaHIy8uzWpeXl4eAgABoNBrI5XLI5fJyy4SGhpZbp0qlgkqlKrNeoVA4tSFL1yXKIKkvHSn94Zbm7H9DV3OkT4ilLtGIguv7Ven6S5arQzt7C353uAf7tHu48nf2QVzeZLGxscjMzLRal5GRgdjYWAB3Z5Nt166dVRmz2YzMzExLGU9RKBSYNm0aBg4cyKn5SbLMPj44MOJ5HBjxPKfmJyLJsvvbq6CgAKdOnbK8P3v2LHJyclC7dm3Ur18fKSkpuHTpEtauXQsAGDVqFBYvXoxJkyZh+PDh+P777/Hpp5/im2++sdSRnJyMIUOGoH379ujQoQMWLlwIrVZruWvIU5RKJWbMmMGp+UnSzAofHHjtBQDSGjhORFSa3QnLgQMH0K1bN8v7krEkQ4YMQVpaGq5cuYILFy5YtkdFReGbb77B+PHjsWjRItSrVw8rVqyw3NIMAAMHDsS1a9cwY8YM5ObmonXr1tiyZUuZgbhERERUPdmdsHTt2hWiWPG5hvJmse3atSsOHz5ss96kpCQkJSXZG45Lmc1mHD16FBcuXIBPnToQZBK96EjVm9mMWucuAQBu1X/Ew8EQETmGF7RtKCoqQps2bQAAi75bDaWPdG5rJirhozdg4MuTAAArv1sNwM+zAREROYCnDIiIiMjrMWEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx9uabVAoFEhOTsaZM2c4NT9JltnHBzl/ftayTEQkRfz2skGpVOKtt97i1PwkaWaFD/a+8TIATs1PRNLFS0JERETk9Ziw2GA2m3Hu3Dnk5eXBbOb/mpJEmc3wv3IN/leuAezHRCRRvCRkQ1FRER577DEAnJqfpMtHb8DL/f8KgFPzE5F08QwLEREReT0mLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PV4W7MNPj4+GDVqFM6fPw+ZXO7pcIgcYpbLcaR/D8syEZEUMWGxQaVS4YMPPrg7Nb+SU/OTNJmVCuyaOBwAp+YnIuniJSEiIiLyekxYbBBFEdeuXcPt27chijy/QhIlilDfzIf6Zj7AfkxEEsVLQjYUFhbikUceAfDH1Px+nJqfpMdHp8fQXn8BUDI1v69nAyIicoBDZ1iWLFmCyMhIqNVqxMTE4Mcff6ywbNeuXSEIQplX7969LWWGDh1aZntiYqIjoREREVEVZPcZlg0bNiA5ORnLli1DTEwMFi5ciISEBJw4cQLBwcFlym/atAkGg8Hy/saNG4iOjsaLL75oVS4xMRGrV6+2vFepVPaGRkRERFWU3WdY3nvvPYwcORLDhg1D8+bNsWzZMvj6+mLVqlXllq9duzZCQ0Mtr4yMDPj6+pZJWFQqlVW5WrVqOfaJiIiIqMqx6wyLwWDAwYMHkZKSYlknk8kQFxeH7OzsStWxcuVKvPTSS/Dzs37EfVZWFoKDg1GrVi0888wzmDdvHurUqVNuHXq9Hnq93vI+Pz8fAGA0GmE0Gu35SDaVrkswS+uWUGe2gzuUxCu1uB3pE4JovezqflW6/pLl6tDOnlYSs9Ril1rfqE7fHZ7kqu8Oe+qzK2G5fv06TCYTQkJCrNaHhITg+PHjD9z/xx9/xJEjR7By5Uqr9YmJiejfvz+ioqJw+vRpTJ06FT179kR2djbk5Ux0lZqaitmzZ5dZv23bNvj6Om9AoU6nsywH/66BulA6g27T09M9HYJDMjIyPB2CXUKhsXsfnUmwLIdc00Atd22/kuvuHS/4dw1M6urRzt4i5Ia0Yud3h3tItU87u50LCwsrXdatdwmtXLkSLVu2RIcOHazWv/TSS5blli1bolWrVmjYsCGysrLQvXv3MvWkpKQgOTnZ8j4/Px8RERGIj49HQECA0+LVarWW5au1i6D0k84tocOfeN7TIdjFaDQiIyMDPXr0gEKh8HQ4lbZq/+d276M33UuE84KKoJK7tl/5FN073tXaRahTqK4W7expgvluspJXpwiihCaQ4HeHe0itT5f0Z2e3c8kVksqwK2GpW7cu5HI58vLyrNbn5eUhNDTU5r5arRbr16/HnDlzHnicRx99FHXr1sWpU6fKTVhUKlW5g3IVCoVTG1Kj0eDVV1/Fb7/9BkEhl9SXjpT+cEtz9r+hqznSJ8RSp4JFwbE67GFSyHGi19OWZaB6tLO3EGXSil9K/aI09mn3cHY721OXXU2mVCrRrl07ZGZmWtaZzWZkZmYiNjbW5r6fffYZ9Ho9XnnllQce57fffsONGzcQFhZmT3hOp1KpsHLlSrz55ptQKKXzh0BUmlmpwPbpo7F9+miY2Y+JSKLszvGSk5OxfPlyrFmzBseOHcPo0aOh1WoxbNgwAMDgwYOtBuWWWLlyJfr161dmIG1BQQEmTpyIvXv34ty5c8jMzETfvn3RqFEjJCQkOPixiIiIqCqxewzLwIEDce3aNcyYMQO5ublo3bo1tmzZYhmIe+HCBchk1nnQiRMnsGvXLmzbtq1MfXK5HD///DPWrFmDW7duITw8HPHx8Zg7d67H52IRRRFarRY6ne6PqfmFB+5D5HVEET66u3fVmZSc34iIpMmhQbdJSUlISkoqd1tWVlaZdU2aNKnwWTwajQZbt251JAyXKywstMwHw6n5Sap8dHq89szdM6Ccmp+IpEqiw36IiIioOmHCQkRERF6PCQsRERF5PSYsRERE5PWYsBAREZHXY8JCREREXs+tzxKSGrlcjv79+yM3N7fM3DJEUiHKZDjdLcayTEQkRUxYbFCr1Vi/fj3S09ORqyqCdB59SHSPSaVExvxxAKT3SHsiohL83y0iIiLyekxYiIiIyOsxYbFBq9VCqVSiX79+0BfpPB0OkUN8inQYFTsIo2IHwYf9mIgkigkLEREReT0mLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PU4060NcrkcPXv2xNWrVzk1P0mWKJPhfMfWlmUiIiliwmKDWq3Gl19+yan5SdJMKiW+XTAZAKfmJyLp4v9uERERkddjwkJERERejwmLDVqtFjVr1sTAgQM5NT9Jlk+RDiO6DcWIbkM5NT8RSRbHsDxAYWGhp0MgemgKnd7TIRARPRSHzrAsWbIEkZGRUKvViImJwY8//lhh2bS0NAiCYPVSq9VWZURRxIwZMxAWFgaNRoO4uDicPHnSkdCIiIioCrI7YdmwYQOSk5Mxc+ZMHDp0CNHR0UhISMDVq1cr3CcgIABXrlyxvM6fP2+1/e2338YHH3yAZcuWYd++ffDz80NCQgJ0Op6+JiIiIgcSlvfeew8jR47EsGHD0Lx5cyxbtgy+vr5YtWpVhfsIgoDQ0FDLKyQkxLJNFEUsXLgQ06ZNQ9++fdGqVSusXbsWly9fxubNmx36UERERFS12DWGxWAw4ODBg0hJSbGsk8lkiIuLQ3Z2doX7FRQUoEGDBjCbzWjbti3mz5+PFi1aAADOnj2L3NxcxMXFWcoHBgYiJiYG2dnZeOmll8rUp9frodffuyafn58PADAajTAajfZ8JJtK1yWYpTWHhTPbwR1K4pVa3I70CUG0XnZ1vypdf8lydWhnTyuJWWqxS61vVKfvDk9y1XeHPfXZlbBcv34dJpPJ6gwJAISEhOD48ePl7tOkSROsWrUKrVq1wu3bt/Huu++iY8eOOHr0KOrVq4fc3FxLHffXWbLtfqmpqZg9e3aZ9du2bYOvr689H8mm0pekgn/XQF2otlHau6Snp3s6BIdkZGR4OgS7hEJj9z46k2BZDrmmgVru2n4l1907XvDvGpjU1aOdvUXIDWnFzu8O95Bqn3Z2O9tzY4vL7xKKjY1FbGys5X3Hjh3RrFkzfPjhh5g7d65DdaakpCA5OdnyPj8/HxEREYiPj0dAQMBDx1yiqKgInTt3xu+//45rdfVQaKQz1+3wJ573dAh2MRqNyMjIQI8ePaBQKDwdTqWt2v+53fvoTfcS4bygIqjkru1Xcr0Bl9s0u3u8unrULVBXi3b2NMF8N1nJq1MEUUITSPC7wz2k1qdL+rOz27nkCkll2JWw1K1bF3K5HHl5eVbr8/LyEBoaWqk6FAoF2rRpg1OnTgGAZb+8vDyEhYVZ1dm6dety61CpVFCpVOXW7cyGVCgUyMzMvDs1v0ZaXzpS+sMtzdn/hq7mSJ8QS50KFgXH6rBHsUaJr/41A8Afp3ULqkc7ewtRJq34pdQvSmOfdg9X/M5Wll1NplQq0a5dO2RmZlrWmc1mZGZmWp1FscVkMuGXX36xJCdRUVEIDQ21qjM/Px/79u2rdJ1ERERUtdl9SSg5ORlDhgxB+/bt0aFDByxcuBBarRbDhg0DAAwePBiPPPIIUlNTAQBz5szBk08+iUaNGuHWrVt45513cP78ebz22msA7t5BNG7cOMybNw+NGzdGVFQUpk+fjvDwcPTr1895n5SIiIgky+6EZeDAgbh27RpmzJiB3NxctG7dGlu2bLEMmr1w4QJkpR5hf/PmTYwcORK5ubmoVasW2rVrhz179qB58+aWMpMmTYJWq8Xrr7+OW7duoVOnTtiyZUuZCebcTavVIjIyEgaDAXM3LYLSTzqDbolK+BTp8HL/vwIA1m38AJDoYD8iqt4cGnSblJSEpKSkcrdlZWVZvX///ffx/vvv26xPEATMmTMHc+bMcSQcl7p+/bqnQyB6aJpbdzwdAhHRQ5HosB8iIiKqTpiwEBERkddjwkJERERejwkLEREReT0mLEREROT1XD41v5TJZDK0a9cOt2/fhiBjbkfSJAoyXG326N1l9mMikigmLDZoNBpkZ2ffnZpfVQTpPEmI6B6TWolNq/4PgPSeEEtEVIL/u0VERERejwkLEREReT0mLDYUFhaicePGGDlyJAw6vafDIXKIj06Pl//0Bl7+0xvwYT8mIoniGBYbRFHE+fPnLctEkiSK8M+9blkmIpIinmEhIiIir8eEhYiIiLweExYiIiLyekxYiIiIyOsxYSEiIiKvx7uEbBAEAc2aNUNBQQEEQfB0OESOEQT8HvWIZZmISIqYsNjg6+uLn3766e7U/GpOzU/SVKxW4dN17wL4Y2r+O56Nh4jIEbwkRERERF6PCQsRERF5PSYsNhQWFiI6OhpvvPEGp+YnyfLR6THgzxMw4M8TODU/EUkWx7DYIIoijh07ZlkmkiRRRO2zlyzLRERSxDMsRERE5PUcSliWLFmCyMhIqNVqxMTE4Mcff6yw7PLly9G5c2fUqlULtWrVQlxcXJnyQ4cOhSAIVq/ExERHQiMiIqIqyO6EZcOGDUhOTsbMmTNx6NAhREdHIyEhAVevXi23fFZWFgYNGoTt27cjOzsbERERiI+Px6VLl6zKJSYm4sqVK5bXJ5984tgnIiIioirH7oTlvffew8iRIzFs2DA0b94cy5Ytg6+vL1atWlVu+f/85z8YM2YMWrdujaZNm2LFihUwm83IzMy0KqdSqRAaGmp51apVy7FPRERERFWOXYNuDQYDDh48iJSUFMs6mUyGuLg4ZGdnV6qOwsJCGI1G1K5d22p9VlYWgoODUatWLTzzzDOYN28e6tSpU24der0eev29ux3y8/MBAEajEUaj0Z6PZFPpugTzH5NuSYQz28EdSuKVWtyO9AlBtF52db8qXX/JcnVoZ08riVlqsUutb1Sn7w5PctV3hz312ZWwXL9+HSaTCSEhIVbrQ0JCcPz48UrVMXnyZISHhyMuLs6yLjExEf3790dUVBROnz6NqVOnomfPnsjOzoZcLi9TR2pqKmbPnl1m/bZt2+Dr62vPR7JJr9cjKCgIABBy0xeqQpXT6na19PR0T4fgkIyMDE+HYJdQaOzeR2e6Nz1+yDUN1HK1M0MqQ66XofCPfhx80xcmVfVoZ28RckNasfO7wz2k2qed3c6FhYWVLuvW25rfeustrF+/HllZWVCr731Jv/TSS5blli1bolWrVmjYsCGysrLQvXv3MvWkpKQgOTnZ8j4/P98yNiYgIMCpMT/77LPIyMhAXp0iiLIip9btSsOfeN7TIdjFaDQiIyMDPXr0gEKh8HQ4lbZq/+d276M36SzLeUFFUMldf6vxx5s/AAAIZjNCbqBatLOnCea7ycrd7w5PR1N5/O5wD6n16ZL+7Ox2LrlCUhl2JSx169aFXC5HXl6e1fq8vDyEhoba3Pfdd9/FW2+9he+++w6tWrWyWfbRRx9F3bp1cerUqXITFpVKBZWq7NkOhULhsg4ryiCpLx0p/eGW5sp/Q1dwpE+IpU4Fi4Jn+lV1aGdvwe8O92Cfdg9nt7M9ddnVZEqlEu3atbMaMFsygDY2NrbC/d5++23MnTsXW7ZsQfv27R94nN9++w03btxAWFiYPeERERFRFWV3jpecnIzly5djzZo1OHbsGEaPHg2tVothw4YBAAYPHmw1KPcf//gHpk+fjlWrViEyMhK5ubnIzc1FQUEBAKCgoAATJ07E3r17ce7cOWRmZqJv375o1KgREhISnPQxHVNUVITY2FhMmDABBr3Bo7EQOUquM6D/8L+j//C/Q85+TEQSZfcYloEDB+LatWuYMWMGcnNz0bp1a2zZssUyEPfChQuQye7lQUuXLoXBYMALL7xgVc/MmTMxa9YsyOVy/Pzzz1izZg1u3bqF8PBwxMfHY+7cueVe9nEns9mMgwcPAgBEs8SGdBP9QRDNCD525u4y+zERSZRDg26TkpKQlJRU7rasrCyr9+fOnbNZl0ajwdatWx0Jg4iIiKoJiQ77ISIiouqECQsRERF5PSYsRERE5PWYsBAREZHXc+tMt1JUt25dGAy8FZSkraimv6dDICJ6KExYbPDz88Ply5eRnp6OXE0RXD+BOpHzFWvUWPPtRwD+eIBZgWfjISJyBC8JERERkddjwkJERERejwmLDUVFRYiLi8Pf//53Ts1PkiXXGfDcmDl4bswcTs1PRJLFMSw2mM1m7Ny5EwCn5ifpEkQzwg8fu7vMfkxEEsUzLEREROT1mLAQERGR12PCQkRERF6PCQsRERF5PSYsRERE5PV4l9AD+Pr6wmQyeToMoodiVKs8HQIR0UNhwmKDn58fbt26xan5SdKKNWqs3J4GgFPzE5F08ZIQEREReT0mLEREROT1mLDYoNPp0LdvX8ydOxdGTmlOEiXXG9Dzb/9Az7/9g1PzE5FkcQyLDSaTCd9++y0AYDCnNCeJEsxmNNiTY1kmIpIinmEhIiIir8eEhYiIiLyeQwnLkiVLEBkZCbVajZiYGPz44482y3/22Wdo2rQp1Go1WrZsifT0dKvtoihixowZCAsLg0ajQVxcHE6ePOlIaERERFQF2Z2wbNiwAcnJyZg5cyYOHTqE6OhoJCQk4OrVq+WW37NnDwYNGoQRI0bg8OHD6NevH/r164cjR45Yyrz99tv44IMPsGzZMuzbtw9+fn5ISEiATqdz/JMRERFRlWF3wvLee+9h5MiRGDZsGJo3b45ly5bB19cXq1atKrf8okWLkJiYiIkTJ6JZs2aYO3cu2rZti8WLFwO4e3Zl4cKFmDZtGvr27YtWrVph7dq1uHz5MjZv3vxQH46IiIiqBrvuEjIYDDh48CBSUlIs62QyGeLi4pCdnV3uPtnZ2UhOTrZal5CQYElGzp49i9zcXMTFxVm2BwYGIiYmBtnZ2XjppZfK1KnX66HX6y3vb9++DQD4/fffYTQa7flINmm1Wsuy7k4RzBK6w+LGjRueDsEuRqMRhYWFuHHjBhQKhafDqTTdnUK79zGY9MAfJw/1BUUQ5a7tVz5FeuT/say7U4TCIlW1aGdPE8xAYaEI3Z0iiBIaLcjvDveQWp8u6c/Obuc7d+4AuHvy4kHsSliuX78Ok8mEkJAQq/UhISE4fvx4ufvk5uaWWz43N9eyvWRdRWXul5qaitmzZ5dZHxUVVbkP4oDJfxrjsrpdYTxGeDoEqoTJb7mnX71RsiCxfkzux+8O8oQ7d+4gMDDQZhlJzsOSkpJiddbGbDbj999/R506dSAIglOPlZ+fj4iICFy8eBEBAQFOrZvuYTu7B9vZfdjW7sF2dg9XtbMoirhz5w7Cw8MfWNauhKVu3bqQy+XIy8uzWp+Xl4fQ0NBy9wkNDbVZvuS/eXl5CAsLsyrTunXrcutUqVRQqayfPluzZk17PordAgIC+MfgBmxn92A7uw/b2j3Yzu7hinZ+0JmVEnZdWVUqlWjXrh0yMzMt68xmMzIzMxEbG1vuPrGxsVblASAjI8NSPioqCqGhoVZl8vPzsW/fvgrrJCIiourF7ktCycnJGDJkCNq3b48OHTpg4cKF0Gq1GDZsGABg8ODBeOSRR5CamgoAePPNN9GlSxcsWLAAvXv3xvr163HgwAF89NFHAABBEDBu3DjMmzcPjRs3RlRUFKZPn47w8HD069fPeZ+UiIiIJMvuhGXgwIG4du0aZsyYgdzcXLRu3RpbtmyxDJq9cOECZLJ7J246duyIdevWYdq0aZg6dSoaN26MzZs34/HHH7eUmTRpErRaLV5//XXcunULnTp1wpYtW6BWq53wER+OSqXCzJkzy1yCIudiO7sH29l92NbuwXZ2D29oZ0GszL1ERERERB4kodkBiIiIqLpiwkJERERejwkLEREReT0mLEREROT1qnXCkpqaiieeeAL+/v4IDg5Gv379cOLEiQfu99lnn6Fp06ZQq9Vo2bIl0tPT3RCtdDnSzsuXL0fnzp1Rq1Yt1KpVC3Fxcfjxxx/dFLE0OdqfS6xfvx6CIHA6gUpwtK1v3bqFsWPHIiwsDCqVCo899hi/P2xwtJ0XLlyIJk2aQKPRICIiAuPHj4dOp3NDxNK0dOlStGrVyjIpXGxsLL799lub+3jid7BaJyw7duzA2LFjsXfvXmRkZMBoNCI+Pt7qoYf327NnDwYNGoQRI0bg8OHD6NevH/r164cjR464MXJpcaSds7KyMGjQIGzfvh3Z2dmIiIhAfHw8Ll265MbIpcWRdi5x7tw5TJgwAZ07d3ZDpNLnSFsbDAb06NED586dw8aNG3HixAksX74cjzzyiBsjlxZH2nndunWYMmUKZs6ciWPHjmHlypXYsGEDpk6d6sbIpaVevXp46623cPDgQRw4cADPPPMM+vbti6NHj5Zb3mO/gyJZXL16VQQg7tixo8IyAwYMEHv37m21LiYmRvzLX/7i6vCqjMq08/2Ki4tFf39/cc2aNS6MrGqpbDsXFxeLHTt2FFesWCEOGTJE7Nu3r3sCrEIq09ZLly4VH330UdFgMLgxsqqlMu08duxY8ZlnnrFal5ycLD711FOuDq9KqVWrlrhixYpyt3nqd7Ban2G53+3btwEAtWvXrrBMdnY24uLirNYlJCQgOzvbpbFVJZVp5/sVFhbCaDTatU91V9l2njNnDoKDgzFiBJ/S66jKtPVXX32F2NhYjB07FiEhIXj88ccxf/58mEwmd4UpeZVp544dO+LgwYOWS8hnzpxBeno6evXq5ZYYpc5kMmH9+vXQarUVPh7HU7+DknxasyuYzWaMGzcOTz31lNUsvPfLzc21zOpbIiQkBLm5ua4OsUqobDvfb/LkyQgPDy/zR0Llq2w779q1CytXrkROTo77gqtiKtvWZ86cwffff4+XX34Z6enpOHXqFMaMGQOj0YiZM2e6MWJpqmw7//nPf8b169fRqVMniKKI4uJijBo1ipeEHuCXX35BbGwsdDodatSogS+++ALNmzcvt6ynfgeZsPxh7NixOHLkCHbt2uXpUKo0R9r5rbfewvr165GVleUVj2uQgsq08507d/Dqq69i+fLlqFu3rhujq1oq26fNZjOCg4Px0UcfQS6Xo127drh06RLeeecdJiyVUNl2zsrKwvz58/Gvf/0LMTExOHXqFN58803MnTsX06dPd1O00tOkSRPk5OTg9u3b2LhxI4YMGYIdO3ZUmLR4hEsvOEnE2LFjxXr16olnzpx5YNmIiAjx/ffft1o3Y8YMsVWrVi6Kruqwp51LvPPOO2JgYKC4f/9+F0ZWtVS2nQ8fPiwCEOVyueUlCIIoCIIol8vFU6dOuSli6bKnTz/99NNi9+7drdalp6eLAES9Xu+qEKsEe9q5U6dO4oQJE6zWffzxx6JGoxFNJpOrQqxyunfvLr7++uvlbvPU72C1HsMiiiKSkpLwxRdf4Pvvv0dUVNQD94mNjUVmZqbVuoyMjAqv9ZFj7QwAb7/9NubOnYstW7agffv2Lo5S+uxt56ZNm+KXX35BTk6O5fXcc8+hW7duyMnJQUREhJsilx5H+vRTTz2FU6dOwWw2W9b973//Q1hYGJRKpSvDlSxH2rmwsNDqAbwAIJfLLfVR5ZjNZuj1+nK3eex30KXpkJcbPXq0GBgYKGZlZYlXrlyxvAoLCy1lXn31VXHKlCmW97t37xZ9fHzEd999Vzx27Jg4c+ZMUaFQiL/88osnPoIkONLOb731lqhUKsWNGzda7XPnzh1PfARJcKSd78e7hCrHkba+cOGC6O/vLyYlJYknTpwQv/76azE4OFicN2+eJz6CJDjSzjNnzhT9/f3FTz75RDxz5oy4bds2sWHDhuKAAQM88REkYcqUKeKOHTvEs2fPij///LM4ZcoUURAEcdu2baIoes/vYLVOWACU+1q9erWlTJcuXcQhQ4ZY7ffpp5+Kjz32mKhUKsUWLVqI33zzjXsDlxhH2rlBgwbl7jNz5ky3xy8Vjvbn0piwVI6jbb1nzx4xJiZGVKlU4qOPPir+3//9n1hcXOze4CXEkXY2Go3irFmzxIYNG4pqtVqMiIgQx4wZI968edPt8UvF8OHDxQYNGohKpVIMCgoSu3fvbklWRNF7fgcFUeQ5MiIiIvJu1XoMCxEREUkDExYiIiLyekxYiIiIyOsxYSEiIiKvx4SFiIiIvB4TFiIiIvJ6TFiIiIjI6zFhISIiIq/HhIWI7HLu3DkIgoCcnBynliUisoUJCxFZDB06FIIgQBAEKBQKREVFYdKkSdDpdJYyERERuHLlCh5//HGXxFBYWIiUlBQ0bNgQarUaQUFB6NKlC7788kuXHI+IpMHH0wEQkXdJTEzE6tWrYTQacfDgQQwZMgSCIOAf//gHgLtPvg0NDXXZ8UeNGoV9+/bhn//8J5o3b44bN25gz549uHHjhsuOaTAY+MRkIi/HMyxEZEWlUiE0NBQRERHo168f4uLikJGRYdl+/2Wemzdv4uWXX0ZQUBA0Gg0aN26M1atXl1u3yWTC8OHD0bRpU1y4cKHcMl999RWmTp2KXr16ITIyEu3atcMbb7yB4cOHW8ro9XpMnjwZERERUKlUaNSoEVauXGnZvmPHDnTo0AEqlQphYWGYMmUKiouLLdu7du2KpKQkjBs3DnXr1kVCQgIA4MiRI+jZsydq1KiBkJAQvPrqq7h+/bplv40bN6Jly5bQaDSoU6cO4uLioNVq7W9kIrIbExYiqtCRI0ewZ88em2cfpk+fjl9//RXffvstjh07hqVLl6Ju3bplyun1erz44ovIycnBDz/8gPr165dbX2hoKNLT03Hnzp0Kjzl48GB88skn+OCDD3Ds2DF8+OGHqFGjBgDg0qVL6NWrF5544gn89NNPWLp0KVauXIl58+ZZ1bFmzRoolUrs3r0by5Ytw61bt/DMM8+gTZs2OHDgALZs2YK8vDwMGDAAAHDlyhUMGjQIw4cPx7Fjx5CVlYX+/fuDz48lchOXPw+aiCRjyJAholwuF/38/ESVSiUCEGUymbhx40ZLmbNnz4oAxMOHD4uiKIp9+vQRhw0bVm59JWV/+OEHsXv37mKnTp3EW7du2Yxhx44dYr169USFQiG2b99eHDdunLhr1y7L9hMnTogAxIyMjHL3nzp1qtikSRPRbDZb1i1ZskSsUaOGaDKZRFEUxS5duoht2rSx2m/u3LlifHy81bqLFy+KAMQTJ06IBw8eFAGI586dsxk/EbkGz7AQkZVu3bohJycH+/btw5AhQzBs2DA8//zzFZYfPXo01q9fj9atW2PSpEnYs2dPmTKDBg2CVqvFtm3bEBgYaPP4Tz/9NM6cOYPMzEy88MILOHr0KDp37oy5c+cCAHJyciCXy9GlS5dy9z927BhiY2MhCIJl3VNPPYWCggL89ttvlnXt2rWz2u+nn37C9u3bUaNGDcuradOmAIDTp08jOjoa3bt3R8uWLfHiiy9i+fLluHnzps3PQkTOw4SFiKz4+fmhUaNGiI6OxqpVq7Bv3z6r8SH369mzJ86fP4/x48fj8uXL6N69OyZMmGBVplevXvj555+RnZ1dqRgUCgU6d+6MyZMnY9u2bZgzZw7mzp0Lg8EAjUbzUJ+vhJ+fn9X7goIC9OnTBzk5OVavkydP4umnn4ZcLkdGRga+/fZbNG/eHP/85z/RpEkTnD171inxEJFtTFiIqEIymQxTp07FtGnTUFRUVGG5oKAgDBkyBP/+97+xcOFCfPTRR1bbR48ejbfeegvPPfccduzYYXcczZs3R3FxMXQ6HVq2bAmz2VxhPc2aNUN2drbV2JLdu3fD398f9erVq/AYbdu2xdGjRxEZGYlGjRpZvUqSG0EQ8NRTT2H27Nk4fPgwlEolvvjiC7s/DxHZjwkLEdn04osvQi6XY8mSJeVunzFjBr788kucOnUKR48exddff41mzZqVKffGG29g3rx5ePbZZ7Fr164Kj9e1a1d8+OGHOHjwIM6dO4f09HRMnToV3bp1Q0BAACIjIzFkyBAMHz4cmzdvxtmzZ5GVlYVPP/0UADBmzBhcvHgRb7zxBo4fP44vv/wSM2fORHJyMmSyir/yxo4di99//x2DBg3C/v37cfr0aWzduhXDhg2DyWTCvn37MH/+fBw4cAAXLlzApk2bcO3atXI/KxE5HxMWIrLJx8cHSUlJePvtt8u9hVepVCIlJQWtWrWyXDpZv359uXWNGzcOs2fPRq9evcod6wIACQkJWLNmDeLj49GsWTO88cYbSEhIsCQkALB06VK88MILGDNmDJo2bYqRI0daYnvkkUeQnp6OH3/8EdHR0Rg1ahRGjBiBadOm2fyc4eHh2L17N0wmE+Lj49GyZUuMGzcONWvWhEwmQ0BAAHbu3IlevXrhsccew7Rp07BgwQL07Nmzsk1JRA9BEEXek0dERETejWdYiIiIyOsxYSEiIiKvx4SFiIiIvB4TFiIiIvJ6TFiIiIjI6zFhISIiIq/HhIWIiIi8HhMWIiIi8npMWIiIiMjrMWEhIiIir8eEhYiIiLze/wP/xTPliywnOwAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_palette(sns.color_palette(\"crest\"))\n",
    "\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "mean=analyses_collection_df['risk_score_mean'].item()\n",
    "median=analyses_collection_df['risk_score_median'].item()\n",
    "mode=analyses_collection_df['risk_score_mode'].item()\n",
    "\n",
    "sns.boxplot(data=analyses_df, x=\"risk_score_mean\", ax=ax_box)\n",
    "ax_box.axvline(mean, color='r', linestyle='--')\n",
    "ax_box.axvline(median, color='green', linestyle='-')\n",
    "ax_box.axvline(mode, color='black', linestyle='--')\n",
    "\n",
    "ax_hist.hist(analyses_collection_df[\"risk_scores\"], alpha = 0.8)\n",
    "ax_hist.axvline(mean, color='r', linestyle='--', label=f\"Mean: {round(mean, 2)}\")\n",
    "ax_hist.axvline(median, color='green', linestyle='-', label=f\"Median: {round(median, 2)}\")\n",
    "ax_hist.axvline(mode, color='black', linestyle='--', label=f\"Mode: {mode}\")\n",
    "\n",
    "ax_hist.set(xlabel='Risk Scores')\n",
    "ax_hist.legend()\n",
    "\n",
    "ax_box.set(xlabel='')\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "         Collection Detected PII Type  Count\n0  PII Collection 1          LOCATION      6\n1  PII Collection 1       SCREEN_NAME      7\n2  PII Collection 1               NRP      2\n3  PII Collection 1     EMAIL_ADDRESS      1\n4  PII Collection 1      PHONE_NUMBER      2\n5  PII Collection 1               URL      1",
      "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>Collection</th>\n      <th>Detected PII Type</th>\n      <th>Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PII Collection 1</td>\n      <td>LOCATION</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PII Collection 1</td>\n      <td>SCREEN_NAME</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>PII Collection 1</td>\n      <td>NRP</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PII Collection 1</td>\n      <td>EMAIL_ADDRESS</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>PII Collection 1</td>\n      <td>PHONE_NUMBER</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>PII Collection 1</td>\n      <td>URL</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detected_types = []\n",
    "for detection in analyses_collection_df[\"detected_pii_type_frequencies\"].item():\n",
    "    detected_types.append({\n",
    "        \"Collection\": analyses_collection_df[\"collection_name\"].item(),\n",
    "        \"Detected PII Type\": detection,\n",
    "        \"Count\": analyses_collection_df[\"detected_pii_type_frequencies\"].item()[detection]\n",
    "    })\n",
    "\n",
    "detected_types_df = pd.DataFrame(detected_types)\n",
    "detected_types_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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qefPmyeFwuLWtxYsXKzw8XLt379bIkSM1f/58eXt7m+ZUr15dy5cvN42VLFnS9D0qKkrPPvusaaxs2bIu+6tZs6b69Omj0aNHmx4sctdLL72ke+65R08//XSOy+Pi4tS7d2/n9969e6tly5Z65ZVXVKKE+V/AevXqqWrVqvrwww+1bt06Pfzww/Lyyvkfi9weZ5bo6GiNGDHC+T05OVmhoaHap3PyUcZVj/NG5qFLfxju0Vll2l2MzeiFGf3IRi/M6Ec2emGW1Y+2bdu65Ji8kHWl8WoKXaiULj2w0rZtW7Vt21Zjx47Vo48+qnHjxmnmzJmSpH379qlZs2ZX3U5oaKiqVq2qqlWrKj09XQ888IB2795tuqzu4+OjKlWqXHE7AQEBV52TZcKECapWrZqWLVuWq/k5ufvuu9WuXTtFR0e7PD2+Z88ebd26Vdu2bTPdR5mRkaFFixZp4MCBLtvr37+/XnvtNe3Zs0fbtm277H7dOU5J8vX1dblFQZIy//yAXvwVvTCjH9nohRn9yEYvzLy9vfMlVOZ2mzfE7Qg1a9bU+fPnde+99+rmm2++7EMsf39Q56+6du0qLy8vvf766/lU5SWhoaEaOnSoxowZo4yMaz9bN2XKFH388cfasmWLaTw2NlZ33323vvnmGyUmJjo/I0aMuOzl8l69emnXrl2qXbu2atasec01AQCAoqtQnak8efKkunXrpv79+6tu3boqUaKEtm/frqlTp6pz584qXry45s2bp27duqlTp056/PHHVaVKFf3+++96//33dfToUS1atCjHbRuGoccff1zjx4/XP//5TxUrdumev/T0dJf3PBqGYbrsm5KS4jLH19dXpUqVynFf0dHReuutt3To0CF17979mnpRp04dRUVFafbs2c6xtLQ0/fvf/9bEiRNdnjB/9NFHNX36dH377bfOey2zlCpVSsePH7/qf4m4e5wAAKDoKFRnKv39/dW0aVPNmDFDd999t2rXrq2xY8dq4MCBevXVVyVJnTt31ubNm+Xt7a1evXopPDxcPXv2VFJSkiZNmnTF7fft21dpaWnObUnSt99+q3Llypk+lSpVMq331ltvuczp2bPnZfcTFBSkUaNG6eLFixa6IU2cOFGZmdkn/pcvX66TJ0/qgQcecJlbo0YN1ahR47JnKwMDA1W8ePEr7s/d4wQAAEWH4XD36RbgGiUnJysgIEDPvTdXPsVusrscW3lIqq0S2s1N5vTib+hHNnphRj+y0QuzrH5ERkbm24M6AQEBSkpKcnlY+e91AAAAAJYQKguAo0ePOl/sntMnp1+3AQAAKEgK1YM6N6ry5csrMTHxissBAAAKMkJlAeDl5eXW+x8BAAAKGi5/AwAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDLD4XA47C4CRUNycrICAgL0+++/Kzg42O5ybJWWlqaVK1cqMjJS3t7edpdjK3phRj+y0Qsz+pGNXpjldz+y/v5OSkpSyZIlLzuPM5UAAACwjFAJAAAAywiVAAAAsIxQCQAAAMsIlQAAALCMUAkAAADLCJUAAACwjFAJAAAAy645VP7xxx/av3+/0tPT87IeAAAAFEJuh8qUlBQNGDBAxYoVU61atXT06FFJ0rBhwzRlypQ8LxAAAAAFn9uhMjo6Wt98843Wr18vPz8/53hERIQWL16cp8UBAACgcPByd4Vly5Zp8eLFuuOOO2QYhnO8Vq1a+v777/O0ONyYnl+8WF43+V194g3MU4ZaBgVp1DvvKEMOu8uxVVYvAACFm9tnKn/77TeVKVPGZfz8+fOmkAkAAICiw+1Q2ahRI33yySfO71lBct68eWrWrFneVQYAAIBCw+3L35MnT9Z9992nPXv2KD09XbNmzdKePXu0efNmbdiwIT9qBAAAQAHn9pnKu+66S4mJiUpPT1edOnW0evVqlSlTRlu2bFHDhg3zo0YAAAAUcG6fqZSk2267TW+99VZe1wIAAIBC6ppCZUZGhpYuXaq9e/dKkmrWrKnOnTvLy+uaNgcAAIBCzu0U+O2336pTp046ceKEqlevLkl66aWXVLp0aX388ceqXbt2nhcJAACAgs3teyofffRR1apVSz/99JN27NihHTt26Mcff1TdunX12GOP5UeNAAAAKODcPlOZmJio7du3q1SpUs6xUqVK6cUXX1Tjxo3ztDgAAAAUDm6fqaxWrZp++eUXl/Fff/1VVapUyZOiAAAAULi4HSpjYmL0+OOP68MPP9RPP/2kn376SR9++KGGDx+ul156ScnJyc4PAAAAiga3L3/ff//9kqSHHnrI+Ws6Dsel3y7u2LGj87thGMrIyMirOgEAAFCAuR0qP/vsM37jGwAAACZuh8pWrVrlQxkAAAAozNy+p7Jy5cqaOHGijh49mh/1AAAAoBByO1Q+8cQTWrJkiW699Va1bdtWixYtUmpqan7UBgAAgELC7VA5fPhwJSYmatu2bapRo4aGDRumcuXKaejQodqxY0d+1AgAAIACzu1QmeX222/X7NmzdezYMY0bN07z5s1T48aNVb9+fcXFxTmfCAcAAMCNz+0HdbKkpaVp6dKlio+PV0JCgu644w4NGDBAP/30k8aMGaM1a9bovffey8taAQAAUEDlOlQuWLBA3bt317fffqv4+HgtXLhQHh4e6tOnj2bMmKHw8HDn3AceeICfbAQAAChCch0qH3nkEbVv316NGzdW27ZtNWfOHHXp0kXe3t4ucytXrqwePXrkaaEAAAAouHJ9T2XWPZI//PCDPv30U3Xr1i3HQClJxYsXV3x8fN5UiGvWr18/GYahKVOmmMaXLVvmfIH9+vXrZRiG81O6dGlFRkZq165dOW7LMAz5+PioSpUqmjhxotLT06/b8QAAgILLrQd1DMNQpUqV8qsW5AM/Pz+99NJLOn369BXn7d+/X8ePH9eqVauUmpqqDh066I8//jDNad++vY4fP66DBw/qqaee0vjx4/Xyyy/nZ/kAAKCQcOtBnTZt2sjL68qr8FqhgiUiIkLfffedYmJiNHXq1MvOK1OmjAIDAxUSEqLhw4erU6dO2rdvn+rWreuc4+vrq5CQEEnS4MGDtXTpUi1fvlzR0dE5bjM1NdX0DtPk5GRJkqckTxXtn/r0NP0vvZAuPfyH7D7QD3rxd/QjG70wy+9+5Ha7boXKdu3ayd/f/5oKgj08PT01efJk9erVS48//rgqVKhwxflJSUlatGiRJMnHx+eKc2+66SadPHnysstjYmI0YcIEl/FmpUqpWLFiuaj+xndXUJDdJRQYCQkJdpdQoNCPbPTCjH5koxdm+dWPlJSUXM1zK1Q+88wzKlOmzDUVBPs88MADql+/vsaNG6fY2Ngc52SFzfPnz0uSOnXqZHqi/68cDofWrl2rVatWadiwYZfdb3R0tEaMGOH8npycrNDQUG05fVpeFy9e6+HcEDx1KVD+79QpZdhdjM2yetG2bdvL3qddlKSlpSkhIYF+iF78Hf3IRi/M8rsfWVcarybXoTLrwQ4UTi+99JLuuecePf300zku37Rpk4oVK6atW7dq8uTJeuONN1zmrFixQv7+/kpLS1NmZqZ69eql8ePHX3afvr6+8vX1dRnPkGSoqL8c/9K/TxmSMuiFJMnb25u/HP6CfmSjF2b0Ixu9MMuvfuR2m7kOlfxCTuF29913q127doqOjla/fv1clleuXFmBgYGqXr26fv31V3Xv3l0bN240zWndurXmzJkjHx8flS9f/qr31wIAgKIj109/Hzp0SKVLl87PWpDPpkyZoo8//lhbtmy54rwhQ4Zo9+7dWrp0qWm8ePHiqlKliipWrEigBAAAJrkOlZUqVeISeCFXp04dRUVFafbs2VecV6xYMQ0cOFDjxo3jDDUAAMgVt95TicJv4sSJyszMvOq8oUOHau/evfrggw+uQ1UAAKCw4xrmDWz+/PkuY2FhYaZ3R7Zq1SrHs5GhoaGm91LltC0AAIAsnKkEAACAZbk+U7lz585czfvrL7AAAACgaMh1qKxfv74Mw8jxUmnWuGEYysgo6q9yBgAAKHpyHSoPHTqUn3UAAACgEMt1qKxUqVJ+1gEAAIBCjHsqAQAAYFme3FOZhXsqAQAAiibuqQQAAIBl3FMJAAAAy3L98vPz589r8ODBuuWWW1S6dGn16NFDv/32W37WBgAAgEIi16Fy7Nix+ve//637779fvXr10meffabHHnssP2sDAABAIZHry99Lly5VfHy8unXrJknq06eP7rjjDqWnp8vLi58QBwAAKMpyfabyp59+UvPmzZ3fGzZsKG9vbx07dixfCgMAAEDhketQmZmZKW9vb9OYl5cXrxACAABA7i9/OxwOtWnTxnSpOyUlRR07dpSPj49zbMeOHXlbIQAAAAq8XIfKcePGuYx17tw5T4sBAABA4WQpVAIAAACSG6ESyCsTu3dXcHCw3WXYKi0tTStXrtRLvXu73Ktc1GT1AgBQuOX6QR0AAADgcgiVAAAAsIxQCQAAAMsIlQAAALAsVw/qzJ49O9cbfPzxx6+5GAAAABROuQqVM2bMMH3/7bfflJKSosDAQEnSmTNnVKxYMZUpU4ZQCQAAUATl6vL3oUOHnJ8XX3xR9evX1969e3Xq1CmdOnVKe/fu1e23364XXnghv+sFAABAAeT2PZVjx47VK6+8ourVqzvHqlevrhkzZui5557L0+IAAABQOLgdKo8fP6709HSX8YyMDP3yyy95UhQAAAAKF7dDZZs2bfTPf/5TO3bscI599dVXGjx4sCIiIvK0OAAAABQObofKuLg4hYSEqFGjRvL19ZWvr6+aNGmismXLat68eflRIwAAAAo4t3/7u3Tp0lq5cqUOHDigffv2SZLCw8NVrVq1PC8ON6Yn3npHHr5+dpdhKy/DUOfKZe0uAwCAPON2qMwSFhYmh8Oh2267TV5e17wZAAAA3ADcvvydkpKiAQMGqFixYqpVq5aOHj0qSRo2bJimTJmS5wUCAACg4HM7VEZHR+ubb77R+vXr5eeXfQkzIiJCixcvztPiAAAAUDi4fd162bJlWrx4se644w4ZhuEcr1Wrlr7//vs8LQ4AAACFg9tnKn/77TeVKVPGZfz8+fOmkAkAAICiw+1Q2ahRI33yySfO71lBct68eWrWrFneVQYAAIBCw+3L35MnT9Z9992nPXv2KD09XbNmzdKePXu0efNmbdiwIT9qBAAAQAHn9pnKu+66S4mJiUpPT1edOnW0evVqlSlTRlu2bFHDhg3zo0YAAAAUcNf0gsnbbrtNb731Vl7XAgAAgELK7TOVnp6e+vXXX13GT548KU9PzzwpCgAAAIWL26HS4XDkOJ6amiofHx/LBQEAAKDwyfXl79mzZ0u69LT3vHnz5O/v71yWkZGhjRs3Kjw8PO8rBAAAQIGX61A5Y8YMSZfOVL7xxhumS90+Pj4KCwvTG2+8kfcVAgAAoMDLdag8dOiQJKl169ZasmSJSpUqlW9FAQAAoHBx++nvdevW5UcdAAAAKMTcflDnwQcf1EsvveQyPnXqVHXr1i1PigIAAEDh4nao3LhxoyIjI13G77vvPm3cuDFPigIAAEDh4naoPHfuXI6vDvL29lZycnKeFAUAAIDCxe1QWadOHS1evNhlfNGiRapZs2aeFAUAAIDCxe0HdcaOHat//OMf+v7773XPPfdIktauXauFCxfqgw8+yPMCAQAAUPC5faayY8eOWrZsmb777jv93//9n5566in99NNPWrNmjbp06eLWtvr16yfDMFw+7du3lySFhYXJMAwtWrTIZd1atWrJMAzNnz/fZVlMTIw8PT318ssvuyybP3++AgMDL/v9WmzZskWenp7q0KGDy7LDhw+bjq1EiRKqVauWhgwZooMHD7rUljXP09NTpUqVUtOmTTVx4kQlJSWZ5v61d97e3qpcubJGjhypixcvmubl1N+/9/Stt95SvXr15O/vr8DAQDVo0EAxMTHO5SkpKYqOjtZtt90mPz8/lS5dWi1bttRHH31kqW8AAODG4faZSknq0KFDjgHqWrRv317x8fGmMV9fX+f/Dw0NVXx8vHr06OEc27p1q06cOKHixYvnuM24uDiNHDlScXFxeuaZZ/KkziuJjY3VsGHDFBsbq2PHjql8+fIuc9asWaNatWopJSVFu3bt0qxZs1SvXj19/PHHatOmjXNeyZIltX//fjkcDp05c0abN29WTEyM4uPj9fnnn5u2ndW7tLQ0ffXVV+rbt68Mw3B5Oj8+Pt4Z1LNkBem4uDgNHz5cs2fPVsuWLZWamqqdO3dq9+7dzrmDBg3SF198oVdeeUU1a9bUyZMntXnzZp08eTIv2gcAAG4A1xQqz5w5ow8//FA//PCDnn76aQUFBWnHjh0qW7asbrnlFre25evrq5CQkMsuj4qK0owZM/Tjjz8qNDRU0qUgFBUVpQULFrjM37Bhgy5cuKCJEydqwYIF2rx5s+688073DtAN586d0+LFi7V9+3adOHFC8+fP15gxY1zmBQcHO4/z1ltvVceOHdWmTRsNGDBA33//vfMXigzDcM4rV66catSooY4dO6pWrVoaOXKk3nnnHec2/9q70NBQRUREKCEhwSVUBgYGXrbHy5cv10MPPaQBAwY4x2rVquUyZ9asWc6n/sPCwtSwYUO3+gQAAG5sbofKnTt3KiIiQgEBATp8+LAeffRRBQUFacmSJTp69GiOQc+KsmXLql27dnr77bf13HPPKSUlRYsXL9aGDRty3FdsbKx69uwpb29v9ezZU7GxsfkaKt9//32Fh4erevXq6t27t4YPH67o6GgZhnHF9Tw8PPTEE0/ogQce0FdffaUmTZpcdm6ZMmUUFRWluLg4ZWRkmH4iM8vu3bu1efNmVapUya36Q0JCtGHDBh05cuSy64aEhGjlypX6xz/+oRIlSuR626mpqUpNTXV+z3o7gJdhyOMq/bnRef15/GlpaTZXYr+sHtCLS+hHNnphRj+y0Quz/O5HbrfrdqgcMWKE+vXrp6lTp5oCRmRkpHr16uXu5rRixQr5+/ubxsaMGWM629e/f3899dRTevbZZ/Xhhx/qtttuU/369V22lZycrA8//FBbtmyRJPXu3VstWrTQrFmzXPaRV2JjY9W7d29Jly5HJyUlacOGDWrVqtVV1w0PD5d06b7LK4XKrLlnz57VyZMnVaZMGUnZvUtPT1dqaqo8PDz06quvuqzbs2dPlyC6Z88eVaxYUePGjdM//vEPhYWFqVq1amrWrJkiIyPVtWtXeXhcuuV27ty5ioqKUnBwsOrVq6e77rpLXbt2VfPmza9Yc0xMjCZMmOAy3q5iaRUrVuyK6xYVCQkJdpdQYNALM/qRjV6Y0Y9s9MIsv/qRkpKSq3luh8ovv/xSb775psv4LbfcohMnTri7ObVu3Vpz5swxjQUFBZm+d+jQQf/85z+1ceNGxcXFqX///jlua+HChbrttttUr149SVL9+vVVqVIlLV682HR5N6/s379f27Zt09KlSyVJXl5e6t69u2JjY3MVKh0OhyRd9azm5eZm9e78+fOaMWOGvLy89OCDD7qsO2PGDEVERJjGsu7NLFeunLZs2aLdu3dr48aN2rx5s/r27at58+bp008/lYeHh+6++2798MMP2rp1qzZv3qy1a9dq1qxZmjBhgsaOHXvZmqOjozVixAjn9+TkZIWGhmrV0d/k4et31WO+kXkZhjqElVHbtm3l7e1tdzm2SktLU0JCAr34E/3IRi/M6Ec2emGW3/3I7XvI3Q6Vvr6+OW78wIEDKl26tLubU/HixVWlSpUrzvHy8tLDDz+scePG6YsvvnCGuL+LjY3Vt99+Ky+v7MPKzMxUXFxcvoTK2NhYpaenmx6ecTgc8vX11auvvqqAgIArrr93715JUuXKla+6r71796pkyZIKDg52jv21d3FxcapXr55iY2NdjjUkJOSqPa5du7Zq166t//u//9OgQYPUokULbdiwQa1bt5Z06eX2LVq0UIsWLTRq1ChNmjRJEydO1KhRo3J8Gb506Z+Vvz50lSXd4ZDHnyG5qPP29uYPxD/RCzP6kY1emNGPbPTCLL/6kdttuv1KoU6dOmnixInO6+uGYejo0aMaNWpUjmfJ8kr//v21YcMGde7cWaVKlXJZvmvXLm3fvl3r169XYmKi87N+/Xpt2bJF+/bty9N60tPTtWDBAk2bNs20v2+++Ubly5fXwoULr7h+ZmamZs+ercqVK6tBgwZXnPvrr7/qvffeU5cuXZyXpP/Ow8NDY8aM0XPPPacLFy5c83FJcr7E/vz581eck56e7vIKIwAAUDS5faZy2rRp6tq1q8qUKaMLFy6oZcuWOnHihJo1a6YXX3zR7QJSU1NdLpt7eXnp5ptvNo3VqFFDv//++2XvxYuNjVWTJk109913uyxr3LixYmNjc3xvpSRlZGQoMTHRNObr66saNWpctu4VK1bo9OnTGjBggMsZyQcffFCxsbEaNGiQc+zkyZM6ceKEUlJStHv3bs2cOVPbtm3TJ598Yrrf0eFw6MSJE85XCm3ZskWTJ09WQECApkyZctl6JKlbt2565pln9Nprr+npp592jp85c8alxyVKlFDx4sU1ePBglS9fXvfcc48qVKig48ePa9KkSSpdurSaNWsmSWrVqpV69uypRo0aKTg4WHv27NGYMWPUunVrlSxZ8oo1AQCAosHtUBkQEKCEhAR9/vnn+uabb3Tu3DndfvvtLvfs5dann36qcuXKmcaqV6+e45nFv176/as//vhD77zzjkaNGpXj8gcffFDTpk3T5MmTc1x+7tw5l7OFt912m7777rvL1h0bG+t8Cj6n/U2dOlU7d+50hq6s/hQrVkyVKlVS69atNXfuXJfL0snJySpXrpwMw1DJkiVVvXp19e3bV0888cRVA5yXl5eGDh2qqVOnavDgwc73eD7yyCMuc2NiYjR69GhFREQoLi5Oc+bM0cmTJ3XzzTerWbNmWrt2rbPfWU/fjxkzRikpKSpfvrzuv/9+Pf/881esBwAAFB2Gw+HezW0LFixQ9+7dXe6V++OPP7Ro0SL16dMnTwvEjSM5OVkBAQGKipnJgzqGoc6VyyoyMrLI3w+UlpamlStX0os/0Y9s9MKMfmSjF2b53Y+sv7+TkpKueILL7XsqH3nkEZefDJSks2fP5nhGDAAAADc+t0Olw+HI8RU4P/3001Wfdi5Mjh49Kn9//8t+jh49aneJAAAABUau76ls0KCBDMOQYRhq06aN6bU9GRkZOnTokMvvSxdm5cuXd3l45+/LAQAAcEmuQ2WXLl0kSYmJiWrXrp3pF2p8fHwUFhaWr68Uut68vLyu+m5HAAAAXJLrUDlu3DhJUlhYmLp37y4/v6L9oAUAAACyuX1PZd++fXXx4kXNmzdP0dHROnXqlCRpx44d+vnnn/O8QAAAABR8br+ncufOnc73Mx4+fFgDBw5UUFCQlixZoqNHj2rBggX5UScAAAAKMLfPVD755JPq16+fDh48aLoEHhkZqY0bN+ZpcQAAACgc3D5TuX37ds2dO9dl/JZbbnH5KUAAAAAUDW6fqfT19VVycrLL+IEDB1S6dOk8KQoAAACFi9uhslOnTpo4caLS0tIkSYZh6OjRoxo1atQN9UohAAAA5J7boXLatGk6d+6cypQpowsXLqhly5aqUqWKSpQooRdffDE/agQAAEAB5/Y9lQEBAUpISND//vc/7dy5U+fOndPtt9+uiIiI/KgPAAAAhYDboTLLXXfdpbvuuisvawEAAEAh5VaozMzM1Pz587VkyRIdPnxYhmGocuXK6tq1qx5++GEZhpFfdQIAAKAAy/U9lQ6HQ506ddKjjz6qn3/+WXXq1FGtWrV05MgR9evXTw888EB+1gkAAIACLNdnKufPn6+NGzdq7dq1at26tWnZZ599pi5dumjBggXq06dPnhcJAACAgi3XoXLhwoUaM2aMS6CUpHvuuUejR4/Wu+++S6jEVc0a2FvBwcF2l2GrtLQ0rVy50u4yAADIM7m+/L1z5061b9/+ssvvu+8+ffPNN3lSFAAAAAqXXIfKU6dOqWzZspddXrZsWZ0+fTpPigIAAEDhkutQmZGRIS+vy18t9/T0VHp6ep4UBQAAgMIl1/dUOhwO9evXT76+vjkuT01NzbOiAAAAULjkOlT27dv3qnN4SAcAAKBoynWojI+Pz886AAAAUIjl+p5KAAAA4HIIlQAAALCMUAkAAADLCJUAAACwjFAJAAAAywiVAAAAsCzXrxQC8sqgaW/L8PGzuwxbeXkY6l6vvPrGzFN6psPucmyV1QsAQOHGmUoAAABYRqgEAACAZYRKAAAAWEaoBAAAgGWESgAAAFhGqAQAAIBlhEoAAABYRqgEAACAZYRKAAAAWEaoBAAAgGWESgAAAFhGqAQAAIBlhEoAAABYRqgEAACAZYRKAAAAWEaoBAAAgGWESgAAAFhGqAQAAIBlhEoAAABYRqgEAACAZbaGyn79+skwDBmGIR8fH1WpUkUTJ05Uenq61q9fL8MwdObMGZf1wsLCNHPmTNPY5s2bFRkZqVKlSsnPz0916tTR9OnTlZGRYZpnGIb8/Px05MgR03iXLl3Ur1+/HGv766d9+/a5OrawsDAZhqGtW7eaxocPH65WrVqZ9tOlSxeX9f9+/PPnz5dhGKpRo4bL3A8++ECGYSgsLMw5ljU/6+Pv76+GDRtqyZIlpnVbtWqV43EOGjTIOeev4yVLllTjxo310Ucf5aoPAACgaLD9TGX79u11/PhxHTx4UE899ZTGjx+vl19+2a1tLF26VC1btlSFChW0bt067du3T0888YQmTZqkHj16yOFwmOYbhqHnn38+17X99bNw4cJc1+Xn56dRo0a5dSxXUrx4cf3666/asmWLaTw2NlYVK1Z0mV+yZEln3V9//bXatWunhx56SPv37zfNGzhwoMtxTp061TQnPj5ex48f1/bt29W8eXN17dpVu3btyrNjAwAAhZuX3QX4+voqJCREkjR48GAtXbpUy5cvV7NmzXK1/vnz5zVw4EB16tRJc+fOdY4/+uijKlu2rDp16qT3339f3bt3dy4bOnSopk+frmeeeUa1a9fOVW3X4rHHHtMbb7yhlStXKjIy8pq3k8XLy0u9evVSXFycsz8//fST1q9fryeffNIl8BqG4aw/JCREkyZN0r/+9S/t3LlT1atXd84rVqzYVY8zMDBQISEhCgkJ0QsvvKBZs2Zp3bp1qlOnzmXXSU1NVWpqqvN7cnKyJMnTw5CHh+Hewd9gvP48fq8i3gcpuwdpaWk2V1IwZPWBftCLv6Mf2eiFWX73I7fbtT1U/t1NN92kkydP5nr+6tWrdfLkST399NMuyzp27Khq1app4cKFplDZvHlzHThwQKNHj9aKFSvypO6cVK5cWYMGDVJ0dLTat28vDw/rJ4b79++vVq1aadasWSpWrJjmz5+v9u3bq2zZsldcLyMjQwsWLJAk3X777de8//T0dMXGxkqSfHx8rjg3JiZGEyZMcBnvXCtExYoVu+YabiQP1ilndwkFRkJCgt0lFCj0Ixu9MKMf2eiFWX71IyUlJVfzCkyodDgcWrt2rVatWqVhw4Y5xytUqOAy968Hd+DAAUnK8V5DSQoPD3fO+auYmBjVrVtXmzZtUosWLXJcd8WKFfL39zeNjRkzRmPGjLn6Af3pueeeU3x8vN599109/PDDuV7vcho0aKBbb71VH374oR5++GHNnz9f06dP1w8//OAyNykpyVn/hQsX5O3trblz5+q2224zzXv99dc1b94809ibb76pqKgo5/eePXvK09NTFy5cUGZmpsLCwvTQQw9dsdbo6GiNGDHC+T05OVmhoaH66NsT8vDxc/vYbyReHoYerFNO/9l1XOmZjquvcAPL6kXbtm3l7e1tdzm2S0tLU0JCAv0Qvfg7+pGNXpjldz+yrjReje2hMiu4paWlKTMzU7169dL48eP15ZdfSpI2bdqkEiVKmNb564MuWf5+3+TV1KxZU3369NHo0aP1+eef5zindevWmjNnjmksKCjIrf2ULl1aTz/9tJ5//nnT2VIr+vfvr/j4eFWsWFHnz59XZGSkXn31VZd5JUqU0I4dOyRdCuJr1qzRoEGDFBwcrI4dOzrnRUVF6dlnnzWt+/cznzNmzFBERIR++OEHPfnkk5o9e/ZVe+Hr6ytfX1+X8YxMhzKLeJDKkp7pKPKhMou3tzd/OfwF/chGL8zoRzZ6YZZf/cjtNm0PlVnBzcfHR+XLl5eXl7mkypUrKzAw0DT21znVqlWTJO3du1d33nmny/b37t2rmjVr5rjvCRMmqFq1alq2bFmOy4sXL64qVaq4cTQ5GzFihF5//XW9/vrrLstKlizp8iS6JJ05c0aenp4qXry4y7KoqCiNHDlS48eP18MPP+zSsyweHh6m+uvWravVq1frpZdeMoXKgICAqx5nSEiIqlSpoipVqig+Pl6RkZHas2ePypQpc8X1AABA0WD7099Zwa1ixYqXDUdXcu+99yooKEjTpk1zWbZ8+XIdPHhQPXv2zHHd0NBQDR06VGPGjHF59VBe8vf319ixY/Xiiy/q7NmzpmXVq1fXt99+a3qgRZJ27NihypUr5/hfB0FBQerUqZM2bNig/v37u1VL1iVsK5o0aaKGDRvqxRdftLQdAABw47A9VFpVvHhxvfnmm/roo4/02GOPaefOnTp8+LBiY2PVr18/de3a9Yr3/kVHR+vYsWNas2aNy7LU1FSdOHHC9Pn999+vqc7HHntMAQEBeu+990zjUVFRMgxDffr00VdffaXvvvtOcXFxmjlzpp566qnLbm/+/Pn6/fffFR4eftk5DofDWfehQ4c0d+5crVq1Sp07dzbNS0lJcTnO06dPX/F4hg8frjfffFM///xzLo4eAADc6Ap9qJSkrl27at26dTp69KhatGih6tWra8aMGXr22We1aNEiGcblX9sSFBSkUaNG6eLFiy7LPv30U5UrV870ueuuu66pRm9vb73wwgsu+wkMDNSmTZuUlpamTp06qX79+po9e7amT5+uf/7zn5fd3k033aTg4OAr7jM5OdlZd40aNTRt2jRNnDjR5f7Jt956y+U4L3d2N0v79u1VuXJlzlYCAABJkuFw9wkX4BolJycrICBAXaOnyeDpb3WvV16LvzlW5B/UyepFZGQkN9zr0lOcWe+2Ler9oBdm9CMbvTDL735k/f2dlJSkkiVLXnbeDXGmEgAAAPYiVF6Dd999V/7+/jl+atWqZXd5AAAA153trxQqjDp16qSmTZvmuIzT8AAAoCgiVF6DEiVKuLyQHQAAoCjj8jcAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAs87K7ABQ9bzzVV8HBwXaXYau0tDStXLlSb0c/Km9vb7vLsVVWLwAAhRtnKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBm//Y3rbsiEWHl4+dpdhq28PA09eFclu8sAACDPcKYSAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEgAAAJYRKgEAAGAZoRIAAACWESoBAABgGaESAAAAlhEqAQAAYBmh8gbXqlUrDR8+3GV8/vz5CgwMlCSNHz9ehmHIMAx5enoqNDRUjz32mE6dOmVaJywsTDNnzsz/ogEAQKHjZXcBKBhq1aqlNWvWKCMjQ3v37lX//v2VlJSkxYsX210aAAAoBAiVkCR5eXkpJCREknTLLbeoW7duio+Pt7kqAABQWBAq4eLw4cNatWqVfHx8LG0nNTVVqampzu/JycmSJC9PycPTsLTtws7rz+NPS0uzuRL7ZfWAXlxCP7LRCzP6kY1emOV3P3K7XUIlJEm7du2Sv7+/MjIydPHiRUnS9OnTLW0zJiZGEyZMcBnv0CRUxYoVs7TtG0VCQoLdJRQY9MKMfmSjF2b0Ixu9MMuvfqSkpORqHqESkqTq1atr+fLlunjxot555x0lJiZq2LBhlrYZHR2tESNGOL8nJycrNDRUn2z7UR5eflZLLtS8PA11blZRbdu2lbe3t93l2CotLU0JCQn04k/0Ixu9MKMf2eiFWX73I+tK49UQKm9wJUuWVFJSksv4mTNnFBAQ4Pzu4+OjKlWqSJKmTJmiDh06aMKECXrhhReued++vr7y9fV1GU/PkDwMxzVv90bi7e3NH4h/ohdm9CMbvTCjH9nohVl+9SO32+SVQje46tWra8eOHS7jO3bsULVq1S673nPPPad//etfOnbsWH6WBwAAbhCEyhvc4MGDdeDAAT3++OPauXOn9u/fr+nTp2vhwoV66qmnLrtes2bNVLduXU2ePNk0/vPPPysxMdH0OX36dH4fBgAAKOAIlTe4W2+9VRs3btS+ffsUERGhpk2b6v3339cHH3yg9u3bX3HdJ598UvPmzdOPP/7oHPvXv/6lBg0amD6ffPJJfh8GAAAo4Linsgho3LixVq9efdnl48eP1/jx413Ge/TooR49eji/Hz58OB+qAwAANwLOVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACzzsrsAFD2vjRug4OBgu8uwVVpamlauXGl3GQAA5BnOVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALONBHVw3DodDknT27Fl5e3vbXI290tLSlJKSouTkZHpBL0zoRzZ6YUY/stELs/zuR3JysqTsv8cvh1CJ6+bkyZOSpMqVK9tcCQAAcNfZs2cVEBBw2eWESlw3QUFBkqSjR49e8R/KoiA5OVmhoaH68ccfVbJkSbvLsRW9MKMf2eiFGf3IRi/M8rsfDodDZ8+eVfny5a84j1CJ68bD49ItvAEBAfwh8KeSJUvSiz/RCzP6kY1emNGPbPTCLD/7kZuTQTyoAwAAAMsIlQAAALCMUInrxtfXV+PGjZOvr6/dpdiOXmSjF2b0Ixu9MKMf2eiFWUHph+G42vPhAAAAwFVwphIAAACWESoBAABgGaESAAAAlhEqAQAAYBmhEtfFa6+9prCwMPn5+alp06batm2b3SXZYuPGjerYsaPKly8vwzC0bNkyu0uyTUxMjBo3bqwSJUqoTJky6tKli/bv3293WbaZM2eO6tat63x5cbNmzfTf//7X7rIKhClTpsgwDA0fPtzuUmwxfvx4GYZh+oSHh9tdlm1+/vln9e7dW8HBwbrppptUp04dbd++3e6ybBEWFubyz4ZhGBoyZIgt9RAqke8WL16sESNGaNy4cdqxY4fq1aundu3a6ddff7W7tOvu/Pnzqlevnl577TW7S7Hdhg0bNGTIEG3dulUJCQlKS0vTvffeq/Pnz9tdmi0qVKigKVOm6KuvvtL27dt1zz33qHPnzvr222/tLs1WX375pd58803VrVvX7lJsVatWLR0/ftz5+d///md3SbY4ffq0mjdvLm9vb/33v//Vnj17NG3aNJUqVcru0mzx5Zdfmv65SEhIkCR169bNlnp4pRDyXdOmTdW4cWO9+uqrkqTMzEyFhoZq2LBhGj16tM3V2ccwDC1dulRdunSxu5QC4bffflOZMmW0YcMG3X333XaXUyAEBQXp5Zdf1oABA+wuxRbnzp3T7bffrtdff12TJk1S/fr1NXPmTLvLuu7Gjx+vZcuWKTEx0e5SbDd69Gh9/vnn2rRpk92lFEjDhw/XihUrdPDgQRmGcd33z5lK5Ks//vhDX331lSIiIpxjHh4eioiI0JYtW2ysDAVNUlKSpEtBqqjLyMjQokWLdP78eTVr1szucmwzZMgQdejQwfTnR1F18OBBlS9fXrfeequioqJ09OhRu0uyxfLly9WoUSN169ZNZcqUUYMGDfTWW2/ZXVaB8Mcff+idd95R//79bQmUEqES+ez3339XRkaGypYtaxovW7asTpw4YVNVKGgyMzM1fPhwNW/eXLVr17a7HNvs2rVL/v7+8vX11aBBg7R06VLVrFnT7rJssWjRIu3YsUMxMTF2l2K7pk2bav78+fr00081Z84cHTp0SC1atNDZs2ftLu26++GHHzRnzhxVrVpVq1at0uDBg/X444/r7bfftrs02y1btkxnzpxRv379bKvBy7Y9A8CfhgwZot27dxfZ+8SyVK9eXYmJiUpKStKHH36ovn37asOGDUUuWP7444964oknlJCQID8/P7vLsd19993n/P9169ZV06ZNValSJb3//vtF7taIzMxMNWrUSJMnT5YkNWjQQLt379Ybb7yhvn372lydvWJjY3XfffepfPnyttXAmUrkq5tvvlmenp765ZdfTOO//PKLQkJCbKoKBcnQoUO1YsUKrVu3ThUqVLC7HFv5+PioSpUqatiwoWJiYlSvXj3NmjXL7rKuu6+++kq//vqrbr/9dnl5ecnLy0sbNmzQ7Nmz5eXlpYyMDLtLtFVgYKCqVaum7777zu5Srrty5cq5/EdWjRo1iuztAFmOHDmiNWvW6NFHH7W1DkIl8pWPj48aNmyotWvXOscyMzO1du3aIn2vGCSHw6GhQ4dq6dKl+uyzz1S5cmW7SypwMjMzlZqaancZ112bNm20a9cuJSYmOj+NGjVSVFSUEhMT5enpaXeJtjp37py+//57lStXzu5SrrvmzZu7vHrswIEDqlSpkk0VFQzx8fEqU6aMOnToYGsdXP5GvhsxYoT69u2rRo0aqUmTJpo5c6bOnz+vRx55xO7Srrtz586Zzi4cOnRIiYmJCgoKUsWKFW2s7PobMmSI3nvvPX300UcqUaKE8x7bgIAA3XTTTTZXd/1FR0frvvvuU8WKFXX27Fm99957Wr9+vVatWmV3adddiRIlXO6tLV68uIKDg4vkPbdPP/20OnbsqEqVKunYsWMaN26cPD091bNnT7tLu+6efPJJ3XnnnZo8ebIeeughbdu2TXPnztXcuXPtLs02mZmZio+PV9++feXlZXOscwDXwSuvvOKoWLGiw8fHx9GkSRPH1q1b7S7JFuvWrXNIcvn07dvX7tKuu5z6IMkRHx9vd2m26N+/v6NSpUoOHx8fR+nSpR1t2rRxrF692u6yCoyWLVs6nnjiCbvLsEX37t0d5cqVc/j4+DhuueUWR/fu3R3fffed3WXZ5uOPP3bUrl3b4evr6wgPD3fMnTvX7pJstWrVKockx/79++0uxcF7KgEAAGAZ91QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAUcSdOnNCwYcN06623ytfXV6GhoerYsaPWrl17XeswDEPLli27rvsEkHds/uVxAICdDh8+rObNmyswMFAvv/yy6tSpo7S0NK1atUpDhgzRvn377C4RQCHBb38DQBEWGRmpnTt3av/+/SpevLhp2ZkzZxQYGKijR49q2LBhWrt2rTw8PNS+fXu98sorKlu2rCSpX79+OnPmjOks4/Dhw5WYmKj169dLklq1aqW6devKz89P8+bNk4+PjwYNGqTx48dLksLCwnTkyBHn+pUqVdLhw4fz89AB5DEufwNAEXXq1Cl9+umnGjJkiEuglKTAwEBlZmaqc+fOOnXqlDZs2KCEhAT98MMP6t69u9v7e/vtt1W8eHF98cUXmjp1qiZOnKiEhARJ0pdffilJio+P1/Hjx53fARQeXP4GgCLqu+++k8PhUHh4+GXnrF27Vrt27dKhQ4cUGhoqSVqwYIFq1aqlL7/8Uo0bN871/urWratx48ZJkqpWrapXX31Va9euVdu2bVW6dGlJl4JsSEiIhaMCYBfOVAJAEZWbu5/27t2r0NBQZ6CUpJo1ayowMFB79+51a39169Y1fS9Xrpx+/fVXt7YBoOAiVAJAEVW1alUZhmH5YRwPDw+XgJqWluYyz9vb2/TdMAxlZmZa2jeAgoNQCQBFVFBQkNq1a6fXXntN58+fd1l+5swZ1ahRQz/++KN+/PFH5/iePXt05swZ1axZU5JUunRpHT9+3LRuYmKi2/V4e3srIyPD7fUAFAyESgAowl577TVlZGSoSZMm+s9//qODBw9q7969mj17tpo1a6aIiAjVqVNHUVFR2rFjh7Zt26Y+ffqoZcuWatSokSTpnnvu0fbt27VgwQIdPHhQ48aN0+7du92uJSwsTGvXrtWJEyd0+vTpvD5UAPmMUAkARditt96qHTt2qHXr1nrqqadUu3ZttW3bVmvXrtWcOXNkGIY++ugjlSpVSnfffbciIiJ06623avHixc5ttGvXTmPHjtXIkSPVuHFjnT17Vn369HG7lmnTpikhIUGhoaFq0KBBXh4mgOuA91QCAADAMs5UAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAMkIlAAAALCNUAgAAwDJCJQAAACwjVAIAAMAyQiUAAAAsI1QCAADAsv8HqLEJM9A9jl4AAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=detected_types_df, y=\"Detected PII Type\", x=\"Count\", alpha = 0.8)\n",
    "plt.grid()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}