{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymongo import MongoClient\n",
    "import pandas as pd\n",
    "import json\n",
    "from bson.json_util import dumps\n",
    "from pandas.io.json import json_normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ip = '127.0.0.1'\n",
    "port = 27017\n",
    "client = MongoClient(ip, port)\n",
    "db = client['Vulnerabilities']\n",
    "joinedVulnerabilitiesDateFormat = db['Vulnerabilities']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getSeverityAndImpact(component):\n",
    "            return joinedVulnerabilitiesDateFormat.aggregate([\n",
    "            { \"$project\" : \n",
    "                { \n",
    "                    \"_id\" : 0,\n",
    "                    \"severity\" : \"$webScrapingInformation.cvssScoreNVD2.severity\",\n",
    "                    \"impact\" : \"$webScrapingInformation.cvssScoreNVD2.\"+component+\"Impact\",\n",
    "                    \"impactLabel\": {\"$literal\": component.title()}\n",
    "                }\n",
    "            }])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getDataFrameSeverityAndImpact():\n",
    "    dataFrame = pd.DataFrame([])\n",
    "    array = [\"confidentiality\",\"availability\",\"integrity\"]\n",
    "    for element in array:  \n",
    "        MongoResponse = getSeverityAndImpact(element)\n",
    "        dataFromMongoResponse = json.loads(dumps(MongoResponse))  \n",
    "        dataFrameTemp = json_normalize(dataFromMongoResponse)\n",
    "        dataFrame = dataFrame.append(pd.DataFrame(dataFrameTemp))\n",
    "    return dataFrame.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfResult = getDataFrameSeverityAndImpact()\n",
    "del dfResult['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def returnImpact(imp):\n",
    "    if \"None\" in imp:\n",
    "        return 1\n",
    "    if \"Partial\" in imp:\n",
    "        return 2\n",
    "    if \"Complete\" in imp:\n",
    "        return 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def returnSeverity(sev):\n",
    "    if \"LOW\" in sev:\n",
    "        return 1\n",
    "    if \"MEDIUM\" in sev:\n",
    "        return 2\n",
    "    if \"HIGH\" in sev:\n",
    "        return 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfResult['impact'] = dfResult['impact'].apply(returnImpact)\n",
    "dfResult['severity'] = dfResult['severity'].apply(returnSeverity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfResult = dfResult[['severity','impact','impactLabel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataframe created and appended due visualizations reasons\n",
    "dataTemp = {'severity': [0, 4], 'impact': [0, 4], 'impactLabel': ['Integrity', 'Integrity']}\n",
    "dfTemp = pd.DataFrame(data=dataTemp)\n",
    "dfTemp = dfTemp[['severity','impact','impactLabel']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfResult = dfResult.append(dfTemp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfResult.to_csv(path_or_buf=\"vsaiEMSE.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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