{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with RAIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rail import Controls, Impact, Likelihood, Risks, ThreatEvents, ThreatSources, Tree, Vulnerabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Test System\n",
      "/Test System/Test Child\n",
      "/Test System/Test Child/Test Grandchild\n"
     ]
    }
   ],
   "source": [
    "test_system = Tree(name=\"Test System\")\n",
    "test_system.add_child(name=\"Test Child\")\n",
    "test_system[\"Test Child\"].add_child(name=\"Test Grandchild\")\n",
    "test_system.to_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Threat Source 1': {'name': 'Threat Source 1'}}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threat_sources = ThreatSources()\n",
    "threat_sources.new(name=\"Threat Source 1\")\n",
    "threat_sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12653ab50>]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "impact_one = Impact(name=\"Impact 1\", mu=.5, sigma=.5)\n",
    "impact_one.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6499: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([1.72032e+00, 0.00000e+00, 8.31600e-01, 0.00000e+00, 0.00000e+00,\n",
       "        2.07760e-01, 0.00000e+00, 0.00000e+00, 3.64000e-02, 0.00000e+00,\n",
       "        0.00000e+00, 3.36000e-03, 0.00000e+00, 5.60000e-04]),\n",
       " array([0.        , 0.35714286, 0.71428571, 1.07142857, 1.42857143,\n",
       "        1.78571429, 2.14285714, 2.5       , 2.85714286, 3.21428571,\n",
       "        3.57142857, 3.92857143, 4.28571429, 4.64285714, 5.        ]),\n",
       " <a list of 14 Patch objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_likelihood = Likelihood(lam=.5)\n",
    "test_likelihood.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Control 1': {'name': 'Control 1', 'cost': 100000, 'reduction': 0.01, 'implemented': True}}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "controls = Controls()\n",
    "controls.new('Control 1', cost=100000, reduction=.01)\n",
    "controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Threat Source</th>\n",
       "      <th>Threat Event</th>\n",
       "      <th>System</th>\n",
       "      <th>Controls</th>\n",
       "      <th>Impact</th>\n",
       "      <th>Impact (mean)</th>\n",
       "      <th>Likelihood (mean)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Threat Source 1</td>\n",
       "      <td>Threat Event 1</td>\n",
       "      <td>/Test System/Test Child</td>\n",
       "      <td>[Control 1]</td>\n",
       "      <td>Impact 1</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Threat Source    Threat Event                   System     Controls  \\\n",
       "0  Threat Source 1  Threat Event 1  /Test System/Test Child  [Control 1]   \n",
       "\n",
       "     Impact  Impact (mean)  Likelihood (mean)  \n",
       "0  Impact 1 1.87           0.50                "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threat_events = ThreatEvents()\n",
    "vulnerabilities = Vulnerabilities()\n",
    "risks = Risks()\n",
    "\n",
    "risks.new(\n",
    "    vulnerabilities.new(\n",
    "        threat_events.new(name=\"Threat Event 1\", threat_source=threat_sources[\"Threat Source 1\"]),\n",
    "        test_system[\"Test Child\"],\n",
    "    [controls['Control 1']]),\n",
    "    test_likelihood,\n",
    "    impact_one)\n",
    "\n",
    "risks.dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.00e+00, 5.25e-03, 5.25e-03, ..., 1.00e-05, 1.00e-05, 1.00e-05]),\n",
       " array([0.00000000e+00, 8.49821044e-04, 1.69964209e-03, ...,\n",
       "        8.49651079e+00, 8.49736062e+00, 8.49821044e+00]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "risks.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cost</th>\n",
       "      <th>implemented</th>\n",
       "      <th>reduction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Control 1</th>\n",
       "      <td>100000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cost  implemented  reduction\n",
       "name                                     \n",
       "Control 1  100000  False       0.01      "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risks.set_optimum_controls(controls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/matplotlib/axes/_base.py:3116: MatplotlibDeprecationWarning: \n",
      "The `xmin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `left` instead.\n",
      "  alternative='`left`', obj_type='argument')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_subplot(1,1,1)\n",
    "risks.plot_risk_cost_matrix(controls, axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}