From 1ebd1ca1514d8efc89a7ed54a4b8be9acbfe3157 Mon Sep 17 00:00:00 2001
From: Theo Naunheim <theonaunheim@gmail.com>
Date: Fri, 13 Sep 2019 19:57:54 -0500
Subject: [PATCH] Remove scratchpad ipynb

---
 pyfair_scratch.ipynb | 1812 ------------------------------------------
 1 file changed, 1812 deletions(-)
 delete mode 100644 pyfair_scratch.ipynb

diff --git a/pyfair_scratch.ipynb b/pyfair_scratch.ipynb
deleted file mode 100644
index d1d5bb5..0000000
--- a/pyfair_scratch.ipynb
+++ /dev/null
@@ -1,1812 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pyfair"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2., 2., 3.])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = pd.Series([1,2,3])\n",
-    "np.clip(s.values, 2, np.inf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'vddsf'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-8-57dfd20b7df3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFairModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Sample'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tef'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50_000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstdev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10_000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'vddsf'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.66\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstdev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.01\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Loss Magnitude'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstdev\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\model.py\u001b[0m in \u001b[0;36minput_data\u001b[1;34m(self, target, **kwargs)\u001b[0m\n\u001b[0;32m    272\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_input\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_simulations\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m         \u001b[1;31m# Update dependency tracker captive class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 274\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Supplied'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    275\u001b[0m         \u001b[1;31m# Update the model table with the generated data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_model_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\model_tree.py\u001b[0m in \u001b[0;36mupdate_status\u001b[1;34m(self, node_name, new_status)\u001b[0m\n\u001b[0;32m    122\u001b[0m         '''\n\u001b[0;32m    123\u001b[0m         \u001b[1;31m# Get the target node\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 124\u001b[1;33m         \u001b[0mnode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnode_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    125\u001b[0m         \u001b[1;31m# If data is supplied\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnew_status\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Supplied'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'vddsf'"
-     ]
-    }
-   ],
-   "source": [
-    "from pyfair import FairModel\n",
-    "from pyfair import FairSimpleReport\n",
-    "\n",
-    "\n",
-    "# Create our model and calculate (don't worry about understanding yet)\n",
-    "model = FairModel(name='Sample')\n",
-    "model.input_data('tef', mean=50_000, stdev=10_000)\n",
-    "model.input_data('vddsf', mean=.66, stdev=.01)\n",
-    "model.input_data('Loss Magnitude', mean=100, stdev=50)\n",
-    "model.calculate_all()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "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>Risk</th>\n",
-       "      <th>Loss Event Frequency</th>\n",
-       "      <th>Threat Event Frequency</th>\n",
-       "      <th>Vulnerability</th>\n",
-       "      <th>Contact</th>\n",
-       "      <th>Action</th>\n",
-       "      <th>Threat Capability</th>\n",
-       "      <th>Control Strength</th>\n",
-       "      <th>Loss Magnitude</th>\n",
-       "      <th>Primary Loss</th>\n",
-       "      <th>Secondary Loss</th>\n",
-       "      <th>Secondary Loss Event Frequency</th>\n",
-       "      <th>Secondary Loss Event Magnitude</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>15227.5</td>\n",
-       "      <td>30.455</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.6091</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.639671</td>\n",
-       "      <td>0.514301</td>\n",
-       "      <td>500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Risk  Loss Event Frequency  Threat Event Frequency  Vulnerability  \\\n",
-       "0  15227.5                30.455                      50         0.6091   \n",
-       "\n",
-       "  Contact Action  Threat Capability  Control Strength  Loss Magnitude  \\\n",
-       "0     NaN    NaN           0.639671          0.514301             500   \n",
-       "\n",
-       "  Primary Loss Secondary Loss Secondary Loss Event Frequency  \\\n",
-       "0          NaN            NaN                            NaN   \n",
-       "\n",
-       "  Secondary Loss Event Magnitude  \n",
-       "0                            NaN  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = pyfair.FairModel('Woot')\n",
-    "m.input_data('Threat Capability', mean=.59, stdev=.1)\n",
-    "m.input_data('Control Strength', mean=.65, stdev=.20)\n",
-    "m.input_data('Loss Magnitude', constant=500)\n",
-    "m.input_data('Threat Event Frequency', constant=50)\n",
-    "m.calculate_all()\n",
-    "m.export_results().head(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "#. **Risk (\"R\")**\n",
-    "\n",
-    "    Description: a vector of currency values/elements, which represent the\n",
-    "    ultimate loss for a given time period.\n",
-    "\n",
-    "    Restrictions: all elements must be positive\n",
-    "\n",
-    "    Derivation: multiply the Loss Event Frequency vector by the Loss\n",
-    "    Magnitude vector.\n",
-    "\n",
-    "    \n",
-    "    \n",
-    "    Example: For a given year the following dollar amounts:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2221794.699516662"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.full(10, 5)\n",
-    "\n",
-    "pd.Series([\n",
-    "    6_937_920,\n",
-    "    3_895_200,\n",
-    "    2_612_009\n",
-    "]).std()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\n",
-    "    \\sum\\limits^m_{i=1}\n",
-    "    \\sum\\limits^n_{i=1}\n",
-    "$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\n",
-    "        \\begin{bmatrix} \n",
-    "             \\text{SL}_{1,1} & \\text{SL}_{1,2} & \\dots  & \\text{SL}_{1,n} \\\\\n",
-    "             \\text{SL}_{2,1} & \\text{SL}_{2,2} & \\dots  & \\text{SL}_{2,n} \\\\\n",
-    "             \\vdots          & \\vdots          & \\ddots & \\vdots \\\\\n",
-    "             \\text{SL}_{m,1} & \\text{SL}_{m,2} & \\dots  & \\text{SL}_{m,n} \\\\\n",
-    "        \\end{bmatrix}\n",
-    "        \\quad\n",
-    "        =\n",
-    "        \\quad\n",
-    "        \\begin{bmatrix} \n",
-    "             \\text{SLEF}_{1,1} & \\text{SLEF}_{1,2} & \\dots  & \\text{SLEF}_{1,n} \\\\\n",
-    "             \\text{SLEF}_{2,1} & \\text{SLEF}_{2,2} & \\dots  & \\text{SLEF}_{2,n} \\\\\n",
-    "             \\vdots            & \\vdots            & \\ddots & \\vdots \\\\\n",
-    "             \\text{SLEF}_{m,1} & \\text{SLEF}_{m,2} & \\dots  & \\text{SLEF}_{m,n} \\\\\n",
-    "        \\end{bmatrix}\n",
-    "        \\quad\n",
-    "        \\circ\n",
-    "        \\quad\n",
-    "        \\begin{bmatrix} \n",
-    "             \\text{SLEM}_{1,1} & \\text{SLEM}_{1,2} & \\dots  & \\text{SLEM}_{1,n} \\\\\n",
-    "             \\text{SLEM}_{2,1} & \\text{SLEM}_{2,2} & \\dots  & \\text{SLEM}_{2,n} \\\\\n",
-    "             \\vdots            & \\vdots            & \\ddots & \\vdots \\\\\n",
-    "             \\text{SLEM}_{m,1} & \\text{SLEM}_{m,2} & \\dots  & \\text{SLEM}_{m,n} \\\\\n",
-    "        \\end{bmatrix}\n",
-    "        \\quad\n",
-    "$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\n",
-    "        \\begin{bmatrix} \n",
-    "             \\text{SL}_{1} \\\\\n",
-    "             \\text{SL}_{1} \\\\\n",
-    "             \\vdots        \\\\\n",
-    "             \\text{SL}_{1} \\\\\n",
-    "        \\end{bmatrix}\n",
-    "        \\quad\n",
-    "        =\n",
-    "        \\quad\n",
-    "            \\sum\\limits^n_{j=1}\n",
-    "        \\quad\n",
-    "        \\left(\n",
-    "            \\quad\n",
-    "            \\begin{bmatrix} \n",
-    "                 \\text{SLEF}_{1,1} & \\text{SLEF}_{1,2} & \\dots  & \\text{SLEF}_{1,n} \\\\\n",
-    "                 \\text{SLEF}_{2,1} & \\text{SLEF}_{2,2} & \\dots  & \\text{SLEF}_{2,n} \\\\\n",
-    "                 \\vdots            & \\vdots            & \\ddots & \\vdots \\\\\n",
-    "                 \\text{SLEF}_{m,1} & \\text{SLEF}_{m,2} & \\dots  & \\text{SLEF}_{m,n} \\\\\n",
-    "            \\end{bmatrix}\n",
-    "            \\quad\n",
-    "            \\circ\n",
-    "            \\quad\n",
-    "            \\begin{bmatrix} \n",
-    "                 \\text{SLEM}_{1,1} & \\text{SLEM}_{1,2} & \\dots  & \\text{SLEM}_{1,n} \\\\\n",
-    "                 \\text{SLEM}_{2,1} & \\text{SLEM}_{2,2} & \\dots  & \\text{SLEM}_{2,n} \\\\\n",
-    "                 \\vdots            & \\vdots            & \\ddots & \\vdots \\\\\n",
-    "                 \\text{SLEM}_{m,1} & \\text{SLEM}_{m,2} & \\dots  & \\text{SLEM}_{m,n} \\\\\n",
-    "            \\end{bmatrix}\n",
-    "            \\quad\n",
-    "        \\right)\n",
-    "$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FairException",
-     "evalue": "Not ready for calculation. See statuses: \nRisk                                  Required\nLoss Event Frequency                  Supplied\nThreat Event Frequency            Not Required\nContact                           Not Required\nAction                            Not Required\nVulnerability                     Not Required\nControl Strength                  Not Required\nThreat Capability                 Not Required\nLoss Magnitude                        Required\nPrimary Loss                          Required\nSecondary Loss                        Required\nSecondary Loss Event Frequency        Required\nSecondary Loss Event Magnitude        Required\ndtype: object",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFairException\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-2-3bd5aad8b448>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[1;34m'Loss Event Frequency'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'mean'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m.3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'stdev'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m.1\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m })\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\model.py\u001b[0m in \u001b[0;36mcalculate_all\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    363\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mready_for_calculation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m             \u001b[0mstatus_str\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_node_statuses\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 365\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mFairException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Not ready for calculation. See statuses: \\n{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    366\u001b[0m         \u001b[0mstatus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_node_statuses\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    367\u001b[0m         \u001b[0mcalculable_nodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Calculable'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mFairException\u001b[0m: Not ready for calculation. See statuses: \nRisk                                  Required\nLoss Event Frequency                  Supplied\nThreat Event Frequency            Not Required\nContact                           Not Required\nAction                            Not Required\nVulnerability                     Not Required\nControl Strength                  Not Required\nThreat Capability                 Not Required\nLoss Magnitude                        Required\nPrimary Loss                          Required\nSecondary Loss                        Required\nSecondary Loss Event Frequency        Required\nSecondary Loss Event Magnitude        Required\ndtype: object"
-     ]
-    }
-   ],
-   "source": [
-    "    from pyfair import FairModel\n",
-    "    \n",
-    "\n",
-    "    # Create an incomplete model\n",
-    "    model = FairModel('Tree Test')\n",
-    "    model.input_data('Loss Event Frequency', mean=5, stdev=1)\n",
-    "    model.calculate_all()\n",
-    "    \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([198.47586524, 150.11389796,  86.89533848])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import scipy.stats as stats\n",
-    "\n",
-    "s = stats.norm(loc=100, scale=50)\n",
-    "s.rvs(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5319104.079542093"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "pd.Series([\n",
-    "    10_512_018,\n",
-    "    0,\n",
-    "    3_841_190\n",
-    "]).std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats as stats\n",
-    "\n",
-    "import matplotlib\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline\n",
-    "matplotlib.style.use('fivethirtyeight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,3)\n",
-    "\n",
-    "heights = stats.norm.rvs(loc=175, scale=40, size=10_000)\n",
-    "weights = stats.norm.rvs(loc=80, scale=20, size=10_000)\n",
-    "\n",
-    "# Height\n",
-    "axes[1].hist(heights, bins=50)\n",
-    "axes[1].set_title('Height (Centimeters)')\n",
-    "axes[1].set_xlim((0, 300))\n",
-    "axes[1].grid(False)\n",
-    "axes[1].text(0,500, 'Mean: 175 cm\\nStdev: 40 cm')\n",
-    "axes[1].yaxis.set_ticks([])\n",
-    "\n",
-    "# Weight\n",
-    "axes[0].hist(weights, bins=50)\n",
-    "axes[0].set_title('Weight (Kilos)')\n",
-    "axes[0].set_xlim((0, 300))\n",
-    "axes[0].grid(False)\n",
-    "axes[0].text(120,500, 'Mean: 80 kg\\nStdev: 20 kg')\n",
-    "axes[0].yaxis.set_ticks([])\n",
-    "\n",
-    "# BMI\n",
-    "axes[2].set_title('BMI')\n",
-    "\n",
-    "axes[2].xaxis.set_ticks([])\n",
-    "axes[2].yaxis.set_ticks([])\n",
-    "axes[2].text(.4,.3,'?', fontsize=90)\n",
-    "\n",
-    "fig.set_size_inches(12, 3)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\n",
-    "\\Large{\\text{BMI}} = \\huge{\n",
-    "    \\frac\n",
-    "        {\\text{Weight}_{kg}}\n",
-    "        {(\\text{Height}_{cm} \\times .01) ^2}\n",
-    "}\n",
-    "$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "35.55555555555556"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "80 / (150 * .01) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "5 + 5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import scipy.stats as stats\n",
-    "\n",
-    "heights = stats.norm.rvs(loc=175, scale=40, size=3)\n",
-    "weights = stats.norm.rvs(loc=80, scale=20, size=3)\n",
-    "\n",
-    "df = pd.DataFrame({'height': heights, 'weight':weights})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "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>height</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>bmi</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>164.606021</td>\n",
-       "      <td>63.909336</td>\n",
-       "      <td>23.587009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>230.353776</td>\n",
-       "      <td>74.972730</td>\n",
-       "      <td>14.129040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>246.915053</td>\n",
-       "      <td>58.501501</td>\n",
-       "      <td>9.595594</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       height     weight        bmi\n",
-       "0  164.606021  63.909336  23.587009\n",
-       "1  230.353776  74.972730  14.129040\n",
-       "2  246.915053  58.501501   9.595594"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def get_bmi(row):\n",
-    "    h = row['height']\n",
-    "    w = row['weight']\n",
-    "    bmi = w / (h * .01) ** 2\n",
-    "    return bmi\n",
-    "\n",
-    "df['bmi'] = df.apply(get_bmi, axis=1)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "heights = pd.Series(stats.norm.rvs(loc=175, scale=40, size=10_000))\n",
-    "weights = pd.Series(stats.norm.rvs(loc=80, scale=20, size=10_000))\n",
-    "df = pd.DataFrame({'height': heights, 'weight':weights})\n",
-    "df['bmi'] = df.apply(get_bmi, axis=1)\n",
-    "df = df.round(2)\n",
-    "bmis = df['bmi']\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1,3)\n",
-    "\n",
-    "# Height\n",
-    "axes[1].hist(heights, bins=50)\n",
-    "axes[1].set_title('Height (Centimeters)')\n",
-    "axes[1].set_xlim((0, 300))\n",
-    "axes[1].grid(False)\n",
-    "axes[1].text(25,480, 'Mean: {} cm\\nStdev: {} cm'.format(round(heights.mean()), round(heights.std())))\n",
-    "axes[1].yaxis.set_ticks([])\n",
-    "\n",
-    "# Weight\n",
-    "axes[0].hist(weights, bins=50)\n",
-    "axes[0].set_title('Weight (Kilos)')\n",
-    "axes[0].set_xlim((0, 300))\n",
-    "axes[0].grid(False)\n",
-    "axes[0].text(120,500, 'Mean: {} kg\\nStdev: {} kg'.format(round(weights.mean()), round(weights.std())))\n",
-    "axes[0].yaxis.set_ticks([])\n",
-    "\n",
-    "# BMI\n",
-    "axes[2].set_title('BMI')\n",
-    "axes[2].hist(bmis, bins=1000)\n",
-    "axes[2].set_xlim((0, 300))\n",
-    "axes[2].grid(False)\n",
-    "axes[2].text(60, 210, 'Mean: {}\\nStdev: {}'.format(round(bmis.mean()), round(bmis.std())))\n",
-    "axes[2].yaxis.set_ticks([])\n",
-    "\n",
-    "fig.set_size_inches(12, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([20.99692202, 23.99979115, 23.02600969, ..., 42.82278335,\n",
-       "       14.7383338 , 24.56640589])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bmis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 1440x432 with 1 Axes>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x26403b71198>)"
-      ]
-     },
-     "execution_count": 121,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.patches import Rectangle\n",
-    "from matplotlib.collections import PatchCollection\n",
-    "\n",
-    "\n",
-    "class FairTreeGraph(object):\n",
-    "    '''Provides a pretty tree diagram to summarize calculations.\n",
-    "    \n",
-    "    '''\n",
-    "    \n",
-    "    # Class attribute\n",
-    "    DIMENSIONS = pd.DataFrame.from_dict({\n",
-    "    'Contact'                       : ['Contact'   ,    0,    0,  600,  800, 'green', None],\n",
-    "    'Threat Event Frequency'        : ['Threat\\nEvent\\nFrequency' ,  600,  800, 1800, 1600, 'green', 'multiply'],\n",
-    "    'Action'                        : ['Action'   , 1200,    0,  600,  800, 'green', None],\n",
-    "    'Threat Capability'             : ['Threat\\nCapability'  , 2400,    0, 3000,  800, 'green', None],\n",
-    "    'Vulnerability'                 : ['Vulnerability'   , 3000,  800, 1800, 1600, 'green', 'step'],\n",
-    "    'Control Strength'              : ['Control\\nStrength'  , 3600,    0, 3000,  800, 'green', None],\n",
-    "    'Loss Magnitude'                : ['Loss\\nMagnitude'  , 6600, 1600, 4200, 2400, 'green', 'add'],\n",
-    "    'Loss Event Frequency'          : ['Loss\\nEvent\\nFrequency', 1800, 1600, 4200, 2400, 'green', 'multiply'],\n",
-    "    'Risk'                          : ['Risk'   , 4200, 2400, 4200, 5000, 'green', 'multiply'],\n",
-    "    'Primary Loss'                  : ['Primary\\nLoss'  , 5400,  800, 6600, 1600, 'green', None],\n",
-    "    'Secondary Loss'                : ['Secondary\\nLoss'  , 7800,  800, 6600, 1600, 'green', 'multiply'],\n",
-    "    'Secondary Loss Event Frequency': ['Secondary\\nLoss Event\\nFrequency', 7200,    0, 7800,  800, 'green', None],\n",
-    "    'Secondary Loss Event Magnitude': ['Secondary\\nLoss Event\\nMagnitude', 8400,    0, 7800,  800, 'green', None],\n",
-    "}, orient='index', columns=['tag', 'self_x', 'self_y', 'parent_x', 'parent_y', 'color', 'function'])\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        self._colormap = {'Not Required': 'grey', 'Supplied': 'green', 'Calculated': 'blue'}\n",
-    "\n",
-    "\n",
-    "    def _process_statuses(self):\n",
-    "        '''Turn dict into df and add color column'''\n",
-    "        self._statuses = pd.DataFrame.from_records([self._statuses]).T\n",
-    "        self._statuses.columns = ['status']\n",
-    "        self._statuses['color'] = self._statuses['status'].map(self._colormap)\n",
-    "        \n",
-    "    def _tweak_axes(self, ax):\n",
-    "        # Set limits\n",
-    "        ax.set_title('Calculation Functions', fontsize=20)\n",
-    "        ax.set_xlim(0, 9_400)\n",
-    "        ax.set_ylim(0, 2_900)\n",
-    "        # Disappear axes and spines\n",
-    "        for axis in [ax.xaxis, ax.yaxis]:\n",
-    "            axis.set_visible(False)\n",
-    "        for spine_name in ['left', 'right', 'top', 'bottom']:\n",
-    "            ax.spines[spine_name].set_visible(False)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_rects(self, ax):\n",
-    "        '''Cannot be done via apply'''\n",
-    "        patches = []\n",
-    "        patch_colors = []\n",
-    "        for index, row in self.DIMENSIONS.iterrows():\n",
-    "            rect = Rectangle(\n",
-    "                (row['self_x'], row['self_y']),\n",
-    "                1000,\n",
-    "                500,\n",
-    "                alpha=.3,\n",
-    "            )\n",
-    "            patches.append(rect)\n",
-    "            patch_colors.append(row['color'])\n",
-    "        collection = PatchCollection(patches, facecolor=patch_colors, alpha=.3)\n",
-    "        ax.add_collection(collection)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_text(self, row, ax):\n",
-    "        '''Apply-able function'''\n",
-    "        # Draw header\n",
-    "        plt.text(\n",
-    "            row['self_x'] + 500, \n",
-    "            row['self_y'] + 240, \n",
-    "            row['tag'], \n",
-    "            horizontalalignment='center',\n",
-    "            verticalalignment='center',\n",
-    "            fontsize=14,\n",
-    "            fontweight='medium',\n",
-    "        )\n",
-    "        # Draw data\n",
-    "        if row['function']:\n",
-    "            plt.text(\n",
-    "                row['self_x'] + 500, \n",
-    "                row['self_y'] - 130, \n",
-    "                row['function'], \n",
-    "                horizontalalignment='center',\n",
-    "                verticalalignment='center',\n",
-    "                fontsize=14,\n",
-    "                fontweight='bold',\n",
-    "                bbox={'facecolor':'salmon', 'alpha':.75, 'pad':5},\n",
-    "                zorder=3\n",
-    "            )\n",
-    "       \n",
-    "\n",
-    "    def _generate_lines(self, row, ax):\n",
-    "        '''Generate lines between boxes'''\n",
-    "        if row.name != 'Risk':\n",
-    "            ax.annotate(\n",
-    "                None,\n",
-    "                xy=(row['parent_x'] + 500, row['parent_y']), \n",
-    "                xytext=(row['self_x'] + 500, row['self_y'] + 500),     \n",
-    "                arrowprops=dict(\n",
-    "                    arrowstyle=\"-\",\n",
-    "                    connectionstyle=\"angle3,angleA=0,angleB=-90\",\n",
-    "                    ec=row['color'],\n",
-    "                    alpha=.3,\n",
-    "                    linestyle='--', \n",
-    "                    linewidth=3\n",
-    "                ),\n",
-    "            )\n",
-    "            \n",
-    "    def _generate_legend(self, ax):\n",
-    "        # Gen legend\n",
-    "        patches = [Patch(color=color, label=label, alpha=.3) for label, color in self._colormap.items()]\n",
-    "        plt.legend(handles=patches, frameon=False)\n",
-    "\n",
-    "    def generate_image(self):\n",
-    "        fig, ax = plt.subplots()\n",
-    "        fig.set_size_inches(20,6)\n",
-    "        self.DIMENSIONS.apply(self._generate_lines, args=[ax], axis=1)\n",
-    "        ax = self._tweak_axes(ax)\n",
-    "        self.DIMENSIONS.apply(self._generate_text, args=[ax], axis=1)\n",
-    "        self._generate_rects(ax)\n",
-    "\n",
-    "        ax.text(0, -500, 'Copyright 2019, Theo Naunheim\\nFreely available for use under the CC BY 2.0 License')\n",
-    "        #self._generate_legend(ax)\n",
-    "        return (fig, ax)\n",
-    "\n",
-    "    \n",
-    "FairTreeGraph().generate_image()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Step 4: Analyze your Risk outputs'"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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AL7/8Mq+99hrt2rXLt05OnTp1GDhwIFFRUQwdOpTY2NizPoOHhweffvopY8aMITo6mpiYGFasWAHAzJkzue++++jYsWO+KV5/lbEsq6TzJZ4UERGRym3RoorugZwvffpUdA9ERKSyq4jtuSsjPz8/kpOTK7obBRU7b0pr1IiIiIiIiIhchKpiqCKa+iQiIiIiIiIiF7FKOJqmRApqREREREREREQqCQU1IiIiIiIiIiKVhIIaERERqZLuvDOSBQumlKrutGm38dRTvc9zj0RERET+Oi0mLCIiIpXOtGm38f337wHg4uJKcHAEbdtey7Bhz+DnFwTA1Klr8PLyrchuioiIiJQ7jagRERGRSik6uifvvXeYd97ZwwMPvMOaNYv4z3/udZ4PDKyBp6dPBfZQREREijJ58mRatGhBVFQUMTExrFq1qlyvHxkZSXx8PACdOnUqU9tJkyYxZUrpRuRWFI2oERERkUrJ3d2ToKAwAEJCanP55YP4/vtZzvN33hnJtdfeT//+owH4+us3WbhwKseO7cPb25+GDVszceL/cHUt/HVn9+5NTJp0NT17DueWWyZfkOcRERG50OZvmU9cSly5XS/UN5Trm19fYp2VK1fyxRdfsH79ejw9PYmPjycjI6Pc+lDQihUrztu1K4qCGhEREan0jhzZxfr1X+Pq6l7k+e3b1/LGG/cxatR7NG9+OSkpJ9m8+fsi6/7++09MntyPQYMm0K/fQ+ez2yIiIhUqLiWO2gG1y+16B5IOnLXO4cOHCQkJwdPTE4CQkBDAHgWzdu1aQkJCWLt2LaNHj2bp0qVMmjSJnTt3cvDgQfbv389jjz3GXXfdxdKlS5k4cSLVq1dn27ZtdOnShddffx0Xl/wTg/z8/Jzbb7/wwgvMnTuX9PR0+vfvz5NPPgnYI3z++9//UqdOHWrUqEGbNm3K7TM5HxTUiIiISKW0fv3XDBzoh8ORTUZGGgB33PFikXWPHduHl5cv7dv3xcfHH6hH/frRheqtWfMFU6bcxN13v8oVVww7n90XERH5W+rVqxdPPfUUTZo0oWfPngwaNIiuXbuW2Gbz5s388ssvpKSkEBsby7XXXgvA6tWr2bJlC/Xq1ePqq69m/vz53HDDDUVeY/HixWzfvp3Vq1djWRZ9+/blxx9/xNfXl48//pgNGzaQlZVF69atFdSIiIiInIsWLbpw331vkZGRyuLFb3PkyE56936wyLoxMVdSo0Y97rqrPq1bX0VMTC86drw+J7Sx7dy5jmee6c8jj3zI5ZffeKEeQ0RE5G/Fz8+PdevW8dNPP/HDDz8waNAgnnvuuRLb9OvXD29vb7y9venevTurV6+mWrVqtG/fngYNGgAwZMgQli9fXmJQs3jxYmJjYwFITk5m+/btnDp1iv79++PjY69r17dv33J82vNDiwmLiIhIpeTp6UNERCMiI1sxYsR00tNPM2fOv4qs6+Pjz7Rp63nssbmEhNTl00+f5d57m3H8+CFnndDQ+tSp05wlS2aQmZl+oR5DRETkb8fV1ZVu3brx5JNP8uqrrzJv3jzc3NxwOBwApKWl5atvjCnyuLjyoliWxbhx49i4cSMbN25kx44d3HHHHWdtVxkpqBEREZEqYfDgfzJ//r/zhS95ubq6ER19Bbfe+izTp28mPT2FNWu+cJ738wvm6ae/IyHhEM88019hjYiIyHmwbds2tm/f7jzeuHEj9erVIzIyknXr1gEwb968fG0+++wz0tLSOH78OEuXLqVdu3aAPfVp9+7dOBwO5syZw+WXX17sfa+66ipmzJjhXK/m4MGDHD16lC5durBgwQJSU1M5deoUixYtKu9HLncKakRERKRKaNWqG3XrtmDu3KcLnVuz5gs+//xldu7cwNGje/nxxw9JTT1FnTqX5KsXEBDC009/R3z8AZ599nqFNSIiIuUsOTmZW2+9lebNmxMVFcWWLVuYNGkS//znPxk5ciSdO3fG1dU1X5v27dtz7bXX0qFDByZMmEBERAQAHTt2ZOzYsbRs2ZL69evTv3//Yu/bq1cvbrrpJjp27EirVq244YYbOHXqFK1bt2bQoEHExMQwYMAAOnfufF6fvzxojRoRERGpMvr1e5iXX76dAQPG5Cv39a3GqlULmTPnKdLTTxMW1pD773+HFi0KfxkLCAhh8uTvGT/+Cp59dgDjxs3D3d3zQj2CiIjIBRPqG1qqnZrKcr2zadOmTZFbZnfu3Jk///yzyDZNmjThrbfeKlTu4+PDnDlzCpXv2bPH+T53BA3AyJEjGTlyZKH648ePZ/z48Wfte2VhLMsq6XyJJ0VERKRyqwKje+Uc9elT0T0QERH56yZNmoSfnx+jR4/OV7506VKmTJnCF198UUzLKq/YhXMU1IiIiFzEFNRcvBTUiIiIVGnFBjVao0ZEREREREREpJJQUCMiIiIiIiIiUkkoqBERERERERERqSQU1IiIiIiIiIiIVBIKakRERERERESk3Bw5coTBgwfTsGFDmjdvzjXXXFPs1twAfn5+53SfpUuX0rt37xLrbNy4kS+//LLM1+7WrRtr1649p379VW4VclcREREREREROa/mz4e4uPK7XmgoXH99yXUsy6J///7ceuutfPzxx4AdlsTFxdGkSZPy60wpbdy4kbVr13LNNddc8HufK42oEREREREREbkIxcVB7drl9ypN6PPDDz/g7u7OP/7xD2dZTEwMsbGx9OjRg9atW9OqVSs+++yzIts///zztGrViujoaMaOHQvkH90SHx9PZGRkoXarV6+mU6dOxMbG0qlTJ7Zt20ZGRgYTJ05kzpw5xMTEMGfOHFJSUhg+fDjt2rUjNjbW2Y/U1FQGDx5MVFQUgwYNIjU1tYyfdvnRiBoRERERERERKRe//fYbbdq0KVTu5eXFggULCAgIID4+ng4dOtC3b1+MMc46X331FQsXLmTVqlX4+PiQkJBQ6vs2a9aMH3/8ETc3N5YsWcLjjz/OvHnzeOqpp1i7di2vvvoqAI8//jhXXHEFM2bM4OTJk7Rv356ePXvy5ptv4uPjw+bNm9m8eTOtW7f+6x/GOVJQIyIiIiIiIiLnlWVZPP744/z444+4uLhw8OBB4uLiCAsLc9ZZsmQJt99+Oz4+PgAEBweX+vqJiYnceuutbN++HWMMmZmZRdZbvHgxn3/+OVOmTAEgLS2Nffv28eOPP/Lggw8CEBUVRVRU1Lk+6l+moEZEREREREREykWLFi349NNPC5XPnj2bY8eOsW7dOtzd3YmMjCQtLS1fHcuy8o2wyeXm5obD4QAo1CbXhAkT6N69OwsWLGDPnj1069atyHqWZTFv3jyaNm1a6FxR964IWqNGRERERERERMrFFVdcQXp6Om+//bazbM2aNezdu5eaNWvi7u7ODz/8wN69ewu17dWrFzNmzOD06dMAzqlPkZGRrFu3DqDIEAjsETW1atUCYNasWc5yf39/Tp065Ty+6qqreOWVV7AsC4ANGzYA0KVLF2bPng3Y07c2b958Ts9fHhTUiIiIiIiIiEi5MMawYMECvv32Wxo2bEiLFi2YNGkS11xzDWvXrqVt27bMnj2bZs2aFWp79dVX07dvX9q2bUtMTIxzetLo0aP5z3/+Q6dOnYiPjy/yvo899hjjxo3jsssuIzs721nevXt3tmzZ4lxMeMKECWRmZhIVFUXLli2ZMGECAPfccw/JyclERUXx/PPP0759+/Pw6ZSOyU2RilHiSREREancFi2q6B7I+dKnT0X3QEREKruK2J5bSq3YeVZao0ZERERERETkIqRQpWrS1CcRERERERERkUpCQY2IiIiIiIiISCWhoEZEREREREREpJJQUCMiIiIiIiIiUkkoqBERERERERERqSQU1IiIiIiIiIiIVBIKakREREREREREKgljWVZJ50s8KSIiIlLRLAs2bEkiIe0oHVqF4efhV9FdEhERETkbU9wJtwvZCxEREZHy9uef8MXPO8nMzuRE9iEGtLscF6NBwyIiIlI16VuMiIiIVFkOB+zdC8bYf5RKz04lLjmugnslIiIicu4U1IiIiEiVtW8fpKdDiE8I7p7ZBARl8OfxPyu6WyIiIiLnTFOfREREpMw2r1tLRkpKRXeDnTsh/hhkWlm4eG1h//rTHMAFr73ZuBrXiu4eHr6+RLVpW9HdEBERkSpEQY2IiIiUWUZKCm0aNajobtC8FuzZA35+4F8jjIOnDlDduzphfqEV3TUA1u3YVdFdEBERkSpGQY2IiIhUKYlJcPw4hIRAgD9ccknumQCqeTUHIMvKZn/ifjxdPQnzC8PFFLuxgoiIiEiloqBGREREqoSsbNi9Gw4fto/jjkDHjkXX3Z+4n/2J+wE4lHyIptWb4q9tu0VERKQK0GLCIiIiUumdOAnr158JaQC8vMCyiq7v5eblfH864zQbj2xk98k9OIprICIiIlJJKKgRERGRSisjE7bvgF9/hQVff0K3wXXpNrguqRn7ad4CipvRFO4XRrdOQ+nWaSgz35mHZVnsT9zP+iPrOZmWWOI9l65YiUuturjUqsvSFSvPw1OJiIiIFE9BjYiIiFQ6ySmw7U9YtSr/KJpcTZuApwfMmvOJM1TZs39/vjqXxsbSLiaauhERzrLTGafZHLeZDYc3cjTlmEbYiIiISKWjNWpERESk0jhxAg4csKc6FeRXxiVmVn7xGWBPjzqYfJA9J/fgcDgAOJVxij/i/8DD1YNaAbWI8I/A1ejvVyIiIlLx9I1EREREyk39SzvhUqsutz44ijGTnyG4eUsiYtvwxn/fJz4hgRvvuhu/Rk1p1rkbixZ/C+QfFbP4+/2cOAmHj+53TnP64ZdPuOQSqBWR/163j3qY4Q8/4jxu0OEyXGrVZdLUFwGc13zyxRep7V+LlN1ZzulQX/1vGY89/G+6db6Jtl17M+n1F4p9pm+WLnNea+v27c7yGR/PwaVWXXwaNuZkYsnTqURERERKS0GNiIiIlLu5X/yPmR/PxcvTkyNHj3Hf40/Q9fob+GX9Bjw83Plz1y6G3j+STb+eJCOzcPu8S8/Uqws1QgrXaVCvHg3q1XUex7RowaWxsdQODy+yT55uHs73L70wiwP74/Dy9uTY0QQmP/MaX373PQDHTyfka9eraxca168PwIyP5zrL5/3vSwD69epFtcDAEj8PERERkdJSUCMiIiLlLsDPjz+XL+PH+fMAsCwLcGHppz/x7Jg3AEhOSWbx0o3ExZ1pFxEOdetCVPTZ7zHhoZE8MXKk83j+u2+x8ovPuPOmIWdtO7D3texbtZrV3y0iIjwUgOdf/w9HU45x4NQBZ72t8VvZeXIXt950AwDvfzqPrKwsEpOS+G75zwDcOvDGs3dWREREpJQU1IiIiEi5u7x9OwIDAqkeVNtZ1rJxFw4e8KSa35lRMCcSj+HqeqZdnToQWQ+8PM9v/27s0xsXY2gS3pBrr+gBwG9/bMPd1T1fvYzsTA4lHSK6S2O8vDw5Gh/Ph18uZME335CRkUF4aE2u7NL5/HZWRERE/la0mLCIiIiUu+xMP1ashOzsM181fLxzVwM+M7GpRg0ICsrTLjsbgMSkUxeimwBYnNn5KcirGg2DGhaq4+/vyxVXduTLRUt584P3cXGx/9Z17TVXkOHIxDtv2iQiIiLyF2hEjYiIiJyTrCxIOAF5M5WsLPtnegbkZC5OxtihTM5yLwAEVYPQkDML0Ozcuw+ABV9/Xao++Hh7Od+nnD5d6r7nri9zKjmZb5f9BEDLZk0B8PPwddZrENSAcP9wPFw96H/9lQCs+mUTq1dtBuDynrFsOLKB3F2+07MziE89zunM1FL3RURERCQvjagRERGRs8rOhqSkM6/Nv0LK4TPnL2lmj44pyOPM+r3Urg2tWsKe/fnrXBobi5+vL8kpKdx07/20aNqEFWvXlapfzRo1cr6/cvBN1KtVmyn/fILL2rUrsd28L79i+Zo1JCad4viJEwA8es8/CtXz9/CjcXAjGgU1onmN5rwe/SEbNv2OI9tBs0saEFm/NsYYHDiwLIsNhzeQkZ0BgLurO3uOnyTweASBXoEEeAbg5eZV6B4iIiIieSmoERERkXyysyE+/kwok5gIKSn566SmAHl2Ysopy6KnAAAgAElEQVTduckt55tFYCC0awd5BrzgUsw43uCganz42is8+q/J7DlwgOzsbD545WWG3Hv/Wfsa1fwSnhg1krdnf8iRo8c4cvQYJ06efavsN/79LB8v/Izvf15BrbAwxj5wH9f27FFsfWMgwNOfh+8cwS0P2AsY3z5wMI2CGxHoFYircSHbcpDpOLOFVWZ2JidSE/gj/g9nmYerBwGeAc5XsHcwvnlG8IiIiIgYy7JKOl/iSREREam6srMhORkyM+0pSa6u9tSlZcvgbLOI9m1dRqt6DfDzg+oh9mgZF1Nym4q2dMVKrrhxEADffzKHbp06lvka63/9lbZXX4uXlycH1q4hOKhavvMn0xI5cOoASWlJZDmy+G33fuq2aV7s9YwxtA5vTYR/BABJ6UlkZGfg7+GPp9t5XlFZREREKlKx35w0okZEROQilp1tj4Yp6pWWdqZezZpw6aV2QFNUSGMM+PpCQID98gE6xoDb32QN3a3bt/P0tOn8tGo1AMMHDy4U0gBU8wqkmlcglgUpWSkkn3CjbrVIktKTSEq3w5u8LMsiITWBCP8IjiQfYc3BNc5zbi5u+Lj74Ofhh6+HL77uvs6fCnFEREQuXgpqREREqjjLsoOUXAkJ8OefcOpU/jCmJKk5a98GBMAll8Dx4/mDGX9/8m2jnXj47xPSAMQdi+ejhZ/h6+PDwD69ef6Jx0usbwz4ufsS6hdKq9BWgB3KpGalkpSeRGJaIknpSbi5uDl3mTqdmT8hy3JkOQOegtxc3PD18CXcL5yGwQ1xMS7OexhTyYc2iYiISIk09UlERKQSy8qyw5aCr9TUM+/T0+0RMe3a2QHB998XXlOmKMaAj4+9nkyTJnYYU1prf1xGm0YNzv3B/ibW7dhF2y5dS1U325HNjoQdHE05SnJGcqHRN8XpWKcjIT4hHEg6wG9Hf8OyLLzdvfFy83K+vN3yH3u4eijQERERqVia+iQiIlKZWBZkZJwJW/z97dAE7MV7f/vNHhGTmVnydXLFxdlTlnJHweQGNblhjK9v4Ze3d/EL/MqF5+riStOQpjQNsbcJz8jOIDkjmdOZp0nOSCYlI4WUzBRSMlKcIY6riyu+7vZixPsS95GZbf+DOZV+ilPpp4q+EeBiXPBy8yKyWiQNg+0RPdmObBJSE5znvN29nSN1RERE5MJRUCMiInIeZGbaOyYVNQIm91VwUGvXrnbIsn27PX2ptIyxF/P1zdk8qE0bO+RxdVUYU5V5uHoQ7B1MsHdwoXPpWemkZqXi6+6Lu6s7AA2CGpCckUx6VvpZr+2wHJzOPM2WY1uoE1gHD1cPfjnwCwmp+f/hubu6FxqNkztaJ3f9HBERESlfCmpERESKkZlpj3rJzDz7ex8faN4cPD3tkGXVKnvaUlmkpdlBTWgoHDliBzkuLuDllf/l7V24LG8YY4x9Hbl4ebp5FlpQOMwvjDC/MNKz0knLSnO+UrNS8x2nZaU5R95U96mOu4sd9BRcIwfsLcYzszOLXCcHILJapHMNnl0ndnEk+QiuxhV3V3fcXdxxd3XHw9Wj2PcasSMiIlKYghoREbmoZWWdCVQKBix5jz08oGFDO3BJT4dffrFHxJRWQoLdtmlTeyHe0oQ07u5nQpcaNSAkxC6vUwfCw8HhsPslUha5IU4ggcXWyXZkk+nIxMvNy1nWNqItu07syhfsnGUtQ+JS4mhFK1IzU/n96O9l6qebixtRoVHUCqgF2EFPQmoCbi5uuLvkhDo5gU/e97k/tcaOiIhcrBTUiIhIpedwnH1Ui4sL1KtnT/+xLFi/3h6V4nCU/j7Z2RATA/HxZQtpwL5/cM4Mlbp17alHqamFR77kfbmWsGuSWyX/L7SHry/rduyq6G5Ueh6589EqGVcXV1xd8v8DDPIOoo13G+exZVlkZGcUOyrHYTloEGQvKO3l5oW/p3+J6+IUlOXIYm/iXmoF1OJE6okyBz1+Hn7EhMUQ5B2EZVnsTdxLUnrSWUfyuBpXhTwiIlKpVfKvgSIiUpk5HHa4kZ1tH3t55T939KgdouTWKenl4gL169sjSwC2bIF9++yRKWf5o75TYiJ07Gj/PHSo7M+TO12oZk17dEtioj3qxd3dHtmS+77gsYcH+PnZ057A/tm6ddnvX5VEtWlb0V2Q88wYU6rRObl1u9brysm0k2RkZ5DpsKdMZToy7eMi3rsaV2fQ4+3ujZuLW6l3ugJIzkhmX+I+gryDOJx8mF/jfi11WxfjQjWvasSGx+Lj7kN6Vjrbjm8jJSPFDrGM61l/BnkH4ePu47xmliOLzOxM53kX46JASEREzomCGhGRi1juqJPSBCWWZQcUQUF22xMnYOdOexpQcW0KCg2F9u3t9ytW2Ncoi8REuPJKu887d5b9eXODooAAqF7dnoLk4lI4VCnqvZ+fvU012McdO5b9/iJ/Z8YYgryDzqmtl5sXPRr04Pjp46UKeTKzM/F083ROm/J09TzLHfJzWA4SUhM4mHSQxtUbszdxL3tP7i3TNYwxdK7bmUCvQA6dOsSGwxtwWPmH8LkYl2KDnpq+NZ07bmVkZ3Ag6UC+oKekn24ubvmmrYmIyMVFQY2IXPRyp75Ylv3ess688h47HPZ6Ie7uZ9qmpdlTWArWK+4a/v72SAxj7PIDByA5ueh7FfU+KAgaNbLDheRk2LrV3nK5tO1r1IB27ez2e/faWzyXZerPjh3Qs6c9ImTTJvvZy+Lo0TOjY3K3hy6L3JDIwwNq1YKDB+1jFxd7KlBRI1tyy7y9ISzsTP1Onexn145HIlWDh6sH4f7hpaqbu3ZO7oiV6j7V6RrZlYTUBDKzM0sc1ZPtsFNmb3dvwvzs/9MI9AzEGHPWNXkK9uF05mkCvQI5mnK0UEgDdiDkyHaQSWahc/Gn4wn2DibIO4hf437l0KmyDQMM9g6mU51OGGPYe3IvW+O3ku3IxhiDwWCMsUf1FPHe1bhSr1o96gbWBeBYyjH2Ju7FYTmKrG8wzhFCBoO7qzu1A2o7RxSlZqZyJPkIFlax98z73sPVg2pe1fJ9lskZyQD57lPUNQDnsYjIxUpBjYicV1u32r9s5373zfsduKj3Hh7QooU9MsOyYN06OHas9O29vOw1RmrUsEeT/PILnDxZ+v66uEDbtvb9ExLsUSFl+N4O2FsjR0TA7t329J2yOHrUDnvCw+3Q5MiRsrdPSLDDokOHyhbS5MoNNgICzh7UuLjY66y4utpBSmTkmXVX2raF/fvtzy+3TkkvDw+oduZ7O61bQ3S0fY9z/T6ukEbk4lTUL+kBngEEeJZuu7NsR3a+qUmhfqH0bNCTU+mnyLayyXZkO386LEehsmwrm0DPQGfQ0yCoAamZqaRkpuSrVxJXF1dn0HEuu18lpCZwOvM0vh6+7Dyx07mTF6X8b1ZSXBIR/hG4GlfWH15PRnZGme5/MOkg3et3x2E5+GnfT6XaFj6vuoF1iQ6LxmE5WLZnmTOoKQ0PVw9ahbYiwj8Ch+Vg9cHVHEs5BuT/t2HI/+8k95yPuw9RoVEEeweTmZ3J+sPrOZF2osh2RV3Pz8OPqNAofD0q5xpUIlL1KagRkfPm9Gk7bCiLzEy7TWioPW3m8OGytU9Ls9c1qVHDDnjKEtKAHWwcP27fPymp7CENnJkSdC4hgYuLPQUH7Kk7Bw6UrQ8hIWcWtG3c2P48s7LyByJ5w5W8Lzc3e0RK7oii2Fh7zRiHo+j6Li4lP2P16vbrryhpsV0RkXNVcCFlsKdfnet0ogDPADrWyT9f0rKsYkMeh+Ug0DMQd1f7/3CjQqOo7lOdtKy0QnWL+umwHIT7hzuDgvrV6vNH/B9lWuMnyDsINxc3Z//jT8eX6Zlzw6Xc5yyrxPREwB6NU5aQBuypYvsS9xHhH0FCaoIzpMntj/N9wdQq5/BU+in2ntxLsHcwcSlxHE05Wqb7p2WlsS9xH5fUuKRM7URESsucZYjnOfyKIiIlWbSoontw4ViWPaImuQzfv3J37qlRww4I/vijbO3d3aFBA3utkaws2L79THtjzozMyA0YcstyX15e9o49np524HLwoB04GVN8m9zrde9u3zciwj62LIiLs6cA5dbNHR1S8H3ucWDgmQVpwV4fJj29cL2SriEiIn8/lmVhYTmDk5LeGwz+nv752iakJpDlyMIip17O9Yp672JciPCPwNPN/g9WUnoSB5MOkm1l88PuH5z3s7DAAgcO7Lf2NVxdXAn3C8fPw/7LxKFTh0hITXD2r2Cbgtdzc3UjMjCSIO8gHJaDP4//SVJ66bfqc3Nxo2FQQwK9AsnIzmBr/NYyjQhydXGlSXCTfJ/hxa5P0z4V3QWRi1GxY8YV1IhcYH+noCZXRhGjqYubypI72iOvgovWFmyb97gip6z30XcYERH5m1u0reK+6OT+XlNwJE3B33cK7shlWRbZVnaxaxQVvJ6bi9s5TVeryhTUiJwXxf7moqlPInLeeXj8tfaa/iIiIiJnkxu+FFybpvhfhc60czP6tUhEKo+/VxQsIiIiIiIiIlKJKagREREREREREakkFNSIiIiIiIiIiFQSCmpERERERERERCoJBTUiclbTpt3GU0/1ruhuiIiIiIiIXPQU1IiIiIiIiIiIVBLah05E/pJjx/bx9tsj2bRpCQDR0VcyYsR0QkJq55zfz5tv3s+WLT+RkZFGjRp1GTJkEl26DAbg44+f4ttv3+XEiSP4+QURG9uLhx76b4U9j4iIiIiISEVSUCMi58yyLCZPvg4PDy+efvp7wPDmm/fzzDPXMXXqGowxvPHGvWRkpDF58g94ewdw8OA2Z/sVK+axYMEURo/+iMjIVpw8eZRt236puAcSERERERGpYApqROScbdy4hD17NvHmmzsJDY0EYPToD7n77kZs2vQdMTE9OXp0L506DaB+/WgAwsLqO9sfPbqX4OBwYmN74ebmTo0adWncuG1FPIqIiIiIiEiloDVqROScHTiwleDgCGdIAxAW1oDg4Aj2798CQJ8+I5k792kefbQjH3zwBDt2rHPWveyyG8nISOOuu+ozffodLF/+CZmZ6Rf6MURERERERCoNBTUics4sywJMkeeMsct79bqDt9/eTY8et3Pw4J889lgnPvxwEgA1atThP//Zxr33vomPTwAzZjzCQw+1IS0t5QI9gYiIiIiISOWioEZEzlmdOs1JSDhIXNweZ9mRI7tISDhEnTrNnWUhIbW5+uoRjBkzl6FDn2Lx4rec5zw8vGjX7lruvPMlpk5dw759v7N1688X8jFEREREREQqDa1RIyKlcvp0Ert2bcxXFh7eiMjIaKZOHcqIEdOxLIu33nqAhg1bExV1BQBvvz2S1q3/j1q1mnD6dBLr13/tDHG++24W2dlZNGlyKV5efixfPgc3N3fCwxtf8OcTERGRv6dpY6eRdCKJiW9OrOiuiIgACmpEpJS2bPmJUaNi85V16jSA8eMX8tZbDzJ+fDcAoqN7MmLEK86pTw6Hg7feeoD4+P14e/sTHd2D4cOnAuDrW4158/7NzJmjycrKpE6d5owbNz/fgsMiIiIiIiJ/J8ZeY6JYJZ4UkbJbtKiieyDnS58+Fd0DERGRirVoW9X7olPSiJpjh47x9jNvs2nFJgCiL4tmxPgRhISF2OcPH+PNf73JlnVbyEjPoEZ4DYbcP4Qu13YB4OPXPubbed9y4tgJ/AL9iL0slof+/dCFe7hy0qepvuSInAdFL/aJRtSIiIiIiIgUYlkWk++fjIeHB0+/9zQAbz79Js/c9wxTP52KMYY3nnyDjIwMJr83GW8/bw7uPuhsv+KbFSyYsYDRU0cT2SSSk8dPsm3Ttop6HBGpQhTUiIiIiIiIFLBxxUb2/LGHNxe/SWjtUABGTxnN3b3uZtPKTcR0iuHooaN06tWJ+s3sadthtcOc7Y8eOkpwjWBiL4vFzd2NGhE1aNxK6/CJyNlp1ycREREREZECDuw8QHDNYGdIAxBWJ4zgmsHs37EfgD7D+jD3jbk8OuhRPpj2ATt+2+Gse9nVl5GRkcFdPe9i+vjpLP96OZkZmRf8OUSk6lFQIyIiIiIiUoCFVewKErmbJvS6oRdvL3mbHtf34OCegzw25DE+fOVDAGqE1+A/X/2He5+8Fx8/H2b8ewYPDXiItNNpF+oRRKSKUlAjIiIiIiJSQJ2GdUiISyDuQJyz7Mj+IyQcTaBOozrOspCwEK4edDVjpo1h6INDWTx3sfOch6cH7bq1485xdzL1k6ns276Preu3XtDnEJGqR2vUiEgh06bdxvffv1eovEmTS5ky5ZcL0odff13K+PHd+eCDYwQEhFyQe4qIiMjf0+mU0+zauitfWXi9cCKbRTL10amMGD8Cy7J46+m3aNi8IVEdogB4e/LbtO7SmlqRtTidfJr1P613hjjfzf+O7OxsmkQ1wcvXi+VfLsfN3Y3wyPAL/nwiUrUoqBGRIkVH9+Thh9/PV+bm5lFBvRERERE5f7as3cKo/qPylXXq1Ynxr47nrclvMX7YeACiO0Uz4okRzqlPDoeDt55+i/jD8Xj7ehPdMZrhY4YD4Bvgy7y35zHz+ZlkZWVRp2Edxk0fl2/BYRGRohjLsko6X+JJESm7RYsqugdnN23abSQlxTNx4heFzr3wwhCysjIYN26es8zhcHDnnfXo1+9h+vV7CMuymD//Bb755k0SEg4RHt6I668fQ/fuNwMQF7eHu+6qz9ixn/LVV2+wdevPhIZGcuedLxMbe6XzfF5XXHEro0bNOq/P/Vf16VPRPRAREalYi7ZVgS86UmZ9mupLjsh5UMwqWFqjRkTKqFu3m1m79n8kJ590lv322zISEg7TpcsQAD744AmWLHmXu+9+jVdf3cINN4zj9dfvZs2a/+W71vvvj6dPnweZPn0TjRq1Y8qUwaSmJhMSUoexY+0g6NVXf+e99w5z110vX7iHFBERERERqSCa+iQiRVq//msGDvTLV3bNNfdxyy2T8fEJYMWKefTqdQcAy5bNJjq6B0FBYaSlpfDZZy/y5JOLadGiMwBhYfX588/VfPnla7Rrd63zev36PUT79vZfaIYNe4Yffvgvu3dvpHnzy/H3DwagWrWaWqNGRERERET+NhTUiEiRWrTown33vZWvzM+vGq6ublx++SCWLZtNr153kJmZzooV8xgxYjoA+/dvISMjjUmTrnbO3wbIysokNDQy3/UiI6Oc74ODIwA4efLoeXoiERERERGRyk9BjYgUydPTh4iIRkWe69btZsaM6cTx4wfZtm0VWVkZdOjQH7DXqwF44olF1KhRN187Nzf3fMeurmeOc0Mdy3KU2zOIiIiIiIhUNQpqRKTMmja9lLCwhvz440f88cdKOnS4Dm9ve5pUnTrNcXf35NixvURHX3HO98jdYSo7O7tc+iwiIiIiIlIVaDFhESlSZmY6J04cyfdKTDzmPN+161AWL36HtWv/R7duNzvLfXz86d9/NDNnjubbb2dw6NAOdu3ayFdfvcHXX79V1K2KVLNmPYwxrF37PxITj5GamlyuzyciIiLyV8UdiKNvs75s/3X7ebn+U3c/xbSx087LtUWk8lJQIyJF2rRpCbfeGp7vNWpUrPN8t243c/DgNnx8AomJuTJf26FD/8WQIZNYuHAK99/fgokTr2TFinmEhtYveJtiVa9eiyFDnuSDD8YzbFgob755f7k9m4iIiFQ908ZOo2+zvrzyxCuFzs18YSZ9m/XlqbufuqB9CgkP4b2f3qPBJQ0A+HXVr/Rt1pekE0kXtB8icnHR1CcRKWTUqFmMGjWrxDrh4Q35/HOryHPGGHr3foDevR8o8nxoaGSRbQuWDR48gcGDJ5Su0yIiInLRCwkPYfmXy7nr8bvw8vECIDsrm6WfL6VGRI0L3h9XV1eCagRd8PuKyMVNI2pERERERKRKiGwSSXhkOMu/Wu4sW7NsDe4e7rRs19JZtv3X7UwcPpGhHYYyqM0gxtw0hj82/JHvWgd3H2TczeMYEDWAe66+h7XL1jKw9UC+m/8dcGZa04pvVjBh+ARuiLmB+669jw0/b3BeI+/Up7gDcYy/dTwAN3e8mb7N+jqnLT1+y+O88dQb+e4/bey0fCOA0lPTmTZ2GgNbD+SWy25h7htzCz1/ZkYms6bM4vaut3Nj7I08fMPDrP9p/bl+nCJSSSmoERERERGRKuPKAVeyZP4S5/GSeUvoeX1P5w6SAKdTTtO9X3eem/0cUz+ZSv1m9Xny7iedU5IcDgfPPPAMrm6uvDDnBUY+O5KPX/uYzIzMQvd7f9r79Lm5D9MXTqdRy0ZMeXgKqSmpheqFhIcwdvpYAF794lXe++k97hp/V6mfa8bzM9i4YiNjXx7L0zOfZtfWXfy+9vd8daY/Pp3f1vzGI1Me4ZXPX+GK667g6XufZvcfu0t9HxGp/BTUiIiIiIhIldG1d1d2/LaDQ3sOceLYCdb/tJ4e/XvkqxPdIZru/bpTp2Edajeozd0T7sbD04N1P60DYOPPGzm4+yAP/fshGlzSgGaxzbhj7B1kZxXebbLfrf1of0V7IiIjGPbwME4lnioyGHF1dcU/0B+AatWrEVQjCF9/31I9U2pKKt9++i23PXobrTu3pl6Teox8ZiQuLmd+XTu87zA//u9Hxrw0hpbtWhJWJ4zeN/emTZc2fD3n61J/fiJS+WmNGhERERERqTL8Av3o0LMD3877Fr8AP1q1b1VofZqTx08y++XZ/LrqV04eP4nD4SAjLYNjh+wdLA/sPkBwzWCqh1Z3tmncqnG+YCRXZNNI5/vgmsHO65enI/uPkJWZRbOYZs4yb19v6jWp5zzeuWUnlmVxX+/78rXNzMgk6tKocu2PiFQsBTUiIiIiIlKl9BzQk2ljp+Ht481ND95U6Py0sdM4efwkd4y7g9Baobh5uDHh9glkZWYBYFlWvqlSJXF1c3W+z21jOYreUKE4xqXwvfKO3rGss1/Pcth9nvrJ1Hx9AvD08ixTf0SkctPUJxERERERqVKiO0bj5u5G0okkOvTsUOj81nVb6T20N+26taNu47p4+3pz4tgJ5/k6DepwPO44x+OOO8t2/LYDh8Pxl/rl5m7/HTw7O/8UqsDgQBKOJeQr273tzPSp8LrhuLm7sW3jNmdZ2uk09m7f6zxucEkDLMvixLETRNSLyPfKOzJIRKo+jagRuchMm3Yb33//XhHlG2jQIKYCeiQiIiJSvowxTP9sOgDuHu6FzkdERrB00VKaRDchPTWdWS/McoYoADGXxVCrfi2mjZvG8MeGk56WzrvPvWuPVCndQJsi1axVE2MMa5etpX339nh4euDt603UpVG88+w7rPp+FbUia/HNnG+IPxxPzYiagD3NqeeAnrw39T0CggOoXrM6H7/+MY7sM8FRrfq16NqnKy8//jLDHxtOwxYNOXXyFL+t/o3QOqF06tXp3DsuIpWKghqRi1B0dE8efvj9fGUBASGF6mVmZuDu7nGhuiUiIiJSbnz8fIo99+AzD/LaxNd4eMDDBNcMZsj9Q0g8keg87+LiwuOvPM4rE17hkRsfIbRWKMPHDOfZB5/Fw/PcvxtVD63OkAeG8MG0D3j1iVfp3q87o54bRc8BPdnz5x6mP26HS9fcdA0denZw7kIF2IFRajrPPvAsnl6eXHvztaSdTst3/ZHPjGTuG3OZNWUWx+OO4xfoR5NWTWh1aatz7rOIVD7mLPMhyzb5UkTOatGi83v9adNuIykpnokTvyh0bsyYy6lfPwZ3dw9++OF9wsMb8cILK0lOPsnMmaNZvfpzMjLSaNiwNXfc8SING7Z2tl2yZCYffvhPkpLiiYnpSXR0T95992EWLrTnen/wwROsWfMFL7+80dlm8eJ3mDlzNB99dGbBvV9++YyPP36S/fu3EBwcQdeuQxk0aIIzMLr99tpcc819HDmyk+XL5+LrG0jfvg9x3XUPO6+RnHyS994bw6pVn5GScpKwsAbcdNNTxMZexW23hfPwwx/QocN1zvrr1n3F5MnXMWvWwSIDq/LSp895u7SIiEiVsGjbef6icx7t/mM3I68byYufvkijlo0qujuVSp+m+pIjch4UO35PI2pE/mZ++OE9rr76Hp57bjmW5cDhcPDkk/9HYGANJk78El/fQJYsmckTT1zB669vIygolK1bV/DKK3dw882T6dRpAJs2fcfs2U+U+d5r137JtGnDuOuul2nevDNHj+7l9dfvJjs7k1tvfc5Zb+HCKQwd+i8GDBjLmjWLePfdh2ne/HKaNGmPw+Fg0qSrSUtL5qGH/kt4eCMOHPiDrKxMfHz8ufzyQSxZMiNfUPPttzNo377veQ1pREREpGpZ+e1KvLy9CI8M5+iBo7z773ep36w+DVs0rOiuicjfnIIakYvQ+vVfM3Cgn/O4efPOTJr0FQBhYY24/fbnnec2bFjM/v1beOaZo7i72zsGDBv2DKtXf86yZbO57rqH+fzzacTGXsWNN44DoFatJvz55yqWLv2gTP2aO/dpBgwYS48etwEQHt6QYcOe5ZVX7sgX1LRpcw3XXHMvAP36PcSiRdPZvPl7mjRpz4YN37Bjxxpee20rtWo1yXmmBs62vXrdxbhxnTlx4ghBQWEkJR1n9f+zd+fxMd37H8dfk30VCZJILCEkIdRWraW1L7cL2qrWVi6qy42tXD+KqlJKpa0mekt7RalqVblqqypKudQSS7WVtISSIImsRCLLzO+P3EyNxFqSEe/n4zGPe+Z7vud7PmfuydH5zHfZs5qJE7++qVhFRESkfMvOymZR+CLOnT2HWwU3GjzQgCGvDrnh1aBERO4UJWpEyqHQ0DaEhX1kfu/o6Gzerlv3fou6R49Gk5NzgX79LFcLyM3N4ezZYwDExx+hdeteFvtDQlredKLm6NFo4uIOsHz5dHOZ0WgkNzebjIxkPDyqABAQcJ/FcV5efmRkJAFw7NgBKqQiSugAACAASURBVFWqZk7SXCkkpAXVqoWwZctievb8P7ZuXULFit40adLlpmIVERGR8q3DEx3o8ESHsg5DRKQYJWpEyiFHRxf8/EoeW+3o6Grx3mQy4ulZlenTtxar6+rq8b8615+uymCwKVYvPz/vilom+vR5g5Ytnyp2vJubl3nb1tZy9QaDwXDZcpnXj6Vz5+fZsGEePXv+H5s3L6Rjx0HY2Nhc9zgREREREZGypkSNyD0uMLApaWlnsbW1w8cnoMQ61avXJzb2R4uyK997eFQhLe0sJpPJ3GX4+PGDFnVq125CQkLsVZNINxpvSko8CQm/XbVXTfv2z7F48XjWro3kxImfePXV/9zy+UREREREREqTfmIWucc1adKVoKAHmDHjCfbv/5bExBPExOzis88mc+TITgC6dRvB/v0bWLHibU6f/p1vvpnHnj2rLdpp2LA9mZnJrFgxizNnjvHttx/z44+WCZLevV/n++8Xs3TpFE6e/IX4+Bh27FjOokXjbyrewMBmvPXWUxw4sJGzZ49z4MBGdu/+Mx53dy9atnyKqKh/0rBhe3x9a/2FT0hERERERKT0KFEjco+zsbFhypQN1K//MBERg3n55SDefvsZTp/+HU/PqgDUr/8QYWEfsW5dJCNG3MfevWvp3ft1i3Zq1mzAiy/OZf36fzFixH0cPvw9PXtaJmDuv/9RJk1aw8GD3zF6dHPGjHmAlSvfpkqVGjcdb1DQg7zzTj/Cwurx73+PoqDAcphVp05DyM/PpXPnIbf4yYiIiIiIiJQ+w3Xmnrj+ZBAiclPWrCnrCG6PH374gnff7c+qVfllHUqJtm79jI8+Gs4nn5zGwcGpVM7ZrVupnEZERMRqrYktJ/+hIxa6Bes/ckTugKsuMac5akSkXLl06SKJicf56qu36Nr1xVJL0oiIiIiIiNwOGvokIuXK8uUzGDmyMRUr+vDMMxPLOhwREREREZGboqFPIqWsvAx9kuI09ElERO51GvpUPmnok8gdcdWhT+pRIyIiIiIiIiJiJZSoERERERERERGxEhr6JCJ3xE+JP3Eq4xSta7SmolPFsg5HRERERETEmmjVJxEpPclZyaz8dSUFpgIyL2XSp2Gfsg5JRERERETkrqChTyJy2yVlJWE0GQFIy0kjryCvjCMSERERERG5OyhRIyK3XVpOGs72zgC42LuQmp1axhGJiIiIiIjcHTRHjUg58VP0PnKzsso6DLLyLvJz4k8kZSWTlpNGoGdtKrtUIbhycFmHZubg6sp9ze4v6zBEREREROTepTlqRMq73KwsmtWpXdZh8HvqUXCpzqUCb5KykqheoToA9f39cLZzKuPoCkUfjSvrEEREREREREqkoU8ictvkGfNJzEoEwNHWkdqefyaOTp8/XVZhiYiIiIiI3DWUqBGR2yYuLQ6jsXASYVcHV4IqBZn3nT5/mot52WUVmoiIiIiIyF1BiRoRuW0yL2Wat2tWrImXkxcVHCsAYDKZLPaLiIiIiIhIcUrUiMgt+2TZcmz8a2DjX4MTp05Ry7MWHk4e1K1Ul8rOlTAYoF6VelRxqUIV1yr4BTfHxr8GU95596bOs3XnLvN5tu7cdYeuRkREREREpOwpUSMit0WesYDKzpU4sDUW/+AHzMkbR1sH6lUJoV7lEB5s0oRmjRri4eWG8dorzomIiIiIiNyTtOqTiNyyC3kXzNvH0o5Rt2bANetvXPkZB84cAOBw0mFCq9THzkaPIRERERERkSLqUSNSztV6sBU2/jUYOGIU46bPwKt+A/yaNGPe4k85l5pKr6Ev4lYnmJCH27Fm43dA8SFNACdOnTKXfbJsOQnnT5OQmWBxrkGjRjN49Bjz+9otWlsMdfKoVZ92rfqx8N8ryMjJYMG6zyzafLT/AFwC61Lj/geZt/jTq17Tt1u3mY878vvv5vKoL5Zh418Dl8C6pGdk3LbPUEREREREpLQoUSNyj/hy7ToWfvElTo6OnE1KJmzCJNo+9TQ/7j+Ag4M9v8XF0X/4SFLT0q/bVsL5BI6lHrMoC/CoSe2aNalds4a5rHFoKA82aUK1qlVLbCcnP8e8/fKrE/gtLg5XZxfiz5zhH69OZP3mLSUe16VtG+rWqgVA1BdfmstXrFsPQI8uXajo4XHd6xAREREREbE2StSI3CMquLnx245t/LByBVC4CpONjQ1Hd25n+fx5AJy/cIE9Bw+WePzlU8pcyL1QbL+TnSOvvTKSSSNHmstWLviIXWu/5vm+fSzqVnaphMFgsCjr1LEVh7dt4ujO7dTw9wfg7X99WGIsBoOBlwY8B8CnX60gPz+fjMxMNu/4LwADn+l11c9BRERERETEmilRI3KPeOiB5lT08CCgejVzWec2bXB0dLToBZOYnFzs2Ky8ixw5d6RYuYfTrfVacXVwpaF3QxxsHcxlrds1Zd+ZfWSSSdd2bQH4OSb2qm0MerYXLs7OJJ07x9pNm1m98Ttyc3Op6uNN5zYP31JcIiIiIiIiZU2zeIrcIyq4uQFgZ/fnn30F98Kyy3u3mExweWeXmOTfOGl04GxqkrnMwdaB+3zuI8717C3HU9HJg+BKQRZlJpOJ42nHSbyQeP3jPTzo/UQPoj7/gqjPv8DW1haA/j2fMm+LiIiIiIjcbdSjRkSK8a5c2bz9y9EYALZv22cuC6gYQMWr9KZxcXYyb2ddvHjN89gY/nwE/XfbfgAuZmWze/chAOrUqXnN48P+PgCAb77fyoat2wAY2Ovpax4jIiIiIiJizdSjRkQsXCq4xINNmuDm6sqFrCymvf4BAbX8+eXnP1dXMmC46vEhdeqYtzv37ktN/2qEvz6J1s2bX/O8W7/fzeGffiMjM5OMjPMADPp74VwzxssnyLlMkwYNaNmsGbuioykoKKB540bUDwoqsa6IiIiIiMjdQD1qRO5lJkjPySAu7YS56I+MP3B0c2DpB5HUqR1ATk4uznYuLImMuKEm76tfj0mjRuJTpQpnk5LZfeAAaenXXyp73qy3aBgUTO6lPKr6ePPGpFcY0qMvULjKVJHfUn4n/nwCuQV5APzjf71qAAaoN42IiIiIiNzlDKar/FL9P9fcKSLWY98P22hWp/YN1c3OyyExK5GkrCSLJbKLNPZtTAVH99sdYjFbd+6iQ69nAdiyfBntWrUssd7Pyb+QejHVosxgMODp7EniiVQ6P9EPJydH4vftxcuz4nXPG300jvvbtP3rFyAiIiIiInJrrjpMQUOfRO4BRpOJjJwMUnNSSc1OJTsvu8R6zvbOVKtQrVSSNDcjyCuIY4ZjnLt4jqLk8onj8UxdGMlPhwpXhurRozPZdhfJznfC2c7pWs2JiIiIiIhYLSVqRMq5U5nx/JHxB0ajscT9djZ2VHGtgo+rj9UlaIo42NpTr3II+cZ8ki+eI/FCIqmpv7L5u104OTvSvuODDHrxKY6lHuMYx3BzcKNe5Xo42ythIyIiIiIidxcNfRIpB4wmIz9s+obAGpXIzsvG09mTys6VMJpM7Di5o1h9GxsbPJ088Xb1ppJzJWwMV58c2FoVDd9KvphcYg+hahWqUduzFvnGfE5lxmMymXB3dMfdwZ1fTiRo6JOIiIiIiJQlDX0SKS9MJhNZeVmkZaeRnpNOWk4amZcy+SP5Fwxu1QE4e+EsD/o/iL2NPZVcKpFyMQVne2e8nL3wcvLCw8njrkzOXM7Z3omAijUJqFiT7Pwc0rLTSM1JJT0nHZPJhKezJwAnM04RnxlvcWzMmbMYE5zxdPKkolNFKjpVxN7WviwuQ0RERERExIISNSJ3gdTsVJKykkjPSSc9J528/614dDV2NnbY2dhhMEBolfoYTaa7PjFzLc52Tji7V8XPvSpGkwkDBoou16mE+WryC/JIvJBI4oVEc5mbgxsVnSri6eyJv7u/EjciIiIiIlImlKgRKWMmk4ns/Gwu5l0kKzeLrLwsnOycqOlRE1sbW+Iz4zlw5sB123Gyc8Lb1Rt3R3e8Xb0tEjPlOUlzpSuv1c+9Ks72zqTnpHP+0nnO554v8bgLuRe4kHuB+Mx4jqYepWOtjhgMBtKy0zh74Sx2Nna4Orjiau+Ki72LEjkiIiIiInJHKFEjUgqKhisVJWIuT8pk52VjNBWf6NdoMlLHqw4X8y4W2+dg64Cns6fF0J1DiTsJqXxjy3PfazydKuLpVLhst8kEpgseBPiGWgwdu3y+rtyCXApMBdhgw56EPeQW5BZr08HWAVeHwqSNq72redvNwQ0HW4dSuzYRERERESlflKgRuQ1MJhN5xjyy87LNiZUqrlWws7EjryCPnad2knkp86badLR1BCDQMxCAfGO+OSnjYu9SrL6DqyvRR+P+4pXcGzwqVqa6R3WqexTO6VNgLCDjUgbpOelczLuIj6sPdjaFj0cHW4cSEzW5BbnkZueSlp1mUW4wGAitEkotz1oAZF7KJCMnAyc7J1wdXHG0dcTWxvYOX6GIiIiIiNyttOqTSAmMJiM2Bhvz+5z8HE6fP82l/EuFX9CveOUZ87jyb8m/gj9NqzYlOSuZH+N/vOb5HO0czUNqXB1c8XL2orJL5TtybXJz8grySDifYO4BlZVb2COqpF5QRSo6VeThmg+TlZvF1hNbi9W1tbHFwdahxFcFxwr4uPpguGwI15X3o4iIiIiI3PW06pNI5qVMc6LlUkHJCZeilwEDgV6BhFQOwWgysvXE1utO4HulAmMBAJVdKuNfwZ/0nHSc7ZxLHC5T1HtDrI+9rT0BFQMsykwmE5cKLhVL3mTlZWEymahbqS5QmGApKaFTYCwg25hd4rLiAPWq1KOOVx2y87LZFb+LrNws7GzsrprcuTLRo/lzRERERETuXvp2KHeEyWSy6BEAhV9a8435GE1GCowFFJgKrrptMpmo7FIZVwdXALLzsjmRfoJLBZeue6zRZMRkMlHdozoNvBsAcODMgWJLNF8zfkycSD9BSOUQCowFxXrLlMTe1h5HW0dcHVxxd3An0KtwyJLBYKBp1aY3fG6xfgaDASc7J5zsnKhEpavWc3d05wH/Bzh9/jQX8y5yMe8iuQW51+yNA5iTgklZSWTlZgGFQ9/yjfklzll0OTsbO1pUa4GnsyeZlzLZf2Y/F/MuYmuwxcZgg62NLbYGW2xt/vf+ivJKLpXwd/fHYDBgNBlJykriUv6lq9a/svzKJFHR386VzwMRERERESmZEjVyQzJyMvgp8Scu5l3E9L8RcSaTCRMm8xexy7cBvJy9aFGtBbY2tpw+f5qfEn+6qV4pNgYbOtXuhKOdIwfPHuTcxXM3FfPxtOMEVQrCwdaB5IvJN3Wsg60DQZWCgMIETItqLUjMSsTWUPKQFXtbew1NkRL5uPng4+ZjUZZvzL9qby4HWwdqeNQwH1vpfCVSs1NvKFlY1HZaThqezp7EZ8Zz/lLhKlcFFNzQ8SczTuJo60gV1yr8lvIbv6f8fhNXC57OnrSq3gobgw0JmQkcTjps/rsvStYULp9efNvGYIO/uz+h3qE3dU4RERERkfJEiRq5ISfST5Cek35Tx6Rmp5KSnYK3qzenMk7d9NChy4eN3MpQjuoe1c2r79zncx/H045jMBhuaPjIlUkXT2dPPJ09bzoGkZLY2dhhZ2NX4qTQl3Oyc6JV9VZAYS+baw3XK3q5OrhSvULhJMl+7n6cPn/6qkOsrqbo7+Zm/2YB0rLTSM9Jx8vZixPpJyzauDype7UZ0OLS4qjlWeu6n42IiIiISHmlyYRv0ZrYNWUdQqnKyMngaNpR87wrN6KCYwXqetXF1saWjJwMTmScIN+Yj43BBhtsCv/XYIPBYDAPnSjatjHY4OnkiYeTB1A4p0f6pXSMRqN5/+XHXu11K7oFd7ul40TupFt95phMJnPSs+hl4s+yoqGGRUMGi+ZNgsLeOUVDn4xcdnwJbRa16+HoQaBnIAaDgZTsFE6mnyTPeOMJnwqOFQiuFHxPDZXSM0esUWxsbFmHIHdIcHBwWYcgUoyeOeWXnjnXpMmE5a/xcPKgiW8Tc6LGYDBguOy+KmkYw5XHN3JqdMvnt7WxpZLz1ecCEZGSGQyGwrlkuPklwe1s7PBz97vlc1dyrmTxd1s0XLJoGyj2XhMhi4iIiMi9TokauWE2BhtsbDUPi4jcGosE773TYUZERERE5KboW7eIiIiIiIiIiJVQokZERERERERExEooUSMiIiIiIiIiYiWUqLnHJMYn0j2kO78f/r2sQxERERERERGRK2gy4XKke0j3a+7v8EQH+gzrU0rRlOzw7sNMHDiRJbuWUMGzQpnGIiIiIiIiImJtlKgpRxZtX2Te3rt1L3Nfm2tR5uDkwIWMC7fUdn5ePnb2ul1ERERERERE7iR98y5HPKt4mrdd3V2LlQHmRE3y6WQWv7eYI/uP4OPvw/MTnqdJ6ybAn71eJs+fzOdzP+d4zHFejXiV5u2bs2fLHj6f+zknj57Es4onbR9vS++w3tg72APw/ervWbN4DQlxCTg4ORDaPJShE4ZSyacSifGJTBw4EYD+LfsDhb18Rs0cdWc/GBEREREREZG7hBI196hP53zKoLGDeHnyyyz7cBnho8P595Z/4+zqbK6zKHwRg8cNpmrNqji7OrN/+37eGfsOQycMJbR5KMmnk/lwyofk5eYxeNxgoLDnTd/hfalWuxqZaZksCl/E7DGzmblkJpWrVmZ8xHhmjpjJ3LVzcfdwx8HJoaw+AhERERERERGro8mE71E9BvbggQ4P4Bfgx4DRAzifcZ7jMcct6vQZ1ocmDzXBt7ovHl4efDn/S54c8iSdenaiao2q3NfiPgb+cyAblm3AZDIB0LlnZ+5vez++1X0Jui+Il6e8zK/7fuXc2XPY2tri7uEOQMVKFfGs4mnu+SMiIiIiIiIi6lFzzwoIDjBve3l7AZCekm5Rp06DOhbvj/1yjN9/+p2V/15pLjMajeTm5JKWnIaXtxfHfjnG5x8UDpe6kH4BE4UJnOTTyVT2rXyHrkZERERERESkfFCi5h5la2dr3jYYDACYjCaLOk4uThbvTUYTvcN60/pvrYu15+HlQc7FHF5//nUatWzEK7NeoWKlimSmZTK+33jy8/LvwFWIiIiIiIiIlC8a+iQ3rHb92sTHxeNX06/Yy9bOlvi4eDLTMnlu9HM0aN6AarWrFeulU7RyVEFBQVlcgoiIiJRzK1eupGnTpmUdhojIX3b48GFCQkKIj48v61CklKlHjdyw3v/ozbSXp+Ht781Df3sIWztb/vjtD347/BuDxg6iil8V7B3sWbdkHY/1e4xTx07xWcRnFm14+3tjMBjYt20fD7R/AAdHB4sJjEVERESKjB8/nlWrVgFgZ2eHr68vnTt3Zvjw4bi4uJR4zKOPPkrbtm1LM0wRsXKpqalERkaybds2kpOTqVChAnXr1mXo0KG0bl18tIBIWVOiRm5Y04ebMnneZJZ9uIz/RP0HW1tb/AP86fBkB6Bw+NOomaP49L1PWb90PQHBAQwZN4QpQ6eY26jkU4k+w/uwZM4S5k6aS/se7bU8t4iIiFxVq1atmDVrFvn5+ezbt4/XXnuN7OxspkyZUqxuXl4eTk5OODk5FW+oFOTl5WFvb18m5xaRqxsxYgTZ2dlMnz6dGjVqkJqayp49e0hPT7/+wXe53NxcHBy00u7dxlC0Ws9VXHPnvWxN7JqyDkHukG7B3co6BJFi9Mwpv/TMEWsUGxtb1iEAhT1q0tLSmD9/vrnstddeY+vWrYSHhzNw4EDmz5/P3LlziYmJISIigrS0NN588032798PQGRkJBs3bmTw4MFERkaSnp5O165deeONN/jqq6/46KOPyM7O5oknnmDcuHHY2BTODLB69WoWL15MXFwcTk5ONG/enAkTJuDj4wPA7t27i51/3LhxzJgxg2XLltGwYUNzzF9++SXvvvsuP/zwQ5l/YQoODi7T84uU5E4+czIzM3nggQeIioqiVatWJdbJzc0lIiKCNWvWkJmZSWBgICNHjuThhx8214mLi2P27Nns3bsXo9FI3bp1mTp1KsHBwRiNRubNm8eXX35JSkoKAQEBjBo1io4dOwIQHx9Pp06deP/991m2bBn79+/H39+fCRMmWPTo2b59OzNmzCAhIYGGDRvSu3dvxo4dy6ZNm6hWrRppaWlMmzaN6Oho0tPTqV69OoMGDaJnz57mNp577jkCAwNxdnZm1apV+Pv7ExQUREpKisWz1Gg00rFjRwYMGMCgQYNu98dupmfONRmutkM9akRERETkruHo6EheXp75fXh4OOPGjaNmzZq4urqydevWYsckJCSwZcsW5s2bR2JiIiNHjuTcuXNUrlyZBQsWEBcXxyuvvELTpk3p2rUrUNg7Zvjw4dSuXZu0tDTCw8MZM2YMS5YssWj7yvN///33rFy50iJRs2LFCnr06FHmSRqRe5GLiwsuLi5s2bKFZs2a4ejoWKzOhAkTOHXqFOHh4fj6+rJt2zb+8Y9/sHz5ckJCQkhMTKRv3740bdqUqKgo3N3dOXz4MEajEYDFixezYMECpkyZQoMGDVizZg3Dhw9nxYoV1KtXz3yeOXPmMHbsWCZPnsyHH37I6NGj2bJlC66urpw5c4awsDB69epFv379iI2NZebMmRZx5ubmEhoaytChQ3Fzc2Pnzp1MmTIFPz8/WrZsaa63evVqnnnmGT777DNMJhOZmZn079+fpKQkvL29Afjvf//LuXPn6NGjx5342OUvUqJGRERERO4KP/30E2vXrrX4QjJs2DAeeuihax5XUFDAjBkzcHd3JygoiIceeoi9e/eybds2HBwcCAwMpEmTJuzevducqLn8F+rq1aszZcoUHn30Uc6ePYuvr+9Vz9+rVy8mT57M+PHjcXR05NixYxw6dIhp06bdro9BRG6CnZ0db731FpMnT+bLL7+kXr16NG3alL/97W80atSIkydPsm7dOjZv3oyfnx8A/fv3Z9euXSxbtozXX3+dpUuX4uLiwpw5c8wJ11q1apnPERUVxeDBg+nWrbCX7IgRI9i7dy9RUVHMnj3bXG/gwIF06FA4bcTo0aP5+uuviYmJoVmzZnz++edUrVqVSZMmYTAYqF27NidOnOD99983H+/j48OQIUPM75999ll2797NunXrLJ6L1apVY/z48RafQ+3atVm1ahUvvPACUDjxevv27fHy8rotn7PcXkrUiIiIiIjV2rFjB02bNiU/P5/8/Hw6duzIpEmTOHr0KAANGjS4bhtVq1bF3d3d/L5y5coEBARY9HCpXLkyqamp5ve//PILH3zwATExMRbzWJw+fdoiUXPl+Tt27Mi0adPYuHEj3bp1Y8WKFdx3330EBQXd/MWLyG3RtWtX2rVrx759+zh48CDbt29n4cKFjBo1ioCAAEwmE48//rjFMbm5uTz44IMAHDlyhKZNm5bYK+7ChQskJSUVW22uWbNm/PDDDxZllw8DKurZkpKSAhQOrWrcuDEGw5+jYRo3bmxxfEFBAR9//DHr168nMTGRvLw88vLyaN68uUW90NDQYnH26tWLpUuX8sILL5Cens7mzZuZO3duyR+YlDklakRERETEat1///1MnToVOzs7vL29zZP1FiVqrrb60+VKmuDXzs7yP4MNBgMFBQUAXLx4keeff56WLVsya9YsKlWqRFpaGv369bMYdlXS+e3t7enRowcrV67kkUceYfXq1QwfPvzGL1hE7ghHR0dat25N69atCQsLY9KkSXzwwQfMmjULg8HA8uXLiz0XiiYmv868rgAWCZarubz9ovpFw6du5BxRUVEsXLiQCRMmEBQUhIuLC++995452VPE2bn4qrrdu3cnPDyc6Ohofv31Vzw9PbXilRWzKesARETk7lCQX0D3kO78uOnHsg4FgIM7D9I9pDsXMi/ccJ3rvRcR6+Pk5ETNmjXx9/cvtRWV4uLiSEtLY/To0TRv3pzatWsX+yJ0Lb169WL37t0sXbqUrKwsHnvssTsYrYjcisDAQPLz8wkMDMRkMpGcnEzNmjUtXkWTh9evX5/9+/eTm5tbrB03Nze8vb2Jjo62KI+OjqZOnTo3Fc+hQ4csEjaHDh0q1ma7du3o0aMH9erVo0aNGpw4ceKG2q9YsSKdO3dmxYoVrFy5kieffBJbW9sbjk9KlxI15dic8XPoHtK92Oufz/6z1GI4vPsw3UO6k5mWWWrnFBFL016axmuDXitx36ljp+ge0p0D/z1QylGVjtD7Q1m0fRGu7q43tH/j8o30ad6nNEMUESvk5+eHg4MDS5Ys4dSpU2zdupWIiIgbPr5WrVo0bdqU2bNn06VLF9zc3O5gtCJyLWlpaQwcOJDVq1cTGxtLfHw8GzZsYMGCBbRs2ZLg4GC6devGhAkT2LBhA6dOneLw4cMsWLCAjRs3AtC3b1+ysrJ45ZVXOHz4MH/88Qdr167lyJEjAAwZMoSoqCjWrl3L8ePHiYiIIDo6+qZWU+rduzcJCQnMmDGDuLg4NmzYwBdffGFRJyAggB9//JHo6Gji4uKYNm0a8fHxN3yOXr16sWbNGmJiYnjqqadu+DgpfRr6VM41atWI0bNGW5TZ2ev/dpF7SedenXlr2FskxifiU83HYt93X32Ht583jVo2KqPoisvLzcPe4fb8am7vYI9nFc9b3i8i9yYvLy9mzpzJe++9x9KlSwkODmbcuHEMHTr0htt4+umn2bdvH08//fQdjFRErsfV1ZVGjRqxePFiTp48SW5uLj4+Pjz22GO8/PLLAMyYMYN58+YRHh5OYmIiHh4eNGzY0DxHjY+PD0uWLGH27NkMHDgQgKCgIKZOnQoULomdlZVFeHi4eXnuiIgIixWfrsfPz4/IyEhmzpzJsmXLCA0NZcyYMYwdO9Zc5+WXXyYhIYGhQ4fi5OTEk08+Sbdu3cxDQa/nwQcfxNfXFz8/P2rUqHHDsUnpM1xnLNz1B8rdLcs9MwAAIABJREFUo9bErinrEK5rzvg5ZKZlMnn+5GL7Zo+eTX5ePq9GvmouMxqNPN/heXr8vQc9/t4Dk8nEygUr+XbZt6QmpVK1RlWeGvoU7bu3ByAxPpGhnYYy/v3xfLPsG47sP4KPvw/PT3ieJq2bmPdfrsMTHRg1c9SdvfC/qFtwt7IOQaSYv/LMKcgvYHD7wXTt1ZW+I/qay/Pz8hnUbhCP9X2MNo+34aWuLzHnP3OoXa+2+bgnGzzJhLkTaNGpRbH3p/84zUtdX+LVyFdZ99k6Yg7G4FPNhxcmvUCjFn8mfv747Q8Wzl7IkegjODg50LhVYwaPH4xn5cIEyTtj3yHnYg51G9Zl/dL1mEwmFm1fxOb/bGbdknUkHE/AwdmBhg805PlXn8fLu3B1goM7DzJ58GRem/can773KQnHE6gZVJOwqWEE1g+0qLN0z1LcKrhd8/3Rn48yebDl87LfyH7k5+WzZ8se3l/1vsW+fz7zT+o1qceQV4fwV+iZI9YoNja2rEO463388cd89dVXfPvtt2UdioXLJzMVsRZ65pSOnJwc2rZty6RJk8wrVN1peuZc01UnNtLQp3tUu+7t2Ldtn8W8DD/v+ZnU5FTaPNYGgCVzlrDpq028OPlF5q6by9MvPM2/Xv8Xe7futWjr0zmf0q1/NyJWRVCnQR3CR4eTnZVN5aqVGR9RuCzc3LVzWbR9EUMn3vgvUSJye9ja2dLhiQ5sXrXZPGEdwJ7v93A+7Twdn+r4l9r/9L1P6fH3HkSsiqB2vdrMfmU2l7IvAZCSmMKE5yZQu15t3vnqHaZGTSXrfBZvDXvLYgz2T7t+IuF4AlMXTGXqgsJfpwryC+g3sh/vf/0+k/41ibTkNN755zvFzv/J7E8Y9H+DePerd6lctTLTXprGpZxLN30dofeHMnjcYFzcXFi0fRGLti+ix8AedO7ZmT9++4Njvxwz1z35+0l+++k3Oj/d+abPIyLlW1ZWFocPH2bx4sUMGDCgrMMREcFoNJKUlERkZCSOjo507dq1rEOS61Cippzbv2M/zzR9xuL1SfgnNH2oKS5uLuzcuNNcd9vabTRq0QjPKp7kXMzh60++Ztibw2j2cDN8q/nStltbuvTqwvql6y3O0WNgDx7o8AB+AX4MGD2A8xnnOR5zHFtbW9w9CpfCrFipIp5VPK86T4SI3Fmde3Ym+XQyh3b+OSndphWbaNy6MVWqVvlLbT8x+Amat2uOX4Afz416jsy0TI7HHgdg3WfrqNOgDgNGD6Ba7WrUCqnFqJmjiDkYQ9yvceY2HF0cGTZtGDXq1qBmUE0AuvTqQrM2zfCt7ktwo2BemvwSh/ccJi05zeL8vcN606R1E2oG1WTUW6PIzspmx/odN30d9g72uLi5YDAY8KziiWcVT5xcnPD296ZRq0Z8t+I7c93vVnxHUKMgatRVt2ERsTRt2jT69u1L06ZNefbZZ8s6HBERTp8+TZs2bVi3bh0zZswocZlxsS6arKScC70/lLCpYRZlbhXcsLWz5aFHHmLbmm10eboLebl57Ny4kxcmvgAUTjCaeymXKUOnWCw1l5+Xj4+/5RwXAcEB5u2iIQnpKel36IpE5Fb4BfgRen8om1ZuoslDTUhJTGH/jv3837v/95fbrhVcy7xd9AzISM0A4Ngvxzi85zDPNH2m2HFnTp0hMLRwiFLNoJrF5qX5/fDvLPvXMo7HHudC+gWMpsLeQMlnki3mlQlpHGLednFzoUadGpw8dvIvX9fluvbqytzJcxkyfggGg4Gta7bSf2T/23oOESkfZs6cycyZM8s6DBERs2rVqhETE1PWYchNUKKmnHN0csSvpl+J+9p1b8e4PuNISUwh9lAs+Xn5tOjUAsA8PGLSh5OK/dpuZ2d529ja/bmsW1FSx2TU9EYi1qZLry7MfW0u59PPs/k/m3H3cOeBDg8AYGNT2MHy8uFI+fn5N9TutZ4BRpOR5u2aM/CfA4sdVzRHDYCTs5PFvosXLjLl+Sk0fbgpo98ejYeXB+nn0pkwYAL5eTcW1+30QIcHsJ1qy67vdmHvYE/OxRwefvThUo9DRERERMo/JWruYcGNgvGt7ssPa38g5mAMLTq2wNnVGYDqgdWxd7AnOSHZYlLQm1W0wlRBQcFtiVlEbl2rrq346M2P2Lp6K5tWbqJ9j/bmv1EPLw8Ai2FFx48c/8vnDKwfyO7Nu/Hx97FI6FzPqWOnOJ9xngFjBpiTxSdiT5RYN/ZQLFX8CutcvHCRU8dO0fWZWxt7bWdvh7HAWGJ5hyc6sGnFJuwd7GndtTUubi63dA4RERERkWvRHDXlXF5eHmnJaRavoiEJAG27tWXjVxvZt20f7bq3M5e7uLnw5OAnWfj2Qr5b8R2n/zhN3JE4vvniGzYs23DD5/f298ZgMLBv2z4yUjPIzsq+nZcnIjfB0cmRNo+34fMPPufsybMWE+E6uzpTp0Edvvr4K04ePcmv0b/ySfgnf/mcj/d/nMy0TGaPmc1vP/3G2VNnOfDfA0ROirzmhL/e/t7Y2duxdslazsafZe/3e1kaubTEul/86wsO7jzIyd9PEjEhAidnJx5+7NZ6u3j7e5N9MZtDPx4iMy3TIsYuT3fhp90/Eb09WpMIi1ip8ePH8+KLL5Z1GCJyD9DzRu4kJWrKuUM7DzHw4YEWr1FP/rk8drvu7Ug4noCLuwuNWzW2OLbfyH70GdaHVVGrGPb4MCYPnszOjTvxqeZz5WmuqpJPJfoM78OSOUsY0HoA86fNv23XJiI3r8vTXbiQcYGQJiFUD6xusW/UW6Mw5hsZ/fRoPnzjw9syB0tl38rMWjoLo9HI68+/zrDHh/HRtI9wdHIsNozycp6VPRn51kh2fruTsEfDWDZvGUPGl7wM9sDRA1nw1gJGPTWKpIQkJn04CUcnx1uKN/T+ULr06sLbo96mf8v+rFq4yrzPv5Y/IU1CqFq9KqH3h95S+yIiIiIi12O4fD6CEmiikatYE7umrEOQO6RbcLeyDkGkGD1zyp7JZOIfj/yDTj070XNoz9vWrp45Yo1iY2PLOoRbMn78eNLS0pg/v/gPQ6dPn2bGjBns3Fm44mXr1q2ZOHEivr6+AJw5c4Zp06YRHR3NpUuXqFq1KsOGDeOxxx4D4IMPPmDFihUkJyfj4eFB69atmTVrVuld3G0SHBxc1iGIFHM3PnP0vLkxeuZck+FqOzRHjYiIyHWknUtj6+qtpCSl0KVXl7IOR0RukslkYtiwYTg4OLBo0SIA3nzzTcLCwvjqq68wGAy88cYb5ObmsmjRItzc3Dh+/M95ur799luioqJ45513CAoKIiUlhUOHDpXV5YiIFdPzRm4HJWpERESuoSC/gIEPDaSCZwXC3gjDvaJ7WYckIjdp586dxMTEsHHjRqpVqwZAeHg4Xbp0YdeuXbRq1YrTp0/TpUsXQkJCAMz1oPDX8SpVqtC6dWvs7e3x8/OjYcOGZXItImLd9LyR20Fz1IiIiFyDrZ0tq2NWs2TXEtp2a1vW4YjILTh27Bje3t4WX4aqV6+Ot7c3R48eBWDAgAHMmzePZ599ljlz5vDzzz+b6/7tb38jNzeXTp06MXHiRDZs2EBubm6pX4eIWD89b+R2UKJGRERERMo9g6HkqQCKyp9++mk2bdrEU089xYkTJ+jTpw+RkZEAVK1alW+++YY33ngDNzc3Zs2aRc+ePbl48WKpxS8idw89b+SvUqJGRERERMq1wMBAEhMTiY+PN5edOnWKpKQk6tSpYy7z9fU1/8I9YsQIvvzyS/M+R0dH2rVrx6uvvsry5cv5/fff2b9/f6leh4hYPz1v5HbQHDUiIiIiUm5kZWVx5MgRi7KaNWsSEhLC2LFjmThxIiaTiTfffJP69evTokULAKZPn06bNm0ICAjgwoULbN++3fylauXKlRQUFHDffffh6urK+vXrsbe3JyAgoLQvT0SsiJ43cqcoUWOl5oyfw5ZVW4qX/2cOtevVLoOIRERERKzfvn37ePLJJy3KunTpwty5c5k+fToDBgwAoFWrVkyaNMk8FMFoNPLmm29y5swZXF1dadmyJePGjQOgQoUKfPzxx7z99tvk5+cTGBhIRESExRwUInLv0fNG7hSDyWS61v5r7ryXrYldc0fbnzN+DilJKYyeNdqivIJnBWztbC3K8nLzsHewv6Px3Eu6BXcr6xBEirnTzxwpO3rmiDWKjY0t6xDkDgkODi7rEESK0TOn/NIz55pKnswI9aixavb29nhW8SxWPq7vOGqF1MLe3p7vV39P1RpVmb1sNhcyL7Dw7YXs2bKH3Eu5BNYPZMj4IQSGBpqP3bRiE0vnLiUzLZPGLRvTqGUjFsxawKpfVgGwZM4S9m7dy/ur3jcfs3H5Rha+vZDP935uLvtx84988cEXnDp6Ci9vL9p2a8uzLz9rThgNajuIR/s+ytlTZ9nxzQ5c3V3pPrA7Twx6wtzGhcwLLApfxO4tu8nKzMK3ui99h/elyUNN+HubvzP67dG06NTCXD/6h2imh03nkx8+oYJnhdv3QYuIiIiIiIhYCSVq7lLfr/qev/X5GzM/m4nJZMJoNPLG0Dfw8PJg8vzJuLq7smnlJib9fRL/+uZfeFb25Mj+I0ROiqT/qP606tKKQz8e4rM5n930ufdt28eccXMYOmEo9e+vT9LpJP71+r8oyC9g4JiB5nqrolbRb2Q/eg7tyd7v97Jg5gLqN6tP0H1BGI1GpgydQs7FHF6Z+QpVa1YlPi6e/Px8XNxceOiRh9i0YpNFoua7Fd/xQIcHlKQRERERERGRckuJGiu2f8d+nmn6jPl9/Wb1mfLxFAB8a/oyaOwg874DOw5w6tgpZnw6w9yrZcDoAez5fg/b1mzjiUFPsHrxapo81IReL/YCwL+WP78d+o2ta7beVFxffvglPYf2pONTHQGoWqMqA14ZQOSkSItETbO2zXi076MA9Ph7D9Z8uoaffvyJoPuCOLDjAEcPH+WDdR/gX8u/8Jqq+5qP7dKrC6/2f5W05DQ8q3iSmZbJni17mPiviTcVq4iIiIiIiMjdRIkaKxZ6fyhhU8PM7x2dHM3bdRvUtah79Jej5FzMoV+LfhbluZdyOXvqLADxx+Jp/bfWFvtDGofcdKLm6C9HiTsSx/L5y81lRqOR3JxcMlIz8PDyACAgOMDiOC9vLzJSMgA49usxKvlWMidprhTSOIRqtaqx5est9Hy+J1vXbKVipYo0ad3kpmIVERERERERuZsoUWPFHJ0c8avpV/I+Z0eL9yaTCc8qnkxfPL1YXVd318I6NzA3tMHGwJUTTOfn5xer12d4H1p2blms3M3Dzbx95aTHBoMBo8l43RiKdO7VmQ1fbKDn8z3ZvHIzHZ/qiI2NzQ0fLyIiIiIiInK3UaKmnAisH0jauTRsbW3xqeZTYp3qgdWJPWQ5o/qV7z08PUg7l4bJZDIvH3f8yHGLOrXr1SbheMJVk0g3Gm/K2RQSjidctVdN++7tWfzOYtZ+upYTsSd4de6rt3w+ERERERERkbuBuieUE00eakJQwyBmDJvB/u37SYxPJOZADJ9FfMaR/UcA6PZcN/Zv38+Kf6/g9InTfPPFN+zZsseinYYPNiQzNZMVH6/gzMkzfPvlt/y46UeLOr3DevP919+zNHIpJ38/SXxcPDs27GDRO4tuKt7A0EDeGvEWB3Yc4Gz8WQ7sOMDuLbvNddwrutOyc0ui3o6i4YMN8a3me40WRURERERERO5+StSUEzY2Nkz59xTqN6tPxMQIXn7kZd5+5W1OnziNp3fhEt/1m9UnbGoY65asY0SPEez9fi+9w3pbtFMzqCYvvvYi6z9fz4geIzi8+zA9h/a0qHN/2/uZ9OEkDu48yOheoxnTawwr/72SKlWr3Fy8H08h6L4g3hn7DmGPhvHvt/5NQV6BRb1OT3ciPy+fzj073+InIyIiIiIiInL3MFw5H8kVrj+pyT1qTeyasg7htvhh3Q+8+3/vsuqXVWUdSom2rtnKR29+xCc/fIKDo0OpnLNbcLdSOY/IzSgvzxwpTs8csUaxsbHXryR3peDg4LIOQaQYPXPKLz1zrslwtR2ao0as0qXsSyTGJ/LVR1/R9ZmupZakERERERERESlLGvokVmn5/OWMfHIkFStV5JmXninrcERERERERERKhXrU3OPaPNaGNo+1Keswiuk/qj/9R/Uv6zBERERERERESpV61IiIiIiIiIiIWAklakRERERERERErIRWfRIRERERERERKV1XXfVJPWpERERERERERKyEEjUiIiIiIiIiIlZCiRoRERERERERESuhRI2IiIiIiIiIiJVQokZERERERERExEooUSMiIiIiIiIiYiWUqBERERERERERsRJK1IiIiIiIiIiIWAklakRERERERERErIQSNSIiIiIiIiIiVkKJGhERERERERERK6FEjYiIiIiIiIiIlVCiRkRERERERETESihRIyIiIiIiIiJiJZSoERERERERERGxEkrUiIiIiIiIiIhYCSVqRERERERERESshBI1IiIiIiIiIiJWQokaEREREREREREroUSNiIiIiIiIiIiVUKJGRERERERERMRKKFEjIiIiIiIiImIllKgREREREREREbESStSIiIiIiIiIiFgJJWpERERERERERKyEEjUiIiIiIiIiIlZCiRoRERERERERESuhRI2IiIiIiIiIiJVQokZERERERERExEooUSMiIiIiIiIiYiWUqBERERERERERsRJK1IiIiIiIiIiIWAklakRERERERERErITdtXbGxsaWVhxSyoKDg+9o+7p3yifdN3KrdO/IrbjT942ISGnSv1Xl153890r3Tfl1rftGPWpERERERERERKyEEjUiIiIiIiIiIlZCiRoRERERERERESuhRI2IiIiIiIiIiJVQokZERERERERExEooUSMiIiIiIiJl4vDhw4SEhBAfH1/WochdpjzfO9dcnltERERERESsX2pqKpGRkWzbto3k5GQqVKhA3bp1GTp0KK1bty7r8MSK6d6xPkrUiIiIiIiI3OVGjBhBdnY206dPp0aNGqSmprJnzx7S09PLOrQ7Ljc3FwcHh7IO466le8f67h0lakRERERERO5imZmZ7Nu3j6ioKFq2bAmAv78/DRs2NNfJzc0lIiKCNWvWkJmZSWBgICNHjuThhx8214mLi2P27Nns3bsXo9FI3bp1mTp1KsHBwRiNRubNm8eXX35JSkoKAQEBjBo1io4dOwIQHx9Pp06deP/991m2bBn79+/H39+fCRMmWPTK2L59OzNmzCAhIYGGDRvSu3dvi2tJS0tj2rRpREdHk56eTvXq1Rk0aBA9e/Y013nuuecIDAzE2dmZVatW4e/vT1BQECkpKcyfP99cz2g00rFjRwYMGMCgQYNu74deTujesc57R3PUiIiIiIiI3MVcXFxwcXFhy5YtXLp0qcQ6EyZMYO/evYSHh7N69WqeeOIJ/vGPfxATEwNAYmIiffv2xWAwEBUVxYoVK+jXrx9GoxGAxYsXs2DBAsaMGcPq1avp3Lkzw4cP58iRIxbnmTNnDv3792fVqlU0aNCA0aNHk5WVBcCZM2cICwujVatWrFq1iv79+xMeHm5xfG5uLqGhocybN4+1a9fy3HPPMWXKFHbt2mVRb/Xq1ZhMJj777DNmzZpFr1692LFjB0lJSeY6//3vfzl37hw9evT4ax9wOaZ7xzrvHSVqRERERERE7mJ2dna89dZbrFmzhubNm/Pss88ya9YsDh06BMDJkydZt24d7733Hs2bN6d69er079+fNm3asGzZMgCWLl2Ki4sLc+bM4b777qNWrVp0796devXqARAVFcXgwYPp1q0btWrVYsSIETRr1oyoqCiLWAYOHEiHDh0ICAhg9OjRZGRkmL/Qf/7551StWpVJkyZRu3ZtHnnkkWK9Inx8fBgyZAj16tWjevXqPPvss3Tu3Jl169ZZ1KtWrRrjx4+ndu3aBAYG0qRJE2rXrs2qVavMdVauXEn79u3x8vK6vR94OaJ7xzrvHQ19EhERERERuct17dqVdu3asW/fPg4ePMj27dtZuHAho0aNIiAgAJPJxOOPP25xTG5uLg8++CAAR44coWnTpiXO13HhwgWSkpJo2rSpRXmzZs344YcfLMqCg4PN297e3gCkpKQAhcNjGjdujMFgMNdp3LixxfEFBQV8/PHHrF+/nsTERPLy8sjLy6N58+YW9UJDQ4vF2atXL5YuXcoLL7xAeno6mzdvZu7cuSV/YGKme8f67h0lakRERERERMoBR0dHWrduTevWrQkLC2PSpEl88MEHzJo1C4PBwPLly7Gzs/wK6OTkBIDJZLpu+5d/Sb6ay9svql80BOZGzhEVFcXChQuZMGECQUFBuLi48N5775m/sBdxdnYudmz37t0JDw8nOjqaX3/9FU9PT61adIN071jXvaNEjYjIX1A0+dny5cstJl0Tuds899xz1K1bl8mTJ5d1KCIicpsEBgaSn59PYGAgJpOJ5ORkWrRoUWLd+vXrs2bNmhJXwXFzc8Pb25vo6GiL46Ojo6lTp85NxbNx40ZMJpP5i3jREJvL22zXrp15bhCTycSJEydwd3e/bvsVK1akc+fOrFixgiNHjvDkk09ia2t7w/HJn3TvlO29ozlqRESuIiQk5Jqv8ePHl3WI7N69m5CQENLS0so6FLnMuXPnmD59Op07d6Zhw4a0adOGoUOHsm3bttt2jvHjx/Piiy/etvZEROTulZaWxsCBA1m9ejWxsbHEx8ezYcMGFixYQMuWLQkODqZbt25MmDCBDRs2cOrUKQ4fPsyCBQvYuHEjAH379iUrK4tXXnmFw4cP88cff7B27VrzhK9DhgwhKiqKtWvXcvz4cSIiIoiOjr6pFXF69+5NQkICM2bMIC4ujg0bNvDFF19Y1AkICODHH38kOjqauLg4pk2bRnx8/A2fo1evXqxZs4aYmBieeuqpGz7uXqV750/WdO+oR42IyFVs377dvL1161Zee+01izInJycyMjJuqe28vDzs7e3/coxifeLj4+nbty+urq6MHj2a4OBgTCYTu3btYsqUKXz//felGo/uNRGR8s/V1ZVGjRqxePFiTp48SW5uLj4+Pjz22GO8/PLLAMyYMYN58+YRHh5OYmIiHh4eNGzY0DzPiI+PD0uWLGH27NkMHDgQgKCgIKZOnQoU9rzMysoiPDzcvMRyRESEecLYG+Hn50dkZCQzZ85k2bJlhIaGMmbMGMaOHWuu8/LLL5OQkMDQoUNxcnLiySefpFu3bhw9evSGzvHggw/i6+uLn58fNWrUuOHY7lW6d/5kTfeO4VpjvWJjY68/EEzuSpdP1HQnxMbG3tH2pWzcy/fNhg0bGDVqlHnm+SJFQ5/ef/99li1bxv79+/H392fChAnmca27d+9m4MCBzJ8/n7lz5xITE0NERATt27dny5YtzJ07l6NHj1KlShUef/xxwsLCzN1GV69ezeLFi4mLi8PJyYnmzZszYcIEfHx8zOe+3BNPPMHMmTNL50O5CffSvfPCCy9w5MgRNmzYgKurq8W+jIwMPDw8OH36NDNmzGDnzp0AtG7dmokTJ+Lr6wtAZGQkGzdu5KWXXmLOnDmkpKTQsmVL3nzzTTw9PYmMjOSDDz6waHvRokX4+/vTqVMnwsPDWb58OQcPHmTs2LH079+fjRs3EhkZyYkTJ6hUqRK9e/fmxRdfNHcftsahT3f6vhERKU3W9G9VeZaTk0Pbtm2ZNGkS3bp1K5Vz3sl/r3TflJ7SvneCg4OvOnGPhj6JiNwGc+bMoX///qxatYoGDRowevRosrKyLOqEh4czcuRI1q9fT6NGjdi+fTtjx46lX79+rF27lunTp/Ptt9/y3nvvmY/Jy8tj+PDhfP3118ybN4+0tDTGjBkDQNWqVYmIiABg7dq1bN++nYkTJ5beRUsx6enpbN++nX79+hVL0gB4eHhgMpkYNmwY586dY9GiRSxatIikpCTCwsIsJspLSEjgm2++Ye7cuSxYsIAjR46Y743BgwfzyCOP0KpVK7Zv38727dtp0qSJ+dh3332XPn36sG7dOjp16sTPP//MqFGj6Ny5M6tXr2bMmDF89NFHLFmy5M5/KCIiIqXAaDSSlJREZGQkjo6OdO3ataxDkruENd47GvokInIbDBw4kA4dOgAwevRovv76a2JiYmjWrJm5zrBhw3jooYfM7+fPn8+Q/2/v3sNyvv8Hjj9Lt1PMoW+FotIoDXOYQ3KYJufJoWyaMJMMtWkk5ItRRCbSfM25zZwqLLbY1pbbaibHWFoHfmpZQvhOo1K/P7r6fN3uWFopvB7XdV+Xz+n9fn/uXpf7/rzu9+G99xg1ahQALVq0YObMmXh7e+Pt7Y2Ojo5yDKB58+YsXLiQwYMH88cff9CkSRMaNGgAgIGBAY0aNXoatyoe4/LlyxQVFWFpafnIc2JjY7lw4QKHDx/G1NQUKE7i9e/fn7i4OHr06AFAQUEBS5cuVSbAGz16NBEREUBxN+VatWqhUqkwNDTUqmPs2LEMHDhQ2Q4MDKRLly54enoCYGFhwaVLl9i4cSOurq4Vc/NCCCFEFcrMzKRfv340adIEf3//UpeKFqI01TF2JFEjhBAV4MEur0ZGRgBaSwG2bdtWY/v8+fOcPXuWjRs3KvsKCwu5e/cu2dnZGBkZcf78eUJCQrhw4QI3b95UzsvMzFSGyYjqoyxLR6ampmJkZKQkaaA4CWdkZERKSoqSqGnWrJnGKgWGhoZaMfUoD8daWloaffr00djXuXNnQkJC+PPPP6lXr16ZyhVCCCGqK1NTU60h6kKURXWMHUnUCCFEBdDT+99/pyVzfhQWFmqcU7duXY3twsJCpk2bptHzoUTjxo3Jzc1l0qRJ2NraEhAQgIGBATk5Obzzzjvk5+dXwl0KI2CSAAAgAElEQVSIf8rMzAwdHR1SU1NxcHB45HklMfK4/Q9PAKyjo1OmRBBAnTp1NLYfXMpSCCGEEEJUbzJHjRAVwN7enk2bNlV1M8QzxsbGhrS0NMzMzLReenp6pKWlkZOTg5eXF126dKFly5ZaPSpKHubv379fFbcgHtKwYUN69uzJ9u3bteYoArh9+zaWlpZkZWVpLBeZnp7O1atXefnll8tcl0ql0koGPoqlpSUnTpzQ2HfixAmaNGkivWmEEEIIIaqZZyZRc+3aNfz8/HBwcKBdu3b07t0bNzc3YmJiKqwOHx8f3N3dK6y8EvIQX/38+uuv2NjYMGbMmCe6Ljg4uNQZwMPCwnBxcamo5okXxNSpUzl48CBr1qzht99+Iy0tjaioKFasWAEUD32pWbMmX3zxBenp6fz444/K5MElTExM0NHRISYmhhs3bpSaHBBP14IFCwBwcnIiKiqKtLQ00tLS2LFjB46OjvTo0QNra2tmzZrFuXPnSEhIYObMmdjY2NC9e/cy12NiYkJycrKS0HtcL6t3332X48ePExwczMWLF4mMjGTLli289957//h+hRBCVL3Keo4pj4yMDKytrUt9qdXqp9oWV1dXZYlooU3ipnTVIW6eiaFPGRkZuLi4oK+vj5eXF1ZWVhQVFREXF8fChQv54YcfqrqJ4hmzZ88exowZw/79+0lNTX3sxJ9l0bhx4wpqmXiR9OrVi//85z+sW7eOzZs3U6NGDczNzRkxYgRQHFfLli1j1apVfPnll1hZWTF79mzc3NyUMoyNjfHw8CAoKAhfX18cHR2r5fLcLxJTU1PCw8NZv349gYGBZGVl0bBhQ6ytrVm0aBE6OjqsXbsWPz8/xo0bB0CPHj3w9fV9ouFJo0eP5pdffsHJyYnc3Fxlee7SvPLKKwQFBREcHMxnn32GgYEBkydPZuzYsRVyz0IIIcTDNmzYgLW1tca+kkUQhHgUiZtiOo8b756UlFS2wfCVbPLkySQmJhIVFaW13OmtW7do0KABmZmZ+Pv7ExsbC4CdnR3z5s1TJtsMDg7m8OHDTJkyhaCgIK5fv46trS1LliyhUaNGBAcHExISolH2tm3b6NatGytXruTbb7/lypUrGBgYMGjQIDw9PalVq5Zy7o8//sinn35KUlIStWvXpmPHjqxevZpJkyZx/PhxjXKrw0RFD058WhmSkpIqtfx/4u7du/Tq1YsvvviC0NBQXnrpJWbPnq0cz8rKYsWKFajVau7du4e5uTk+Pj5kZmYyd+5cjbL8/f0ZOXIk9vb2vPPOO8qv0/80HqurFzluxD8jsSPKo7LjRgghnqbK/Kzy8fEhJyeH9evXl3r8776bXrlyhcWLF3PixAnu3btH06ZNmT59OkOGDAEgJCSE8PBwsrOzadCgAXZ2dgQEBJRaV0ZGBv369WPPnj20a9dO6/jFixcZNGgQ+/fv1/h/fteuXaxatQq1Wo1KpSIlJYUVK1Zw/PhxateuTffu3ZkzZ46y2mHJPdvZ2bFx40bu3r3LG2+8wb///W/q1KmDj48P+/bt06j7u+++05jMv6JU5ueVxE2x5zRuHvkLXbXvUXPz5k3UajUffPCBVpIGirNrRUVFTJ8+nZo1a7Jt2zYAlixZwrRp0wgLC1N+ofz999/55ptvWLt2Lbm5uXz00UesWrWKjz/+mIkTJ5KWlsatW7eU4CnJ3NWpUwd/f3+MjIxITU1l4cKF1KxZkw8++AAAtVrNtGnTcHNzw9/fn/v373P06FEKCwsJDg5m+PDhjBw58omH2YjKcejQIZo1a4aVlRXDhg1jxowZeHl5oVKpyM3NZdy4cTRu3Ji1a9dibGysJNYGDx5McnIyP/74I6GhoQAaK7KUqIh4FEIIIYQQoiKU5bvpokWLyMvLY9u2bdSrV4+LFy8q1x86dIjNmzezcuVKWrduzfXr1zlz5ky522NhYUHbtm05cOCAxgN3ZGQkgwcPRqVScfXqVcaOHYuTkxPe3t4UFBSwatUqpk6dyq5du9DVLZ7B48SJExgZGbFlyxauXLnCjBkzMDc3x93dnXnz5nHp0iVatmzJjBkzAOkF/yQkbqo2bqp9ouby5csUFRU9dmhKbGwsFy5c4PDhw0qmKzAwkP79+xMXF6csdVpQUMDSpUuVh+vRo0cTEREBgL6+PrVq1UKlUinZthJTp05V/m1qaoq7uzubN29WEjWffvopAwYM4MMPP1TOKwmeOnXqoKuri76+vla5omrs2bOHYcOGAdC1a1dq165NdHQ0AwYM4MCBA2RnZ7Nz506lZ0uLFi2Ua+vWrYuent5j/5YVEY9CCCGEEEJUhLJ8N83MzKR///7KkJMHew9kZmZiaGiInZ0dKpWKZs2aldrj4WGurq7Kg3GJmJgY6tevz7Bhw9i6dSteXl7o6Ohw5coVTpw4wUcffQTAzp07sba2ZubMmcq1AQEBdOvWjXPnztG+fXsA6tWrx4IFC9DT08PS0pKBAwfy888/4+7uTv369VGpVNSuXVuew8pB4qZq46baJ2rKshRpamoqRkZGGoHRvHlzjIyMSElJUR6MmzVrptEDwtDQUGsFldJERUURGhrK5cuXyc3N5f79+xorbSQmJipzSojq7f/+7/84deoUK1euBIqXu33zzTcJCwtjwIABJCYmYmVl9Y+GH1V2PAohhBBCCFFWZfluOm7cOBYuXIharcbW1pZ+/frRtm1bAAYOHMjnn39Ov3796NmzJ7169cLe3p6aNWs+tt7AwEBatWqlsa9khMSQIUNYvnw58fHxdOnShQMHDtC8eXM6duwIwPnz54mPj6dTp05a5V6+fFl54La0tERP73+PtIaGhv+o14b4H4mbqlXtEzVmZmbo6OiQmpqKg4PDI8971ASMD+4vWcb2wWN/lwg6ffo0H330EdOmTaNXr17Ur1+f6Oholi9f/gR3IaqLsLAw7t+/j729vbKvJAauXLlSpsRgWVRWPAohhBBCCPGk/u67qZOTEz179iQmJoa4uDjGjBnD5MmT8fDwoGnTpnzzzTfExcURFxdHQEAAISEh7Nq1i7p16z6yTmNjY8zMzEo9ZmBggK2tLZGRkXTp0oXIyEiGDh2qHC8sLKRPnz54e3uXem2JBx+2S+5Hvk9XHImbqlPtl+du2LAhPXv2ZPv27aUuO3v79m0sLS3JysoiIyND2Z+ens7Vq1d5+eWXy1yXSqXS6CkDcPLkSYyNjZk6dSrt2rXD3NyczMxMjXPatGnDzz///ETliqevoKCAffv24eXlxd69e5XXvn37sLKyIiIiAhsbG5KSksjJySm1DJVKxf379x9bT0XFo3gx2Nvbs2nTpic65++2hSiP6rAUpRBCiIpX1u+mTZo04a233iIoKAhPT092796tHKtVqxavv/46c+bMYc+ePSQnJ3Py5Ml/1K5hw4Zx6NAhzp07x2+//aZMTQBgY2NDSkoKzZo1w8zMTONVr169Mtchz2HlJ3FTtXFT7RM1AAsWLACKM3ZRUVGkpaWRlpbGjh07cHR0pEePHlhbWzNr1izOnTtHQkICM2fOxMbGhu7du5e5HhMTE5KTk0lLSyMnJ4f8/HzMzc3JysoiMjKS9PR0duzYwcGDBzWumzJlClFRUQQFBZGSkkJycjJbt27lr7/+UsqNj48nKyvrkQkAUfliYmLIycnB2dmZ1q1ba7wGDx5MeHg4Q4cOxcDAgGnTphEfH09GRgbR0dFKIs7ExITMzEzOnz9PTk4OeXl5WvVUVDyKynXt2jX8/PxwcHCgXbt29O7dGzc3N2JiYqq6aVrCwsJwcXEp83Fra2uioqKeRtNeeDdu3GDRokXY29vTrl077OzsmDBhAj/99BNQPZNox44dw9raWj6PhBDiOXPnzh0SExM1XhkZGWX6burn54darSY9PZ3ExETUarXyMB4REcGePXtISkoiIyODiIgIVCoV5ubmj23PzZs3yc7O1njdvXtXOd6vXz/y8/Px9fWlffv2GuW5uLjw3//+Fy8vL86cOUN6ejqxsbHMnz+fP//8s8zviYmJCWfPniUjI4OcnJwqf/iujiRutFWHuKn2Q5+geFKi8PBw1q9fT2BgIFlZWTRs2BBra2sWLVqEjo4Oa9euxc/Pj3HjxgHFD8u+vr6P7K5VmtGjR/PLL7/g5OREbm4u27Ztw97enokTJ+Lv78+9e/ews7PD09OTRYsWKdf16dOHtWvXsnbtWjZt2oS+vj4dO3ZUVnny9PRkwYIFODg4kJeXVy2W534RhYWF0bVr11Lnnxk4cCArV67k5MmTfP755wQEBPD++++Tn5+PhYUFPj4+AAwYMIBvv/2Wd999l9u3byvLcz+oouJRVJ6MjAxcXFzQ19fHy8sLKysrioqKiIuLY+HChfzwww9V3UQNfzfTvKxgUHU8PT3566+/8PPzo0WLFty4cYNffvmFmzdvlrmMwsJCioqKqFGjRiW2VAghxPMuPj5ea97M/v37s2bNmr/9blpYWMiSJUu4cuUK+vr62NraMnv2bABeeuklNmzYwPLlyykoKMDS0pI1a9b87XLFbm5uWvsWL16Ms7MzULzoioODA/v372fevHka5xkbG/Pll1/yySef4Obmpiz9bGdn97dznDxo4sSJ+Pj4MHToUO7evVtpyyw/yyRutFWHuNF53FispKSk6jVQS1SYB5c0qwxJSUmVWr6oGs9L3EyePJnExESioqKUyclK3Lp1iwYNGrBlyxb27t1Leno69evXp3fv3nh7e/PSSy8Bxb8SLFmyhMDAQAICArhy5QodOnTAz8+P5s2bA8WTli1btoyzZ89y584dLCws8PDwoG/fvkp99vb2jBgxgsuXL/P9999Tt25d3n33Xd577z2Nc9555x1l3+O27e3tNYZnNmvWjNDQUPr378+uXbs0ZtvfvXs3n3zyCUeOHHmiD6/yeF5i50G3b9+ma9eubN68WZkk/EGurq4cP35cY9+FCxeU2Fm1ahWBgYGkpaWxd+9eWrduTXh4OJs3byY9PZ2mTZsyZswYxo0bp6x+UPIDRWxsLEeOHMHAwABPT0+Nbr9nzpxh0aJFpKSkYGlpyYcffoi7uzvbtm3DxMSEfv36abRp+PDhLFu2DFdXV15++WXq16/P7t270dXVxdHRkVmzZmmtvvC0VHbcCCHE0yTfj59flfl5JXHz/LKysnrkr/jPxNAnIYSoKDdv3kStVvPOO+9oJWkAGjRoAICuri5z584lMjKSwMBAzp49y5IlSzTOzcvLIyQkhKVLl7Jz504KCwuZPn26MhlZbm4uvXv3ZtOmTezbt4/+/fvj6elJWlqaRjlbt27F0tKS8PBwpk+fTlBQEIcPHy7X/YWFhQHFvzyo1WrCwsIwNTXF1tZWa/n38PBwHB0dKz1J87yqW7cudevWJTo6mnv37mkdDw4OpkmTJkydOhW1Wo1arVaO3bt3j3Xr1rFo0SIOHDhAs2bN2L17N0FBQXh4ePD1118ze/ZsNm7cyJdffqlR7qeffsobb7zBvn37GDRoEPPmzeP3338HirsvT5kyBQsLC8LDw5k5cyYrVqxQrm3atClr1qwB4MCBA6jVao1foyIjI9HT02PHjh3Mnz+f0NBQvv766wp934QQQgghxONJokYI8UK5fPkyRUVFWFpaPva88ePH0717d0xNTenatSuzZs3im2++0RijWlBQwLx58+jUqRM2NjYEBASQnJxMXFwcUNz74e2338bKygozMzOmTJmCjY0Nhw4d0qjr1VdfVR6u3377bRwdHdm6dWu57q9kGFT9+vUxNDRUtp2dnTl48KCSUEhNTeXMmTOMGjWqXPWI4hUDli5dqqw88NZbbxEQEKAs79iwYUN0dXXR19fH0NAQQ0ND5dr79+/j6+tLp06dsLCwoF69eqxbt46ZM2cycOBATE1Nsbe3x83NjR07dmjUO2zYMIYNG4aZmRkffPABNWrU4MSJE0BxoqWwsBA/Pz9atWqFnZ0d7u7uyrU1atRQkpEGBgYYGhpSv3595bilpSWenp5YWFgwaNAgunXr9tjJ8oUQQgghRMV7JuaoEUKIilLWpfd+/vlnPvvsM1JTU/nvf/9LYWEh+fn5ZGdnY2xsDBT3unlwKJGJiQlGRkakpKTQo0cPcnNzCQkJ4ccffyQ7O5uCggLu3btH69atNerq0KGD1va33377D+9U0xtvvMHixYs5fPgwb775JuHh4bRv316rLeLJDBgwgNdff534+HhOnz6NWq1my5YtfPjhh0yZMuWR1+np6dGmTRtl+8aNG1y5coUFCxZozIFWUFCgFbMPdq/W09OjcePGXL9+HYCLFy/SqlUrateurZzz6quvlvl+Hu66bWhoqJQthBBCCCGeDknUCCFeKGZmZujo6JCamoqDg0Op5/z++++4u7vj7OyMh4cHDRs25Ndff+Wjjz4iPz+/zHUtX74ctVqNt7c35ubm1K5dm9mzZz9RGRVFpVLh6OhIREQEgwYN4quvvsLDw+Opt+N5VKtWLezs7LCzs2PatGn4+voSEhLCxIkTH3lNzZo1NSYPLumptXDhQjp27PjY+vT0tD+6S64vayKyrGXr6OjIChlCCCGEEE+ZDH0SQrxQGjZsSM+ePdm+fTt37tzROn779m3OnTtHfn4+c+bMoWPHjlhYWHD16lWtcwsLC0lISFC2MzMzuXr1qjKs6sSJEzg6OjJgwACsrKxo0qQJ6enpWuWcPn1aY/vMmTO0bNmy3PeoUqlKfbh2dnbm2LFjfPnll9y5c4chQ4aUuw7xaJaWlhQUFJCXl/fIv8XD/vWvf2FsbMzly5cxMzPTepVVy5YtSU5O1ljG8uzZsxrnqFQqoHj4lRBCCCGEqH4kUSOEeOEsWLAAACcnJ6KiokhLSyMtLY0dO3bg6OiIubk5hYWFbNu2jYyMDA4cOEBoaKhWOSVzlJw6dYrExER8fHx4+eWXlRWAzM3N+e677zh//jxJSUnMmjWr1Elnz5w5w/r167l06RK7d+9m3759jB8/vtz316xZM+Li4sjOzubWrVvKfgsLCzp16sSKFSvo378/9erVK3cdAnJychg/fjxfffUVSUlJZGRkEBUVxaZNm7C1taVevXqYmJgQHx9PVlYWOTk5jy1v+vTpbNq0ia1bt5KWlsZvv/3Gvn37WL9+fZnb9Oabb6Krq8v8+fNJSUkhNjZWub5kKU0TExN0dHSIiYnhxo0bpSYshRBCCCGqSkZGBtbW1ho/iFYkd3d3fHx8KqXsiiKJGiHEC8fU1JTw8HB69OhBYGAgjo6OjB8/nujoaBYtWoSVlRVz585l69atDBkyhLCwMLy9vbXKqVmzJlOmTMHHx4fRo0dTWFhIcHCw8kDs4+ODgYEBY8eOZfLkybz66qt07txZq5wJEyaQlJTEyJEjWb16NZ6engwcOLDc9zd79mx++eUX+vbty4gRIzSOOTk5kZ+fj5OTU7nLF8X09fV59dVXCQ0NxdXVlaFDh7Jq1SqGDBnCJ598AoCnpyd//PEHDg4O2NraPrY8Z2dn/Pz8+Oqrrxg+fDhjx45l9+7dmJqaPlGb1q1bR0pKCiNGjGDFihVMnz4dKB6iBWBsbIyHhwdBQUHY2dmxePHicr4DQgghqhMfHx+sra21XomJiVXdNFGNlcSNr6+v1rEVK1ZgbW2tsTDB09C0aVPUarUyn9+xY8ewtrb+2x+9nic6jxvPnpSU9M8Gu4tq6+EJIytaUlJSpZYvqobEzf9ERESwZMkSTp48WdVNeSIbNmwgLCxMa+WpyiaxU3W+//57pk+fTmxsLI0aNarq5jyRyo4bIYR4mir7s8rHx4erV68SEBCgsb9Ro0Zac5Dl5eVRs2bNSm3Pi6QyP6+eRtwcO3aMW7ducfToUerWrQsUL2jQt29fVCoVrVq1eqIevhXt2LFjjB8/nri4uAr5LuPu7k6jRo1YtmxZBbSu/KysrHQedUx61AghxAvgzp07JCQkEBoayrhx46q6OaIS7d27l/j4eDIyMvjhhx/w9/enb9++z1ySRgghxJNTqVQYGhpqvPT09HBxceHjjz9m6dKl2Nra4urqChTPzefr60uPHj3o3Lkzrq6unD9/XqPM8PBw+vbtS4cOHXj//fcJDQ3llVdeUY4HBQUxfPhwjWv27NlDly5dNPZ9//33jBw5kvbt29OvXz9Wr15NXl6ecrxPnz6sX78eX19fOnfuzOuvv86WLVs0yrh9+zb//ve/6dmzJ+3bt2fIkCFERUXx559/0qlTJ7777juN848cOUK7du1eqJ4Y5dG6dWvMzc355ptvlH0xMTHUrFlT4++YkJDAxIkT6d69O507d8bFxYVTp05plHXx4kXGjh1L+/btGThwIDExMXTq1ImIiAjgf8OaDh06xMSJE+nQoQNDhgzhp59+Usp4cOhTRkaGMiWAra0t1tbWyrAlV1dXPv74Y436fXx8NHoA/fXXX/j4+NCpUyfs7Oz4z3/+o3X/eXl5BAYG0qdPHzp27IiTkxNqtbq8b2eFkESNEEK8ABYvXoyLiwudOnXirbfequrmiEp0/fp1vL29GTRoEIsXL6Z3794sX768qpslhBCiiu3btw89PT22b9+Ov78/hYWFuLm5cf36ddavX094eDgdO3ZkwoQJXLt2DYCTJ0/i6+vL22+/zd69e+nduzchISFPXHdMTAyzZ89m7NixHDhwgCVLlvD1118THByscd7mzZuxsbEhIiKCCRMmEBAQoEyKX9LeU6dOsWzZMg4ePIi3tzcqlYp69eoxaNAgwsPDNcoLDw/H3t5efqwog1GjRinJFCh+70aOHKkM6YfiH/4cHR3Zvn07e/bsUYZFlSTCCgsL8fDwQE9Pj127drF06VJCQkI0EnIlgoKCGDt2LPv27aNt27Z4eXmVOm9e06ZNWbNmDQAHDhxArVYzb968Mt/X8uXLiY2NZfXq1WzZsoXExETi4+M1zpk7dy7Hjx8nMDBQGYI+depULly4UOZ6KpokaoQQohxGjhz5TA17WrZsGQkJCaxevbrU5Z3F82PSpElER0eTkJBAdHQ0CxYskImjhRDiBXH06FE6deqkvNzc3JRjZmZmzJo1i5YtW2JpaUlsbCypqamsXr2adu3aYW5ujpeXF02aNCEyMhKA0NBQevbsibu7OxYWFowZM4a+ffs+cbvWrVuHm5sbI0eOpEWLFnTv3p0ZM2awY8cOjfP69OmDi4sLZmZmTJgwARMTE37++Wfl3hISElizZg09e/akefPm9OnThzfeeAMonutNrVaTnZ0NFE/6Hx0dLfPyldHQoUM5d+4cly5dIjs7G7VarTXXYffu3XF0dMTS0pKWLVsyf/58atWqpfQ++emnn7h48SIBAQG0adOGjh074uPjQ0FBgVZ948ePx97eXom7W7dulZoYqVGjBg0aNADAwMAAQ0ND6tevX6Z7unPnDmFhYcyaNYtevXrRunVr/P390dX9Xxrk8uXLHDx4kFWrVtGlSxeaN2/O2LFj6d27N7t27Srz+1fR5Nu6EEIIIYQQQjwHXnvtNY2hILVr11b+3bZtW41zz58/T25uLt27d9fYf+/ePdLT0wFITU3VWuCgQ4cOSiKnrM6fP09iYqLGPCeFhYXcvXuXGzdu0LhxY0B7rhcjIyOuX78OwK+//kqTJk2wsLAotY4OHTpgYWHB/v37mTRpEpGRkRgYGGBnZ/dEbX1RNWjQgH79+hEeHs5LL71E165dadasmcY5169fZ/Xq1Rw7dozr168rf8PMzEygeNiTkZERxsbGyjXt2rXTSIyUePBvbWRkpJRfkdLT08nPz6dDhw7KPn19fVq3bq1s//rrrxQVFTF06FCNa/Py8ujWrVuFtudJSKJGCCGEEEIIIZ4DtWvXxszMrNRjderU0dguKirC0NCQ0NBQrXPL2mMBQFdXl4cXqCmtB4WHhwcODg5a+0t6SwBavX51dHS0yn4cZ2dndu7cyaRJk4iIiGDkyJGlJglE6UaNGoWPjw9169bF09NT67iPjw/Xr19nzpw5mJiYULNmTd59913y8/OB4ph6cKjU4zz4ty65prCw8InaW9rf9sHYK0vsFBYWoqOjw549e7Ti78FE59MmiRohhBBCCCGEeMHY2Nhw7do1atSogampaannWFpacubMGY19D283atSIa9euaTykP7wkeJs2bbh48eIjk0hlbe8ff/zBxYsXH9mrZtiwYaxcuZLPP/+cpKQk1q5dW+76XkS2traoVCpycnLo16+f1vETJ04wb948Xn/9dQCuXbumDDUDaNmyJVlZWWRlZSm9as6dO/fECZiHqVQqAO7fv6+xv3Hjxhr1Q/EqWSU9gVq0aIFKpeL06dM0b94cgNzcXJKTk5XtNm3aUFRURHZ2tlbvsqok6UUhhBBCCCGEeMH07NmTdu3aMX36dNRqNRkZGZw6dYo1a9Yo8/C5urqiVqvZuHEjly5dYufOnURHR2uU061bN27cuMGGDRu4fPkyu3fv1lp9adq0aezfv5/g4GCSk5NJS0sjKiqKlStXPlF7X3nlFTw9PTl69CgZGRkcPXpUoz0NGzbEwcGB5cuX061bt0cmoETpdHR02L9/P99//32py7ebm5sTGRlJSkoKCQkJeHl5KUkUADs7OywsLJgzZw4XLlzg9OnTLFu2DD09vTL3tCmNiYkJOjo6xMTEcOPGDWXS4W7dunHkyBGio6NJS0tj6dKlXLlyRblOX1+fUaNGsXLlSn766SeSk5OZN2+eRsLHwsKCN998k7lz5xIVFUV6ejoJCQls2rSJw4cPl7vN/5QkaoQQQgghhBDiBaOrq8vGjRvp3Lkz8+bNY9CgQcyYMYNLly4pc4Z07tyZjz/+mC+++AJHR0d++OEHpk2bplFO69atmT9/Pjt27MDR0ZFjx45pTGIMxZMEr1u3jtjYWJydnXF2dmbjxo00bdr0iVa6QcQAAAI4SURBVNq7YcMG2rdvz6xZsxg8eDBLly5Vht2UcHJyIj8/n1GjRpXznXmx1atX75GLEPj7+5Obm8uoUaPw8vJi1KhRmJiYKMd1dXUJDg4mLy8PZ2dnfHx8mDJlCjo6OtSqVavcbTI2NsbDw4OgoCDs7OxYvHgxUDxUa9SoUcydOxcXFxf09fW1egJ5e3vTrVs3PDw8GD9+PK1ateK1117Tuq8RI0YQGBjI4MGDef/994mPj9eao+dp0nncuK2kpKSyDwgUz5SHJ+qqaElJSZVavqgaEjeivCR2RHlUdtwIIcTT9Lx8VpUsi33+/PmqbkqpIiMjWbJkCUeOHPlHyYEnUZmfV8963Fy4cIHhw4cTFhamNaH1i87KyuqR3YxkjhohhBBCCCGEEM+0v/76i4yMDD777DNGjx791JI0QtO3335LnTp1MDc3JyMjg4CAAKytrXnllVequmnPFEnUCCGEEEIIIYR4pq1fv56NGzfy2muvMWXKlKpuzgvrzp07BAYG8scffyjLfM+ZM+cfzVHzIpKhTy8oGYYgykPiRpSXxI4oDxn6JIR4nshn1fNLhj6J8njc0CeZTFgIIYQQQgghhBCimpBEjRBCCCGEEEIIIUQ18dihT0IIIYQQQgghhBDi6ZEeNUIIIYQQQgghhBDVhCRqhBBCCCGEEEIIIaoJSdQIIYQQQgghhBBCVBOSqBFCCCGEEEIIIYSoJiRRI4QQQgghhBBCCFFNSKJGCCGEEEIIIYQQopr4f8RUG7FUx21dAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1440x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.patches import Rectangle\n",
-    "from matplotlib.collections import PatchCollection\n",
-    "\n",
-    "\n",
-    "class FairTreeGraph(object):\n",
-    "    '''Provides a pretty tree diagram to summarize calculations.\n",
-    "    \n",
-    "    '''\n",
-    "    \n",
-    "    # Class attribute\n",
-    "    DIMENSIONS = pd.DataFrame.from_dict({\n",
-    "    'Contact'                       : ['Contact'   ,    0,    0,  600,  800, 'gray', None],\n",
-    "    'Threat Event Frequency'        : ['Threat\\nEvent\\nFrequency' ,  600,  800, 1800, 1600, 'green', None],\n",
-    "    'Action'                        : ['Action'   , 1200,    0,  600,  800, 'gray', None],\n",
-    "    'Threat Capability'             : ['Threat\\nCapability'  , 2400,    0, 3000,  800, 'gray', None],\n",
-    "    'Vulnerability'                 : ['Vulnerability'   , 3000,  800, 1800, 1600, 'green', None],\n",
-    "    'Control Strength'              : ['Control\\nStrength'  , 3600,    0, 3000,  800, 'gray', None],\n",
-    "    'Loss Magnitude'                : ['Loss\\nMagnitude'  , 6600, 1600, 4200, 2400, 'green', None],\n",
-    "    'Loss Event Frequency'          : ['Loss\\nEvent\\nFrequency', 1800, 1600, 4200, 2400, 'blue', 'multiply'],\n",
-    "    'Risk'                          : ['Risk'   , 4200, 2400, 4200, 5000, 'blue', 'multiply'],\n",
-    "    'Primary Loss'                  : ['Primary\\nLoss'  , 5400,  800, 6600, 1600, 'gray', None],\n",
-    "    'Secondary Loss'                : ['Secondary\\nLoss'  , 7800,  800, 6600, 1600, 'gray', None],\n",
-    "    'Secondary Loss Event Frequency': ['Secondary\\nLoss Event\\nFrequency', 7200,    0, 7800,  800, 'gray', None],\n",
-    "    'Secondary Loss Event Magnitude': ['Secondary\\nLoss Event\\nMagnitude', 8400,    0, 7800,  800, 'gray', None],\n",
-    "}, orient='index', columns=['tag', 'self_x', 'self_y', 'parent_x', 'parent_y', 'color', 'function'])\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        self._colormap = {'Not Required': 'grey', 'Supplied': 'green', 'Calculated': 'blue'}\n",
-    "\n",
-    "\n",
-    "    def _process_statuses(self):\n",
-    "        '''Turn dict into df and add color column'''\n",
-    "        self._statuses = pd.DataFrame.from_records([self._statuses]).T\n",
-    "        self._statuses.columns = ['status']\n",
-    "        self._statuses['color'] = self._statuses['status'].map(self._colormap)\n",
-    "        \n",
-    "    def _tweak_axes(self, ax):\n",
-    "        # Set limits\n",
-    "        ax.set_title('FAIR by Example', fontsize=20)\n",
-    "        ax.set_xlim(0, 9_400)\n",
-    "        ax.set_ylim(0, 2_900)\n",
-    "        # Disappear axes and spines\n",
-    "        for axis in [ax.xaxis, ax.yaxis]:\n",
-    "            axis.set_visible(False)\n",
-    "        for spine_name in ['left', 'right', 'top', 'bottom']:\n",
-    "            ax.spines[spine_name].set_visible(False)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_rects(self, ax):\n",
-    "        '''Cannot be done via apply'''\n",
-    "        patches = []\n",
-    "        patch_colors = []\n",
-    "        for index, row in self.DIMENSIONS.iterrows():\n",
-    "            rect = Rectangle(\n",
-    "                (row['self_x'], row['self_y']),\n",
-    "                1000,\n",
-    "                500,\n",
-    "                alpha=.3,\n",
-    "            )\n",
-    "            patches.append(rect)\n",
-    "            patch_colors.append(row['color'])\n",
-    "        collection = PatchCollection(patches, facecolor=patch_colors, alpha=.3)\n",
-    "        ax.add_collection(collection)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_text(self, row, ax):\n",
-    "        '''Apply-able function'''\n",
-    "        # Draw header\n",
-    "        plt.text(\n",
-    "            row['self_x'] + 500, \n",
-    "            row['self_y'] + 240, \n",
-    "            row['tag'], \n",
-    "            horizontalalignment='center',\n",
-    "            verticalalignment='center',\n",
-    "            fontsize=14,\n",
-    "            fontweight='medium',\n",
-    "        )\n",
-    "        # Draw data\n",
-    "        if row['function']:\n",
-    "            plt.text(\n",
-    "                row['self_x'] + 500, \n",
-    "                row['self_y'] - 140, \n",
-    "                row['function'], \n",
-    "                horizontalalignment='center',\n",
-    "                verticalalignment='center',\n",
-    "                fontsize=14,\n",
-    "                fontweight='bold',\n",
-    "                bbox={'facecolor':'salmon', 'alpha':.2, 'pad':5},\n",
-    "                zorder=3\n",
-    "            )\n",
-    "       \n",
-    "\n",
-    "    def _generate_lines(self, row, ax):\n",
-    "        '''Generate lines between boxes'''\n",
-    "        if row.name != 'Risk' and row.color != 'gray':\n",
-    "            ax.annotate(\n",
-    "                None,\n",
-    "                xy=(row['parent_x'] + 500, row['parent_y']), \n",
-    "                xytext=(row['self_x'] + 500, row['self_y'] + 500),     \n",
-    "                arrowprops=dict(\n",
-    "                    arrowstyle=\"-\",\n",
-    "                    connectionstyle=\"angle3,angleA=0,angleB=-90\",\n",
-    "                    ec=row['color'],\n",
-    "                    alpha=.3,\n",
-    "                    linestyle='--', \n",
-    "                    linewidth=3\n",
-    "                ),\n",
-    "            )\n",
-    "            \n",
-    "    def _generate_legend(self, ax):\n",
-    "        # Gen legend\n",
-    "        patches = [Patch(color=color, label=label, alpha=.3) for label, color in self._colormap.items()]\n",
-    "        plt.legend(handles=patches, frameon=False)\n",
-    "        \n",
-    "    def generate_image(self):\n",
-    "        fig, ax = plt.subplots()\n",
-    "        fig.set_size_inches(20,6)\n",
-    "        self.DIMENSIONS.apply(self._generate_lines, args=[ax], axis=1)\n",
-    "        ax = self._tweak_axes(ax)\n",
-    "        self.DIMENSIONS.apply(self._generate_text, args=[ax], axis=1)\n",
-    "        self._generate_rects(ax)\n",
-    "\n",
-    "\n",
-    "        self._generate_legend(ax)\n",
-    "        return (fig, ax)\n",
-    "\n",
-    "    \n",
-    "FairTreeGraph().generate_image()\n",
-    "\n",
-    "'Step 1: Generate random values to supply TEF, V, and LM\\n\\n'\n",
-    "'Step 2: Multiply your TEF and V values to calculate LEF\\n\\n'\n",
-    "'Step 3: Multiply your LEF and LM to calculate Risk\\n\\n'\n",
-    "'Step 4: Analyze your Risk outputs'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 1440x432 with 1 Axes>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x193e1f405c0>)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.patches import Rectangle\n",
-    "from matplotlib.collections import PatchCollection\n",
-    "\n",
-    "\n",
-    "class FairTreeGraph(object):\n",
-    "    '''Provides a pretty tree diagram to summarize calculations.\n",
-    "    \n",
-    "    '''\n",
-    "    \n",
-    "    # Class attribute\n",
-    "    DIMENSIONS = pd.DataFrame.from_dict({\n",
-    "    'Contact'                       : ['Contact'   ,    0,    0,  600,  800, 'green', None],\n",
-    "    'Threat Event Frequency'        : ['Threat\\nEvent\\nFrequency' ,  600,  800, 1800, 1600, 'blue', 'x'],\n",
-    "    'Action'                        : ['Action'   , 1200,    0,  600,  800, 'green', None],\n",
-    "    'Threat Capability'             : ['Threat\\nCapability'  , 2400,    0, 3000,  800, 'gray', None],\n",
-    "    'Vulnerability'                 : ['Vulnerability'   , 3000,  800, 1800, 1600, 'gray', 'step'],\n",
-    "    'Control Strength'              : ['Control\\nStrength'  , 3600,    0, 3000,  800, 'gray', None],\n",
-    "    'Loss Magnitude'                : ['Loss\\nMagnitude'  , 6600, 1600, 4200, 2400, 'green', '+'],\n",
-    "    'Loss Event Frequency'          : ['Loss\\nEvent\\nFrequency', 1800, 1600, 4200, 2400, 'green', 'x'],\n",
-    "    'Risk'                          : ['Risk'   , 4200, 2400, 4200, 5000, 'blue', 'x'],\n",
-    "    'Primary Loss'                  : ['Primary\\nLoss'  , 5400,  800, 6600, 1600, 'gray', None],\n",
-    "    'Secondary Loss'                : ['Secondary\\nLoss'  , 7800,  800, 6600, 1600, 'gray', 'x'],\n",
-    "    'Secondary Loss Event Frequency': ['Secondary\\nLoss Event\\nFrequency', 7200,    0, 7800,  800, 'gray', None],\n",
-    "    'Secondary Loss Event Magnitude': ['Secondary\\nLoss Event\\nMagnitude', 8400,    0, 7800,  800, 'gray', None],\n",
-    "}, orient='index', columns=['tag', 'self_x', 'self_y', 'parent_x', 'parent_y', 'color', 'function'])\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        self._colormap = {'Not Required': 'grey', 'Supplied': 'green', 'Calculated': 'blue'}\n",
-    "\n",
-    "\n",
-    "    def _process_statuses(self):\n",
-    "        '''Turn dict into df and add color column'''\n",
-    "        self._statuses = pd.DataFrame.from_records([self._statuses]).T\n",
-    "        self._statuses.columns = ['status']\n",
-    "        self._statuses['color'] = self._statuses['status'].map(self._colormap)\n",
-    "        \n",
-    "    def _tweak_axes(self, ax):\n",
-    "        # Set limits\n",
-    "        ax.set_title('LEF and LM Example', fontsize=20)\n",
-    "        ax.set_xlim(0, 9_400)\n",
-    "        ax.set_ylim(0, 2_900)\n",
-    "        # Disappear axes and spines\n",
-    "        for axis in [ax.xaxis, ax.yaxis]:\n",
-    "            axis.set_visible(False)\n",
-    "        for spine_name in ['left', 'right', 'top', 'bottom']:\n",
-    "            ax.spines[spine_name].set_visible(False)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_rects(self, ax):\n",
-    "        '''Cannot be done via apply'''\n",
-    "        patches = []\n",
-    "        patch_colors = []\n",
-    "        for index, row in self.DIMENSIONS.iterrows():\n",
-    "            rect = Rectangle(\n",
-    "                (row['self_x'], row['self_y']),\n",
-    "                1000,\n",
-    "                500,\n",
-    "                alpha=.3,\n",
-    "            )\n",
-    "            patches.append(rect)\n",
-    "            patch_colors.append(row['color'])\n",
-    "        collection = PatchCollection(patches, facecolor=patch_colors, alpha=.3)\n",
-    "        ax.add_collection(collection)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_text(self, row, ax):\n",
-    "        '''Apply-able function'''\n",
-    "        # Draw header\n",
-    "        plt.text(\n",
-    "            row['self_x'] + 500, \n",
-    "            row['self_y'] + 240, \n",
-    "            row['tag'], \n",
-    "            horizontalalignment='center',\n",
-    "            verticalalignment='center',\n",
-    "            fontsize=14,\n",
-    "            fontweight='medium',\n",
-    "        )\n",
-    "\n",
-    "    def _generate_operators(self, row, ax):\n",
-    "        if row.color == 'blue':\n",
-    "            ax.text(\n",
-    "                row['self_x'] + 500, \n",
-    "                row['self_y'] - 180,\n",
-    "                row['function'],\n",
-    "                horizontalalignment='center',\n",
-    "                verticalalignment='center',\n",
-    "                fontsize=15,\n",
-    "                fontweight='bold',\n",
-    "            )\n",
-    "\n",
-    "    def _generate_lines(self, row, ax):\n",
-    "        '''Generate lines between boxes'''\n",
-    "        if row.color != 'gray' and row.name != 'Risk':\n",
-    "            ax.annotate(\n",
-    "                None,\n",
-    "                xy=(row['parent_x'] + 500, row['parent_y']), \n",
-    "                xytext=(row['self_x'] + 500, row['self_y'] + 500),     \n",
-    "                arrowprops=dict(\n",
-    "                    arrowstyle=\"-\",\n",
-    "                    connectionstyle=\"angle3,angleA=0,angleB=-90\",\n",
-    "                    ec=row['color'],\n",
-    "                    alpha=.3,\n",
-    "                    linestyle='--', \n",
-    "                    linewidth=3\n",
-    "                ),\n",
-    "            )\n",
-    "            \n",
-    "    def _generate_legend(self, ax):\n",
-    "        # Gen legend\n",
-    "        patches = [Patch(color=color, label=label, alpha=.3) for label, color in self._colormap.items()]\n",
-    "        plt.legend(handles=patches, frameon=False)\n",
-    "\n",
-    "    def generate_image(self):\n",
-    "        fig, ax = plt.subplots()\n",
-    "        fig.set_size_inches(20,6)\n",
-    "        self.DIMENSIONS.apply(self._generate_lines, args=[ax], axis=1)\n",
-    "        ax = self._tweak_axes(ax)\n",
-    "        self.DIMENSIONS.apply(self._generate_text, args=[ax], axis=1)\n",
-    "        self.DIMENSIONS.apply(self._generate_operators, args=[ax], axis=1)\n",
-    "        self._generate_rects(ax)\n",
-    "\n",
-    "        #ax.text(0, -500, 'Copyright 2019, Theo Naunheim\\nFreely available for use under the CC BY 2.0 License')\n",
-    "        self._generate_legend(ax)\n",
-    "        return (fig, ax)\n",
-    "\n",
-    "    \n",
-    "FairTreeGraph().generate_image()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 1440x432 with 1 Axes>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x1870aaca3c8>)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.patches import Rectangle\n",
-    "from matplotlib.collections import PatchCollection\n",
-    "\n",
-    "\n",
-    "class FairTreeGraph(object):\n",
-    "    '''Provides a pretty tree diagram to summarize calculations.\n",
-    "    \n",
-    "    '''\n",
-    "    '''<TDF, TC, CS, PL, SL EXAMPLE>'''\n",
-    "    # Class attribute\n",
-    "    DIMENSIONS = pd.DataFrame.from_dict({\n",
-    "    'Contact'                       : ['Contact'   ,    0,    0,  600,  800, 'gray', None],\n",
-    "    'Threat Event Frequency'        : ['Threat\\nEvent\\nFrequency' ,  600,  800, 1800, 1600, 'green', 'multiply'],\n",
-    "    'Action'                        : ['Action'   , 1200,    0,  600,  800, 'gray', None],\n",
-    "    'Threat Capability'             : ['Threat\\nCapability'  , 2400,    0, 3000,  800, 'green', None],\n",
-    "    'Vulnerability'                 : ['Vulnerability'   , 3000,  800, 1800, 1600, 'blue', 'step'],\n",
-    "    'Control Strength'              : ['Control\\nStrength'  , 3600,    0, 3000,  800, 'green', None],\n",
-    "    'Loss Magnitude'                : ['Loss\\nMagnitude'  , 6600, 1600, 4200, 2400, 'blue', 'add'],\n",
-    "    'Loss Event Frequency'          : ['Loss\\nEvent\\nFrequency', 1800, 1600, 4200, 2400, 'blue', 'multiply'],\n",
-    "    'Risk'                          : ['Risk'   , 4200, 2400, 4200, 5000, 'blue', 'multiply'],\n",
-    "    'Primary Loss'                  : ['Primary\\nLoss'  , 5400,  800, 6600, 1600, 'green', None],\n",
-    "    'Secondary Loss'                : ['Secondary\\nLoss'  , 7800,  800, 6600, 1600, 'green', 'multiply'],\n",
-    "    'Secondary Loss Event Frequency': ['Secondary\\nLoss Event\\nFrequency', 7200,    0, 7800,  800, 'gray', None],\n",
-    "    'Secondary Loss Event Magnitude': ['Secondary\\nLoss Event\\nMagnitude', 8400,    0, 7800,  800, 'gray', None],\n",
-    "}, orient='index', columns=['tag', 'self_x', 'self_y', 'parent_x', 'parent_y', 'color', 'function'])\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        self._colormap = {'Not Required': 'grey', 'Supplied': 'green', 'Calculated': 'blue'}\n",
-    "\n",
-    "\n",
-    "    def _process_statuses(self):\n",
-    "        '''Turn dict into df and add color column'''\n",
-    "        self._statuses = pd.DataFrame.from_records([self._statuses]).T\n",
-    "        self._statuses.columns = ['status']\n",
-    "        self._statuses['color'] = self._statuses['status'].map(self._colormap)\n",
-    "        \n",
-    "    def _tweak_axes(self, ax):\n",
-    "        # Set limits\n",
-    "        ax.set_title('TEF, TC, CS, PL, and SL Example', fontsize=20)\n",
-    "        ax.set_xlim(0, 9_400)\n",
-    "        ax.set_ylim(0, 2_900)\n",
-    "        # Disappear axes and spines\n",
-    "        for axis in [ax.xaxis, ax.yaxis]:\n",
-    "            axis.set_visible(False)\n",
-    "        for spine_name in ['left', 'right', 'top', 'bottom']:\n",
-    "            ax.spines[spine_name].set_visible(False)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_rects(self, ax):\n",
-    "        '''Cannot be done via apply'''\n",
-    "        patches = []\n",
-    "        patch_colors = []\n",
-    "        for index, row in self.DIMENSIONS.iterrows():\n",
-    "            rect = Rectangle(\n",
-    "                (row['self_x'], row['self_y']),\n",
-    "                1000,\n",
-    "                500,\n",
-    "                alpha=.3,\n",
-    "            )\n",
-    "            patches.append(rect)\n",
-    "            patch_colors.append(row['color'])\n",
-    "        collection = PatchCollection(patches, facecolor=patch_colors, alpha=.3)\n",
-    "        ax.add_collection(collection)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_text(self, row, ax):\n",
-    "        '''Apply-able function'''\n",
-    "        # Draw header\n",
-    "        plt.text(\n",
-    "            row['self_x'] + 500, \n",
-    "            row['self_y'] + 240, \n",
-    "            row['tag'], \n",
-    "            horizontalalignment='center',\n",
-    "            verticalalignment='center',\n",
-    "            fontsize=14,\n",
-    "            fontweight='medium',\n",
-    "        )\n",
-    "\n",
-    "\n",
-    "    def _generate_lines(self, row, ax):\n",
-    "        '''Generate lines between boxes'''\n",
-    "        if row.color != 'gray' and row.name != 'Risk':\n",
-    "            ax.annotate(\n",
-    "                None,\n",
-    "                xy=(row['parent_x'] + 500, row['parent_y']), \n",
-    "                xytext=(row['self_x'] + 500, row['self_y'] + 500),     \n",
-    "                arrowprops=dict(\n",
-    "                    arrowstyle=\"-\",\n",
-    "                    connectionstyle=\"angle3,angleA=0,angleB=-90\",\n",
-    "                    ec=row['color'],\n",
-    "                    alpha=.3,\n",
-    "                    linestyle='--', \n",
-    "                    linewidth=3\n",
-    "                ),\n",
-    "            )\n",
-    "            \n",
-    "    def _generate_legend(self, ax):\n",
-    "        # Gen legend\n",
-    "        patches = [Patch(color=color, label=label, alpha=.3) for label, color in self._colormap.items()]\n",
-    "        plt.legend(handles=patches, frameon=False)\n",
-    "\n",
-    "    def generate_image(self):\n",
-    "        fig, ax = plt.subplots()\n",
-    "        fig.set_size_inches(20,6)\n",
-    "        self.DIMENSIONS.apply(self._generate_lines, args=[ax], axis=1)\n",
-    "        ax = self._tweak_axes(ax)\n",
-    "        self.DIMENSIONS.apply(self._generate_text, args=[ax], axis=1)\n",
-    "        self._generate_rects(ax)\n",
-    "\n",
-    "        #ax.text(0, -500, 'Copyright 2019, Theo Naunheim\\nFreely available for use under the CC BY 2.0 License')\n",
-    "        self._generate_legend(ax)\n",
-    "        return (fig, ax)\n",
-    "\n",
-    "    \n",
-    "FairTreeGraph().generate_image()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAABACAYAAABP0uObAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAABVlJREFUeJzt3V2opVUdx/HvT0cdzWAyUNDJEMoLS6FhmF5AFJ2BgrSLMfClFPKiGwkyRCOCoAupCbwSfLnxJVQ8g4HjFDFNMYlMUzQTo+N0TI0mHfEtFMmYEv9d7GfizOmMe2/Hffbae38/cDj7Wc+znmdxOJvfXutZz9qpKiRJas1x426AJElLMaAkSU0yoCRJTTKgJElNMqAkSU1aMeTxTvmTJB2WUZ7cHpQkqUkGlNSQ+fl55ufnx90MqQkZ8kFdh/gkSYeNdIhv2HtQkob0wK4DR2xf/dmzj7p/9+O/AmDNhesHOn7xPmmaGFBSQ37+wN3AkQElzSoDSppgi3tni9nD0iQzoKQhDTNkN0h9SUszoKRjNOrAOZbz9wtTqWUGlDSAae31GGBqmdPMpQEsV0C9/vJBAD56xpnLcr3FDCgNyWnm0qwYVzBJLTKgpCWMa0hv57YtAHx+w2Vjub7UEgNKM6nVe0rbH/kpYEBJYEBJWsDnqtQSA0ozodUek6SjM6A0lQyk0XBaupaTX7chSWqSz0FpKkxLj+mtN/4BwIdXnTbmlrw/C3tU9rZmgs9BSbNiUoPpsGn5oKA2OMQnNWTHY3PseGxu3M2QmmAPSk061hXDJ9XjWzcDcNGXvzrmlkjjZ0BpIkxrIM0Sn7HSsBzikyQ1yR6UxsZekRZy1p8WM6AkNckhQRlQUkNuuu2ecTdBaoYBpZFxyGZ4J608edxNkJrhShIaGe8xDW/b5vsA2HDFtWNuyeTxA9BYuJKE2mQAffB2bd8KGFDvx3v9Pxpek8mA0lEZQJoWTriYTAaU/sdA0qzyfmmbDKgZYwhJ/Q3b4zLgRsOAGjMDQ5o8/d63x/K+HnbdyWkOw2Fn8UmStCxci0+S1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KSBAirJF5PMJ3k2yS2jbpQkafSSfDvJviRPJXkwycoklyTZ3ZXdm+T/FnRIsiHJH5M82f2+ZMG+E5PcleSZJH9OsrErvzHJ00n2Jtme5OP92tc3oJIcD9wOfAk4D7gqyXnD/BEkSW1JchbwLWBtVX0aOB64GrgXuLIr+xtw3RLVXwMuq6rzu/33L9j3PeCVqjqXXmbs6Mr3dNe6ANgM/LhfGwfpQa0Dnq2q56vq38BDwFcGqCdJatsK4OSul3QK8E/gUFU90+3fBmxcXKmq9lTVwW5zH7AyyUnd9jeAW7vj3q2q17rXv6mqt7tjfges7te4QQLqLODvC7Zf6MokSROqql4EfgIcAF4C3gQeBk5IsrY77ArgY31OtRHYU1WHkqzqyn7YDRPOJTljiTrXA7/o18ZBAmqpb0x0AT9JmmBJPkJvNOwc4EzgQ8A1wJXAbUl+D7wFvPMe5/gU8CPgm13RCno9oyeqag2wk14ILqzzNWAtsKlfGwcJqBc4MkFXAwePcqwkaTKsB/5aVa9W1X+AR4AvVNXOqrqwqtYBvwX+slTlJKuBnwHXVtVzXfHrwNtdOcAcsGZBnfX07lFdXlWH+jVwkID6A/DJJOckOZFeuj46QD1JUrsOAJ9LckqSAJcC+5OcDtDdU7oZuKPbXpfkvu71KmAr8N2qeuLwCav39RhbgIu7okuBp7s6nwHupBdOrwzSwL4BVVXvADcAvwT2Aw9X1b5BTi5JalNV7aI3m2438CS9PLgLuCnJfmAvsKWqft1VORv4V/f6BuATwPeT/Kn7Ob3bdzPwgyR7ga8D3+nKNwGnAnPd8X07On4flCSprySbgPurau+yXdOAkiS1yKWOJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTfovVi6TqPPSIOQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x72 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x72 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x72 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from pyfair import FairModel, FairSimpleReport\n",
-    "\n",
-    "\n",
-    "# Create our model and calculate (don't worry about understanding yet)\n",
-    "model = FairModel(name='Sample')\n",
-    "model.input_data('Threat Event Frequency', mean=50_000, stdev=10_000)\n",
-    "model.input_data('Vulnerability', p=.66)\n",
-    "model.input_data('Loss Magnitude', mean=100, stdev=50)\n",
-    "model.calculate_all()\n",
-    "\n",
-    "FairSimpleReport(model).to_html('C:/Users/theon/Desktop/crap.html')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(<Figure size 1440x432 with 1 Axes>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x1c87a4774a8>)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from matplotlib.patches import Patch\n",
-    "from matplotlib.patches import Rectangle\n",
-    "from matplotlib.collections import PatchCollection\n",
-    "\n",
-    "\n",
-    "class FairTreeGraph(object):\n",
-    "    '''Provides a pretty tree diagram to summarize calculations.\n",
-    "    \n",
-    "    '''\n",
-    "    '''<TDF, TC, CS, PL, SL EXAMPLE>'''\n",
-    "    # Class attribute\n",
-    "    DIMENSIONS = pd.DataFrame.from_dict({\n",
-    "    'Contact'                       : ['Contact'   ,    0,    0,  600,  800, 'gray', None],\n",
-    "    'Threat Event Frequency'        : ['Threat\\nEvent\\nFrequency' ,  600,  800, 1800, 1600, 'gray', 'multiply'],\n",
-    "    'Action'                        : ['Action'   , 1200,    0,  600,  800, 'gray', None],\n",
-    "    'Threat Capability'             : ['Threat\\nCapability'  , 2400,    0, 3000,  800, 'gray', None],\n",
-    "    'Vulnerability'                 : ['Vulnerability'   , 3000,  800, 1800, 1600, 'gray', 'step'],\n",
-    "    'Control Strength'              : ['Control\\nStrength'  , 3600,    0, 3000,  800, 'gray', None],\n",
-    "    'Loss Magnitude'                : ['Loss\\nMagnitude'  , 6600, 1600, 4200, 2400, 'gray', 'add'],\n",
-    "    'Loss Event Frequency'          : ['Loss\\nEvent\\nFrequency', 1800, 1600, 4200, 2400, 'green', 'multiply'],\n",
-    "    'Risk'                          : ['Risk'   , 4200, 2400, 4200, 5000, 'gray', 'multiply'],\n",
-    "    'Primary Loss'                  : ['Primary\\nLoss'  , 5400,  800, 6600, 1600, 'gray', None],\n",
-    "    'Secondary Loss'                : ['Secondary\\nLoss'  , 7800,  800, 6600, 1600, 'gray', 'multiply'],\n",
-    "    'Secondary Loss Event Frequency': ['Secondary\\nLoss Event\\nFrequency', 7200,    0, 7800,  800, 'gray', None],\n",
-    "    'Secondary Loss Event Magnitude': ['Secondary\\nLoss Event\\nMagnitude', 8400,    0, 7800,  800, 'gray', None],\n",
-    "}, orient='index', columns=['tag', 'self_x', 'self_y', 'parent_x', 'parent_y', 'color', 'function'])\n",
-    "    \n",
-    "    def __init__(self):\n",
-    "        self._colormap = {'Not Required': 'grey', 'Supplied': 'green', 'Calculated': 'blue'}\n",
-    "\n",
-    "\n",
-    "    def _process_statuses(self):\n",
-    "        '''Turn dict into df and add color column'''\n",
-    "        self._statuses = pd.DataFrame.from_records([self._statuses]).T\n",
-    "        self._statuses.columns = ['status']\n",
-    "        self._statuses['color'] = self._statuses['status'].map(self._colormap)\n",
-    "        \n",
-    "    def _tweak_axes(self, ax):\n",
-    "        # Set limits\n",
-    "        ax.set_title('Incomplete Example', fontsize=20)\n",
-    "        ax.set_xlim(0, 9_400)\n",
-    "        ax.set_ylim(0, 2_900)\n",
-    "        # Disappear axes and spines\n",
-    "        for axis in [ax.xaxis, ax.yaxis]:\n",
-    "            axis.set_visible(False)\n",
-    "        for spine_name in ['left', 'right', 'top', 'bottom']:\n",
-    "            ax.spines[spine_name].set_visible(False)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_rects(self, ax):\n",
-    "        '''Cannot be done via apply'''\n",
-    "        patches = []\n",
-    "        patch_colors = []\n",
-    "        for index, row in self.DIMENSIONS.iterrows():\n",
-    "            rect = Rectangle(\n",
-    "                (row['self_x'], row['self_y']),\n",
-    "                1000,\n",
-    "                500,\n",
-    "                alpha=.3,\n",
-    "            )\n",
-    "            patches.append(rect)\n",
-    "            patch_colors.append(row['color'])\n",
-    "        collection = PatchCollection(patches, facecolor=patch_colors, alpha=.3)\n",
-    "        ax.add_collection(collection)\n",
-    "        return ax\n",
-    "    \n",
-    "    def _generate_text(self, row, ax):\n",
-    "        '''Apply-able function'''\n",
-    "        # Draw header\n",
-    "        plt.text(\n",
-    "            row['self_x'] + 500, \n",
-    "            row['self_y'] + 240, \n",
-    "            row['tag'], \n",
-    "            horizontalalignment='center',\n",
-    "            verticalalignment='center',\n",
-    "            fontsize=14,\n",
-    "            fontweight='medium',\n",
-    "        )\n",
-    "\n",
-    "\n",
-    "    def _generate_lines(self, row, ax):\n",
-    "        '''Generate lines between boxes'''\n",
-    "        if row.color != 'gray' and row.name != 'Risk':\n",
-    "            ax.annotate(\n",
-    "                None,\n",
-    "                xy=(row['parent_x'] + 500, row['parent_y']), \n",
-    "                xytext=(row['self_x'] + 500, row['self_y'] + 500),     \n",
-    "                arrowprops=dict(\n",
-    "                    arrowstyle=\"-\",\n",
-    "                    connectionstyle=\"angle3,angleA=0,angleB=-90\",\n",
-    "                    ec=row['color'],\n",
-    "                    alpha=.3,\n",
-    "                    linestyle='--', \n",
-    "                    linewidth=3\n",
-    "                ),\n",
-    "            )\n",
-    "            \n",
-    "    def _generate_legend(self, ax):\n",
-    "        # Gen legend\n",
-    "        patches = [Patch(color=color, label=label, alpha=.3) for label, color in self._colormap.items()]\n",
-    "        plt.legend(handles=patches, frameon=False)\n",
-    "\n",
-    "    def generate_image(self):\n",
-    "        fig, ax = plt.subplots()\n",
-    "        fig.set_size_inches(20,6)\n",
-    "        self.DIMENSIONS.apply(self._generate_lines, args=[ax], axis=1)\n",
-    "        ax = self._tweak_axes(ax)\n",
-    "        self.DIMENSIONS.apply(self._generate_text, args=[ax], axis=1)\n",
-    "        self._generate_rects(ax)\n",
-    "\n",
-    "        #ax.text(0, -500, 'Copyright 2019, Theo Naunheim\\nFreely available for use under the CC BY 2.0 License')\n",
-    "        self._generate_legend(ax)\n",
-    "        return (fig, ax)\n",
-    "\n",
-    "    \n",
-    "FairTreeGraph().generate_image()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pyfair.report.base_curve import FairBaseCurve\n",
-    "from pyfair.model.model import FairModel\n",
-    "from pyfair.model.meta_model import FairMetaModel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fbc = FairBaseCurve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'model': <pyfair.model.model.FairModel at 0x193dd289668>}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = FairModel('model')\n",
-    "fbc._input_check([model, model])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FutureWarning",
-     "evalue": "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;31mTypeError\u001b[0m: ufunc 'equal' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('bool')",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mFutureWarning\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-4-091753ff11c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mmeta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFairMetaModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'meta'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\meta_model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, models, model_uuid, creation_date)\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[1;31m# If model, load\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mFairModel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m             \u001b[1;31m# If metamodel, load components.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\meta_model.py\u001b[0m in \u001b[0;36m_load_model\u001b[1;34m(self, model)\u001b[0m\n\u001b[0;32m    150\u001b[0m         \u001b[1;34m\"\"\"Loads an individual model into the metamodel\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_record_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 152\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_calculate_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    153\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_load_meta_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeta_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\meta_model.py\u001b[0m in \u001b[0;36m_calculate_model\u001b[1;34m(self, model)\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[0mmodel_json\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m         \u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_json\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m         \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m         \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexport_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_risk_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Risk'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\model\\model.py\u001b[0m in \u001b[0;36mcalculate_all\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    379\u001b[0m         \u001b[1;31m# Needs to be string to avoid weird numpy error with empty status array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[1;31m# https://stackoverflow.com/questions/40659212/futurewarning-elementwise-comparison-failed-returning-scalar-but-in-the-futur\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 381\u001b[1;33m         \u001b[0mstatus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstatus\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Calculable'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    382\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    383\u001b[0m         calculable_nodes = (status.loc[status == 'Calculable']\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other, axis)\u001b[0m\n\u001b[0;32m   1764\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1765\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1766\u001b[1;33m                 \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1767\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1768\u001b[0m                 raise TypeError('Could not compare {typ} type with Series'\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1647\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1648\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1649\u001b[1;33m                     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1650\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1651\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0minvalid_comparison\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mFutureWarning\u001b[0m: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison"
-     ]
-    }
-   ],
-   "source": [
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"error\")\n",
-    "\n",
-    "\n",
-    "meta = FairMetaModel('meta', models=[model, model])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\theon\\AppData\\Local\\Temp\\tmp2icpjbs5\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import tempfile\n",
-    "tf = tempfile.NamedTemporaryFile()\n",
-    "dir(tf)\n",
-    "\n",
-    "with tempfile.NamedTemporaryFile() as tf:\n",
-    "    print(tf.name)\n",
-    "    \n",
-    "tf.delete"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 10.0 20 0\n"
-     ]
-    },
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-1-1ed12ba7d11d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpyfair\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutility\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbeta_pert\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFairBetaPert\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mfbp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFairBetaPert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mvariates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfbp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_variates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10_000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mvariates\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\utility\\beta_pert.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, low, mode, high, gamma)\u001b[0m\n\u001b[0;32m    121\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mean\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    122\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stdev\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_stdev\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_alpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    124\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_beta\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_beta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m         \u001b[1;31m# Generate curve\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\development\\pyfair\\pyfair\\utility\\beta_pert.py\u001b[0m in \u001b[0;36m_generate_alpha\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    169\u001b[0m             \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_high\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_low\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m         )\n\u001b[1;32m--> 171\u001b[1;33m         \u001b[0mgroup_2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup_2_numerator\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mgroup_2_denominator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    172\u001b[0m         \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup_1\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mgroup_2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "import matplotlib\n",
-    "%matplotlib inline\n",
-    "\n",
-    "\n",
-    "from pyfair.utility.beta_pert import FairBetaPert\n",
-    "\n",
-    "fbp = FairBetaPert(low=0, mode=10, high=20)\n",
-    "variates = pd.Series(fbp.random_variates(10_000))\n",
-    "variates.plot.hist(bins=100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "WindowsPath('C:/Users/theon')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pathlib\n",
-    "\n",
-    "p = pathlib.Path().home()\n",
-    "pathlib.Path(p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sqlite3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dir(sqlite3)"
-   ]
-  }
- ],
- "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": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}