{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis Example in R\n",
    "### Adapted by Carrie Brown from \"The Bigmemory Project: Introductory Example\" by Michael Kane and John Emerson\n",
    "\n",
    "Using USA Commerical Flight Data from 1997 to 2008, this analysis covers data loading, integration and basic visualization to attempt to discern possible causes of flight delays.\n",
    "\n",
    "**If you wish to run this notebook interactively:** you will be need to run the `get_data.submit` job (using the command `sbatch get_data.submit`) to download the necessary datafiles.  It is also recommended to change the data load step to use the truncated data set to improve run times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "randomForest 4.6-12\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "Attaching package: ‘randomForest’\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    margin\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load required libraries\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(maps)\n",
    "library(randomForest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Year</th><th scope=col>Month</th><th scope=col>TailNum</th><th scope=col>ArrDelay</th><th scope=col>Origin</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1987</td><td>10  </td><td>NA  </td><td>NA  </td><td>JFK </td></tr>\n",
       "\t<tr><td>1987</td><td>11  </td><td>NA  </td><td> 2  </td><td>MCI </td></tr>\n",
       "\t<tr><td>1987</td><td>11  </td><td>NA  </td><td> 0  </td><td>SAN </td></tr>\n",
       "\t<tr><td>1987</td><td>12  </td><td>NA  </td><td>31  </td><td>ORD </td></tr>\n",
       "\t<tr><td>1987</td><td>10  </td><td>NA  </td><td>12  </td><td>LAX </td></tr>\n",
       "\t<tr><td>1987</td><td>12  </td><td>NA  </td><td> 0  </td><td>ATL </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Year & Month & TailNum & ArrDelay & Origin\\\\\n",
       "\\hline\n",
       "\t 1987 & 10   & NA   & NA   & JFK \\\\\n",
       "\t 1987 & 11   & NA   &  2   & MCI \\\\\n",
       "\t 1987 & 11   & NA   &  0   & SAN \\\\\n",
       "\t 1987 & 12   & NA   & 31   & ORD \\\\\n",
       "\t 1987 & 10   & NA   & 12   & LAX \\\\\n",
       "\t 1987 & 12   & NA   &  0   & ATL \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Year | Month | TailNum | ArrDelay | Origin | \n",
       "|---|---|---|---|---|---|\n",
       "| 1987 | 10   | NA   | NA   | JFK  | \n",
       "| 1987 | 11   | NA   |  2   | MCI  | \n",
       "| 1987 | 11   | NA   |  0   | SAN  | \n",
       "| 1987 | 12   | NA   | 31   | ORD  | \n",
       "| 1987 | 10   | NA   | 12   | LAX  | \n",
       "| 1987 | 12   | NA   |  0   | ATL  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Year Month TailNum ArrDelay Origin\n",
       "1 1987 10    NA      NA       JFK   \n",
       "2 1987 11    NA       2       MCI   \n",
       "3 1987 11    NA       0       SAN   \n",
       "4 1987 12    NA      31       ORD   \n",
       "5 1987 10    NA      12       LAX   \n",
       "6 1987 12    NA       0       ATL   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t2200000 obs. of  5 variables:\n",
      " $ Year    : int  1987 1987 1987 1987 1987 1987 1987 1987 1987 1987 ...\n",
      " $ Month   : int  10 11 11 12 10 12 11 12 10 10 ...\n",
      " $ TailNum : chr  NA NA NA NA ...\n",
      " $ ArrDelay: int  NA 2 0 31 12 0 -12 -2 0 -5 ...\n",
      " $ Origin  : chr  \"JFK\" \"MCI\" \"SAN\" \"ORD\" ...\n"
     ]
    }
   ],
   "source": [
    "# Load airline.csv data - to run this interactively, change the file to './data/airline_truc.csv'\n",
    "flights <- read.csv(\"./data/airline_subsample.csv\",\n",
    "  sep=\",\", \n",
    "  header=TRUE,\n",
    "  stringsAsFactors=FALSE)\n",
    "\n",
    "# Explore the data\n",
    "head(flights)\n",
    "str(flights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1366017</li>\n",
       "\t<li>5</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1366017\n",
       "\\item 5\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1366017\n",
       "2. 5\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1366017       5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove flights with missing data\n",
    "flights <- na.omit(flights)\n",
    "\n",
    "dim(flights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>N304DQ</dt>\n",
       "\t\t<dd>24106</dd>\n",
       "\t<dt>N348AT</dt>\n",
       "\t\t<dd>24102</dd>\n",
       "\t<dt>N816NW</dt>\n",
       "\t\t<dd>24096</dd>\n",
       "\t<dt>N805NW</dt>\n",
       "\t\t<dd>24101</dd>\n",
       "\t<dt>N822SK</dt>\n",
       "\t\t<dd>24107</dd>\n",
       "\t<dt>N516AS</dt>\n",
       "\t\t<dd>24107</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[N304DQ] 24106\n",
       "\\item[N348AT] 24102\n",
       "\\item[N816NW] 24096\n",
       "\\item[N805NW] 24101\n",
       "\\item[N822SK] 24107\n",
       "\\item[N516AS] 24107\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "N304DQ\n",
       ":   24106N348AT\n",
       ":   24102N816NW\n",
       ":   24096N805NW\n",
       ":   24101N822SK\n",
       ":   24107N516AS\n",
       ":   24107\n",
       "\n"
      ],
      "text/plain": [
       "N304DQ N348AT N816NW N805NW N822SK N516AS \n",
       " 24106  24102  24096  24101  24107  24107 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to estimate birthmonth of aircraft by finding the month and year of first flight\n",
    "birthmonth <- function(y){\n",
    "  minYear <- min(y[,'Year'], na.rm=TRUE)\n",
    "  these <- which(y[,'Year']==minYear)\n",
    "  minMonth <- min(y[these, 'Month'], na.rm=TRUE)\n",
    "  return(12 * minYear + minMonth - 1)\n",
    "}\n",
    "\n",
    "# Create vectors for each aircraft (aircrafts) and store their birthmonth (acStart)\n",
    "aircrafts <- unique(flights[,'TailNum'])\n",
    "aircrafts <- aircrafts[!is.na(aircrafts)]\n",
    "\n",
    "acStart <- rep(0, length(aircrafts))\n",
    "\n",
    "for (i in aircrafts) {\n",
    "  acStart[i] <- birthmonth(flights[flights$TailNum==i,])\n",
    "}\n",
    "\n",
    "tail(acStart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Year</th><th scope=col>Month</th><th scope=col>TailNum</th><th scope=col>ArrDelay</th><th scope=col>Origin</th><th scope=col>acStart</th><th scope=col>Age</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1995  </td><td> 3    </td><td>N240AU</td><td>  5   </td><td>BWI   </td><td>23940 </td><td> 3    </td></tr>\n",
       "\t<tr><td>1995  </td><td>12    </td><td>N605AU</td><td>108   </td><td>TPA   </td><td>23940 </td><td>12    </td></tr>\n",
       "\t<tr><td>1995  </td><td>10    </td><td>N178AW</td><td>  1   </td><td>PHX   </td><td>23940 </td><td>10    </td></tr>\n",
       "\t<tr><td>1995  </td><td>11    </td><td>N966VJ</td><td> -7   </td><td>PIT   </td><td>23940 </td><td>11    </td></tr>\n",
       "\t<tr><td>1995  </td><td> 1    </td><td>N903DE</td><td> -4   </td><td>PNS   </td><td>23940 </td><td> 1    </td></tr>\n",
       "\t<tr><td>1995  </td><td> 2    </td><td>N997Z </td><td>  7   </td><td>AUS   </td><td>23941 </td><td> 1    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Year & Month & TailNum & ArrDelay & Origin & acStart & Age\\\\\n",
       "\\hline\n",
       "\t 1995   &  3     & N240AU &   5    & BWI    & 23940  &  3    \\\\\n",
       "\t 1995   & 12     & N605AU & 108    & TPA    & 23940  & 12    \\\\\n",
       "\t 1995   & 10     & N178AW &   1    & PHX    & 23940  & 10    \\\\\n",
       "\t 1995   & 11     & N966VJ &  -7    & PIT    & 23940  & 11    \\\\\n",
       "\t 1995   &  1     & N903DE &  -4    & PNS    & 23940  &  1    \\\\\n",
       "\t 1995   &  2     & N997Z  &   7    & AUS    & 23941  &  1    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Year | Month | TailNum | ArrDelay | Origin | acStart | Age | \n",
       "|---|---|---|---|---|---|\n",
       "| 1995   |  3     | N240AU |   5    | BWI    | 23940  |  3     | \n",
       "| 1995   | 12     | N605AU | 108    | TPA    | 23940  | 12     | \n",
       "| 1995   | 10     | N178AW |   1    | PHX    | 23940  | 10     | \n",
       "| 1995   | 11     | N966VJ |  -7    | PIT    | 23940  | 11     | \n",
       "| 1995   |  1     | N903DE |  -4    | PNS    | 23940  |  1     | \n",
       "| 1995   |  2     | N997Z  |   7    | AUS    | 23941  |  1     | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Year Month TailNum ArrDelay Origin acStart Age\n",
       "1 1995  3    N240AU    5      BWI    23940    3 \n",
       "2 1995 12    N605AU  108      TPA    23940   12 \n",
       "3 1995 10    N178AW    1      PHX    23940   10 \n",
       "4 1995 11    N966VJ   -7      PIT    23940   11 \n",
       "5 1995  1    N903DE   -4      PNS    23940    1 \n",
       "6 1995  2    N997Z     7      AUS    23941    1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate flight age using the birthmonth\n",
    "age <- data.frame(names(acStart), acStart, stringsAsFactors = FALSE)\n",
    "colnames(age) <- c(\"TailNum\", \"acStart\")\n",
    "flights <- left_join(flights, age, by=\"TailNum\")\n",
    "flights <- mutate(flights, Age = (flights$Year * 12) + flights$Month - flights$acStart)\n",
    "\n",
    "head(flights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = ArrDelay ~ Age, data = flights)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-182.32  -15.37   -7.42    3.64 1437.54 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) 7.279e+00  2.802e-02   259.8   <2e-16 ***\n",
       "Age         2.113e-03  1.189e-05   177.7   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 32.68 on 1379269 degrees of freedom\n",
       "Multiple R-squared:  0.02238,\tAdjusted R-squared:  0.02238 \n",
       "F-statistic: 3.158e+04 on 1 and 1379269 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate linear model for response: ArrDelay and predictor: Age \n",
    "lm <- lm(ArrDelay ~ Age, data=flights)\n",
    "summary(lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert Months from number to factor\n",
    "flights$Month <- factor(flights$Month)\n",
    "levels(flights$Month) <- month.abb\n",
    "\n",
    "# Select a subset of fields needed to graph arrival delays by month\n",
    "subset_month <- select(flights, Month, ArrDelay)\n",
    "\n",
    "# Create violin graph showing arrival delays by month\n",
    "ggplot(subset_month, aes(Month,ArrDelay, fill=factor(Month))) + \n",
    "       geom_violin(aes(group=Month)) + \n",
    "       theme(legend.position=\"none\") +\n",
    "       labs(y = \"Arrival Delay (in minutes)\") + \n",
    "       labs(title = \"Average Flight Arrival Delay by Month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nCAiCHQAAQEAQ7AAAAAKCYAcAABAQ/w/8EQw8CA8UUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load state list for airport codes and join departure state to flights dataframe\n",
    "airport_codes <- read.csv(\"./data/airport_codes.csv\",\n",
    "                        col.names=c(\"OriginState\", \"Origin\"),\n",
    "                        stringsAsFactors = FALSE)\n",
    "flights <- left_join(flights, airport_codes, by=\"Origin\")\n",
    "\n",
    "# Create subset of data containing origin state and arrival delay\n",
    "subset_state <- select(flights, OriginState, ArrDelay)\n",
    "subset_state <- subset_state[!is.na(subset_state$ArrDelay),]\n",
    "subset_state <- group_by(subset_state, OriginState)\n",
    "subset_summary <- summarise(subset_state, AveDelay=mean(ArrDelay))\n",
    "\n",
    "# Create graphic of US States colored by average delay time\n",
    "map = map_data(\"state\")\n",
    "\n",
    "ggplot(subset_summary, aes(fill=AveDelay)) + \n",
    "  geom_map(aes(map_id=OriginState), map=map) +\n",
    "  scale_fill_distiller(name = \"AveDelay(mins)\", palette = \"Spectral\", direction=-1) +\n",
    "  expand_limits(x=map$long, y=map$lat) +\n",
    "  theme_void() +\n",
    "  labs(title = \"Average Flight Arrival Delay by State\") +\n",
    "  theme(legend.justification = c(1, 0), legend.position = c(1, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1070222"
      ],
      "text/latex": [
       "1070222"
      ],
      "text/markdown": [
       "1070222"
      ],
      "text/plain": [
       "[1] 1070222"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "275881"
      ],
      "text/latex": [
       "275881"
      ],
      "text/markdown": [
       "275881"
      ],
      "text/plain": [
       "[1] 275881"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bin based on arrival times\n",
    "flights$ArrCategory <- cut(flights$ArrDelay, c(-120,-15,15,120,400), c(\"Early\", \"On-Time\", \"Late\", \"Very Late\"))\n",
    "\n",
    "# Remove any remaining NA's\n",
    "flights_subset <- select(flights, Month, Year, OriginState, Age, ArrCategory)\n",
    "flights_subset <- na.omit(flights_subset)\n",
    "flights_subset$OriginState <- as.factor(flights_subset$OriginState)\n",
    "\n",
    "# Divide data into training and testing sets\n",
    "flights.train <- rownames(sample_frac(flights_subset, .8))\n",
    "flights.test <- rownames(flights)[!(rownames(flights_subset) %in% flights.train)]\n",
    "\n",
    "length(flights.train)\n",
    "length(flights.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       " randomForest(formula = ArrCategory ~ OriginState + Month + Year,      data = flights_subset[flights.train, ], ntree = 10) \n",
       "               Type of random forest: classification\n",
       "                     Number of trees: 10\n",
       "No. of variables tried at each split: 1\n",
       "\n",
       "        OOB estimate of  error rate: 31.16%\n",
       "Confusion matrix:\n",
       "          Early On-Time Late Very Late class.error\n",
       "Early        54  108157  263         0 0.999502185\n",
       "On-Time     395  728141 1229         0 0.002225374\n",
       "Late        130  204159 1137         0 0.994465160\n",
       "Very Late    11   15748   42         0 1.000000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create random forest to predict ArrCategory\n",
    "forest <- randomForest(ArrCategory ~ OriginState + Month + Year, flights_subset[flights.train,], ntree=10)\n",
    "forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>MeanDecreaseGini</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>OriginState</th><td>5906.318</td></tr>\n",
       "\t<tr><th scope=row>Month</th><td>4530.451</td></tr>\n",
       "\t<tr><th scope=row>Year</th><td>6233.670</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       "  & MeanDecreaseGini\\\\\n",
       "\\hline\n",
       "\tOriginState & 5906.318\\\\\n",
       "\tMonth & 4530.451\\\\\n",
       "\tYear & 6233.670\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | MeanDecreaseGini | \n",
       "|---|---|---|\n",
       "| OriginState | 5906.318 | \n",
       "| Month | 4530.451 | \n",
       "| Year | 6233.670 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "            MeanDecreaseGini\n",
       "OriginState 5906.318        \n",
       "Month       4530.451        \n",
       "Year        6233.670        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forest$importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94\nAAAgAElEQVR4nOzdeXxU1f3/8fckIQvZIWyJSUCBCIRFIazKVqxIsRXFFZAiWhCpC5Uvoqio\noLWi6U+gEINCra2ICEpVRBQ07BAUZFfEEJIga0gyJJBtfn/M7UwSWQUymcPr+fDh4y7n3vlM\ngMl7zr3nHpvD4RAAAAC8n4+nCwAAAMDFQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAA\nMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEO\nAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQ\nBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAA\nAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDs\nAJimoKDgoYceio2N9fPzCwgImDNnjqcrAoBqQrADYJqnnnrqH//4R1ZWVllZWXFxcWlpqacr\nOq3S0lLb/7zzzjueLgeA1/PzdAEAcJGtXLnSuRAbG3vPPfe0bdvWs/UAQLUh2AEwTWFhoXNh\n8ODBL774omeLAYDqxKVYAOa46667bDbbrl27nKsvvfSSzWabMGGCq0FeXt5LL7108803N27c\nOCwsrHPnzvfff//XX39d5TyTJk1yXh5t3LixpPfff79t27bNmzd3NVizZs0dd9wRHR0dEBDQ\npEmTAQMGfP755w6Ho+JJysvLFyxYcOONN1555ZWBgYFNmjT5zW9+M2fOnJKSEle1tWrVcrUf\nMmSIzWZbu3btxfyJALjM0GMH4HLx7bff9u/fPycnx7Vl3bp169ate/PNNx988MGpU6f6+vr+\n8qjZs2ffd999kuLj451bXnzxxaeeesrVICMjIyMj48MPP7z//vtnzJjh5+cnqby8/NZbb/3o\no4+qNFu2bNnf//73FStWhIaGXqK3CeByRo8dAHNMnjx5w4YNcXFxztVhw4Zt2LDhwQcflHT4\n8OGKqa5Zs2Y9e/YMDg52rs6YMePll1/+5QkLCgrGjBlTccuaNWtcqa5Xr16jRo3q0qWLc3XW\nrFkvvPCCc3nmzJmuVNe6devBgwf37NnTZrNJ2rx58+jRo53VrlmzxnXm5557bsOGDYmJiRf+\ncwBw+XIAgFkSEhKcn29PPfWUa+P48eNdn3szZ850bty/f/8111zj3BgcHHz06FHndlc+kxQY\nGDh27Nj33nvvv//9b3l5ebdu3ZzbJ0yY4GxcXl7uah8QEHDgwAGHw/H73//eueXuu+921TBz\n5kznxoiIiLKyMofD4bosK+lf//pX9fx8ABiMS7EALguLFy92LvTv33/EiBHO5YYNG6akpHTs\n2FHS8ePHV65cefPNN1c58N///vett97qXP7pp59WrVrlXH700UedCzabbezYsc8991xpaenJ\nkye//PLLu+++29U1uGbNmgULFvTt27d27dr33HNPgwYNnNuLi4sDAwMv1bsFcLniUiwA85WX\nl7tGVNx+++0VdyUlJTlHSEhytXEJCAi45ZZbXKu7d+92LUdFRbkeQRcYGOh6Wp7zJK7rsxkZ\nGbfddludOnX69u375ptvXn311bfccsstt9xCqgNwKRDsAJgvJyenqKjIueyKcS6uURHff/99\nlV0NGjTw8XF/TlYMdqdz5MgRSc8++2zPnj1dG0+ePLlkyZLHHnusRYsWnTp1Sk9PP9+3AADn\ngkuxAMwXHh7uWs7Ly6uyNz8/37kQFhZWZZdzuINLQECAq+WXX355yteqV6+epLp16y5btuzb\nb7+dO3fuRx99VDEyrl+/vnfv3rt3765fv/6veTMAcHoEOwDmCw0NjYmJyc7OlvTtt99WvJHO\nbre7UleLFi3OfJ5mzZo5FwoKCtq0aePv73+Gxjab7dprr7322mv/9re/7dy586OPPnrrrbec\nr1VQUPDf//53+PDhF/KmAOCXuBQL4LLgmlhs+vTpubm5ru3JycnHjx+v0uZ0rr76aueVWYfD\n8fHHH7u2Hzx4sF27domJiYmJievWrduxY0fP/zl8+LDzwHHjxn333XdBQUHOQyqOhz3dFgA4\nX/TYAbgsjB49+tNPP5V08ODBbt26Pf3009HR0R999FFycrKzQZcuXdq3b3/mk9SrV++BBx5I\nSUmRNGzYsNLS0uuuu27jxo0vvPDC5s2bJSUmJiYlJRUXF69Zs6a4uFjS4MGDx40bFxsbm5GR\nsWTJEtetfl27dpXk4+Pj6+tbVlYm6euvvx4wYEBwcHDF6SgA4Px4+nkrAHCRnfI5dg6HY+TI\nkaf7JKxTp86uXbtcLV3PpYuPj69y8gMHDtStW/eUJ6lXr962bduczSZOnHiGD97BgweXl5c7\nW1a5/rtmzZpL+KMBYDouxQK4XEyfPv3ll1/+5QiJ3r17r1u3ruJUsGdQv379FStWJCUlVdne\nv3//FStWtGzZ0rn6zDPP/PWvf3U9tc4lICBg9OjRb7zxhmtYxqRJk+iiA3Cx2ByVZ60GALMV\nFhZu375969atR44cadmyZWJiYmxs7PmexOFw7N69e/PmzXv27ImNjW3Xrt0pB16cPHly1apV\nWVlZ+/fvDw4OjouL69ixY8OGDas0271797Jly44dO9a4ceO+ffv+MnoCwDki2AEAABiCS7EA\nAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg\n2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAA\nGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAH\nAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAI\ngh0AAIAhCHYAAACG8PN0Ad5h8+bNpaWlnq4CAADUCH5+fm3btvV0FadAsDu79PT0pKQkT1cB\nAABqkA0bNnTo0MHTVVRFsDu74uJiSSdPnvT39/d0LQAAwMOKi4sDAgKc8aCm4R47AAAAQxDs\nAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADCEtwa7wsLCzMzM/Px8h8Ph6VoA\nAABqBG8KdsuWLRs6dGhCQkJERERwcHB8fHx4eHhISEizZs3GjBmzZcsWTxcIAADgSd4x84TD\n4RgxYkRqaqqk8PDwpk2b1qlTJzQ0tKCgIDc3d8+ePcnJycnJycOGDUtNTfX19fV0vQAAAB7g\nHcFu6tSpqampSUlJU6ZM6dq1q59fpbLLysrS09MnTJgwe/bshISEcePGeapOAAAAD7J5xT1q\nnTt3zsrK2r17d2Bg4OnalJaWJiUl2e32H3744eK++urVq7t168ZcsQAAQP+bK3bVqlVdu3b1\ndC1Vecc9dtu3b+/SpcsZUp0kPz+/Hj16ZGZmVltVAAAANYp3BLvExMS1a9eeOHHiDG3KyspW\nrFgRGxtbbVUBAADUKN4R7AYNGpSVldW9e/e0tLTS0tIqe8vKytavX9+3b99vvvlm+PDhHqkQ\nAADA47xj8MSoUaO2bt06c+bMHj16hIeHN2vWzDkq1m635+bm7t69++jRo5KGDBkyduxYTxcL\nAADgGd4R7Gw224wZMx5++OFp06YtXbp0586ddrvduSsoKCg6OnrIkCHDhg1r27atZ+sEAADw\nIO8Idk4tWrSYPn26c9lutx85ciQyMjI0NNRms3m2MAAAgJrAm4JdRSEhISEhIZ6uAgAAoAbx\njsETAAAAOCtv7bH7pZycnH79+knatGnTuR9VXl5+ypG2FW3btu1Ci6tgv3SG03WTgiRJq6Si\n07SJllpyKk7FqTgVp+JUnOo8T9VKanT69mbwjpknzkVGRkaTJk0kndc7+umnnzp16nTmYHfy\n5MnCwkK73R4cHHyhVUqTpNdOs8smzZd6SflSi9P/he4ofcapONXlcCqHo2Np6Wd2u6RJgYGv\nBQVV3KX//Uu3SfMPHOhVr16+n591qgp7rca+vu6qCgtfCwio9EL/m2DaJs0/dqzXjz/m+/q2\naNWqyMdHknx85LqRt7xcDkfH48c/++EHSZMaNXqtUaNKe8vK3KfasqVX8+b5ISFWVRX2Wvz8\nOtpsVlW5ua9Vub3Ez895Zps0f9++XmvX5teq1eLGG4uc1daq5X7dsjKVlXU8evSzFSskTWrR\n4rVWrarsdVe1Zk2vhIT8hg2tqsrKVOUDsFatjj4+VlVZWa/Vq1dlr3x8rFNt29Zr1ap8f/8W\nd9zhrsrnfxeCSktVWtpx//7P5s2TNKlr19e6dauy113VwoW9OnTIb9bMqqqkpGpV/v4dfX2t\nqnbseK1x4yp7nX+INml+WlqvTz/NDwhoMWZMUa1akhQQ4PojVnGxSks7/vTTZ6+/LmlSv36v\n9e9fZa9VlcMxPyWlV7du+Z06WVWdPFm1qoCAjn5+VlXr17/WqlWVvfLzs6qaP79XSkp+7dot\n3nqryPnXLzBQrukxT5xQaWnHnTs/Gz9e0qRBg14bPLjKXndVzz/f64Yb8n/7W6uqEydUXFzp\ndYOCOtaqZVX15ZevJSVV2atatayq3nyz14wZ+cHBLebPLwoIkMOh2rWdeyWpqEjFxR03b/5s\n2DBJk0aPfm30aPfewkKVlLireuihXgMG5A8caFXl2uv6l1i7dkd/f6uqRYte6969UlW1a8vf\n36rq1Vd7TZ6cHxraYt26IufEBP/ba71ucXHHb775bOBAnTgx6fHHX5swwb33+HHXT8PmcMy/\n/fZe996bP3SoVdXx42PWrp3wm9/ogtXkmSfMCXZFRUWrVq2S1KdPn4t75pSUlJEjRxYUFHBX\nH86urEz5+ZVW69Rx/zLLz9e+far4qO2WLeXKKxkZ2r1bkoqLdfy4bDb17KmoKGvvxo3auNF9\nYHGx+veX69fb559r7VoVFrobDB6sxERr+Z13tGJFpTM/+qi6dLH2TpumtDRrlySbTU88od69\nrb0vvaRly5SXp/Jya+9zz6lfP2vvk09q3rxKKeqVV3TrrdbyQw/p3Xcr/XymT9fdd1vLQ4fq\n7bcr7Z09W3/8o7V8552aN6/S3jfe0AMPWMu3367583/l3jvu0PvvV9qbmqr77z/t3lmz5HpA\n5l136b33Ku196y0NG2Yt33NP1fc7Z46GDrWWhwzRO++4d9lsmjNH995rrf7xj/r3vxUa6m7w\n+usaPNhafuABffBBpTNX/EmOHFnpZ+Xjo+nTdeed1urDD+uTTyod+/LLGjjQWn7sMS1aVGnv\nlCkaMMBafvxxLVxY6cxTpugPf7BWn3xSn39e6diJE9W/v7X8zDP69NNK77fi35znntN//+ve\nGxiop5/WjTdaq3/9q778stKZn3hCrt/Hr76qzz6rVNX48erZ01p1/n2u6OGHdd111vLMmVq2\nrNKxjzzi/rcwe7bWrq107P33yxWMXP+OXO9oxAhdc421Oneuli+3liMiZLPpnnvUpo21ZeFC\nrVtX6cz33quWLa3lRYu0enWlqu69V1dfba0uWaIqV6JuvVXNmlnLX3xR6ZPBZtPAgbrySmv1\nq68qva6Pj26/3f25sXq1tm6tdOabbpLrOf/r1mnz5kpn/t3vFB1traan65tvKp25f381bGit\nfveddu2qdOaePeX6krBtm7Zvr3TmXr1Ut661unOntmypdObevRUZaa3u2aM9eyqdOSlJ4eHW\nsutT1HXmjh3d/6z27VNQkPtD9QIQ7Lwbwa6GKizUzz9Lkt1ufTW8+mq5elWdHxzHjlmZ49gx\n3XabrrrK2jtvnvVrw5VmRo+W6xvkxIn6+GMVFurkSWtLxV+EI0ZYv/hPnFBRkWw2vfWWO478\n8hd/Sor+9Cdr+cyRwhl0wsPdQbDiL+8xY/TRR9ays82zz+rmm60tzvjlUuVX3Rtv6IsvFBrq\n7gMYMULXXmstf/CBNmxwH2uzacgQ96+czz/Xt99W2lvx18aaNZU+gqt89G/Zop07K+3t3Vt1\n6lirP/2kH3+sVHPHjnL9Kzt4UPv2VdrbqpX7e7ndroMHK+2Ni3P/3Fx/rC7OX7ROJSX63/OS\nrKoiItyrVaK5j4/7dwYAb1Nerrw8a7mkRPXrX5zTEuy8G8HuLAoKtHu3ioqs7qKTJ9W5s/u7\n17p1Wr3a6hvPz1dZmQYNUuvW1t5Zs/T553I4dOyYteWxx9zf6f/yF82bZ/2GPnlShYWV+k7u\nvltz51aqpGKvzJ//rA8/tHJASIhq1arUlzBjhpYvd/9G9/HRiBFq187a+9VX2rjR/SWvyjdR\n57dJ19fHKt8IDx2Sa8JiZ5CKjXVfvKgSOAgNALyc8+JnxQ+znBwVFKhZM+ur1vr11ncl53fh\nkBDddJMkHTmihQsVEKDBg2WzKTtbb7zh/jadm6t27fTgg5I0f77mzVObNpowQZLeekv//Kf7\no7SgQI8+arXs21fr12v8eDknK+jcuWo/6Rdf6GJcia3Rwc6cwRO4IM5olZen/Hzl56ugQO3b\nu7/azJ+vTz6xduXmKj9fEyfqnnusvQ8+qH//21r281NoqJKT3ZefPv3UugwUGKigINls6tPH\nHexCQhQZqZAQqy8tNNTdqSZp6FB17iz9LwA5I5RLaqomT5a/v9VLFxQk590YTlOnaurU077f\nBx+0PgZOqWdPd1/XL119tfsSyS/Vq6cq9yRV5O/v7nMCgAtjt+vwYesuiWPHVLu29eF04oTS\n0uRwWBe3jx7VggXub98nTqhpUw0aJEmffaaFC3X11XrsMUn69781Z44kFRRY9/U98IBGjJCk\ngQOVlqb/+z89/rgkde9e6bq0pM8/1w036NgxxcertNS92quXCgtVq5bVHR8Wpm3bFBysnTv1\nt7+pQQPddptq19b+/VqzxrrE4rwm4fq+7/xF0aCBtdqypfvrf3Cw/P3dl1uef15796pbN2v1\nX/9y978HBCgkRPHxF/ozr/nosTs77+uxcyaw/Hw1bqzata2Nq1Zp6VL3rtxcjRrlvpNmxAi9\n8Yb7DLVr6+9/d/d+zZmjr75SWJj1X0SE+vXTFVdYe529UBUvdQHA5cHh0N69ysuzYlB+vqKj\nlZAgSRkZ2rBBDRpYsWPTJi1Z4r7UX1ysfv3kvCd88mRt3qzbb9ftt0vS6NHWnX7O+0FsNs2e\nreuvV0GBYmNVUKAlS9Snj/LzFRXlGsAgSQEBOnhQYWFavly33abgYG3bprAwrV6t++6zcpWv\nr8LCdM01+tvfJOmjjzRvnpKS9OijkpSWpk8/tb6GOxv37WvdrLtmjbKy1LGjlY127lRWlvsL\ntZ+fWre2uujy8uRwVLrBwTz02OHXKiy0ermcdu/WypXucJaXp4EDdcMN1t5nn9XUqcrNdR/+\n4osaP95aTk/X119byaxePV11lfs2KUmTJ2vkSIWHKyJCYWHuO7Gc/vhH9z1kv0QvFICaqrhY\nx44pIsL6lHLe2FlaavVIHT+uAQNUt67Ky/XXvyo/XyNGqEkTnTypBx7Q/v3WHVoOh0pLlZam\n8HB9/bX69FGtWsrOVmSkli51D/lw6tZNK1dK0gcf6P/9P3XubAW7jRv1/vvue2gjI909UhER\niopy39P/m98oNtZ9NcLfX875MkNDtXixTpyQM0uEhemnn6xrl86Oq4AA67t8r146etRdUteu\nlW52regPf3APhpHUvbuqDFd1cY0wcTrDpQtuMPEs7wh2EeeT/I+5/q3UTLm52rzZHc4KCtSr\nl/sKY0qKUlKUl6djx5Sfr9JSPfOMnnvO2jt3rt58091zFhZW6cxDhqhdO6tHLTzcCnAujzyi\nRx45bVUVP1QAoBqVlVnPG3E4tG2bfHysoTtFRVq8WHa7dW/WiROKj9ddd0nSF1/oX/9Skyaa\nOFGS3nlHf/+7dSOu8xLC6NF6/nlJuvFGffWVHn1UycmS9MAD+vJL67bbkBAFBKh5c3XvrpIS\nbdyosjIVFEiSr6+aNlV0tNUjFRSkunWtO2k7d9aSJQoNte6z/e1v9fPPVmqscj/IX/6iv/zF\nvTp8uPsO4SoeeqjSqutSyi9VSVcxMWf6weLy5B3BbsqUKSkpKenp6ZIaN24c7tVfB6ZM0Ysv\nVgpn0dHuYNe+vYYMsbZHRioszD04UdKECda9o6fUtKmaNr20xQPA/zj7vZwhSdLu3dqxQ4WF\nysuzbqi/8041bqyyMo0cqZwcPfOMOnVSSYk6dlROjkpKrGa1amnfPjVooJUr1b27/PyUna36\n9bV1q0aOtOKUM0V17GgFu/JyBQa6v9u2bq3bb7c6w8LC5OurTp2sXXPnKi/P/QSPL7449XsJ\nCKj0PBk/Pz3zzGlbup4F5OS69wuoCbzmHrvS0tL+/fsvWbJk4cKFt9xyS3W+tPfdYwcAZ+OM\nZQUFKiqS3a7QUDVvLkl79ujjjxUVZY2PWr5cM2bo+HEVFSk/X3a77r5bzz4rSb/7nT79VH/6\nk1JSJGnAAC1frqAg1a6tiAjVrq2//lXduqm8XE8/LYdD999v3QDy4YcqKbHGRPn4qH599yMX\n8/JUq5b73mCgZuIeu4vAz89v9OjRS5Ys8XQhAFCD7N+vnBy1amVdBPzoI2VlyW5Xfr5z4gC9\n8IIiI7VnjwYNUkmJPvpIMTHavFnt21eaCKN5c+uBsmvXavZsXX21Fexq1VJkpJo0UVCQgoIU\nGWkNVZc0a5YOHnQPZK/4JOOKfHw0eXKlLWf4bu7V12OAmsBrgp2ka6+9Njg42Nc19wsAeLnC\nQvcNt8eOqXFj636Kjz/WV18pKcmaP+Lll/XBB9bjKo4d08mT+sc/rEkr2rTR4cN6803dd5/V\n0m5XUJDCwhQSotBQ6/kR9epp4EBFRFh33rZurbVr5eNjda0FBbkT1T33uJ9lJOm669yzNlTR\nqJEaGT/vJuBtvCnYRUdH2ys+Lx4AaqTVq3X0qHvwekCA9SyJr77SxImqXduaamvOHPdsZJJ8\nfHTffUpNlaRdu7Rrl3vWKOe0VSEhCgpSaKhCQ+W6/pORodJSdyyrODFVRaGhlW7k9/FRhw4X\n6d0CqEm8KdgBQHXKydH+/Wra1IpN8+Zpxw53XCso0LRpuuoqZWWpTRvl5Sk9Xddco7171bOn\nAgLc46OuukoOh2w2NWyo3r3dj0i9/Xa1bq3gYKtZxZt4qwyo/O1v9dvfnrpI1yx6ACCCHQCz\nOZ9DduyYwsOtWWq3b1damuLjrXmN3n1X777rjmvHjumZZ6xHA/Xpox079Le/WdMTffihcnKs\nEBYVpebNrUewxsRowQL5+loPG4uPt6bQ+6Wrr6401jI4WO3bX7q3DuByRLAD4GWOH9eRIzpy\nRIcOqbRUN90km027d+uVV1Raqn/8QwEB+vprDRqkvDy5bt/o3l1ffy1Jixbpn//UDTdYwS4s\nzOqTc3WwuR7Q+s03Kitzd4n95z+nrsdmO9MUdABQnQh2AGqKggIdPqxDh5Sfr86dFRKi0lKN\nH6+sLD35pFq3lt2uuDj37Cp+frriCm3ZopAQFRerqEgNGliPum3bVq++qtq13YktOto66okn\n9MQT7hf93e/0u9+dup6KD5sFAK9AsANwyeXm6vBhHTmiuDgrYL33npYvV+/euuMOSbrrLi1c\n6L6CGRCgDz9U374qL1duriIjrQebhYTok0/k46O6dVWvXqVHY7Rsqbffdq9GRFjjSQHgskKw\nA/ArlZdbl0Rr1bIeZrZtm95+W6Gh1gwpb7+tsWN15Ij7eWmjRmn6dEnas0d5eXI9vGj8eP3x\nj4qKUr16qlvXPYzA31+zZlV60SpTKgEAKiLYAaiqpERHjshut56pVlSk6dOVm6u//EV16igj\nQ/366eBBHTlitY+NVWamJO3dq+++U6tW1vbevTV1qurUUVSU6tZV3bruGQXGj6/0im3bWiMP\nAAAXgmAHXEbKynTggOrVsyb3/Ogjbd2q3/1O7dpJ0h136JtvdPiw8vKs9t9+q3btdPSoPv5Y\noaEqKpKkhg01dqzCwqys5vzPqV8/9evnfrkrrrCutAIAqgfBDjBNebn275ePjzUrwMcf61//\nUlaWMjP1888qLdXkyXrySUn697+Vna1Wraxgd9ttuvFGd1arX19RUZIUE6OvvnKfPzCw0mN1\nAQA1B8EO8FY5OcrIUHa2srO1d68aNtS4cZI0YYJeeklduliTEBQXq04dtW6tuDhdcYWio92T\ne86bV+mEjDYAAG9HsANqrtJS5eZak3seOaKXXtLhw0pOVmSkVq9Wt26SFBWlmBjFxrpn7XQO\nRHBNb3Drrbr1Vk9UDwCodgQ7wMNKSqz73gICJGnyZH37rbKzrSun5eX6/ns1a6aCAmVkKDra\nuj2uSxdlZKh+fQUFVT2hcy5RAMBliGAHVIeyMmVna98+7dtnLQwYoB49JKl1a+3apVdf1Zgx\nknT4sBo2VPv2uuIKxcUpLk5NmkhS48aaP999QpvN3ScHAIATwQ64aEpKVFZmTVewZo3ee08x\nMdY0o2PHKjlZkho0UEyMYmJUUmIdtXixysqs9CZZzQAA+BUIdsD5KS5WXp5139uBA/rrX7V3\nr9UJd+CA2rTRt99K0o8/6qef3HFt4kQ99JCuuMK63lqRqw0AABeIYAec2tGj2r5dmZnKztaR\nI3riCUVEaNMmJSWprEw//aT4eNntys7WFVeoUyfFxCguTgkJ1uGDB2vwYPfZnNOVAgBwSRHs\nAEtenoKC5O8vSddfr5UrJalhQ8XEqHFj68pp69ZasUKNGln3t111VdUnhgAA4EEEO1y+Cgrk\n62tNcnXbbVq4UOPHa/JkSZo0SbVrq02bqldOfX3VubMHSgUA4FwQ7HAZKS3VukY5lrsAACAA\nSURBVHXauFHp6UpP165dGjpUb70lSU88obFjdc01VkvneFUAALwLwQ4mczi0fr327tXAgfLx\n0fz5GjRIzZurQwf96U/q0EHt21stk5I8WigAABcDwQ5GKS7Wd99p1y7ddpsCA7VihXr21NVX\nq29fhYXprrv0+99b114BADAPwQ4mWLRIixcrPV3ffafiYl15pa6/XnFx6t5dx49XmpuBVAcA\nMJiPpwsAfo0vv9To0XrvPWt13jz9/LNuuUWLFunQIf34o+LirF2/nHELAABT0WOHmq68XLt2\nWSMeEhL04IOS9Mknysx0z4j6zjseLBAAgJqCYIeaa8sW/fnP+uYbFRSofn21b+9+1Mhrr3m0\nMgAAaiSCHWqQzEwNH64fftCaNWrUSKGh6tlTDz+sDh3cl1YBAMDpEOzgGZmZ1tXVjRu1bZs+\n/VStW6t2bXXrpgcfVMOGktS4sSZO9HCdAAB4EYIdqklOjgIDVaeOJN14oz7/XKGhuuYadeig\noUN19dWSFBVFkgMA4Ncj2OFSOXhQ5eVW39vQoXr7bQ0frlmzJOnvf5ekhAT5MCwbAICLh2CH\ni6asTF9+ac3WtXGjMjN100369FNJevZZPf64Wra0WrZo4cEyAQAwFsEO5+3ECQUGSlJ+vl5/\nXYcO6fnnFR6u1av1+98rMVEdOujpp9W+vRITrUOuvNKD9QIAcLkg2OHUDh1SZKT8/CTp9de1\ncaOys5WTo8xMFRZqxw4lJOjIEaWlqWFDlZdL0vXX68QJz1YNAMBljWB3+Sov188/a98+5eRY\n/+/fX9ddJ0mJidq2TVOm6C9/kaTt2yWpc2dFRys2VnFxSkiQpCZN9PnnnnsDAACgMoKd4UpK\nVFZmXTldt05z5yo2VmPGSNL//Z9efVWSIiIUE6PYWHXvbh01b57Ky630JmnmzOovHAAAnDeC\nnQlOnNDRo4qOlqSff9ZLL2nvXmVnKztbP/+sNm20aZMk/fBDpUlUJ0zQ/fcrLk61a1c9oWuU\nAwAA8CIEO69RWKiMDGVlWTe65ebqmWcUGanNm3XttXI4lJGhuDgdP66DBxUfr27dFB2tuDg1\nb26dYfBgDR7sPmFEhCIiPPJWAADAJUGwq1mOHlVYmDVkYcYMbdigIUPUq5ckJSQoK0v+/mrU\nSFdcofh4lZVJUuvW2rBBDRtaPXZXXaV33/XcGwAAAJ5DsKtuDod+/ln+/qpbV5IWL9a772rf\nPmVnKytLRUV68UWNHy9J332n0lIFBVkHrl0rHx81bCibrdIJfXx07bXV+x4AAECNRLCrbk89\npZdeUteuWrVKkvLz5eur7t0VE2ONYHBOriVpxoxKB8bEVHepAADAuxDsqtu4cRo8WE2aWKt3\n3qk77/RoQQAAwBQEu+oWHq7wcE8XAQAATMQc7AAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYA\nAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg\n2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAA\nGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAH\nAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAI\ngh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAivD3ZZ\nWVmrV68+cuSIpwsBAADwMK8JdoWFhS+//PItt9zyhz/8YdGiRZLsdvvAgQNjY2O7desWFRV1\n3XXX/fDDD54uEwAAwGP8PF3AOcnLy+vSpcuOHTucqx9//PGHH344Z86cBQsW/OY3v2natOnW\nrVtXrVrVqVOnnTt31q9f37PVAgAAeIR39Ni98MILO3bsGD9+/N69ezds2NC2bdvbbrttwYIF\nixYt+uKLL2bOnLly5crU1NTc3NznnnvO08UCAAB4hncEu08++aR9+/aTJ0+Oi4vr0KFDSkpK\nSUnJTTfddPPNN7vaDB8+/Nprr01LS/NgnQAAAB7kHcFu7969LVu2tNlsztVWrVpJSkhIqNjG\nZrO1aNFiz549HqgPAACgBvCOYBcXF7d9+3aHw+Fc3bZtm6Rdu3ZVafb9999feeWV1V0cAABA\nzeAdwa5fv34bN258+umn9+3bl56ePmLECF9f38WLF3/88ceuNm+99daGDRt69OjhwToBAAA8\nyObqBqvJcnNzO3Xq5HqaSXBw8BdffHHfffft2LHjhhtuuPLKK7dt27Zy5cqoqKgdO3ZERUVd\n3FdPSUkZOXJkQUFBSEjIxT0zAADwOsXFxQEBAatWreratauna6nKOx53EhkZuXHjxuTk5PXr\n1wcGBo4ZM6Zz585Lliy55557li5d6mzTs2fPN99886KnOgAAAG/hHcFOUmho6DPPPFNxS2xs\nbFpa2o8//njw4MEWLVpERkZ6qjYAAICawGuC3SnZbLamTZs2bdrU04UAAAB4nncMngAAAMBZ\neXePXUU5OTn9+vWTtGnTpnM/qqSkZO7cuUVFRWdos2LFigstDgAA4NIzJ9gVFxdv3rz5fI/a\nv3//5MmTS0pKztAmPz9fklcMHwYAAJczc4JdgwYNXCNkz11cXNzOnTvP3Mb5uBPXvBcAAAA1\nkznBLigoqE+fPp6uAgAAwGO8dfBEYWFhZmZmfn4+V0gBAACcvCnYLVu2bOjQoQkJCREREcHB\nwfHx8eHh4SEhIc2aNRszZsyWLVs8XSAAAIAnecelWIfDMWLEiNTUVEnh4eFNmzatU6dOaGho\nQUFBbm7unj17kpOTk5OThw0blpqa6uvr6+l6AQAAPMA7gt3UqVNTU1OTkpKmTJnStWtXP79K\nZZeVlaWnp0+YMGH27NkJCQnjxo3zVJ0AAAAe5B2XYv/zn//ExMSkpaV17969SqqT5Ovr26lT\np8WLF7dr127WrFkeqRAAAMDjvCPYbd++vUuXLoGBgWdo4+fn16NHj8zMzGqrCgAAoEbxjmCX\nmJi4du3aEydOnKFNWVnZihUrYmNjq60qAACAGsU7gt2gQYOysrK6d++elpZWWlpaZW9ZWdn6\n9ev79u37zTffDB8+3CMVAgAAeJx3DJ4YNWrU1q1bZ86c2aNHj/Dw8GbNmjlHxdrt9tzc3N27\ndx89elTSkCFDxo4d6+liAQAAPMM7gp3NZpsxY8bDDz88bdq0pUuX7ty50263O3cFBQVFR0cP\nGTJk2LBhbdu29WydAAAAHuQdwc6pRYsW06dPdy7b7fYjR45ERkaGhoYyiysAAIC8K9hVFBIS\nEhIS4ukqAAAAahDvGDwBAACAsyLYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcA\nAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiC\nHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACA\nIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYA\nAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg\n2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAA\nGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAH\nAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAI\ngh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAA\ngCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2\nAAAAhiDYAQAAGMLP0wVckOLi4h9++KGkpKRFixYBAQGeLgcAAMCTvKbH7ueff37ooYfuvfde\n52phYeGTTz4ZGhqamJh4zTXXBAcHDx069ODBg54tEgAAwIO8o8duz549nTt3PnTo0O9//3tJ\nDodj6NCh8+fPb9iwYc+ePUNCQtavX//222+vWLFi8+bNoaGhnq4XAADAA7yjx27cuHGHDh2a\nNWvWwoULJS1fvnz+/Pn9+vXbvXv3u+++m5qaumnTpldfffWnn3569tlnPV0sAACAZ3hHsEtL\nS+vTp8/w4cN9fHwkrV69WtKUKVOCg4OdDWw222OPPda+ffsvvvjCk4UCAAB4jncEu8LCwpCQ\nENdqSUmJpOjo6IptbDZb06ZN9+7dW93FAQAA1AzeEew6duy4fPny/fv3O1c7deokaeXKlRXb\nFBUVrV69+pprrvFAfQAAADWAdwS7p556Ki8v7/rrr1+0aFFxcfFvf/vbfv36jRo1Kj093dng\nwIED99xzz759+2666SbPlgoAAOAp3jEqtnfv3m+99daoUaP+8Ic/hIeHN23aNCQkJDMzMykp\nqUmTJkFBQd9//31paemAAQMee+wxTxcLAADgGd7RYydp2LBhOTk5r7/+ekJCQkZGxtdff+3c\nvnfv3qNHj95xxx0rV6784IMP/P39PVsnAACAp3hHj51TZGTkn//85z//+c+SSkpKDh486Ofn\nFxUV5evr6+nSAAAAPM+bgl1FtWrViomJ8XQVAAAANYjXXIoFAADAmXlrj90v5eTk9OvXT9Km\nTZvO/agTJ06kpKScOHHiDG3WrVt3ocUBAABceuYEu+Li4s2bN5/vUUeOHJk7d67zicenc+jQ\nIUkOh+PXFwcAAHDpmRPsGjRosHTp0vM9KiYmZs2aNWduk5KSMnLkSJvN9mtLAwAAqA7mBLug\noKA+ffp4ugoAAACP8dbBE4WFhZmZmfn5+VwhBQAAcPKmYLds2bKhQ4cmJCREREQEBwfHx8eH\nh4eHhIQ0a9ZszJgxW7Zs8XSBAAAAnuQdl2IdDseIESNSU1MlOacUq1OnTmhoaEFBQW5u7p49\ne5KTk5OTk4cNG5aamsrzigEAwOXJO4Ld1KlTU1NTk5KSpkyZ0rVrVz+/SmWXlZWlp6dPmDBh\n9uzZCQkJ48aN81SdAAAAHuQdl2L/85//xMTEpKWlde/evUqqk+Tr69upU6fFixe3a9du1qxZ\nHqkQAADA47wj2G3fvr1Lly6BgYFnaOPn59ejR4/MzMxqqwoAAKBG8Y5gl5iYuHbt2jPPD1FW\nVrZixYrY2NhqqwoAAKBG8Y5gN2jQoKysrO7du6elpZWWllbZW1ZWtn79+r59+37zzTfDhw/3\nSIUAAAAe5x2DJ0aNGrV169aZM2f26NEjPDy8WbNmzlGxdrs9Nzd39+7dR48elTRkyJCxY8d6\nulgAAADP8I5gZ7PZZsyY8fDDD0+bNm3p0qU7d+602+3OXUFBQdHR0UOGDBk2bFjbtm09WycA\nAIAHeUewc2rRosX06dOdy3a7/ciRI5GRkaGhocziCgAAIO8KdhWFhISEhIR4ugoAAIAaxDsG\nTwAAAOCsCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAA\ngCEIdgAAAIYg2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2\nAAAAhiDYAQAAGIJgBwAAYIizB7tp06bNnTu3GkoBAADAhfA7a4snnngiKirqrrvuqoZqAAAA\n8Kudvcdu+PDhe/fuXbduXTVUAwAAgF/t7D12ycnJderUueGGGyZNmtStW7f4+Hg/v0pHRURE\nXLLyAAAAcK7OHuzq168v6fjx44888sgpGzgcjotcFAAAAM7f2YNd//79q6EOAAAAXKCzB7s5\nc+Zc+jIAAABwoc7vOXZ5eXnbtm07evToJaoGAAAAv9o5Bbv8/Pxnn322QYMGERERiYmJdevW\njYqKevrpp/Pz8y91fQAAADhHZ78UW1hY2KVLl+3btzdo0GDAgAGNGjU6cODAqlWrJk2atHDh\nwg0bNgQFBVVDoQAAADizs/fYTZw4cfv27WPHjs3IyFiwYMH06dPnz5//008/jRkzZtu2bc89\n91w1VAkAAICzOnuwW7p0aevWrV9++eXAwEDXxsDAwFdeeaVly5ZLly69lOUBAADgXJ092H3/\n/fft2rWz2WxVj/Txufbaa3ft2nVpCgMAAMD5OXuwa9KkyY4dO375FGKHw7Fjx44rr7zy0hQG\nAACA83P2YNejR4/09PRXXnmlvLzctbG8vHzKlCkbN27s3r37pSwPAAAA5+rso2InT578ySef\njBs37p///Gfv3r0bNGhw4MCB5cuXb9u2rXHjxpMnT66GKgEAAHBWZw92ERERa9asmThx4ltv\nvbV9+3brMD+/ESNGPPvss+Hh4Ze4QgAAAJyTswc7SY0aNUpJSZk6dWpGRkZOTk5MTEx8fLy/\nv/+lLg4AAADn7uzBbtq0aVFRUXfddZe/v3/z5s2bN29eDWUBAADgfJ092D3xxBPOYFcN1QAA\nAOBXO/uo2OHDh+/du3fdunXVUA0AAAB+tbP32CUnJ9epU+eGG26YNGlSt27d4uPj/fwqHRUR\nEXHJygMAAMC5Onuwq1+/vqTjx48/8sgjp2zwy2cXAwAAoPqdPdj179+/GuoAAADABTp7sJsz\nZ86lLwMAAAAX6uyDJ6ZNmzZ37txqKAUAAAAXgsedAAAAGILHnQAAABiCx50AAAAYgsedAAAA\nGILHnQAAABiCx50AAAAY4uyDJ87gu+++++KLLy5WKQAAALgQpw52DRs2vP/++ytuSUlJGTly\nZJVmzz///A033HCpSgMAAMD5OHWwO3DgwLFjxypuWbp0aUpKSrWUBAAAgF/jgi7FAgAAoOYg\n2AEAABiCYAcAAGAIgh0AAIAhCHYAAACGINgBAAAY4rQzT6xdu/auu+6quCqp4hbXRgAAANQE\npw122dnZ7733XpWNv9wCAACAGuLUwW7Dhg3VXAcAAAAu0KmDXYcOHaq5DgAAAFwgBk8AAAAY\ngmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcA\nAGAIgh0AAIAhCHYAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiC\nHQAAgCEIdgAAAIYg2AEAABjChGA3c+bMr776ytNVAAAAeJgJwe7BBx985513PF0FAACAh/l5\nuoBz8vHHH5+5QWZmpqtN//79L31FAAAANY53BLubb775zA2WLl26dOlS57LD4bj0FQEAANQ4\n3hHs3nvvvYceeujw4cOJiYn33nuvzWaruHfs2LFJSUl33HGHp8oDAACoCbwj2N1xxx09e/Yc\nPXr0+++/v3Tp0tTU1Pj4eNfesWPHtmnT5vHHH/dghQAAAB7nNYMn6tevP2/evPfff3/Tpk2J\niYkzZ84sLy/3dFEAAAA1iNcEO6eBAwdu3769f//+Dz74YJ8+ffbs2ePpigAAAGoKLwt2kqKi\not59990FCxZs3769devWU6dO9XRFAAAANYL3BTunAQMGbNu2bcCAAQ8//LCnawEAAKgRvGPw\nxCnVrVv3nXfeGTJkyI4dO1q1auXpcgAAADzMi4Od04033njjjTd6ugoAAADP89ZLsQAAAKjC\n63vsXHJycvr16ydp06ZN536U3W5/5ZVXTp48eYY253VCAAAATzEn2BUXF2/evPl8jzp+/Hh6\nenpxcfEZ2mRnZ4uZygAAQI1nTrBr0KCBa7rY8zrqk08+OXOblJSUkSNHVpnHDAAAoKYxJ9gF\nBQX16dPH01UAAAB4jLcOnigsLMzMzMzPz+cKKQAAgJM3Bbtly5YNHTo0ISEhIiIiODg4Pj4+\nPDw8JCSkWbNmY8aM2bJli6cLBAAA8CTvuBTrcDhGjBiRmpoqKTw8vGnTpnXq1AkNDS0oKMjN\nzd2zZ09ycnJycvKwYcNSU1N9fX09XS8AAIAHeEewmzp1ampqalJS0pQpU7p27ernV6nssrKy\n9PT0CRMmzJ49OyEhYdy4cZ6qEwAAwIO841Lsf/7zn5iYmLS0tO7du1dJdZJ8fX07deq0ePHi\ndu3azZo1yyMVAgAAeJx3BLvt27d36dIlMDDwDG38/Px69OiRmZlZbVUBAADUKN4R7BITE9eu\nXXvixIkztCkrK1uxYkVsbGy1VQUAAFCjeEewGzRoUFZWVvfu3dPS0kpLS6vsLSsrW79+fd++\nfb/55pvhw4d7pEIAAACP847BE6NGjdq6devMmTN79OgRHh7erFkz56hYu92em5u7e/fuo0eP\nShoyZMjYsWM9XSwAAIBneEews9lsM2bMePjhh6dNm7Z06dKdO3fa7XbnrqCgoOjo6CFDhgwb\nNqxt27aerRMAAMCDvCPYObVo0WL69OnOZbvdfuTIkcjIyNDQUGZxBQAAkHcFu4pCQkJCQkI8\nXQUAAEAN4h2DJwAAAHBWBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGw\nAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAw\nBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4A\nAMAQBDsAAABD+Hm6gMvOjBkz3n777Xr16kVFRTVo0KB+/fpRUVH16tVr0KCBc2NgYKCnawQA\nAF6JYFfdrr/++ry8vIMHDx4+fHjz5s0HDhxwLp84ccLZIDQ01BnynDmvYcOGzoV69eo1bNgw\nKioqKioqICDAs+8CAADUQAS76paYmJiYmPjL7UVFRbm5ubm5ufv378/JyXEtf/fdd86F7Ozs\nkydPOhsHBgZGRkZGR0c3atQoMjLyl8tXXHGFv79/9b4zAADgYQS7miIoKCgoKCg6OrpVq1an\na+MMf1WSX05Ozp49ezZu3Lh///6srKzi4mJnY8IfAACXG4KdN3GFv/bt25+uzcUNf7GxsbVq\n1aqu9wcAAC4Iwc40FyX87du3r6SkxNk4MDDwDMmvUaNG9evX9/PjLxIAAJ7H7+PL0bmEv0OH\nDh0+fPjw4cOHDh36+eefnQuHDh1at26dc9ehQ4fKy8sl2Wy2evXqhYSESIqIiLDZbJJ8fHzC\nw8Odp6pVq5Zzr6TAwMCgoCDncnBwsOtacHh4uI+Pj/NsERERzo1+fn6hoaHO5YCAgNq1azuX\na9eu7Ro+Ehoa6oqVp3x1AAAuHwQ7nJpzWO4ZGjgcDlfCO3To0NGjR53bc3NznQtlZWX5+fnO\n5eLi4uPHjzuXi4qKnEOAc3NzCwoKSktLnduPHTvmcDgklZeX5+XlOTeWlJTY7Xbn8okTJ4qK\nis73jfj7+wcHBzuXg4KCXE+TCQkJcV1lPmuslBQZGelarhhJK56/4uFVXqJiMK3SrGI2rRh8\nAQA4XwQ7/Eo2m61+/fr169f3yKsfP37cdadgfn5+WVmZc9kVK0tLSwsKCpzLJ0+eLCwsdC4X\nFha6BhefNVZWPElubq6rTZXzVEyfFU9Vpc7zVaXfMSwszNfX17lcMf/5+vqGhYWdslnFIFsl\np1anitm3mlXsLT6Dc8zTFZP6Gbh6js+gyp/aebmQY01VsRcfuMwR7OCVgoODXVmhYl9aTVax\n21IVMqgku93uuqmxYjOHw3Hs2DFXs4pJtGJarRhGqzSr2M154MCBis28S5UfxSU6vEpGPx1X\nrzNwLs7xC8a5qPht7QJVvFbgcef4renCDR06dPDgwdXwQh5UU/5QAeP5+/tXfL6Mt+RR/GoV\nE/YZVIz41S8vL895s+zl6UL61C9ElS9jF6LKN8YL4UXfWCp+GT4vTZo0uejF1DQEOwC4JM7x\n2jcRH8BF5OPpAgAAAHBxEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATB\nDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADA\nEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsA\nAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ\n7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAA\nDEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQXhbs7Hb7li1bjh07dsq9\n+/fvz8jIqNaCAAAAagyvCXa7du3q2bNnWFhYmzZt6tSpM3DgwOzs7CptBgwY0KRJE4+UBwAA\n4HF+ni7gnGRmZnbo0MFut3ft2jUuLm758uUffPDBunXrVq1aFRcX5+nqAAAAagTv6LEbP368\n3W5/++23V61a9e677+bk5Dz66KNZWVmDBw8uLy/3dHUAAAA1gncEuzVr1lx33XVDhgxxrvr4\n+Lz66qsDBw5csWLFnDlzPFoaAABATeEdwS47O7vKJVcfH5+pU6eGhoaOHz/+dGMpAAAALive\nEexiYmLS0tJKS0srbmzYsOFLL7108ODBoUOHckEWAADAO4LdrbfempWVdeedd+bk5FTcPmrU\nqJtuumnRokWPP/748ePHPVUeAABATeAdwe7pp59u1arVggULYmJioqOjbp4k8QAAIABJREFU\nv//+e+d2m8329ttvd+7cOTk5OTY2dufOnZ6tEwDw/9u784Co6n//459h2GUcFgUFURT4gktp\nJqVokomFqWXdKG9GRH5z66amKWZ2s2sWlUsWJorlkpXpTfO6Vai5ZKKCoiK4ACIiyjZsI+ss\nvz+Ov/mSmnwr5TDH5+MvXmcOw3s09dXnbABkZB3FTqvVHjx4MC4urnfv3nV1ddXV1ZaX2rRp\ns3v37rffftvR0bGiokLGIQEAAOSlMpvNcs9wexiNxry8vNzc3EGDBt3ed162bNn48eOrqqpc\nXFxu7zsDAACrU19f7+DgcODAgdDQULlnuZ513KD436FWqzt37syTJwAAwF3LOg7FAgAAoEnK\nWbErKCh4/PHHhRBpaWn//neVl5e//fbb9fX1t9gnMzPz7w4HAABw5ymn2NXX1x8/fvzPfpfR\naKyoqKitrb3FPo2v1QAAAGixlFPsvLy8kpKS/ux3eXh4rFmz5tb7LFu2LDU19a/OBQAA0EyU\nU+ycnJzCw8PlngIAAEA21nrxRHV1dV5eXmVlpWJu1wIAAPA3WVOx2717d3R0dFBQkKura6tW\nrTp16qTVal1cXAIDA6dOnXry5Em5BwQAAJCTdRyKNZvN48aNS0xMFEJotdqAgAB3d3eNRlNV\nVVVWVpaTk7No0aJFixbFxMQkJiaq1Wq55wUAAJCBdRS7zz77LDExMSQkZP78+aGhoba2vxvb\naDSmpKTMnj175cqVQUFBsbGxcs0JAAAgI+s4FPvNN9/4+Pjs27dv4MCB17U6IYRarX7wwQd3\n7NjRq1evFStWyDIhAACA7Kyj2GVkZPTr18/R0fEW+9ja2oaFheXl5TXbVAAAAC2KdRS7Hj16\nJCcn3/o2wkajcf/+/b6+vs02FQAAQItiHcVu9OjR+fn5AwcO3Ldvn8FguO5Vo9F4+PDhiIiI\no0ePjhkzRpYJAQAAZGcdF09MnDgxPT09ISEhLCxMq9UGBgZKV8Xq9fqysrKsrCydTieEiIqK\nmj59utzDAgAAyMM6ip1KpVq6dOmkSZPi4+OTkpJOnz6t1+ull5ycnLy9vaOiomJiYnr27Cnv\nnAAAADKyjmIn6dq165IlS6Sv9Xp9aWmpm5ubRqNRqVTyDgYAANASWFOxa8zFxcXFxUXuKQAA\nAFoQ67h4AgAAAE2i2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAA\noBAUOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAU\nOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAUOwAA\nAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg\n2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEA\nACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgE\nxQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4A\nAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAh\nKHYAAAAKQbEDAABQCIodAACAQlDsmtsHVz7okt5lWv40KV41XS01lMo7EgAAUAaKXXN7wf2F\nN9u9GeoSKsVp+dPanGjz3PnnpHii5sTPlT/rDDr5BgQAANbKVu4B7jq+9r6vtHnFEhf7Lh7X\nZpxGrZHiqtJVS4qXRLpFrvVbK4RYo1uTX58/QjviHqd75BkXAABYD4qdzBxUDvc532eJCzss\n/MjnI0ssbijeXLHZRthIxW5C3oRCQ+H4NuMfbf2oDLMCAICWjUOxLY6tytZWda1wT/Oadijo\n0Mx2M6UYoY3wsfOpN9dL8d7Me31P+n5b9q0Us+uyrzRcaf6BAQBAC8GKnTV5Uvvkk9onLfG7\nzt8dqT7Sx7mPFB8++3B+Q/4Xnb542eNlIcQ3um80ak24JtzJxkmecQEAQPOi2Fmxro5duzp2\ntcTsHtlZdVmd7TsLIYxmY0JJQlpN2ppOa0a6jqw11U69NNXT1vPVtq+2tW0r38gAAOAO4lCs\nctir7Ls5dpPW59Qq9b5/7KvsWTnSdaQQwkZlYyts9+v3X6y/KIQoMhTdl3nfiOwRefV50vfW\nmmplnBwAANwWrNjdFexV9p/6fmqJHmqP1zxfy6rLclA5CCFO157ultGtnV27A0EHOtt3rjfX\nJ19N7uLQpYNdB/lGBgAAfxrF7m6kVqml8/AkwY7BR7sevVB/QWpy6TXpj2U9Vm+qv3TPpXZ2\n7QoaCr7RfdPVsesw7TD5RgYAAE2j2EEIIXo59erl1Ev6urdz76qeVaWGUi87LyFEsaH4f8v/\nt8RQEqYJc7FxOXj14MLChb2ce73V7i1ZRwYAANfjHDvchK3KVmp1QoieTj2Tg5Kzume52LgI\nITxtPb3svM7WnjUJkxDia93XndI7PX/+eWnngoaCH8p/OFV7Sq7JAQC4m7Fihz/H38E/3jfe\nEiNaR9Sb66WSJ4RIqkx6Pf/1trZtz3Q/I4RYUbIirjDuAecHvun8jRAiozZjT9WeXs69QluF\nyjI8AADKRrHD3+Jh6xHjEWOJ0R7R0R7RljhUO1StUktLfUKIo9VHlxQv8bX3/THgRyHE+1fe\nf+/Ke4M1g7f4bxFC7NPv21m1s49znye0TwghDGZDjanG8rA1AADQJIod7iAfO5/Gte8F9xde\ncH/BEse3Gd/TqaebrZsUiwxFv+p/vdxwWSp2swpmfVz48SOaR3YF7hJCfF/+/Y+VPz7s8vBo\n99FCiDJjWWFDYSf7Ttx+GQAAC4odZONu6974SttnXJ95xvUZS5zTfs5/uv2ni/raap+jyrHG\nVJNdly3F/y747/ji+CGth/wc8LMQYknxkp8qf3qs9WOvtn1VCJFbn5tXnxfsGOxp69l8nwcA\nALlZa7Grrq4uKSlxdXXVaDQqlUrucXD7Ods43+d8nyUO0w5r3AIX+y6e1W6WZbmum2O37Lps\nRxtHKS4qWhRfFP+E6xObumwSQrxV8NbPlT9HukXO8JohhDhSfSS7Lrtvq75+9n7N9nEAAGgG\n1lTsdu/evXr16uTk5MLCwoqKCmmjs7Ozt7f3iBEjYmJi7rnnHnknRLOxETbt7dpb4iDNoEGa\nQZa4uMPihT4LzcIsxadcn3JXu1sev7a+bP1a3dontU8mdEwQQsRciEmqTPpnm3/OaT9HCLGt\nYtu5unODNIN6OvUUQlSbquvMdVq11oZLyAEALZ51FDuz2Txu3LjExEQhhFarDQgIcHd312g0\nVVVVZWVlOTk5ixYtWrRoUUxMTGJiolqtlnteyE+t+td/Bn2c+/Rx7mOJH/t8/LHPx5YY6xU7\nWDM42DFYiinVKRvLN1YYK6Ri93TO0z9V/jSh7YTPfT8XQrx28bWDVw9Ge0S/1vY1IcTq0tWZ\ntZmPax8f6DJQCJFZm3mp4VJPp548kBcAIAvrKHafffZZYmJiSEjI/PnzQ0NDbW1/N7bRaExJ\nSZk9e/bKlSuDgoJiY2PlmhPWKNgx2NLqhBDvtH/nnfbvWOJ3nb87X3++o11HKT7j9kxH+469\nnXpLsdBQeKzmWGeHzlKxe6vgrR/Kf3jd8/UFHRYIIcLPhe+q2jWr3ax53vOEELGXYo/VHHvJ\n/aXn3Z8XQmws35hVlxXROuJep3uFEBfrLxYbioMdg51tnJvpkwMAFEdlNpvlnqFpffv2zc/P\nz8rKcnR0/KN9DAZDSEiIXq8/d+7c7f3py5YtGz9+fFVVlYuLy+19ZyjbpYZLmbWZ3Ry7edt5\nCyE2lW86dPXQkNZDBmsGCyGmX5r+S9UvL3m89F9t/0sIEXY2bJ9+3xteb0iriY9nPX64+vBM\nr5lveL0hhJhzec6Z2jNR7lGPax8XQvxc+fPFhouPaB7pbN9ZCKEz6PQmvY+dT+N1SgDAHVJf\nX+/g4HDgwIHQ0BZ3W1brWLHLyMh47LHHbtHqhBC2trZhYWFLly5ttqmAW/Ox8/Gx87HEp1yf\nesr1KUtsfDhYCLHnH3vKjeUam2v37fvQ58PTtad7O19bGmxv1/5yw2XLWYNrdWsPXD1QZaya\n4jlFCPFo1qOp1amz282e6z1XCPF0ztMZtRnTvaaP8RgjhJhfOP9C/YVRbqP6u/QXQhzQHyho\nKOjn0k96NPDp2tOlhtIeTj20aq0QQmfQlRvLfe197VR2d+rXBQBwx1jH+eA9evRITk6ura29\nxT5Go3H//v2+vr7NNhVwG6mEyk3tZqu69v9a9zjdE+kW6e/gL8VxbcYt67jMcl3wGr812d2z\npVYnhPgl8Jfs7tmz2s2S4qS2k6Z6Tu3Xqp8Ua821VxquFBmKpBhXGDfh4oSNZRul+ELuCwPO\nDogvvvY0kYfPPex/yv+DKx9IsVdmL9VR1YeFH0px8LnBXie8Pi/+XIpRuVF9Tvf5Wve1FN+4\n9Maz55/dVrFNigsKF4zPG39Af0CK68rWfVj4YXpNuhR3Vu38ruy7i/UXpXiq9tTOqp3lxnIp\nlhhKcupy6s31Uqw311u+BgDcgnUUu9GjR+fn5w8cOHDfvn0Gg+G6V41G4+HDhyMiIo4ePTpm\nzBhZJgRkpFFrujh0sdz85WHNw2PbjO3m2E2Ks9vN3tBlg2WxcIv/lpJ7SyZ5TpJiSnCKubf5\nrXZvSXHfP/Zld8+ObXftRNXN/pv3/mPv2DZjpTjPe168b/xw7XApPu36dKRbpHSOoBAi0CGw\ng10HaeVPCFFuLNcZdWXGMinurNy5oWzD8ZrjUlxctHjmpZlbK7ZK8cXcF4ecG7K8ZLkUw86G\n+Z/y/7jw2qLm/afvdzjmYOmXA88OdD/uvrT42vL8qPOjGvfLKflTInMit1dsl+L8wvlT8qck\nX02W4ndl331U+NGZ2jNSPKA/sKFsQ2FDoRRz63NTq1OrTdVSrDHVlBnLLAulANDyWceh2IkT\nJ6anpyckJISFhWm12sDAQOmqWL1eX1ZWlpWVpdPphBBRUVHTp0+Xe1jAirmqXV3VrpbYyb5T\nJ/tOlti3Vd++rfpaYuMjy0KIcW3GNY7ScWGLFZ1WNI7Sc+QsUoNTG8cjwUeuNFzxtb+2AL/N\nf1uxoTjAIUCKH/t8nFefF+py7dSWKPeo07WnLXc9DHYMtlPZudpe+xQ6o66goaDEUCLFrRVb\nM2szve28gxyDhBBvFbx1oubEXO+50q2th2UNy6jNiPOJi/WKFULcd/q+M7Vn/hUz7ztff36e\n9zxp5xHZI3Lqct5s96b0PJUJeROy67OntJ0inQf5wZUPcutzX/J4SVo6Xatbe6nh0hPaJ6Tb\n7uzV7y1qKOrv0l86/zKzNvOq6WqwY7D0/D2dQVdrrm1n146b7AD408zWIyMjY+LEiYGBgY0v\nYnBycvL39588eXJaWtod+rkJCQlCiKqqqjv0/gBaiAZTg86gM5lNUixuKE6rTrtqvCrFE9Un\ndlTsKGwolOL2iu3LipedrT0rxWXFy2LzYw/pD0nxrUtvReZE/lTxkxRjcmPuz7x/nW6dFIec\nG+J53HN58XIpdj3VVaSK+VfmSzEgPUCkio+ufCTFoFNBIlV8UviJFEMyQ9zS3BKKEyxv1SW9\ny+rS1VJ8NufZ+zPvX69bL8WxF8aGnw3fWr5VirH5sc/mPLu7crcU467EjbswLlmfbPkI7xa8\ne7L6pBQ3lG1YVrwsqzZLinuq9qzXrb9Uf0mKadVpSZVJOoNOijl1OSlXUyy/VsUNxdm12Q2m\nBinWGGssewIKUFdXJ4Q4cOCA3IPchDUVu8aqqqpyc3MrKipMJtOd/lkUOwB3mqUDmc3mkoaS\n7NrsWlOtFHPqcvZV7Ss3lEsx9WqqdPhYij9V/LSseFluXa4U1+nWxV2Jy6zJlGJCcUJsfmzq\n1VQpfnD5g7EXxu6v2i/FGfkzInMif6z4UYr/vPDP8LPhm8o2SfE/sv/j/sz7v9F9I8XBZwf7\nnPD5ouQLKd6feb8qVRVfFC/FwPRAkSoWFC6QYpf0Lo2rqn+6f+NXg08Fi1Rh+d4+mX3c0twS\nixMtP6hxVR2ZPbJxJx6VM6r/mf6WIcdeGDvk3JDtFdulOD1/emROpKW5vnf5vbEXxv6m/02K\nnxZ9Gpsfe/TqUSmuKlkVdyXuVM0pKX6n+25p8VJLkd1esX158fKL9Rel+GvVr41/2Y9XH0+q\nTLL8plzXa4sairJrs+tMdVLUGXQX6y8aTAYp1ppqywxlZlgzip11o9gBwK3Vmeoar8npDLrG\nK3YX6i407j3pNelJlUmWcnNQf3C9br2lM/1S+cvy4uV5dXlS3Fy+eVnxsnO156T4VelXcVfi\n0mvSpbikaMmNzfWA/to/tzMvzWzc817Ne3XIuSHbyrdJMSY35uGzD28s2yhFqch+q/tWitJS\n6KqSVVJ84PQDjduntMi6pGiJFKXmuqhwkRT9TvqJVLGwcOFNY+eTnRvv/I/0fzR+q96Zvd3S\n3Cyruf3P9G88xqPnHvVP97e07Weyn7k/8/7vy763fKLGC7STL06OzIn8ueJnKcbmxz6X89ze\nqr1SnHd53tgLYy3rtVLrPVZ9TIpS682oyZDi16Vff3jlQ8v6tPSbcr7uvBSvK8H7qvat162/\nUn9Figf1B3+s+LGkoUSK13XirNqslKspVcZr/8Lm1uWmXE2pNlZL8bqKXNpQmlOXY6nI1cbq\nxkvsdaY6y/vcaRQ760axAwDcQp2prvEiXLWxurih2NI/pAPTlnYidRdLzT1Vc6px0Tl69eim\nsk1FDUVS3Fu1d71ufX59vhS3lm9dVrwspy5Hit/ovom7Ene65rQUlxcvn3lp5vHq41JcXLg4\nNj/28NXDUpx7eW7j9VppdXNX5S4pjrswLvxsuGXtMzo3+qEzD/1Q/oMUpda7oWyDFCPORTTu\nl9Ii65rSNVJ88PSDbmluX5Z8KUWpqi4rXiZF6dSCz4s+l6LUiT8t+lSKUgm2nHjQ6WSnxiX4\nuijtbIlSY7a8lbSKbGnMPTJ6NB7jb2rJxc46blD87ygoKHj88ceFEGlpaf/+dxUXF0+ZMqWh\noeEW++Tk5KSmplZWVmo0mr87JQAA+AO1ptoac42r2lUlVEKIalN1kaHI185Xuvt6qaG0wlhh\nudGm9MCebo7dHG0chRDn68/rDLpgx+BWNq2EEKdqT11uuBziHCJdqn+85nixofg+p/s8bD3+\n/pzcoLg51NfXHz9+/M9+l4ODQ9u2bWtqam6xj5eXl7TnXx8OAAA0xdHG0VH862EEzjbOfvZ+\nluhh69G4lvna+1qunRdCdLbvLD2MR9LdsXt3x+6WKD3++26gnGLn5eWVlJT0Z7+rdevWn3zy\nya33+e2337Zv3/5X5wIAAGgmyil2Tk5O4eHhck8BAAAgG2u9+2V1dXVeXl5lZaVizhEEAAD4\nm6yp2O3evTs6OjooKMjV1bVVq1adOnXSarUuLi6BgYFTp049efKk3AMCAADIyToOxZrN5nHj\nxiUmJgohtFptQECA9EixqqqqsrKynJycRYsWLVq0KCYmJjExUa1Wyz0vAACADKyj2H322WeJ\niYkhISHz588PDQ21tf3d2EajMSUlZfbs2StXrgwKCoqNjZVrTgAAABlZx33s+vbtm5+fn5WV\n5ejo+Ef7GAyGkJAQvV5/7ty52/vTf/vtt/79+9fV1dnb29/edwYAAFanJd/HzjrOscvIyOjX\nr98tWp0QwtbWNiwsLC8vr9mmAgAAaFGso9j16NEjOTm5trb2FvsYjcb9+/f7+vreYh8AAAAF\ns45iN3r06Pz8/IEDB+7bt89gMFz3qtFoPHz4cERExNGjR8eMGSPLhAAAALKzjosnJk6cmJ6e\nnpCQEBYWptVqAwMDpati9Xp9WVlZVlaWTqcTQkRFRU2fPl3uYQEAAORhHcVOpVItXbp00qRJ\n8fHxSUlJp0+f1uv10ktOTk7e3t5RUVExMTE9e94tT4IDAAC4kXUUO0nXrl2XLFkifa3X60tL\nS93c3DQajUqlkncwAACAlsCail1jLi4uLi4uck8BAADQgljHxRMAAABoEsUOAABAISh2AAAA\nCkGxAwAAUAiKHQAAgEJQ7AAAABTCWm930pzs7e2FEA4ODnIPAgAAWgqpHrQ0KrPZLPcMVuD4\n8eM3PqMWFp999llKSsrMmTPlHgRNSEtLi4+PX7FihdyDoAlXr14dP378+++/7+vrK/csaMKM\nGTOGDh06aNAguQdBExITE52dnd97773b8m62trYt83lXFDvcBm+//XZycnJSUpLcg6AJ27Zt\ne+655yxP5EOLpdPpPDw8jh8/fu+998o9C5oQHBz8+uuvjxs3Tu5B0ISYmBghxMqVK+Ue5M7i\nHDsAAACFoNgBAAAoBMUOAABAISh2AAAACkGxAwAAUAiKHQAAgEJQ7AAAABSCYgcAAKAQFDsA\nAACF4FmxuA3s7Oxa5iPzcB17e3t+p6yCnZ2dSqXiN8sq8MfKWtwlv008Ugy3wdWrV/V6vZeX\nl9yDoAkmkykvL8/Pz0/uQdC0nJycLl26yD0Fmpafn+/p6XmXlAarVlZWJoRwc3OTe5A7i2IH\nAACgEJxjBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDsAAAA\nFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIod/pbKysoZM2YEBAQ4\nOjr6+/tHRUVdvHhR7qHQhA0bNqhUqq1bt8o9CG5uz549Q4YMad26dfv27UeNGnX+/Hm5J8JN\nlJWVTZs2rVu3bs7Ozt27d58xY0ZFRYXcQ+F3VqxY4erqeuN2s9n8+eef9+/fv3Xr1qGhofHx\n8WazufnHu0NUSvowaGZVVVWhoaHp6ekdOnR46KGHLly48Ntvv7m4uKSkpAQFBck9HW6uuLi4\nW7duJSUlW7ZsGT58uNzj4HqrVq16+eWXW7du/fDDD+v1+l27dnl6ep44ccLLy0vu0fAvVVVV\nffr0OXv2bO/evbt163bq1Kljx45169btyJEjzs7Ock8HIYQwGAyhoaFnz54tLy+/7qUJEyYk\nJCT4+Pj07dv34MGDBQUFr7zyyvLly2WZ8/YzA39VXFycEOKpp55qaGiQtqxevVoIERYWJutc\nuJVnn31W+rO/ZcsWuWfB9XQ6naOjY5cuXQoKCqQtiYmJQohXX31V3sFwnf/5n/8RQsyaNctk\nMpnNZpPJ9Oabbwoh4uLi5B4N5oKCgm3btkVERAghtFrtda9mZGQIIUJCQmpqasxmc3V1dZ8+\nfYQQu3fvlmPY249Dsfjr1q9fL4SIj4+3tbWVtrz44ouhoaH79u3T6/Wyjoab+/7779evX9+j\nRw+5B8HNrV27tra2duHChe3bt5e2vPzyyyNGjKisrJR3MFzn0KFDQojx48erVCohhEqlGj9+\nvBDi4MGDMk8GIQIDA4cNG/bjjz/e9NX4+HghxPz58x0dHYUQTk5OCxYsEEKsWrWqGWe8gyh2\n+Otyc3Pbt2/v7e3deGPHjh3NZjNnBbVAJSUlEyZMGDJkyIsvvij3LLi5tWvXarXaoUOHWrbY\n2Nj83//935o1a2ScCjdycHAQQhQWFlq2FBUVCSHMnN3UAnz77bebNm3atGmTn5/fja8mJSVp\ntdrQ0FDLln79+mm12uTk5OYb8U6i2OGv27Vr1549expvMZlMv/zyi0ql8vX1lWko/KHXXnut\npqYmMTFRWmNAC5SdnR0QEGBjY7Njx445c+a8//77e/bsoSu0QFOnTm3VqtXYsWNTUlKqq6tT\nUlLGjh1rY2MzZswYuUeDGDFixMiRI0eOHKnVam989fLly4GBgZYDTUIIOzu7gICAxjXdqtk2\nvQvwB3r16tU4mkymadOmFRYWPv300ze9EAky2rRp07p165YuXdqpUye5Z8HNNTQ0lJaW9ujR\nY+TIkdu2bbNsj4yMXLVqFafktyj9+/ffuXPngAEDQkJCpC12dnabNm164okn5B0Mt6bX6/V6\nvbu7+3Xb3d3dKyoqampqnJycZBnsNmLFDrfHlStXRo0a9cknn/j4+Hz66adyj4PfKS0tnTBh\nwqBBg8aOHSv3LPhD0rG8vXv3njhxYsuWLeXl5enp6UOHDt2wYcO8efPkng6/c+nSpX/+859G\nozEiImLy5MmPPfZYQ0PDmjVrOL24hSstLRVCaDSa67ZLW4qLi2WY6XZjxQ5/l9lsXrp06Ztv\nvllZWTlgwIC1a9f6+PjIPRR+Z/LkyVVVVStWrLCx4f/lWi7LsaHvv/9eWgfSarUbNmzw9/df\nsGDBnDlz7OzsZB0Q15jN5lGjRp06dWrz5s2WJbrNmzePHDnSxsZGuqoMLZObm5sQ4sb+XVVV\nJYRQxrEm/pbH31JaWjp8+PBXX33V0dFxxYoVe/bs4UhfS/PTTz99/fXXcXFxXbp0kXsW3Eqb\nNm1sbGw6d+5sObonhGjVqtWgQYPq6urOnj0r42xoLDs7+9dffw0PD2984PXJJ58cPHjwhg0b\n8vPzZZwNt6bRaBwdHcvKyq7bXlZW5uzsfONKnjWi2OGvq6mpGT58+Pbt24cPH37mzJkxY8ao\n1Wq5h8L1MjMzhRCTJk1S/X/Tp08XQowYMUKlUiUkJMg9IK5Rq9Wenp43nuLj4uIihGhoaJBj\nKNyEdNDc39//uu0BAQFCiLy8PBlmwr9HpVJ5e3tnZWUZjUbLRqPRmJOT4+3trYwLyzgUi7/u\ngw8+SE5OnjJlyoIFCzjG12J17979uiv1Tpw4ceTIkSFDhnTs2DHpj0opAAAHHklEQVQ4OFiu\nwXCjRx55ZP369YWFhZbnTJjN5sOHD6vV6q5du8o7GyykPzWnTp26bnt6eroQgufutHAjRoxY\nvHhxamrqAw88IG1JSUnR6XTKuQ+UnHdHhjUzGAze3t5ubm56vV7uWfDnfPzxx4InT7RIv/76\nqxBi5MiR0j3xTSbTwoULhRBRUVFyj4bfCQsLE0KsXLlSiiaTacWKFUKI0NBQWefC7/Ts2fPG\nJ08cO3ZMCPHoo48aDAaz2dzQ0BAeHi6EOHHihBwz3n6s2OEvysvLKygo0Gq1gwcPvvHVTZs2\nWW6dD+DfFBoa+tRTT23atCk4OLhfv35ZWVkpKSl+fn5SF0fL8cUXX/Tt2zcmJiYhISEgIOD0\n6dOpqalubm4rV66UezQ0oVevXqNHj/7666/79u07YMCAPXv2pKWlRUdH33PPPXKPdntQ7PAX\n5ebmCiEqKiqkR+tcp66urrkHAqyfSqVat27dwoULd+zYsW3bNl9f30mTJs2dO7d169Zyj4bf\n8ff3z8zMfOedd/bu3btx40Y/P7/x48e/++67np6eco+Gpq1cuTI4OPiLL75YsmSJr6/v+++/\nL515rAwqM/c0BwAAUAROeAcAAFAIih0AAIBCUOwAAAAUgmIHAACgEBQ7AAAAhaDYAQAAKATF\nDgAAQCEodgAAAApBsQMAAFAIih0AAIBCUOwAAAAUgmIHAACgEBQ7AAAAhaDYAQAAKATFDgAA\nQCEodgAAAApBsQMAAFAIih0AAIBCUOwAAAAUgmIHAACgEBQ7AAAAhaDYAQAAKATFDgAAQCEo\ndgAAAApBsQMAAFAIih0AAIBCUOwAAAAUgmIHAACgEBQ7AAAAhaDYAQAAKATFDgAAQCEodgAA\nAApBsQMAAFAIih2Au8vWrVtVKtXatWvlHgQAbj+KHQAAgEJQ7AAAABSCYgfgLhIRETFixAgh\nRFRUlEqlKikpEUK89NJLbdq0MZlMr7/+ukajiY+Pl3Y2GAzz5s3r27dvq1atOnToEB0dnZGR\n0fjdbr2DyWT68ssvH3jgAa1W6+HhMXDgwB07djTjZwVwN6LYAbiLTJs2bfLkyUKIV155ZeXK\nlS4uLpaX5s6d+8knn7i7u3t4eAgh6urqHnnkkdmzZ6vV6ueeey4oKOirr7564IEHDhw4IO3f\n5A7vvvvumDFjzp07N2jQoJ49ex46dGjYsGF79uxp7s8M4K5iBoC7yZYtW4QQX331lWVLdHS0\njY2NRqNJTk62bFy4cKFard66datly8GDB52dnbt27WoymZrcwWQyubm5+fn56fV66dX9+/cL\nIaKjo+/0BwRwN7OVu1gCgPxMJtPcuXMffPBBy5aFCxcOGDCge/fuubm50pZ27dpFRERs3Ljx\nwoULfn5+t97Bx8ensrKydevWdnZ20quhoaHHjh1rvEYIALcdxQ4AhBBiwIABlq+rqqry8/Pz\n8/M7d+58455Xrlzx8PC49Q5+fn6RkZHr1q0LDg6Ojo4ODw/v06dPr1697uAHAACKHQBIOnXq\nZPk6Ly9PCDF06NCxY8feuKe/v3+TOwghVq9eHRISsnr16jlz5syZM0ej0URGRs6bN69du3Z3\n6jMAuOtR7ABACCFsbf/192H79u2FEBqNZuTIkTfdWa1W33oHIYS9vf3UqVOnTp166dKlvXv3\nfvXVV19++eWRI0fS0tJsbLhwDcAdwV8uAHA96drYnTt31tXVWTaazeaePXsGBASYzeYmdzh3\n7twbb7yxdetWIYSPj8/zzz+/Y8eOiIiIkydPXrhwQYaPBODuQLEDcDdqXMhuauLEiTqd7sUX\nXzQajdKWxYsXnzhx4sknn1SpVE3u4ODgsGDBghkzZlRXV0uv1tTUXL582dbW1svL6459LAB3\nO5XZbJZ7BgBoPrt27QoPD7/33nuHDRs2a9YsFxeXl156afXq1WVlZa6urpbd9Hp9aGjoyZMn\nO3ToMHDgwAsXLhw4cCA4OPjw4cMajabJHcxmc2Rk5Pfff9+hQ4eHHnpIrVb/8ssvly5dmj59\n+kcffSTfpwegcKzYAbi79O/f/4knnjhz5szy5cvr6+v/aDcXF5dDhw7NnDnT09Pzhx9+KCoq\nev311w8ePCi1uiZ3UKlUq1ateuedd1xcXDZv3rxjx46OHTuuXr06Li6umT4ngLsSK3YAAAAK\nwYodAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIod\nAACAQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACA\nQlDsAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAhKHYAAAAKQbEDAABQCIodAACAQlDs\nAAAAFIJiBwAAoBAUOwAAAIWg2AEAACgExQ4AAEAh/h9FekJlbsEAOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “forest”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(forest)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 3.4",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}