NohupPlotter.ipynb

{
 "cells": [
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "#!/usr/bin/Rscript\n\nif (!require(\"pacman\")) install.packages(\"pacman\")\npacman::p_load(ggplot2, sqldf, tidyr, stringr, dplyr, doBy, plyr)\nRESEARCH_HOME = Sys.getenv(c(\"RESEARCH_HOME\"))\nPATH = \"Scripts/Python/data/\"\nSAVE_PDF = F\nW = 6\nH = 6\nmuDefault      = 4\ndeltaDefault   = 5\nepsilonDefault = 30\n\ndata = read.csv(paste0(RESEARCH_HOME, PATH, 'methods_epsilon.csv'), header = F, sep = \";\")\n",
   "execution_count": 45,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "head(data)\nnrow(data)",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/html": "<table>\n<thead><tr><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th></tr></thead>\n<tbody>\n\t<tr><td>0          </td><td>SpatialJoin</td><td>10         </td><td>4          </td><td>5          </td><td>462.226    </td></tr>\n\t<tr><td>1          </td><td>SpatialJoin</td><td>10         </td><td>4          </td><td>5          </td><td>444.708    </td></tr>\n\t<tr><td>2          </td><td>SpatialJoin</td><td>10         </td><td>4          </td><td>5          </td><td>446.822    </td></tr>\n\t<tr><td>3          </td><td>SpatialJoin</td><td>10         </td><td>4          </td><td>5          </td><td>455.671    </td></tr>\n\t<tr><td>4          </td><td>SpatialJoin</td><td>10         </td><td>4          </td><td>5          </td><td>453.472    </td></tr>\n\t<tr><td>5          </td><td>MergeLast  </td><td>10         </td><td>4          </td><td>5          </td><td>484.132    </td></tr>\n</tbody>\n</table>\n",
      "text/latex": "\\begin{tabular}{r|llllll}\n V1 & V2 & V3 & V4 & V5 & V6\\\\\n\\hline\n\t 0           & SpatialJoin & 10          & 4           & 5           & 462.226    \\\\\n\t 1           & SpatialJoin & 10          & 4           & 5           & 444.708    \\\\\n\t 2           & SpatialJoin & 10          & 4           & 5           & 446.822    \\\\\n\t 3           & SpatialJoin & 10          & 4           & 5           & 455.671    \\\\\n\t 4           & SpatialJoin & 10          & 4           & 5           & 453.472    \\\\\n\t 5           & MergeLast   & 10          & 4           & 5           & 484.132    \\\\\n\\end{tabular}\n",
      "text/markdown": "\nV1 | V2 | V3 | V4 | V5 | V6 | \n|---|---|---|---|---|---|\n| 0           | SpatialJoin | 10          | 4           | 5           | 462.226     | \n| 1           | SpatialJoin | 10          | 4           | 5           | 444.708     | \n| 2           | SpatialJoin | 10          | 4           | 5           | 446.822     | \n| 3           | SpatialJoin | 10          | 4           | 5           | 455.671     | \n| 4           | SpatialJoin | 10          | 4           | 5           | 453.472     | \n| 5           | MergeLast   | 10          | 4           | 5           | 484.132     | \n\n\n",
      "text/plain": "  V1 V2          V3 V4 V5 V6     \n1 0  SpatialJoin 10 4  5  462.226\n2 1  SpatialJoin 10 4  5  444.708\n3 2  SpatialJoin 10 4  5  446.822\n4 3  SpatialJoin 10 4  5  455.671\n5 4  SpatialJoin 10 4  5  453.472\n6 5  MergeLast   10 4  5  484.132"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": "66",
      "text/latex": "66",
      "text/markdown": "66",
      "text/plain": "[1] 66"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "names(data) = c(\"runID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Time\")\ndata$Epsilon = as.numeric(as.character(data$Epsilon))\ndata$Mu      = as.numeric(as.character(data$Mu))\ndata$Delta   = as.numeric(as.character(data$Delta))\ndata$Time    = as.numeric(as.character(data$Time))\n\ndf = summaryBy(data=data, Time ~ Epsilon + Mu + Delta + Method, FUN = function(x){ c(m=mean(x), s=sd(x)) })\ndf",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/html": "<table>\n<thead><tr><th scope=col>Epsilon</th><th scope=col>Mu</th><th scope=col>Delta</th><th scope=col>Method</th><th scope=col>Time.m</th><th scope=col>Time.s</th></tr></thead>\n<tbody>\n\t<tr><td>10         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>483.4434   </td><td> 4.166888  </td></tr>\n\t<tr><td>10         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>452.5798   </td><td> 7.044824  </td></tr>\n\t<tr><td>15         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>496.5968   </td><td> 5.760131  </td></tr>\n\t<tr><td>15         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>505.2922   </td><td>10.092524  </td></tr>\n\t<tr><td>20         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>505.2672   </td><td>11.067578  </td></tr>\n\t<tr><td>20         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>585.2887   </td><td>15.958481  </td></tr>\n\t<tr><td>25         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>519.9606   </td><td> 5.149485  </td></tr>\n\t<tr><td>25         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>722.0828   </td><td>27.904416  </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>531.4094   </td><td> 6.904833  </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>722.1781   </td><td>49.764907  </td></tr>\n</tbody>\n</table>\n",
      "text/latex": "\\begin{tabular}{r|llllll}\n Epsilon & Mu & Delta & Method & Time.m & Time.s\\\\\n\\hline\n\t 10          & 4           & 5           & MergeLast   & 483.4434    &  4.166888  \\\\\n\t 10          & 4           & 5           & SpatialJoin & 452.5798    &  7.044824  \\\\\n\t 15          & 4           & 5           & MergeLast   & 496.5968    &  5.760131  \\\\\n\t 15          & 4           & 5           & SpatialJoin & 505.2922    & 10.092524  \\\\\n\t 20          & 4           & 5           & MergeLast   & 505.2672    & 11.067578  \\\\\n\t 20          & 4           & 5           & SpatialJoin & 585.2887    & 15.958481  \\\\\n\t 25          & 4           & 5           & MergeLast   & 519.9606    &  5.149485  \\\\\n\t 25          & 4           & 5           & SpatialJoin & 722.0828    & 27.904416  \\\\\n\t 30          & 4           & 5           & MergeLast   & 531.4094    &  6.904833  \\\\\n\t 30          & 4           & 5           & SpatialJoin & 722.1781    & 49.764907  \\\\\n\\end{tabular}\n",
      "text/markdown": "\nEpsilon | Mu | Delta | Method | Time.m | Time.s | \n|---|---|---|---|---|---|---|---|---|---|\n| 10          | 4           | 5           | MergeLast   | 483.4434    |  4.166888   | \n| 10          | 4           | 5           | SpatialJoin | 452.5798    |  7.044824   | \n| 15          | 4           | 5           | MergeLast   | 496.5968    |  5.760131   | \n| 15          | 4           | 5           | SpatialJoin | 505.2922    | 10.092524   | \n| 20          | 4           | 5           | MergeLast   | 505.2672    | 11.067578   | \n| 20          | 4           | 5           | SpatialJoin | 585.2887    | 15.958481   | \n| 25          | 4           | 5           | MergeLast   | 519.9606    |  5.149485   | \n| 25          | 4           | 5           | SpatialJoin | 722.0828    | 27.904416   | \n| 30          | 4           | 5           | MergeLast   | 531.4094    |  6.904833   | \n| 30          | 4           | 5           | SpatialJoin | 722.1781    | 49.764907   | \n\n\n",
      "text/plain": "   Epsilon Mu Delta Method      Time.m   Time.s   \n1  10      4  5     MergeLast   483.4434  4.166888\n2  10      4  5     SpatialJoin 452.5798  7.044824\n3  15      4  5     MergeLast   496.5968  5.760131\n4  15      4  5     SpatialJoin 505.2922 10.092524\n5  20      4  5     MergeLast   505.2672 11.067578\n6  20      4  5     SpatialJoin 585.2887 15.958481\n7  25      4  5     MergeLast   519.9606  5.149485\n8  25      4  5     SpatialJoin 722.0828 27.904416\n9  30      4  5     MergeLast   531.4094  6.904833\n10 30      4  5     SpatialJoin 722.1781 49.764907"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "title = \"Execution time by Epsilon\"\ng = ggplot(data=df, aes(x=factor(Epsilon), y=Time.m, fill=Method, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(epsilon,\"(mts)\"))) \nplot(g)\n",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "image/png": 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191cHBo0aLFzJkzlUrjX0tw4sQJo48J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",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "stages = read.csv(paste0(RESEARCH_HOME, PATH, 'stages_epsilon.csv'), header = F, sep = \";\")\nnames(stages) = c(\"runID\", \"stageID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Stage\", \"Time\")\nstages$Epsilon = as.numeric(as.character(stages$Epsilon))\nstages$Mu      = as.numeric(as.character(stages$Mu))\nstages$Delta   = as.numeric(as.character(stages$Delta))\nstages$Time    = as.numeric(as.character(stages$Time))\nstages = stages[stages$Stage != \"Running MergeLast\", ]\n\nstages = stages[stages$Method == \"MergeLast\",]\nstages = summaryBy(data=stages, Time ~ Method + Epsilon + Stage, FUN = function(x){ c(m=mean(x), s=sd(x)) })\nhead(stages, n = 15)\n",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/html": "<table>\n<thead><tr><th scope=col>Method</th><th scope=col>Epsilon</th><th scope=col>Stage</th><th scope=col>Time.m</th><th scope=col>Time.s</th></tr></thead>\n<tbody>\n\t<tr><td>MergeLast                   </td><td>10                          </td><td>0.Reporting locations       </td><td> 5.040714                   </td><td> 0.3771953                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>10                          </td><td>1.Set of disks for t_i      </td><td>13.952857                   </td><td>12.7806190                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>10                          </td><td>2.Set of disks for t_i+delta</td><td>30.803429                   </td><td> 3.8494468                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>10                          </td><td>3.Joining timestams         </td><td> 3.733143                   </td><td> 0.1945851                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>10                          </td><td>4.Checking internals        </td><td> 9.350286                   </td><td> 0.9824294                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>15                          </td><td>0.Reporting locations       </td><td> 5.173286                   </td><td> 0.3436535                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>15                          </td><td>1.Set of disks for t_i      </td><td>14.172571                   </td><td>13.0889982                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>15                          </td><td>2.Set of disks for t_i+delta</td><td>31.362571                   </td><td> 3.8219400                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>15                          </td><td>3.Joining timestams         </td><td> 4.136571                   </td><td> 0.2949870                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>15                          </td><td>4.Checking internals        </td><td> 9.764286                   </td><td> 1.0453462                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>20                          </td><td>0.Reporting locations       </td><td> 5.194571                   </td><td> 0.2162036                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>20                          </td><td>1.Set of disks for t_i      </td><td>14.358286                   </td><td>13.2221985                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>20                          </td><td>2.Set of disks for t_i+delta</td><td>31.480000                   </td><td> 3.8224145                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>20                          </td><td>3.Joining timestams         </td><td> 4.987143                   </td><td> 0.1814577                  </td></tr>\n\t<tr><td>MergeLast                   </td><td>20                          </td><td>4.Checking internals        </td><td> 9.766000                   </td><td> 0.9910399                  </td></tr>\n</tbody>\n</table>\n",
      "text/latex": "\\begin{tabular}{r|lllll}\n Method & Epsilon & Stage & Time.m & Time.s\\\\\n\\hline\n\t MergeLast                    & 10                           & 0.Reporting locations        &  5.040714                    &  0.3771953                  \\\\\n\t MergeLast                      & 10                             & 1.Set of disks for t\\_i       & 13.952857                      & 12.7806190                    \\\\\n\t MergeLast                      & 10                             & 2.Set of disks for t\\_i+delta & 30.803429                      &  3.8494468                    \\\\\n\t MergeLast                    & 10                           & 3.Joining timestams          &  3.733143                    &  0.1945851                  \\\\\n\t MergeLast                    & 10                           & 4.Checking internals         &  9.350286                    &  0.9824294                  \\\\\n\t MergeLast                    & 15                           & 0.Reporting locations        &  5.173286                    &  0.3436535                  \\\\\n\t MergeLast                      & 15                             & 1.Set of disks for t\\_i       & 14.172571                      & 13.0889982                    \\\\\n\t MergeLast                      & 15                             & 2.Set of disks for t\\_i+delta & 31.362571                      &  3.8219400                    \\\\\n\t MergeLast                    & 15                           & 3.Joining timestams          &  4.136571                    &  0.2949870                  \\\\\n\t MergeLast                    & 15                           & 4.Checking internals         &  9.764286                    &  1.0453462                  \\\\\n\t MergeLast                    & 20                           & 0.Reporting locations        &  5.194571                    &  0.2162036                  \\\\\n\t MergeLast                      & 20                             & 1.Set of disks for t\\_i       & 14.358286                      & 13.2221985                    \\\\\n\t MergeLast                      & 20                             & 2.Set of disks for t\\_i+delta & 31.480000                      &  3.8224145                    \\\\\n\t MergeLast                    & 20                           & 3.Joining timestams          &  4.987143                    &  0.1814577                  \\\\\n\t MergeLast                    & 20                           & 4.Checking internals         &  9.766000                    &  0.9910399                  \\\\\n\\end{tabular}\n",
      "text/markdown": "\nMethod | Epsilon | Stage | Time.m | Time.s | \n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| MergeLast                    | 10                           | 0.Reporting locations        |  5.040714                    |  0.3771953                   | \n| MergeLast                    | 10                           | 1.Set of disks for t_i       | 13.952857                    | 12.7806190                   | \n| MergeLast                    | 10                           | 2.Set of disks for t_i+delta | 30.803429                    |  3.8494468                   | \n| MergeLast                    | 10                           | 3.Joining timestams          |  3.733143                    |  0.1945851                   | \n| MergeLast                    | 10                           | 4.Checking internals         |  9.350286                    |  0.9824294                   | \n| MergeLast                    | 15                           | 0.Reporting locations        |  5.173286                    |  0.3436535                   | \n| MergeLast                    | 15                           | 1.Set of disks for t_i       | 14.172571                    | 13.0889982                   | \n| MergeLast                    | 15                           | 2.Set of disks for t_i+delta | 31.362571                    |  3.8219400                   | \n| MergeLast                    | 15                           | 3.Joining timestams          |  4.136571                    |  0.2949870                   | \n| MergeLast                    | 15                           | 4.Checking internals         |  9.764286                    |  1.0453462                   | \n| MergeLast                    | 20                           | 0.Reporting locations        |  5.194571                    |  0.2162036                   | \n| MergeLast                    | 20                           | 1.Set of disks for t_i       | 14.358286                    | 13.2221985                   | \n| MergeLast                    | 20                           | 2.Set of disks for t_i+delta | 31.480000                    |  3.8224145                   | \n| MergeLast                    | 20                           | 3.Joining timestams          |  4.987143                    |  0.1814577                   | \n| MergeLast                    | 20                           | 4.Checking internals         |  9.766000                    |  0.9910399                   | \n\n\n",
      "text/plain": "   Method    Epsilon Stage                        Time.m    Time.s    \n1  MergeLast 10      0.Reporting locations         5.040714  0.3771953\n2  MergeLast 10      1.Set of disks for t_i       13.952857 12.7806190\n3  MergeLast 10      2.Set of disks for t_i+delta 30.803429  3.8494468\n4  MergeLast 10      3.Joining timestams           3.733143  0.1945851\n5  MergeLast 10      4.Checking internals          9.350286  0.9824294\n6  MergeLast 15      0.Reporting locations         5.173286  0.3436535\n7  MergeLast 15      1.Set of disks for t_i       14.172571 13.0889982\n8  MergeLast 15      2.Set of disks for t_i+delta 31.362571  3.8219400\n9  MergeLast 15      3.Joining timestams           4.136571  0.2949870\n10 MergeLast 15      4.Checking internals          9.764286  1.0453462\n11 MergeLast 20      0.Reporting locations         5.194571  0.2162036\n12 MergeLast 20      1.Set of disks for t_i       14.358286 13.2221985\n13 MergeLast 20      2.Set of disks for t_i+delta 31.480000  3.8224145\n14 MergeLast 20      3.Joining timestams           4.987143  0.1814577\n15 MergeLast 20      4.Checking internals          9.766000  0.9910399"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "title = \"Execution time MergeLast by Epsilon and Stage\"\ng = ggplot(data=stages, aes(x=factor(Epsilon), y=Time.m, fill=Stage, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(epsilon,\"(mts)\"))) \nplot(g)\n",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
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g4dqnx85/fff1c+hjxkyBDz+4BY4tq1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cIUSNtTZljAfIj74UO5VKpVAoCi0s+HvX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\nGvgW6QAAAABJRU5ErkJggg==",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "stages = read.csv(paste0(RESEARCH_HOME, PATH, 'stages_epsilon.csv'), header = F, sep = \";\")\nnames(stages) = c(\"runID\", \"stageID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Stage\", \"Time\")\nstages$Epsilon = as.numeric(as.character(stages$Epsilon))\nstages$Mu      = as.numeric(as.character(stages$Mu))\nstages$Delta   = as.numeric(as.character(stages$Delta))\nstages$Time    = as.numeric(as.character(stages$Time))\nstages = stages[stages$Stage != \"Running SpatialJoin\", ]\n\nstages = stages[stages$Method == \"SpatialJoin\",]\nstages = summaryBy(data=stages, Time ~ Method + Epsilon + Stage, FUN = function(x){ c(m=mean(x), s=sd(x)) })\nhead(stages, n = 15)\n",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/html": "<table>\n<thead><tr><th scope=col>Method</th><th scope=col>Epsilon</th><th scope=col>Stage</th><th scope=col>Time.m</th><th scope=col>Time.s</th></tr></thead>\n<tbody>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>0.Reporting locations </td><td> 5.0883636            </td><td>0.4701724             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>1.Set of disks for t_i</td><td>28.0189091            </td><td>5.0414279             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>2.Distance Join phase </td><td> 3.7920000            </td><td>0.9103330             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>3.Getting candidates  </td><td> 1.8594000            </td><td>0.3782797             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>4.Found flocks        </td><td> 0.5089091            </td><td>0.1974342             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>5.Updating times      </td><td> 0.5776364            </td><td>0.2020631             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>10                    </td><td>6.Filter phase        </td><td> 0.8583636            </td><td>0.2104296             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>0.Reporting locations </td><td> 4.9087879            </td><td>0.3216357             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>1.Set of disks for t_i</td><td>28.0469697            </td><td>5.8036743             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>2.Distance Join phase </td><td> 5.4528889            </td><td>1.4224044             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>3.Getting candidates  </td><td> 2.8878889            </td><td>0.5966470             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>4.Found flocks        </td><td> 0.7144444            </td><td>0.2746806             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>5.Updating times      </td><td> 0.9506061            </td><td>0.3194441             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>15                    </td><td>6.Filter phase        </td><td> 1.4690909            </td><td>0.3716870             </td></tr>\n\t<tr><td>SpatialJoin           </td><td>20                    </td><td>0.Reporting locations </td><td> 4.9326263            </td><td>0.3304715             </td></tr>\n</tbody>\n</table>\n",
      "text/latex": "\\begin{tabular}{r|lllll}\n Method & Epsilon & Stage & Time.m & Time.s\\\\\n\\hline\n\t SpatialJoin            & 10                     & 0.Reporting locations  &  5.0883636             & 0.4701724             \\\\\n\t SpatialJoin              & 10                       & 1.Set of disks for t\\_i & 28.0189091               & 5.0414279               \\\\\n\t SpatialJoin            & 10                     & 2.Distance Join phase  &  3.7920000             & 0.9103330             \\\\\n\t SpatialJoin            & 10                     & 3.Getting candidates   &  1.8594000             & 0.3782797             \\\\\n\t SpatialJoin            & 10                     & 4.Found flocks         &  0.5089091             & 0.1974342             \\\\\n\t SpatialJoin            & 10                     & 5.Updating times       &  0.5776364             & 0.2020631             \\\\\n\t SpatialJoin            & 10                     & 6.Filter phase         &  0.8583636             & 0.2104296             \\\\\n\t SpatialJoin            & 15                     & 0.Reporting locations  &  4.9087879             & 0.3216357             \\\\\n\t SpatialJoin              & 15                       & 1.Set of disks for t\\_i & 28.0469697               & 5.8036743               \\\\\n\t SpatialJoin            & 15                     & 2.Distance Join phase  &  5.4528889             & 1.4224044             \\\\\n\t SpatialJoin            & 15                     & 3.Getting candidates   &  2.8878889             & 0.5966470             \\\\\n\t SpatialJoin            & 15                     & 4.Found flocks         &  0.7144444             & 0.2746806             \\\\\n\t SpatialJoin            & 15                     & 5.Updating times       &  0.9506061             & 0.3194441             \\\\\n\t SpatialJoin            & 15                     & 6.Filter phase         &  1.4690909             & 0.3716870             \\\\\n\t SpatialJoin            & 20                     & 0.Reporting locations  &  4.9326263             & 0.3304715             \\\\\n\\end{tabular}\n",
      "text/markdown": "\nMethod | Epsilon | Stage | Time.m | Time.s | \n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| SpatialJoin            | 10                     | 0.Reporting locations  |  5.0883636             | 0.4701724              | \n| SpatialJoin            | 10                     | 1.Set of disks for t_i | 28.0189091             | 5.0414279              | \n| SpatialJoin            | 10                     | 2.Distance Join phase  |  3.7920000             | 0.9103330              | \n| SpatialJoin            | 10                     | 3.Getting candidates   |  1.8594000             | 0.3782797              | \n| SpatialJoin            | 10                     | 4.Found flocks         |  0.5089091             | 0.1974342              | \n| SpatialJoin            | 10                     | 5.Updating times       |  0.5776364             | 0.2020631              | \n| SpatialJoin            | 10                     | 6.Filter phase         |  0.8583636             | 0.2104296              | \n| SpatialJoin            | 15                     | 0.Reporting locations  |  4.9087879             | 0.3216357              | \n| SpatialJoin            | 15                     | 1.Set of disks for t_i | 28.0469697             | 5.8036743              | \n| SpatialJoin            | 15                     | 2.Distance Join phase  |  5.4528889             | 1.4224044              | \n| SpatialJoin            | 15                     | 3.Getting candidates   |  2.8878889             | 0.5966470              | \n| SpatialJoin            | 15                     | 4.Found flocks         |  0.7144444             | 0.2746806              | \n| SpatialJoin            | 15                     | 5.Updating times       |  0.9506061             | 0.3194441              | \n| SpatialJoin            | 15                     | 6.Filter phase         |  1.4690909             | 0.3716870              | \n| SpatialJoin            | 20                     | 0.Reporting locations  |  4.9326263             | 0.3304715              | \n\n\n",
      "text/plain": "   Method      Epsilon Stage                  Time.m     Time.s   \n1  SpatialJoin 10      0.Reporting locations   5.0883636 0.4701724\n2  SpatialJoin 10      1.Set of disks for t_i 28.0189091 5.0414279\n3  SpatialJoin 10      2.Distance Join phase   3.7920000 0.9103330\n4  SpatialJoin 10      3.Getting candidates    1.8594000 0.3782797\n5  SpatialJoin 10      4.Found flocks          0.5089091 0.1974342\n6  SpatialJoin 10      5.Updating times        0.5776364 0.2020631\n7  SpatialJoin 10      6.Filter phase          0.8583636 0.2104296\n8  SpatialJoin 15      0.Reporting locations   4.9087879 0.3216357\n9  SpatialJoin 15      1.Set of disks for t_i 28.0469697 5.8036743\n10 SpatialJoin 15      2.Distance Join phase   5.4528889 1.4224044\n11 SpatialJoin 15      3.Getting candidates    2.8878889 0.5966470\n12 SpatialJoin 15      4.Found flocks          0.7144444 0.2746806\n13 SpatialJoin 15      5.Updating times        0.9506061 0.3194441\n14 SpatialJoin 15      6.Filter phase          1.4690909 0.3716870\n15 SpatialJoin 20      0.Reporting locations   4.9326263 0.3304715"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "title = \"Execution time SpatialJoin by Epsilon and Stage\"\ng = ggplot(data=stages, aes(x=factor(Epsilon), y=Time.m, fill=Stage, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(epsilon,\"(mts)\"))) \nplot(g)\n",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "image/png": 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Wrcu3cvw2OxDh06dP/+/Ro1amR1PMCK\nsl10mVKS0Keffprpu8ePHxeRDBcDrVu3zvRldHT0jh077O3t33jjjcTExIsXL5YrV+6DDz4w\nfquJyJkzZyyfkVx0UrZsWRG5ceOGcUh6errpybiCX6EZHjEYHR39448/Ojk5GeOCr69vQEBA\neHj4l19+mZqamueH68Tsmrp27VpgYODQoUOVtzQaTVBQ0Jo1a4oWLXr37t1sH/aWf8szTzYh\nEalZs6aTk9O+ffvu3r1rOnzt2rXmR8zFHpGj4qtUqeLm5hYWFqacbDXK8PzC3O3OAAqV1yXY\nPXny5N1333V3d1+2bJmLi4vyyM3hw4crlyQr/0MdOXKkcoObiKSkpLz//vuHDh2qUaOGiGi1\n2hkzZqSlpQ0dOtQY3X755Zdvv/3W09NTeYKUciHRqlWrjP8137Rp0/bt218u5sWLF1nVaXw2\nhDEuXLt2bdSoUca3XpGZSedOtosuU++++65Op/v++++HDBlieswgOjp6ypQp27dvL126dOfO\nnU1HWb169ffff6/8/eTJk759+yYkJAwdOrR06dJFihTx8vKKjo6+d++e0sBgMKxcuVL50soQ\ntbNaAjnqRNGwYUMR2bRpk3JrgoisXLny/v37JUqUqFWrljIkv1doBitXrtywYYPy9+PHj3v3\n7p2QkPD++++bXgnaq1cvg8Hw8ccfi8iAAQPyvAYza6p8+fLnz59fv3790aNHje2PHTv29OnT\nypUru7i4ZNt5Pi3PXKz9THl6er7//vvJycn9+vWLjo5WBu7evXvhwoXmR8zFHmHcki0p3tHR\ncdKkSSkpKUOHDjWOuGnTpo0bN5r2mbvdGUDhYq3bcV+d8UEM9evXb5CFb775RmmsHJn4+uuv\njaP37NlTRBYtWqS8VJ5HoDxTtG3btsqv+jRq1CgxMdE4udatW4uIq6tr06ZNQ0JCtFqtRqPZ\nvHmz0uDWrVseHh4iUq1atUGDBgUHB4uI8vVpfNzJ48ePRcTBwaF3796rV682vPRwh/T09H79\n+iltgoKCAgMDlQv7BgwYYKz85WcaK1MXs09zyHbSyuNOjE92UCg3AMbFxRmHKIcijI87sWTR\nZWrr1q3e3t7KGvTy8qpTp07p0qWVp2oVL17c9AEryrnCTp06iUjlypUbNmyoPBWsZs2a9+7d\nU9pMmzZNRLy9vfv169evX7+qVau6uLiMHz9eRFxcXMaNG2fJErCkkwxP31AeCOzo6NioUSPl\nqJhWq922bZux+Fys0NytX39//6pVqyoPa6xYsWLDhg2VS0hDQkJiYmJMWyonZ0UkODjYzApS\n5Ggvs2RNzZkzR/7/WF2HDh2MC23Hjh2Zzn6GBZ5/O4gla9+Snh8/fhwQECAiTk5OwcHByh3l\nwcHBwcHB5h93Yvke8fKWbEnxcXFxyn9F3N3dmzdvXr16da1W+9lnn7m7u3fv3t3Yee52ZwCF\nhxqCnRkff/yxwWDYsWOHiDRu3Nj0OUz37t1zd3d3cXGJjIxUhvz0008dO3YsW7as8kM6ixYt\nyvCYUL1ev3DhwmbNmnl4eCg/KfbLL7+YNjh79mzHjh19fHycnZ0DAwO3bduWmJjYq1ev5cuX\nG9vMnTvX29vb2dlZedxXpl8Va9asadu2bYkSJUqUKNG2bdvvvvvO9N2svl2cnZ0HDx5sZomZ\nn7Tlwc7Z2dn0e9SSRZepZ8+ezZ49W3kEg5OTU+XKldu0aRMaGhofH2/aTIkLCQkJn376aUhI\niJubm7+//8SJE43PYjUYDKmpqYsWLapdu7aLi0vNmjWHDh36+++/GwyGr776qkmTJlOmTLFk\nCVjSSYackZaW9sUXXwQGBrq7u5ctW7ZDhw4ZNglFjlZo7tbvpEmTJk6cGB8fP3ny5PLlyzs7\nOwcHB//lL39JTk5+ubHyNDjjM4HNsHwvM1i2pvR6/fr16xs3blyiRAllpfft2/fUqVNZLY1M\nHxyYHzuIJWvfwp6VnxQLCgpSfrRw4sSJcXFxs2bNGjlypPmlbeEeYXhpS7Zw+09JSZk5c2ZA\nQECRIkXeeOONrVu3JiQkiMn/PBW5250BFBIaw0u30QGFSmBg4OnTp5OSkpRDUHhFBoOhcuXK\nf/zxx/37901/b/7VsaZszqVLl+rUqTN79mxumABU43W5xg6AIiwsLDIy0niWDa+J6tWrOzs7\nK3dtG/39738XEQsfNAPAJhDsgNfF8+fP79+/r1yP9fbbb1u7HBSo3r17JyYm9unT58KFC8nJ\nyZGRkTNnzvz666/r16+v3P4FQB1er+fYAa+zFi1aKI/mCQkJUX4kFK+P2bNnR0ZGbty40fRR\neWXKlFm1apUVqwKQ57jGDnhdfPTRRwcPHmzQoMGsWbO8vLysXQ6s4OLFi8eOHbt3717JkiWr\nVKnSvHlz5c5lAKpBsAMAAFAJrrEDAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEA\nAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACASthbu4BcSkhISExMtHYVuefp6Skiz549s3Yh\ntsrBwcHV1TUhISEpKcnatdgqd3d3Ozu7p0+fWrsQW6XT6dzc3BITE236s8i63NzcdDrd06dP\nbfrHLYsWLWrtEoD/stVgJyI2/UGg0WjExmfB6liGr0ij0Wg0/Fr0K2EjfEXGjZBlCOQVTsUC\nAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACo\nBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEO\nAABAJawZ7C5dutS1a9fY2FjlpV6vX7169fDhw4cOHbps2bLU1FQr1gYAAGBzrBbsEhISFi1a\nZDAYjENWr1599OjRkSNHjhs37uzZs19++aW1agMAALBFVgt2y5Yt8/DwML5MTEzcv3//8OHD\ng4KCAgICRo0adfTo0efPn1urPAAAAJtjb5WpHj58+Pr162PGjJk+fboy5Pbt20lJSf7+/spL\nPz8/vV5/8+bNevXqKUP0ev21a9eMPbi5ubm6uhZw2XnO3t46y18F7OzsRESr1bIMc02j0Qgb\n4SvQarXCRvhqjBuh6dkb22K7lUOtrPB59PDhw5UrV86ePVvZpRVPnz61t7d3cXH5T1n29q6u\nrk+ePDE2ePHixeDBg40vR44cOXLkyAKrOZ94enpauwTbVqRIkSJFili7CtvGRviKnJycnJyc\nrF2FbTM9e2NzkpOTrV0C8D8KOtilp6d//vnnXbt2rVq16vXr143DDQaDac5T6PV6499OTk5D\nhgwxvqxTp05iYmJ+V5t/lG+CpKQkaxdiq+zs7BwcHFJTU9PS0qxdi61ydHTUarU2vR9Zl7IR\npqWlcadXrqlgI0xLS3N0dLR2FcB/FXSw27lz54sXL0JCQu7duxcdHS0i9+/fL168uLe3d2pq\namJionIARq/Xx8XFFStWzDhikSJFxo4da3yZkJAQHx9fwMXnIeWDwKZnwbocHR0dHBxSUlJs\n+ivBunQ6nUajYSPMNQcHB2UjTEhIsHYttsre3l6r1SYkJNj0CU03NzdrlwD8V0EHu6ioqHv3\n7o0ZM8Y4ZPLkya1btx4xYoSjo+Nvv/0WFBQkIpcvX9ZqtRUrVizg8gAAAGxXQQe70aNHjx49\nWvn7+vXrH3zwwYYNG5T/7rRp0+bbb78tWrSoRqNZtWpV8+bNvby8Crg8AAAA21WIbuYaPnz4\n6tWr582bl56eHhwcPHz4cGtXBAAAYEs0NnplQ0JCgk1f1+Lt7S0iprf9IkccHR3d3Nzi4+O5\nxi7XPD097ezsYmJirF2IrXJwcHB3d7f1zyLr8vDw0Ol0MTExNvpNpDC9HBywOn4rFgAAQCUI\ndgAAACpBsAMA5NiRI0d27txp7SoAZFSIbp4AANiKTz755Ndff1UeRwqg8OCIHQAAgEoQ7AAA\nAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSC\nYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcA\nAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKAS\nBDsAAACVINgBAACoBMEOAABAJQh2AF47ERERc+fOPXHihLULAYA8RrAD8Nq5cePGkiVLzpw5\nY+1CACCPEewAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0A\nAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBK\nEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwA\nAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABU\ngmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh1ge6ZPnz5+/HhrVwEA\nKHTsrV0AgBw7cODAs2fPrF0FAKDQ4YgdAACAStjqETt7e3sPDw9rV5F7Go1GRGx6FqxLq9WK\niJOTk4ODg7VrsQKtVqvRaF5x+7Gzs3v1TmyUi4uLiDg5Ob3K7Ct7sZOTk06ny7PKbIednZ28\n8oeYvb29iLi7u+dNTdaQmppq7RKA/2GrwU6v1yclJVm7itxTPg3j4uKsXYitcnBwcHFxSU5O\nTk5OtnYtVmAwGAwGwytuP+7u7lqt9vXcCBMTE0UkJSXlVWZfp9O5urqmpKQovb1u9Hq9vPKH\nmJubm729fXx8vMFgyKO6CprBYHB2drZ2FcB/2WqwMxgMyseKTVPBLFhLenq6qGUzyAXlW/AV\n5z1POrFRyvaTnp7+KrOvHLJ6xU5sXV5thLYb7IDChmvsAAAAVIJgBwAAoBIEOwAAAJUg2AEA\nAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgE\nwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgh4J24MCBXr16HThwwNqFAACgNgQ7\nFLSoqKiwsLCoqChrFwIAgNoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AIAc\n0+v11i4BQCYIdgAASz1//vzjjz/29/c/d+6cwWCoVq3a+PHjb9++be26APwHwQ4AYJHIyMiW\nLVt+8cUX9+7dU4Y8ffr0+++/b9GixaFDh6xbGwAFwQ4AkL2UlJRBgwbduXPn5bfi4uLefvtt\nY9oDYEUEOwBA9rZv337t2rWs3o2Pj1+6dGlB1gMgUwQ7AED2/v3vf5tvsH///oKpBIAZBLsc\n27Nnz8GDB61dBQAUqPv372fbwGAwFEwxALJib+0CbM/48eO9vb3Dw8OtXQgAFBz9i1PmG9jb\npWk0moIpBkBWOGIHAMhepTLZNKicXQMABYBgBwDI3p9CxPzxuHYNC6oUAFkj2AEAslfTV7o1\ny/LdOpWkc5MCrAZAFgh2AACLTOgvfdqI9qXjdo3ryqdjxd7OGjUB+F/cPAEAsIidVsb1ke7N\nZf8vsvWgvIiXfm2leYC8UdnalQH4fxyxAwDkQLkSMqyzlC0uGo2M6U2qAwoXgh0AAIBKEOwA\nAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABU\ngmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgBeO3FxcSISHx9v7UIAII8R7AC8RrZs2dKi\nRYuxY8eKyMKFC4OCglauXKnX661dFwDkDYIdgNdCenr62LFj33vvvUuXLhkHRkZGTp8+vV+/\nfsnJyVasDQDyCsEOwGth+fLlmzZtyvStw4cPz549u2DLAYB8QbADoH5paWmLFy8202DNmjWP\nHz8usHoAIJ8Q7ACo3/nz5588eWKmQVpa2pEjRwqsHgDIJwQ7AOr34MGDbNvcv3kvCCwAACAA\nSURBVH+/ACoBgHxFsAOgfs7Oztm2cXFxKYBKACBfEewAqF+tWrW0mmza1KlTp0BqAYB8RLAD\noH4lSpQINhvbfEtJ/fr1C6ocAMgvBDsAr4XxfcUji3OtDjr582DRavk8BGDz+CAD8FooW1yW\nfCiVymQcXsJbFo6TN6pYoyYAyGv21i4AAApI5bKy5iP55ZL8GCbHzkvdKtK7tTTxEx0fhADU\ngs8zAK8RrUZC6khyihw7L439pCWX1QFQF07FAgAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0A\nAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJ+4Kf5N27\nd1evXn3lyhU7O7s33nhj2LBhxYoVExG9Xv/dd9+dOHEiLS0tKChoxIgROp2u4MsDAACwUQV9\nxC41NXXOnDmOjo5z5swZO3bs48eP58+fr7y1evXqo0ePjhw5cty4cWfPnv3yyy8LuDYAAACb\nVtDBLjIy8sGDB++//36VKlWCgoIGDRp07dq1pKSkxMTE/fv3Dx8+PCgoKCAgYNSoUUePHn3+\n/HkBlwcAAGC7CvpUbJUqVTZv3uzk5JSUlBQVFXX8+PGqVas6OTlduXIlKSnJ399faebn56fX\n62/evFmvXj1lSHp6elRUlLEfBwcHe3srnEc2srOzKySd2BytVqv8+yqzr3Si0Whez2Wo0Wjk\nlbefPOlETXK6KPJkS1aZnC4K40ZoMBjyp6J8Z7uVQ60KOhtptVonJycRmT179uXLl11dXRcs\nWCAiT58+tbe3d3Fx+U9Z9vaurq5Pnjwxjvj8+fOuXbsaX44cOXLkyJEFW/t/KGHCy8vr1bvK\nk05sjrOzs/Lvq8++s7Oz0tvrRqvVajQaNsK8lbtF4eTkpHymQXK7DD09PfO8kgKTnJxs7RKA\n/2G1g14zZsxITEz817/+NW3atJUrVxoMBuW/bqb0er3xb0dHxx49ehhfVqtWLSkpqYBq/V8G\ng8FgMLzi1B0dHeV1/URITU1V/n2VZWhnZ6fT6VJTU003ktdHnmyEDg4OWq3WWvtRIZTTRaHV\nah0cHNLS0tLS0vKpJJuT02Wogo0wLS1N+TwHComCDna3b9+OiYkJCAhwc3Nzc3MbOHDgjh07\nfvvtN29v79TU1MTExCJFioiIXq+Pi4tT7pZVODs7T58+3fgyISEhLi6ugIs3Sk9Pf8WpOzg4\niIgVZ8GKlDibnJz8KrPv6Oio0+lSUlISExPzrjSbkZ6eLq+8/Xh6emo0mtdzI8xUTheFg4OD\ng4NDSkpKQkJCPpVkc3K6DD08PLRabXx8vE2f0HR1dbV2CcB/WeHmiUWLFhmPsiQkJKSkpNjb\n25cvX97R0fG3335Thl++fFmr1VasWLGAywMAALBdBR3sAgIC0tPTly5dev369YiIiE8//bRU\nqVK1a9d2dnZu06bNt99+e+PGjZs3b65atap58+Zc/QMAAGC5gj4V6+7uPmvWrG+//XbmzJmO\njo516tR5//33lQsUhg8fvnr16nnz5qWnpwcHBw8fPryAawMAALBpVrh5olq1an/7299eHm5n\nZzdixIgRI0YUfEmArUhKSlIe8ZiUlLR3795mzZq9nvcFAwAyxW/FAjbj+++/9/f3HzBgQExM\nTEJCwuDBg/39/VetWmXTF54DAPIQwQ6wDYsXLx4/fnxMTIzpwKdPn06bNm3u3LnWqgoAUKgQ\n7AAbEBERYfxV5Zd9+eWXv/zyS0HWAwAonAh2gA347rvvzDyK2WAwfPvttwVZDwCgcCLYATbg\nzJkz5hucPn26YCoBABRmBDvABjy+f858gyePbhVIIQCAQo1gB9gAz+x+ssjbvUDqAAAUbgQ7\nwAbUq55dg2oFUgcAoHAj2AE2oHtzcXLI8l17O+ndugCrAQAUVgQ7wAYU95ZpQ0Sb2f6q0ciH\nA6V8yQKvCQBQ+FjhJ8UA5ELrQPHxki+3yOXI/w6sVl7e6ykNalqvLABAYUKwA2xG3SqyYppE\nxcjoBZKYJN/MlLLFrV0TAKAw4VQsYGNKFRUnB7G3I9UBADIi2AEAAKgEwQ4AAEAlCHYAAAAq\nQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2OVAQkLCvn37kpKSnj9/fujQoZSU\nFGtXZGMuXLgwduzYjz/+WEQ+/vjjcePGXbx40dpFAQCgHgQ7ixgMhlWrVvn7+w8aNCgxMfHx\n48d9+vSpV6/eli1brF2azVi8ePGbb765adOmmJgYEYmJidm4cWObNm2WLl1q7dIAAFAJgp1F\n5syZM23atKdPn5oOjI6Ofu+995YtW2atqmzIpk2b5s2bp9frMwzX6/Vz5swhHwMAkCcIdtk7\nefLkV199ldW7c+fOvXr1akHWY3PS0tLmzp1rpsGcOXNeznwAACCnCHbZW716tcFgyOrdtLS0\ntWvXFmQ9NufUqVPR0dFmGjx48ODMmTMFVg8AAGpFsMve6dOnX7HBa+727dvZtomMjCyASgAA\nUDeCXfaePv7DfIPou78WTCU2SqPRZNtGq2VTBADgVfFtmj0P12waeLkXSB02q3Llytm2qVKl\nSgFUAgCAuhHsshdQPbsG1QqkDpsVEBBQqqi5BqV9xM/Pr6DKAQBAtQh22evdWuyyXk5OjtK1\neQFWY4O0Wu24vpLV+VitRib05VQsAAB5gG/T7FUsLRP6ZZ5LtFqZPkSKexV4Tbamqb/8ebA4\n6DIOd9TJ1CHSqK41agIAQHXsrV2AbejeQkoVk2Xb5Oa9/w6sVVHG9Ja6XBtmmU5NpEFN+TFM\nDv8qd6OlbHFp1UC6NpMS3tauDAAAtSDYWSqkjoTUkdsPZMQn4uwkyyZLaR9r12RrShaVUT2k\nTHFZsFYGtpPOTaxdEAAA6sKp2JypUFLs7aSII6kOAAAUOgQ7AAAAlSDYAQAAqATBDgAAQCUI\ndgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAA\nACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEvbWLgAAYHtWTLN2BQAywxE7AAAAlSDYAQAA\nqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIWRXq9fvnx5o0aNfHx8vL29AwMD58yZExsba+26\nUKgR7AAAKHQMBkOnTp1GjRql0+nee++9sWPHlihRYvbs2QEBAS9evFDaLFy4UKPRxMTEWLdU\nFCo8xw4AgEJn3bp1e/funT179qxZs4wD//GPf/To0WPWrFmLFi2yYm0ozDhiBwBAoXPkyBER\nmTBhgunA7t27165d+9ixY1YqCjaAYAcAQKETHx8vInfv3s0wfO/evRs3bhSRli1bTpo0SUSK\nFSs2ePBg5d3vv/8+ODjYy8vL3d09ICBg1apVGcZt0aKFp6dncHDwihUrQkND3dzcjO9GRkb2\n7dvX19fXw8OjefPmu3fvztcZRD4h2AEAUOi0b99eRJo1azZz5sybN28ah5ctW7ZKlSoisnjx\n4tGjR4vIjh07ZsyYISLbt28fOHCgRqOZMmXKqFGj0tLSRowYsXXrVmXEH374oWPHjs+ePfvg\ngw8CAgLGjRu3ePFiY7fnz5/39/c/duxYv379PvjggydPnnTq1Ombb74pyFlGnuAaOwAACp3B\ngwdHRkZ+9tln8+bNmzdvXuXKlVu3bt2uXbtOnTrpdDoR8fPzq1y5sog0bty4aNGiIrJ+/fqy\nZcseOXLEwcFBRObOnVu8ePH9+/f36tUrJSVlypQp9evXP3LkiJOTk4h06NChS5curq6uyuTG\njx/v6el59uxZb29vEZk+ffqbb745ceLEvn37GtvAJnDEDgCAQkej0cyaNevBgwfbt29///33\ndTrdihUrevToUbly5ZMnT2Y6ysqVKy9cuKCkOhGJjY3V6/UJCQkiEh4e/scff3zwwQdKqhOR\nzp0716hRQ/n76dOnYWFhI0eOVFKdiOh0ujFjxsTGxmY1LRRaBDsAAAopV1fX7t27f/nllxER\nEZcuXXrnnXeioqK6du2a6dPsihYtGhMTs27dug8//LBFixZly5ZVLtQTkevXr4tIrVq1TNsb\nX169elVEZs6cqTHRq1cvEXn06FG+ziDyHKdiAQAoXOLj44cOHdqlSxfjXREiUqtWrVWrVvn4\n+MyfP//48ePt2rXLMNbSpUs//PBDNze3Dh069O/ff9GiRV27dlXeSklJeXkqdnZ2yh/KQb6p\nU6e+3Gf16tXzaqZQMAh2AAAULi4uLkeOHHn+/LlpsFP4+vqKSSYzio+Pnzx58oABA7755hvj\nu8nJycofVatWFZErV67UrVvXOIpyoE5ElLsxtFpt8+bNje9GRUVdu3bN09Mz72YLBYFTsQAA\nFDodOnTYv3//3//+d9OBsbGxK1ascHZ2DgwMNA5MT08XkcjIyOTk5AYNGhhT3b59+6Kjo5V3\ng4ODfXx8Fi9ebDx0d+DAgQsXLih/u7u7t27desWKFcYTr+np6UOGDOnXr59yowZsCEfsAAAo\ndBYvXnz8+PHRo0cvX748MDDQ29v7/v37u3btevbs2YYNG5QDaUrqWrRoUYcOHYKCgsqWLfvJ\nJ588evSoUqVKv/zyy7Zt28qWLfvvf/97zZo1Q4cOnT9//jvvvNO4cePu3btHR0d/9913zZs3\nv3jxojK5zz77rFmzZn5+fm+//badnd0///nPX3/9dd26dS8fGkQhxxE7AAAKHQ8Pj/Pnz3/6\n6aeOjo47duz48ssvf/31106dOl24cKF///5Km65du7Zs2fKLL77YtGmTg4PD7t27a9euvXjx\n4o8++ujp06cnT57csmVLjRo1jh8/LiLDhg3bunWrnZ3dggULzp8/v3379iZNmpQoUULpql69\nemfOnAkJCVm7du2SJUuKFCmya9euQYMGWW3+kVscsQMAoDAqUqTI5MmTJ0+enFWDChUqHDx4\n0PjyjTfe2L9/f4YGYWFhIqLX6589e9axY8eePXsa3125cqUx2IlItWrVtm/fnpczAGvgiB0A\nACqXlJRUunRp01+effjw4Y4dO9q0aWPFqpAfOGIHAIDKubi4DB06dMWKFWlpaa1atXr69OnC\nhQvt7e1HjBhh7dKQxwh2AACo39KlS8uXL7927drvv//ex8fH399/0aJFPj4+1q4LeYxgB+C1\n0zxAjq2wdhFAwXJwcJgxY8aMGTOsXQjyF9fYAQAAqATBDgAAQCVs9VSsnZ2dm5ubtav4r5wW\no9FocjGWiuV0UWi1WhFxdHS0t7fVbTjP5XQZ2tnZaTQaNkKjXG+EPMHVKBcboYi4urrmTzkF\nIS0tzVZ65tPyNWGrqzk9Pd34E3iFQWJiYo7aK48Lz+lYKpbTReHg4KDT6VJTUwvVZmBdOV2G\n9vb2Wq2WjdAoFwtQ2QiTkpLyqSSbk9NlaGdnp9Vqk5KSDAZDPpWU3wwGg4uLS350q5/xQd72\nab9gSd52iMLJVoOdwWDIv/8n5ULuiilUs2BdOV0Uyn/009PTWYZGOV0UylcpC9Aop4tCOWLH\nRmgq1xuh7QY767oa8/Ti45hGZUqVcs37cAkbxTV2AADYpB2/3xi4Y8/pqIfWLgSFCMEOAABA\nJQh2AAAAKkGwAwAAUAmCHQAAgErY6l2xAAC8tp4kJm24dOWHy9dEZOmZc0+TknvXrFqEJ9WB\nI3YAANiWzRHXaiz/bvLBo5cex4jIkT/ujdzz7zdWrvv5XlTeTmjw4MEaE0WKFPH399+8eXPe\nTiW/6fV6jUZz5swZaxdSQAh2AADYjJ9+vzl0179epKRkGH43Nq7Llp0XH8VY2E9aWtqHH37o\n6+tbpkyZUaNGZfWw95CQkPD/9+OPP1arVq1///75HZKaNm26cOFCEbl9+7ZGo1m+fHm+Ti6n\nCnl5BDsAAGxDil4/4d9h6Vk8zzk2JWXywaMWdvXhhx/+8MMPS5cu/eabb/71r3+NGDEi02ae\nnp7B/+9Pf/rT+vXrnZyc9u/fn8sZMCslJSUyMtJ0iIeHx9SpU/39/fNjcq+ucJZHsAMAwDYc\nvXP/XmycmQaHb9+JiovPtp/Y2NjVq1cvWrSoc+fO7dq1++qrr3744YdHjx5lO6KDg4Ojo2PR\nokWVl8+fPx81alSFChU8PDy6dOly/fp1ETlz5oyPj8/x48cbNmxYrFixVq1aRUREKO0fPXo0\ncODAkiVLli5detCgQcYp6nS6Xbt2lSlTZty4cYGBgceOHZs0aVL79u09PT1DQ0OVXzTR6XTh\n4eF9+vSpVKlSlSpVtm7dqox79+7dLl26eHl5BQQE7Ny5083N7dKlS1nVn1UB0dHRffv29fHx\nKVWq1Pjx41NSUkTk6tWr7dq18/Lycnd3b9GixYULF0Qkq/LMzFqmZe/atSsgIMDZ2blixYpf\nfPFFtkvecgQ7AABsw+XH2ZxpNYhcfvwk234uXrwYFxfXtm1b5WXr1q1TU1PPnj1rfqwXL158\n9tlnjo6O7du3V4Z069btypUra9eu3b9/v4uLS7NmzZ49eyYisbGxU6ZM2bRp0507d4KDg5s1\naxYbG2swGDp27Hjjxo1NmzZt3Ljx+vXrHTp0MP6a3IcffrhgwYKlS5eeOnWqSZMmoaGhe/bs\nyVDA1KlT58+f//vvvw8cOHDw4MFJSUlpaWmtW7cWkT179syYMePdd9+Nj88y12ZVQHp6etu2\nbWNjY3fu3Pnpp59u3Lhx7ty5IjJw4MDk5OStW7fu2LHDYDAoBzUzLc/8rL1c9p07d3r27Nm2\nbduwsLBRo0ZNmDAhPDw827VmIe6gAQDANiTr9dm2SbLgF3ujoqIcHBw8PT2Vlw4ODl5eXvfv\n33+55d69ezUajfGlnZ3d4cOHy5YtKyInT548fvz4w4cPvby8RGT9+vW+vr5Hjx4tXbp0cnLy\nRx99VKFCBRGZN2/e5s2b165dW7t27bNnz968ebNcuXIisnnz5kqVKh09erRZs2YiMmLEiGHD\nhpkvWznuJSLDhw+fM2fOvXv3zp07Fx0dfebMGVdXVxGJjY19++23sxo9LCws0wJiY2Nv3rwZ\nFhbm6enZsGHDhISEEydOGAyGPn369OrVS5ni/fv3J0yYkNOelVl7uezIyMjU1NT33nuvQoUK\ngYGBVapUKV68uPl5txzBDgAA21DWzS3bNuU9sm9jMBhM45oiLbNEGBISsnjxYuXvuLi4pUuX\n9uzZ886dOw4ODhEREampqaaJJC0t7fr166VLlxaRwMBAZaBWq61fv/7ly5e1Wq2vr68SfUSk\nfPnyFSpUiIiIUNKPn59ftmXXrFlT+cPZ2Vn54+LFi/7+/kqqE5EmTZqYGT0iIiLTAp49e1a7\ndm1jzH333XffffddEZk4cWJ4ePju3bvPnDmze/fuXPSszNrLZYeEhAQGBtapU6dbt25t27bt\n3bt3kSJFsp19C3EqFgAA29C2YjkHOzszDcq7u9XxKZZtP6VKlUpOTo6NjVVepqWlPXv2TDkO\nl4HpzROtW7f++uuvo6OjlWvpPDw8vL29U00YDIaJEycqI6anpxs7SU1NTU9PT09Pz5AmtVqt\nMU0aQ48ZDg4OGYakpqaa9qnVmks1WRWQmppq/9IjABMSEpo3bz5s2LDbt2937979b3/7Wy56\nzqpsV1fXkydP7tmzx83Nbc6cOeXLl3/5vHOuEewAALANRYsUGdvA3JGt2U1DMh6Iy0zt2rWd\nnZ0PHTqkvDx27JidnZ0ld3eWKlVKRK5du6Z08uTJk4sXLypvPX78uFu3bpcvX1ZeHjlyRPkj\nMTHxxIkTNWvWrFGjRmRk5L1795Thd+/evXXrVq1atSyo19yMnD9/PiEhQXlp/kq1rAqoWbOm\nctGhMnzjxo1vvvnmoUOHLly4cPHixc8++6xLly6mOdXynrNqHxYWFhoa2qRJk2XLlv3+++8N\nGzZcuXKl5XNtHsEOAACb8demDXtUr/LycI3ItIaBA2rXsKQTDw+PYcOGTZ48+ddffz137tyE\nCRP69+9fsmRJEVmzZs3XX39tZlw3N7cHDx6ISLVq1Xr06DFw4MBDhw4dPXp08ODBERER1apV\nU5pNnDhx586d4eHhffv21ev1Q4cObdWqVd26dfv27XvixInjx4/37dvXz8+vRYsWL09Cq9Xe\nuHFDuQ/DvB49eri7uw8ePFg5Wzpv3jx7e/usjttlVUCXLl18fHwGDRp0+vTpH3/8cfr06bVq\n1XJzc4uPj9+2bdvNmzdXrVr117/+NTY2Vrkx9uXyLJ81RUpKyp///OelS5deuHBh586dly5d\nsuRMtIUIdgAA2Ax7rXZD1/bfdf5TwzKltBqNiDja2bWr5LuvX49ZTUMs72fRokXt27fv1q1b\nx44dGzZsuGLFCmX4hg0bVq9ebWbEWrVqrVq1Svl73bp1jRs3fuuttzp37uzo6Lh3717jOc2v\nv/76L3/5y5tvvhkbG3vkyBF3d3eNRrNnz55y5cr16NGjZ8+evr6+e/bseflSPxEZMmTI5s2b\n33nnHTNlFClSRKvVOjo6Hjhw4MWLF61atZo3b966detEJKsbEbIqQKfTHTx4UETefPPN9957\nr2vXrvPnz2/WrNmsWbM++OCDoKCgffv2HT58uH379jNmzMi0PMtnTSm7bdu28+fPX7RoUVBQ\n0NixY3v06DF9+nQzM5sjGkMWzzks5BISEoyHXvPb1X/4mL7sMFE8XGXj3P9pU7179o//MeXt\n7S0iT55kf1O6Opguw5+OyYK18ue3pLPJRa45XYCOjo7Kf6cSExPzqsjCLMNG2G+mxMbLPxf9\nT5ucLkNPT087O7uYGEufU2/rMizDl+V0ATo4OLi7uxfkZ5F1ZbsAJefL0MPDQ6fTxcTE2Og3\nkaJYseyvacspg8GQMnV8ts3m/3xq9tHwLd07dq5aKdvGjguW5EVp2Ttz5kyDBg3S0tLszF4O\nmCcePnx44sSJ7t27Ky8vXrwYHBwcFxeXaah6TXDEDgAAm6R9jeOLwmAwDBgw4PPPP3/48OG1\na9fef/995fdtrV2XNRHsAACATSpZsuSOHTu+//77ihUrtmrVqmrVqgsWLLB2UVbGc+wAAECe\nqV+/fkGeW3/zzTfffPPNAptc4ccROwAAAJUg2AEAYJNqFPXuWb1KaTdXaxeCQoRTsQAA2KQu\nVSt1seB+WLxWCHaA7enaTJJSrF0EgPykmRual7293jeKvlYIdoDt6c+FwoCqaTSal39gFLAE\nwQ4AgMLFYDCkpOTlYXmS4uuDYAcAQKFzeq1T3nbYeIQN/7wHLMddsQAA2KSzV+Xr7XIrytp1\noDAh2AEAYJMu3pQNe+XOQ2vXgcKEYAcAAKASBDsAAACV4OaJHFvyodiRhwEAQOFDsMuxKmWt\nXQEAAEBmOPQEAICNuXhT5q6Wjf8SEVm0UT7fmF+3UKxfvz4oKMjd3b1UqVIdOnQIDw/P80kc\nOXKkQoUKLVq0MN/Mzc3twIED5ttcvXpVo9E8f/48n2qwCQQ7AABsydfbZPQC2RcuL+JFRKKf\nyvZD8tZfZdexHHeVkpJStGjRmJiYTN9dtmzZO++806tXr4MHD27cuLFkyZItWrQ4f/68+T6b\nNm26cOFCy2tYsmRJvXr1tmzZYknj27dvazSa5cuXW95/ntfwsmxnOZ/KzhSnYgEAsBmb9suG\nfZkMT02TT9eJj5cE17aon6SkpBMnTixfvvzJkydZtVm6dOmECROmTJmivGzRosWNGze++eab\nJUuW5Kb0LMTHx4eEhPj4+FjS2MPDY+rUqf7+/nlYQI5qSElJuXfvXsWKFXPUfz6VnSmO2AEA\nYBsSkmT1T1m+m26QLy0+5LR06dIhQ4YcPnzYTJsHDx4kJiaaDvniiy/69eun/P38+fNRo0ZV\nqFDBw8OjS5cu169fF5HAwMBjx45NmjSpffv2GXp79OjRwIEDS5YsWbp06UGDBj169EhEWrZs\nuXfv3tmzZzds2DBD+2vXrr355puenp716tX76af/zLanp2doaGhaWpqI7Nq1KyAgwNnZuWLF\nil988UWG0U+dOuXp6fn1119n2zJDDZnWKSI6nW7Xrl1lypQZN26c6ehmZtnItOz8RrADAMA2\nnLosCUnmGkTet/Riu8mTJ9+5c2f37t1m2nTq1Onrr7/u37//vn37lITn7+/fqFEj5d1u3bpd\nuXJl7dq1+/fvd3Fxadas2bNnz06dOtWkSZPQ0NA9e/aYdmUwGDp27Hjjxo1NmzZt3Ljx+vXr\nHTp0MBgMhw4dateu3ezZs3/++WfT9vHx8c2bNxeRnTt3fvTRR+PGjUtISDBtcOfOnZ49e7Zt\n2zYsLGzUqFETJkwwvf7v/PnzSrejR48231JETGvIqk6l5YcffrhgwYKlS5eajp7VLFsLp2IB\nALAN9x9n3+ZutJQrkTeTW7VqVfXq1Tdu3NiuXTtHR8fGjRu//fbbgwYNEpGTJ08eP3784cOH\nXl5eIrJ+/XpfX9+jR4927tw5067CwsLOnj178+bNcuXKicjmzZsrVap09OjRZs2aZdp+w4YN\nycnJ27Ztc3NzE5EiRYpkOB529erV1NTU9957r0KFCoGBgVWqVClevHhqaqqIXL58uWvXrqNH\nj54wYUJWLbOaZfN1jhgxYtiwYblZlAWII3YAANgGjSb7Ntq8+2J3dHScOXPmpUuX7t27t3Ll\nSi8vrxEjRkycOFFEIiIiUlNTixcvrtPpdDqdk5PT3bt3lbOxmYqIiPD19VXSkoiUL1++QoUK\nERERZtoHBQUpqU5EWrZsqfnfmQ8JCQkMDKxTp87gwYPXrl3boUOHSpUqKW917tzZYDDcuHEj\n25Y5rdPPz8/sAisUCHYAANiGslkeafqvcha0scSlS5fatm2rnIEtXbr04MGDt27d+tVXX335\n5ZcpKSkeHh7e3t6pJgwGg5L5MpWenp4hmWm1WjPXnNnZ2Zm+1Gg0GUZ3LAp+/wAAIABJREFU\ndXU9efLknj173Nzc5syZU758eeOZ0OnTp2/evHnTpk0HDx403zKndTo7O2c1YuFBsAMAwDY0\nqCnuLuYa1KggpS26uzR7pUqV+ve///3Pf/7TdKBer3d0dLS3t69du/aTJ08uXryoDH/8+HG3\nbt0uX76cZWE1akRGRt67d095effu3Vu3btWqVSur9jVr1jx9+nRcXJzy8tixY+np6aYNwsLC\nQkNDmzRpsmzZst9//71hw4YrV65U3nrnnXdatmzZr1+/MWPGpKammmn56nUWQgQ7AABsg5OD\njOqR5bv2djK2z6tOYs2aNcqdpN7e3uPHj3/77bdnz569f//+w4cPh4aGTpkyZdy4cVqttlq1\naj169Bg4cOChQ4eOHj06ePDgiIiIatWqiYhWq71x48azZ89Mu23VqlXdunX79u174sSJ48eP\n9+3b18/Pz8wDgfv37+/g4NCnT5+ff/559+7do0ePdnH5n0ibkpLy5z//eenSpRcuXNi5c+el\nS5cynCcNDQ29c+fOF198kW3LV6lTkeksWwvBDgWtdDFpWV9KF7N2HQBgg7o0lVE9MvnJcmcn\nmTVc/Kq+av8bNmxYvXq18ndoaOinn366c+fOXr169ezZc8uWLaGhoR9//LHy7rp16xo3bvzW\nW2917tzZ0dFx79699vb2IjJkyJDNmze/8847pt1qNJo9e/aUK1euR48ePXv29PX13bNnjybr\nawadnZ3DwsLS0tLat28/derUBQsWdO3a1d3d3digbdu28+fPX7RoUVBQ0NixY3v06DF9+nTT\nHsqUKTNr1qy//vWvtWrVMt/yVepUZDrL1qIx3sRrWxISEjLc+Zx/rv4j++Pa1bs/ylGf3t7e\nImLmsZAqk+0yzOkCdHR0dHNzi4+Pz/CMJbXKj43Q09PTzs4uqyfOq0+eb4QODg7u7u4F+Vlk\nXfmxEXp4eOh0upiYGBv9JlIUK5b3/081GAwnVpk78nIrSnaEyeFf5dEz8S0lLQKkW3Mp5mmu\nz8YjbHghw3I87gQAABvjW0rG9xNvD1n+D3m3uzQtiF80gG0g2AEAALyqo0ePzpkzJ9O3hg4d\nOnDgwIIpg2AHAADwqpo2bbp//35rV8HNEwAAAGpBsAMAAFAJgh0AADYpoLqM7iEVS1u7DhQm\nXGMHAIBNql1Jamf5q6d4TRHsAAAoXDQaTciwLH9HFTCDYAcAQKFjZ2dn7RJgkwh2AAAULgaD\nIW9/Vkej0RQpUiQPO0ShRbADAKDQeeuQS/aNcmJrR35S7LXAXbEAANikO0fk6EyJibB2HShM\nLD1iZzAYLl68GBYWduvWrYcPHxoMhpIlS/r6+jZv3rxOnToajSZfqwQAABkkPJToc1K5k7Xr\nQGGSfbC7efPm8uXL16xZEx0dbW9vX7Ro0aJFi4rIkydPHj9+nJaW5uPjM3To0FGjRlWqxF3X\nAAAAVmPuVGxMTMyIESOqVat24sSJadOmhYeHx8XFPXjw4NKlS5cuXYqKioqLizt58uSMGTPC\nw8OrVas2YsSImJiYAisdAAAApswdsfPz8+vdu/eNGzcqVKiQaQNHR8egoKCgoKDx48f/8ccf\nixYt8vf3v3PnTv6UCgAAAHPMBbvTp0+XLFnSwo7Kly+/aNGiKVOm5EVVAAAgG/oUEZH0VGvX\ngcLE3KlYM6lOr9fv2rVr586dL168MB1eqlSpPCsNAAC8xJAuN3bJv0ZLxEYRkZOfyuFJcv/n\nvJ9Q586dNf/P2dm5UaNGy5cvz/vJ5I/bt29rNJqwsLD8npBOpzt+/Hh+T8Vylj7uJD4+fsSI\nEdWrV1deduvWrXPnzl27dq1Xr94ff/yRb+UBAID/0qfI8Vly7u8Sa7zuySAxV+TneXLu7yIW\nP6vu4cOHb731VunSpb28vNq1a3fhwoVMm7Vp0yY8PDw8PHzbtm3NmjUbM2bMBx98oLylJCcz\nUa9p06YLFy60eM4KWrb12yhLg92sWbNWrVrl7+8vIj///POuXbuGDx++c+fOZ8+effzxx/lZ\nIQAA+I/fVsvDs5m/dWOX3NxraT8DBw68cOHChg0b9u3b5+7u3qpVq6ioqJebFStWLDg4ODg4\nuH379vPnz1+zZs2SJUt+//13EfHw8Jg6daoSDGyRrdefFUuD3bZt2zp16vTDDz+IyK5duxwd\nHUNDQzt37tytW7cDBw7kZ4UAAEBEJOmp3NxjrkHERjFYcNDu3r17Bw4cWLZsWcuWLYOCgjZs\n2GAwGH766adsR+zfv3+5cuXWrl0rIp6enqGhoWlpaSKya9eugIAAZ2fnihUrfvHFFyISGBh4\n7NixSZMmtW/fXkSuXr3arl07Ly8vd3f3Fi1aGA8Q6nS68PDwPn36VKpUqUqVKlu3blWGR0dH\n9+3b18fHp1SpUuPHj09JSRGR58+fjxo1qkKFCh4eHl26dLl+/Xr2syoiIo8ePRo4cGDJkiVL\nly49aNCgR48eZag/qzKMzpw54+Pjc/z48YYNGxYrVqxVq1YREf99MPTDhw87derk6elZuXLl\nLVu2KAOzmuWXl9WrzNrLLA12Dx48CA4OVv4+duxYUFCQh4eHiFSvXv3+/fu5njwAALBQ9Fkx\n6M01SHoiz29m349er589e3b9+vWVl6mpqUlJSenp6dmOqNVq69ate/Pm/0zjzp07PXv2bNu2\nbVhY2KhRoyZMmBAeHn7q1KkmTZqEhobu2bNHRAYOHJicnLx169YdO3YYDIYRI0YYR586der8\n+f/H3n0HNHXv/+N/JSEgO0w3QxFFrCjIahEEhYoDERVvRRx1oPgRsbVqcWtVVKzW9urttVXr\nqNZRK/WKXusAHNDKT0UQEUG9gBUVCkZmQvL7I/2mKSMDQxbPx1/JOe+8zzNvj/DifVZiQUFB\nVFRUdHS0KElwcDCXy01OTt66devRo0c3bNhAROHh4Q8ePDh48ODFixeNjY39/f0rKytlZhYK\nhaNHjy4sLDx27NjRo0cfPXo0atQoYbP6t3mMJg24XO7SpUuPHTtWXFzs7e3t7+/P5XJFqxYt\nWjRz5szr16/7+flNnz5dVIa2+JVbHKs2f7UWyfvkie7du9+5c4eISkpKrl+/vmrVKtHy3Nxc\nGxubtm0bAAAA5Fcrx71ia18Rp7eMNnZ2dmvWrBG9rqmpmT59uqWlZWRkpDwZbG1tm8wn5efn\n83i82NhYe3t7T09PJycnW1tbyQZCoTAyMnLixImiBxk8e/YsPj5evFY0T0ZEs2fPXr9+fWlp\n6YMHD4qKilJTUzkcjq+vb01NzY0bNzIzM69fv15WVmZhYUFEhw8fdnBwSE9PHzt2rPTAqamp\nt2/fLioq6tmzJxEdP368V69e6enp/v7+ks2ax+jd+2/jWF9fv3r1atEN4DZu3Hj8+PGDBw8u\nWLCAiGJjYydMmEBEa9euPXjw4O+//25nZ9fiV25xrNr81Vokb2E3ceLE7du3x8fHp6eni/6F\nampqvv7665MnT4aFhbVhwwAAAKAQloEcbTrJ25tQKDx06NDKlSsdHR2zsrIsLS3l+dSLFy+6\ndesmucTHx8fT03PAgAHh4eHBwcGTJk0yNDSUbMBgMBYvXpyRkXHu3LmsrKxz585JrnVxcRG9\nMDIyEr3IyclxdXXlcDiitzExMTExMQcOHODxeJIlI5/Pl+eQZV5enoODg6iqIyI7Ozt7e/u8\nvLwmhV3zGM15enqKXjCZTA8Pj/v37zdZLv5sa1+5xbFKS0tr21drkbyHYlesWDF69Ohdu3bd\nvn173bp1Li4uxcXFH330UefOndevX9+2bQMAAID8zB1lNGAwyLzlRwo09fLly8DAwFWrViUm\nJl65cqXJHFtrRA+ObzKVZWJikpmZmZKSYmpqun79ejs7O9HhV7GampqAgIAPP/zw6dOn48eP\n37x5s+RafX39Jlvh8Xh6ek0nnszNzS0tLXkShELh4sWLJdusXLlSfB2u6OQ5IhIIBE2eaM9k\nMsVrpcRoTvJoNY/HE79tUshS61+5xbGS56vJT97CztTU9KeffqqsrKyqqlq5ciURdenS5Zdf\nfrl3716fPn3atm0AAACQn3V/MpZ6u9jOQ8iAI7sfoVA4atQoc3Pz7OzsKVOmMJnyFgM//PDD\n06dPp06dKrkwNTU1KSnJz89v9+7dBQUFvr6+e/fulWxw5cqV7OzsnJycbdu2hYWFyTyZz8XF\nJScn582bN6K3R48eDQkJcXV1raioyMnJES189epVeHi4eM5MJC8v7/vvvxe9Ft2LrXPnzv36\n9Xv8+HFpaaloeUlJyZMnT/r37y/nV5aUlpYmelFbW3vjxg3xJF9zrX3lFsdKnq8mP2mHYktL\nS7t37y65xMzMTPza3Nx8+PDhMj8CAAAASsFgkfv/0fU1JGg630REpG9KbnNaWN7c5cuXs7Ky\nFi9efOvWLfHCvn379ujR48CBA7W1tfPnzxctrKioyMrKIqI//vgjNTV1y5YtcXFxzs7Okr01\nNDQsW7bMwMAgICDg8ePHubm506ZNIyImk1lYWFhZWWlqalpdXX3q1CkfH5/Lly+vW7eOy+Vm\nZ2cPHDiwxXhhYWE2NjZTp05duXJlSUlJQkLCuHHjnJ2dIyIioqKidu7cqaent2nTpqKioiZJ\nRowYsWDBgsOHD/fv33/9+vV9+/bt06dP3759Bw4cOHny5K1btwqFwqVLl7q5uQ0bNkyukfq7\nxYsX6+np2drabtq0qbGxccaMGa21bO0rtzhW8nw1+Ukr0n18fOLj4588eSJPR48fP46Li/Px\n8WlbDgAAAJDJ1o18V7YwLWdmR/6byKRbS59p5u7du0KhMCoqaoSEM2fOENGRI0f27dsnbvnf\n//53yJAhQ4YMGTt27C+//LJr167PP/+8SW/BwcGJiYk7duzw8vJauHBhREREQkICEU2fPv34\n8eOzZs3y9/dfs2bNRx995OXldeHChatXr4aGhq5YsaJ5MENDQyaTyWazL1++TEQhISGxsbHj\nxo1LTEwkokOHDr333nvTpk0bO3asgYHB+fPnmxyxjYmJSUhI+PTTT4cNG8Zms8+cOcNisRgM\nRkpKSs+ePSMiIiZMmODg4JCSktLk4GyLMZov37Nnz6pVq0JCQrhcblpamuRsVxOtfeXWxkrm\nV5Mfo/kVv2IVFRWffvrpt99+6+PjExER4evrO3jw4E6d/jots66u7vbt2zdv3jx16lRmZuas\nWbM2b94s59mXb6mmpqampkYFGyKi/NOyL/vtO/6lQn2KRqmioqKNmbSNzDFUdAANDAxEfw/V\n1ta+RS6t0R47IYfDYbFY5eVyXGWnE5S+E+rr65uZmanyZ5F6tcdOaG5uzmazy8vLpfwm0nzW\n1tZK71MoFE46J23mpbGeSq9T4VmqeEhdvcjxferiSQypB1RPjtbiQVa7rKysIUOG8Pl8Foul\n7iwySKsHLS0tv/766+XLl3/99dfbtm17/vw5i8WytLQUFyUVFRWNjY1dunSZPn364cOHHR1l\nndUJAAAAb41lQHZBVFtOFQ/JIYS6eqs7EGgM2RN9jo6OiYmJiYmJubm5aWlpjx8/LisrYzAY\ntra2jo6O/v7+rq6uKggKAAAAANIpcATX1dUVNRwAAAB0NB4eHtpywoBip+a9efMmMzPz5cuX\nw4YN43A4bDa7DQebKysr9+/ff+fOnYaGhr59+86YMcPBwYGIGhsbv/vuuxs3bvD5fC8vrzlz\n5rDZbEU7BwAAAOiw5L11DRHt3bu3W7duI0aM+OCDD/Lz8zMzM3v27HnkyBFFN7l9+/YnT54s\nWbJk3bp1hoaGK1as+OOPP4ho37596enpc+fOjYuLu3379ldffaVozwAAAAAdmbyF3X/+85+Y\nmBgPD49Tp06Jljg7O7u6uk6dOrXJs0GkKy8vv3v37vz589955x1nZ+clS5YQ0a+//lpbW3vx\n4sXZs2d7eXm5u7vPmzcvPT29qqpK0e8DAADQQfT0p6GfkVWrd8mFjkjeQ7GJiYkDBgy4ePGi\n+MYqXbt2vXDhgqenZ2Ji4qhRo+TsRyAQfPDBB+KnkfD5/IaGBoFA8PTp07q6ukGDBomWu7m5\nNTY2FhUVDR48WLREKBRyuVzJfqTfhEbF2hZGo76Ceik6FOL2GEMx7IRvqc0DiDEUw06oYkad\nyaizukOAhpG3sLt79+6SJUua3C6PyWSOHj36yy+/lH97NjY2H3zwgeh1fX39zp07TU1N/fz8\ncnJy9PT0jI2N/4ylp2diYiJ5m7fKysrg4GDx27lz586dO1f+7bY3KysrlX1KJ7VtKIyNjcX7\nDGAnfEttGwpDQ8Pmj4nssNo2hqq5+2k7qa+vb49uGQzGiVEynrsF0CJ5CzsLC4u6urrmy/l8\nvqmpqaJbFQqFV65cOXz4cOfOnXfs2GFqaioUCpv/0dbY2Ch+zWazvby8xG+7du3K4/EU3W77\nUTSMqERu/hDiDkvRAWQymSwWq7GxUeYzBzuONuyEDAZDo/4fqZeiQ8FgMPT09AQCgeRPqg6u\nA+6E7fevj4lMaBt5Cztvb++DBw9+8sknFhYW4oUvXrw4cOCAoo8Rq6qq2rJlS1lZ2fTp0/39\n/UX7rqWlJY/Hq62tFf3t29jY+ObNG8nbeZuYmOzevVv8tqamRqPOwFM0jOgvVI36Cuql6FCI\nnjxRV1fXQZ48IQ9Fx1D05AnshGKKDoXoyRN1dXUd5MkT8lB0DEVPnnj9+rW23EiiRUZGRkrv\nUygUMtN2y26nUJ8BC5TbIWgmeS+e2LJly+vXrwcNGrRp0yYiOn/+fEJCgqurK5fL3bJli/zb\nEwqF69atMzIy+vLLLwMCAsR/kdjZ2RkYGNy7d0/09v79+0wmE4+yAAAAAJCfvDN2jo6O6enp\ncXFxoqf2ih7HO3z48G3btvXp00f+7WVnZxcWFo4bN66goEC8sHv37tbW1iNGjNi/f7+VlRWD\nwfjmm28CAgIkZwcBAADgb05eoe9SaOUM8u6v7iigKRS4QbGbm1tqampFRcXDhw/19fWdnJzM\nzMwU3d7jx4+FQuH27dslF8bExIwePXr27Nn79u3buHGjQCDw9vaePXu2op0DAAB0IPU84tYQ\nTtcGCYo9eYKILC0tFT2pTlJ4eHh4eHiLq1gs1pw5c+bMmdPmzgEAAAA6MnkLu5KSksWLF2dm\nZjY/V93CwuLhw4fKDgYAAAAAipG3sJs7d+4vv/wyatSoLl26NLkGuw2PiwUAAAAApZO3sLt2\n7dqRI0cmTZrUrmkAAABALtW1VPqSiOjpc/J0IX22ugOBRpD3dic2NjYeHh7tGgUAAABke11D\nWw5T+HI6d5OI6N/JNP5T+vZn4in/KorGxsZt27YFBQVZWFj06NEjLCzs5s2bSt+K2pmaml66\ndOltGmgOeQu7sLCwI0eOtGsUAAAAkKG8iuZtpZQM4kk89KK6jg5doCX/pAZ5H+Px4MGD0aNH\nW1padu7cefLkycXFxc3bvH79OiAgYMuWLSNGjDh27NiuXbs6derk5+f3448/Su986NCh4ttf\niF8/ffqUwWB8/fXXciZUvbdJKPmV1UveQ7Fbt2597733cnJyhg8f3vzpnFFRUcoOBgAAAM0k\nHaVnr1pedbeAvkuhOWEy+6ivrx89erSHh8d///vfZ8+ebdmyZeLEiZmZmU2abdiwoaioKDs7\nu1u3bqIlERERCxcunD9/flhYWJPHx8tkbm6+fPnyQYMGKfQpVdL8hPKQd8buP//5z927d48f\nPx4TEzO1mXaNCAAAAERExS/oZo60BqfT5Dkge+fOnaKioq+//nrIkCFhYWEbNmz49ddf37x5\nI9mGy+Xu2rVrw4YN4qpOZO3atbt27RI1rqqqmjdvnr29vbm5eVhY2KNHj4jI09Pz2rVrS5Ys\nCQ0NlXzN4XCSkpJED0lns9kZGRmRkZG9evVycnI6efKkqPOSkpKwsDALCwt3d/fk5GRTU9Pc\n3Nwm4V+8eDF58mQbG5uuXbsuWrSooaGBiPLz80eOHGlhYWFmZjZs2LDs7GxR49Y29PDhw5CQ\nEA6HM3jw4J9//lm0UDJhiw1a25Dk12xtWIjo7Nmz7u7uRkZGjo6OX3zxhcx/praRt7DbsGGD\nr6/v1atX8/LyHjTTTuEAAADgL3cLZDSoqaOHLRxUbWLIkCFv3ryxsLCorq6+e/fuiRMnPD09\nTUxMJNvk5eU1NDQEBgY2+ayVldXkyZM5HA4RhYeHP3jw4ODBgxcvXjQ2Nvb396+srPztt9/8\n/PySkpJSUlIkXzfpZ/ny5YmJiQUFBVFRUdHR0XV1dXw+f/jw4USUkpKyYsWKmJiY6urqJp8S\nCATBwcFcLjc5OXnr1q1Hjx7dsGEDEUVFRdXX1588efLMmTNCoVDynrjNN1RdXR0QEEBEycnJ\nq1evjouLa/LEZykNWtxQk6/Z4rAUFxdPmDAhODg4NTV13rx58fHxGRkZMv+l2kDeedTCwsKb\nN2+6uLi0RwgAAACQrfKNHG24MpuwWCzRWVUjR468du2ahYXF9evXm7QpLS0loq5du4reVlVV\niYo5kX/961+DBg26fv16WVmZ6Pmfhw8fdnBwSE9PHzt2rDxfRTSLRkSzZ89ev359aWnpnTt3\nXrx4kZWVJSoxuVzuzJkzm3wqJSWlqKgoNTWVw+H4+vrW1NTcuHFDKBRGRkZOnDhR1OGzZ8/i\n4+OlbOjSpUv19fWnTp0yNTUlIkNDQ9FMm9iRI0dabCB9QyKZmZktDouhoSGPx4uNjbW3t/f0\n9HRycrK1tZVnoBQlb2Hn6enJ5creVwAAAKC9mBjK0cZI/v7OnDnD5XL37t3r7+9fVFQkqmNE\nHBwciOjp06f9+vUjIhMTE/EM05QpU4goLy+Px+NJVid8Pl982FEm8VSRkdGfgXNycgYNGiSe\nOPTz82v+qZycHFdXV3GJGRMTExMTQ0SLFy/OyMg4d+5cVlbWuXPnpG8oLy/Py8tL/GUDAwOb\n3KC3tQYMBkPKhsSfbXFY5syZ4+npOWDAgPDw8ODg4EmTJhkayvGvqTh5D8UmJiYmJCQ8ffq0\nPUIAAACAbAN6yWigz6Y+PWR2c+/evQsXLhCRpaWlvb39hg0bampqrl69Ktmmb9++bDb7/Pnz\norcsFsvb29vb29vd3f3Zs2dEZG5ubmlpyZMgFAoXL14s51fR19dvsoTH40kWWExmCyUKj8dr\nftFGTU1NQEDAhx9++PTp0/Hjx2/evFn6hpo8WIHBYEh/8oK4gfQNibQ2LCYmJpmZmSkpKaam\npuvXr7ezs2t+eFop5C3sPvvss5KSkt69ezs7Ow9upj2SAQAAwN849ZBR273vRUadZHZz9+7d\n6Oho0VUCRFRVVVVXV8dm/+0Wx0ZGRnFxcZs2bXry5Ink8s2bN9fV1RGRq6trRUVFTs6fF3O8\nevUqPDz8/v37Cnydv3N1db179674bLYWT0FzcXHJyckRX+dx9OjRkJCQK1euZGdn5+TkbNu2\nLSwsTCAQSN+Qi4vLrVu3xJ1cu3atyUdaayDPhlobltTU1KSkJD8/v927dxcUFPj6+u7du1fO\nkVGIvIUdn8/v06dPaGho3759ezTTHskAAACgqaVRZNb0pmN/cuhCc8bJ08fIkSMFAsHs2bOz\nsrKuX78+efLk3r17+/v7E9GBAwf27NkjarZq1aqePXu6u7tv3rz54sWLp0+fjoqKOnnypKur\nKxE5OztHRERERUVduXIlPT09Ojo6Ly/P2dmZiJhMZmFhYWVlZZPX0kVERJiZmUVHR4uOcm7c\nuFFPT6/JvF1YWJiNjc3UqVNv3br1008/JSQk9O/f39TUtLq6+tSpU0VFRd988826deu4XK74\nwtjmPvjgA319/cjIyJs3b547d27+/PlN7uPWWgMpGxJ/zdaGpaGhYdmyZV9++WV2dnZycnJu\nbq6bm5s8/1iKkvccO8lrfQEAAEA97DrT7iWU9D3dkbhClsmgIA+Km0Rmcp1gZ21tfe7cuSVL\nlgQFBRkZGQUEBPz73/8WnYJ25MiRysrK+fPnE5G5ufnNmzc/++yz06dPb9q0ycnJafjw4Xv3\n7j1w4IDoHiiHDh1asmTJtGnTuFzusGHDzp8/LzpOOn369KVLl5aVlZ06dUrydWt5DA0NmUym\ngYHBpUuXYmJigoKCBgwYcOjQIW9v7yZXGLDZ7MuXLy9cuDAkJKRTp06RkZGJiYmdOnVas2bN\nRx99xOfzAwMDr169umTJkhUrVjQvXUQbMjIySk1NXbBgQWhoqJ2d3ZYtW06cOGFmZiZu1loD\nT0/P1jYk+TVbHJbg4ODExMQdO3Z88skntra2kydPTkhIkOcfS1EMoVDYHv22t5qamiYXJ7ef\n/NM2Mtv0Hf9SoT4tLS2JqKKioo2ZtI3MMVR0AA0MDER/OdXW1r5FLq3RHjshh8NhsVjl5eVt\nDaVllL4T6uvrm5mZqfJnkXq1x05obm7OZrPLy8u19DeRiLW1tdL7FAqFzLTdMho9eU7f/kzp\nd2lSIE0Mos4WMvoMWKC0fO2jrKzsxo0b48ePF73Nycnx9vZ+8+ZNkxPgQDoZM3YMBqNLly6/\n//67p6enlGa//fabUlMBAACAVA5dqJ89pd+lQX1kVnVaQSgUTpkyZePGjVFRUVVVVQsWLIiO\njkZVpygZhV2XLl1sbGyoff4iAQAAABDp0qXLmTNnEhISVq5caWlpOXLkyC1btqg7lPaRUdj9\n/vvvohftdFEuAAAAgEhISEhISIi6U2g3aVfF9unTZ8eOHSqLAgAAAABvQ1ph9+jRo45zdj8A\nAACAtpP3dicAAACgWSYFUpgfGRqoOwdoEBR2AAAA2kmfTfps2c2gI5FR2KWnp2/cuFFmLytW\nrFBSHgAAgI6OwWBo/m3nQDPJKOxSU1NTU1Nl9oLCDgAAQIlkPvBUUU2ezQW6SkZhN2PGjHnz\n5qkmCgAAABCRUChkXVDyxYvCUNyPtkOQUdj16NHD29tbNVEAAAC3AGA5AAAgAElEQVRAAdVV\nxK0kC1syMFR3FNAUmJgFAADQTv/ZTx8OodtX1Z0DNAgKOwAAAAAdIa2wmzFjxuDBg1UWBQAA\nAADehrRz7Pbv36+yHAAAAADwlnAoFgAAAEBHoLADAADQNkIh5WbSvZtERL/+l57kqTsQaAoU\ndgAAAFqlKIcWBtInYyjrMhHR+cMU60+rJlP578rdzvjx4xnNyPM8qjYzNTW9dOlSk4VpaWn2\n9vbDhg0jIgcHh/PnzyvUZ35+PoPBqKqqUlZIDYdnxQIAAGiPwnu0NIxq3zRdnnWZPhlD21PI\nwlah/tLT04cNG/bixQsrK6vmawMDAzdv3iy5pEePHgomflu7du0aPHjw3r17iai6urqxsVHF\nAbQLCjsAAAAtIRTSzkUtVHUiz/9H366lJbvl76+qqio6OlrK48usrKzU/pyC6upqHx8fGxsb\n9cbQFjgUCwAAoCUe3qbCe9IapP1E1Qocc5w/f76trWIzfCIvX76Miorq0qVLt27dpk6d+vLl\nSyKqrq5mMBi5ubmiNpLHQNlsdkZGRmRkZK9evZycnE6ePPnnF3r4MCQkhMPhDB48+Oeff26+\nocDAwPPnz69du9bX11dmACJ68eLF5MmTbWxsunbtumjRooaGBslP/fbbbxwOZ8+ePUR09uxZ\nd3d3IyMjR0fHL774og2DoJlQ2AEAAGiJgtsyGvB5VJgjZ2eHDx++devWtm3bpLSpqKjIkvDg\nwQMiEgqFo0ePLiwsPHbs2NGjRx89ejRq1CihUCh9c8uXL09MTCwoKIiKioqOjq6rq6uurg4I\nCCCi5OTk1atXx8XF1dTUNPnUlStXRo4cuXbt2ps3b4oXthZAIBAEBwdzudzk5OStW7cePXp0\nw4YN4k/dvXtX1NX8+fOLi4snTJgQHBycmpo6b968+Pj4jIwMOcdNw+FQLAAAgJao4crR5rU8\nPT1+/Dg+Pj4lJYXJlDbFc/ny5SFDhojf+vj43Lx5MzU19fbt20VFRT179iSi48eP9+rVKz09\n3cPDQ0pXouk6Ipo9e/b69etLS0svXbpUX19/6tQpU1NTIjI0NAwNDZUnfGsBuFxuUVFRamoq\nh8Px9fWtqam5ceOG6CP3798fN27c/Pnz4+PjiSg/P5/H48XGxtrb23t6ejo5ObVt5lIDYcYO\nAABAS5jLcZ6ZHBdPNDY2RkdHL1682NPTU3rLiRMnCiWIps3y8vIcHBxERRUR2dnZ2dvb5+XJ\nuOWKi4uL6IWRkZHoRV5enpeXl6iqI6LAwEAGgyEzvJQAOTk5rq6uHA5HtDwmJua7774TvR47\ndqxQKCwsLBS99fHx8fT0HDBgQHR09MGDB0eNGiUqOnUACjsAAAAt4TaUpJc+JhzqPVBmN198\n8cWrV6/Cw8Pz8/OfPHlCRAUFBc+fP5czhUAgaFKBMZlMPp/fpFltba3kW319/SYNWCyW5FvR\n7VTeJgCPx9PTa/lQZEJCwvHjx48dO3b58mUiMjExyczMTElJMTU1Xb9+vZ2dXUpKijyb1nwo\n7AAAALREFzsaOk5ag/HzSY8ts5uCgoL8/PwBAwb069dv4sSJROTr6/vpp5/KmaJfv36PHz8u\nLS0VvS0pKXny5En//v1Fb//44w/Ri6ysLOn9uLi43Lp1682bPy/yvXbtmpTrc+UJ4OLikpOT\nI+7w6NGjISEhotezZs0KDAz8xz/+8X//9388Hi81NTUpKcnPz2/37t0FBQW+vr6i26noABR2\nAAAA2mPBVnJwaXmVVwhNWihPH3v27BEfXb116xYRvXr1SvSA+AMHDoguGpUiKCho4MCBkydP\nvnHjxvXr1ydPnuzm5jZs2DBjY2MbG5vExMTc3NxLly599dVX0vv54IMP9PX1IyMjb968ee7c\nufnz5xsbG8uTv7UAYWFhNjY2U6dOvXXr1k8//ZSQkCAuN0WSkpKKi4u/+OKLhoaGZcuWffnl\nl9nZ2cnJybm5uW5ubvJsWvOhsAMAANAepha0PYUiYsnI9K+Flp1pznpafVCe6Trpjhw5sm/f\nPultGAxGSkpKz549IyIiJkyY4ODgkJKSIjo2eujQoUePHvn6+m7cuPHIkSM+Pj5NjreKGBoa\nMplMIyOj1NRUPp8fGhq6fPnyLVu2jBs3zszMTGbI1gKw2WzRkdaQkJDY2Nhx48YlJiZKfrB7\n9+5r1qxZt25d//79ExMTd+zY4eXltXDhwoiIiISEBAWGSYMxZF6frJlqamqaXxTdTvJPyz5Z\nte/4lwr1aWlpSUQVFRVtzKRtZI6hogNoYGBgampaXV3d5BwOXdUeOyGHw2GxWOXl5W0NpWWU\nvhPq6+ubmZmp8meRerXHTmhubs5ms8vLy7X0N5GItbW10vsUCoXM87L+b/J5tHcV/fwtzd9M\nYz4khoyZGmGo8nOCBsKMHQAAgBbSY5NVFyIim+4yqzroOLArAAAAAOgIFHYAAAAAOgKFHQAA\nAICOQGEHAACgnUw41MWeDIzUnQM0CJ4VCwAAoJ1GzaBRM9QdAjQLCjsAAACN08uohdu/AciE\nwg4AAECzMBiMwgALdacArYTCDgAAQOPw+Xzldqinh9/4HQL+mQEAADSLUCg8vkPJv6CnfKLc\n/kBD4apYAAAArfTsxYPM7BN/vH6m7iCgQVDYAQAAaKVbuad3HY4sKv5N3UFAg6CwAwAAANAR\nKOwAAAAAdAQKOwAAAAAdgcIOAAAAQEfgdicAAABa5k1N+bWsQzduf09E56/tfFNb4ev2D322\nobpzgfphxg4AAECb3LxzLD6x16GfFxc/zyGi+4VX/338wyXb+j58cl25Gxo6dOicOXOaLOza\ntev27dsV6sfU1PTSpUvKywXSoLADAADQGlm5Z/55NKq27nWT5eWVxVu+DS1+fk/OfhITExkS\n2Gy2spMqYOjQoaJi8enTpwwG4+uvv9a0DrUIDsUCAABoB35jw4Gf/k8oFLS4tq6eeyh5ccLc\nX+TpKj8/f/To0QsXLhS9ZTAYSkv5FszNzZcvXz5o0CCN7VDzYcYOAABAO+QVpVZUlUhpcL/w\nspwPosjPzw8ODn7//wkJCVE0TFZWlo2NzfXr1319fa2trYOCgvLy8kSrHj58GBISwuFwBg8e\n/PPPP0tudOTIkRYWFmZmZsOGDcvOziYiT0/Pa9euLVmyJDQ0lMPhJCUliZ6Ty2azMzIyIiMj\ne/Xq5eTkdPLkSVEnJSUlYWFhFhYW7u7uycnJpqamubm5ksFa69DQ0PD8+fOjRo3q0qVLUFBQ\naWlpfHy8i4tL586d//nPf4o+W1VVNW/ePHt7e3Nz87CwsEePHomWnz171t3d3cjIyNHR8Ysv\nvlB0rFQJhR0AAIB2KH2eK72BUCgsKZPRRiQ/P/+XX37p0aOHpaXlmDFjHj582IY8XC536dKl\nx44dKy4u9vb29vf353K51dXVAQEBRJScnLx69eq4uLiamhpR+6ioqPr6+pMnT545c0YoFIpO\n4Pvtt9/8/PySkpJSUlKa9L98+fLExMSCgoKoqKjo6Oi6ujo+nz98+HAiSklJWbFiRUxMTHV1\ndZNPSekwKSnp6NGjeXl5ZWVlzs7OQUFB9+/fX7p0aXx8vKif8PDwBw8eHDx48OLFi8bGxv7+\n/pWVlcXFxRMmTAgODk5NTZ03b158fHxGRkYbhks1cCgWAABAO/Aa62W34dXJbPPq1auKigom\nk/n999/z+fwNGzaIShwzMzOF8tTX169evdre3p6INm7cePz48YMHD7LZ7Pr6+lOnTpmamhKR\noaFhaGgoEQmFwsjIyIkTJ/bq1YuInj17Fh8fL71/0XQdEc2ePXv9+vWlpaV37tx58eJFVlaW\niYkJEXG53JkzZ8ofOCYmxtzcnIhCQ0PT09PDwsKIKCoqasmSJc+fP3/16tX169fLysosLCyI\n6PDhww4ODunp6YaGhjweLzY21t7e3tPT08nJydbWVqGBUiUUdgAAANrByrynzDbWFvYy23A4\nnJKSkq5duzKZTCJyd3fv1q3b2bNnp0yZomgkT09P0Qsmk+nh4XH//n19fX0vLy9RVUdEgYGB\nohP4GAzG4sWLMzIyzp07l5WVde7cOZmdu7i4iF4YGRmJXuTk5AwaNEhU1RGRn5+fQmnFBZmh\noaHka9GLvLw8Ho8nWbTx+fxHjx7NmTPH09NzwIAB4eHhwcHBkyZNEn9EA+FQLAAAgHZ4xzlE\nj6UvpYG1hX3PLu/I7EdPT6979+6iqo6IOByOg4NDcXFxk2b6+vr19U3nCOvr6/X1/8ogEPx1\nJQePxxMIBCwWS7K96KpbIqqpqQkICPjwww+fPn06fvz4zZs3y8wpuSHxJiSv8xB/BaUwNze3\ntLTkSRAKhYsXLzYxMcnMzExJSTE1NV2/fr2dnV3zg7yaA4UdAACAdjA1th45VNrhy0nvb5Dn\n+tazZ88OHDiwvLxc9PbNmzfFxcX9+vVr0szV1TUjI6OhoUG85M6dO3/88cfAgQPFS9LS0kQv\namtrb9y44eLi4uLicuvWrTdv3oiWX7t2TVT8XblyJTs7OycnZ9u2bWFhYZIVofxcXV3v3r0r\nPmlPuee6ubq6VlRU5OTkiN6+evUqPDz8/v37qampSUlJfn5+u3fvLigo8PX13bt3rxK3q1wo\n7AAAALRG5MiNXu9MbL6cwWCED1/p5x4tTyf+/v7l5eVRUVEXL168du3apEmTHB0dR40aRUQH\nDhzYs2ePqNmiRYvKyspCQ0NPnz6dnp6+Z8+esWPHjhgxYujQoeKuFi9enJycnJGRMXny5MbG\nxhkzZnzwwQf6+vqRkZE3b948d+7c/PnzjY2NicjU1LS6uvrUqVNFRUXffPPNunXruFyu6MJY\nJpNZWFhYWVkpM3lERISZmVl0dLToYO7GjRv19PSaz9vJ36EkZ2fniIiIqKioK1eupKenR0dH\n5+XlOTs7NzQ0LFu27Msvv8zOzk5OTs7NzXVzc1OoZ1VCYQcAAKA1WEy9uKnHF0z53tnhPQaD\nSUR6egaD+o1aMffypPc3yNmJmZnZhQsXBALBxIkTIyMjbWxsLl68KLpH8ZEjR/bt2ydq1rt3\n74yMDCsrq0WLFr3//vtfffXVrFmzfvrpJ8lCas+ePatWrQoJCeFyuWlpaWZmZkZGRqmpqXw+\nPzQ0dPny5Vu2bBk3bpyZmZm/v/+aNWs++ugjLy+vCxcuXL16NTQ0dMWKFUQ0ffr048ePz5o1\nS0pmQ0NDJpNpYGBw6dKl169fBwUFbdy48dChQyRx5pyYPB0275yIDh069N57702bNm3s2LEG\nBgbnz5/X09MLDg5OTEzcsWOHl5fXwoULIyIiEhIS5OxZ9RhCoVDdGdqipqZGPBPb3vJP28hs\n03f8S4X6tLS0JKKKioo2ZtI2MsdQ0QE0MDAQ/fFXW1v7Frm0RnvshBwOh8ViiY/F6Dyl74T6\n+vpmZmaq/FmkXu2xE5qbm7PZ7PLyci39TSRibW2t9D6FQuHRJNlHVM9c3nj8/MqPpv/k4TpO\nZuMpnygjmYSsrKwhQ4bw+fwmJ9W1q7Kyshs3bowfP170Nicnx9vb+82bNxpyg2VNgBk7AAAA\nrSSasetQhELhlClTPv/887KysocPHy5YsCA6OhpVnaQOt08AAACAlurSpcuZM2e+//57R0fH\noKCgPn36bNmyRd2hNAvuYwcAAAAK8/DwUMsx9JCQkDY8AK3jwIwdAAAAgI5AYQcAAKCVutm6\neA+cZGHeXd1BQINo66FYFoslfqKIJlA0jOhMT436Cuql6FCILsLS19dX5dVYGk7RMWQymQwG\nAzuhWBsGkIj09fWVe+97rda2/8iim5xpKT6fr8atD3ENH+IarsYAoIG0tbATCASS98JWu+YP\nXZFO9JgURT+lwxQdCjabzWazGxsbMYZibRjDNnxKhyk6FHp6evr6+tgJJbVhDJlMZkNDg/be\n7qT9kk9cpMzfcQwGg4itxA5BY2lrYScUCnk8nrpT/KVtYTTqK6iXokMhmiNpbGzEGIopOhRC\noVDT/h+pl6JDIZp3x04oqQ07oehT2lvYtRMGg9H8MakA8tDWwg4AAEBXCYVC5R6VQqXYcaCw\nAwAA0Dis+Y+V3OO+fkruEDQSzvkFAADQSqnPf024tT2vslDdQUCDoLADAADQShkv7my7903B\n6yfqDgIaBIUdAAAAgI5AYQcAAACgI1DYAQAAAOgIFHYAAAAAOgKFHQAAgJbJeHFnRtqyz3P2\nEdGijM/iMja0xyUU48ePZzSzceNGcYPGxkYGg5GVlaX0TYux2ezr16+3X/+6B4UdAACANvn0\nVpL/f6YcKUyuqK8iopLq53vyvh/807j9D08p1M+BAwc8PT3NzMxGjBiRn5/fYpvAwMCMv5sx\nY8bTp08ZDMbXX38t2XLo0KHbt29v85cCZcENigEAALTGztwDSfe+bb68vrFh3o3V3Yxt3+8+\nVJ5+Dhw4sHDhwl27dtnb22/atGns2LF5eXksFqtJMysrK29v7yYLKysrly9fPmjQoLZ9hYaG\nhtLSUkdHx7Z9HKTDjB0AAIB24PKq19/+qrW1AqFg6a9b5elHKBRu3rx58+bNM2fODAoK2r9/\nv5ubW3FxsZwxOBxOUlISn88XL/H09Lx27dqSJUtCQ0OJqKqqat68efb29ubm5mFhYY8ePRI1\nY7PZZ8+e7d69e1xcnGSHWVlZNjY2169f9/X1tba2DgoKysvLE68tKysbM2YMh8Pp3bv3iRMn\nRAvz8/NHjhxpYWFhZmY2bNiw7Oxs0fKzZ8+6u7sbGRk5Ojp+8cUXooWt5dFJKOwAAAC0wy/P\nbnB51VIa3K98JM/Jdg8ePHj48GFERIRAIHjx4kXPnj1PnDjh4ODQvGVFRUWWhAcPHrTY4W+/\n/ebn55eUlJSSkkJE4eHhDx48OHjw4MWLF42Njf39/SsrK0UtP/744y1btnz55ZdNeuByuUuX\nLj127FhxcbG3t7e/vz+XyxWtWrRo0cyZM69fv+7n5zd9+nTRU3SjoqLq6+tPnjx55swZoVA4\nZ84cIiouLp4wYUJwcHBqauq8efPi4+MzMjKk59E9OBQLAACgHR5zZU+qPXr9vz5mDtLblJSU\n6OnpHTlyZMOGDVwut1u3brt27ZowYULzlpcvXx4yZIj4rY+Pz82bN6V3npmZef369bKyMgsL\nCyI6fPiwg4NDenr62LFjiWjOnDkffvhh80/V19evXr3a3t6eiDZu3Hj8+PGDBw8uWLCAiGJj\nY0XZ1q5de/Dgwd9//93Ozi4yMnLixIm9evUiomfPnsXHxxNRfn4+j8eLjY21t7f39PR0cnKy\ntbWVnkf3oLADAADQDgxiyGzDZMhu8+rVKz6ff+PGjXv37llYWPzzn/+cMmXKnTt3XFxcmrSc\nOHGi+OinnPLy8ng8nq2trXgJn88XH/10c3Nr7YOenp5/fgUm08PD4/79+02WGxkZiV4wGIzF\nixdnZGScO3cuKyvr3LlzouU+Pj6enp4DBgwIDw8PDg6eNGmSoaFhWlqalDy6B4diAQAAtIOT\nmb3MNs6ypuuIyMbGhoh2795tb29vZmb26aefdu3a9cKFC2+fkIjMzc0tLS15EoRC4eLFi0Vr\nxcVZcwKBQPyax+OJ3xoaGjZpWVNTExAQ8OGHHz59+nT8+PGbN28WLTcxMcnMzExJSTE1NV2/\nfr2dnV1KSor0PLoHhR0AAIB2GN7tXUsDcykNPKwHOJr2lNlPv379mEym+DwzPp9fW1vL4XCU\nEtLV1bWioiInJ0f09tWrV+Hh4eLpNynS0tJEL2pra2/cuNF8+lDsypUr2dnZOTk527ZtCwsL\nE5eAqampSUlJfn5+u3fvLigo8PX13bt3b5vzaCkcigUAANAORnqdNg35eN711S2uZTP1kryW\ny9NPjx49Jk6cOHXq1K1bt5qbm+/YsUNPTy8sLIyIDhw4UFtbO3/+fEWzMZnMwsLCyspKZ2fn\niIiIqKionTt36unpbdq0qaioyNnZWWYPixcv1tPTs7W13bRpU2Nj44wZM1praWpqWl1dferU\nKR8fn8uXL69bt47L5WZnZzc0NCxbtszAwCAgIODx48e5ubnTpk1rcx4thRk7AAAArTHLedJG\nj4/0mE1vOGfKNj4UkOTX2UPOfkR3J545c+b777//5s2bq1evWlpaEtGRI0f27dvXhmDTp08/\nfvz4rFmziOjQoUPvvffetGnTxo4da2BgcP78eT092RNJe/bsWbVqVUhICJfLTUtLMzMza62l\nv7//mjVrPvroIy8vrwsXLly9ejU0NHTFihXBwcGJiYk7duzw8vJauHBhREREQkJCm/NoKYZQ\nKFR3hraoqampqalRzbbyT9vIbNN3/EuF+hT9/6moqGhjJm0jcwwVHUADAwPRX2y1tbVvkUtr\ntMdOyOFwWCxWeXl5W0NpGaXvhPr6+mZmZqr8WaRe7bETmpubs9ns8vJyLf1NJGJtba30PoVC\nYeOslh8FIZJXWfjv/B9+fPLfZzVlLpzeEQ4hc/v+o5uRrZSP6O3rp+yYSpOVlTVkyBA+n9/8\nDsmgKJ2tWAEAAHSVC6f3Du+ELobWK7N2fOaxOMxuuLoTgabAoVgAAAAAHYEZOwAAAFAnDw8P\nrT4cr1EwYwcAAACgI1DYAQAAAOgIFHYAAABaaVhX701DPu7PcVJ3ENAgOMcOAABAK3nbuHnb\ntProVeiYUNgBAABoHMG/eqk7AmglFHYAAACahcFg6OvrqzsFaCWcYwcA0ILU1NSff/5Z3SkA\nABSDGTsAgBZ89tlnOTk5v//+u7qDAAAoADN2AAAAADoChR0AAACAjkBhBwAAAKAjUNgBAAAA\n6AgUdgAAAAA6AoUdAAAAgI5AYQcAAACgI1DYAQAAAOgIFHYAAAAAOgKFHQAAKF9eXt6sWbNO\nnz6t7iAAHQsKOwAAUL6XL18mJyfn5eWpOwhAx4LCDgAAAEBHoLADAAAA0BEo7AAAAAB0BAo7\nAAAAAB2Bwg4AAABAR6CwAwAAANARKOwAAAAAdAQKOwAAAAAdgcIOAAAAQEegsAMAAADQESjs\nAAAAAHQECjsAAAAAHYHCDgAAAEBHoLADAAAA0BEo7AAAAAB0BAo7AAAAAB2Bwg4AAABAR6Cw\nAwAAANARKOwAAAAAdAQKOwAAAAAdgcIOAAAAQEegsAMAAADQESjsAAAAAHQECjsAAAAAHYHC\nDgAAAEBHoLADAAAA0BEo7AAAAAB0BAo7AAAAAB2Bwg4AoKm6urq6ujqhUPj69Wt1ZwEAUAAK\nOwCAv/zvf/+bO3euk5PTgwcPGhsbnZ2dJ02adPv2bXXnAgCQCwo7AIA/3bp1KzAw8PTp0/X1\n9aIljY2NV69eHT169KlTp9SbDQBAHijsAACIiN68eTNjxowWj73yeLy4uLhHjx6pPhUAgEJQ\n2AEAEBEdPXq0rKystbUNDQ1fffWVKvMAALQBCjsAACKiq1evSm+QmpqqkiAAAG2Hwg4AgIjo\nxYsX0htImc8DANAQKOwAAIiIShvvSG/AMOCpJgkAQJuprbDj8/lRUVFcLle8pLGxcd++fbNn\nz54xY8bu3bt5PPwMBQDV4fSW1cBJJTkAAN6CGgq7hoaG7Ozszz//XLKqI6J9+/alp6fPnTs3\nLi7u9u3bOE8ZAFTJfgQxpP5EdAhRVRQAgLZSQ2F39uzZnTt33rt3T3JhbW3txYsXZ8+e7eXl\n5e7uPm/evPT09KqqKtXHA4COydyB+kW2urabL/UcqsI0AABtoobCLiIiYt++fWvWrJFc+PTp\n07q6ukGDBoneurm5NTY2FhUVqT4eAHRY/aPonQ9Jr9PfFjJY1HsseX1CxFBTLAAAuempO8Cf\n/vjjDz09PWNjY9FbPT09ExOTiooKyQbBwcHit3Pnzp07d65qsuXL0cba2roNPbftU9pI5hi2\nbSiMjY3F+4xuw0749uQZQ2KQcwQ5BNOzDMr5juqryG0OdfMhI9s/1zcfLiMjIyMjI+VG1Uzy\n74SvXr365ptvjh8/TkTHjx9nMBhz587t27dva5+ysrJSWkqVEz+kBEBDaEphJxQKGYymfw43\nNjaKX7NYLBcXF/FbKysrPp+vonByUDSMnp5eGz6lwxQdCgaDwWKxBAKBQCBop0haR9ExZLFY\nDAYDO2Fz+qbkEExF56iBS05hf1slOVzYCZvj8/kXLlyYPn16eXm5aElpaennn3++a9eudevW\nLV26tEl7HdgJ8a8PmkZTCjtLS0sej1dbW2toaEhEjY2Nb968kfzj2MzM7NChQ+K3NTU1lZWV\nagjaCkXDWFpatuFTOkzRoTAwMDA1Na2tra2trW2nSFpH0THkcDgsFgs7oUIkh0tfX9/MzKyu\nrq6mpkaNkTRKamrqhAkTmk9i8fn8FStWsNnsmTNnSi43Nzdns9lVVVVCoVCFMZVM9GsLQENo\nyn3s7OzsDAwMxFdU3L9/n8lkOjo6qjcVAADIb+3atVIOTX722WdNboYAAEqnKYWdkZHRiBEj\n9u/fX1hYWFRU9M033wQEBFhYWKg7FwAAyKXiNd24cUNKg9evX1++fFlleQA6Jk05FEtEs2fP\n3rdv38aNGwUCgbe39+zZs9WdCAAA5FXyQvYJZ48ePVJNGIAOS22FnZOTU3JysuQSFos1Z86c\nOXPmqCsSAAC0mcTVblLayNEIAN6CphyKBQAlun///oYNGzIyMtQdBDqQbjay29jb27d/EIAO\nDYUdgA4qKCjYtWvX7du31R0EOpDOljKet8vSpxEjRqgqDkAHhcIOAACU452Z1OyGpH9xnqDd\n9yIG0Aoo7AAAQDlsB5H7QmKyW1jlOJJcpqg8EEDHo0FXxQIAgLZzCCGr/vToDD3LpLoK0jOk\nzoOp12iydVN3MoCOAYUdAAAok2kPGryAuvtR+gpyCiPXaHUHAuhIcCgWAAAAQEegsAMAAADQ\nESjsAAAAAHQECjsAAAAAHYHCDgAAAEBHoLADAAAA0BEo7MFlKsUAACAASURBVAAAAAB0BAo7\nAAAAAB2Bwg4AAABAR6CwAwAAANARKOwAAAAAdAQKOwAAAAAdgcIOAAAAQEegsAMAAADQESjs\nAAAAAHQECjsAAAAAHYHCDgAAAEBHoLADAAAA0BEo7AAAAAB0BAo7AAAAAB2Bwg4AAABAR6Cw\nAwAAANARKOwAAAAAdAQKOwAA+JuHDx9u2LDh2rVr6g4CAApDYQcAAH9TVFS0a9euW7duqTsI\nACgMhR0AAACAjkBhB6BT0tPTp0yZEhsbS0Tr16+PiIhISUlRdyiAlv3+++9FRUVCoVDdQQB0\nBwo7AN2xcePGCRMmXLx4saGhgYj4fH56evq0adMWLVokEAjUnU7XlJWVnThxIi8vT91BtNjM\nmTN79+5dW1ur7iAAugOFHYCO+P7773fu3Nni5IdoleojaTULZ7J1k9YgJycnMjLyxx9/VFUi\nAADZUNgB6AKBkDZv3iylwc6dO9+8eaOyPDpg8HzyW6/uEAAACkJhB6AL8p/S8+fPpTSora1N\nS0tTWR4AAFALFHYAuqCsQnabkpKS9g8CAADqhMIOQBewWbLb6Ovrt38QAABQJxR2ALrAoZvs\nNv369Wv/IAAAoE4o7AB0QXcbspJathl3Jk9PT1XFAQAA9UBhB6Aj3GKI1cqxVgaTBs0nFkuO\n47UAAKDNUNgB6AiLPvTuGjLgNF3ONiavpdRliDoyAQCAaumpOwAAKI2tG438hv53hf53mcrz\nyNyBHILJLoj0TdWdDAAAVAKFHYBO0etEvUJJ34TK88h+BDmNU3cgAABQIRyKBQAAANARKOwA\nAED52EZk4USdLNWdA6CDwaFY0EQeHh61tbX3799XdxAAaCOLPhS0U90hADoezNgBAAAA6AgU\ndgAAbfHHH38QUWVlpVAoVHcWAIA/obADAFDMsWPH3nvvvVmzZhHR7t27BwwYkJSU1NDQoO5c\nAAAo7AAA5CYUChcuXLhw4cKHDx+KF7548WLLli1jxozhcrlqzKZdcnJy1qxZc+fOHSL6+OOP\nU1JSBAKBukMB6AIUdgAA8vr222+PHTvW4qrbt28vXbpUxXm0EZ/PX7ZsWWBg4O7duysqKojo\nxIkT06ZNGz169LNnz9SdDkDrobADAJCLUEg7d0q7zvPUqVNPnz5VWR4t9emnn+7bt6/58lu3\nbk2aNKmmpkb1kQB0CQo7AAC5vH5KZWVlUhoIhcLU1FSV5dFG2dnZ3333XWtrHz58+K9//UuV\neQB0Dwo7AAC51FXIbvP8+fP2D6LFfvzxR+kXEZ86dUplYQB0Ego7AAC56HWS3cbExKT9g2gx\nyYtOWlRYWMjn81UTBkAnobADAJCLmT0RmyW9zcCBA1UTRktd/KNYeoPGxsbGxkbVhAHQSSjs\nAADkwjYmChgsrUVPWx8fH1XF0U6dZT071srcwMBAJVEAdBMKOwAAuc0LJxtOy6vYerRkip6e\n1j+AOzc39/z580R08+bNzMxMJT9X4913ZDUYoMzNAXQ8KOwAAORmzaEv4mlAr6bLu1pR0gJy\nc1JHJqV59uzZpEmThg0bduTIESK6fPnymDFjgoKCcnNzlbaNdwe0MHpiRp1o6vtK2xZAh6T1\nf1wCAKhUN2v66iO6V0g/X6f//krv9KYPRpC3K7G0++/ksrKyMWPGFBc3PQcuJydn7NixycnJ\nbKVshsGg9bPpk39SYWnTVSaGtH627GO1ACCVdv8kAgBQj3d6U7AnEZFHX3r3HW2v6oho7dq1\nzas6ES6Xu2jRIqUdkrU0oz1LKDaCnHoQg4iIbC0oIoD2JZB7XyVtA6Dj0vofRgAA8JYqKyvP\nnDkjpUF2dvYDJT5TQ59NkUH0zXIa3JeI6OAqiptEthbK2wBAx4XCDgCgo8vNzeXxeNLb5D1W\nTRYAeCso7ECzPHjwYOvWra9evXr9+nViYmJeXp66EwHoPi6XK7PNm1oVBAGAt4XCDjRFQ0PD\nRx995O/vv23btpqamvr6+u3btwcEBMTFxdXV1ak7HYAus7a2ltnGylwFQQDgbaGwA00RGxt7\n6NChJjfNEgqFR48enTt3rrpSAXQEbm5uHKm3BWYQufdTWRwAaDsUdqARfvnlFynnbqekpPzn\nP/9RZR6ADoXNZsd5SnuoxiQX565WKosDAG2Hwu5vrl69+vHHH9+9e1fdQTqcH3744S0bAMDb\n+MTHI7S3Q4ur3rGx/iJ4mErTAEBbobD7m/v37x88ePDpUyVe1g9yefDggfQG9+/fV00SgI6J\nzWSejBiTGOjXxcRYvNDcQP9jb/erUydadMLzWwG0Awq7v+Tk5GRmZhJRampqUVGRuuN0LMWV\nMgq7steotgHaF4vBiPccXDR/5s7gYUQ0z31g6cI5GwPeM2Yr56kTAKACKOyIiAoLC8eOHRsY\nGHju3DkiOnjwoI+Pz7Rp0168eKHuaB2Foaxr8oxsVJIDtB+Xy719+3ZWVlZlZaW6s2glJoPR\nw9SEiLqaGOsx8TsCQMvgWbH06NGj0aNHV1RUSC4UCoUpKSl5eXkpKSnqCtahdPGgVznSGnQe\noqooHUZJSYlAIDAzM1N3EKXJz8//7LPPfvnlFz6fT0RMJnPo0KErV64cNGiQuqMBAKgI/hqj\n+Pj4JlWd2JMnT1avXq3iPNqroaEhLS1t3759+/fvv3nzpswb2UvqNYo6tf48IQMzchqrhIQa\nrrS0dPPmzYGBgX379h02bNimTZtKSkrab3Ph4eGurq7t17+KpaWlBQcHnz9/XlTVEZFAIEhN\nTR01alRycrJ6swEAqExHn7HLy8sTnVfXmp9++unD98jUSGWJtNXRo0c/++wzyYPX3bt3X7du\n3bhx4+T5ONuY3l1N19dQ/eumq/RNyHcV6ZsqMawm+uGHHz755JPa2j/v7l9RUZGbm7tnz55t\n27b94x//ULQ328EUtJOMZN90VkdUVFTMmjVLPHqSeDxebGwsJu0AoIPo6DN2WVlZ0hvweLwH\nT1QSRZslJSXFxcU1OSWxtLR09uzZ//73v+XsxKIPjfiKeo0mg/93g3sDM+oVSiO+IisX5ebV\nOBcvXoyLi2tel9TV1S1atOj8+fOKdqhvQhZOZMBRUj6N991330k5o66+vn7Pnj2qzAMAoC4d\nvbCrqqqS2QZPSJTu9u3b27Zta23t2rVrHz58KGdXnSxp8Hwac5iMbEjfnEYfocELZF9Xoe2E\nQmFCQoJAIGhxrUAgSEhIEAhbXAl/Sk1Nld4gLS1N+Vs1MyaPvoT79gKAJtHWQ7EsFsvY2Fh2\nO1m6d+8us42FHAcBFQ3DYDDa8CnNdOjQodaKEiLi8XhHjhyZotBxMAYxmMRgEIPxt8WSw8Vi\nsYhIX1+fqf1X7WVlZT158kRKg+Li4vuPaUCvt91Qk/1Nl3bCV69eSW9QVlam/K32taPtC5ss\n043xVC/tGkPxOZ0AGkJbCzuhUKiU/07vvvsug0jKbIgxm+3iKPsigLaF0Y2fCDdv3pTZQLHC\nrhWSwyUqSgQCgQ6MoTwzmsVlSijsWhwrHRhAIjJ9LePOJmbCVv/2UC7tHU99dQcQ064xbGxs\nVHcEgL/R1sJOIBDU19e/fT+2trYT+vU5+aCgtQbz3N8xYP9/MvtRNIzoT1KlfAW1q3xWKr3B\nH0WFStlQk+Hq1KkTn8/XgTGsq6uT2ab1KVEFNBkroVDYfKGWcu9ie+t3aXNyHl1sibgqSKK9\n46k5hZ32jiGAJtD6w1hv74vggH5Wli2uGtqz+6r3fFScR+tYGXaS3sDGyFA1SbSUvb29zDbd\ndP1Ew7c0453+zCZH7v/uQzfdubGLTokMpI8/ILa2TjEAaCAUdmRlaJgWPWme+0BDvb9+uHAM\nDFa86/WfyHGd9FhqzKYV/HrIOE/Rr6fsExk7Mg8PD8mnczZnY2T4jpPK4mgl9y62H3m5t7Z2\n2jsuIY6yq2dQA58BNPY9YuE3EYDS4L8TEZGZvv7OEQGlC+fEDB5IROv8fYsXzl7l563PQlUn\nW6zHQCnPHTJgseYOGqDKPFpHT09vo/+7Uhp8FvAu/r6QaUPAu82famrAYi339dz9fpC6UinF\n48eP7969q11nngGAuqCw+4sRW8+RY0ZEThYc9ltca3ngwAEbG5tjx44pL5pGc7W22hLo1+Iq\nBtGXIYGOHPMW14JY1IB+6/x9Wc0OJjIZjNV+PtPf6a+WVNqFQfSxt3vB/BnfjBrxQf++E/r2\n+dfI4Q/nzVg71EfbH3iakJAwYsQILlcV5wgCgLbDmQ1/M9yh5z/fD3TvjAfOK2aBh5udmWlC\n6vWCir8uTnS1ttoS5DfCwU6NwbTIMp8hwQ52u/+/7IuPn5bX1lkadgp2sFvg4ebexVbd0bSJ\nZadOUwe4TB2g67e0BgBoBQq7v3nHxvodG5ym3hZj+/Qa7eSY87L8QXkFk8Hob23lYm0p7Wx2\naMa9i+03o0aoYEPp6ek//vjjo0ePGhoaYmNjR48eHRoaqgN3BAQAABR2oDRMBmOgrfVAW1TG\nmqumpmbBggVnz54VLzlx4sSJEye8vLz2799va4vZQQAA7Ya/0QE6kJiYGMmqTuzXX3+NjIxs\naGhQfSQAAFAiFHYAHcWlS5fOnz/f2trc3NzvvvtOlXkAAEDpUNgBdBSnT5+W3uDHH39UTRIA\nAGgnKOwAOopHjx5Jb1BQ0Oqz9QAAQCugsAPoKLK4L6Q3qKqvVU0SAABoJyjslOx///tfVlYW\nEf32228lJSXqjgMgoYuVjAZ4JC0AgJZDYac0z58/j46OHjJkiOiZEwcPHvTw8Jg1a9arV6/U\nHQ2AiIj8Bspo8J6sBgBaqK6u7u7du8XFxeoOAqAKKOyU49mzZyNHjjx//rxQKBQvFAgEycnJ\noaGhL1++VGM2beQ6jQZ+qO4Qumf4EHLq0epaSzOK1O5nqgK06PHjxyNGjNixY4e6gwCoAgo7\n5Vi2bFlpaWmLq548ebJixQoV59F2PQPIDjWG0rGYtHkeOXRtYZU1h7bEkqmRyjMBAIAyobBT\ngufldOHCBSkNzpw5gwOyoBFsOPT1UloQQf3sicEgInLsRjNG0b4E6tP6ZB6oQ0NDw969e0eM\nGHHp0iUiGjZs2PLly3HmLgBIh8JOCXKKSPIIbHMCgeDWrVsqywMgjQGbJgXRvz6h3t1Jn037\nE2jGKDLDXJ1mqaioGD16dEJCwt27d0U/Xp49e/btt9/6+/tfuXJF3ekAQHOhsFMCbo3sNpWV\nle0fBAB0gVAonDVr1p07d5qv4nK5M2bMePLkicpDAYB2QGGnBBwT2W2srGTdaQIAgIiIrl69\neu3atdbW1tTUbN++XZV5AECL6Kk7gC4Y6EQMBkk5GMvUoyFDhqgwEQBoMenn7BKRlGf+KoVP\nty7nJof35pi361YAoD1gxk4JrMypR4C0BvbDycLCQlVxAEC7ybzjWmVl5evXr9svgLWRYZB9\nT3tzs/bbBAC0ExR2yjEohkx7trzK3JHewS3ZAEBuRvkCmW309fVVkAQAtA4KO+XQN6XAJHJ8\nn5gSB7dZ+tR7DA3bSmxj9SXTUSUlJSdOnCgsLFR3EADl62feW3oDB9MenTp1Uk0YANAuKOyU\nhm1M7gtpzBHqPZaIyDmCxhyhQfNIz1DdyXRRRkZGZGSkzFORALTR5F6jmAxpP5w/6DVGZWEA\nQLugsFMytjGZ2RERmdmhpAOAthhg4RzrEtXa2t5mdkvemaXKPNqruLh45cqVU6dOJaKTJ09O\nnTq1va87AVA7XBULAKBxkryWCYXC3XlHhPS36+3drVx/CNxpxjbBjTFlOnfuXGxsbHV1teht\nbW3thQsXLly4MG7cuN27d+MkRdBVmLEDrSS6F79AIPsccwBtxGKwdvqsyBp3+qMBH1oZcIho\nkmPoD4Ff3Bj7g4MpHv4m27179+bMmSOu6iSdOXNm5cqVqo8EoBoo7EDLXLp0adKkSTExMUS0\nYsX/396dxjVx7W8A/yUBwr4EEVwhRkQWZdGCaMtSl1ZF3C0WKy5Q9VoXsNoq2lb928Xl4lIt\nXi1o1Vbb3lrUVtS2rgTrUhFExOJCUUSBsEoQSPJ/kV4uFyFBhUxmeL5v6sw5SR7mM4VfZs6c\nEzt69OgjR44wHQqgTfQRuXz20mJfu75E9Ln/h+Ochgl4AqZDscOaNWtqamqaa929e/ft27d1\nmQdAZ1DYAZusWLEiLCzs1KlTdXV1RKRQKKRS6fTp0+fNm4erd89s9hhaHsF0CIDWV1lZeebM\nGQ0dlEolBtsBV2GMHbBGYmJifHx8k0379+93cnJatGiRjiOxW//eTCcAaBP5+fm1tbWa+2C9\nXeAqXLEDdlApaO3atRo6bNq0qbKyUmd5AEBv8fna/7QJBLipDdyEwg7YQXaTioqKNHSQy+Ua\n1k0HgPaja9euJgItEzhLJFpmgQZgKRR2rY9vSEbmxDdkOge3yDUVdX+7d+9e2wcBAH1nbGwc\n0j1YQwehwGjEiBE6ywOgSyjsWp/TEBq1n7oFMp2DW1pSKAuFwrYPAs/m/v37mzdvTklJYToI\ntC+rfRZaGVk017qkT1Tnzp11mQdAZ1DYATuo1/PQrHdvPA2gd3Jzc1evXv3bb78xHURP5eXl\nrV69+pdffmE6CNdILLsfGhrf2bRjo/084sV4zFjhPZeRVAA6gKdigR3MOxO5OlHW3WZ7dLXr\n16+f7gIBtIb8/PzNmzcrFIohQ4YwnYVrBnb0yRj3066bP3x/Nzn10RU7Y9FE8fAZvSZ4ivAN\nELgMV+yAPRZMJGEzd2QFfIoOa8mjcADAMUeOHNm8ebNcLn+6ydLQfL771G0DPyKi0O6DNw1Y\njqoOOA9/CIE9ejvSJ3PI1qrxfkszWhVJ/VyYyAQADDtw4MDq1aubXD0MoB3CrVhgFZ9etO9D\n+vUSJf9OGbeoVzca4U9DfclMy9QGoHtKpfL06dP79u0jopMnTzo7O4eGhpqamjKdCwCAy3DF\nDtjG2IhGDqQxrxARDfOjMQGo6vTQrVu3hg4dOmnSpKSkJCLKyMiYN2+ej4/P0aNHmY6mXyoq\nKogIc2sDQGtBYQcArSw/P3/06NHp6emN9hcXF0+fPv3YsWOMpNI3P//882uvvfbmm28S0e7d\nuz09PePi4ppct/7Q0O2107NEwqcGIQAAPAWFHQC0slWrVj18+LDJJoVCsXjx4idPnug4kr75\n8MMPIyIi/vjjD5VKpd6Tn5//8ccfh4SElJeXM5sNAFgNhR0AtKbKysrDhw9r6PDgwYNTp07p\nKo4++u6777Zt29Zk05UrV6Kjo3Wch9Vyc3PV3yKuXLlSW1vLdBwA5qGwY8Zff/31119/MZ2C\nzQb3p1Of04QgpnNAYzk5OU3eT2zo2rVrugmjnzZs2KCh9dChQzdKb+ksDHtlZmaOGjWqf//+\nV65cIaI333zTy8srMTGx/iIoQPuEwk7XCgsL9+zZ07dv3z59+uzZs6ewsJDpRACtqcnpxBqp\nqqrSQRL9lJeXd+uWlrrtRD5WYNNCKpW+/vrr58+fb7jz0aNHS5YsWbJkCVOpAPQBCjvdUSgU\n69at8/b2jomJqaioqKysjImJ8fb2XrdunUKhYDodQOtwcHDQ2qc9L9P54MEDrX3uPW56hCKo\nPX78eNasWdXV1U227tq168iRIzqOBKA/UNjpzrJly9auXdto2PiTJ0/Wrl27bNkyplIBtC6x\nWNzT0lFDBx7xXn31VZ3l0TctmcnP3BCz/WmSlJRUUFCgoUN8fLzOwgDoGxR2OpKampqQkNBc\na0JCglQq1WUegLaz3OsfGlrflIwSi8U6C6NvJBKJkba6DcteaZaamqq5w6VLl2qVdfWbrtaS\nR+G//9MP35+hXUBhpyN79+59wQ4AbBEuCY3xmNFkk39H7y3+H+g4j14xMTHx93xDQwcbqy5D\nuwzSWR42Kikp0dxBoVDInpTVbwp4AhsjS1MDzGQO7QIKOx25evXqC3YA0ENbtmxZtGiRUqls\ntP+zlxYfHLJtYEcfPu/vXzI9LLqvfWnJr8O/sjA003lM/TJp+Me21t2abOLzBTPHxZsIUIJo\nUlVkr7kDj8e3EVrqJgyAvkFhpyPZJY80d7gp09IBGHHmzJlDhw4xnUJ/JScnf/XVV01OMBHS\nLfj0yH2HhsYT0VzXKdkTjkV7TDfkt6/1qbdt27Zo0aK6urqGO60tHJbPPtWz+4BGnS3NO0ZP\n/cHbNUSHAVnJ2XGg5g6Sbi8Z8Q11EwZA36Cw0xVbbV8ftXYAHaqrq9u/f/+ECRPCwsJmzpw5\nfPjwLVu2qJf1hHqFhYXqZRJu377dXB/1xad2exfs2LFjX3311dNXNDuKenw0Vxo767dB3uFE\n5NTF+x+T925aesfHLZSJmCzj7xVmaWanocPQge/oLAyAvkFhpyv9tI2G7o/h0vpCJpOFhobO\nmzfv9OnT6rnsL126tGrVqsDAwKysLKbT6YWsrKyJEye6u7vfuHGDiAYOHBgQEIBFYBtSqVTp\n6emPHj0iopSUlKdXUePxeG6S4MH+s4nIvefgQd7hWh+qaIfq6upKS0sbTY5oamz19qREQTNX\nfwd6TVaXywDtEwo7XRkbQKbNX7QwNaZxgTpMA81SqVTTpk27ePHi0015eXmTJk0qLS3VfSq9\ncvbs2WHDhp06darhHdisrKwpU6Zs3bqVwWD649y5cwEBAYMHD87JySGiSZMmeXp67ty5E4si\nPKu0tDRnZ+c1a9Y02u/tOjJ29smuDh4Nd5oYW0587f/mhO3h8Xg6zAigX1DY6YqtFX0wjYya\nGvZhZEgfTCNbK51ngiYkJydrmEyhoKCguVU+24mysrKoqKjm5oZduXLl5cuXdRxJ3xw5cmTC\nhAnqa5n1iouLly5dihkrn1VZWRkRlZeXP10Tuzi9/Gl0+v/Nv+TY2YuI5k7e+/ny+2MGx/L5\nAgaCAugNFHY6NMCD4heTvwcJ/nPYBXzy96D4xTTAQ+MrQXd++umnF+zAbQcOHCguLm6uVaVS\nNbpoF+DwUu30rI/7L2r7aHqhqKhowYIFza0ls3Pnzl9++UXHkVjq0KFDr776alhYGBF98803\nbm5uH3/8caN7sjweT9y1n611dyLycB5qbGTOTFYAfYLCTrd6dKZPZtPhteTrRi+50uG19Mls\n6tF+l1fSQ3fv3tXaoZ3cUMvJyVm9evWZM2ca7kxJ0bKMqdYO3HbgwAH1AyXN+de//qWzMOwV\nGxs7c+bMjIyM+j1FRUVxcXGvv/66TCZjMBiA/mtfUw/oC1NjWqtpan5gkOHdOs0d+Ir2Mnzn\n7t27mzdvNjExCQgIqN9ZWFio+VUlJSV1SoVBe70dduHCBa0dIof/d9PF6eV9a9vF94SW+/bb\nb5srf69fvz5//vypg5N1HAmARXDFDuB/OFs5aelg6dQehmZnZWUlJycT0e+///7777/XX6Ss\nqeig+YVCI/N2W9UR0c9/aVkU4fHjx7V1TY9QBLW4uDgNrceOHfvrAaZzB2gWCjtov6qrq/Py\n8h4/ftxw50Sn4c31/7uD+PW2DMW8goKCsLCwgICA3bt3E9GpU6dCQkKCg4OvXbtGRD27+Wl+\nec/uWjpwnIWNlg4mZobtdVa/FnkoUz9KrEH6zeO6yQLARijsoD06evToyJEjHR0dfXx8xGJx\nYGDgnj171LPIDu0yaGS3oOZeKLHsPs9tqu6C6lxhYWFISMivv/7aaH9mZuaoUaMyMjIC+k/T\nXJcM8Z/TlgH1nkfj9SQac9fWoZ0rLtPapaQ8v+Gmb59xo4LeMzLCLIAARCjsOCwtLW3Lli2r\nV6+Oi4uTSqXtZLx/Syxbtmzq1KkXLlxQV3Iqler69esxMTFTpkypqakhoj2B64d3DXj6he42\nzkeH7TTn9Cyyq1atys3NbbKpsrJy/vz5ttbd3gpt9k7Zyz5TXvIY12bp2GDwG2RuranD6Fm6\nisJOJsIWdLFouPlKv4iwEZ/ikVgANTw8wUF5eXnvvPOOVCptuNPNzW3r1q0eHu19XpXdu3fv\n2LGjyaYTJ06sXLlyK0VbGJolDY3/Oe/0N7cOH8r7TV5XPaJb4FjHoZN7jBIKjHQcWKceV//w\nww8a2q9du3Yr78LgAbNNja33Ho4urSiobzIyNA0JWjxm8PK2T6nfLEUUs5nWzCBFU0/hjJ1N\n/YLpms5TsUgXOzIxJ3mlhi5Onb11FgeAdVDYcU1+fn5ISEh+fn6j/devXw8NDT18+LC7uzsj\nwXRPJpMplUqRSMTn/31lWqlUrl+/XsNLEhISPpw4taOxLY94I7sFjewWdKkoo6ymcnBnf51E\nZtrt++prlhrc+utCz+4D/L3C+nuMvfbnL7t+nFtUkvuPyXs9XYabm4p0E1PfDRhOnyXRF+/T\nrf/O1kEie5ryHr3+FnOxWMLIkIIn0M+7mu1g26mvC8fHuQK8CNyK5ZoVK1Y8XdWpVVRULFy4\nkPP3ZIuKipYvX+7u7u7i4uLq6tq7d+/o6Oi8vDwiyszMLCgo0PDaurq6X+7/z5XO/h36tJeq\njogey7V2qar+ewiUoYHQ23WkyKorEfl7hqGq+x9uvrTlN/riLHVzJiL65CDtuoKqrqWmLiUH\nx6abBAY0/59Ghia6DQTAJijsOEUmk2leFyEtLS09PV1neXQvKysrKCho+/bt6sXXiaikpGTv\n3r1BQUEpKSnNlbwN3XusqfLjOBtLrV2sLRwabnaycxF37dce5n95Ho69ydqOiMjNlwyaWk4Q\nmmQpos+SqO/LjffbOtAHe+ilIUxkAmAN3IplpdTU1IMHD2ZnZ6tUKolEEhoaGhQUxOPx0tPT\nm1vLqF5aWpqnp6ducuqYXC4PDw9/+PDh003l5eUREREbNmzQ+iYmBtrHbnNWz65kKaJyTTP7\nu/V8teHm2xO/bONMLOf3GnXtSXx8hX5Gdl3o04N04xId/5qS91DPvjRpIfm9RoacHuQK0BpQ\n2OkdpVJ5+vRpqVQqk8lsbGz8/f2Dg4PrR4lVVVW9U5j5AgAAErVJREFU8847hw8fru+fmpq6\nd+/e4ODg7du3V1ZqGnGspnm9I1bbt2+f+pZrk8rKyqRSKY/HV6mUGt6kr6h3G0RjCQGfxs6h\n3Wua7fBKqL2tRIeB2G8c1ph5Ab37ExEl7yEPf3p5FNNpANgBhZ1+uX79+uzZs7Oysur3bNq0\nydnZedu2bV5eXkQUFRV1/HgTk3OePHkyPDz8gw8+0PoR9vb2rRhYr2hdXl0qlfbtNexqdrPr\nEXUU9RjUsV9r52KVCe9Q9mU639QhcnSld9ZT03OhALQNu640fQU5ezGdA4A1cINAj/z555+j\nR49uWNXV7x87dmxmZubx48ebrOrULl68mJ2dbWVk0VwHIuLz+K+88krrxNU/WofQ3b9//63Q\njaYmTU8zJhAYzhgf356XwyIiEhjQ8l0UtYpEDb4AmFrQuH/QP49qX1YBoHXZOtDE+eTVxLyS\nANAkFHZ65L333istLW2yqbKyctGiRf/+9781v0NSUtIC9wgNHaZIQjt16vT8EfVblkLLSk0V\nAuNOdi7Lon7pZNerUZOlecfoqQf7OA9ts3TswRfQ2Dm0J53eWU9ENGIa7c+myJVkYsZ0MgAA\n0AK3YvXGg+KzZ89qaL98+bLW4XE3b9489vq2S0UZP+edfrq1XwePuAGxmsaXsZ2TK928oqmD\n2I2IxF37fbYo83Jm0tXso0UluZbmHfs4D/XrO1FohMKlAR6f7LoQEYns8UQnAABboLDTGzn3\ntHYpflSlucPjijpDvsEPg7duub4n7lpiftXfU36IhFazek9e6jnLRGDc9CVBbhj8Bh3/WmOH\nSer/CvgGvn3G+/YZr4tU7OXiQ2u+p05ipnMAAEBLobDTG/InWruYGlvLyu5r6GAnciIiAU+w\n0H3aAveIm2V3HlQV2hmLelv3EPDawdCxPgNpaBid2N90q3cgBU+gTN1GYjVLEXkHMh0CAACe\nAQo7vWFrpbWLi/iVew81FSY+bqH1/+YRz8Wqh4tVj1bIxiLz/klCM/opkRrNaRIwhhbEEQ+D\nSgEAgMtQ2OkNjx5kYkbyx812MDAaP+yj67dOPijMbrLd2rLT64Pm0912vHACERkY0j8+pVEz\n6cyP9NcNUiiomzMNCqGe3JyTGQAAoCEUdnpDaEhj59DXzS9RP2qmlbn94hlHPt3x2iPZ7UaN\n1padFk//ydTEmqh9F3Zq3ZwpfDHTIQAAAHRNjwo7hUKxe/duqVRaV1fn6+sbFRVlaNjOnsUL\ni6Hb15qeG9YnmKYtp2yyt+35SXRa8rnNqWnf5BfeUKmUDh16+fYZPyIgxtzUVueJAQAAQI/o\nUWGXkJAglUrnzJljYGDwxRdffP7559HR0UyH0i0DQ1qxm37aRT/GU/6dv3c6dKfRs2jUTPrP\nxLnGQosxg2PHDI5VqVQqlZLfzifUBQAAgP/Ql8JOLpefOHFiwYIFvr6+RDR79uw1a9bMmDHD\nykr7IwWcwuNTyAwKmUHFBVTykKztqEPnZvvyeLz28KwrAAAAtIy+PCSYm5tbXV2tXg6ViDw9\nPRUKxe3bjUeStSO2DtTTU0NVBwAAANCIvlyxKykpMTAwMDP7e+p/AwMDc3NzmUxW36G0tHTc\nuHH1mxEREVOnTm2Vj65plXdhD1vbVh6K194OIOEYvjAcwBeHY/iCWusA1tS0tyMH+o6nUqmY\nzkBEJJVKN2zY0HAt1PDw8IiIiGHDhqk3y8vL33rrrfrWsLCwSZMm6Tpl6xEIBESkUCiYDsJW\nPB6Pz+crlUo9OYHZiM/n83g8nITPTX0SqlQqpZLLC/W1KQ6chLW1tcbGWlapBtAlfbliJxKJ\namtr5XK5iYkJESkUisrKyg4dOtR3sLS0TEpKqt+sqqoqKSlhIGgrEYlERMTqH4FZQqHQwsJC\nLpfL5XKms7CVtbW1QCDASfjcjIyMLC0t5XJ5VZWWtf6gOVZWVoaGhqWlpaz+hobCDvSKvoyx\n6969u1AozMjIUG9ev36dz+eLxVikEgAAAKCl9OWKnamp6ZAhQxITE21tbXk83s6dOwMDA21s\nbJjOBQAAAMAa+lLYEVFkZGRCQsKaNWuUSqWfn19kZCTTiQAAAADYRI8KO4FAEBUVFRUVxXQQ\nAAAAAFbSlzF2AAAAAPCCUNgBAAAAcAQKOwAAAACOQGEHAAAAwBEo7AAAAAA4AoUdAAAAAEeg\nsAMAAADgCBR2AAAAAByBwg4AAACAI1DYAQAAAHAECjsAAAAAjkBhBwAAAMARKOwAAAAAOAKF\nHQAAAABHoLADAAAA4AgUdgAAAAAcgcIOAAAAgCNQ2AEAAABwBAo7AAAAAI5AYQcAAADAESjs\nAAAAADgChR0AAAAAR6CwAwAAAOAIFHYAAAAAHIHCDgAAAIAjUNgBAAAAcAQKOwAAAACO4KlU\nKqYzPI+qqqqqqiqmUzy/Y8eOEdFrr73GdBC2ys/Pv3DhQt++fXv06MF0FrY6efJkZWXlqFGj\nmA7CVoWFhSkpKa6uri4uLkxnYSupVPro0aOQkBADAwOmszy/Dh06MB0B4L/Y+v+Sqampqakp\n0yme39dff61SqcLDw5kOwlaXL1+Oj49ftGiRr68v01nY6uDBg3fv3p0+fTrTQdjqzz//jI+P\nf/vttwcNGsR0FrZKTk6+cOFCeHi4iYkJ01kAOAK3YgEAAAA4AoUdAAAAAEegsAMAAADgCLY+\nPMF2lZWVRGRubs50ELaqra2Vy+XGxsZGRkZMZ2Grx48fK5VKCwsLpoOwVV1dXVVVlVAoFAqF\nTGdhq6qqqrq6OgsLCx6Px3QWAI5AYQcAAADAEbgVCwAAAMARKOwAAAAAOAKFHQAAAABHsHWC\nYjaqq6uLiIiIj4+vH66uUCh2794tlUrr6up8fX2joqIMDQ2ZDannnj6G33///VdffVXfQSAQ\nHDx4kKF0eq20tDQxMTEtLa2mpsbFxWXatGlOTk6Ek/BZNHcMcRK20L179xISEm7cuCEQCPr0\n6TNjxgz1mg04CQFaER6e0IWampobN24kJyefO3du37599UXJjh07pFLpnDlzDAwMvvjiCzc3\nt+joaGaj6q3mjuGmTZvKyspCQkLUmzwez9vbm7mY+mvFihXl5eWRkZFCofDgwYPp6emff/65\njY0NTsKWa+4Y4iRsidra2rlz50okkvHjx8tksu+//16pVK5fv57wmxCgVeFWrC4cOXJk48aN\nGRkZDXfK5fITJ05ERkb6+vr6+PjMnj377NmzZWVlTIXUc00eQyK6f/++t7e3z3/gD2qTiouL\nr169OmfOnD59+vTq1evdd98logsXLuAkbLnmjiHhJGyZO3fuFBQUzJ07t2fPnr6+vlOmTLl5\n82Z1dTVOQoDWhVuxujBu3Lhx48bl5OTExMTU78zNza2urvby8lJvenp6KhSK27dv469Ck5o8\nhkR0//79tLS0H3744cmTJ7179545c2aXLl2YCqm3lErl5MmTJRKJerOurq6mpkapVOIkbLnm\njiHhJGyZnj17fvvtt8bGxtXV1Q8ePEhJSXF2djY2Nr5x4wZOQoBWhCt2jCkpKTEwMDAzM1Nv\nGhgYmJuby2QyZlOxS3l5eUVFBY/He/fdd99///0nT54sX768qqqK6Vx6x87ObvLkyepxS0+e\nPNm4caOFhcXLL7+Mk7DlmjuGOAlbiM/nGxsbE9FHH320YMGCs2fPLly4kPCbEKC14YodY1Qq\n1dOTrSsUCkbCsJSZmVliYqJIJFIfSYlEEhERcfHixcDAQKaj6SOVSnXy5Mm9e/fa29vHxcVZ\nWFjgJHxWTx9DhUKBk/CZxMbGyuXy48ePL126dMeOHTgJAVoXCjvGiEQi9bpYJiYmRKRQKCor\nK9XPiEELCQQCW1vb+k0zMzN7e/uioiIGI+mtsrKyzz777OHDhxEREQEBAeo/pTgJn0mTxxAn\nYQvl5uYWFxf7+PhYWFhYWFiEh4cnJSVlZGTgJARoXbgVy5ju3bsLhcL6pwGuX7/O5/PFYjGz\nqdjl4sWL8+bNq6ioUG9WV1cXFhZ27dqV2VR6SKVSrVy50tTUdMuWLYGBgfUXSHAStlxzxxAn\nYQvduXMnLi6u/lJcVVVVTU2NgYEBTkKA1oUrdowxNTUdMmRIYmKira0tj8fbuXNnYGCgjY0N\n07nYxN3dvaKiYsOGDWPGjDEyMvr222/t7e379+/PdC69k56efuvWrdGjR//555/1O7t06dKh\nQwechC3U3DHESdhCPj4+O3bs2LJlS0hISG1t7f79+zt16uTu7i4UCnESArQizGOnO+onOhvO\nwaZQKBISElJTU5VKpZ+fX2RkJKbl1OzpY5ibm/vll1/evHlTKBR6eXlNnz7d2tqa2ZB66Mcf\nf0xISGi0c9asWSNHjsRJ2EIajiFOwha6efNmYmLinTt3hEKhh4dHREREx44dCb8JAVoVCjsA\nAAAAjsAYOwAAAACOQGEHAAAAwBEo7AAAAAA4AoUdAAAAAEegsAMAAADgCBR2AAAAAByBwg4A\nAACAI1DYAQAAAHAECjsADpLJZN26dbt//34bvf+DBw+6du1aXFzcRu8PAADPB4UdAActXbp0\n3LhxXbp0edYXbtiwgcfjaa3YOnXqNGHChKVLlz5vQAAAaBMGTAcAgFZ29+7dL7/8suFa9W0h\nOjpaIpG8//77PXr0aNMPAgCAlsMVOwCu2b59u5+fn1gsbtNPcXR09Pf33759e5t+CgAAPBMU\ndgCslJubO23atCFDhgwaNMjV1XXz5s31Tbt27Ro/fnz95qhRo8aOHXv58uVhw4bZ2Nj0798/\nKSmptrY2JibG2dnZysoqJCREPRovODj43XffJaIOHTq89dZbRFRRUbFs2TJnZ2dTU1OJRLJ4\n8eLHjx/Xv/P48eN37dqlUql092MDAIBGKOwA2Ke0tPSVV14pLCw8duxYSkqKs7PzggUL/vjj\nDyLKzMwsKCjw9/dv2D8rK2vJkiWrVq1KSUkxMzObNGnSoEGDrKyskpOTd+zYcfz48ejoaCLa\nuHHjnDlziCgpKSk2NpaIpk6dum7dOk9Pz6VLl7q6uq5fv37hwoX1b+vv7//o0aPMzEyd/vAA\nANA8jLEDYJ/z58/n5eUtXrxYIBAQ0datW/38/Nzc3IjozJkzAoHAy8urYf+cnJxjx445OjoS\n0apVq4KCgsRi8YcffkhEEonk66+/Pn/+PBF5enpKJBIiGjRokK2tbXl5eVJS0vz58zdu3Kh+\nnzfeeOPMmTP1b+vl5WVgYHD69GkPDw8d/eQAAKARrtgBsI+3t7dIJPrmm2/u3btHRN26dYuN\njTU2NiaiO3fuiEQiExOThv179OihruqIyN7enogGDx5c3+rg4CCXy5/+FB6PR0Rnz56tnzbl\nwIED2dnZ9R2EQqGtre2dO3da96cDAIDnhsIOgH3s7e1TUlJycnIcHR0tLCwkEolSqVQ3PXr0\nSCQSNepvZmZW/291ufb0nqdZWFisXLkyLS3N0dExKCgoNjZWfWGvIVtb24cPH774TwQAAK0C\nhR0A+xQUFIwdO1YkEuXl5RUUFKSlpfH5f/+/3LqPMqxYsSI9PX358uUKhWLDhg3+/v6hoaEK\nhaJhHzw8AQCgP1DYAbDPzp07b9y4ER4e3rlzZzMzMwsLi/ome3t7mUzWKp9SVlaWnZ0tFos/\n+uijs2fPFhQUREZGHj58+OjRo/V9ZDKZg4NDq3wcAAC8OBR2AOyjvvFqZGTUcOd3331HRGKx\nWCaTVVdXv/j7X7p0qXfv3vUz1VlbW4eGhta3EtGTJ0+KioqcnJxe5LMAAKAV4alYAPaZPHly\nXFzcunXrJBLJ8OHDBQLBl19+qX5gIiAgQKFQXLlypdGMJy1kaGhIRHFxcSNGjBgwYIBYLF6+\nfPnVq1fd3d2zs7N//PFHsVgcFBSk7nz16tW6urrAwMDW+8kAAOCF4IodAPs4OzufO3fu1Vdf\nXbhwoZ2dnY+Pj1KpnDFjBhG5u7s7ODikpqa2/N34fL6NjY3636NHjw4ODt60adP+/fvNzMyS\nk5NDQkJOnDixYsWKX3/9dezYsadOnbK0tFR3Tk1NtbOzw1wnAAD6g4eBzwAc895776WkpJw7\nd66tPygwMNDPz2/t2rVt/UEAANBCKOwAuOb27du9evXKyclp09FveXl5YrE4OztbPacxAADo\nAxR2ABwUFRVlamq6adOmtvuImJiY8vLynTt3tt1HAADAs0JhB8BBMpnM09Pz/PnzXbp0aYv3\nLygo6N+/f1paWocOHdri/QEA4PmgsAMAAADgCDwVCwAAAMARKOwAAAAAOAKFHQAAAABHoLAD\nAAAA4AgUdgAAAAAcgcIOAAAAgCNQ2AEAAABwBAo7AAAAAI5AYQcAAADAESjsAAAAADgChR0A\nAAAAR6CwAwAAAOAIFHYAAAAAHPH/egPPt9EUf3sAAAAASUVORK5CYII=",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "data = read.csv(paste0(RESEARCH_HOME, PATH, 'methods_delta.csv'), header = F, sep = \";\")\nnames(data) = c(\"runID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Time\")\ndata$Epsilon = as.numeric(as.character(data$Epsilon))\ndata$Mu      = as.numeric(as.character(data$Mu))\ndata$Delta   = as.numeric(as.character(data$Delta))\ndata$Time    = as.numeric(as.character(data$Time))\n\ndf = summaryBy(data=data, Time ~ Epsilon + Mu + Delta + Method, FUN = function(x){ c(m=mean(x), s=sd(x)) })\ndf",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/html": "<table>\n<thead><tr><th scope=col>Epsilon</th><th scope=col>Mu</th><th scope=col>Delta</th><th scope=col>Method</th><th scope=col>Time.m</th><th scope=col>Time.s</th></tr></thead>\n<tbody>\n\t<tr><td>30         </td><td>4          </td><td>3          </td><td>MergeLast  </td><td>641.5292   </td><td>116.504948 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>3          </td><td>SpatialJoin</td><td>619.2548   </td><td> 12.763986 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>4          </td><td>MergeLast  </td><td>564.5756   </td><td>  8.385703 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>4          </td><td>SpatialJoin</td><td>633.2694   </td><td>  6.662870 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>534.4168   </td><td>  6.950416 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>646.5072   </td><td>  7.386751 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>6          </td><td>MergeLast  </td><td>493.6746   </td><td>  5.790759 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>6          </td><td>SpatialJoin</td><td>662.3992   </td><td>  8.573007 </td></tr>\n</tbody>\n</table>\n",
      "text/latex": "\\begin{tabular}{r|llllll}\n Epsilon & Mu & Delta & Method & Time.m & Time.s\\\\\n\\hline\n\t 30          & 4           & 3           & MergeLast   & 641.5292    & 116.504948 \\\\\n\t 30          & 4           & 3           & SpatialJoin & 619.2548    &  12.763986 \\\\\n\t 30          & 4           & 4           & MergeLast   & 564.5756    &   8.385703 \\\\\n\t 30          & 4           & 4           & SpatialJoin & 633.2694    &   6.662870 \\\\\n\t 30          & 4           & 5           & MergeLast   & 534.4168    &   6.950416 \\\\\n\t 30          & 4           & 5           & SpatialJoin & 646.5072    &   7.386751 \\\\\n\t 30          & 4           & 6           & MergeLast   & 493.6746    &   5.790759 \\\\\n\t 30          & 4           & 6           & SpatialJoin & 662.3992    &   8.573007 \\\\\n\\end{tabular}\n",
      "text/markdown": "\nEpsilon | Mu | Delta | Method | Time.m | Time.s | \n|---|---|---|---|---|---|---|---|\n| 30          | 4           | 3           | MergeLast   | 641.5292    | 116.504948  | \n| 30          | 4           | 3           | SpatialJoin | 619.2548    |  12.763986  | \n| 30          | 4           | 4           | MergeLast   | 564.5756    |   8.385703  | \n| 30          | 4           | 4           | SpatialJoin | 633.2694    |   6.662870  | \n| 30          | 4           | 5           | MergeLast   | 534.4168    |   6.950416  | \n| 30          | 4           | 5           | SpatialJoin | 646.5072    |   7.386751  | \n| 30          | 4           | 6           | MergeLast   | 493.6746    |   5.790759  | \n| 30          | 4           | 6           | SpatialJoin | 662.3992    |   8.573007  | \n\n\n",
      "text/plain": "  Epsilon Mu Delta Method      Time.m   Time.s    \n1 30      4  3     MergeLast   641.5292 116.504948\n2 30      4  3     SpatialJoin 619.2548  12.763986\n3 30      4  4     MergeLast   564.5756   8.385703\n4 30      4  4     SpatialJoin 633.2694   6.662870\n5 30      4  5     MergeLast   534.4168   6.950416\n6 30      4  5     SpatialJoin 646.5072   7.386751\n7 30      4  6     MergeLast   493.6746   5.790759\n8 30      4  6     SpatialJoin 662.3992   8.573007"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "title = \"Execution time by Delta\"\ng = ggplot(data=df, aes(x=factor(Delta), y=Time.m, fill=Method, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(delta,\"(timestamps)\"))) \nplot(g)",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "image/png": 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hQMf+isFH/oDJUrV87SJaCwZAt2Qgje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\nAwAIhLADAAiEsAMACMT/A0k0zqUl/Co1AAAAAElFTkSuQmCC",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "stages = read.csv(paste0(RESEARCH_HOME, PATH, 'stages_delta.csv'), header = F, sep = \";\")\nnames(stages) = c(\"runID\", \"stageID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Stage\", \"Time\")\nstages$Epsilon = as.numeric(as.character(stages$Epsilon))\nstages$Mu      = as.numeric(as.character(stages$Mu))\nstages$Delta   = as.numeric(as.character(stages$Delta))\nstages$Time    = as.numeric(as.character(stages$Time))\nstages = stages[stages$Stage != \"Running MergeLast\", ]\n\nstages = stages[stages$Method == \"MergeLast\",]\nstages = summaryBy(data=stages, Time ~ Method + Delta + Stage, FUN = function(x){ c(m=mean(x), s=sd(x)) })\n\ntitle = \"Execution time MergeLast by Delta and Stage\"\ng = ggplot(data=stages, aes(x=factor(Delta), y=Time.m, fill=Stage, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(delta,\"(timestamps)\"))) \nplot(g)",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "image/png": 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maIBasBgAKJYJePxMfHh4WFZffc36lTp2bPnm3hkgCLGt1WGlV/xnJ3J/mil5TM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\noBMUOwAAAJ34F2O4XvMuKFazAAAAAElFTkSuQmCC",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "stages = read.csv(paste0(RESEARCH_HOME, PATH, 'stages_delta.csv'), header = F, sep = \";\")\nnames(stages) = c(\"runID\", \"stageID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Stage\", \"Time\")\nstages$Epsilon = as.numeric(as.character(stages$Epsilon))\nstages$Mu      = as.numeric(as.character(stages$Mu))\nstages$Delta   = as.numeric(as.character(stages$Delta))\nstages$Time    = as.numeric(as.character(stages$Time))\nstages = stages[stages$Stage != \"Running SpatialJoin\", ]\n\nstages = stages[stages$Method == \"SpatialJoin\",]\nstages = summaryBy(data=stages, Time ~ Method + Delta + Stage, FUN = function(x){ c(m=mean(x), s=sd(x)) })\n\ntitle = \"Execution time SpatialJoin by Delta and Stage\"\ng = ggplot(data=stages, aes(x=factor(Delta), y=Time.m, fill=Stage, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(delta,\"(timestamps)\"))) \nplot(g)",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "image/png": 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7//9vDw0Gq1qampjo6O4r/ZSHpaVmJnZxcUFGR8mZKSkpKSYuHi8yX9LtNqteb9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+/foePXrExcWJzqN80NgBAAAohhFd/mWi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\nAAAAAJZAsQMAAABgif8Fi/kqcdbRF3MAAAAASUVORK5CYII=",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "data = read.csv(paste0(RESEARCH_HOME, PATH, 'methods_mu.csv'), header = F, sep = \";\")\nnames(data) = c(\"runID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Time\")\ndata$Epsilon = as.numeric(as.character(data$Epsilon))\ndata$Mu      = as.numeric(as.character(data$Mu))\ndata$Delta   = as.numeric(as.character(data$Delta))\ndata$Time    = as.numeric(as.character(data$Time))\n\ndf = summaryBy(data=data, Time ~ Epsilon + Mu + Delta + Method, FUN = function(x){ c(m=mean(x), s=sd(x)) })\ndf",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/html": "<table>\n<thead><tr><th scope=col>Epsilon</th><th scope=col>Mu</th><th scope=col>Delta</th><th scope=col>Method</th><th scope=col>Time.m</th><th scope=col>Time.s</th></tr></thead>\n<tbody>\n\t<tr><td>30         </td><td>3          </td><td>5          </td><td>MergeLast  </td><td>778.2196   </td><td>143.024927 </td></tr>\n\t<tr><td>30         </td><td>3          </td><td>5          </td><td>SpatialJoin</td><td>779.3038   </td><td>  2.017583 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>5          </td><td>MergeLast  </td><td>536.8898   </td><td>  4.503238 </td></tr>\n\t<tr><td>30         </td><td>4          </td><td>5          </td><td>SpatialJoin</td><td>644.7652   </td><td>  4.976606 </td></tr>\n\t<tr><td>30         </td><td>5          </td><td>5          </td><td>MergeLast  </td><td>511.7100   </td><td>  7.101651 </td></tr>\n\t<tr><td>30         </td><td>5          </td><td>5          </td><td>SpatialJoin</td><td>630.5092   </td><td>  8.354047 </td></tr>\n\t<tr><td>30         </td><td>6          </td><td>5          </td><td>MergeLast  </td><td>504.7188   </td><td>  5.380213 </td></tr>\n\t<tr><td>30         </td><td>6          </td><td>5          </td><td>SpatialJoin</td><td>524.6336   </td><td>  9.569199 </td></tr>\n</tbody>\n</table>\n",
      "text/latex": "\\begin{tabular}{r|llllll}\n Epsilon & Mu & Delta & Method & Time.m & Time.s\\\\\n\\hline\n\t 30          & 3           & 5           & MergeLast   & 778.2196    & 143.024927 \\\\\n\t 30          & 3           & 5           & SpatialJoin & 779.3038    &   2.017583 \\\\\n\t 30          & 4           & 5           & MergeLast   & 536.8898    &   4.503238 \\\\\n\t 30          & 4           & 5           & SpatialJoin & 644.7652    &   4.976606 \\\\\n\t 30          & 5           & 5           & MergeLast   & 511.7100    &   7.101651 \\\\\n\t 30          & 5           & 5           & SpatialJoin & 630.5092    &   8.354047 \\\\\n\t 30          & 6           & 5           & MergeLast   & 504.7188    &   5.380213 \\\\\n\t 30          & 6           & 5           & SpatialJoin & 524.6336    &   9.569199 \\\\\n\\end{tabular}\n",
      "text/markdown": "\nEpsilon | Mu | Delta | Method | Time.m | Time.s | \n|---|---|---|---|---|---|---|---|\n| 30          | 3           | 5           | MergeLast   | 778.2196    | 143.024927  | \n| 30          | 3           | 5           | SpatialJoin | 779.3038    |   2.017583  | \n| 30          | 4           | 5           | MergeLast   | 536.8898    |   4.503238  | \n| 30          | 4           | 5           | SpatialJoin | 644.7652    |   4.976606  | \n| 30          | 5           | 5           | MergeLast   | 511.7100    |   7.101651  | \n| 30          | 5           | 5           | SpatialJoin | 630.5092    |   8.354047  | \n| 30          | 6           | 5           | MergeLast   | 504.7188    |   5.380213  | \n| 30          | 6           | 5           | SpatialJoin | 524.6336    |   9.569199  | \n\n\n",
      "text/plain": "  Epsilon Mu Delta Method      Time.m   Time.s    \n1 30      3  5     MergeLast   778.2196 143.024927\n2 30      3  5     SpatialJoin 779.3038   2.017583\n3 30      4  5     MergeLast   536.8898   4.503238\n4 30      4  5     SpatialJoin 644.7652   4.976606\n5 30      5  5     MergeLast   511.7100   7.101651\n6 30      5  5     SpatialJoin 630.5092   8.354047\n7 30      6  5     MergeLast   504.7188   5.380213\n8 30      6  5     SpatialJoin 524.6336   9.569199"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "title = \"Execution time by Mu\"\ng = ggplot(data=df, aes(x=factor(Mu), y=Time.m, fill=Method, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(mu,\"(moving objects)\"))) \nplot(g)",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "image/png": 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ACAJAh2AAAAkiDYWd6xY8fWrVt39+5d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\nAACSIOwAAAAkQdgBAABI4v8CViuKS+URyaIAAAAASUVORK5CYII=",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "stages = read.csv(paste0(RESEARCH_HOME, PATH, 'stages_mu.csv'), header = F, sep = \";\")\nnames(stages) = c(\"runID\", \"stageID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Stage\", \"Time\")\nstages$Epsilon = as.numeric(as.character(stages$Epsilon))\nstages$Mu      = as.numeric(as.character(stages$Mu))\nstages$Delta   = as.numeric(as.character(stages$Delta))\nstages$Time    = as.numeric(as.character(stages$Time))\nstages = stages[stages$Stage != \"Running MergeLast\", ]\n\nstages = stages[stages$Method == \"MergeLast\",]\nstages = summaryBy(data=stages, Time ~ Method + Mu + Stage, FUN = function(x){ c(m=mean(x), s=sd(x)) })\n\ntitle = \"Execution time MergeLast by Mu and Stage\"\ng = ggplot(data=stages, aes(x=factor(Mu), y=Time.m, fill=Stage, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(mu,\"(moving objects)\"))) \nplot(g)",
   "execution_count": 78,
   "outputs": [
    {
     "data": {
      "image/png": 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gY1/ytD+NaQd3vK6jFStYypi1M7nxr5NPDyEIs8r5Xj5fz+++9t2rTZvHlzSkqK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XqMyMkuYVZspPhM10c9PXv2rE+fPra2tj///POGDRuS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InchYHD161NPTc8mSJbGxsUKIO3fufPnl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\nQGIHAAAAIBFI7AAAAAAk4i+xFH2UnTm1WQAAAABJRU5ErkJggg==",
      "text/plain": "plot without title"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "stages = read.csv(paste0(RESEARCH_HOME, PATH, 'stages_mu.csv'), header = F, sep = \";\")\nnames(stages) = c(\"runID\", \"stageID\", \"Method\", \"Epsilon\", \"Mu\", \"Delta\", \"Stage\", \"Time\")\nstages$Epsilon = as.numeric(as.character(stages$Epsilon))\nstages$Mu      = as.numeric(as.character(stages$Mu))\nstages$Delta   = as.numeric(as.character(stages$Delta))\nstages$Time    = as.numeric(as.character(stages$Time))\nstages = stages[stages$Stage != \"Running SpatialJoin\", ]\n\nstages = stages[stages$Method == \"SpatialJoin\",]\nstages = summaryBy(data=stages, Time ~ Method + Mu + Stage, FUN = function(x){ c(m=mean(x), s=sd(x)) })\n\ntitle = \"Execution time SpatialJoin by Mu and Stage\"\ng = ggplot(data=stages, aes(x=factor(Mu), y=Time.m, fill=Stage, ymin = Time.m-Time.s, ymax = Time.m+Time.s)) +\n  geom_bar(stat=\"identity\", position=position_dodge(width = 0.75),width = 0.75) +\n  geom_pointrange(position=position_dodge(width = 0.75)) +\n  labs(title=title, y=\"Time(s)\", x=expression(paste(mu,\"(moving objects)\"))) \nplot(g)",
   "execution_count": 79,
   "outputs": [
    {
     "data": {
      "image/png": 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I1G4+zsnJycnJiYaKw+S4nU1FQhxLNnz4z4AVGr1W5ubmlpaS9evDBWn8WftbW1\nuUsA/svU/+PeunXrr7/+EkI4OjqWK1du8ODBqampf//9t5ubm1arTU5OlptlZGQkJCTId8sW\nc19++WWOqU62du3aixcvmrAcAABQepnh5omwsDDdobikpKS0tDRLS8tq1apZW1v//fff8vBL\nly6p1erq1aubuLwCkK+ry01mZuaOHTtMVgwAACjNTB3smjVrlpmZuWzZsuvXr1++fHnBggUV\nK1Zs2LChnZ1dhw4dvvvuu6ioqBs3bqxevTogIMDV1dXE5b2stLS0W7du5d3mypUrpikGOpmZ\nmXv27BkzZkxcXNyDBw/GjRu3f/9+bj0BACieqa+xc3JymjVr1nfffTdz5kxra+tGjRqNGzdO\nvkBh9OjRa9asmTt3bmZmpq+v7+jRo01cWwFc3l4537QQd3fflR3udXs9NklFELGxsSNHjvzz\nzz91Q7Zu3bp169Z27dqtWrXK2dnZjLUBAFCkzHDzRJ06dT7//PPswy0sLEJCQkJCQkxfUoHZ\nWAsXRxEXn1ebimVMVQ2ESE9PHzRokO4OZX0HDx4cPnz49u3bs9+mAwCAMvCAgMJq7ZVfA+M8\nzA4G2bx5c46pTnb06NFdu3aZsh4AAEyJYFdYw7oI29xvdW9WV/g0MGE1pd7OnTsL2QAAgJKL\nYFdYFcuKz8YKB7scRtXzEJ+8KTjvZ0pRUVF5N7h27ZppKgEAwPQIdkbg3UCs+0j0bf/vy+lU\nKtGgunhnoPj6PeHsYO7iSpMrO9xTEu7m3Sb5+dUrO9xNUw8AACZGsDOOcm5iYn/x/SwhhGhe\nX6x8X/RuJzSl5Td1ipGK+T3TulIJeOg1AAAFRLCDovjnd6uKf1OT1AEAgDkQ7KAovdrmddDO\ns6Lo0tKE1QAAYFoEOyiKjZVYNEFUyukiOs+KYuEEzo+bQUZGxo8//ti/f/9z584JIYKDg7/6\n6quEhARz1wUACsS3HJSmWgWx9kOx45A4eFpcuS2EEA2qi/bNRXd/YWNl7uJKn7i4uKFDh0ZG\nRuqGnDx58uTJk99+++3GjRvr169vxtoAQHk4YgcFsrMRgzuJ1TOEi4OoWFasmC76dSDVmYEk\nSSNHjtRPdTp37tzp169fXFyc6asCAAUj2AEoKvv37z9y5EhuYx8+fPjVV1+Zsh4AUDyCHYCi\n8vPPP+fdYM+ePaapBABKCYIdgKJy69atfBtkZmaaphgAKA0IdgCKxJUd7slP/iMUN10AACAA\nSURBVMynkaS9urO8ScoBgFKBYAegqFQtl0+Dyu78mDIAGBPBDkBRadc8nwaB+TUAALwUgh2A\nouLTQLR8JdexFcuI/kEmrAYASgGCHYAiNHu08GuUw3DPiiJssrC3MXlBEEIIkZaWJj9EMDY2\n1ty1ADAmgh2AImRnIxaOF/PHifbewtpKCCH8GolpQ8WamaJKflfgoSg8ffr03XffrV279m+/\n/SaECA4Obteu3S+//GLuugAYB8EOQNFSqUQrL/FxiKhTVQghFowX3dsIK425yyqVbt++HRgY\nuHbt2qSkJN3ACxcuDB8+/LPPPjNjYQCMhWAHAKVCZmbm6NGj7927l+PYsLCwffv2mbgkAEZH\nsAOAUiE8PPzMmTN5NFiyZInJigFQRAh2AFAqHD58OO8Gp0+fjo+PN00xAIoIwQ4ASoWYmJi8\nG0iSlG8bAMUcwQ4AlO/KDndtzNZ8m8Uc9TNBMQCKDsEOAEqFeh75NCjnJlwdTVIKgCJDsAOA\nUiGgqXC2z6tBcCt+uhco8Qh2AFAqONiJKYOFOpfoVs9DDPqXaQsCUAQszV0AAMBEApsLSwsR\nulE8ffHfgSqVaPeqeHeIsLEyX2UAjIRgBwCliH9T4ddIHPtbrPlZ3LgnhnQSnVoIz4rmLguA\nkXAqFgBKFyuNaNtM1KkmhBBdW5HqAEUh2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACA\nQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDs\nAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAA\nFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJg\nBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAA\noBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAE\nOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAAFIJgBwAAoBAEOwAA\nAIUg2AEAACiEpbkLKCCVSmVpWZKKL0y1arVaCGFhYSFJkvEqKtUKvznUanXJ2gOLs8KsSQsL\nC8HmMKrCfzpK3N/nwuDPMoqbkvrZU6vVtra25q7iJRSmWvlPpLW1tUajMV5FpVphNoecJKys\nrOQXKLzCbA45SVhaWpasPwjFWWHWpEqlEkJYWFiUns2Rnp5u7hKA/1FSg11GRkZSUpK5q3gJ\n8fHxBZ7Wzs7Ozs4uKSlJq9UasaTSrDCbw8bGxsHBISUlJSUlxYgllWaF2RwajcbZ2TktLS0x\nMdGIJZVmhdkcarXazc0tPT29MJ2UOPb29uYuAfgvrrEDAABQCIIdAACAQhDsAAAAFIJgBwAA\noBAEOwAAAIUoqXfFFk+WFqK7v/CsYO46AABAqUSwMyYrjZg2xNxFAACA0opTsQAAAArBETso\n2aZPhFCZuwgAAEyFYAclc7AzdwUAAJgQp2IBAAAUgmAHAACgEAQ7AAAAhSDYAQAAKATBDgAA\nQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgl+eAGAiiyaKjEyh5kfeAKDIEOwAmIidjbkr\nAACl41QsAACAQhDsAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAheNwJAJRGTesIK42w\ntzV3HQCMimAHAKVR11aiaytzFwHA2DgVCwAAoBAEOwAAAIUg2AEAACgEwQ4AgOIoIyNjxYoV\nLVu2dHd3d3Nz8/b2njNnTnx8vLnrQrFGsAMAoNiRJCk4OHjMmDEajeatt94aP358+fLlZ8+e\n3axZsxcvXshtFi1apFKpnjx5Yt5SUaxwVywAAMXO+vXr9+3bN3v27FmzZukG7tixo3fv3rNm\nzQoLCzNjbSjOOGIHAECxc/jwYSHEpEmT9Af26tWrYcOGR48eNVNRKAEIdgAAFDuJiYlCiLt3\n72YZvm/fvk2bNgkh2rVrN3XqVCFE2bJlhw4dKo/94YcffH19XV1dnZycmjVrtnr16izTtm3b\n1sXFxdfXd+XKlaGhoY6Ojrqx0dHR/fv39/T0dHZ2DggI+PXXX4t0AVFECHYAABQ7nTt3FkL4\n+/vPnDnzxo0buuFVqlSpVauWEGLx4sVjx44VQuzatWvGjBlCiO3btw8ePFilUk2bNm3MmDHp\n6ekhISHbtm2TJ9yyZUvXrl3j4uLeeeedZs2aTZgwYfHixbpuz50716RJk6NHjw4YMOCdd955\n+vRpcHDwt99+a8pFhlGoJEkydw0FkZSUlJSUZO4qxJUd7ga2rNvrcYHnYmdnZ2dn9/z5c61W\nW+BOSgPTbA4bGxsHB4eEhISUlJQCd1IamGZzaDQaZ2fn5ORk+fAGcmOazaFWq93c3NLS0nRX\n95cGZcuWLYpuJUmaM2fOwoUL5X27Zs2a7du379SpU3BwsEajkdssWrRo6tSpsbGxZcqUEUL0\n7t375MmTUVFRVlZWQojU1NRy5coNGDBgxYoVaWlptWvXLl++/OHDh21sbIQQP//8c/fu3R0c\nHOTbbNu2bRsdHX3mzBk3NzchhFar7dix4+nTp+/fv+/g4FAUC4giwhE7AACKHZVKNWvWrIcP\nH27fvn3cuHEajWblypW9e/euWbPm8ePHc5xk1apV58+fl1OdECI+Pj4jI0M+CBIZGXn79u13\n3nlHTnVCiG7dutWrV09+/ezZs/Dw8DfeeENOdUIIjUbz9ttvx8fH5zYvFFsEOwAAiikHB4de\nvXp9+eWXly9fvnjx4qhRox48eNCjR48cn2ZXpkyZJ0+erF+/fsqUKW3btq1SpYruSPb169eF\nEA0aNNBvr3t75coVIcTMmTNVel577TUhxOPHBT+CC7PgcScAABQviYmJw4cP7969u+6uCCFE\ngwYNVq9e7e7uPm/evIiIiE6dOmWZatmyZVOmTHF0dOzSpcvAgQPDwsJ69Oghj0pLS8s+FwsL\nC/mFfJBv+vTp2fusW7eusRYKpkGwAwCgeLG3tz98+PDz58/1g53M09NT6GUyncTExHfffXfQ\noEHffvutbmxqaqr8onbt2kKIf/7555VXXtFNIh+oE0LId2Oo1eqAgADd2AcPHly9etXFxcV4\niwVT4FQsAADFTpcuXfbv3//NN9/oD4yPj1+5cqWdnZ23t7duYGZmphAiOjo6NTW1efPmulT3\n22+/xcTEyGN9fX3d3d0XL16sO3R34MCB8+fPy6+dnJzat2+/cuVK3YnXzMzMYcOGDRgwQHej\nBkoKjtgBAFDsLF68OCIiYuzYsStWrPD29nZzc7t///6ePXvi4uI2btwoH0iTU1dYWFiXLl18\nfHyqVKny2WefPX78uEaNGidOnPjpp5+qVKny+++/r127dvjw4fPmzRs1alSrVq169eoVExPz\n/fffBwQEXLhwQZ7dwoUL/f39vby8RowYYWFh8csvv/z111/r16/PfmgQxRxH7AAAKHacnZ3P\nnTu3YMECa2vrXbt2ffnll3/99VdwcPD58+cHDhwot+nRo0e7du2WLFmyefNmKyurX3/9tWHD\nhosXL/7oo4+ePXt2/PjxH3/8sV69ehEREUKIkSNHbtu2zcLCYv78+efOndu+fXvr1q3Lly8v\nd9W0adPTp0/7+fmtW7du6dKltra2e/bsGTJkiNmWHwXFc+wKhefYFSs8x65Y4Tl2xQrPsSs6\nRfQcO+PKyMiIi4uzt7fXPe5ECDFo0KCHDx/+8ccfZiwMRscROwAAFC4lJaVSpUr6vzz76NGj\nXbt2dejQwYxVoShwjR0AAApnb28/fPjwlStXpqenBwYGPnv2bNGiRZaWliEhIeYuDUZGsAMA\nQPmWLVtWrVq1devW/fDDD+7u7k2aNAkLC3N3N/QcPUoKgh0AAMpnZWU1Y8aMGTNmmLsQFC2u\nsQMAAFAIgh0AAIBCEOwAAAAUgmvsAAAoXiRJSps+0bh9Ws9fatwOUTxxxA4AAEAhCHYAAJRI\nq85eqL9y3R+37pi7EBQjBDsAAEqkZykp0XHPE9P4tUn8F8EOAABAIQh2AAAACkGwAwAAUAiC\nHQAAJU+GJMUkJgkhHiQkmrsWFCMEuxIgKirq999/f/bsmbkLAQCYX3J6+qzDf1ZdtvrL0+eE\nEBP2H6rzzdpv/jqfKUnGndHQoUNVemxtbZs0abJ161bjzqWoZWRkqFSq06dPm7sQEyHYlQDr\n168PCgr6+++/zV0IAMDMnqemBW7cNj/y1NOUFN3A2y/iJ/0ePnjX3gyDs116evqUKVM8PT0r\nV648ZsyY1NTUHJv5+flF/sfOnTvr1KkzcODAog5Jbdq0WbRokRDi1q1bKpVqxYoVRTq7l1XM\nyyPYAQBQYkzcf+jMo8c5jtpxNeqL438Z2M+UKVO2bNmybNmyb7/99v/+7/9CQkJybObi4uL7\nH//61782bNhgY2Ozf//+Alafp7S0tOjoaP0hzs7O06dPb9KkSVHMrvCKZ3kEOwAASobbL+K3\nXLqSR4Owk3+lZ2bm2098fPyaNWvCwsK6devWqVOnr776asuWLY8f55wX9VlZWVlbW5cpU0Z+\n+/z58zFjxnh4eDg7O3fv3v369etCiNOnT7u7u0dERLRo0aJs2bKBgYGXL1+W2z9+/Hjw4MEV\nKlSoVKnSkCFDdHPUaDR79uypXLnyhAkTvL29jx49OnXq1M6dO7u4uISGhqanp8ttIiMj+/Xr\nV6NGjVq1am3btk2e9u7du927d3d1dW3WrNnu3bsdHR0vXryYW/25FRATE9O/f393d/eKFStO\nnDgxLS1NCHHlypVOnTq5uro6OTm1bdv2/PnzQojcystj0XIse8+ePc2aNbOzs6tevfqSJUvy\nXfOGI9gBAFAyHLp1N+9TrU+TU3I7nqfvwoULCQkJQUFB8tv27dtrtdozZ87kPdWLFy8WLlxo\nbW3duXNneUjPnj3/+eefdevW7d+/397e3t/fPy4uTggRHx8/bdq0zZs337lzx9fX19/fPz4+\nXpKkrl27RkVFbd68edOmTdevX+/SpYv0n3PHU6ZMmT9//rJly06ePNm6devQ0NC9e/dmKWD6\n9Onz5s27du3a4MGDhw4dmpKSkp6e3r59eyHE3r17Z8yY8eabbyYm5norSW4FZGZmBgUFxcfH\n7969e8GCBZs2bfrkk0+EEIMHD05NTd22bduuXbskSZIPauZYXt6Llr3sO3fu9OnTJygoKDw8\nfMyYMZMmTYqMjMx3qxnI0lgdAQCAIvUw99Ty3zYG3CT74MEDKysrFxcX+a2VlZWrq+v9+/ez\nt9y3b59KpdK9tbCwOHToUJUqVYQQx48fj4iIePTokaurqxBiw4YNnp6eR44cqVSpUmpq6kcf\nfeTh4SGEmDt37tatW9etW9ewYcMzZ87cuHGjatWqQoitW7fWqFHjyJEj/v7+QoiQkJCRI0fm\nXbZ83EsIMXr06Dlz5ty7d+/s2bMxMTGnT592cHAQQsTHx48YMSK3ycPDw3MsID4+/saNG+Hh\n4S4uLi1atEhKSjp27JgkSf369XvttdfkOd6/f3/SpEkv27O8aNnLjo6O1mq1b731loeHh7e3\nd61atcqVK5f3shuOYAcAQMngaGWVbxsn6/zbSJKkH9dk8inFLPz8/BYvXiy/TkhIWLZsWZ8+\nfe7cuWNlZXX58mWtVqufSNLT069fv16pUiUhhLe3tzxQrVa/+uqrly5dUqvVnp6ecvQRQlSr\nVs3Dw+Py5cty+vHy8sq37Pr168sv7Ozs5BcXLlxo0qSJnOqEEK1bt85j8suXL+dYQFxcXMOG\nDXUx980333zzzTeFEJMnT46MjPz1119Pnz7966+/FqBnedGyl+3n5+ft7d2oUaOePXsGBQX1\n7dvX1tY238U3EKdiAQAoGZpVyOe4jkatblyubL79VKxYMTU1NT4+Xn6bnp4eFxcnH4fLQv/m\nifbt23/99dcxMTHytXTOzs5ubm5aPZIkTZ48WZ4wU+9SP61Wm5mZmZmZmSVNqtVqXZrUhZ48\nWGXLtVqtVr9PtTqvVJNbAVqt1tIy63GupKSkgICAkSNH3rp1q1evXp9//nkBes6tbAcHh+PH\nj+/du9fR0XHOnDnVqlXLft65wAh2AACUDN4Vyzd2zyu39apby83GJt9+GjZsaGdnd/DgQfnt\n0aNHLSwsDLm7s2LFikKIq1evyp08ffr0woUL8qjY2NiePXteunRJfnv48GH5RXJy8rFjx+rX\nr1+vXr3o6Oh79+7Jw+/evXvz5s0GDRrkO9O8F+TcuXNJSUny27yvVMutgPr168sXHcrDN23a\n1LFjx4MHD54/f/7ChQsLFy7s3r17Zp63pLzsooWHh4eGhrZu3Xr58uXXrl1r0aLFqlWrDF/q\nvBHsAAAoGdQq1dedAu01mhzHVnF0mN8ur3OROs7OziNHjnz33Xf/+uuvs2fPTpo0aeDAgRUq\nVBBCrF279uuvv85jWkdHx4cPHwoh6tSp07t378GDBx88ePDIkSNDhw69fPlynTp15GaTJ0/e\nvXt3ZGRk//79MzIyhg8fHhgY+Morr/Tv3//YsWMRERH9+/f38vJq27ZtDoupVkdFRcn3YeSt\nd+/eTk5OQ4cOlc+Wzp0719LSMrfjdrkV0L17d3d39yFDhpw6dWrnzp0ffPBBgwYNHB0dExMT\nf/rppxs3bqxevfrjjz+Oj4+Xb4zNXp7hiyZLS0t77733li1bdv78+d27d1+8eNGQM9EGItgB\nAFBiNK9Yft+AXnXLuGYZ7l+t8oFBfSo62BvYT1hYWOfOnXv27Nm1a9cWLVqsXLlSHr5x48Y1\na9bkMWGDBg1Wr14tv16/fn2rVq1ef/31bt26WVtb79u3T3dO8+uvv/7www87duwYHx9/+PBh\nJycnlUq1d+/eqlWr9u7du0+fPp6ennv37s1+qZ8QYtiwYVu3bh01alQeZdja2qrVamtr6wMH\nDrx48SIwMHDu3Lnr168XQuR2I0JuBWg0mj/++EMI0bFjx7feeqtHjx7z5s3z9/efNWvWO++8\n4+Pj89tvvx06dKhz584zZszIsTzDF00uOygoaN68eWFhYT4+PuPHj+/du/cHH3yQx8K+FJVk\n7F8gMY2kpCTdoVczurLD3cCWdXvlf/95bhYsWLBw4cLdu3e3aNGiwJ2UBqbZHDY2Ng4ODgkJ\nCSl6j31HdqbZHBqNxtnZOTk5OY9nHECYanOo1Wo3N7e0tLQXL14UuJMSp2zZ/K9pe1mSJKVN\nn5hHg0xJOnjrbujx0wdv3RnUsO5bzbyaVyyfd5/W85catcZcnT59unnz5unp6RYWFkU9r0eP\nHh07dqxXr17y2wsXLvj6+iYkJOQYqkoJjtgBAFDCqFWq9p5V23lUEUL0qlMr31SnVJIkDRo0\n6Isvvnj06NHVq1fHjRsn/76tuesyJ4IdAAAokSpUqLBr164ffvihevXqgYGBtWvXnj9/vrmL\nMjOeYwcAAIzm1VdfNeVVXh07duzYsaPJZlf8ccQOAABAIQh2AAAACsGpWAAASqRhjRt08KxW\nw9XZ3IWgGCHYAQBQIpW3tytvn/8vcaFUIdgBAFC8qFQqq3lLjNuhEXtDccY1dgAAKFwJ/TEC\nFABH7AAAKF4kSTq22shHXlqFkO1KBY7YAQBQIqVpRXySSM8wdx0oTgh2AACUSFt+F50niT//\nNncdKE4IdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUwgyPO4mLi/vuu+/Onj2blpZWt27d4cOH\ne3p6CiEyMjK+//77Y8eOpaen+/j4hISEaDQa05cHAEDxdzdGXL0thBDnromGNYSbk7kLQvFg\nhiN2ixYtunnz5tSpUz/++GNbW9sZM2Y8e/ZMCLFmzZojR4688cYbEyZMOHPmzJdffmn62gAA\nKOYePBFTlogBM8XB00IIsXm/6DVNfP69SEg2/rw2bNjg4+Pj5ORUsWLFLl26REZGGn0Whw8f\n9vDwaNu2bd7NHB0dDxw4kHebK1euqFSq58+fF1ENJYKpg92TJ0/OnTs3duzYxo0b16lTZ+rU\nqUKIEydOJCcn79+/f/To0T4+Ps2aNRszZsyRI0cKsG0AAFCwuzHijc/E8Yv/MzAjU/wSIcYt\nfOlsl5aWVqZMmSdPnuQ4dvny5aNGjXrttdf++OOPTZs2VahQoW3btufOncu7zzZt2ixatMjw\nGpYuXdq0adMff/zRkMa3bt1SqVQrVqwwvH+j15BdvotcRGXnyNSnYjMzMwcOHFizZk35bXp6\nelpaWmZm5q1bt1JSUpo0aSIP9/LyysjIuHHjRtOmTeUhKSkpW7Zs0fXTsGHD+vXrm7j4wrC1\ntS3wtBYWFkIIjUZTmE6grzBr0tLSUgih0Wj47UVjKfynw9LSkk+HsRRmTcofCrVaXXo2R0aG\nqZ8OPO978Sw+51FRd8WKHWLKIIP6SUlJOXbs2IoVK54+fZpbm2XLlk2aNGnatGny27Zt20ZF\nRX377bdLly596bpzl5iY6Ofn5+7ubkhjZ2fn6dOn69KC6WtIS0u7d+9e9erVX6r/Iio7R6YO\ndu7u7gMHDpRfp6amLl682NHRsXXr1hcuXLC0tLS3t/93WZaWDg4O+ntbcnLysmXLdG/feOON\n5s2bm7LyQtItWgGo1WohhJWVVWE6gb7Cr0lra2tra2ujFIPCbw6NRsMlucZS+M1haWkp//9T\nGqSmpppydlF3xdlreTX4NUKM6yNsDPjjtGzZsqVLl6alpeXR5uHDh8nJ/3MMcMmSJUlJSfLr\n58+fv/fee3v37o2LiwsICPjiiy9q1arl7e196tSpo0eP/v7773v37tWf9vHjx5MmTTpw4IBa\nrQ4MDAwLC3N3d2/Xrt2hQ4f27du3b9++P//8U7/91atX33777RMnTlSvXn3OnDnyQBcXl9DQ\n0ODgYCHEnj17Pvroo3/++ad8+fKTJk2aOHGi/uQnT54MCgr6/PPPx44dm3fLLDXkWKcQQqPR\n7NixY8SIEX5+fj///LNu8jwWWUe/7KJmns+eJEkHDx7csGFD+fLlw8LCHB0dJUnKfvxD/z8h\nBweH5cuX696WLVu2ZJ2oLUy18npISUkpWYtcnBVmTVpZWdna2iYnJ+f9BxGGK8zmkP8hTE1N\nTUlJMWJJpVlhNodarXZ0dNRqtbrvfsWTJMmU/+P9HZVPg1StuHJbeNXOv6t333333XffPX36\ndB5HSYKDg7/++uvHjx8PHz7c39/f1tZW/5hTz549JUlat26dra1tWFiYv7//pUuXTp482aZN\nm549e06ZMkW/K0mSunbtqlarN2/erFKp3nvvvS5dupw4ceLgwYOdO3f28/ObNWuWfvvExMSA\ngIDGjRvv3r37yZMnEyZMyLJT3blzp0+fPpMmTVqxYsUff/wxadIkX19fV1dXeey5c+c6deo0\ne/bssWPH5tjSz89P15V+DbnVKUeUKVOmzJ8/PzAwUL+S3BbZXMwQ7J4/fz5//vxHjx4NGzbM\n399fXllubm5arTY5OVk+gJ+RkZGQkFC2bFndVBqNxsfHR/c2KSmpZP3h0Gq1BZ42MzNTCJGR\nkVGYTqCvMGtSPvfH5jCiwq/JzMxMNoexFGZNyqcXJElicxSRF4nGaWOg1atX161bd9OmTZ06\ndbK2tm7VqtWIESOGDBkihDh+/HhERMSjR4/kLLVhwwZPT88jR45069Ytx67Cw8PPnDlz48aN\nqlWrCiG2bt1ao0aNI0eO+Pv759h+48aNqampP/30k6OjoxDC1ta2c+fO+g2uXLmi1Wrfeust\nDw8Pb2/vWrVqlStXTt7xLl261KNHj7Fjx06aNCm3lrktct51hoSEjBw5siCr0oRMffOEJEkf\nf/yxnZ3dsmXLAgICdEfpqlWrZm1t/fff//7Fu0uXLqnV6pc9hw0AgIK5OBinjYGsra1nzpx5\n8eLFe/furVq1ytXVNSQkZPLkyUKIy5cva7XacuXKyRdC2NjY3L179/r167l1dfnyZU9PTzkt\nCSGqVavm4eFx+fLlPNr7+PjIqU4I0a5duyyn9fz8/Ly9vRs1ajR06NB169Z16dKlRo0a8qhu\n3bpJkhQVFZVvy5et08vLK88VViyYOtidP38+KiqqTZs2165dO/cfsbGxdnZ2HTp0+O6776Ki\nom7cuLF69eqAgADdMVUAAOBVJ58Gttairodx5nXx4sWgoCD5GrtKlSoNHTp027ZtX3311Zdf\nfpmWlubs7CyfatORJEnOfDnKzMzMkszUanV6enpu7eVzIzoqlSrL5A4ODsePH9+7d6+jo+Oc\nOXOqVaumu77tgw8+2Lp16+bNm//444+8W75snXZ2drlNWHyYOthFR0dLkrRo0aIP9Rw/flwI\nMXr06GbNms2dO3fOnDn16tUbN26ciWsDAKA486ggfBvm1aBngLAy0n1EFStW/P3333/55Rf9\ngRkZGdbW1paWlg0bNnz69OmFCxfk4bGxsT179rx06VJuvdWrVy86OvrevXvy27t37968ebNB\ngwa5ta9fv/6pU6cSEhLkt0ePHpWvStIJDw8PDQ1t3br18uXLr1271qJFi1WrVsmjRo0a1a5d\nuwEDBrz99ttarTaPloWvsxgy9TV2PXv27NmzZ46jLCwsQkJCQkJCTFwSAAAlxXuvi7HzxaOc\nHlHSqIYY1b2w/a9duzY5OXns2LFubm4TJ04cMWLEhQsXWrVqpdFoTp06NXfu3AkTJqjV6jp1\n6vTu3Xvw4MGLFy+2tLT87LPPbty4UadOHSGEWq2OioqKi4tzcXHRdRsYGPjKK6/0799/wYIF\nkiRNmzbNy8srjwcCDxw48MMPP+zXr9+HH3747NmzyZMnZ7lfOy0t7b33U3P7RgAAIABJREFU\n3rO2tg4ICIiOjr548eLrr7+u3yA0NLRevXpLlizx8vLKu6W+l61TluMimwu/FQsAQIlRzlWs\nniGCfIRa7wvcxkoMCBJLpggbq8L2v3HjxjVr1sivQ0NDFyxYsHv37tdee61Pnz4//vhjaGjo\np59+Ko9dv359q1atXn/99W7dullbW+/bt09+zM2wYcO2bt06atQo/W5VKtXevXurVq3au3fv\nPn36eHp67t27N4+ngdrZ2YWHh6enp3fu3Hn69Onz58/v0aOHk9N/fzctKCho3rx5YWFhPj4+\n48eP79279wcffKDfQ+XKlWfNmvXxxx83aNAg75aFqVOW4yKbi0qSJHPXUBDF5K7YKzsMeqCi\nEKJur8cFnsuCBQsWLly4e/fuFi1aFLiT0sA0m8PGxsbBwSEhIYHna+TNNJtDo9E4OzsnJycn\nJhrvVkAlMs3mUKvVbm5uaWlpL168KHAnJY7+AxyMRZKkY6vzOfISFy++3Cb2/SlGdhMDOwrb\n/B660iqkRH7d42VxxA4AgJLHxVF4VBBCiNpV8091KD1Ky8PBAQAAis6RI0d0v5CRxfDhwwcP\nHmyaMgh2AAAAhdWmTZv9+/ebuwpOxQIAACgFwQ4AAEAhOBULAECJNLSzGNo5/2YoVThiBwAA\noBAcsQMAoHhRqVQ8dg4FQ7ADAKDYycjIMG6HFhYWxu0QxRPBDgCA4kWSpP77jPwFva0rhwBL\nBa6xAwCgREp6JGLOitTn5q4DxYmh/xBIknThwoXw8PCbN28+evRIkqQKFSp4enoGBAQ0atQo\n39/HBQAAxnXnsLjwvWgxU1TyM3cpKDbyD3Y3btxYsWLF2rVrY2JiLC0ty5QpU6ZMGSHE06dP\nY2Nj09PT3d3dhw8fPmbMmBo1ahR9wQAAAMhZXqdinzx5EhISUqdOnWPHjr3//vuRkZEJCQkP\nHz68ePHixYsXHzx4kJCQcPz48RkzZkRGRtapUyckJOTJkycmKx0AAAD68jpi5+Xl1bdv36io\nKA8PjxwbWFtb+/j4+Pj4TJw48fbt22FhYU2aNLlz507RlAoAAIC85BXsTp06VaFCBQM7qlat\nWlhY2LRp04xRFQAAAF5aXsEuj1SXkZGxd+/ezMzMtm3bOjk56YZXrFjRmNUBAIBsMrXifqR4\ncEIIIW79LiysRLmmgvsYIQx/3EliYmJISEjdunXltz179uzWrVuPHj2aNm16+/btIisPAAD8\nj5hzYl+IOD5fPLkshBD3I8XRj8QfE8ULY38bd+vWTfUfdnZ2LVu2XLFihZHnUWRu3bqlUqnC\nw8OLekYajSYiIqKo52I4Q4PdrFmzVq9e3aRJEyHEn3/+uWfPntGjR+/evTsuLu7TTz8tygoB\nAMC/Pb4gImaL5Nisw+NuiMMfiMRHhvbz6NGj119/vVKlSq6urp06dTp//nyOzTp06BAZGRkZ\nGfnTTz/5+/u//fbb77zzjjxKTk55RL02bdosWrTI0IJMLt/6SyhDn2P3008/BQcHb9myRQix\nZ88ea2vr0NBQZ2fnnj17HjhwoCgrBAAAQgghZYq/lolMbc5jU+PE+VWixUyDuho8eHBsbOzG\njRvt7e1DQ0MDAwP//vvv7NdTlS1b1tfXV37duXPnxo0bDxs2bOzYsbVr13Z2dp4+fbp8xKck\nKun158bQI3YPHz7UbdqjR4/6+Pg4OzsLIerWrXv//v2iqg4AAPzHk0si4V5eDR6cMOiHKO7d\nu3fgwIHly5e3a9fOx8dn48aNkiT9/PPP+U44cODAqlWrrlu3Tgjh4uISGhqanp4uhNizZ0+z\nZs3s7OyqV6++ZMkSIYS3t/fRo0enTp3auXNnIcSVK1c6derk6urq5OTUtm1b3QFCjUYTGRnZ\nr1+/GjVq1KpVa9u2bfLwmJiY/v37u7u7V6xYceLEiWlpaUKI58+fjxkzxsPDw9nZuXv37tev\nX89/UYUQQjx+/Hjw4MEVKlSoVKnSkCFDHj9+nKX+3MrQOX36tLu7e0RERIsWLcqWLRsYGHj5\n8mXd2EePHgUHB7u4uNSsWfPHH3+UB+a2yNnXVWEWLTtDg13lypXPnj0rhLh7925ERET79u3l\n4RcvXnR3dy/w7AEAgIGeR+fTQMoUL27m309GRsbs2bNfffVV+a1Wq01JScnMzMx3QrVa/cor\nr9y4cUN/4J07d/r06RMUFBQeHj5mzJhJkyZFRkaePHmydevWoaGhe/fuFUIMHjw4NTV127Zt\nu3btkiQpJCREN/n06dPnzZt37dq1wYMHDx06VK4kKCgoPj5+9+7dCxYs2LRp0yeffCKE6Nmz\n5z///LNu3br9+/fb29v7+/vHxcXlW7MkSV27do2Kitq8efOmTZuuX7/epUsXScr6y7nZy8jS\nID4+ftq0aZs3b75z546vr6+/v398fLw8auLEiSNGjIiIiGjduvWwYcPkGJrjIue4rgq8aDky\n9FTsa6+9tmjRokmTJh05ckSSpH79+iUlJa1YsWLbtm3du3cv2LwBAIDh0rOGjQK2qVat2qxZ\ns+TXSUlJw4YNc3Nz69evnyE1lCtXLsvxpCtXrmi12rfeesvD4//Zu/O4GvP3f+DXOXXal1PJ\nNrRJJCNKqZGsRShJmJEsY0szUcxYsgyZCA2jsXyM+RgTMbYxYpQxlmSpoaGUSmRMQpZoX845\nnd8f5/vr05zqnFPqrK/nHx7n3Pd13vfr3De5uldzJycna2vrjh07NiwQtA3+/v6CJ1Q9e/Ys\nNDS0fq5gPxkRzZ07NyIiorCwMCcnJz8/Pykpic1mu7q6VlZW3rhxIzU19fr160VFRUZGRkR0\n6NAhCwuL5ORkb29v0YGTkpLu3LmTn5/fvXt3Ijp27JiVlVVycrK7u3vDssYxevTo0bCgpqZm\n7dq1gjv7RkZGHjt2LDY29rPPPiOi4ODgSZMmEdG6detiY2OfP39uZmbW5Fducl21+qs1SdLG\nbtWqVTk5OTExMUQUERFha2ubm5u7ZMkSS0vLiIiIViwYAAAAWkTLpG1qBPh8/sGDB1evXm1p\naZmWlmZsbCzJp16+fNm1a9eGU1xcXJycnPr27evr6+vh4TF58mRtbe2GBQwGIywsLCUl5dy5\nc2lpaefOnWs419bWVvBCR0dH8CIzM9POzo7NZgveLliwYMGCBQcOHOBwOA1bRi6XK8khy+zs\nbAsLC0FXR0RmZmbm5ubZ2dlCjV3jGI05OTkJXjCZTEdHx/v37wtNr/9sc1+5yXV19erV1n21\nJkl6KFZfX//XX3999+5dSUnJ6tWriahz585//PHHvXv3evbs2bplAwAAgOQ69SeGyP+3NdnE\nluyx7a9evRo+fPiaNWuioqIuX74stI+tOXw+PzMzU2hXlp6eXmpqakJCgr6+fkREhJmZmeDw\na73KysqhQ4d++umnT548mThx4qZNmxrO1dDQEFoKh8NRVxfe8WRoaGhsbMxpgM/nh4WFNaxZ\nvXp1/XW4gpPniKiuro7x71v8MZnM+rkiYjTW8Gg1h8OpfyvUyFLzX7nJdSXJV5OcqL8ghYXC\np2gaGBjo6+sLXhsaGo4cOVJXV1f0RwAAAKBNaBmThaeoAtupYjo/AT6fP3bsWENDw4yMjGnT\npjGZku7lOXr06JMnT6ZPn95wYlJSUnR0tJub2+7du/Py8lxdXfft29ew4PLlyxkZGZmZmVu3\nbvXx8RF7Mp+trW1mZmZ5ebng7ZEjRzw9Pe3s7IqLizMzMwUTX79+7evrW7/PTCA7O/vw4cOC\n14Kb7Hbq1Kl3796PHz+u70+ePn36999/9+nTR8Kv3NDVq1cFL6qqqm7cuFG/k6+x5r5yk+tK\nkq8mOVGHYl1cXCZNmhQaGmphYSF2oMePH2/fvv3UqVN4ViwAAEA7sZ9H5U/pVWYTsyw8qcd4\niQa5dOlSWlpaWFjY7du36yf26tWrW7duBw4cqKqqWrhwoWBicXFxWloaEb19+zYpKWnz5s2L\nFi2ysbFpOFptbe3y5cs1NTWHDh36+PHjrKysGTNmEBGTyXz06NG7d+/09fUrKipOnjzp4uJy\n6dKl9evXl5WVZWRk9OvXr8l4Pj4+pqam06dPX7169dOnT8PDwydMmGBjY+Pn5xcQEPDtt9+q\nq6tv3LgxPz9fKMmoUaM+++yzQ4cO9enTJyIiolevXj179uzVq1e/fv2mTp26ZcsWPp+/bNky\ne3v7YcOGSbSm/i0sLExdXb1jx44bN27k8XizZs1qrrK5r9zkupLkq0lOVJOenp5eVVVlbW3t\n5ua2bdu2mzdvCl0kUl1dffPmzW3btg0ePLhnz541NTXp6emtywEAAABiqWnSkEjqN5d0Gzz1\nk92DnL8kx0VEkj1VLD09nc/nBwQEjGrg9OnTRBQXF7d///76yt9//33gwIEDBw709vb+448/\nYmJitm3bJjSah4dHVFTU9u3bnZ2dQ0JC/Pz8wsPDiWjmzJnHjh2bM2eOu7v7V199tWTJEmdn\n5/Pnz1+5csXLy2vVqlWNg2lrazOZTBaLdenSJSLy9PQMDg6eMGFCVFQUER08eHDw4MEzZszw\n9vbW1NRMTEwUOmK7YMGC8PDwlStXDhs2jMVinT59Wk1NjcFgJCQkdO/e3c/Pb9KkSRYWFgkJ\nCQyRz18TxGg8fc+ePWvWrPH09CwrK7t69WrDR6oKae4rN7euxH41yTEaX/Er5PHjx3v37v3p\np59evHihpqZmbGwsOL+yuLi4uLiYx+N17tx55syZCxYssLS0bF2IVqisrKysrJTa4pqTe0rS\nW730mviqdYu4c+fO5s2bL168uGjRolmzZtWf/gmNSWFzEJGWlpaenl55eXnji+GhIelsDhaL\nZWhoWFVVVVFR0epBVIF0NgeTyTQ2Nq6trS0tLW31IAqnQ4cObT4mn8+ffE784dGsWMo5Rk5L\nyWy4+DFPjBPz3z2IkJaWNnDgQC6Xq6amJussYojvBy0tLaOioqKiorKysq5evfr48eOioiIG\ng9GxY0dLS0t3d3c7OzspBFVB9+/fX7x4seD2gUQUExOzc+fOSZMmbdy4sf5aIQAAUFnq2v/7\nE0CgBTv67Ozs0MNJTXp6uq+vb/2powJ1dXXHjx+/f//+mTNn6q9iAQAAABCQ9EIYgfLy8osX\nL/78888vXryorq7m8XjtFEvF8Xi8kJAQoa6uXlZW1ubNm6UcCQAAQGU5Ojry+Xz5Pw5LLWrs\n9u3b17Vr11GjRn3yySe5ubmpqandu3ePi4trv3AqKzU1teFD6BqLi4sTPLEEAAAAoJ6kjd1v\nv/22YMECR0fHkydPCqbY2NjY2dlNnz5d6BbS8P4aXn/epPLy8pycHOmEAQAA+aRlREbWxNKT\ndQ6QJ5KeYxcVFdW3b98LFy7UX3/bpUuX8+fPOzk5RUVFjR07tt0SqhzTrKOUnyq2bGT66VfN\n3AEIAABUgfkoMh8l6xAgZyRt7NLT07/44guhu6owmcxx48Z999137RBMtbEl+P3LCL+jAQAo\nrUMjq2QdARSSpI2dkZFRk3ft4nK5uDyz7fUXd79pI30y7yKVKAAAIG0MBkNLS0vWKUAhSdrY\nDRo0KDY29ssvvzQyMqqf+PLlywMHDri4uLRPNhXWsxs59KK/cpstmDKSmJLdXxwAABQNn8/n\ncDhtO6YkD7kHJSBpY7d582Z7e/v+/fsvWLCAiBITE8+fP79v377q6mrceqNdrAykz7+hordN\nzProQ5oyQuqBAABAejRv7mvbAflDP2vbAUE+SXpVrKWlZXJysoWFheDhblFRUZs2bbK3t796\n9WrPnj3bM6GqMmXT98tpjAuxGtw1x1CP5k+gr+eRWstuQAgAAEro/mM6fIGetv4pcKB8WvDk\nCXt7+6SkpOLi4gcPHmhoaFhbW4t4/C20AUM9WjGdFvnT9qN04RaFTiXvwWjpAADg/9zJo33x\nZN6Jukn6LGBQei1o7ASMjY1xUp1U6WhRJ2MiIrNO6OoAAABABEkbu6dPn4aFhaWmplZVCV+A\nbWRk9ODBg7YOBgAAAAAtI2ljN3/+/D/++GPs2LGdO3dmMP51PaZCPDoNAAAAQOlJ2thdu3Yt\nLi5u8uTJ7ZoGAAAAAFpN0sbO1NTU0dGxXaMAAACARApf0dnrdOkvIqL/nqXHz2n8YImeWgTK\nTtKT8X18fOLi4to1CgAAAIh34grN/JqO/EFFxURE+c/ohzM0fT3duNfmi+LxeFu3bh0xYoSR\nkVG3bt18fHxu3rzZ5kuROX19/YsXL75PgfyQtLHbsmXLmTNnpk6d+v3338c10q4RAQAA4P8k\nptDOE8TlCU8vr6KvfqCsxxIOk5OTM27cOGNj406dOk2dOrWgoKBxTWlp6dChQzdv3jxq1Kif\nf/45JiZGS0vLzc3tl19+ET34kCFDvvnmG6HXT548YTAYe/fulTCh9L1PwoZfWbYkPRT722+/\npaen37p169ixY43nBgQEtGkqAAAAaKSGQ3t+bXYuh0c7T9KeL8QPU1Mzbtw4R0fH33///dmz\nZ5s3b/b3909NTRUq27BhQ35+fkZGRteuXQVT/Pz8QkJCFi5c6OPjo67esjumGRoarlixon//\n/i36lDTJf0JJSLrHbsOGDa6urleuXMnOzs5ppF0jAgAAABHRX7lUUi6qIPtvevFG7DB3797N\nz8/fu3fvwIEDfXx8NmzY8Oeff5aX/2vksrKymJiYDRs21Hd1AuvWrYuJiREUl5SUBAUFmZub\nGxoa+vj4PHz4kIicnJyuXbv2xRdfeHl5NXzNZrOjo6O5XC4RsVislJSUKVOmWFlZWVtbnzhx\nQjD406dPfXx8jIyMHBwc4uPj9fX1s7KyhMK/fPly6tSppqamXbp0Wbx4cW1tLRHl5uaOGTPG\nyMjIwMBg2LBhGRkZguLmFvTgwQNPT082mz1gwIAzZ84IJjZM2GRBcwtq+DWbWy1EdPbsWQcH\nBx0dHUtLyx07dojdTK0jabv96NGjmzdv2tratlMOAAAAEKPgpUQ1nU1ElwwcOLC8vFxXV7ei\nouLhw4fHjx93cnLS0/vXtRfZ2dm1tbXDhw8X+qyJicnUqVMFr319ffl8fmxsrLa29vbt293d\n3e/fv3/r1q0hQ4b4+vouXbqUiBq+bmjFihX79+83NzePiIgIDAwcP368urr6yJEje/XqlZCQ\nUFhYuGDBgoqKCqFP1dXVeXh4fPDBB/Hx8Q8fPly6dKmBgcGGDRsCAgL09fVPnDjBZDLXrVs3\nb968+h2QjRfE4/GGDh364YcfxsfHv3nzZtGiRZWVlQ2XUlFR0VxBkwsS+spNrpaysrJJkyaF\nhobu3bv30qVLoaGhgwYNao8nPkja2Dk5OZWVlbX54gEAAEBSdXXia3iNTr9rRE1NTVdXl4jG\njBlz7do1IyOj69evC9UUFhYSUZcuXQRvS0pK2Gx2/dz//Oc//fv3v379elFRkZGREREdOnTI\nwsIiOTnZ29tbkq8i2ItGRHPnzo2IiCgsLLx79+7Lly/T0tIELWZZWdns2bOFPpWQkJCfn5+U\nlMRms11dXSsrK2/cuMHn86dMmeLv7y8Y8NmzZ6GhoSIWdPHixZqampMnT+rr6xORtra2YE9b\nvbi4uCYLRC9IIDU1tcnVoq2tzeFwgoODzc3NnZycrK2tO3bsKMmKailJG7uoqKjly5f/97//\nNTc3b48cAAAAIEYXMbviiIi6tuC5sadPny4rK9u3b5+7u3t+fr6gjxGwsLAgoidPnvTu3ZuI\n9PT0UlJSBLOmTZtGRNnZ2RwOp2F3wuVy6w87ilV/DFBHR0fwIjMzs3///vU7Dt3c3Bp/KjMz\n087Orr7FXLBgwYIFC4goLCwsJSXl3LlzaWlp586dE72g7OxsZ2fn+i87fPhwoScvNFfAYDBE\nLKj+s02ulnnz5jk5OfXt29fX19fDw2Py5Mna2toSrakWkrSx+/rrr58+fdqjRw8rKytBm9/Q\nnTt32joYAAAA/NvA3qSlQdW1zRaYdyazTmKHuXfv3rNnz0aPHm1sbGxsbLxhw4bt27dfuXKl\n4c62Xr16sVisxMREQWOnpqY2aNAgIuJwOM+ePSMiQ0NDY2PjN2/En9LXJA0NDaEpHA6nYYPF\nZDZxGQCHw2l80UZlZeWoUaNevXrl6+s7ceLEIUOGLF++XMSChJ6YxWAwRD9Sq75A9IIERKyW\n1NTUa9euHT58OCIiYunSpbGxsUJ7CtuEpBdPcLncnj17enl59erVq1sjbR4LAAAAhOlq04zm\nWwEGg4J8JRkmPT09MDBQcJUAEZWUlFRXV7NYrIY1Ojo6ixYt2rhx499//91w+qZNm6qrq4nI\nzs6uuLg4MzNTMP3169e+vr73799vwdf5Nzs7u/T09Pqz2ep3EDZka2ubmZlZf53HkSNHPD09\nL1++nJGRkZmZuXXrVh8fnzpxB6xtbW1v375dP8i1a9eEPtJcgSQLam61JCUlRUdHu7m57d69\nOy8vz9XVdd++fRKumRaRdI9dw0tCAAAAQDY+GUVvSujkFeHp6mr0+SRy7SvJGGPGjAkNDZ07\nd25ISEh1dXVERESPHj3c3d2J6MCBA1VVVQsXLiSiNWvWXL582cHB4csvvxRcb3HixIl79+7Z\n2dkRkY2NjZ+fX0BAwLfffquurr5x48b8/HwbGxsiYjKZjx49evfuHZvNbvhadCo/P7/w8PDA\nwMDw8PCioqLIyEh1dXWh/XY+Pj6mpqbTp09fvXr106dPw8PDJ0yYoK+vX1FRcfLkSRcXl0uX\nLq1fv76srCwjI6Nfv35Nr8JPPlmzZs2UKVPWrFnz9u3bsLAwoUORzRWIWFD912xutRQWFi5f\nvlxTU3Po0KGPHz/OysqaMWOGJBurpSTdYwcAAACyx2BQiD99u5iGDSAdLSIiI30aP5h+WEG+\n7hKO0aFDh3PnzuXn548YMcLf39/IyOjChQuCU9Di4uL2798vKDM0NLx58+bnn39+6tQpPz+/\niIiILl26pKSkBAcHC+6BcvDgwcGDB8+YMcPb21tTUzMxMVFwnHTmzJnHjh2bM2eO0OvmaGtr\nM5lMTU3NixcvlpaWjhgxIjIy8uDBg0QkdIUBi8W6dOkSEXl6egYHB0+YMCEqKsrd3f2rr75a\nsmSJs7Pz+fPnr1y54uXltWrVquYWpKOjk5SUxOVyvby8VqxYsXnz5gkTJhgYGNSXNVcgYkEN\nv2aTq8XDwyMqKmr79u3Ozs4hISGCLlbC7dUiDD6fL2o2g9G5c+fnz587OTmJKLt161ZbBxOj\nsrJS6OJkmcg9Jek5qr0mvpKw0jTrqPCkH87QofO0bRE52DSc/MpuqoRjqoj22ByNaWlp6enp\nlZeXCw5GQHOkszlYLJahoWFVVVXj2yJAQ9LZHEwm09jYuLa2trS0tNWDKJwOHTq0+Zh8Pp95\ndbf4urjfaV88Rc6nwU3vmvrXmEM/a4Nk7amoqOjGjRsTJ04UvM3MzBw0aFB5ebnQCXAgmphD\nsZ07dzY1NaX2+YsLAAAAIMDn86dNmxYZGRkQEFBSUvLZZ58FBgaiq2spMY3d8+fPBS8SEhLa\nPwwAAACoqM6dO58+fTo8PHz16tXGxsZjxozZvHmzrEMpHlGNXc+ePYODg8PCwqSWBgAAAFSW\np6enp6enrFMoNlEXTzx8+LC4uFhqUQAAAADgfeCqWAAAAMXUsxt5Dxb7ZFhQKZLexw4AAADk\ni3Mfcu4j6xAgX8Q0dsnJyZGRkWJHafJuMQAAANA6tR/Nb8PRcG2p6hDT2CUlJSUlJYkdBY0d\nAABAW2EwGEIP+AKQkJjGbtasWUFBQdKJAgAAAETE5/Pb9hboDAZDS0urDQcEuSWmsevWrdug\nQYOkEwUAAAAEdK608cNU+F5o7FQCrooFAABQTDfP0aY5lHdX1jlAjqCxAwAAUEwFDyg5nt48\nl3UOkCOiGrtZs2YNGDBAalEAAAAA4H2IOsfuxx9/lFoOAAAAAHhPOBQLAAAAoCTQ2AEAAAAo\nCTR2AAAAiubGbxQ+iQ5uJiLaHESRsyn7VpsvZOLEiYxGJHkeVavp6+tfvHhRaOLVq1fNzc2H\nDRtGRBYWFomJiS0aMzc3l8FglJSUtFVIOYdnxQIAACiOOh5tW0SXjv1vSk0lXT9LN87RjJU0\nNbSl4yUnJw8bNuzly5cmJiaN5w4fPnzTpk0Np3Tr1q3lod9LTEzMgAED9u3bR0QVFRU8Hk/K\nARQLGjsAAADFcWjLv7q6evw6+imSuliQu6/kg5WUlAQGBtbV1TVXYGJiIvPnFFRUVLi4uJia\nmso2hqLAoVgAAAAFUfaWftklquDA18TnSz7ewoULO3bs2Iogr169CggI6Ny5c9euXadPn/7q\n1SsiqqioYDAYWVlZgpqGx0BZLFZKSsqUKVOsrKysra1PnDghqHnw4IGnpyebzR4wYMCZM2ca\nL2j48OGJiYnr1q1zdXUVG4CIXr58OXXqVFNT0y5duixevLi2trbhp27dusVms/fs2UNEZ8+e\ndXBw0NHRsbS03LFjRytWgnxCYwcAAKAg/rpCtTWiCl48oX9yJBzs0KFDt2/f3rp1q4ia4uLi\ntAZycnKIiM/njxs37tGjRz///PORI0cePnw4duxYvriGcsWKFVFRUXl5eQEBAYGBgdXV1RUV\nFUOHDiWi+Pj4tWvXLlq0qLKyUuhTly9fHjNmzLp1627evFk/sbkAdXV1Hh4eZWVl8fHxW7Zs\nOXLkyIYNG+o/lZ6eLhhq4cKFBQUFkyZN8vDwSEpKCgoKCg0NTUlJkXC9yTkcigUAAFAQr56K\nrykqIHNbsVWPHz8ODQ1NSEhgMkXt4rl06dLAgQPr37q4uNy8eTMpKenOnTv5+fndu3cnomPH\njllZWSUnJzs6OooYSrC7jojmzp0bERFRWFh48eLFmpqakydP6uvrE5G2traXl5f4L0jUXICy\nsrL8/PykpCQ2m+3q6lpZWXnjxg3BR+7fvz9hwoSFCxeGhoYSUW6T5eyfAAAgAElEQVRuLofD\nCQ4ONjc3d3Jysra2bt2eSzmEPXYAAAAKQo0lvkZdfA2PxwsMDAwLC3NychJd6e/vz29AsNss\nOzvbwsJC0FQRkZmZmbm5eXZ2tuihbG3/r93U0dERvMjOznZ2dhZ0dUQ0fPhwBoMhNryIAJmZ\nmXZ2dmw2WzB9wYIFP/30k+C1t7c3n89/9OiR4K2Li4uTk1Pfvn0DAwNjY2PHjh0raDqVABo7\nAAAABWHWS0wBgyG+hmjHjh2vX7/29fXNzc39+++/iSgvL+/FixcSpqirqxPqwJhMJpfLFSqr\nqqpq+FZDQ0OoQE1N7d/ZGRI2ds0F4HA46upNH4oMDw8/duzYzz//fOnSJSLS09NLTU1NSEjQ\n19ePiIgwMzNLSEiQZNHyD40dAACAgug3mEw6iyro60oduoodJi8vLzc3t2/fvr179/b39yci\nV1fXlStXSpiid+/ejx8/LiwsFLx9+vTp33//3adPH8Hbt2/fCl6kpaWJHsfW1vb27dvl5eWC\nt9euXRNxfa4kAWxtbTMzM+sHPHLkiKenp+D1nDlzhg8f/vHHH3/++eccDicpKSk6OtrNzW33\n7t15eXmurq6C26koATR2AAAACoKlQQs2UnO7tTS1aYFEdw/es2dP/dHV27dvE9Hr168FD4g/\ncOCA4KJREUaMGNGvX7+pU6feuHHj+vXrU6dOtbe3HzZsmK6urqmpaVRUVFZW1sWLF3fu3Cl6\nnE8++URDQ2PKlCk3b948d+7cwoULdXV1JcnfXAAfHx9TU9Pp06ffvn37119/DQ8Pr283BaKj\nowsKCnbs2FFbW7t8+fLvvvsuIyMjPj4+KyvL3t5ekkXLPzR2AAAAisPNm0JjSEtHeLpxJ1p/\nmKz6vufwcXFx+/fvF13DYDASEhK6d+/u5+c3adIkCwuLhIQEwbHRgwcPPnz40NXVNTIyMi4u\nzsXFReh4q4C2tjaTydTR0UlKSuJyuV5eXitWrNi8efOECRMMDAzEhmwuAIvFEhxp9fT0DA4O\nnjBhQlRUVMMPfvDBB1999dX69ev79OkTFRW1fft2Z2fnkJAQPz+/8PDwFqwmOcYQe32yfKqs\nrGx8UbT05Z6S9H6JvSa+krDSNOuo8KQfztCh87RtETnYNJz8ym6qhGOqiPbYHI1paWnp6emV\nl5dXV1e3ehBVIJ3NwWKxDA0Nq6qqKioqWj2IKpDO5mAymcbGxrW1taWlpa0eROF06NChzcfk\n8/nMxDeiKt69ogtH6I+fqSCP+jjRiKk03J+0Re3u4nu1fU6QQ9hjBwAAoGjYpjR5EY2cQkTk\nH0JjZ4ru6kB1KOp97NTV1Y2MjGSdogXaI61irQG58j6rTnC4QUdHR1tbu+0SqbT33xxaWlqN\nL7iD1nn/HywsFkt1fjpxOBxZRwD4F0Vt7LhcrmLt6q+/SkjOx1QR77PqBIdiKysrcSi2rbzP\n5hAcihXcwr4NI6my99kcgkOxHA5HsX4+vyctLS1ZRwD4HxyKBQAAAFASaOwAAAAAlISiHooF\nAABQdQNHkb7x+9/iBJQJGjsAAADFZNUXXR0IQWMHAAAgXxgMBm+0saxTgEJCYwcAACB3mEyc\nBA+tgcYOAABAvvD5/CPRzTwQtrWmfdm244Gcwi8EAAAAAEoCjR0AAIBCupjyn7CoHpl5f8g6\nCMgRNHYAAAAKqaLq7cvi/JpaPHYF/geNHQAAAICSQGMHAAAAoCTQ2AEAAAAoCTR2AAAAiqeu\njldSVkREb0ufyToLyBE0dgAAAIqkllN1LHHVwoiOidd2ENGPp4IXb7K4cGMXn1/XtgsaMmTI\nvHnzhCZ26dLlm2++adE4+vr6Fy9ebLtcIAoaOwAAAIVRWV2yfrfb6UsbyyuL6ye+fvvkwK+f\nxxyaUlfHk3CcqKgoRgMsFqt98kpkyJAhgmbxyZMnDAZj79698jagAsGTJwAAABTGgVOf/V34\nV5Oz/rx38mzSVp/hKyQZJzc3d9y4cSEhIYK3DEYbP+iidQwNDVesWNG/f3+5HVD+YY8dAACA\nYnj99smNu4dFFPyWFM2r40oyVG5uroeHx+j/z9PTs6Vh0tLSTE1Nr1+/7urq2qFDhxEjRmRn\nZwtmPXjwwNPTk81mDxgw4MyZMw0XOmbMGCMjIwMDg2HDhmVkZBCRk5PTtWvXvvjiCy8vLzab\nHR0dzeVyiYjFYqWkpEyZMsXKysra2vrEiROCQZ4+ferj42NkZOTg4BAfH6+vr5+VldUwWHMD\namtrJyYmjh07tnPnziNGjCgsLAwNDbW1te3UqdOuXbsEny0pKQkKCjI3Nzc0NPTx8Xn48KFg\n+tmzZx0cHHR0dCwtLXfs2NHSdSVNaOwAAAAUQ9ajS3w+X0RBeeWb5vbnCcnNzf3jjz+6detm\nbGw8fvz4Bw8etCJPWVnZsmXLfv7554KCgkGDBrm7u5eVlVVUVAwdOpSI4uPj165du2jRosrK\nSkF9QEBATU3NiRMnTp8+zefzBSfw3bp1y83NLTo6OiEhQWj8FStWREVF5eXlBQQEBAYGVldX\nc7nckSNHElFCQsKqVasWLFhQUSF8f2YRA0ZHRx85ciQ7O7uoqMjGxmbEiBH3799ftmxZaGio\nYBxfX9+cnJzY2NgLFy7o6uq6u7u/e/euoKBg0qRJHh4eSUlJQUFBoaGhKSkprVhd0oFDsQAA\nAIqhpOyF2Jp3pc/F1rx+/bq4uJjJZB4+fJjL5W7YsEHQ4hgYGLQoT01Nzdq1a83NzYkoMjLy\n2LFjsbGxLBarpqbm5MmT+vr6RKStre3l5UVEfD5/ypQp/v7+VlZWRPTs2bPQ0FDR4wt21xHR\n3LlzIyIiCgsL7969+/Lly7S0ND09PSIqKyubPXu25IEXLFhgaGhIRF5eXsnJyT4+PkQUEBDw\nxRdfvHjx4vXr19evXy8qKjIyMiKiQ4cOWVhYJCcna2trczic4OBgc3NzJycna2vrjh07tmhF\nSRMaO0UQOIamjiQdLVnnAAAAWdLS1Bdbo60lvjljs9lPnz7t0qULk8kkIgcHh65du549e3ba\ntGktjeTk5CR4wWQyHR0d79+/r6Gh4ezsLOjqiGj48OGCE/gYDEZYWFhKSsq5c+fS0tLOnTsn\ndnBbW1vBCx0dHcGLzMzM/v37C7o6InJzc2tR2vqGTFtbu+FrwYvs7GwOh9OwaeNyuQ8fPpw3\nb56Tk1Pfvn19fX09PDwmT55c/xE5hEOxikCTRfo6pIaNBQCg0iw/cBRdoKbGMutiL3YcdXX1\nDz74QNDVERGbzbawsCgoKBAq09DQqKmpEZpYU1OjoaFR/7au7n/3WOFwOHV1dWpqag3rBVfd\nElFlZeXQoUM//fTTJ0+eTJw4cdOmTWJzNlxQ/SIaXudR/xXahKGhobGxMacBPp8fFhamp6eX\nmpqakJCgr68fERFhZmbW+CCv/ECvAAAAoBiszQaZdeknosD5w0l6OsZixzl79my/fv3evHkj\neFteXl5QUNC7d2+hMjs7u5SUlNra2vopd+/effv2bb9+/8tw9epVwYuqqqobN27Y2tra2tre\nvn27vLxcMP3atWuC5u/y5csZGRmZmZlbt2718fFp2BFKzs7OLj09vf6kvbY9183Ozq64uDgz\nM1Pw9vXr176+vvfv309KSoqOjnZzc9u9e3deXp6rq+u+ffvacLltC40dAACAYmAwmPP8f9DU\n0G1yrgm7e8B4iW4d7O7u/ubNm4CAgAsXLly7dm3y5MmWlpZjx44logMHDuzZs0dQtnjx4qKi\nIi8vr1OnTiUnJ+/Zs8fb23vUqFFDhgypHyosLCw+Pj4lJWXq1Kk8Hm/WrFmffPKJhobGlClT\nbt68ee7cuYULF+rq6hKRvr5+RUXFyZMn8/Pzf/jhh/Xr15eVlQkujGUymY8ePXr37p3Y5H5+\nfgYGBoGBgYKDuZGRkerq6o3320k+YEM2NjZ+fn4BAQGXL19OTk4ODAzMzs62sbGpra1dvnz5\nd999l5GRER8fn5WVZW8vfreorKCxAwAAUBhW3Z1WLbjUtaPw3jXbHsPWLLxqZNBVkkEMDAzO\nnz9fV1fn7+8/ZcoUU1PTCxcuCO5RHBcXt3//fkFZjx49UlJSTExMFi9ePHr06J07d86ZM+fX\nX39t2Ejt2bNnzZo1np6eZWVlV69eNTAw0NHRSUpK4nK5Xl5eK1as2Lx584QJEwwMDNzd3b/6\n6qslS5Y4OzufP3/+ypUrXl5eq1atIqKZM2ceO3Zszpw5IjJra2szmUxNTc2LFy+WlpaOGDEi\nMjLy4MGD1ODMuXqSDNh4cCI6ePDg4MGDZ8yY4e3trampmZiYqK6u7uHhERUVtX37dmdn55CQ\nED8/v/DwcAlHlj6G6Aun5VZlZWX9nlgZyj1lKmFlr4mvJKw0zToqYeUru6kSVqqI9tgcjWlp\naenp6ZWXl1dXV7d6EFUgnc3BYrEMDQ2rqqoa3/IAGpLO5mAymcbGxrW1taWlpa0eROF06NCh\nzcfk8/lHokXdMZjPr8t8ePHslc2ZeRfdHKaPHrzIqruT6DGnfdmmEYnS0tIGDhzI5XKFTqpr\nV0VFRTdu3Jg4caLgbWZm5qBBg8rLy+XkBsvyAHvsAAAAFAyDwfywp4ed9Ugicv7QX2xXpzT4\nfP60adO2bdtWVFT04MGDzz77LDAwEF1dQ2jsAAAAQDF07tz59OnThw8ftrS0HDFiRM+ePTdv\n3izrUPIF97EDAACAFnN0dJTJ2Vyenp6teACa6sAeOwAAAAAlgcYOAAAAQEngUCwAAIBCch84\n+8Oenp1Mesg6CMgRNHYAAAAKia3fma3fWdYpQL6gsQMAAJAvDAbjky/a8roE3BBEdeAcOwAA\nACWnoA8jgFbAHjsAAAD5wufzeXNy23ZM9f3CTyEDpYQ9dgAAAAqpmlfztraUU8eVdRCQI2js\nAAAAFNKOrJ86xg1KeJok6yAgR9DYAQAAACgJNHYAAAAASgKNHQAAAICSQGMHAAAAoCTQ2AEA\nACieR6X/3Hlzn4iuFaW9qHot6zggL9DYAQAAKJK/ywvH/z6/98nRJ/8+T0TbM3+0PDZs7rVV\nJbVlbbugiRMnMhqJjIysL+DxeAwGIy0trW2X2xCLxbp+/Xr7ja980NgBAAAojEel/ww+M/V8\nYXLDidw63k95vww/F9ii3u7AgQNOTk4GBgajRo3KzW36fsjDhw9P+bdZs2Y9efKEwWDs3bu3\nYeWQIUO++eabVnwjaFt48gQAAIDCmHd91cvqN03Ouvc2d3Xa9u9c10oyzoEDB0JCQmJiYszN\nzTdu3Ojt7Z2dna2mpiZUZmJiMmjQIKGJ7969W7FiRf/+/VuRn4hqa2sLCwstLS1b93EQDXvs\nAAAAFMO94tzkF7dFFPyUd6qCWyV2HD6fv2nTpk2bNs2ePXvEiBE//vijvb19QUGBhDHYbHZ0\ndDSX+78nXjg5OV27du2LL77w8vIiopKSkqCgIHNzc0NDQx8fn4cPHwrKWCzW2bNnP/jgg0WL\nFjUcMC0tzdTU9Pr1666urh06dBgxYkR2dnb93KKiovHjx7PZ7B49ehw/flwwMTc3d8yYMUZG\nRgYGBsOGDcvIyBBMP3v2rIODg46OjqWl5Y4dOwQTm8ujlNDYAQAAKIabL++ILqjiVQuuqBAt\nJyfnwYMHfn5+dXV1L1++7N69+/Hjxy0sLBpXFhcXpzWQk5PT5IC3bt1yc3OLjo5OSEggIl9f\n35ycnNjY2AsXLujq6rq7u797905QuXTp0s2bN3/33XdCI5SVlS1btuznn38uKCgYNGiQu7t7\nWdn/HVZevHjx7Nmzr1+/7ubmNnPmzNraWiIKCAioqak5ceLE6dOn+Xz+vHnziKigoGDSpEke\nHh5JSUlBQUGhoaEpKSmi8ygfHIoFAABQDMW1JeJrasS3LE+fPlVXV4+Li9uwYUNZWVnXrl1j\nYmImTZrUuPLSpUsDBw6sf+vi4nLz5k3Rg6empl6/fr2oqMjIyIiIDh06ZGFhkZyc7O3tTUTz\n5s379NNPG3+qpqZm7dq15ubmRBQZGXns2LHY2NjPPvuMiIKDgwXZ1q1bFxsb+/z5czMzsylT\npvj7+1tZWRHRs2fPQkNDiSg3N5fD4QQHB5ubmzs5OVlbW3fs2FF0HuWDxg4AAEAxdNA0El+j\nJb7m9evXXC73xo0b9+7dMzIy2rVr17Rp0+7evWtraytU6e/vX3/0U0LZ2dkcDqdjx471U7hc\nbv3RT3t7++Y+6OTkJHjBZDIdHR3v378vNF1HR0fwgsFghIWFpaSknDt3Li0t7dy5c4LpLi4u\nTk5Offv29fX19fDwmDx5sra29tWrV0XkUT44FAsAAKAYhnQeKLpAj6XjYGIndhxTU1Mi2r17\nt7m5uYGBwcqVK7t06XL+/Pk2CWloaGhsbMxpgM/nh4WFCebWN2eN1dXV1b/mcDj1b7W1tYUq\nKysrhw4d+umnnz558mTixImbNm0STNfT00tNTU1ISNDX14+IiDAzM0tISBCdR/mgsQMAAFAM\nvQytRn8wRETB/F4fa6lpih2nd+/eTCaz/jwzLpdbVVXFZrPbJKSdnV1xcXFmZqbg7evXr319\nfet3v4lw9epVwYuqqqobN2403n1Y7/LlyxkZGZmZmVu3bvXx8alvAZOSkqKjo93c3Hbv3p2X\nl+fq6rpv375W51FQOBQLAACgMP4zOGLob9P+qXjeeJZLx/5fDQiRZJBu3br5+/tPnz59y5Yt\nhoaG27dvV1dX9/HxIaIDBw5UVVUtXLiwpcGYTOajR4/evXtnY2Pj5+cXEBDw7bffqqurb9y4\nMT8/38bGRuwIYWFh6urqHTt23LhxI4/HmzVrVnOV+vr6FRUVJ0+edHFxuXTp0vr168vKyjIy\nMmpra5cvX66pqTl06NDHjx9nZWXNmDGj1XkUFPbYAQAAKIxuup1v+hz/xGq8GuN/95zTUdcK\n6zv79zE/6qhrSTiO4O7Es2fPHj16dHl5+ZUrV4yNjYkoLi5u//79rQg2c+bMY8eOzZkzh4gO\nHjw4ePDgGTNmeHt7a2pqJiYmqquL35G0Z8+eNWvWeHp6lpWVXb161cDAoLlKd3f3r776asmS\nJc7OzufPn79y5YqXl9eqVas8PDyioqK2b9/u7OwcEhLi5+cXHh7e6jwKisHn82WdoTUqKysr\nKytlnYJyT5lKWNlr4iuhKZMmTcrMzGx8s2/TrKMSjvnKbqqElSrifTaH5LS0tPT09MrLy6ur\nq1s9iCqQzuZgsViGhoZVVVUVFRWtHkQVSGdzMJlMY2Pj2tra0tLSVg+icDp06NDmY/L5fN6c\nph8FUe9VdfHyW1sOPjy9dsBnYXaf6rGaPXdNQH1/77YL2MbS0tIGDhzI5XIb3yEZWgp77GSm\nvLxcie+jAwAA7cpUy7iXoRUR2Rvbiu3qQHWgsQMAAABQEkp7jBkAAAAUgqOjo4KeGCaHsMcO\nAAAAQEmgsQMAAABQEjgUCwAAoJCW95u/vN98WacA+YI9dgAAAABKAnvsAAAA5AuDwZDn286B\nPMMeOwAAAAAlgcYOAAAAQEmgsQMgIqqsrHz37l1dXZ2sgwAAALQeGjsAIqIlS5b07NmzoKBA\n1kEAAABaD40dAAAAgJJAYwcAAACgJNDYAQAAACgJNHYAAAAASgKNHQAAAICSQGMHAHInMTFx\n6dKlubm5sg4CAKBg0NgBgNxJT0+PjY199uyZrIMAACgYNHYAAAAASgKNHQAAAICSQGMHAAAA\noCTQ2AEAAAAoCTR2AAAAAEoCjR0AAACAkkBjBwAAAKAk0NgBAAAAKAk0dgAAAABKAo0dAAAA\ngJJAYwcAAACgJNDYAQAAACgJNHYAAAAASgKNHQAAAICSQGMHAAAAoCTQ2AEAAAAoCZk1dlwu\nNyAgoKysrH4Kj8fbv3//3LlzZ82atXv3bg6HI6tsAAAAAIpIBo1dbW1tRkbGtm3bGnZ1RLR/\n//7k5OT58+cvWrTozp07O3fulH426aitrb18+XJRURGfzz979mxpaamsEwEAAIAykEFjd/bs\n2W+//fbevXsNJ1ZVVV24cGHu3LnOzs4ODg5BQUHJycklJSXSj9fefv31V0dHxylTphQWFvL5\n/NmzZ9vb23/77bd1dXWyjgYAAACKTV36i/Tz8/Pz83v48OGSJUvqJz558qS6urp///6Ct/b2\n9jweLz8/f8CAAYIpPB7vwYMH9fX6+vp6enrSjP2e1NXViWj//v1Lly4VmlVeXh4ZGVlQULBj\nx45WjAmt0HjVMRgMIlJTUxO7VplMpuBPrP+20nhNClayJJtDTU2NsDna1PusScGGYzAYqrM5\n+Hy+rCMA/Iu8/Nt7+/aturq6rq6u4K26urqenl5xcXF9QWlpaWBgYP3b+fPnz58/X9op3wOb\nzX78+PGqVauaK4iNjZ08efL48eNbNGZbRFNFjVedhoYGERkYGEi4VnV0dHR0dNo+mUpqvM61\ntLSISE9PT8LNoampqamp2fbJVNL7/2BhsViq89OppqZG1hEA/kVeGjs+ny/YZdIQj8erf62p\nqenn51f/1sbGprq6Wkrh2kJ1dfW+fftEZ961a9eoUaNaNOZ751JRjVed4C9bTU2N2LWqpqbG\nYrE4HE7Dv5/wPhqvcy6XS0S1tbViNweTydTQ0OByuYKPwPt7nx8sDAZDU1OTx+OpztVvXC4X\nv1SAXJGXxs7Y2JjD4VRVVWlraxMRj8crLy/v0KFDfYGOjk54eHj928rKyvLychkEba3y8vKU\nlBTRNampqS36Uoq1BuRK41UnaAsk+XulpaXFYrEkaQFBQo3XeW1tLRFVVVWJ3RwsFktDQ4PD\n4VRUVLRXPhXzPj9YmEymoLFTqZ9OinVeECg9ebmPnZmZmaamZv0VFffv32cymZaWlrJN1YaC\nUk3TCy+Lrnn37m1Qiql08gAAAIDykZc9djo6OqNGjfrxxx9NTEwYDMYPP/wwdOhQIyMjWedq\nSxqGYgo02UTCh6MBAAAAJCUvjR0RzZ07d//+/ZGRkXV1dYMGDZo7d66sE7Ux0w/pxS0xBQAA\nAACtJrPGztraOj4+vuEUNTW1efPmzZs3T1aR2puFB+Uep9qypucyGNRzonQDAQAAgHKRl3Ps\nVIGGPjl/SUxW03M//JSMeko3EAAAACgXNHZS1cmBhkcLH3I1MKOP1mJ3HQAAALwvOTrHTkWw\ne5D7JqoupqsrqfwZeewh/W6yzgQAAABKAXvsZEPLmFi6RISuDgAAANoMGjsAAAAAJYHGDgAA\nAEBJoLEDAAAAUBJo7EDVVVZWnjx5MiMjg4h++umn7OxsWScCAABoJTR2oNJOnTo1YMCAoKCg\nvLw8Ivruu+/c3d1nz5799u1bWUcDAABoMTR2oLqOHj26YMGC4uJioelnz56dPHlyVVWVTFIB\nAAC0Gho7UFHFxcXh4eF8Pr/Juenp6bt27ZJyJBD4559/cnJyiOj27dvYdQoA0CJo7EBFxcfH\nl5aWiiiIi4trru2DdlJQUPDxxx8PHDjw7NmzRLRly5a+ffuuXLmysrJS1tEAABQDGjtQUffu\n3RNd8PTp08ZHaaH9PHr0yMPD4+LFiw376dra2h9++GHSpEno7QAAJIHGDlRRUKrp5SexYstC\nr/WWQhggIj6fHxIS8ubNmybn3r59Ozo6WsqRAAAUERo7UFFaRmIKGEzxNdBW7t69e+vWLREF\nP/30U21trdTyAAAoKDR2oKI6DRBTYNKH1DSlEgWIUlNTRReUlpbiFoMAAGKpyzoAgGx0dCCj\nnvQ2r9mC3lOlmEa1BaWaZmWJL1t3c1SnavrPoFftnwgAQFFhjx2oKAaDXFaQjmmT88guUPwu\nPWhDmvriazQkqAEAUHFo7EB16XSikTHUYzyxdP//JAYZ9ya3ddhdJ20mdmIKWLpkaCGNJAAA\nCg2NHag0DX3qH0Teh6mzMxHRyO00PJo6Oco6luox6kEd+ogq6DGOmDhzBABAHDR2AMRQI5YO\nERFLT9ZRVBaDBoaRJrvpmR36UO+PpZsHAEAxobEDALmg24VGfktdXYjB+N9ENU3qOZHcviY1\nDdklAwBQHDi2AQDyQrsDua6mqjeUsY+eXiO7QOrh/X87UwEAQBLYYwcA8kXbhPS7ExEZ2aCr\nAwBoGTR2AAAAAEoCjR0AAACAkkBjBwAAAKAk0NgBAAAAKAk0dgAAAABKAo0dAAAAgJJAYwcA\nAACgJNDYAQAAACgJNHYAAAAASgKNHQAAAICSQGMHAAAAoCTQ2AEAAAAoCTR2AAAAAEoCjR0A\nAACAkkBjBwAAAKAk1GUdQHUZ2ZC6rqxDAAAAgBJBYycz/YNknQAAAACUCw7FAgAAACgJNHYA\nAAAASgKNHQAAAICSQGMHAAAAoCTQ2AEAAAAoCTR2AAAAAEoCjR0AAACAkkBjBwAAouTn58fG\nxubm5so6CACIh8YOAABEuXXr1tKlS69fvy7rIAAgHho7AAAAACWBxg4AAABASaCxAwAAAFAS\naOwAAAAAlAQaOwAAAAAloS7rAAAK5vnz50+ePDEzMzM2NpZ1FgAAgH/BHjuAlvnjjz88PDzO\nnz8v6yAAAADC0NgBAAAAKAk0dgAAAABKAo0dAAAAgJJAYwcAAACgJHBVLAARkbEN8bmkriXr\nHEBEREbWZDmGtE1knQMAQNGgsQMgIrL2IWsfWYeA/6/LIOoySNYhAAAUEA7FAgAAACgJNHYA\nAAAASgKNHQAAAICSwDl2AKDAKioqHj58yGazDQ0NZZ0FAED2sMcOABRYenr6wIEDd+/eLesg\nyqy6urr+TwCQc2jsAACgacePHx85cuSXX35JROvWrRs+fPjPP//M5/NlnQsAmoXGDqBlKioq\niKiyslLWQQDaUV1d3cKFC4ODgzMyMgSdHJ/Pz8zMDAkJmTNnDpfLlXVAAGgaGjsAifD5/MOH\nD7u7uwv2XqxYscLd3f3w4cPYewFKKSYm5sSJE03OOnPmzOFTz3wAACAASURBVNatW6WcBwAk\nhMYOQDwulztnzpzFixdnZ2fXT8zOzl68eDH2XoDyqampiYmJEVGwe/fu8vJyqeUBAMmhsQMQ\nb9u2bWfOnGly1pkzZ7755hsp5wFoV3/++WdZWZmIgurq6hs3bkgtDwBIDo0dgBhVVVW7du0S\nUbBr1y6ccgfK5Pnz52JrCgsLpZAEAFoKjR2AGCkpKaL7tqqqqpSUFKnlAWhXQammB59+Jrbs\n2ItlQammUsgDAC2Cxg5AjBcvXoitwd4LUCaGluJr2BLUAID0obEDEMU06+ii13fFli0pzjDN\nOiqFPABSoNeVTGxFFbB7SNT8AYD0obEDEMf6g7apAVAc/ReSulbTs9Q0aID4Q7UAIBto7ADE\nMetEfSxEFdiak0UXKYUBkAq2Fbl9TbqdhKfrmJJbBBnbyCITAEgAjR2ABMI+Jm3NpmdpaVDY\nx9JNAyANJr3Jcy8NWkEd+xMRdbAj52U0+nvq0FfWyQCgeWjsACTQsxtFf05dOwhP79qBoj8n\nm+6yyATQ7pjq1M2NzEYQEXVzp+7uxGTJOhMAiKQu6wAACsLOkmLX0I17dDqZ0nLJwYZ83emj\nD0ldTdbJVBSHw/ntt9+OHz9OROfOnTMxMfH39zc2NpZ1LgAAWcIeOwCJqauRe38aOZCIaLgj\nufdHVycrWVlZQ4YMmTdv3u+//05EOTk5q1atcnBwOHoU1yYDgErDHjsxcnJyrl27VlRUZGJi\n4uTk5OjoKOtEAKruyZMnfn5+xcXFQtMrKipCQkI0NDQmTpwok2AAADKHxq5Zz58/DwsLu3jx\nYsOJDg4OMTExvXr1klUqADlRV1d3/vz533777bfffjM0NJw4ceLkyZP79OkjhUWvW7eucVcn\nwOfzV65cOXr0aB0dHSkkAQCQNzgU27SXL1+OGzdOqKsjor/++mvcuHE5OTkySQUgJ549ezZu\n3LgZM2YcPXq0vLy8sLBw586dw4YNW7lyJY/Ha9dFl5aWCg6/NufNmzeXLl1q1wwAAHILe+ya\ntnbt2oKCgiZnlZSUhIaGJiYmSjkSgJyorKz09/fPy8sTms7n83/44Qc+nx8VFdV+S3/06FFt\nba3omuzs7PHjx7dfBvn06tWrxMTErKwsBoPRt2/fMWPGmJiYyDoUAEgbGrsmvH37Nj4+XkRB\nWlravXv3PvzwQ6lFApAf//nPfxp3dfX2798fEBDQfv86PHMSxNZseXpnS9bRV3ZT2ymDvOHx\neNHR0d99911NTU39RC0trbCwsNDQUCYTR2YAVAj+wTfh3r17HA5HdM1ff/0lnTBQ7+XLlxs3\nbhw1apStra2zs/P8+fOTk5NlHUoVnThxQsRcPp//yy+/tOPiO7LF13RSrZueLF++PDo6umFX\nR0TV1dWbNm1avXq1rFIBgEwo6h47NTU1PT299hiZ8dUyzoNHYsuqz/yi//IJWbdHBEm10xqQ\nT7///vvMmTPfvXsnePv69evHjx+fOnVq5syZu3btUlOTi9uOqMIW4fF4+fn5omsePXrUjqui\nswlZdKG/nzdbwGSQcx9Sjc1BRFeuXPnpp5+am/vDDz98/PHHH330UTstXWglMxgMas+fz3KI\ny+XKOgLAvyhqY1dXVyf2PJvW0SLqpCv+errOEtS0N6Ff0JVYdnb21KlTq6urG8/66aef9PT0\nIiMjpZ+qMVXYIowN4WIvj6i5n8n4alk7/tozZzyt2dfs3LEfURcTUo3NQUT//e9/RcwVnPjY\nfvdpElrJTCZTU1Ozrq5ORVY+EfH5fFlHAPgXRW3s+Hy+2KOlraNF5NC5o76GRlnzjSODyN2s\nW3ssvUXaaQ3IofXr1zfZ1Qns3bv3008/NTMzk2akJqnCFtFXU+ukq1NUUSmixtzQoH1DDLGn\nud7037PU+P9UJ1sK8Re8VIXNQUR3794VW9B+q0JoZMH5fO338xkAxMI5dk3QVFP73NFeRMFk\nWxszA32p5VFxlZWVou9eweVyExLEn1APbWWctaXogvHiCtrA9NG0I5Rc+hJLnYiIwSAbM/py\nGm0OJk0Vepqp/taI8hfNH5UmIqLywqf6WyOkkwcAZA6NXdNWfuQ00qLpJ7v3NTXZ4TFMunFU\nWmFhodjD7o8fP5ZOGCCi5S4D9TSabZ5cPugyVgqNHRH160FRQbTlMyKiKcPp+2U07iNiMqSx\naHki9rSQzrq60kkCAPIAjV3TNNTUTvv7rHd37aCjXT9RX0NjsdOAKwGTjbQ0ZZhN1ej+d7fY\nGtadW9gnITXmhgZHfccZamo0nvWhaYdjvmOl2lsJOjkVvqNHc7+C1hslrgAAlIminmMnBepM\n5nKXgV8Mcsx89fpNVbW+hoZ9xw4a8nH1pUrpZqCnqaZWI/KE/Z5GEtwCo60Y6pGNGbFV5aK/\nJo206H579rRv/vwr/kH+s/JyIrLvaDrNrteCAf201PFvRKo+c7T/z1/3Kpo5p81AQyPIoZ+U\nIwGADKGxE0ONwbDvaCrrFCpNl8UaY2VxOq/Ze9CwmEzvnlbSCzT4QxqMe1NTdwP9b0cN3T5q\n6IM3xfoaGl31VbrTlaGuenoHxntOj09s/MuPlrraQZ8xklzmDwBKQ3WPX4ACWe/uostq9qSu\nxU4DcC2LrDCIepkYo6uTLe+eVlcDJ4+2Mldj/N9hcHUmc5y15fUZU0dbmcs2GwBIGfbYgQLo\nbWJ8wm/c9Pjzb6qqhGbNH/BhhLurTFIByA/7jqan/X1Kamrzit8yGAwbY7a+RhMnQbaOGos0\n9EhNha42BlBgaOxAMQw3754xN+D7u5m/5z95Vl6ho64+sEunT+3tXD/oIutoAPLCUFNjYJdO\nbT5styHUbUibjwoA7QKNHSgME23tla5OK12dZB0EAABATuEcOwAAAAAlgcYOAAAAQEmgsQMA\nAABQEmjsAAAAAJQEGjsAAAAAJYHGDgAAAEBJ4HYnAKDIOhrRNA+y7ynrHAAAcgGNHQAosi4m\nNH+CrEMAAMgLHIoFAAAAUBJo7AAAAACUBBo7AAAAACWBxg4AAABASaCxAwAAAFASaOwAAAAA\nlAQaOwAFc/LkyaVLlz59+lTWQQCEFRcXe3h4fP3117IOAqC60NiBeCtWrBg1alRlZaWsgwAR\n0Z9//hkbG1tcXCzrIADCamtr//jjj3v37sk6CIDqQmMH4uXn56enp/N4PFkHAQAAAFHQ2AEA\nQBuoqqq6ceMGERUUFOTk5Mg6DoCKQmMHANB6BQUFSUlJr169knUQWeLxeN9+++2HH344b948\nIkpLSxsyZMjo0aPT09NlHQ1A5aCxAwBovTNnzvj7+9+8eVPWQWSmrq4uODg4MjKypKSk4fS/\n/vpr3Lhx165dk1UwANWExg4AAFrv2LFjv/zyS5OzampqgoODcd0VgDShsQMAgNbbt2+fiLnP\nnz+Pj4+XWhgAQGMHAACtVFVVJfbmJqmpqdIJAwCExg5E43A4SUlJBQUFRHT27FncOw0AGjL7\nM5bP54uuOfTknmnWUenkAQA0dtCs8+fPOzs7+/v7P3z4kIgWLVpkb2+/ceNGDocj62gq6vr1\n60FBQSdOnCCi4ODgqKgoFb8YE2RPX5cYDDE1hnpSiQIARGjsoDnHjx8PDAwUem5VdXX19u3b\ng4KCxP6ODm2Lx+MtWbLE19f35MmTpaWlRJSbm/vNN9989NFHly9flnU6lVZXV0dEqvsvQpNF\nNt3F1PTrIZUoAECExg6a9PLly2XLljX3f1V8fLxgpxFIzddff33w4MHG09+9ezdjxozs7Gzp\nR4LTp097e3tv2LCBiBYuXDhlypSkpCRZh5IF/+Gi5pqyyb2/tKIAABo7aMrx48fLy8tFFPz4\n449SCwMFBQV79+5tbm51dTWeuS5lPB4vKCho7ty5KSkpgj12HA7n8uXL/v7+gj5PtYwaSB5O\nTc/SYNHqWaSlId1AACoNjR00IS0tTXTBnTt3BP+fgRQkJiaKPq/x8uXLFRUVUssDW7duPXny\nZJOzYmJi4uLipJxHxhgMWjmDgnzJQOdf0/ta0e6lZG8to1gAKgqNHQhjL3tdlfZGdA2Xy1Vf\n+o908sDjx49FF3A4HMGVyyAFJSUlO3fuFFEQFRWlcr/2MBn08Sg6EUlfzSEi6m1OB9fSziVk\n3U3WyQBUDho7aIKplrHoAm01LX2WrnTCqDj2stea16vFlhlseyeFMEBEycnJNTU1IgpevHiR\nkZEhtTxyRINFH1oREXU0ou4dZZ0GQEWhsYMmDO3iLLaAQeLucQBtxNrATHSBBpNlpttVOmGg\nsLBQbI3Q5eQAAFKDxg6a8LHV+A90OokoWNJ3ttTCgLfZSBZTXUTB6G5D9Fg6IgqgrbCXvWb/\nViu2zDiuhr3stRTyyB0NdXLsRVb4NQNAZtDYQRN01bXjhn2jq67d5NxV/RcO7+Ii5UiqzEy3\nS1jznbSuunak4xJp5lFxfdg9xdcYqeoVAwa69E0IzRor6xwAqguNHTRtcCfHm97HPT9wa3jI\n1drAPG7YtnUDFskwmGra4BC6oPcnjaebaLJPjtxly8YNYKXno04DLPRFXRPg1snRQu8DqeUB\nAGgIjR00y5bd4zfPfQUfJzuY2BHR9fFHsyclTrH0knUuVcRkMHe6rr0y9tAMa18jDQMi6mfc\na92ARff8fhvZ1VXW6VSLGkNtp+taNYZak3N11bW/dVkt5UgAAPXQ2IEYnbRNTLTYRNSbbSXr\nLKpucCfH/w7ZNNVqHBH94LZxVf+FYq9fhvYw+oMhh4dtY2voC03vptv5jMf39sa9ZZIKAICI\nRJ2RDQAATfKz8BzR1fXwo/j9D06kF+e4d3aaYe07xWqstpqWrKMBgEpDYwcA0BpsDf1g24Aa\nXm16cc5C22n+FmNknQgAAIdiAeTDwYMHv//+e1mnAAAAxYbGDkAu7Nq1Kzo6WtYpAABAsaGx\nU11z586NjIyUdQoAeRQfHx8TEyP60WEAAHIIjZ3qOnPmzNWrV2WdAkAeHTlyZMOGDZWVlbIO\nAgDQMrh4AsTz6uZurveBBpMl6yAAAAAgCho7FcXhcAR/8vl8BoMhujikzwyphAJQPFpqmkYa\nBvi1BwDkBA7Fqpzc3Ny5c+daW1vX1dXdu3fP1tY2PDz8zZs3ss6luvLy8rZu3VpUVFRRUREV\nFXX//n3R9R91cpjba4qJlpF04qmgf/7559WrV0R07949Ho8nunih7bSXAak+ZiOlEg0AQAw0\ndqolISFh1KhRp0+frj956M2bN/v27Rs2bNiDBw9km00F1dbWLlu2zM3NbcuWLeXl5bW1td98\n882wYcM+//zzqqqq5j71idX4PR+tN9PtIs2oKiIrK8vHx8fR0TE9PZ2IJk2aNGDAgLi4OFnn\nAgCQFA7FqpAnT57Mnz+/urq68awXL15Mnz792rX/196dxjVx7X0AP0mAQAj7Doos4n4LUgSR\nKlgpgqJCtWBdQRFF61KfutvW+lxbKy5o3agVl2u9Ra2tVFSweLWKFwtKURTxeguylNUgICEE\nkjwv5nNzeVgCWpgJk9/3Veack5lfSIt/Zs6cuaWjo0N/MI21cuXKc+fOtWlUKBSJiYkikejb\nb7/t8io59KD09PTw8PA2d8KWlZWtWrUqLy/vr3/9K1PBoJuePn164cKF7OxsFxeXkJAQV1dX\nphMBMABn7DTIvn37OqzqKAUFBWfPnqUzj4a7ceNG+6pO6erVq0lJSXTm0XANDQ2LFy/ubH2T\n+Pj41NRUmiNB91VWVs6dO3fMmDHbt29PSUnZv3+/v7//pEmTcCECNBAKOw1y7do11QN+/vln\nepIAIeTMmTOqByQmJtKTBAgh58+fr6ioUDHg8OHDtIWBV1JdXT158uQrV64oFIrW7ZmZmZMn\nT378+DFTwQAYgcJOUygUivLyctVjysrK6AkDhJAub5LocgD0oIyMDNUD7ty5I5fL6QkDr2TL\nli2FhYUddr148WLVqlVtCj4AdkNhpymuHrfQ4uqqHtP4wjD1mDk9eTRc6jHz5xVS1WNqayT4\nOmjT5Y3hUqm0traWnjDQfbW1tT/88IOKAXfv3n3w4AFteQAYh8JOg9jbvNHFAFvMNaaPqVF/\n1QPMjLsYAD0l9Zh543NL1WO4XN6d80705IHuy83NlUq7+Bvp7t279IQBUAco7DTIOI8IFb0c\nDnesOxYipo/bkKCuBkyiJwkQQlwcxqgeMNB+NJfLoycMdJPx2uqWr0q6HCZNLDNeW01DHgB1\ngMKOVaqrq5OSkvLy8jrs9R21YKiTb2fvDRq7ysFuZK9Fg7be9oo2MbTtrFdfzyRw7Co682i4\nMW6zhAJTFQPeGbOMtjDQfZa6qr41ipWeGQ1JANQECjtWycvLW7hw4fnz5zvs5XJ5/xORNNo1\nrE07j6cdMmHzrMmxvR9Qs5SUlFhYWERFRXXYq8s3+CjyJ0N9i/ZdAl2j1fN/7LAL/oz79+93\ntv6FUGC66L2jnZ2T8xk529t1Zm9Gg9fkbjbcUFuoeoyftRc9YQDUAQo7lpDL5SkpKQkJCYSQ\nn3/++fTp0w0NDe2H6ekaLp+duG3l3VD/jwnhGOibz5ocu2fd0/cm/i+Hg/8Yehj1QF4VE4Ac\n7Ny/+DDnnTHLlDWcUGA63jPqiw9zhjiNoymlxmhpaQkMDFy0aFFnAzyGh2xYdNXWckjrRj1d\nw/cm/nVJ+AksFq2e+DydD4bNVTFgusNEB4N+tOUBYBz+LWeD/Px8Pz+/OXPmXLx4kRCSm5u7\ncuVKd3f35OTkDsc72LnPCNjK4XAsTZ0m+35kZmxPb172u3fv3pw5c9566y1CyOXLl93d3Xfu\n3NnhU8KMDW0iQvYf/KTCysxZX8/48KdVUTOOmJsMoD0ym/3+++/Lli0bNGhQc3Pzo0ePhg8f\n/vHHH4tEovYjhzmP3/E/Dz/74J/9rEcQQpa+/+3+TSUhEzZhdp062+i65G2b0R12DTF23u/9\nKc15AJiFwq7PKy4uDgkJaT+vTiQSLVy4MCUlhZFUmuzkyZOTJ09OSUlRnqsrLi7+8ssvAwIC\nqEfLt8fhcLhcHofDxXnTHpeWljZ+/PgzZ87U19dTLZWVlYcPH+7s+cgcDneg/WhzY3tCiOvg\nQF2+Aa1x4f9LTk4ODw93cnKysrJyc3Nbs2ZNQUFBmzF8ns7FgCNbRq4w5RspGwVausuGzkkP\n/s5c14TeyAAMw78ifd6WLVuqqzu+4Usmk61du7azpyRBb8jMzFyzZk1LS0v7rsePH6u4Dgi9\noaSkZMGCBWKxuH1XWVnZnDlzulwpA5jS3Ny8cOHCiIiIa9eu1dfXy+Xy0tLS48ePjxs3rv3C\nddpcrU1uMaUz01MCj+3y2nDe/2D5rH/Gjd7U5fQ7APZBYde31dXVXb58WcWAP/7448aNGx12\n/e/yX5eEn+idXJorNjZWxfMJ0tPTb968SWceDXfgwIEOqzpKQUEBntumtj755JMOH5cskUiW\nLl2alZXVvkuLy3vbZvSKYfOm9B+vx+tiPXYAtkJh17c9efKEmqGvQmdPpnLs92abeeLwJ0ml\n0lu3bqkek5aWRk8YIN14/HFnX4fnGzOm+K3T0UZxwIyioqJjx4511tvS0rJt2zY68wD0IVpM\nB4DXZ5FJyH1Jl8O2/Vu8LZN8S0MgjVdVVdVlnV1aWtphu6lRf74OLhv1sD/++EP1gJKSjpe3\n9fWI7IU40F0pKSkymUzFgNu3b7948cKYtkAAfQcKuz7OzKbrMeadroILPcvtsV6XY35s0Pux\nozp7Y3QX55bgVVlkEqKtS1TOosuR6Vngzx7aFRcX//TTT/n5+XK53NnZOTg4eODAga0HFBUV\nqd6DXC4vLi52IN34BQigYVDY9XF2zsTGgZQVdjqAwyXub9MWR9MZmBAza/K8XNUYx+F0pQFC\nHIeR3AxVAxyG0RUFCCFEJpN9/vnnBw8ebH2D0RdffDF37txt27bx+XxCSOox8+K8rs9e/3rR\nfGwvJgXoqzDHru+bvVZVr384scYydXThcMiEcFUDdHTJ2Kl0pQHSxdfB4ZAJbR/EAr1q/fr1\n+/bta3PbuFwuP3HixOLFixUKBdViYzFY9X60tfgWpo69lRKgL0Nh1/e9/R6Zsbzjrr+MIUu/\npDeNxgtbSfoN7LQ3YnO3rp5DT3nnfTLCu9PeyZFk8Js0ptF0v/766/HjxzvrTU5OVq6p/uaw\nqTraAhW7chsyWRdzUgE6gsKOFRZ8QracJiO8Cfc/X2j/QWTx5+Tz7wm/61lf0JMEBuTz7zso\nJnR0yeLPSchiJjJpMC6PfHqK+AR30P7uUrLkcyYyaa6///3vqgecPn2aemEotJz+TqdPjBDo\nGs2ctL0nkwGwCObYsYXnO8TzHZKVRj6ZSUJjyKKtTAfSYOa2ZEcSuX+L3PiBXD5JLPuT0Bji\nG0qMzZlOppH0DcmmY+RJNvnnJXJmHxEakneXkXEhxMaB6WQa5+HDh6oH5ObmKl8H+61tkjb8\neG2bXP7/bo81MbJbMTvR2tylVyIC9H0o7NjF0JQMdCU2eNKoGnjjLWLjQC6fJIPdyTQ8cIJp\ng0aSQSNJzi1i40DCVzGdRhNZZBLyvNPFoilldeLWNylPD/hstGv49cyjD59eEze+sLEY7DZ0\nsq9HBJ7zBqACCjt2GTSS7MOqGWpDV58EzSMDXZnOAf+xW9VjWqDXmVmTonxVA8zbzkC1sxo2\nO3hXL0YCYB0UdgC9xsCELMe/SQD/8ebbJLvjJxz+dwAA/Dm4eQIAAGgROJeYWnXaqyckoTE0\npgFgJxR2AABAC4EB+fgkEXb0JDC+Htl4lJhZ054JgG1Q2AEAAF0Gu5MD/yABs4jef1ah4+uR\ncSHkq2u4DgvQIzDHDgAAaGTRj6zaS5bvIlWlRC4jlv2Ilg7TmQDYA4UdAADQjqdFrLEwE0DP\nw6VYAAAAAJZAYQcAAADAEijsAAAAAFhCjebYyWSyEydO3L59u6WlxdPTc9GiRdra2kyHAgAA\nAOgz1OiMXUJCws2bN6Ojo1esWJGdnb1//36mEwEAAAD0JepS2DU2Nl69ejUqKsrT09Pd3X3J\nkiU3b96sra1lOhcAAABAn6Euhd2zZ88kEombmxu16erqKpPJfv/9d2ZTAQAAAPQh6jLHrqam\nRktLS19fn9rU0tISCoUikUg5oK6ubu7cucrNmTNnhoWF9UaSlt7YKUuZmJjQcBR8I92Er0Pd\n0PCN4Ovovl76Opqbm3tjtwCvjaNQKJjOQAght2/f3rVr1/fff69smT179vz58wMCAqhN2go7\nNcTlcjkcjlwuV5MvS8NxOBwul4uvQ01QX4dCoZDL5UxnAUII4fF4GvV1NDc36+rqMp0C4L/U\n5Yydqalpc3NzY2Ojnp4eIUQmk718+dLc3Fw5wNDQ8MKFC8pNsVhcU1PDQFAmCAQCgUBQX1+P\nPw3Vga6urlAoFIvFEomE6SxAtLW1jYyMJBJJQ0MD01mAcLlc6pd5XV0d01nog8IO1Iq6zLGz\nt7fn8/kPHjygNh89esTlch0dHZlNBQAAANCHqMsZO4FA4O/vf+zYMTMzMw6H88033/j6+tIz\nZwgAAACAHdSlsCOEREVFJSQkbNu2TS6Xe3l5RUVFMZ0IAAAAoC9Rl5snXpVYLBaLxUynoAk1\nx662thZz7NQBNcfu5cuXmGOnDqg5do2NjZhjpw6oOXZSqVSj5ti1ng4OwDh1mWMHAAAAAH8S\nCjsAAAAAlkBhBwAAA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      "text/plain": "plot without title"
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