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": 18,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "head(data)\nnrow(data)",
   "execution_count": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "outputs": [
    {
     "data": {
      "image/png": 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Wrcu3cvw2OxDh06dP/+/Ro1amR1PMCK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\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": 26,
   "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>586.6666   </td><td> 6.594120  </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   & 586.6666    &  6.594120  \\\\\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   | 586.6666    |  6.594120   | \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   586.6666  6.594120\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": 27,
   "outputs": [
    {
     "data": {
      "image/png": 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y2lg2cpHQAAqDwEO6AMZrb3daxRo6StPRo3DPJoZMx6AAAoiGAHlEFjZ6dtg/u5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\nQCD+H+n/WVyNZgkzAAAAAElFTkSuQmCC",
      "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": 28,
   "outputs": [
    {
     "data": {
      "image/png": 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h+lQhSxFEIIGytTl1IYhYeHP336VAhx4cKFlJQUU5cD4B0Q7AAA/7hx40anTp28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xk6GoqKiDBw/GxMRIHcREff/99++8887J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\nAQAAKATFDgAAQCH+AzvVN4kQEH6AAAAAAElFTkSuQmCC",
      "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": 29,
   "outputs": [
    {
     "data": {
      "image/png": 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7//9vDw0Gq1qampjo6O4r/ZSHpaVmJnZxcUFGR8mZKSkpKSYuHi8yX9LtNqteb9\npaZUKu3s7DQaDUniRRkMBoPBYPbPGHt7e7Pv5ZIgIyNDCJGWlmbGYKdSqRwdHfV6PbvjRUk5\nOz093YybTqlUOjk5ZWRksDsAa7HCwxPh4eHGM08pKSkajcbW1rZq1ar29vZ///23NP3ixYtK\npbJ69eoWLq/AYmJiJk6cKN0jeOjQIX9//yVLlqSmplq7LgAAUIJYOtj5+PhkZGQsX748Jibm\n0qVLixYtqlChQsOGDdVqdceOHb/99tvY2Ni4uLg1a9a0bdvW3d3dwuUVzN69ezt06LBt2zbj\nY7xxcXGffvppx44d//nnH+vWBgAASg5LBztXV9fZs2fHx8fPmjVrwYIFzs7O0rB2QogxY8b4\n+PjMnz9/7ty59erVmzBhgoVrK5iYmJixY8fmeHLu6tWrI0aMkC4/wcJiYmKWLl2amJj4+PHj\n5cuX37x509oVAQDw0lnh4Yk6dep8+umn2afb2NgEBwcHBwdbvqTCWLJkSR5PRZ06derXX3/t\n3LmzJUsq4TQazcyZM9evXy9F6vT09Llz586fP3/s2LEffvihNFgJUMLdvHnz9u3bQoiTJ09W\nqlSJR7wB2WBctMLav39/IRvAvEJCQtatW5flRKler1+xYsW0adOsVRVQRFy5cqV3797NmjU7\ncuSIEGLixImNGzdetmwZD90D8kCwK5Tk5OSEhIS829y5c8cyxUAIERkZ+dNPP+X27ubNmw8f\nPmzJeoAi5a+//nr99dejo6MzT0xISJg3b15ISAj3jQAyQLArlBuRXtlG38tK++T3Kz96WqQc\niK1bt+bdYMuWLZapBEbp6ekrV64MDAw8efKkEKJly5bz5s179OiRtesqcTQazbhx46ThNrPb\ntWvX5s2bLVwSALMj2BWKrY2oUi6fNjWK3DefydmFCxfybnD+/HnLVAJJfHx8p06dZs2adebM\nGYPBIISIiYlZtmxZ69atT5w4Ye3qSpZff/31xo0beTRYuXKlpWoB8LIQ7Arr9YC83lUqRaCf\npUop8a786Jn4NJ+nX589vMgJVIvJyMh48803L168mP2tR48eDRky5OHDh5avqsQ6evRo3g0u\nX7789OlTyxQD4CUh2BXWwI55nZMb3El4VbBgNSVemVL5NPAsHmMjysSePXv++uuv3N59+vTp\nsmXLLFlPCZfvDcFCiMePH1ugEgAvD8GusBzsRPgU4V0n63SlUrzZVYzrY42aSj8BmCEAACAA\nSURBVLCARoVtADPat29f3g0iIyMtUwmu/OhpeLQ932ZPjja3QDEAXh6CnRmUdhPLp4nwKaJX\nGyGEcHcRY3qJrfPE2N5Cmd+jFTCv/q8JZ3Wu77q7iN5tLFhNiXfr1q28G9y9e5cnMS2mca18\nGlQrL9ycLVIKgJeGYGceCoXwbSAm9BNCiFpVxIhuoiL3cVmDu4v4OESocxps1VktFkzIK/bB\nvK786KlNOJ53G4XQXd2V3/NHMJNWTUSF0nk16NfBUqUAeGkIdpCbZvXE2lni9QDhaP+fKU4O\noltL8d2HomENq1ZW8lQrn3+DfAcMgrnYqcQHo4W9Kud3WzX5zzUHAMUawQ4yVLms+GCUiFwq\n3JxEudJi71Lx3nBRzsPaZZU8HfN7JJxnxi3slVpixb9Eg+r/b6LaQYzsLj4OEUo+EIDij+/N\nhGzZ2gilUtgohQ0fV1bSpLYI9BP7/8z5Xa8Kot9rli0IQtSpKla9J67fE4s2ir9jxL+Gidd8\nc757AUBxxCcegJfo/RE5j/XYoLpYMiXXy4J42apXFJU8hRDCuw6pDpAVztgBeIlUtuKDUaJP\nO/HrcfHLUZGcJl4PEG2bilbePDMOAOZHsAPw0jWqIRrVENduib9jxcyRRDoAeFm4FAsAACAT\nBDsAAACZINgBAADIBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsA\nAACZINgBAADIBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZ\nINgBAADIBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgB\nAADIBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAADI\nBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAADIBMEO\nAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAADIBMEOAABA\nJgh2AAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAADIBMEOAABAJgh2\nAAAAMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAADIBMEOAABAJgh2AAAA\nMkGwAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZsLV2AQWkUChsbYtT8YWpVqlUCiFs\nbGwMBoP5KirRCr87lEpl8ToCi7LCbEkbGxvB7jCrwv/vKHa/nwuDX8soaorr/z0bGxsnJydr\nV/ECClOt9NHl4ODAbxBzKczukD667O3tVSqV+Soq0QqzOxQKhRBCpVJJ+wWFV/jdUex+PxeG\nVqu1dgnA/1Ncg51Op0tJSbF2FS/g2bNnBZ5XrVar1erk5GR+g5hLYXaHg4ODs7NzampqWlqa\nGUsqyQqzO1QqlZubW3p6enJyshlLKskKszuUSqWHh4dOp3v+/LkZSyri1Gq1tUsA/oe/cQEA\nAGSCYAcAACATBDsAAACZINgBAADIBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAAgEwU1wGKAQCF\nEdROtHxFlHazdh0AzIpgZ05qB3F4lbWLAAATNKguGlS3dhEAzI1LsQAAADJBsAMAAJAJLsUC\nsJD3hovUdKFUWLsOAJAvgh3kbPX7xIgipGp5a1cAAHJHsIOclS9t7QoAALAg7rEDAACQCYId\nAACATBDsAAAAZIJgBwAAIBMEOwAAAJkg2AEAAMgEwQ4AAEAmCHYAAAAyQbADAACQCYIdAACA\nTBDsAAAAZIJgBwAAIBMEOwAAAJkg2AEAAMgEwQ4AAEAmCHYAAAAyQbADAACQCYIdAACATBDs\nAAAAZIJgBwAAIBMEOwAAiiK9Xr9y5coWLVp4enp6eHj4+vrOnTs3MTHR2nWhSCPYAQBQ5BgM\nhu7du4eEhKhUqrfeemvSpEnlypWbM2eOj4/P8+fPpTaLFy9WKBSPHz+2bqkoUmytXQAAAMhq\nw4YN+/btmzNnzuzZs40Tf/zxx6CgoNmzZ4eHh1uxNhRlnLEDAKDIOXjwoBBiypQpmSf26dOn\nYcOGhw8ftlJRKAYIdgAAFDnJyclCiDt37mSZvm/fvi1btggh2rdvHxoaKoQoU6bMsGHDpHc3\nb97s7+/v7u7u6urq4+OzZs2aLPO2a9euVKlS/v7+q1atCgsLc3FxMb57/fr1gQMHenl5ubm5\ntW3bdu/evS91BfGSEOwAAChyunTpIoRo06bNrFmz4uLijNMrV65cq1YtIcTSpUvHjx8vhNi9\ne/fMmTOFED/88MOQIUMUCsX06dNDQkJ0Ol1wcPCOHTukGbdt29atW7eEhIR33nnHx8dn8uTJ\nS5cuNXZ79uxZb2/vw4cPDxo06J133nny5En37t2/+eYbS64yzEJhMBisXUNBpKSkpKSkWLsK\nceVHTxNb1u3zsMBLUavVarX62bNnWq22wJ2UBJbZHQ4ODs7OzklJSWlpaQXupCSwzO5QqVRu\nbm6pqanS6Q3kxjK7Q6lUenh4aDQa4939JUGZMmVeRrcGg2Hu3LmfffaZdGzXrFnztdde69y5\nc/fu3VUqldRm8eLFoaGhjx49Kl26tBAiKCjoxIkTsbGxdnZ2Qoj09PSyZcsOGjRo5cqVGo2m\ndu3a5cqVO3jwoIODgxDip59+6tmzp7Ozs/SYbbt27a5fv3769GkPDw8hhFar7dSp06lTp+7d\nu+fs7PwyVhAvCWfsAAAochQKxezZs//5558ffvhhwoQJKpVq1apVQUFBNWvWPH78eI6zrF69\n+ty5c1KqE0IkJibq9XrpJMixY8du3br1zjvvSKlOCNGjR4969epJPz99+jQqKmrs2LFSqhNC\nqFSqiRMnJiYm5rYsFFkEOwAAiihnZ+c+ffp88cUXly5dunDhwujRo+/fv9+rV68cR7MrXbr0\n48ePN2zYMG3atHbt2lWuXNl4JjsmJkYI0aBBg8ztjS+vXLkihJg1a5Yik379+gkhHj4s+Blc\nWAXDnQAAULQkJyePGDGiZ8+exqcihBANGjRYs2aNp6fnggULoqOjO3funGWu5cuXT5s2zcXF\npWvXroMHDw4PD+/Vq5f0lkajyb4UGxsb6QfpJN+MGTOy91m3bl1zrRQsg2AHAEDR4uTkdPDg\nwWfPnmUOdhIvLy+RKZMZJScnv/vuu2+88cY333xjfDc9PV36oXbt2kKIy5cvv/LKK8ZZpBN1\nQgjpaQylUtm2bVvju/fv37969WqpUqXMt1qwBC7FAgBQ5HTt2nX//v1ff/115omJiYmrVq1S\nq9W+vr7GiRkZGUKI69evp6enN2vWzJjqfvnllwcPHkjv+vv7e3p6Ll261Hjq7rfffjt37pz0\ns6ur62uvvbZq1SrjhdeMjIzhw4cPGjTI+KAGigvO2AEAUOQsXbo0Ojp6/PjxK1eu9PX19fDw\nuHfv3p49exISEjZt2iSdSJNSV3h4eNeuXf38/CpXrvzJJ588fPiwRo0af/75586dOytXrvzv\nf/973bp1I0aMWLBgwejRo1u2bNmnT58HDx589913bdu2PX/+vLS4zz77rE2bNk2aNBk5cqSN\njc3PP//8119/bdiwIfupQRRxnLEDAKDIcXNzO3v27KJFi+zt7Xfv3v3FF1/89ddf3bt3P3fu\n3ODBg6U2vXr1at++/eeff75161Y7O7u9e/c2bNhw6dKlH3744dOnT48fP/7999/Xq1cvOjpa\nCDFq1KgdO3bY2NgsXLjw7NmzP/zwQ6tWrcqVKyd11bRp01OnTgUEBKxfv37ZsmWOjo579uwZ\nOnSo1dYfBcU4doXCOHZFCuPYFSmMY1ekMI7dy/OSxrEzL71en5CQ4OTkZBzuRAjxxhtv/PPP\nP7///rsVC4PZccYOAACZS0tLq1ixYuZvno2Pj9+9e3fHjh2tWBVeBu6xAwBA5pycnEaMGLFq\n1SqdTtehQ4enT58uXrzY1tY2ODjY2qXBzAh2AADI3/Lly6tWrbp+/frNmzd7enp6e3uHh4d7\nepp6jR7FBcEOAAD5s7Ozmzlz5syZM61dCF4u7rEDAACQCYIdAACATHApFgCAIken05m3Q1tb\nPvFLBHYzAABFi8Fg0M98x7x92i5cZt4OUTRxKRYAgGLpyuOnO6/E3E9idG78D8EOAIBiafe1\n2CG7I0/ej7d2IShCCHYAAAAyQbADAACQCYIdAACATBDsAAAAZILhTgAAKGaepKZtunB528Wr\nQojlp848TUvvX7+2IyPVgTN2AAAUL9svXa238rt3fz904dFjIcTBW3fHRv678eoNR+/eN++C\nhg0bpsjE0dHR29t7+/bt5l3Ky6bX6xUKxalTp6xdiIUQ7AAAKDZ+uhY3Ys+vzzWaLNPvJCb1\n/D7i/MPHJvaj0+mmTZvm5eVVqVKlkJCQ9PT0HJsFBAQc+69du3bVqVNn8ODBLzsktW7devHi\nxUKImzdvKhSKlStXvtTFvagiXh7BDgCA4kGj10/5d1SGwZDju4kazbu/HzKxq2nTpm3btm35\n8uXffPPNr7/+GhwcnGOzUqVK+f/X66+/vnHjRgcHh/379xdwBfKk0WiuX7+eeYqbm9uMGTO8\nvb1fxuIKr2iWR7ADAKB4OHT73t3EpDwa/HHztilfRJGYmLh27drw8PAePXp07tz5yy+/3LZt\n28OHD/Od0c7Ozt7evnTp0tLLZ8+ehYSEVKtWzc3NrWfPnjExMUKIU6dOeXp6RkdHN2/evEyZ\nMh06dLh06ZLU/uHDh0OGDClfvnzFihWHDh1qXKJKpdqzZ0+lSpUmT57s6+t7+PDh0NDQLl26\nlCpVKiwsTPraXJVKdezYsQEDBtSoUaNWrVo7duyQ5r1z507Pnj3d3d19fHwiIiJcXFwuXLiQ\nW/25FfDgwYOBAwd6enpWqFDh7bff1mg0QogrV6507tzZ3d3d1dW1Xbt2586dE0LkVl4eq5Zj\n2Xv27PHx8VGr1dWrV//888/z3fKmI9gBAFA8XHyUz5VWgxAXHz3Jt5/z588nJSUFBgZKL197\n7TWtVnv69Om853r+/Plnn31mb2/fpUsXaUrv3r0vX768fv36/fv3Ozk5tWnTJiEhQQiRmJg4\nffr0rVu33r5929/fv02bNomJiQaDoVu3brGxsVu3bt2yZUtMTEzXrl0N/z37OG3atIULFy5f\nvvzEiROtWrUKCwuLjIzMUsCMGTMWLFhw7dq1IUOGDBs2LC0tTafTvfbaa0KIyMjImTNnjhs3\nLjk511ybWwEZGRmBgYGJiYkRERGLFi3asmXLvHnzhBBDhgxJT0/fsWPH7t27DQaDdFIzx/Ly\nXrXsZd++fbtv376BgYFRUVEhISFTpkw5duxYvnvNRDxBAwBA8ZCu1+fbJk2ny7fN/fv37ezs\nSpUqJb20s7Nzd3e/d+9e9pb79u1TKBTGlzY2Nn/88UflypWFEMePH4+Ojo6Pj3d3dxdCbNy4\n0cvL69ChQxUrVkxPT//www+rVasmhJg/f/727dvXr1/fsGHD06dPx8XFValSRQixffv2GjVq\nHDp0qE2bNkKI4ODgUaNG5V22dN5LCDFmzJi5c+fevXv3zJkzDx48OHXqlLOzsxAiMTFx5MiR\nuc0eFRWVYwGJiYlxcXFRUVGlSpVq3rx5SkrKkSNHDAbDgAED+vXrJy3x3r17U6ZMedGepVXL\nXvb169e1Wu1bb71VrVo1X1/fWrVqlS1bNu91Nx3BDgCA4qGyi0u+baq65d/GYDBkjmsSXU6J\nMCAgYOnSpdLPSUlJy5cv79u37+3bt+3s7C5duqTVajMnEp1OFxMTU7FiRSGEr6+vNFGpVL76\n6qsXL15UKpVeXl5S9BFCVK1atVq1apcuXZLST5MmTfItu379+tIParVa+uH8+fPe3t5SqhNC\ntGrVKo/ZL126lGMBCQkJDRs2NMbccePGjRs3TggxderUY8eO7d2799SpU3v37i1Az9KqZS87\nICDA19e3UaNGvXv3DgwM7N+/v6OjY76rbyIuxQIAUDwEVq9iZ2OTR4Oqri6NPMvk20+FChXS\n09MTExOllzqdLiEhQToPl0Xmhydee+21FStWPHjwQLqXzs3NzcPDQ5uJwWCYOnWqNGNGRoax\nE61Wm5GRkZGRkSVNKpVKY5o0hp482NnZZZmi1Woz96lU5pVqcitAq9XaZhsCMCUlpW3btqNG\njbp582afPn0+/fTTAvScW9nOzs7Hjx+PjIx0cXGZO3du1apVs193LjCCHQAAxUNpR8dJzfI6\nszWndUDWE3E5adiwoVqtPnDggPTy8OHDNjY2pjzdWaFCBSHE1atXpU6ePHly/vx56a1Hjx71\n7t374sWL0suDBw9KP6Smph45cqR+/fr16tW7fv363bt3pel37ty5ceNGgwYNTKg3rxU5e/Zs\nSkqK9DLvO9VyK6B+/frSTYfS9C1btnTq1OnAgQPnzp07f/78Z5991rNnz8w51fSec2sfFRUV\nFhbWqlWrr7766tq1a82bN1+9erXpa503gh0AAMXGR62bB9WtlX26Qoj3mvu+0bCeKZ24ubmN\nGjXq3Xff/euvv86cOTNlypTBgweXL19eCLFu3boVK1bkMa+Li8s///wjhKhTp05QUNCQIUMO\nHDhw6NChYcOGXbp0qU6dOlKzqVOnRkREHDt2bODAgXq9fsSIER06dHjllVcGDhx45MiR6Ojo\ngQMHNmnSpF27dtkXoVQqY2Njpecw8hYUFOTq6jps2DDpaun8+fNtbW1zO2+XWwE9e/b09PQc\nOnToyZMnd+3a9f777zdo0MDFxSU5OXnnzp1xcXFr1qz56KOPEhMTpQdjs5dn+qpJNBrNv/71\nr+XLl587dy4iIuLChQumXIk2EcEOAIBiw1ap3NSry3c9Xm9eqYJSoRBC2NvYdK7h9cugoNmt\nA0zvJzw8vEuXLr179+7WrVvz5s1XrVolTd+0adPatWvzmLFBgwZr1qyRft6wYUPLli3ffPPN\nHj162Nvb79u3z3hNc8WKFR988EGnTp0SExMPHjzo6uqqUCgiIyOrVKkSFBTUt29fLy+vyMjI\n7Lf6CSGGDx++ffv20aNH51GGo6OjUqm0t7f/7bffnj9/3qFDh/nz52/YsEEIkduDCLkVoFKp\nfv/9dyFEp06d3nrrrV69ei1YsKBNmzazZ89+5513/Pz8fvnllz/++KNLly4zZ87MsTzTV00q\nOzAwcMGCBeHh4X5+fpMmTQoKCnr//ffzWNkXojDkMs5hEZeSkmI89WpFV370NLFl3T75jw+U\nG7VarVarnz17ptVqC9xJSWCZ3eHg4ODs7JyUlJSWllbgTkoCy+wOlUrl5uaWmpqaxxgHEJba\nHUql0sPDQ6PRPH/+vMCdFDtlyuR/T9uLMhgMmhlv59tswdETcw4d+75Ptx61a+Tb2H7hMnOU\nlr9Tp041a9ZMp9PZ5Hk7oFnEx8cfOXKkT58+0svz58/7+/snJSXlGKpKCM7YAQBQLClLcHyR\nGAyGN954Y8mSJfHx8VevXp0wYYL0/bbWrsuaCHYAAKBYKl++/O7duzdv3ly9evUOHTrUrl17\n4cKF1i7KyhjHDgAAmM2rr75qybu8OnXq1KlTJ4strujjjB0AAIBMEOwAACiW6pX26Fu3VkUX\nZ2sXgiKES7EAABRLPWvX6GnC87AoUQh2AAAUOYp5YebsrWQ/KFqiEOwAAChaFApF9i8YBUxB\nsAMAoGgxGAwajcaMHZIUSw6CHQAARc7J9Q7m7bBlcLH8oim8KJ6KBQCgWDp9Raz4Qdy4b+06\nUJQQ7AAAKJbOx4lN+8TteGvXgaKEYAcAACATBDsAAACZsMLDEwkJCd9+++2ZM2c0Gk3dunVH\njBjh5eUlhNDr9d99992RI0d0Op2fn19wcLBKpbJ8eQAAAMWUFc7YLV68+MaNG6GhoR999JGj\no+PMmTOfPn0qhFi7du2hQ4fGjh07efLk06dPf/HFF5avDQAAoPiydLB7/Pjx2bNnx48f37hx\n4zp16oSGhgoh/vzzz9TU1P37948ZM8bPz8/HxyckJOTQoUPPnj2zcHkAABR95+PEvLViy69C\nCBG+RSzZ8rIeodi4caOfn5+rq2uFChW6du167Ngxsy/i4MGD1apVa9euXd7NXFxcfvvtt7zb\nXLlyRaFQFCA8mFhDsWDpS7EZGRmDBw+uWbOm9FKn02k0moyMjJs3b6alpXl7e0vTmzRpotfr\n4+LimjZtKk15/vz5sGHDjP0MGjRowIABFi6+MNzd3Qs8r1KpFEK4uLgYDIxCZB6F2R3SN/Oo\n1WpHR0fzVVSiFX53ODg4MPiquRRmd0hUKlXhOykutFqt5Re6YqfY/KswfiA8eCp+OCB+OiSm\nvSG6t3qxrjQaTYUKFa5evVq6dOns73711VdTp06dN29ehw4dkpKS1q9f365du+PHjzdp0iSP\nPlu3bt27d+9p06aZWMOyZcuaNm26evVqUxrfvHnTy8vr66+/HjdunIn9m72G7PJd5ZdUdo4s\nfcbO09Nz8ODB0s1z6enpS5cudXFxadWq1dOnT21tbZ2cnKRmtra2zs7OT548sXB5AAAUZVv3\ni02/iOx/5mt1YtEGcfyCqf2kpaX9/vvvw4YNy+Ojdvny5VOmTJk+fXqzZs3atWu3du1af3//\nb775pqC15yw5Oblp06aenp6mNHZzc5sxY4bxNJDla9BoNNevX3/R/l9S2TmyzjdPGAyGAwcO\nbNy4sVy5cuHh4dK5qOxfUazX640/u7q67t692/gyJSVFujOvuChMtWq1Wq1WJyYmWuVPQ1kq\nzO5wcHBwdnZOSUlJS0szY0klWWF2h0qlcnNzS0tLS05ONmNJJVlhdodSqfTw8NBqtc+fPzdj\nSUWcg4OZvyIiDylpYu1Pub6bYRBffC/8G5rU1fLly5ctW5b3F5f9888/qampmad8/vnnKSkp\n0s/Pnj3717/+FRkZmZCQ0LZt2yVLltSqVcvX1/fkyZOHDx/+97//HRkZmXnehw8fTpky5bff\nflMqlR06dAgPD/f09Gzfvv0ff/yxb9++ffv2HT16NHP7q1evTpw48c8//6xevfrcuXOliaVK\nlQoLC+vevbsQYs+ePR9++OHly5fLlSs3ZcqUt99+O/PsJ06cCAwM/PTTT8ePH593yyw15Fin\nEEKlUv34448jR44MCAj46af/7YY8Vtkoc9kvmxUennj27NnMmTM3bdo0fPjwTz75xM3NTQgh\n/S4wHkB6vT4pKalMmTKWLw8AgKLpxEWRkudflNfvmXqz3bvvvnv79u29e/fm0aZ79+4rVqwY\nPHjwL7/8In1Ae3t7t2jRQnq3d+/ely9fXr9+/f79+52cnNq0aZOQkHDixIlWrVqFhYVliTgG\ng6Fbt26xsbFbt27dsmVLTExM165dpbM8nTt3njNnTpZUl5yc3LZtWyFERETEhx9+OHnyZGOg\nlNy+fbtv376BgYFRUVEhISFTpkzJfP/f2bNnpW7Hjx+fd0shROYacqtTajlt2rSFCxcuX748\n8+y5rbK1WPqMncFg+Oijjzw8PJYvX65Wq43Tq1atam9v//fff/v5+QkhLl68qFQqq1evbuHy\nAAAosu49yr/NnQeiSjnzLG7NmjV169bdsmVL586d7e3tW7ZsOXLkyKFDhwohjh8/Hh0dHR8f\nL91PuXHjRi8vr0OHDvXo0SPHrqKiok6fPh0XF1elShUhxPbt22vUqHHo0KE2bdrk2H7Tpk3p\n6ek7d+50cXERQjg6Onbp0iVzgytXrmi12rfeeqtatWq+vr61atUqW7asdF3r4sWLvXr1Gj9+\n/JQpU3Jrmdsq511ncHDwqFGjCrIpLcjSZ+zOnTsXGxvbunXra9eunf2vR48eqdXqjh07fvvt\nt7GxsXFxcWvWrGnbtm3Juf0WAIB8ZbtlKQdK832w29vbz5o168KFC3fv3l29erW7u3twcPDU\nqVOFEJcuXdJqtWXLllWpVCqVysHB4c6dOzExMbl1denSJS8vLyktCSGqVq1arVq1S5cu5dHe\nz89PSnVCiPbt22e5XysgIMDX17dRo0bDhg1bv359165da9SoIb3Vo0cPg8EQGxubb8sXrTPv\np0aKCEsHu+vXrxsMhsWLF3+QyfHjx4UQY8aM8fHxmT9//ty5c+vVqzdhwgQL1wYAQFFWOdcz\nTf9TxYQ2prhw4UJgYKB0BbZixYrDhg3bsWPHl19++cUXX2g0Gjc3N+keKiODwSBlvhxlZGRk\nSWZKpVKn0+XW3sbGJvNLhUKRZXZnZ+fjx49HRka6uLjMnTu3atWqxiuh77///vbt27du3fr7\n77/n3fJF68x8pbHIsvSl2N69e/fu3TvHt2xsbIKDg4ODgy1cEgAAxUKz+sLVSTzP/UmhetVE\nRZOeLs1fhQoV/v3vf//888/9+vUzTtTr9fb29ra2tg0bNnzy5Mn58+cbNWokhHj06NGYMWM+\n+eSTBg0a5FxYvXrXr1+/e/dupUqVhBB37ty5ceNGbo2FEPXr11+3bl1SUpKzs7MQ4vDhwxkZ\nGZkbREVFnThxIjQ0tFWrVgaDoVevXqtXr/7000+FEKNHj3Zzcxs0aNDEiRPPnj175MiR7C2z\nXNgtcJ1FEN8VCwBA8eBgJ0KCcn3X1kZMKvQAr+vWrVuxYoUQwsPD4+233x45cuScOXP279//\nxx9/hIWFTZ8+ffLkyUqlsk6dOkFBQUOGDDlw4MChQ4eGDRt26dKlOnXqCCGUSmVsbGxCQkLm\nbjt06PDKK68MHDjwyJEj0dHRAwcObNKkSR4DAg8ePNjOzm7AgAFHjx7du3fv+PHjjQOiSTQa\nzb/+9a/ly5efO3cuIiLiwoULWa6ThoWF3b59+/PPP8+3ZWHqlOS4ytZCR2KUFwAAIABJREFU\nsAMAoNjo2VqEBAmbbJ/eagcxe4xoUruw/W/atGnt2rXSz2FhYYsWLYqIiOjXr1/fvn2///77\nsLCwjz/+WHp3w4YNLVu2fPPNN3v06GFvb79v3z5bW1shxPDhw7dv3z569OjM3SoUisjIyCpV\nqgQFBfXt29fLyysyMjL7MGf/Wx21OioqSqfTdenSZcaMGQsXLuzVq5erq6uxQWBg4IIFC8LD\nw/38/CZNmhQUFPT+++9n7qFSpUqzZ8/+6KOPGjRokHfLwtQpyXGVrUVRTL/MICUlJcuTz1Zx\n5UdTT3nX7fOwwEuRxrF79uwZ49jlzTK7QxrHLikpiXHs8maZ3SGNY5eamso4dnmzzO6QxrHT\naDQlahy7lzEyl8FgOLImrzMvN+6L3VHij7/EwwThVUG08xG924oypfLqs2Vwsfy4x4uyzgDF\nAACgwLwqiLcHCQ83sfJHMa6PaG2JbzRA8UCwAwAAKKxDhw4ZvyEjixEjRgwZMsQyZRDsAAAA\nCqt169b79++3dhU8PAEAACAXBDsAAACZINgBAFAs+dQV44NE9YrWrgNFCffYAQBQLDWsIRrm\n+q2nKKEIdgAAFC0KhSJgVK7fowrkgWAHAECRY2NjY+0SUCwR7AAAKFoMBkNqaqoZO1QoFI6O\njmbsEEUWwQ4AgCLnzQNO+Td6ETu68ZViJQJPxQIAUCzdPigOzRKPL1m7DhQlpp6xMxgM58+f\nj4qKunHjRnx8vMFgKF++vJeXV9u2bRs1aqRQKF5qlQAAIIuUePHgjKjZ3dp1oCjJP9jFxcWt\nXLly3bp1Dx48sLW1LV26dOnSpYUQT548efTokU6n8/T0HDFiREhISI0aPHUNAABgNXldin38\n+HFwcHCdOnWOHDny3nvvHTt2LCkp6Z9//rlw4cKFCxfu37+flJR0/PjxmTNnHjt2rE6dOsHB\nwY8fP7ZY6QAAAMgsrzN2TZo06d+/f2xsbLVq1XJsYG9v7+fn5+fn9/bbb9+6dSs8PNzb2/v2\n7dsvp1QAAADkJa9gd/LkyfLly5vYUdWqVcPDw6dPn26OqgAAQD70GiGEyNBauw4UJXldis0j\n1en1+j179kRERDx//jzz9AoVKpitNAAAkI0hQ8TuEb+OF5e2CCHE8UXij1Bx76j5F9SjRw/F\nf6nV6hYtWqxcudL8i3k5bt68qVAooqKiXvaCVCpVdHT0y16K6Uwd7iQ5OTk4OLhu3brSy969\ne/fo0aNXr15Nmza9devWSysPAAD8j14jomeLM1+LRON9Twbx+LI4Ol+c+VoIk8eqi4+Pf/PN\nNytWrOju7t65c+dz587l2Kxjx47Hjh07duzYzp0727RpM3HixHfeeUd6S0pOeUS91q1bL168\n2OQ1s7R86y+mTA12s2fPXrNmjbe3txDi6NGje/bsGTNmTEREREJCwscff/wyKwQAAP/x91oR\nfzrnt2L3iLh9pvYzZMiQc+fObdq06ZdffnF1de3QocP9+/ezNytTpoy/v7+/v3+XLl0WLFiw\nbt26ZcuWXbt2TQjh5uY2Y8YMKRgUR8W9/tyYGux27tzZvXv3bdu2CSH27Nljb28fFhbWo0eP\n3r17//bbby+zQgAAIIQQaU9FXGReDS5tEQYTTtrdvXv3t99+++qrr9q3b+/n57dp0yaDwfDT\nTz/lO+PgwYOrVKmyfv16IUSpUqXCwsJ0Op0QYs+ePT4+Pmq1unr16p9//rkQwtfX9/Dhw6Gh\noV26dBFCXLlypXPnzu7u7q6uru3atTOeIFSpVMeOHRswYECNGjVq1aq1Y8cOafqDBw8GDhzo\n6elZoUKFt99+W6PRCCGePXsWEhJSrVo1Nze3nj17xsTE5L+qQgghHj58OGTIkPLly1esWHHo\n0KEPHz7MUn9uZRidOnXK09MzOjq6efPmZcqU6dChw6VL/xsYOj4+vnv37qVKlapZs+b3338v\nTcxtlbNvq8KsWnamBrt//vnH399f+vnw4cN+fn5ubm5CiLp16967d6/AiwcAACZ6cFoY9Hk1\nSHsinsXl349er58zZ86rr74qvdRqtWlpaRkZGfnOqFQqX3nllbi4/7eM27dv9+3bNzAwMCoq\nKiQkZMqUKceOHTtx4kSrVq3CwsIiIyOFEEOGDElPT9+xY8fu3bsNBkNwcLBx9hkzZixYsODa\ntWtDhgwZNmyYVElgYGBiYmJERMSiRYu2bNkyb948IUTv3r0vX768fv36/fv3Ozk5tWnTJiEh\nId+aDQZDt27dYmNjt27dumXLlpiYmK5duxqy5d/sZWRpkJiYOH369K1bt96+fdvf379NmzaJ\niYnSW2+//fbIkSOjo6NbtWo1fPhwKYbmuMo5bqsCr1qOTP3miUqVKp05c0YIcefOnejo6A8+\n+ECafuHCBU9Pz4ItGwAAmC7VhLFiUx+JUjXzaVO1atXZs2dLP6ekpAwfPtzDw2PAgAGm1FC2\nbNks55OuXLmi1WrfeuutatWq+fr61qpVq2zZspkbGAyGAQMG9OvXT/oig3v37k2ZMsX4rnSe\nTAgxZsyYuXPn3r179/Lly3FxcVFRUaVKlWrevHlKSsqRI0eOHz8eHR0dHx/v7u4uhNi4caOX\nl9eh/2Pv7uNqvP8/gL/P6f50n0q5q9xEMtF9XymhJiZJ2ISZu9x8UWZGxtwsQmZsmNnM182Y\n8TWxYogK1WhIqZRiuSk3Kafbc06d3x/X99daN+ecUuf29fzD45zrep/P9TrXRd5dt0lJY8eO\nFR04ISHh1q1b+fn53bt3J6Ljx4/37NkzKSnJy8urYVnTGL16/WM91tTUrFmzhrkBXGRk5PHj\nxw8ePLhw4UIiWrBgwYQJE4ho7dq1Bw8efPbsWY8ePZr9ys2uqzZ/tWZJ2tgFBwdv27YtLCws\nKSmJ2UKVlZV79+49ceJEQEBAGxYMAAAAraKmJUGNtqSjCYXCQ4cOffbZZzY2NmlpaSYmJpJ8\n6vnz5126dGk4xd3d3cXFZcCAAYGBgb6+vhMnTtTR0WlYwGKxwsPDU1JSYmNj09LSYmNjG861\ns7NjXnA4HOZFRkaGvb29kZER8zY0NDQ0NPTAgQN8Pr9hyygQCCQ5ZJmVlWVtbc10dUTUo0cP\nKyurrKysRo1d0xhNubi4MC/YbLaTk9O9e/caTa//bEtfudl1lZiY2Lav1ixJD8WuWrVqzJgx\nO3fuvHXr1rp16+zs7AoLC5cuXdq5c+f169e3bdkAAAAgOUMbMQUsFhk2/0iBxl68eOHj47N6\n9eqoqKjLly832sfWEubB8Y12Zenp6aWmpsbFxenr669fv75Hjx7M4dd6lZWV3t7eM2fOfPTo\n0fjx4zdt2tRwrqamZqOl8Pl8dfXGO54MDQ1NTEz4DQiFwvDw8IY1n332Wf11uMzJc0RUV1fX\n6In2bDa7fq6IGE01PFrN5/Pr3zZqZKnlr9zsupLkq0lO0sZOX1//119/LS0tLSsr++yzz4jI\nwsLi4sWLd+/e7dOnT9uWDQAAAJIz7U+6Im8X29mZtIzEjyMUCkePHm1oaJienj5lyhQ2W9Jm\n4Oeff3706NHUqVMbTkxISIiOjvb09Ny9e3dubq6Hh8e+ffsaFly+fDk9PT0jI2Pr1q0BAQFi\nT+azs7PLyMgoLy9n3h49etTPz8/e3r6kpCQjI4OZ+PLly8DAwPp9ZoysrKyffvqJec3ci61z\n5879+vUrKCh48uQJM/3x48cPHz7s37+/hF+5ocTEROZFVVXV9evX63fyNdXSV252XUny1SQn\n6lDskydPunbt2nCKgYFB/WtDQ8MRI0aI/QgAAAC0C5YaOf6brn1OdY33NxERaeqTw5xmpjcV\nHx+flpYWHh5+8+bN+ol9+/bt1q3bgQMHqqqq5s+fz0wsKSlJS0sjotevXyckJGzevHnx4sW2\ntrYNR+PxeJ9++qmWlpa3t3dBQUFmZub06dOJiM1mP3jwoLS0VF9fv6Ki4uTJk+7u7vHx8evW\nreNyuenp6QMHDmw2XkBAgJmZ2dSpUz/77LPHjx9HRESMGzfO1tY2KCgoJCTkq6++UldX37hx\nY35+fqMkI0eOXLhw4eHDh/v3779+/fq+ffv26dOnb9++AwcOnDx58pYtW4RC4fLlyx0cHIYN\nGybRmvqn8PBwdXV1c3PzjRs31tbWzpgxo6XKlr5ys+tKkq8mOVFNuru7e1hY2MOHDyUZqKCg\nYPHixe7u7m3LAQAAAGKZO5DHZ83sljPoQV4bSa9Lc59p4s6dO0KhMCQkZGQDp0+fJqIjR47s\n37+/vvL33393dnZ2dnYeO3bsxYsXd+7c+eWXXzYazdfXNyoqavv27a6urosWLQoKCoqIiCCi\nDz/88Pjx47NmzfLy8vr888+XLl3q6up6/vz5K1eu+Pv7r1q1qmkwHR0dNputoaERHx9PRH5+\nfgsWLBg3blxUVBQRHTp0aMiQIdOnTx87dqyWlta5c+caHbENDQ2NiIhYuXLlsGHDNDQ0Tp8+\nraamxmKx4uLiunfvHhQUNGHCBGtr67i4uEYHZ5uN0XT6nj17Vq9e7efnx+VyExMTG+7taqSl\nr9zSuhL71STHanrFb72SkpKVK1f+8MMP7u7uQUFBHh4egwcP1tb++7TM6urqW7duJScnnzx5\nMjU1ddasWZs2bZLw7Mu3VFlZWVlZKYUFiZZzStIrgvuOf9HmpXA4HA6HU1ZWxufjiYCiSGdz\naGtr6+nplZeXN70YHhqSzubQ0NAwNDSsqqqqqKho8yCqQDqbg81mm5iY8Hi8Rk+bVG6mpqbt\nPqZQKJwYK2rPS20NPblGD85SyX2ydCWbd8nChVgiD6ieGCPxUymgibS0NGdnZ4FAoKamJuss\nYojqB01MTPbu3btixYq9e/du3bq1qKhITU3NxMSEad1KSkpKSkpqa2stLCw+/PDDw4cP29iI\nO6sTAAAA3pqaFvUYTlWvqOQ+WfuRpZusA4HcEL+jz8bGJioqKioqKjMzMzExsaCgoLi4mMVi\nmZub29jYeHl52dvbSyEoAAAAAIjWiiO49vb26OEAAABA1Tg5OYk4dU2utO7UvPLy8tTU1Bcv\nXgwbNszIyEhDQ0P+DzYDAAAAqAhJb11DRPv27evSpcvIkSM/+OCDnJyc1NTU7t27HzlypOPC\nAQAAAIDkJG3sfvvtt9DQUCcnp5MnTzJTbG1t7e3tp06d2ujZIAAAACAF3b1o6BfUqcW75IIq\nkvRQbFRU1IABAy5cuFB/YxVLS8vz58+7uLhERUWNHj26wxICAABAMzididNZ1iFAzkja2N25\nc2fZsmWNbpfHZrPHjBnz9ddfd0AwAAAAFcVisX4ZLea5WwDNkrSxMzY2bvZ2rAKBQF9fv10j\nAQAAqDrRj0YAaImkjZ2bm9vBgwc/+eQTY2Pj+onPnz8/cOAAHiMGAADQjoRCITtxdzuP6b2w\nfQcE+STpxRObN29+8+bNoEGDNm7cSETnzp2LiIiwt7fncrmbN2/uyIQAAAAAIBFJGzsbG5uk\npCRra2vmqb1RUVGbNm1ycHBITEzs06dPRyYEAACA5py4TGOXU+o9WecAOdKKGxQ7ODgkJCSU\nlJTcv39fU1Ozd+/eBgYGHZcMAAAARKnhE7eSBAJZ5wA50ronTxCRiYkJTqoDAAAAkEOSNnaP\nHz8ODw9PTU2tqqpqNMvY2Pj+/fvtHQwAAAAAWkfSxm7u3LkXL14cPXq0hYVFo2uw8bhYAAAA\nAHkgaWN39erVI0eOTJw4sUPTAAAAgEQqqujJCyKiR0XkYkeaGrIOBHJB0qtizczMnJycOjQK\nAAAAiPemkjYfpsAVFJtMRPRdDI1fST+cIX77X0VRW1u7devW4cOHGxsbd+vWLSAgIDk5ud2X\nInP6+vqXLl16mwL5IWljFxAQcOTIkQ6NAgAAAGK8KqN5Wyguhfi1f0+sqKZD52nZLuLxJRwm\nOzt7zJgxJiYmnTt3njx5cmFhYdOaN2/eeHt7b968eeTIkceOHdu5c6e2tranp+d///tf0YMP\nHTp027ZtjV4/evSIxWLt3btXwoTS9zYJG35l2ZL0UOyWLVuGDBmSkZExYsQIXV3dRnNDQkLa\nOxgAAAA0EX2Unr5sftadXPpPHM0JEDtGTU3NmDFjnJycfv/996dPn27evDk4ODg1NbVR2YYN\nG/Lz89PT07t06cJMCQoKWrRo0fz58wMCAho9Pl4sQ0PDFStWDBo0qFWfkib5TygJSffY/fbb\nb3fu3Dl+/HhoaOjUJjo0IgAAABARFT6n5AxRBacSJTkge/v27fz8/L179zo7OwcEBGzYsOGP\nP/4oLy9vWMPlcnfu3Llhw4b6ro6xdu3anTt3MsVlZWXz5s2zsrIyNDQMCAjIy8sjIhcXl6tX\nry5btszf37/hayMjo+joaIFAQEQaGhopKSmTJk3q2bNn7969T5w4wQz++PHjgIAAY2NjR0fH\nmJgYfX39zMzMRuGfP38+efJkMzMzS0vLJUuW8Hg8IsrJyRk1apSxsbGBgcGwYcPS09OZ4pYW\ndP/+fT8/PyMjo8GDB585c4aZ2DBhswUtLajh12xptRDR2bNnHR0dORyOjY3Njh07xG6mtpG0\nsduwYYOHh8eVK1eysrKym+igcAAAAPC3O7liCiqr6X4zB1UbcXZ2Li8vNzY2rqiouHPnzi+/\n/OLi4qKnp9ewJisri8fj+fj4NPpsp06dJk+ebGRkRESBgYHZ2dkHDx68cOGCrq6ul5dXaWnp\njRs3PD09o6Oj4+LiGr5uNM6KFSuioqJyc3NDQkKmTZtWXV0tEAhGjBhBRHFxcatWrQoNDa2o\nqGj0qbq6Ol9fXy6XGxMTs2XLlqNHj27YsIGIQkJCampqTpw4cfr0aaFQOGfOHBELqqio8Pb2\nJqKYmJg1a9YsXry4srKy4VJEFDS7oEZfs9nVUlhYOGHCBF9f34SEhHnz5oWFhaWkpIjdUm0g\n6X7UBw8eJCcn29nZdUQIAAAAEK+0XIIartgSNTU15qyqUaNGXb161djY+Nq1a41qnjx5QkSW\nlpbM27KyMqaZY3z77beDBg26du1acXGxsbExER0+fNja2jopKWns2LGSfBVmLxoRzZ49e/36\n9U+ePLl9+/bz58/T0tKYFpPL5X700UeNPhUXF5efn5+QkGBkZOTh4VFZWXn9+nWhUDhp0qTg\n4GBmwKdPn4aFhYlY0KVLl2pqak6ePKmvr09EOjo6zJ62ekeOHGm2QPSCGKmpqc2uFh0dHT6f\nv2DBAisrKxcXl969e5ubm0uyolpL0sbOxcWFyxX/dwUAAAA6ip6OBDUcycc7ffo0l8vdt2+f\nl5dXfn4+08cwrK2tiejRo0f9+vUjIj09vfo9TFOmTCGirKwsPp/fsDsRCAT1hx3Fqt9VxOH8\nL3BGRsagQYPqdxx6eno2/VRGRoa9vX19ixkaGhoaGkpE4eHhKSkpsbGxaWlpsbGxoheUlZXl\n6upa/2V9fHwa3aC3pQIWiyViQfWfbXa1zJkzx8XFZcCAAYGBgb6+vhMnTtTRkWBrtp6kh2Kj\noqIiIiIePXrUESEAAABAvAE9xRRoalCfbmKHuXv37vnz54nIxMTEyspqw4YNlZWVV65caVjT\nt29fDQ2Nc+fOMW/V1NTc3Nzc3NwcHR2fPn1KRIaGhiYmJvwGhEJheHi4hF9FU1Oz0RQ+n9+w\nwWKzm2lR+Hx+04s2Kisrvb29Z86c+ejRo/Hjx2/atEn0gho9WIHFYol+8kJ9gegFMVpaLXp6\neqmpqXFxcfr6+uvXr+/Ro0fTw9PtQtLG7osvvnj8+HGvXr1sbW0HN9ERyQAAAOAfencT09u9\n60ocbbHD3LlzZ9q0acxVAkRUVlZWXV2tofGPWxxzOJzFixdv3Ljx4cOHDadv2rSpurqaiOzt\n7UtKSjIy/ncxx8uXLwMDA+/du9eKr/NP9vb2d+7cqT+brdlT0Ozs7DIyMuqv8zh69Kifn9/l\ny5fT09MzMjK2bt0aEBBQV1cnekF2dnY3b96sH+Tq1auNPtJSgSQLamm1JCQkREdHe3p67t69\nOzc318PDY9++fRKumVaRtLETCAR9+vTx9/fv27dvtyY6IhkAAAA0tjyEDBrfdOx/rC1ozjhJ\nxhg1alRdXd3s2bPT0tKuXbs2efLkXr16eXl5EdGBAwf27NnDlK1evbp79+6Ojo6bNm26cOHC\nqVOnQkJCTpw4YW9vT0S2trZBQUEhISGXL19OSkqaNm1aVlaWra0tEbHZ7AcPHpSWljZ6LVpQ\nUJCBgcG0adOYo5yRkZHq6uqN9tsFBASYmZlNnTr15s2bv/76a0RERP/+/fX19SsqKk6ePJmf\nn//999+vW7eOy+XWXxjb1AcffKCpqTlp0qTk5OTY2Nj58+c3uo9bSwUiFlT/NVtaLTwe79NP\nP/3666/T09NjYmIyMzMdHBwk2VitJek5dg2v9QUAAADZ6NGZdi+j6J/odoMrZNksGu5EiyeS\ngUQn2JmamsbGxi5btmz48OEcDsfb2/u7775jTkE7cuRIaWnp/PnzicjQ0DA5OfmLL744derU\nxo0be/fuPWLEiH379h04cIC5B8qhQ4eWLVs2ffp0Lpc7bNiwc+fOMcdJP/zww+XLlxcXF588\nebLh65by6OjosNlsLS2tS5cuhYaGDh8+fMCAAYcOHXJzc2t0hYGGhkZ8fPyiRYv8/Py0tbUn\nTZoUFRWlra39+eefL126VCAQ+Pj4XLlyZdmyZatWrWraujAL4nA4CQkJCxcu9Pf379Gjx+bN\nm3/55RcDA4P6spYKXFxcWlpQw6/Z7Grx9fWNioravn37J598Ym5uPnny5IiICEk2VmuxhEJh\nR4zb0SorKxtdnCwTOafMJKzsO/5Fm5fC4XA4HE5ZWRmfL+ktxVWTdDaHtra2np5eeXk5czAC\nWiKdzaGhoWFoaFhVVdX0tgjQkHQ2B5vNNjEx4fF4b968afMgCsfU1LTdxxQKhezE3WKKHhbR\nD2co6Q5N9KHg4dTZWMyY3gvbLV/HKC4uvn79+vjx45m3GRkZbm5u5eXljU6AA9HE7LFjsVgW\nFhbPnj1zcXERUXbjxo12TQUAAAAiWVtQPytKukOD+ojt6hSCUCicMmVKZGRkSEhIWVnZwoUL\np02bhq6utcQ0dhYWFmZmZtQxv5EAAAAAMCwsLE6fPh0REfHZZ5+ZmJiMGjVq8+bNsg6leMQ0\nds+ePWNedNBFuQAAAAAMPz8/Pz8/WadQbKKuiu3Tp8/27dulFgUAAAAA3oaoxi4vL6+kpERq\nUQAAAADgbUh6uxMAAACQLxN9KMCTdLRknQPkCBo7AAAAxaSpQZoa4stAlYhp7JKSkiIjI8WO\nsmrVqnbKAwAAoOpYLJb833YO5JOYxi4hISEhIUHsKGjsAAAA2pHYB562VqNnc4GyEtPYzZgx\nY968edKJAgAAAEQkFArVzrfzxYtCf9yPViWIaey6devm5uYmnSgAAADQChVlxC0lY3PS0pF1\nFJAX2DELAACgmH77kWY6060rss4BcgSNHQAAAICSENXYzZgxY/DgwVKLAgAAAABvQ9Q5dj/+\n+KPUcgAAAADAW8KhWAAAAAAlgcYOAABA0QiFlJlKd5OJiP74nR5myToQyAs0dgAAAAolP4MW\n+dAn71FaPBHRucO0wItWT6ZXz9p3OePHj2c1IcnzqNpMX1//0qVLjSYmJiZaWVkNGzaMiKyt\nrc+dO9eqMXNyclgsVllZWXuFlHN4ViwAAIDieHCXlgdQVXnj6Wnx9Ml7tC2OjM1bNV5SUtKw\nYcOeP3/eqVOnpnN9fHw2bdrUcEq3bt1amfht7dy5c/Dgwfv27SOiioqK2tpaKQdQLGjsAAAA\nFIRQSF8taaarYxT9RT+spWW7JR+vrKxs2rRpIh5f1qlTJ5k/p6CiosLd3d3MzEy2MRQFDsUC\nAAAoiPu36MFdUQWJv1JFK445zp8/39y8dXv4GC9evAgJCbGwsOjSpcvUqVNfvHhBRBUVFSwW\nKzMzk6lpeAxUQ0MjJSVl0qRJPXv27N2794kTJ/73he7f9/PzMzIyGjx48JkzZ5ouyMfH59y5\nc2vXrvXw8BAbgIieP38+efJkMzMzS0vLJUuW8Hi8hp+6ceOGkZHRnj17iOjs2bOOjo4cDsfG\nxmbHjh1tWAnyCY0dAACAgsi9JaZAwKcHGRIOdvjw4Zs3b27dulVETUlJSVoD2dnZRCQUCseM\nGfPgwYNjx44dPXo0Ly9v9OjRQqFQ9OJWrFgRFRWVm5sbEhIybdq06urqiooKb29vIoqJiVmz\nZs3ixYsrKysbfery5cujRo1au3ZtcnJy/cSWAtTV1fn6+nK53JiYmC1bthw9enTDhg31n7pz\n5w4z1Pz58wsLCydMmODr65uQkDBv3rywsLCUlBQJ15ucw6FYAAAABVHJlaDmjSQjFRQUhIWF\nxcXFsdmidvHEx8c7OzvXv3V3d09OTk5ISLh161Z+fn737t2J6Pjx4z179kxKSnJychIxFLO7\njohmz569fv36J0+eXLp0qaam5uTJk/r6+kSko6Pj7+8vSfiWAnC53Pz8/ISEBCMjIw8Pj8rK\nyuvXrzMfuXfv3rhx4+bPnx8WFkZEOTk5fD5/wYIFVlZWLi4uvXv3btueSzmEPXYAAAAKwlCC\n88wkuHiitrZ22rRp4eHhLi4uoiuDg4OFDTC7zbKysqytrZmmioh69OhhZWWVlSXmlit2dnbM\nCw6Hw7zIyspydXVlujoi8vHxYbFYYsOLCJCRkWFvb29kZMRMDw0N/c9//sO8Hjt2rFAofPDg\nAfPW3d3dxcVlwIAB06ZNO3jw4OjRo5mmUwmgsQMAAFAQDkNJdOujZ0S9BoodZseOHS9fvgwM\nDMzJyXn48CER5ebmFhUVSZiirq6uUQfGZrMFAkGjsqqqqoZvNTU1GxWoqak1fMvcTuVtAvD5\nfHX15g9FRkREHD9+/NixY/Hx8USkp6eXmpoaFxenr6+/fv36Hj1eVZHGAAAgAElEQVR6xMXF\nSbJo+YfGDgAAQEFY9KCh40QVjJ9P6hpih8nNzc3JyRkwYEC/fv2Cg4OJyMPDY+XKlRKm6Nev\nX0FBwZMnT5i3jx8/fvjwYf/+/Zm3r1+/Zl6kpaWJHsfOzu7mzZvl5f+7yPfq1asirs+VJICd\nnV1GRkb9gEePHvXz82Nez5o1y8fH5/333//3v//N5/MTEhKio6M9PT13796dm5vr4eHB3E5F\nCaCxAwAAUBwLt5C1XfOzXP1o4iJJxtizZ0/90dWbN28S0cuXL5kHxB84cIC5aFSE4cOHDxw4\ncPLkydevX7927drkyZMdHByGDRumq6trZmYWFRWVmZl56dKlb775RvQ4H3zwgaam5qRJk5KT\nk2NjY+fPn6+rqytJ/pYCBAQEmJmZTZ069ebNm7/++mtERER9u8mIjo4uLCzcsWMHj8f79NNP\nv/766/T09JiYmMzMTAcHB0kWLf/Q2AEAACgOfWPaFkdBC4ij//dEk840Zz2tOSjJ7jrRjhw5\nsn//ftE1LBYrLi6ue/fuQUFBEyZMsLa2jouLY46NHjp0KC8vz8PDIzIy8siRI+7u7o2OtzJ0\ndHTYbDaHw0lISBAIBP7+/itWrNi8efO4ceMMDAzEhmwpgIaGBnOk1c/Pb8GCBePGjYuKimr4\nwa5du37++efr1q3r379/VFTU9u3bXV1dFy1aFBQUFBER0YrVJMdYYq9Plk88Hk/CI/Ed6o8D\njc8YaInrDJ74ohaoqakxZw8o6MaSGulsDjabraamVltbK+EhA5Ulnc3BYrHU1dXr6upwM3rR\nsDk6CJ/Pr78UoB0JhUL2uVdiigR82reazvxA8zfRezOJJWZPjdDftN3ygRxT1Nud1NbW1tTU\nyDpFK1RUVLT5s9ra2tra2tXV1U1PTYW2eZvNoampyeFwampqGt33EtrsbTaHurq6np4en89v\ndJo2tNnbbA42m21gYFBbW/s2gygWoVDYEY2dRNQ1qJMFEZFZV7FdHagORW3shEKhYnU5b5OW\n2TNUW1urWF9Znr3NmmQuuaqrq8PmaC9vsyaZPffYHO3obdYkc0c0hfv5DKBM0OMDAAAAKAk0\ndgAAAABKAo0dAACAYtIzIgsr0pLRSX4glxT1HDsAAABVN3oGjZ4h6xAgX9DYAQAAyJ2enGZ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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": 30,
   "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>683.3324   </td><td>8.849225   </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   & 683.3324    & 8.849225   \\\\\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   | 683.3324    | 8.849225    | \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   683.3324 8.849225\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": 31,
   "outputs": [
    {
     "data": {
      "image/png": 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YULF/r06RMaGhocHNy/f/+EhISHDx9m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\neYc4SJ/SAAAAAABJRU5ErkJggg==",
      "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": 32,
   "outputs": [
    {
     "data": {
      "image/png": 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CIjoJvwrpnLdOtS4suBolJpvRdUghw4cED7u6qXLVumZS6AIoJLsQCKDDNj8dUg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\nYq2eUAMI5wAAAABJRU5ErkJggg==",
      "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": 33,
   "outputs": [
    {
     "data": {
      "image/png": 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I1G4+zsnJycnJiYaKw+S4nU1FQhxLNnz4z4AVGr1W5ubmlpaS9evDBWn8WftbW1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\nCjsAAAAAlkBhBwAAA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      "text/plain": "plot without title"
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