path: root/Tests/MODELS2020-CaseStudies/case.study.pledge.run/MODELS2020Plots-temp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis for the paper \"Automated Generation of Consistent Models with Structural and Attribute Constraints\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's load some packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: tidyverse\n",
      "\n",
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.0 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.0     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n",
      "\n",
      "Warning message:\n",
      "“package ‘ggplot2’ was built under R version 4.0.1”\n",
      "Warning message:\n",
      "“package ‘tibble’ was built under R version 4.0.2”\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n",
      "Warning message in dir.create(\"plots\"):\n",
      "“'plots' already exists”\n"
     ]
    }
   ],
   "source": [
    "require(tidyverse)\n",
    "dir.create('plots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ProcessDetailedStatistics <- function(str) {\n",
    "    str <- sub('TransformationExecutionTime', 'TransformationExecutionTime:', str)\n",
    "    str <- sub('Backtrackingtime', 'BacktrackingTime', str)\n",
    "    str <- gsub('\\\\(|\\\\)', '', str)\n",
    "    str <- lapply(strsplit(str, '\\\\||:'), function (v) {\n",
    "        dim(v) <- c(2, 12)\n",
    "        values <- as.double(v[2,])\n",
    "        names(values) <- v[1,]\n",
    "        as.data.frame(t(values))\n",
    "    })\n",
    "    str\n",
    "}\n",
    "Load10Log <- function(filename, size) {\n",
    "    read_csv(filename, col_types = cols(\n",
    "      .default = col_double(),\n",
    "      Result = col_character(),\n",
    "      Solution1DetailedStatistics = col_character(),\n",
    "      Solution2DetailedStatistics = col_character(),\n",
    "      Solution3DetailedStatistics = col_character(),\n",
    "      Solution4DetailedStatistics = col_character(),\n",
    "      Solution5DetailedStatistics = col_character(),\n",
    "      Solution6DetailedStatistics = col_character(),\n",
    "      Solution7DetailedStatistics = col_character(),\n",
    "      Solution8DetailedStatistics = col_character(),\n",
    "      Solution9DetailedStatistics = col_character(),\n",
    "      Solution10DetailedStatistics = col_character()\n",
    "    )) %>% transmute(\n",
    "        n = size,\n",
    "        Run = Run,\n",
    "        preprocessingTime = get('Domain to logic transformation time') + get('Logic to solver transformation time') + ExplorationInitializationTime,\n",
    "        Solution0FoundAt = Solution0FoundAt,\n",
    "        additionalTime = Solution9FoundAt - Solution0FoundAt,\n",
    "        Solution1DetailedStatistics = ProcessDetailedStatistics(Solution1DetailedStatistics)\n",
    "    ) %>% unnest() %>% mutate(\n",
    "        # (Logical) constraint evluation should count as refinement.\n",
    "        ForwardTime = ForwardTime + GlobalConstraintEvaluationTime + FitnessCalculationTime,\n",
    "        preprocessingTime = preprocessingTime,\n",
    "        BacktrackingTime = Solution0FoundAt - (StateCoderTime + ForwardTime + NumericalSolverSumTime)\n",
    "    ) %>% select(n, Run, preprocessingTime, StateCoderTime, ForwardTime, BacktrackingTime, NumericalSolverSumTime, additionalTime)\n",
    "}\n",
    "Load1Log <- function(filename, size) {\n",
    "    read_csv(filename, col_types = cols(\n",
    "      .default = col_double(),\n",
    "      Result = col_character(),\n",
    "      Solution1DetailedStatistics = col_character()\n",
    "    )) %>% filter(Result == \"ModelResultImpl\") %>% transmute(\n",
    "        n = size,\n",
    "        Run = Run,\n",
    "        preprocessingTime = get('Domain to logic transformation time') + get('Logic to solver transformation time') + ExplorationInitializationTime,\n",
    "        Solution0FoundAt = Solution0FoundAt,\n",
    "        Solution1DetailedStatistics = ProcessDetailedStatistics(Solution1DetailedStatistics)\n",
    "    ) %>% unnest(cols = c(Solution1DetailedStatistics)) %>% mutate(\n",
    "        ForwardTime = ForwardTime + GlobalConstraintEvaluationTime + FitnessCalculationTime,\n",
    "        BacktrackingTime = Solution0FoundAt - (StateCoderTime + ForwardTime + NumericalSolverSumTime)\n",
    "    ) %>% select(n, Run, preprocessingTime, StateCoderTime, ForwardTime, BacktrackingTime, NumericalSolverSumTime)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ProcessRQ1 <- function(df) {\n",
    "    df %>% group_by(n) %>% summarize(\n",
    "        .groups = 'drop',\n",
    "        time = median(preprocessingTime + StateCoderTime + ForwardTime + BacktrackingTime + NumericalSolverSumTime) / 1000.0\n",
    "    )\n",
    "}\n",
    "ProcessRQ2 <- function(df) {\n",
    "    df %>% group_by(n) %>% summarize(\n",
    "        .groups = 'drop',\n",
    "        preprocessingTime = median(preprocessingTime) / 1000.0,\n",
    "        StateCoderTime = median(StateCoderTime) / 1000.0,\n",
    "        ForwardTime = median(ForwardTime) / 1000.0,\n",
    "        BacktrackingTime = median(BacktrackingTime) / 1000.0,\n",
    "        NumericalSolverSumTime = median(NumericalSolverSumTime) / 1000.0,\n",
    "        additionalTime = median(additionalTime) / 1000.0\n",
    "    )\n",
    "}\n",
    "ProcessRQ3 <- ProcessRQ1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "RQ2Plot <- function(df, name) {\n",
    "    df <- df %>% gather(name, value, -n) %>% filter(name != \"preprocessingTime\")\n",
    "    df$name <- factor(df$name, levels=rev(c('ForwardTime', 'BacktrackingTime', 'StateCoderTime', 'NumericalSolverSumTime', 'additionalTime')))\n",
    "    plot <- df %>% ggplot(aes(x=n, y=value, fill=name)) +\n",
    "        geom_bar(stat='identity') +\n",
    "        scale_fill_brewer(palette='Set2',\n",
    "                          labels=rev(c('Refinement', 'Backtracking', 'State Coding', 'SMT Solver Calls', 'Additional Model Generation')),\n",
    "                          guide=FALSE) +\n",
    "        scale_x_continuous(breaks=c(20, 30, 40, 50, 100), name=\"Model Size (# nodes)\") +\n",
    "        scale_y_continuous(name=\"Runtime (s)\") +\n",
    "        theme_bw()\n",
    "    ggsave(plot=plot, filename=paste0('plots/plot_RQ2_', name, '.pdf'), width=3.5, height=2.5)\n",
    "    plot\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fam domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“`cols` is now required when using unnest().\n",
      "Please use `cols = c(Solution1DetailedStatistics)`”\n",
      "Warning message:\n",
      "“`cols` is now required when using unnest().\n",
      "Please use `cols = c(Solution1DetailedStatistics)`”\n",
      "Warning message:\n",
      "“`cols` is now required when using unnest().\n",
      "Please use `cols = c(Solution1DetailedStatistics)`”\n",
      "Warning message:\n",
      "“`cols` is now required when using unnest().\n",
      "Please use `cols = c(Solution1DetailedStatistics)`”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 4 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>n</th><th scope=col>Run</th><th scope=col>preprocessingTime</th><th scope=col>StateCoderTime</th><th scope=col>ForwardTime</th><th scope=col>BacktrackingTime</th><th scope=col>NumericalSolverSumTime</th><th scope=col>additionalTime</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>20</td><td>1</td><td>33339</td><td> 878</td><td> 2654</td><td>1610</td><td>  7655</td><td> 39044</td></tr>\n",
       "\t<tr><td>30</td><td>1</td><td>36670</td><td>2016</td><td> 5187</td><td>2687</td><td> 33695</td><td>103508</td></tr>\n",
       "\t<tr><td>40</td><td>1</td><td>48868</td><td>3907</td><td> 9219</td><td>4814</td><td> 99075</td><td>309361</td></tr>\n",
       "\t<tr><td>50</td><td>1</td><td>62541</td><td>5581</td><td>12720</td><td>5863</td><td>268490</td><td>552214</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " n & Run & preprocessingTime & StateCoderTime & ForwardTime & BacktrackingTime & NumericalSolverSumTime & additionalTime\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 20 & 1 & 33339 &  878 &  2654 & 1610 &   7655 &  39044\\\\\n",
       "\t 30 & 1 & 36670 & 2016 &  5187 & 2687 &  33695 & 103508\\\\\n",
       "\t 40 & 1 & 48868 & 3907 &  9219 & 4814 &  99075 & 309361\\\\\n",
       "\t 50 & 1 & 62541 & 5581 & 12720 & 5863 & 268490 & 552214\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 8\n",
       "\n",
       "| n &lt;dbl&gt; | Run &lt;dbl&gt; | preprocessingTime &lt;dbl&gt; | StateCoderTime &lt;dbl&gt; | ForwardTime &lt;dbl&gt; | BacktrackingTime &lt;dbl&gt; | NumericalSolverSumTime &lt;dbl&gt; | additionalTime &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 20 | 1 | 33339 |  878 |  2654 | 1610 |   7655 |  39044 |\n",
       "| 30 | 1 | 36670 | 2016 |  5187 | 2687 |  33695 | 103508 |\n",
       "| 40 | 1 | 48868 | 3907 |  9219 | 4814 |  99075 | 309361 |\n",
       "| 50 | 1 | 62541 | 5581 | 12720 | 5863 | 268490 | 552214 |\n",
       "\n"
      ],
      "text/plain": [
       "  n  Run preprocessingTime StateCoderTime ForwardTime BacktrackingTime\n",
       "1 20 1   33339              878            2654       1610            \n",
       "2 30 1   36670             2016            5187       2687            \n",
       "3 40 1   48868             3907            9219       4814            \n",
       "4 50 1   62541             5581           12720       5863            \n",
       "  NumericalSolverSumTime additionalTime\n",
       "1   7655                  39044        \n",
       "2  33695                 103508        \n",
       "3  99075                 309361        \n",
       "4 268490                 552214        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 4 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>n</th><th scope=col>preprocessingTime</th><th scope=col>StateCoderTime</th><th scope=col>ForwardTime</th><th scope=col>BacktrackingTime</th><th scope=col>NumericalSolverSumTime</th><th scope=col>additionalTime</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>20</td><td>33.339</td><td>0.878</td><td> 2.654</td><td>1.610</td><td>  7.655</td><td> 39.044</td></tr>\n",
       "\t<tr><td>30</td><td>36.670</td><td>2.016</td><td> 5.187</td><td>2.687</td><td> 33.695</td><td>103.508</td></tr>\n",
       "\t<tr><td>40</td><td>48.868</td><td>3.907</td><td> 9.219</td><td>4.814</td><td> 99.075</td><td>309.361</td></tr>\n",
       "\t<tr><td>50</td><td>62.541</td><td>5.581</td><td>12.720</td><td>5.863</td><td>268.490</td><td>552.214</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 4 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " n & preprocessingTime & StateCoderTime & ForwardTime & BacktrackingTime & NumericalSolverSumTime & additionalTime\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 20 & 33.339 & 0.878 &  2.654 & 1.610 &   7.655 &  39.044\\\\\n",
       "\t 30 & 36.670 & 2.016 &  5.187 & 2.687 &  33.695 & 103.508\\\\\n",
       "\t 40 & 48.868 & 3.907 &  9.219 & 4.814 &  99.075 & 309.361\\\\\n",
       "\t 50 & 62.541 & 5.581 & 12.720 & 5.863 & 268.490 & 552.214\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 4 × 7\n",
       "\n",
       "| n &lt;dbl&gt; | preprocessingTime &lt;dbl&gt; | StateCoderTime &lt;dbl&gt; | ForwardTime &lt;dbl&gt; | BacktrackingTime &lt;dbl&gt; | NumericalSolverSumTime &lt;dbl&gt; | additionalTime &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 20 | 33.339 | 0.878 |  2.654 | 1.610 |   7.655 |  39.044 |\n",
       "| 30 | 36.670 | 2.016 |  5.187 | 2.687 |  33.695 | 103.508 |\n",
       "| 40 | 48.868 | 3.907 |  9.219 | 4.814 |  99.075 | 309.361 |\n",
       "| 50 | 62.541 | 5.581 | 12.720 | 5.863 | 268.490 | 552.214 |\n",
       "\n"
      ],
      "text/plain": [
       "  n  preprocessingTime StateCoderTime ForwardTime BacktrackingTime\n",
       "1 20 33.339            0.878           2.654      1.610           \n",
       "2 30 36.670            2.016           5.187      2.687           \n",
       "3 40 48.868            3.907           9.219      4.814           \n",
       "4 50 62.541            5.581          12.720      5.863           \n",
       "  NumericalSolverSumTime additionalTime\n",
       "1   7.655                 39.044       \n",
       "2  33.695                103.508       \n",
       "3  99.075                309.361       \n",
       "4 268.490                552.214       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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vjjd9jy9ttvr1u3btKkSSeddFIRpgIAYLf1Kuz+/d///b0bN2/e/KUvfenTn/50\nf48EAMCe2PPP2tl3331vuummBQsW9OM0AADssT59iOKECRP+8Ic/9NcoAAD0xZ6HXS6XW758\n+T777NOP0wAAsMd69Rq79/5OsEwms3nz5oaGhiuuuKIIUwEAsNv28HPsysvLP/jBD55xxhmF\nXywBAEBp9SrsnnvuuWLPAQBAH/XpzRNRFL3xxhv9MQYAAH21i7B74oknPvWpT02ePPlTn/rU\n/fff3/Wqjo6Ov//7v/e7XAEA9hI9hd3TTz89a9as1atXd3Z2Pvroo6effvrPf/7z/FUPPvjg\nEUcccc011+y///7vy5wAAOxCT2F3/fXXV1VVrV279s0339y4ceNRRx21YMGCjRs3nnPOOZ/6\n1KfeeeedxYsXr1u37n2bFQCAHvT05onnn3/+L//yL6dPnx5F0ZgxY6699tpPf/rTkydPTqVS\nl1566aJFi0aNGvV+zQkAwC70FHYbN2485JBDChenTZsWRdFHPvKRW2+99fDDDy/6aAAA7I6e\nnopNp9NDhgwpXKyoqIii6KqrrlJ1AAB7ob5+3AkAAHsJYQcAEIhd/OaJ11577emnn85/3djY\nGEXRyy+/XFdX1/U2M2fOLNJwAAD03i7C7rrrrrvuuuu6brnssst2uE0ul+vnoQAA2H09hd2C\nBQvetzkAAOijnsJu4cKF79cYAAD0lTdPAAAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELY\nAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC\n2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAE\nQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEA\nBELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgB\nAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAASirNQD7LlMJtPU1DR8+PB+2Vs2m926\ndWu/7GpAy+VyURSlUimrkZfJZCxFXi6Xy+VyViPPI8Yg1913P5vNJpPJ93kY9h49HBixWKy1\ntbXvd5FKpbLZbA83GMBhl0gkamtrR4wY0fddpdPptra26urqvu9qoMtms42NjZpwVW8AAB7E\nSURBVOXl5TU1NaWepfRyudy2bdv65RgLQGNjYywWsxp5jY2NlmIw6+67n0wmhw0blkgk3ud5\n2Et0d2C0tLSUlZVVVFT0/S5SqVQ83tPTrZ6KBQAIhLADAAiEsAMACISwAwAIhLADAAjEAH5X\nLAAMID9c83CpR6DITjiv1BM4YwcAEAphBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh\nBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAI\nYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcA\nEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEH\nABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh\nBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQiLKi7r2x\nsXH58uXPP/98Z2fnQQcddMEFFxxyyCFRFDU3Ny9btuyFF15IpVJTpkypr68fM2ZMD9sBANil\n4p6x++53v9vQ0PCd73xnyZIlo0aNWrRoUXt7exRFS5Ys2bJly4IFC2688caqqqpFixZls9ke\ntgMAsEtFDLvt27ePHj3661//+kEHHTRu3LjZs2cnk8kNGzY0NDSsWbPmkksumThx4vjx4+vr\n6zdt2rRu3bruthdvQgCAkBTxqdjq6uqrr766cPHdd9+Nx+OjRo16+eWXy8vLJ06cmN8+fPjw\nCRMmvPLKK62trTvdPn369OINCQAQjOK+xq5g+/btP/jBD84888wRI0Ykk8nq6upYLFa4tra2\ntqmpqba2dqfbCxf/93//9+677y5cbGtra21tbW5u7vt42Ww2k8n0y64GulwuF0VROp22GnnZ\nbNZS5OVyuVwuZzXyLMUg1913P51Ot7a2dv1B1lVlMUdib9DDgZFOp1OpVN/vIpVK5X9Sd+f9\nCLuNGzdee+21Rx555Jw5c/Jbujvou9uet2nTpl/84heFi5MmTero6Mi/aK9fZDKZ/trVQJfN\nZvtxYQc6S9GV1SiwFINZD9/9jo6O7q4SdsHr+WEhkLB7/vnn/+Ef/uHLX/7yZz7zmfyWurq6\nZDKZy+UKGdfU1DRixIjuthd2dfTRR//kJz8pXLzppptqamrq6ur6PmQmk+no6Kiqqur7rga6\nbDabTCbLy8uHDRtW6llKL5fLbd++vaamptSD7BWSyWQURVYjL5lMWorBrLsfPS0tLZWVlfG4\njxIbpLo7MNra2hKJxJAhQ/p+F6lUqucDrLhh94c//OGGG274m7/5m6OOOqqwcfLkyalUav36\n9QcffHAURfl3VEybNm3cuHE73V74g9XV1V0vJhKJRCJRVtY//wuxWKy/djWg5d+GbDXy8v/M\nsBQFVqMrSzGYdffdj8Vi+Z9NO73Ws0LB6+7AiMfj/VUsPZ+ui4r6rtjOzs4lS5Z87nOfO+CA\nAxr+rL29feTIkccee+xtt932+uuvb9q0afHixZMmTTr00EO72168CQEAQlLEf3G+9NJLmzdv\nXrly5cqVKwsb586de/rpp8+bN2/ZsmULFy7MZDKHHXbYNddck3/6tbvtAADsUhHDbvr06f/x\nH/+x06uqqqrmz5/f++0AAOySF3gCAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC\n2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAE\nQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEA\nBELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEQtgBAARC2AEABELYAQAEoqzU\nA8BANfdXK0s9AkV3+wnnlXoEgN3gjB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0A\nQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQd\nAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAgyko9AEBofrjm4VKPQJGdcF6pJ4Cd\nc8YOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4A\nIBDCDgAgEGWlHmDP5XK5zs7Ojo6Ovu8qm81ms9l+2dVAl8vloiiyGnm5XC6Xy1mKway7737P\nB8YAfmCld7r77mez2c7Oznh85ydNHBjB6+7AyGQy+R+vfZdKpXre1QA+zHK5XDqdTqVS/bKr\nbDbbL7sa6AphZzXycrmcpRjMevju93DVAH5gpXe6++7nfzDFYrGdXuvACF53B0Y2m+3HsOv5\nBgP4MIvH41VVVcOHD+/7rtLpdFtbW7/saqDLn6srKyuzGtGfq85SDGbdffc7Ozt7ODAyRZuH\nvUR33/1kMllVVZVIJHZ6rQMjeN0dGC0tLWVlZRUVFX2/i1Qq1d2/HPK8xg4AIBDCDgAgEMIO\nACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDC\nDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQ\nwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAg\nEMIOACAQwg4AIBDCDgAgEMIOACAQZaUeYACY+6uVpR6B4rr9hPNKPQIA9ANn7AAAAiHsAAAC\nIewAAAIh7AAAAiHsAAAC4V2xsId+uObhUo9A8XnHNDCgOGMHABAIYQcAEAhhBwAQCGEHABAI\nYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABCIslIPAACDws37XFrqESiuy0s9QOSMHQBAMIQdAEAghB0AQCC8xg4A3g/nbWkv9QiE\nT9gB9DOvkQ/e3vAaedgpT8UCAARC2AEABMJTsQD9zEupgFIRdrv2wzUPl3oEiuyE80o9ARC+\nRz93c6lHoLi+HB1f6hE8FQsAEAphBwAQCE/Fwh7ykRaDwZ59qoVn3IK3NzzjBjsl7GAPeYE8\nAHsbT8UCAARi7zpj19zcvGzZshdeeCGVSk2ZMqW+vn7MmDGlHgoAYGDYu8JuyZIlzc3NCxYs\nqKioWLly5aJFi2655ZZ4vMSnFd/JXFDaASi2fffoT3kd1WDgpVTAwLIXPRXb0NCwZs2aSy65\nZOLEiePHj6+vr9+0adO6detKPRcAwMCwF52xe/XVV8vLyydOnJi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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FamilyTreeRQ2Raw <- rbind(\n",
    "#     Load10Log(\"measurements/stats/FamilyTree//size010to-1r10n10rt300nsdrealstats_06-0249.csv\", 10),\n",
    "    Load10Log(\"measurements1/stats1010.csv\", 20),\n",
    "    Load10Log(\"measurements1/stats1515.csv\", 30),\n",
    "    Load10Log(\"measurements1/stats2020.csv\", 40),\n",
    "    Load10Log(\"measurements1/stats2525.csv\", 50)\n",
    ")\n",
    "FamilyTreeRQ2Raw\n",
    "FamilyTreeRQ2 <- FamilyTreeRQ2Raw %>% ProcessRQ2\n",
    "FamilyTreeRQ2\n",
    "median(FamilyTreeRQ2Raw$preprocessingTime) / 1000.0\n",
    "FamilyTreeRQ2 %>% RQ2Plot('FamilyTree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}