1 files changed, 67 insertions, 25 deletions

diff --git a/Tests/MODELS2020-CaseStudies/case.study.pledge.run/MODELS2020Plots-temp.ipynb b/Tests/MODELS2020-CaseStudies/case.study.pledge.run/MODELS2020Plots-temp.ipynb
index b91d8e53..cc342a87 100644
--- a/Tests/MODELS2020-CaseStudies/case.study.pledge.run/MODELS2020Plots-temp.ipynb
+++ b/Tests/MODELS2020-CaseStudies/case.study.pledge.run/MODELS2020Plots-temp.ipynb
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +158,7 @@
     "        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, 40, 60, 80, 100), name=\"Model Size (# nodes)\") +\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",
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -184,6 +184,15 @@
      "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"
      ]
     },
@@ -191,39 +200,54 @@
      "data": {
       "text/html": [
        "<table>\n",
-       "<caption>A tibble: 1 × 8</caption>\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>14735</td><td>994</td><td>1890</td><td>1272</td><td>4391</td><td>14750</td></tr>\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: 1 × 8\n",
+       "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 & 14735 & 994 & 1890 & 1272 & 4391 & 14750\\\\\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: 1 × 8\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 | 14735 | 994 | 1890 | 1272 | 4391 | 14750 |\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   14735             994            1890        1272            \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 4391                   14750         "
+       "1   7655                  39044        \n",
+       "2  33695                 103508        \n",
+       "3  99075                 309361        \n",
+       "4 268490                 552214        "
       ]
      },
      "metadata": {},
@@ -233,39 +257,54 @@
      "data": {
       "text/html": [
        "<table>\n",
-       "<caption>A tibble: 1 × 7</caption>\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>14.735</td><td>0.994</td><td>1.89</td><td>1.272</td><td>4.391</td><td>14.75</td></tr>\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: 1 × 7\n",
+       "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 & 14.735 & 0.994 & 1.89 & 1.272 & 4.391 & 14.75\\\\\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: 1 × 7\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 | 14.735 | 0.994 | 1.89 | 1.272 | 4.391 | 14.75 |\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 14.735            0.994          1.89        1.272           \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 4.391                  14.75         "
+       "1   7.655                 39.044       \n",
+       "2  33.695                103.508       \n",
+       "3  99.075                309.361       \n",
+       "4 268.490                552.214       "
       ]
      },
      "metadata": {},
@@ -274,16 +313,16 @@
     {
      "data": {
       "text/html": [
-       "14.735"
+       "42.769"
       ],
       "text/latex": [
-       "14.735"
+       "42.769"
       ],
       "text/markdown": [
-       "14.735"
+       "42.769"
       ],
       "text/plain": [
-       "[1] 14.735"
+       "[1] 42.769"
       ]
      },
      "metadata": {},
@@ -291,7 +330,7 @@
     },
     {
      "data": {
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BbSOesTElQL6SUs/OSU9sMzF0+fdt4i\nIdFHkiF9/9jZnZDOP2PBwxeeslZI9I9kSN99fG47pIHJ81vfSofMExL9IxlSWXZCuv2wda3l\nqdcKif4xAiHNOa49POvS1uKWiS1T7x7oqu7DAL3pOsUfOXiDQ5oxHNKdx7Qcdc+TXdV9GKA3\nXaf44xsc0h2Dp3bXDb3o1I7m6zrFN/zUbvHkB8ry6Sn3CIn+kQxpycBNUwYGVpQXfHzBwnNP\nXyck+kcypOMntX2jXDb72KNnLRl+WUg0XzKklyAkmk9IECAkCBASBAgJAoQEAUKCACFBgJAg\nQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBAS\nBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoKAV0lI/w82akKCACFBgJAgQEgQICQI\nEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQE\nAUKCACFBgJAgQEgQICQIEBIEjEJIJ9z/bFd1HwboTdcp/uSknkN6YEVXdR8G6E3XKb6055Cc\n2tF8Xae4ayToTkgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQ\nIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQ\nEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQB\nQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAkYipI9NajlCSPSR\nkQhpxvUDAwOLhUQfGYmQDr/zRU+FRPONQEirJv3NaTNnLRQSfWQEQnrqw5+5//5zP/xsa/hP\n72858p+f6KruwwC96TrFHz14Q0PqWH7ETa3l3MktR/10SVd1HwboTdcpvqhaSOVH/35o5NSO\n5us6xTf81O6Xn1tdliuOuEVI9I8RCGnptNmPLpw1Y6WQ6B8jEFI5/+wjjzn/seGnQqL5RiKk\nf0NINJ+QIEBIECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBA\nSBAgJAgQEgS8SkL6a9ioCQkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBI\nECAkCBASBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCDgVRLS/4WNmpAg\nQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIEDAKIZ3w\n8+Vd1X0YoDddp/jTk3oO6YEVXdV9GKA3Xaf40p5DcmpH83Wd4q6RoDshQYCQIEBIECAkCBAS\nBAgJAoQEAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoKAV0lI/ws2akKCACFBgJAgQEgQICQI\nEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQIEBIECAkCBASBAgJAoQE\nAUKCACFBgJAgQEgQICQIEBIECAkChAQBQoIAIUGAkCBASBAgJAgQEgQICQKEBAFCggAhQYCQ\nIEBIECAkCBASBAgJAoQEAUKCACFBgJAgYCRCeubi6dPOWyQk+shIhHT+GQsevvCUtUKif4xA\nSAOT57e+lQ6ZJyT6xwiEdPth61rLU68VEv1jBEKac1x7edalrcXcyS1H/XRJV3UfBuhN1ym+\n6OANDmnGhofEiFo8sLjuXeh7Gx7SHYOndtcNPX8lp3aMqJUDy+vehb634ad2iyc/UJZPT7ln\n6LmQaiek+lW4/X3BxxcsPPf0dUNPhVQ7IdWvQkjLZh979Kwlw0+FVDsh1W90/hMhRpSQ6iek\nBhBS/YTUAEKqn5AaQEj1E1IDCKl+QmoAIdVPSA0gpPoJqQGEVD8hNYCQ6iekBhBS/YTUAEKq\nn5AaQEj1E1IDCKl+QmoAIdVPSA0gpPoJqQGEVD8hNYCQ6iekBhBS/YTUAEKqn5AaQEj1E1ID\nCKl+QmoAIdVPSA0gpPoJqQGEVD8hNYCQ6iekBhBS/YTUAEKqn5AaQEj1E1IDCKl+gZA++0Xq\nddnffr7uXeh7l/cc0m1fpWafm3lh3bvAt3sNidrdPPGquneBFwhpYyWkVxUhbayE9KoipI2V\nkF5VhAQBQoIAIUGAkCBASBuhxRceM/XM+8vymYunTztvUd17Q5uQNkKfOGP+IxcdvaI8/4wF\nD194ytq6d4dSSBujpbP+tSwfn/TzgcnzW99Kh8yre38ohbTRum/KktsPW9canHpt3btCKaSN\n1dKPfqGcc1x7dNalde8LpZA2Ug+ddMm6cs6M9lBIrwpC2hjNm3Z9a3nH4KnddXXvDaWQNkr3\nHnVX+2Hx5AfK8ukp99S9O5RC2hg9d+I1Ay0rygs+vmDhuaevq3t/KIW0MZo3qeOGctnsY4+e\ntaTu3aFNSBAgJAgQEgQICQKEBAFCggAhQYCQIEBIo+OcYrtVg6Pji31/9Y+P3GL9Z/vuMjQa\n+Mvf3maz7f5gTmu49y6/8q71PLHzzNbyq0eU5eNv7LozL97akLN+/Rdd38lLEdLoOGfMpl/v\nDJa/buwrD2nxb2x+6peu+YvfHHNNWc6e9TIfv/agCe3fTXFWa505f9R1Z/79kNa8f+LKrm/l\nJQhpdJyz2T6Dv7ng6rF7vvKQPlN8uf2w5E07d/l58quKW9sPH2h9dc06u+vO/PshlT8bc1HX\nt/IShDQ6zilmbfZYe3DQQfu2Q/rmflu+ZteL15XluvN2Gr/bdZ2pfeuBW712j8vL9UL6k+KB\nzuNDyzundncWg3663roda97xvs7j9o+X5eFfe/7F/d774/232u6PF3Xb2iMnvGX8Dofe1xpN\n3f7ZUTgUzSSk0XFO8UDn3/cLx1zxnlZIX9/kA//4ndOLPynLvyqOvvna3XZpTe3vbPq+62/6\nSHHReiFdU3zoyaFPaIW09OaWG7bb6an11u24rbiiLC8cP74YP378JuPHL+y8eMCbf+fmRV/Z\ndHq3rb3nDZfdcvW7t19WljcWfmy9KiGNjnOKFQfu2nr8H69duncrpHe+5bnWk0PGPrFux91a\ng0fGtqb2Hm9rzeVy8lYrXghp7dRi/B/91R2d87qhmw0zxv9w/XU7/rzotPOVQ8vysR2HNnlA\n8YP2cscuW3u6OLM1eHDWw2W5bNzxI34gmkpIo6MV0peKH5Xlu44qWyE9XHyk/eLlxQ3/UvyX\n9uh3tygXFaetaPm71lov3LUr50zbsSi2PXPZcEiXFJ8v11+344M7dB4+8Zmy/OrhQ289YPP2\ncvqYLltbtc3O3xm6Apuw+0geg0YT0uhohbRsq5PLHxXfaof0o+L89ovfLC794eDosC3Knzx/\nAVR8bf2QWuZ/fr/id9c+H9Lt405qLddbt2OvXTsPv/PDwZgGHbBze3l80W1rP/gPxTaHXb26\n/Qf77zSSx6DRhDQ6WiGVM39txSlvXNMO6c7ivPaLNxaX3TE4tQ9pT+2ZczsG/k1IZbluZnHb\nYEiP7rh3+yxtvXU73rlPa/GmwUukceNnDL44HFKXrZVrvvup/1js2b59fuiWI30cGktIo6Md\n0m3FN7b9ZNkO6dGi/b1SXlrMmV+c0h7tvkW5uJg+tPJQSCu/NPh3T+WVxZc6Ia3ab4fOxdB6\n63YMfiPd/t6yXL3l8N8FDYfUZWsdlxRfbC33f1PsH7jfCGl0tENa95t7Fnd3Qip327F9m+AD\nmz+9dtu3ti5Q7t+kdfm/19btO3RXnrV6OKR1b99ufvtxzcHFP3dCOnWz2wb/5IV1OwavkS45\ntSzv/q3hTQ6H9PJbu+vI9g3yB4sLS9dIPRDS6GiHVJ5btKd5O6Qbxxz0jW+dXFzQvuF26Ff/\n584TW1P71rETrvz22WOPW+/29/e23OrEz1723ycUH+vcbLi2mNq+AX7z/PXW7Tirc9fupMvK\n8oszhjf5Qkgvu7VHt5pw+c1f3ud1D5blsvEzSqoR0ujohLRgk4vLwZDKm967xfg9rmgN1pz5\nhnHv/vqp41rDf/pPW419x6dXr//f2t07863jN9vhD79SdkI67fkbBOest27H94ovtNe4qyxP\n+9zwJl8I6eW3dveHth+744d+XLZvR1wzCoeimYTUBKvf+vuJj/njbZ9JfExfElIjXFl8v/cP\nuW/Mp3v/kH4lpEZYe+DuK3r+jAP26Pkz+peQmmGg8/NIPfnz1y9I7EmfEhIECAkChAQBQoIA\nIUGAkCBASBAgJAj4/xrea7N1kYKMAAAAAElFTkSuQmCC",
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GEHABAIYQcAEAhhBwAQCGEHABAIYQcA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\nAAAAAElFTkSuQmCC",
       "text/plain": [
        "plot without title"
       ]
@@ -312,7 +351,10 @@
    "source": [
     "FamilyTreeRQ2Raw <- rbind(\n",
     "#     Load10Log(\"measurements/stats/FamilyTree//size010to-1r10n10rt300nsdrealstats_06-0249.csv\", 10),\n",
-    "    Load10Log(\"measurements1/stats.csv\", 20)\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",