diff --git a/output/last_output.csv b/output/last_output.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c67ec57d40c6fd19ee588b75ca2a06239beb4b70
--- /dev/null
+++ b/output/last_output.csv
@@ -0,0 +1,73 @@
+Tool;Model;RunIndex;PhaseName;MetricName;MetricValue
+EMFSolutionATL;Test.ttmodel;0;Initialization;Time;374834256
+EMFSolutionATL;Test.ttmodel;0;Initialization;Memory;4624528
+EMFSolutionATL;Test.ttmodel;0;Load;Time;162520318
+EMFSolutionATL;Test.ttmodel;0;Load;Memory;3442952
+EMFSolutionATL;Test.ttmodel;0;Run;Time;657545083
+EMFSolutionATL;Test.ttmodel;0;Run;Memory;7009984
+EMFSolutionATL;GeneratedI4O2Seed42.ttmodel;0;Initialization;Time;330053018
+EMFSolutionATL;GeneratedI4O2Seed42.ttmodel;0;Initialization;Memory;4623016
+EMFSolutionATL;GeneratedI4O2Seed42.ttmodel;0;Load;Time;148103042
+EMFSolutionATL;GeneratedI4O2Seed42.ttmodel;0;Load;Memory;3450816
+EMFSolutionATL;GeneratedI4O2Seed42.ttmodel;0;Run;Time;859620938
+EMFSolutionATL;GeneratedI4O2Seed42.ttmodel;0;Run;Memory;6720088
+EMFSolutionATL;GeneratedI8O2Seed68.ttmodel;0;Initialization;Time;327484284
+EMFSolutionATL;GeneratedI8O2Seed68.ttmodel;0;Initialization;Memory;4624560
+EMFSolutionATL;GeneratedI8O2Seed68.ttmodel;0;Load;Time;316426724
+EMFSolutionATL;GeneratedI8O2Seed68.ttmodel;0;Load;Memory;3809208
+EMFSolutionATL;GeneratedI8O2Seed68.ttmodel;0;Run;Time;57113369544
+EMFSolutionATL;GeneratedI8O2Seed68.ttmodel;0;Run;Memory;4563896
+EMFSolutionATL;GeneratedI8O4Seed68.ttmodel;0;Initialization;Time;296808144
+EMFSolutionATL;GeneratedI8O4Seed68.ttmodel;0;Initialization;Memory;4624256
+EMFSolutionATL;GeneratedI8O4Seed68.ttmodel;0;Load;Time;281286053
+EMFSolutionATL;GeneratedI8O4Seed68.ttmodel;0;Load;Memory;3897168
+EMFSolutionATL;GeneratedI8O4Seed68.ttmodel;0;Run;Time;74236382313
+EMFSolutionATL;GeneratedI8O4Seed68.ttmodel;0;Run;Memory;4634624
+EMFSolutionATLGraph;Test.ttmodel;0;Initialization;Time;293030083
+EMFSolutionATLGraph;Test.ttmodel;0;Initialization;Memory;4624656
+EMFSolutionATLGraph;Test.ttmodel;0;Load;Time;109964721
+EMFSolutionATLGraph;Test.ttmodel;0;Load;Memory;3438800
+EMFSolutionATLGraph;Test.ttmodel;0;Run;Time;601353129
+EMFSolutionATLGraph;Test.ttmodel;0;Run;Memory;7036240
+EMFSolutionATLGraph;GeneratedI4O2Seed42.ttmodel;0;Initialization;Time;290350781
+EMFSolutionATLGraph;GeneratedI4O2Seed42.ttmodel;0;Initialization;Memory;4624848
+EMFSolutionATLGraph;GeneratedI4O2Seed42.ttmodel;0;Load;Time;128698535
+EMFSolutionATLGraph;GeneratedI4O2Seed42.ttmodel;0;Load;Memory;3451336
+EMFSolutionATLGraph;GeneratedI4O2Seed42.ttmodel;0;Run;Time;754545110
+EMFSolutionATLGraph;GeneratedI4O2Seed42.ttmodel;0;Run;Memory;7053504
+EMFSolutionATLGraph;GeneratedI8O2Seed68.ttmodel;0;Initialization;Time;294348110
+EMFSolutionATLGraph;GeneratedI8O2Seed68.ttmodel;0;Initialization;Memory;4624480
+EMFSolutionATLGraph;GeneratedI8O2Seed68.ttmodel;0;Load;Time;298509547
+EMFSolutionATLGraph;GeneratedI8O2Seed68.ttmodel;0;Load;Memory;3816544
+EMFSolutionATLGraph;GeneratedI8O2Seed68.ttmodel;0;Run;Time;57133341098
+EMFSolutionATLGraph;GeneratedI8O2Seed68.ttmodel;0;Run;Memory;4567520
+EMFSolutionATLGraph;GeneratedI8O4Seed68.ttmodel;0;Initialization;Time;288669766
+EMFSolutionATLGraph;GeneratedI8O4Seed68.ttmodel;0;Initialization;Memory;4623152
+EMFSolutionATLGraph;GeneratedI8O4Seed68.ttmodel;0;Load;Time;284255246
+EMFSolutionATLGraph;GeneratedI8O4Seed68.ttmodel;0;Load;Memory;3897816
+EMFSolutionATLGraph;GeneratedI8O4Seed68.ttmodel;0;Run;Time;71221152984
+EMFSolutionATLGraph;GeneratedI8O4Seed68.ttmodel;0;Run;Memory;4633824
+RelationalRAGSolution;Test.ttmodel;0;Initialization;Time;68217
+RelationalRAGSolution;Test.ttmodel;0;Initialization;Memory;4843464
+RelationalRAGSolution;Test.ttmodel;0;Load;Time;24011377
+RelationalRAGSolution;Test.ttmodel;0;Load;Memory;2556184
+RelationalRAGSolution;Test.ttmodel;0;Run;Time;6335567
+RelationalRAGSolution;Test.ttmodel;0;Run;Memory;2358984
+RelationalRAGSolution;GeneratedI4O2Seed42.ttmodel;0;Initialization;Time;64151
+RelationalRAGSolution;GeneratedI4O2Seed42.ttmodel;0;Initialization;Memory;4846304
+RelationalRAGSolution;GeneratedI4O2Seed42.ttmodel;0;Load;Time;28456065
+RelationalRAGSolution;GeneratedI4O2Seed42.ttmodel;0;Load;Memory;2612000
+RelationalRAGSolution;GeneratedI4O2Seed42.ttmodel;0;Run;Time;8048195
+RelationalRAGSolution;GeneratedI4O2Seed42.ttmodel;0;Run;Memory;2439608
+RelationalRAGSolution;GeneratedI8O2Seed68.ttmodel;0;Initialization;Time;82400
+RelationalRAGSolution;GeneratedI8O2Seed68.ttmodel;0;Initialization;Memory;4846672
+RelationalRAGSolution;GeneratedI8O2Seed68.ttmodel;0;Load;Time;108396409
+RelationalRAGSolution;GeneratedI8O2Seed68.ttmodel;0;Load;Memory;4692368
+RelationalRAGSolution;GeneratedI8O2Seed68.ttmodel;0;Run;Time;36170605
+RelationalRAGSolution;GeneratedI8O2Seed68.ttmodel;0;Run;Memory;6373216
+RelationalRAGSolution;GeneratedI8O4Seed68.ttmodel;0;Initialization;Time;62352
+RelationalRAGSolution;GeneratedI8O4Seed68.ttmodel;0;Initialization;Memory;4845984
+RelationalRAGSolution;GeneratedI8O4Seed68.ttmodel;0;Load;Time;104493315
+RelationalRAGSolution;GeneratedI8O4Seed68.ttmodel;0;Load;Memory;5085568
+RelationalRAGSolution;GeneratedI8O4Seed68.ttmodel;0;Run;Time;41165426
+RelationalRAGSolution;GeneratedI8O4Seed68.ttmodel;0;Run;Memory;7193608
diff --git a/scripts/.gitignore b/scripts/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..21172f65141f5900200f928294ec32a3c378b8ff
--- /dev/null
+++ b/scripts/.gitignore
@@ -0,0 +1,5 @@
+.ipynb_checkpoints/
+run_time.pdf
+run_time.png
+memory.pdf
+memory.png
diff --git a/scripts/imported.ipynb b/scripts/imported.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dd645ca71b383900740234c135d967a5f8117000
--- /dev/null
+++ b/scripts/imported.ipynb
@@ -0,0 +1,705 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rcParams\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rcParams['font.family'] = 'sans-serif'\n",
+    "rcParams['font.sans-serif'] = ['Open Sans']\n",
+    "# rcParams['font.weight'] = 'semibold'\n",
+    "\n",
+    "haec_h = (0 / 256.0, 113 / 256.0, 157 / 256.0)\n",
+    "haec_a = (14 / 256.0, 180 / 256.0, 142 / 256.0)\n",
+    "haec_e = (138 / 256.0, 200 / 256.0, 101 / 256.0)\n",
+    "haec_c = (178 / 256.0, 210 / 256.0, 51 / 256.0)\n",
+    "\n",
+    "seed_to_plot = 11\n",
+    "max_time = 50\n",
+    "runs = [0,1,2]\n",
+    "\n",
+    "name_to_read = '../output/last_output.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FORMAT = re.compile('GeneratedI(\\d+)O(\\d+)Seed\\d+')\n",
+    "\n",
+    "def change_model_name(old_name):\n",
+    "    match = FORMAT.match(old_name)\n",
+    "    if match:\n",
+    "        return 'Input: {}\\nOutput: {}'.format(*match.groups())\n",
+    "    return old_name\n",
+    "\n",
+    "def normalise_data(data):\n",
+    "    data = data.groupby(['Tool', 'Model', 'RunIndex', 'MetricName']).aggregate({'MetricValue': 'sum'}).reset_index()\n",
+    "    data = data.groupby(['Tool', 'Model', 'MetricName']).mean().reset_index()\n",
+    "    data.loc[:, 'Model'] = data['Model'].apply(change_model_name)\n",
+    "    return data\n",
+    "\n",
+    "#    # get the baselines\n",
+    "#    objective = data[np.where(0 == data['numClusters'])][0]['objective']\n",
+    "#    generationTime = data[np.where(0 == data['numClusters'])][0]['generationTime']\n",
+    "#    solvingTime = data[np.where(0 == data['numClusters'])][0]['solvingTime']\n",
+    "\n",
+    "#    # remove the unclustered measurement\n",
+    "#    data = data[np.where(data['numClusters'] != 0)]\n",
+    "\n",
+    "#    data['objective'] = objective / data['objective']\n",
+    "#    data['objective'][np.isinf(data['objective'])] = 0\n",
+    "\n",
+    "#    return objective, generationTime, solvingTime, data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_measurements(file_name=None):\n",
+    "    if file_name is None:\n",
+    "        file_name = name_to_read\n",
+    "    data = pd.read_csv(file_name, delimiter=';')\n",
+    "                       #, skip_header=0,\n",
+    "                       #  names=('Tool', 'Model', 'RunIndex', 'PhaseName', 'MetricName', 'MetricValue'))\n",
+    "                         #dtype=(string,str,int,str,str,float))\n",
+    "\n",
+    "    # Normalize data\n",
+    "    data = normalise_data(data)\n",
+    "\n",
+    "    # Split time and memory data\n",
+    "    time_data = data[data['MetricName'] == 'Time'].copy()\n",
+    "    memory_data = data[data['MetricName'] == 'Memory'].copy()\n",
+    "    time_data.loc[:, 'MetricValue'] = data['MetricValue'] / 10e6\n",
+    "    memory_data.loc[:, 'MetricValue'] = data['MetricValue'] / 2**20\n",
+    "\n",
+    "#    data['value'] = data['MetricValue'] / 10e6\n",
+    "    return (time_data, memory_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_dpi = 300\n",
+    "# plt.figure(figsize=(1920/my_dpi, 1080/my_dpi), dpi=my_dpi)\n",
+    "\n",
+    "# f, axes = plt.subplots(2, 3, sharey=False,figsize=(1920/my_dpi, 1080/my_dpi), dpi=my_dpi)\n",
+    "\n",
+    "# plt.subplots_adjust(left=.04,right=.96,bottom=.15, top=.96, wspace=.4, hspace=.2)\n",
+    "# plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Original CSV header was: \n",
+    "\n",
+    "run|seed|numClusters|isValid|objective|clusteringTime|generationTime|solvingTime\n",
+    "-|-|-|-|-|-|-|-\n",
+    "0|11|0|true|1104501.5899999999|0|2451|20838\n",
+    "\n",
+    "And new header is:\n",
+    "\n",
+    "Tool|Model|RunIndex|PhaseName|MetricName|MetricValue\n",
+    "-|-|-|-|-|-\n",
+    "EMFSolutionATLGraph|Test.ttmodel|0|Initialization|Time|270028458\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tool</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>MetricName</th>\n",
+       "      <th>RunIndex</th>\n",
+       "      <th>MetricValue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 4\\nOutput: 2</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>133.777700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 8\\nOutput: 2</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5775.728055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 8\\nOutput: 4</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7481.447651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119.489966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 4\\nOutput: 2</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>117.359443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 8\\nOutput: 2</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5772.619875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 8\\nOutput: 4</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7179.407800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.434793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>RelationalRAGSolution</td>\n",
+       "      <td>Input: 4\\nOutput: 2</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.656841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>RelationalRAGSolution</td>\n",
+       "      <td>Input: 8\\nOutput: 2</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.464941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>RelationalRAGSolution</td>\n",
+       "      <td>Input: 8\\nOutput: 4</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14.572109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>RelationalRAGSolution</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.041516</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Tool                Model MetricName  RunIndex  \\\n",
+       "1          EMFSolutionATL  Input: 4\\nOutput: 2       Time         0   \n",
+       "3          EMFSolutionATL  Input: 8\\nOutput: 2       Time         0   \n",
+       "5          EMFSolutionATL  Input: 8\\nOutput: 4       Time         0   \n",
+       "7          EMFSolutionATL         Test.ttmodel       Time         0   \n",
+       "9     EMFSolutionATLGraph  Input: 4\\nOutput: 2       Time         0   \n",
+       "11    EMFSolutionATLGraph  Input: 8\\nOutput: 2       Time         0   \n",
+       "13    EMFSolutionATLGraph  Input: 8\\nOutput: 4       Time         0   \n",
+       "15    EMFSolutionATLGraph         Test.ttmodel       Time         0   \n",
+       "17  RelationalRAGSolution  Input: 4\\nOutput: 2       Time         0   \n",
+       "19  RelationalRAGSolution  Input: 8\\nOutput: 2       Time         0   \n",
+       "21  RelationalRAGSolution  Input: 8\\nOutput: 4       Time         0   \n",
+       "23  RelationalRAGSolution         Test.ttmodel       Time         0   \n",
+       "\n",
+       "    MetricValue  \n",
+       "1    133.777700  \n",
+       "3   5775.728055  \n",
+       "5   7481.447651  \n",
+       "7    119.489966  \n",
+       "9    117.359443  \n",
+       "11  5772.619875  \n",
+       "13  7179.407800  \n",
+       "15   100.434793  \n",
+       "17     3.656841  \n",
+       "19    14.464941  \n",
+       "21    14.572109  \n",
+       "23     3.041516  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data, _ = read_measurements()\n",
+    "pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tool</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>RunIndex</th>\n",
+       "      <th>MetricName</th>\n",
+       "      <th>MetricValue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 4\\nOutput: 2\\nSeed: 42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>133.777700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 8\\nOutput: 2\\nSeed: 68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>5775.728055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 8\\nOutput: 4\\nSeed: 68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>7481.447651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>119.489966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 4\\nOutput: 2\\nSeed: 42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>117.359443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 8\\nOutput: 2\\nSeed: 68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>5772.619875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 8\\nOutput: 4\\nSeed: 68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>7179.407800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>100.434793</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Tool                          Model  RunIndex MetricName  \\\n",
+       "0       EMFSolutionATL  Input: 4\\nOutput: 2\\nSeed: 42         0       Time   \n",
+       "1       EMFSolutionATL  Input: 8\\nOutput: 2\\nSeed: 68         0       Time   \n",
+       "2       EMFSolutionATL  Input: 8\\nOutput: 4\\nSeed: 68         0       Time   \n",
+       "3       EMFSolutionATL                   Test.ttmodel         0       Time   \n",
+       "4  EMFSolutionATLGraph  Input: 4\\nOutput: 2\\nSeed: 42         0       Time   \n",
+       "5  EMFSolutionATLGraph  Input: 8\\nOutput: 2\\nSeed: 68         0       Time   \n",
+       "6  EMFSolutionATLGraph  Input: 8\\nOutput: 4\\nSeed: 68         0       Time   \n",
+       "7  EMFSolutionATLGraph                   Test.ttmodel         0       Time   \n",
+       "\n",
+       "   MetricValue  \n",
+       "0   133.777700  \n",
+       "1  5775.728055  \n",
+       "2  7481.447651  \n",
+       "3   119.489966  \n",
+       "4   117.359443  \n",
+       "5  5772.619875  \n",
+       "6  7179.407800  \n",
+       "7   100.434793  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d1 = data.groupby(['Tool', 'Model', 'RunIndex', 'MetricName']).aggregate({'MetricValue': 'sum'}).reset_index()\n",
+    "d1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Tool</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>MetricName</th>\n",
+       "      <th>RunIndex</th>\n",
+       "      <th>MetricValue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 4\\nOutput: 2\\nSeed: 42</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>133.777700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 8\\nOutput: 2\\nSeed: 68</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5775.728055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Input: 8\\nOutput: 4\\nSeed: 68</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7481.447651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EMFSolutionATL</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119.489966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 4\\nOutput: 2\\nSeed: 42</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>117.359443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 8\\nOutput: 2\\nSeed: 68</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5772.619875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Input: 8\\nOutput: 4\\nSeed: 68</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7179.407800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>EMFSolutionATLGraph</td>\n",
+       "      <td>Test.ttmodel</td>\n",
+       "      <td>Time</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100.434793</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Tool                          Model MetricName  RunIndex  \\\n",
+       "0       EMFSolutionATL  Input: 4\\nOutput: 2\\nSeed: 42       Time         0   \n",
+       "1       EMFSolutionATL  Input: 8\\nOutput: 2\\nSeed: 68       Time         0   \n",
+       "2       EMFSolutionATL  Input: 8\\nOutput: 4\\nSeed: 68       Time         0   \n",
+       "3       EMFSolutionATL                   Test.ttmodel       Time         0   \n",
+       "4  EMFSolutionATLGraph  Input: 4\\nOutput: 2\\nSeed: 42       Time         0   \n",
+       "5  EMFSolutionATLGraph  Input: 8\\nOutput: 2\\nSeed: 68       Time         0   \n",
+       "6  EMFSolutionATLGraph  Input: 8\\nOutput: 4\\nSeed: 68       Time         0   \n",
+       "7  EMFSolutionATLGraph                   Test.ttmodel       Time         0   \n",
+       "\n",
+       "   MetricValue  \n",
+       "0   133.777700  \n",
+       "1  5775.728055  \n",
+       "2  7481.447651  \n",
+       "3   119.489966  \n",
+       "4   117.359443  \n",
+       "5  5772.619875  \n",
+       "6  7179.407800  \n",
+       "7   100.434793  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d2 = d1.groupby(['Tool', 'Model', 'MetricName']).mean().reset_index()\n",
+    "d2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "good_colors = ['#000000','#e69f00','#56b4e9','#009e73','#f0e442','#0072b2','#d55e00','#cc79a7']\n",
+    "good_shapes = ['o', 's', 'v', '^', 'x']\n",
+    "good_dashes = ['-', '--', ':', '-.']\n",
+    "\n",
+    "info = {\n",
+    "    'EMFSolutionATL':        (good_colors[0], good_shapes[0], good_dashes[0], 'ATL'),\n",
+    "    'EMFSolutionATLGraph':   (good_colors[1], good_shapes[1], good_dashes[0], 'ATL (Graph)'),\n",
+    "    'RelationalRAGSolution': (good_colors[2], good_shapes[2], good_dashes[0], 'RelRAG')\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_part(data_to_draw, name, axis_title, legend_position):\n",
+    "    fig = plt.figure(figsize=(1920/my_dpi, 1080/my_dpi), dpi=my_dpi)\n",
+    "    ax1 = fig.add_subplot(111)\n",
+    "\n",
+    "    ax1.set_xlabel('Model instance',weight='semibold',name='Open Sans')\n",
+    "    ax1.set_ylabel(axis_title, weight='semibold',name='Open Sans')\n",
+    "    #ax1.set_xticks(range(320,6401,640), minor=True)\n",
+    "    #ax1.set_xticks(range(640,6401,640), minor=False)\n",
+    "\n",
+    "    #ax2 = ax1.twinx()\n",
+    "    #ax2.set_ylabel('quality', color=haec_h,weight='semibold',name='Open Sans')\n",
+    "\n",
+    "    #objective, generationTime, totalTime, data = normalise_data(data, seed_to_plot)\n",
+    "    #print(data)\n",
+    "\n",
+    "    #ax1.plot(data['Model'], [1] * len(data), color=haec_h, linestyle='dashed', linewidth=1, alpha=0.5,label='memory consumption')\n",
+    "    #ax2.plot(data['Model'], [totalTime] * len(data), color=haec_a, linestyle='dashed', linewidth=1, alpha=0.5,label='total run time')\n",
+    "    #ax2.plot(data['Model'], [generationTime] * len(data), color=haec_e, linestyle='dashed', linewidth=1, alpha=0.5,label='unclustered generation time')\n",
+    "    # first = True\n",
+    "    #for run in runs:\n",
+    "    #    data_for_tool = data[np.where(data['run'] == run)]\n",
+    "    #    if first:\n",
+    "    #        ax1.plot(data_for_tool['Model'], data_for_tool['objective'], color=haec_h,label='solution quality',marker='o')\n",
+    "    #        ax2.plot(data_for_tool['Model'], data_for_tool['clusteringTime'], color=haec_c, label='clustering time',marker='o', mfc='w')\n",
+    "    #        ax2.plot(data_for_tool['Model'], data_for_tool['generationTime'], color=haec_e, label='generation time',marker='o', mfc='w')\n",
+    "    #        ax2.plot(data_for_tool['Model'], data_for_tool['solvingTime'], color=haec_a,label='total solving time',marker='o', mfc='w')\n",
+    "    #        first = False\n",
+    "    #    else:\n",
+    "    for tool in np.unique(data_to_draw['Tool']):\n",
+    "        data_for_tool = data_to_draw[data_to_draw['Tool'] == tool]\n",
+    "        color, shape, dash, nice_name = info[tool]\n",
+    "    #    ax1.plot(data_for_tool['Model'], data_for_tool['objective'], color=haec_h,marker='o')\n",
+    "        ax1.plot(data_for_tool['Model'], data_for_tool['MetricValue'], color=color,\n",
+    "                 label=nice_name, marker=shape, mfc=color, linestyle=dash)\n",
+    "    #    ax2.plot(data_for_tool['Model'], data_for_tool['generationTime'], color=haec_e, marker='o', mfc='w')\n",
+    "    #    ax2.plot(data_for_tool['Model'], data_for_tool['solvingTime'], color=haec_a,marker='o', mfc='w')\n",
+    "\n",
+    "    ax1.legend(loc=legend_position)\n",
+    "    #ax2.legend(loc='upper right')\n",
+    "\n",
+    "    #ax1.set_ylim(0, 1.19)\n",
+    "    ax1.set_ylim(0, 1.19 * data_to_draw['MetricValue'].max())\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    fig.savefig(name + '.pdf', format=\"pdf\", dpi=fig.dpi)\n",
+    "    fig.savefig(name + '.png', format=\"png\", dpi=fig.dpi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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KojkUefjyiHh9RDw2Ij4F/B/wmHKb3wN7TfT3IEnqK/Nw75iHJWmwjmH0DtLbgWdl5opmG5fvHV0p2pzieah11btSf56Z17QSTPl/twonZuZ5zd4sBzvf3OCtUzLzsxO0/Z8Uf98j9m62YUTsRfHs2HvrZubnx2s8M+8E/hH4Q6X4HRHR6DmwI3fSVp+L+50cZ+rqso/rKWYgaeWZ7SOezuhnXQ88OzOvHqePNRR36o78NpnN2N9hQ89BV2k4vbt6RUwzmXkHcEGl6KFt9HENY6cjGs8nKK5CGXF0sw2HXWauz8xjs5iq44GZ+b2Ja7WufPD428vV1RTTCk84f74kaeiYi3uk17k4M/8MPJLi+UgjtqWYqvJi4IUUV5WvAk4AHpHFlIiSpOFhHu4R87AkDdzLK8sfzszrJqqQmb8GLqoUHdpgs+pjze7bbLBN4/pQC9v8hLEXfAF8YKJKWTx27sJK0YPG2fwVleUVwLtaiIvMXAe8sVK0K8XduI08n7HP/n1Li338luI57q2qft/PKutP1Mf1jH1kQaPv+9By0FUaPndRzKXeqj9Vljdvo943q1Mnjae8urh6wPSQiNisjb6mhXLKpDMYfR7DcZnZ1amLJUl9YS6e4jLzVoqrYX84zma/Bs4urwiWJA0P8/AUZx6WpMYiYgfGDrad1Ub1/60sP6bBM0F/WVnekbGDb5rYpZl57UQblb8JqndqXpuZv2uxj6sqy5s23Qr2qyx/uc0ZKc6lePbqiGc02e5J1biyvUfjfXXiTe59fvu+laLJft/vFxHbt1F3oBx0lYbPn9r8j7R6ZU2j+fybaXfqnv+trT+yzfrTwbuB3crlb2XmhA8slyQNJXPxFBYRsyLiDcCVjD2Q/A1jp0HaG/hFRHwqIto5SS9J6i3z8BRmHpakce1ZWV4DXNpG3esry/OApbX3L2Dsc7vfERFfi4iHtBXh9NXO30X1t8dk6zX8zRIROwNbVYrqvz/GVc64eHGlqNk0xo+qLF/YZJtmJrxbtdJH9eKA+nPlx3N9bX1ZG3UHatagA5C0geVtbl+9MredaSP+3mY/l9XWp9QDrHstIvYHXlmu3kwxZZIkaWpa3ub25uIhERFbAF9m7Enec4A3Z+alEXFf4CUUOXszir+vFwL7RsSzMvNX/Y5ZkrSB5W1ubx4eEuZhSZrQbpXlOcA1bcwCvKC2vgVw752ZmZkRcRjwXWCXsvgQ4JCI+CHwSeAr5TS32tCEd7k2cfMk6zX7i9+ptl7//dGK3wEHlssP2KDjYrbG6qB9u31c3uJ2u9XWf97G931ubX2LVisOmne6SsPnjj71U597fiK31NaXdCuQqa48sDyV0WR5bDn3vCRpajIXT0ERsQA4n9ETvbcCh2bmIZl5KUBm/iUz3wrcH3g/o3fc3A/4fkTs3uewJUkbMg9PQeZhSWpJ9c7+mcB2bbzq09HOqzeemdcAj6E4T3lP5a0nAacD10fExyKinWegTxft/i4YsaqrURQXJVXVf3+0YkVleUGDqajrv2HaHTheydiL3pqpz2TRzvd9y1rdDb7vw8pBV2n49Ot5JvdMvMkY9aug2pm2aWN3MnCfcvn0zPzSIIORJHXMXDw1vYfRKZLuAPbJzIbPmsnMOzLzdcD+jB5cLwHOanBAKknqL/Pw1GQelqSJLe51B5l5a2YeQ3GX4UmMHUxcDBwL/DYivhoR9bsqp7N1gw6gVP99sWYSbdQHghdN0Edbdz+Xz7VtJa6ef9+HkYOu0vQ1v83t6/85T/bqn/FMmStWRkTE0cCzy9VrGJ1iWJKkiZiLu6QyXeGIN2bmhM+ZyczvA0dWih4IPLfL4UmShpN5uEvMw5LUsupA1dWZGR28Lm7aC5CZf8rMlwHbUEzl/tPK2wE8E7g0Ip7f9U+pTtSfa9/u7xXYcFC1PotIfZB1Mr8/WhlbrH7fE5jZwff9zEnEOBAOukrTV7tXmtRv6b+1W4FU1A9ip4J/ryxvDlwWEX+Z6AV8sVLvvg222bO/H0OSNADm4u45CJhVLq8BPt1qxcz8GmNPQBzSxbgkScPLPNw95mFJak112tf6dME9kZmrMvOUzHwc8DjgG5W35wGnRcT+/YhFLbmxtj6ZZ5lWp/VdlZn1u3hvr6239ciEiJhFawO11e97ME3ufHXQVZq+2p0+4iG19aubbNfKfO7N7NBB3UGZXVleyOTmpW/0DIf6w8IlSRsfc3H37FZZviwz61cHT+TCyvIuXYhHkjT8zMPdYx6WpNZcVVleEhH3abplD2TmTzPzIOBgRmdsmAG8t59xaFx/rK3Xf3+0olrnivqbmbmSsRePPbDN9pe2uN1VtfXdGm61kXHQVZq+Ht7m9k+uLCfwyybbVacnaPcZN7u3ub0kSVOZubh7qs9/u3sS9aNbgUiSpgzzcPeYhyWpNRfW1p84iCAy8+vAyytFD+v3ALAay8zlwF8rRW19RyJiNsUdzSN+0mTTX1WWH9NOH8DDWtzuImB9ZX0g3/d+c9BVmr72i4iW5oSPiHnAP1aKfpuZ9WkIRtxSWW75CtWIeBywbavbA3dVlvsyHUcjmblsMvPQA8dUmmn0DIcLBvSRJEn9Yy7unhsqy7tExMw26z+0snxdF+KRJA0/83D3mIclqTWXAX+prL+k2YZ98I3a+jYNtrmrtt6rfNNJP9W6cyNiky7EM2hnV5afGRHtPH7gIMZOL3x+k+2qFwA8PCJ2bqOPA1vZqPytVH328IsiYqMfk9zoP6CkpuYDr2px29cwdtqA8Z7Pcmll+SERsazFPt7c4nYjqgeyO7b6H3ZEzIqIT0TErRHxp4g4oM1+JUnqFnNx93LxzyrLmwOHtVoxInZj7N1LP+xCPJKk4WceNg9LUl9lZgIfqhQ9JSL+sdn2PbZVbf2W+gaZuR64o1LU8sBcRDw0In4VEbdHxNkRsfk4m9f7bmcAsF633ccHDKOPMfq4gkXA8a1Uioi5wDsrRcuBrzfZ/LO19be12MdS4HmtbFv678ryzsBxbdSdkhx0laa3t0bEvuNtEBHPAN5eKboBOH2cKhdUlmcAH5zo4C8i3kKLV8hUXFZZXgQ8t8V6/wi8iOIB4bsAn91IroCSJE1N5uLu5OLvAjdV1j8SEXtMVCkitgO+DMwqi+4EPtdhLJKkqcM8bB6WpH47Gbimsv6piDio1coRsVOjqYAjYu+IaGfq/DdUlv9Wi6mqmm+eHxELWmz/o8AjKHLUwYxzcVFmrihjGPGSNu6IvKy2fmyL9YZWZl4KfKZS9OqIePF4dcoB1y8AD6gUv70cOG/Ux5+BcytFz4+Il07Qx0KKPL1kvO1qvsLYRzL8Z0S0fId3RNwnInZso7+Bc9BVmr5WApsA50XExyLisdUpgCJi54h4P3AOowdAAK/KzFtpIjMvZuyc8M8Ezo+IJ5Zzyo+0vzgiDoqI7wMnlMW/biP+bzP2WTEfrB8sR8T8iFhcq1efo34LxiYjSZL6xVxc6DgXZ+Za4E21Nn8SER+JiD3LaSFHYpoREQ+KiLdS3I20W6XeuzLzqk5ikSRNGebhgnlYkvooM+8A/h+j0+IuAM6OiC9HxP71C2EiYssyh/x7RFwI/Bl4cIOm9wZ+HRE/j4g3R8QT6jkgIhZFxNMj4nzghZW3TsrMe5qEXB2YWwqcUd7tWG13m9p6APWLb/Zq0v6Ib1aWHwp8rEH8S9nQH4ErKusvjYhjazl9VkRsOUH/w+ZfGf1cAXw8Ir4aEU+t/Z7YIiKOpsipB1fqfykzT52gj1cCqyrrJ0XEZyPi0eXf4Ugf25aDvr8BnlIWJy0ov1fPAUZ+O80CTo6I70XEMyNizABuRGxa/nZ4XUR8G7i20ueUMGviTSRtpJ4LnAZsRnEF0LHA+oi4hWKapUbPtnl7Zp7VQtsvoZgXfuTAat/ydVfZ/jygfuD3aeAM4HutBJ+Z10XEJxm9emlL4DsR8TdgRfm5lgLPBr7WSpuSJPWZubiLMvOTEfFA4HVl0Wzg5eVrfUTcRnFyegkwt0ETn2bsnUySpI2bebiLzMOS1LrM/Gl5d+sXKPJBAIeWryz/z1xHcZdou7MR7EFlwDMiVlFMETyPxs9K/SFjp6StOwn4Z0anIz4EODAiLgdmluVr2fCZsEF73kuRm0dy50uAoyPiSoq8sTXwO+Bx1UqZmRHxH8CpZdEsiul53xURf6XYh0uBDwJvbDOmgcnMWyNiP4rZJEbu9Hxm+bonIlZQ7JdGz3s9GziqhT6WR8RzKO5GHdnvzytfI79ZGn0H30MxjfOzW/wsV5UXhp3D6PPrn1K+iIg7gNXAQoqLEKY073SVpq9LgMcD/1spm0lxoFY/uLwZeFFmHt9Kw5n5S2A/xj4YHoqDrq0Ze3B5J8X0Ei8BLm8x9hGvAb5VK9sGeBBwHxr/H/eL2vqNFFdESZLUb+biQtdycWYeRzFtYv1zz6R4xtzWbHii90bghZn5T+UzliRJ04N5uGAelqQByMzzKWYfOLf2VlAMjm5N4wHXK4DrGpRfBtzeoHwBRW6oD7jeTfG8zQMyc904cd5IMRi8olI8E3ggxTM6N5hqtvz//Je14gub9VHW+QPFYN+aSvFcihkRdqQYkGtW9zSKgcCqJRT5cHtgznh9D6ty9odHUQwoV+9EnkHxe6U+4HorxcDyoZm5hhZk5nkUv1l+X3tr5DdL9Tt4G8WMH2+guOO6ZeVvo0dTPEu2flf1yMB4owHX6xl7J/PQ805XaRrLzN9HxBOAA4DDgD0pDszmUTyn5o8UD9s+o5xbv522L4yInYEjgH8AHklx5dPcsu3lwDeAz2fmNQAR8ReKA85GV7026mNNRPwDxRQFz6e4imsLiitjrqeY8uCSWrXPAU+kmMbj78AryqmQJEnqO3Nx93NxZn4hIr4MPL18PRLYgeIE92yKA8XrKU46fxv4Smbe2a3+JUlTh3nYPCxJg5SZf6S4a/QRFHeQPgVYRjGgNptiAPJGiotyfgp8KzN/0qStcyLifsBBwFMppiC+P8WA1lyKaWRvorhb9ALgrMysXyTTLM4LI+JBFHe8HkDxTPBNyvauAc5rUO1lwCkUU9h/F/ivFvr5SkQ8GPiX8jMsoxgwvRG4Evj8OHXfEBFfp5gB4vEUA833UDwr9vcUOWfKKR9pcExEvIMi1z8F2JViEH0Vxb65BDgf+EJm3jKJPi6MiN0pfk88kyJ3L6UYXB+5OOtcit8sN5TV2hp0Lfu5DjgyIk6guEt2X4qB+y0pfnutoRjcv4LR3wk/aPZc2mEVXkQmTQ8RsQyoPhtlh8xcPpBgJEmahszFkiQNjnlYkiRJveb0wpIkSZIkSZIkSZLUAQddJUmSJEmSJEmSJKkDDrpKkiRJkiRJkiRJUgccdJUkSZIkSZIkSZKkDjjoKkmSJEmSJEmSJEkdcNBVkiRJkiRJkiRJkjrgoKskSZIkSZIkSZIkdcBBV0mSJEmSJEmSJEnqgIOukiRJkiRJkiRJktSByMxBxyBJkiRJkiRJkiRJU5Z3ukqSJEmSJEmSJElSBxx0lSRJkiRJkiRJkqQOOOgqSZIkSZIkSZIkSR1w0FWSJEmSJEmSJEmSOuCgqyRJkiRJkiRJkiR1wEFXSZIkSZIkSZIkSeqAg66SJEmSJEmSJEmS1IFZgw5AmioiYjGwD7AfsAewFbAlsAlwE3AjcCXwHeD8zLxyMJFqYxERDwAOBPYHdgKWArOBW4A/AxcCp2XmnwYWpCT1kblY/WYulqRR5mENm4g4EfjnStExmXnqgMKRJEkiMnPQMUhDLSK2AN4AvJLiYLJV3wLelJm/6Ulg2mhFxB7AW4CDW9g8gdOBV2bmHT0NTJIGxFysfjMXS9Io87CGUUTsT/Edi0qxg66SJGmgnF5YGkdEPJPiSt3jaHxwuZriat71Dd47APhVRHwgIoby31pE/DkiMiL2GXQsgzQs+yEi5kTE+4Gf0fgk7+3AzcA91WrAUcDFEbFZ76OUpP4yF08Pw7IfzMWSNJZ5eHqYavshIjYHTmHsgKskSdLADeWPXmkYRMSrgS8DiyvFVwBvAx4DLMjMBZm5NcU0c9sARwCfZ/SAM4BXA1+MiHn9ir0VEfEgYOdBxzFoQ7YfPgK8htEDx9XAh4F9gfmZuSQztwQ2BZ4D/K5S90EU3z1J2miYi6eHIdsP5mJJKpmHp4cpuh9OBrYF7gauH3AskiRJ93LQVWogIg4HPsDov5E7KU7A7ZaZJ2TmzzNz9cj2Wfh7Zp6Zmc8FHg5cVGnyUODjfQq/Va1MlzcdDNN+eDvFs5AATgV2zMx/zszvZeaakY0y847M/BLFiY4LKvWfFhFP61ewktRL5uJpZZj2g7lYkjAPTzNTaj9ExNHAYeXqpwGfqy5JkoaGg65STUQsA06qFK0CDszMD2bmXa20kZmXAk8Fzq8UHxkRz+tWnF0wpQ6semho9kNm/gU4HDgsM4/JzL9PsP1q4EiKEyAjhuk7JkmTYi6edoZmP5iLJck8PA1Nmf1QfjdPLFdXUlwsJUmSNDQcdJU29D5gSWX92Mz8bruNlHdDHAZcUyn+cEQs7DC+jkXEQ4HHDjqOQRvG/VDeSfPlNrb/C/C9StHjux+VJPWduXiaGMb9YC6WJPPwdDGV9kP5XODPMDrd9dsz87oBhiRJkrQBB12livKqyWdWir6VmZ+dbHuZuRJ4RaVoU+CYcfrfJyKy8lrWTn+1ui+ovRcRcUBEfBP4LWP//f+gVnfkdWqDPk6tvP/wSvkWEfHyiPhORCyPiLURsSIifhcRH29nqr0psh/2iYg/RMTtEfHFiFjUToxd9vvK8jYDi0KSusBcbC42F0vS4JiHzcNDnIdfDzyhXP4J8MEe9ydJktQ2B12lsY4FZlbW39dpg5n5DeDyStG/dNrmJD0fOA94OhBdanMHgIg4CrgC+AiwL3B/YC6wGfAg4MXAtyLi+xGxU5f6nqxu7YdPAw8EFlFcvf2qzkObtPWV5W793UrSoJiL22MuNhdLUjeZh9tjHu5DHo6IRwAnlKsrgaMyc/04VSRJkgZi1qADkIbM/pXl5cD3u9TuqcB/lss7RcQDMvNPXWq7VWuBv5bLMxl7F8ZNjH0W2YgVE7S5Y0S8GvhArfwOYB2wOWMP4p4M/CginpKZf2w18C7reD9ExOaUB9cVj+5WgJNQ/QzXDywKSeoOc/FY5uKCuViS+sM8PJZ5uDCwPBwRmwBnALPLotdk5hW96EuSJKlTDrpKpYhYADysUvSDzMwuNf/D2vpeQF8PMDPzi8AX4d4po66qvP2czLxgEs2+gOKqXYCLgfcC38vM28p+NgGeBryd0X27LfD1iHhYZq6dRJ8d6dJ+uKdR053G1oG9Ksu/HFgUktQhc7G5GHOxJA2Medg8zOjtlNEAAB/CSURBVHDm4XcDu5XLX8/MT/SoH0mSpI45vbA06lGMnUapmyfMfsPYA5A9u9j2ID2E4v+RjwF7ZeZXRg4uATJzTWZ+DdgD+Fql3i7Av/U10i7KzFsZOz0WwI8GEUtEPAzYuVJ0ziDikKQuMRe3z1w8ylwsSZ0xD7fPPDyq63k4IvYHXlmu3gi8qNt9SJIkdZODrtKobWrrXZvqJzNXAtdVirbrVttD4BLgVeNdAZ2ZdwFHUExPNeLlETGnx7H10tHAZcDtwGkUB9mD8KbK8s3AVwcUhyR1g7l4cszF5mJJ6gbz8OSYh3uQhyNiC+AURqdnfnFm3tDNPiRJkrrNQVdp1Ga19dsabjV5t4/T11T2ocy8e6KNymmTjq8UbQEc1Kugei0zf5KZD87MJZn5gkFMCxURTwUOrxS9NzNX9TsOSeoic/HkmIvNxZLUDebhyTEP9yYPn0wxFTPApzPz7C63L0mS1HUOukqjen2AWW1v8y63PUjfaGPbLwNrKutP6HIs00ZEbA58slJ0CfCBAYUjSd1iLp4cc/EAmIslbYTMw5NjHu6yiDgaeHa5eiXwrwMMR5IkqWUOukqj6lMBRcOtJq/aXtNph6aYGzPz761uXE4p9X+Vokd1P6SNX0TMBr4ALCuL1gBHllNWSdJUZi5un7l4AMzFkjZS5uH2mYe7LCKWASeWq+uBozLzjoEFJEmS1AYHXaVRt9TWl3S5/Wp79b6mqpYPLisuqyzfr1uBTBcRERR31Ty1UvzSzPztgEKSpG4yF7fPXNxn5mJJGzHzcPvMw10UETOA04HFZdF7MvN/BxiSJElSWxx0lUb18wBzRZfbHpSVk6hT3c/d3sfTwfuBoyrr/5GZpw8qGEnqMnNx+8zF/WculrSxMg+3zzzcXW8A9i6Xfw28bYCxSJIktc1BV2nUdbX1XbvVcEQsBrapFP21W20P2D2TqLO6srywW4FMBxFxAvDqStGnMvOtg4pHknrAXNw+c3EfmYslbeTMw+0zD3dJRDwCeHu5uhZ4vtP2S5KkqcZBV2nUr4G7K+vdfLbKwxn7/JqfdLFtACJiXrfbbMH8SdRZVFmezFXB4xrQfui5iHgj8JZK0eeAlwwoHEnqFXNx+8zFfWIuljQNmIfbZx7ugojYBDgDmF0WvSkzLxuniiRJ0lBy0FUqZeZq4DeVon3KZ3Z1wz619a4fYDL2wK1fFk+8yQa2rCzf2q1AKgaxH3oqIl4HvLNS9EXgqMyczFXVkjS0zMWTYi7uA3OxpOnAPDwp5uHuOBTYrbJ+XET8pZUXsGel3gdq73+xvx9DkiRNdw66SmN9u7J8f+ApnTZYHqQeXSn6U2Ze3mTz7KCrHTqoO1n3i4jZE282xkMqy1c32Waq7YeeiYhXA++tFJ0FPDcz1w8oJEnqNXNxe8zFPWYuljTNmIfbYx7ujvo+3BbYrsXXnEq9zWrvbdXTqCVJkmocdJXGOomx0ym9tgttHgzsWFn/0Djbrq6tt/N8l4e3sW23zAIe3OrGEbFVbftfNNl0qu2HnoiIVwEfqBR9DnheZt7dpIokbQzMxe0xF/eQuVjSNGQebo95WJIkSfdy0FWqyMxrga9Wip4eEc+dbHsRsQj4cKVoBXDaOFVuqa3v0kZ3h7ax7V219U3bqFt3SBvbPo/ioHTEj5psNxX3Q1dFxMsZezLidOBI76qRtLEzF0+KubgHzMWSpiPz8KSYhzuUmadmZkzmxdi7hY+pvb/PID6PJEmavhx0lTZ0HHBbZf2kiNin3UYiYj7wFeC+leJXZOaqcapdBVTfP6jFvh4D7NdGePXnxuzcRt26l0XEhM+xiYhNgX+rFP0d+GaTzYd+P0TE3hFxWUTcFhGfLf++uyIiXsrYExOnAC/wuXGSphFzcXvMxeZiSeom83B7zMNdzsOSJElTlYOuUk1mXg0cWylaBJwXEf8cEbOaVBsjInYHfgDsWyk+LTPPnKDv9cCFlaLnR8RjJ+hrGcVUdy3/ey4PcqtXg74oIma2Wr9mKXBGRMwdJ8Z5wJcZ+zyVD2Vm/arakfimwn44BdgNWExxtfIrWu13PBHxIuBjQJRFnwT+yZO8kqYTc3HbzMXmYknqGvNw28zDXczDkiRJU5mDrlID5YHga4CRk2vzgBOByyLizRHxyPKgCYCImBER20TEERFxFvAr4DGVJr8IvLTF7j9aWZ4NfDciXhsR96luFBEPjoi3Ar8BdgKWs+FVquM5t7L8QOCT5ZW31T62maCNleWfBwK/jYijImLrSv25EfEsiv3xlEq9S4D3TdD20O6HiNiMDa8A3quNPhuKiGOAjzN6kvdk4CWZmZ22LUlTjbn43j7MxZiLJanfzMP39mEepn95WJIkaaoLzx9IzUXEsymu3lzUZJM7gLXA5kCjq0HvAT4AvL6dk3UR8QXgOQ3eug24E9iM4qCrGseeFHdlPKEsOyYzTx2nj52A3wILKsXrgCvLtrcGrszMh9fqnQocXa6eVtZ5ca35VcBqYAs2vLhjOfDkzFzeLLZKX0O5H8oDzBW1pr6Wmc+a6DONE8fuFAfi1f11HTDZ/6Sfk5kXTTYeSRoW5mJzMeZiSRoY87B5mD7l4cmKiOXA/cvVcT+rJElSr3mnqzSOzPwysCPFQeLaBpssopgeqNHB5TeBh2fmcZO4O+Ioijsr6pZQHPBUD6p+AeyZmb8DLm+1g8y8Ajicsc+JmQPsSnF1bLOD6rpjgTdQHFCOWECxX+r/x5wLPKGVg8vSUO6HzLyF4gC06sL6dm3ajA3317bAdpN8NZ3aSpKmEnNxS8zFBXOxJHWZebgl5uFCp3lYkiRpymvpWRzSdJaZNwGvjYgTKKYD2g94JMUB1FYUJ9RuAm4ErgC+A3yrjYOoRn2uBY6NiBOBY4C9KQ52NqU4kLuO4oDqC8B55fNeoI0Dq7Kfb0TEg4FXlZ9rWfl5bgSuAr7UQhv3AO+JiDOAIyimVdqJ4gBwJXA9cAHwxcz8YZvxDfN+OJpi+sHtgLOBj7TTpySpdebiCdswF5uLJalnzMMTtmEeNg9LkiQBTi8sqU31qZQy8wWDi0aSpOnHXCxJ0uCYhyVJktSM0wtLkiRJkiRJkiRJUgccdJUkSZIkSZIkSZKkDjjoKkmSJEmSJEmSJEkdcNBVkiRJkiRJkiRJkjrgoKskSZIkSZIkSZIkdcBBV0mSJEmSJEmSJEnqgIOukiRJkiRJkiRJktQBB10lSZIkSZIkSZIkqQMOukqSJEmSJEmSJElSByIzBx2DJEmSJEmSJEmSJE1Z3ukqSZIkSZIkSZIkSR1w0FWSJEmSJEmSJEmSOuCgqyRJkiRJkiRJkiR1wEFXSZIkSZIkSZIkSeqAg66SJEmSJEmSJEmS1AEHXSVJkiRJkiRJkiSpAw66SpIkSZIkSZIkSVIHHHSVJEmSJEmSJEmSpA446CpJkiRJkiRJkiRJHXDQVZIkSZIkSZIkSZI64KCrJEmSJEmSJEmSJHXAQVdJkiRJkiRJkiRJ6oCDrpIkSZIkSZIkSZLUAQddJUmSJEmSJEmSJKkDDrpKkiRJkiRJkiRJUgccdJUkSZIkSZIkSZKkDjjoKkmSJEkaGhGxLCKy9jp+gPFcUI+nS+3OioiPRMQNEXFLRHw+IhZ3o21JkiRJUv/NGnQAkiRJkiRNQy8CXl5ZPxy4GnjjYMKRJEmSJHXCO10lSZIkqQea3LE58vpbRMzssP2Tx2n/+C59DPXOQ1sskyRJkiRNAQ66SpIkSVL/LQWePNnKETEHeE73wtEAXNJimSRJkiRpCnDQVZIkSZIG4/AO6j4d2KxbgWggPgl8FLgJuA04E3jHQCPqoYiYExG3Vu7GXjbomCRJkiSpmxx0lSRJkqTBOLS8Y3UyntfVSNR3mXl3Zr4iM7fKzE0z84jMvH3QcfXQAcCSQQchSZIkSb3ioKskSZIkDcZmwNParRQRi4GDuh+O1FP/OOgAJEmSJKmXHHSVJEmSpP75bW19MlMMHwrMq6z/YPLhSL0XEfOAgwcdhyRJkiT1koOukiRJktQ/Z9bWD46I+W22UZ9a+AsdxCP1wyHAwkEHIUmSJEm95KCrJEmSJPXP14HVlfWFwIGtVo6IbYCnVIpWAN/tNKiI2CUijouIb0XE5RFxW0SsjYi/RMTFEfHOiNizwz4OjIjTIuKPZfsrI+LPEfGViDiig+fb1vtZEBFHRcSZEfGHiFgREXdGxHURcVFE/GdEPKwbfXUY5wsiImuvF4yz/QW1bTevvLew/Mxnl/t0Vfm6JiK+HhGviohFbcY3v/x7OT0ifhsRN0fEuohYExF/K/flJyLiudVYam0sjYiPAKc3ePuqBp8/I+L4CeKaFxEHRcQHI+IHEXFt+VnvjIgbIuIXEXFSROwXEdHiZz21FsMjKu/NjYhnV75Pt5f74K8R8Z2IeFNEbNVKP036fkD5b++88u/uloi4q/w38seIOCciXh8RD26xvdkRcVhEnBIRv4uIm8q/t79FxC8j4v0Rsddk45UkSZLU3KxBByBJkiRJ08gqioHX6vMtD6f1u1UPZ+zFs18C7p5sMBGxLfBO4Pk0vih3u/L1WOCNEfEd4DWZeWkbfewMfAp4YoO3dy5fzwJ+Vw463tTOZ6j19ULgv4BtGrx9n/L1OODfIuIs4JWZefNk+xuwRwHfiYhnAycC2zbYZj6wPcXA/vERcUxmnj1RwxFxFPAeYGmDt2dTTG+9lGJfvgi4KyK+Arw+M68p23gV8A5gQbsfrElMOwGvp7jTu1mbW5WvRwEvBX4dEUe1830t7VHW3Qc4GXhAg222LV/7Am+JiFdn5smtdhAR21P82zuCxv/2FpevB1A8w/ndEfEfmfnWcdo8GHg/xb+puqXl65HAayLi28BLM3N5qzFLkiRJGp93ukqSJElSf9WnGH5GRCxusW59auHPTTaIiHg48DPgKFo/NtwPuCgint5iHw8EfkzjAde6BwMXAI9uMZZqPzPKOyo/ReMB1w2qUAxgXxQRO7bb35DYIyKOpRh4bzTgWrcZ8NWIOGi8jSLizcBpNB5wbWY2xTNbV1bKDqVLA66l04GXtNnmI4AftXqXaMUeEfEPFHeRNxpwrdsEOCkiXtlK4xHxaIp/e8+jvfMyF4zT5puAs2k84NrI/sBPI2KPNvqXJEmSNA4HXSVJkiSpv84Dbqusz6W403NcEfEAijvwRlwL/GgyAUTE/YDvU9zF2shdFHflNrIQOCcinjBBH3OBc2g+CHo3YwfpoBhQO228dpt4F/DyJu/dBdzR5L1dKAYi502iz0F7GfDhBuUrgTub1AnglIhoOHAZEbsDJ4zT5x3AuibvnZGZKyrrtwE3l69G8dxSeb/6Wt1gW4APjhPXynHqbQZ8epy6jTyL4u7zmbXy1cCaceq9v7yDtamI2AH4DuP/u7gdyFr5VcAPmrT5coq7ihtZ36Q9gK2BszuZHlmSJEnSKAddJUmSJKmPMvNO4Gu14sNbqFq/y/XMzGw0kDKu8jmXn6MYjKq6A3gTsENmzsnMhcAWwNHA8tq2s4DPN3uWZ+lNNL5L8NvAXsDczFxEMYh7EHBR+f4mrX8aiIgnAa+tFSdwEvDQsp/FwKbAkcA1tW0fBjSdsnWIbc/ooOBPKO40XZSZizJzHvAg4IwG9bag2A+NNLrr+VcUU+jOzczFmTmXYprmAymmNb663O7EaqXMPCQzt8zMLdnw7m6AR468X3u9p0lsXwGuLJd/Brya4iKEueVnXgAso5het/7v4jETXSRQsxXF1MwAv6OYAniLzFyQmfOBHWg8CDwHeEWzRst/e58HltTeWkcxnfODgTn5/9u782jrzvkO4N+fRAZDqWgttKihRYkaaighJYuSamk1Zq1iEV2UDstUqnShVrtKU0u1LCpqtsxTiZpFTEEIpUTNEpEggzTx6x/7XM677z73nnvPffNe8fms9a7lPPt59rP3vvv8Ed/z/J7uy2T4XtwyyV8nOSnJC6a+77PV5H8/Md0rMpQFP3DufHeZnWvelZI8c9E1AwAAyxO6AgAAXPjGIdQRVXX5Tcbca/R5u6WF75ghzJl3RpJbdvfT5vd47O7Tu/uFGcq0fmw05spJHjE1QVUdkGSq1OqLk/xWd3+gu384m+Os7n5DksOSvGwb9/PkrP9v2z/s7qO7+6S1oKq7z+zuF2UoX/ylUf+HVtWltzH3bvCiJId19+u7+0crh7v75O6+T6aD19svONevTLQd1d3HdfePVrh29ze6+43d/acZAsibdPcnV7iHTXX3BRl+eHDN7r5Zdz+juz8yuq4vdfdfZDoQPWIb0x6X4d5eOr+Kt7tP6e4/y7B/8NiiZ5skR2YIQuedm+QO3f2o7v703Pt6dne/v7uf1N3XXzBXkjwu63+o8PjuPqq7T5g9t7XzvXY2/4dH/Y+arcAFAABWIHQFAAC48L09yWlzn/dPcrdFnavqZtlzr8aTu/vEbc798Im2x28UmnX3GRlWR14wOvTQqppamXpkhhWV805N8pBFq3Nn4dBDkpw+dXxKVR2aIayd98ruPnbRmO7+VpJHj5ovM7vmnzTfSHL0WoC9wBMn2m64oG9NtJ2/0QX04CMb9dkp3X18d//PEl2nyi4vszfrvHOT3L+7z92gz9OzvtzwoVW16P9rmfohwlO6+52bXcxaeDpvVhb4qFHzh7r7bzc4z9lJHjZq3i/J3Te7BgAAYGNCVwAAgAtZd5+f5FWj5o1KDI9LC0+tXtzUbJ/Vw0fNZ2eJPS+7+1MZ9qKcd0iGUsFjt55oe153L9pbdW2OMzKURV3WVFD6L0uMe2PWB8hbKT+7W7x4fnXrlO7+fJKvjprHgfiaT0+0PWtBsL5rdfcXM+zlO2/RPS/ypu7+8ibzfDdD+eV5+2UoZb2H2ervw0fN5yU5ZovXNe+IDHtCz3vOZoO6+/gk3xo1/yS+/wAAsKsIXQEAAPaNcYnhw6rqSuNOVbVf1q9m225p4ZtkfUjz7tnqt2W8aaJtKqy58ZJjp7xryX7J+sC3kxy/2aBZ+PvtUfN1tjDvbnHckv3GoeuiUspTYf6RSU6uqodU1SUmju9W540+H7DF8dt9tsn0871R1n/33jv7ocF2Tf3g4f1Ljj1l9Pkn8f0HAIBdRegKAACwb7w7ydfmPl8s0yU+j0hyhbnPx89W8m3HNSbaPrWF8SdNtE3tA3q1ibZl9/z87NJXsz4oqiTfr6re7F+Snx+N3epKyN3gM0v2G5fAnTQrWf20iUNXTfLsJF+tqmOq6gZLzrujanCzqnpyVb2uqj5bVadV1TkTf99LrjjdKs92qkzztSbaVt0Hdyoo/fSS7/9NR+N+Et9/AADYVYSuAAAA+8BsH85xKd2pEsM7Ulp45mcn2k7dwvjTJtouN9F2mdHn87awou87W7ieqfvZrp/ZwXNdWLbyt1tKdz8mySMzHSZeNsO+pCdW1Qeq6qgN9i/dUVV1ZIaQ8vgkf5Xkzhn2aT0kyUF7YcqdfrbrSg4n+eaK5/xpf/8BAGBXEboCAADsO+MSwzetqquvfZiVc73r3PHzk7x8hfmmyp4utQpyZqoM8VRYM15leNYW5thwj9Il5t6uqdWJu92yZaG3pLufkWEV5XOzvkzvmpsneVmSj1XVVDnpHVNVj0nyhiS/ujfnGdnpZzv1rq46h6AUAAB2kf339QUAAAD8tOru46vqlOxZjvceSZ4y+9+/k+RSc8eO6+5vrTDluRNtW9mnc6rvdyfazsme172VlYj7baHv2dkzeOokp29h/LztjttnuvuCvXjuLyV5UFU9LskDkjw4Q5nhsUOTvKeq7trdb93p66iq2+fH34d5n07yb0nel+SLSb7X3T+YG9crTr3Tz3bqxw2rrtCdCm3HexUDAAAXEqErAADAvvWyJI+a+3zP/Dhk2snSwkkyFdiO9zbdyFTfqbDyzOwZuh5cVZfo7mVW9k2txl3k29kzdD23uy+/hfFsYhbyP7Wq/i7JnZI8OsktR90OTvIfVXWd7t7psrxPnGh7QZIH7s3QeS+YKq/9cyuecypgve6KP8wAAAC2SXlhAACAfWtcYvh6VXXdqjokyR3m2s9J8uoV5zp5ou36Wxh/vYm2z020fXmi7bpLznG1pa8m+cLo88Gz58YO6+4fdvcbuvtWSe6WIVifd0iS++7knLO/5c1Hzd9JcvRGgWtVjfcU3g2mvhNT36etGL//SfKLK54TAADYJqErAADAPtTdJyb5zKj5nkmOSnLxubbXd/dW9jud8rEk3xu13aqqll1deqeJtvcumGfs9kvOcbMl+yXJu1aYh23q7ldlKIM9dtNFQ7Y51VWyfq/dE7p7qkz2vEO3Od/edELWP4dbV9WlpjovyfsPAAC7iNAVAABg33vZ6PM9sn7V4KqlhdPd5yd5/aj5oCRHbza2qq6X5Haj5u8mef9E96kg9oFVdcAmc+yXra2WfONE26Oq6uIT7eysqcDvsgv6ToWkV15ijgMn2g5eYtyDl+hzoeruM5OcOGo+OMlDVjjt25L836jtYbt0pS8AAFzkCV0BAAD2vXGJ4WsmucXc59OTvGWH5vqnibYnVNW4jOuPzEKcFybZb3TouQtW3742ybj9l5I8bZNre1KSX96kz49090eTvGPUfIMkz6+qqcBunaq6eFXdcNk5L8qq6r5Vtewq0akVyYv2Ej1tou2OS8wxtT/sjarqiosGVNVDs34v5N3iXyfanrjRd2/NrOT4HnvAdvc3khw76nrFJK9aNnitqotV1U2W6QsAAGxM6AoAALCPdfdnknx8gy6v6u7zdmiuD2Z9yHvJJO+oqr+pqmuuNVbVZavqPkk+mmQcTJ6e5B8WzHFWkudNHHpkVb2iqm5cVTWb48Cquk1VvTrJY7dxS4/N+tV+907y0ar646r6hfkDVXWJqrpeVd2vqp6f5OtJ/nEb814U3T3Jx6vq41X11Kq6wzjgrKorVNUDkrx8YvzU6tdkutz0X1bVn1TVJWfnvXhVXb+q5sPYL2R9YHupJK+vqlutrZyuqoOq6rZV9Zokz9r8NveZY5N8ZdQ2/927xlpjVe0/C1ofVFVvTXJSplcHPznr99e9XZJPVNXD5885O++BVXXtqjqqqp6d5H+TvHLF+wIAAJLsv68vAAAAgCRDEHqDBcdWLi08cnSSm2RYUbvm4CRPyLDq9bwk52UIuBa5f3d/bYPjT0jyB0muNGq/2+zf+VV1dpJLZ899Oz+X5FrL3EQyhMhV9edZv4L3upkFv7P7OTvDPS61Avan3KGzf49Okqq6IMNewAckucSCMV9L8ooFx96WoRT1z8y1HZDkn5McU1Xfz/AeJENw++Yk6e6uqmOTPHJ0vhsneU+SH86NnX+HvpXk7UnuteFdXsi6+6yqul+Ga5v/Efz4u3du1t/TonOeMjvna0b9r5LkmUmeWVXnZ1h5ftDs39iXtnE7AADAiJWuAAAAu8N49emaryR5905O1N1nJLltkpMXdDkgiwPX85Lct7tft8kc301yl6xfhbdm/wwh3HxQ9MUkhyX5/EbnnpjrmCSPSHLBgi4HZNhvVOC6PftleH6LAtezktx79jdfp7u/l+SpC8ZWfhy4Tnlq1q8OXXOxrH+Hvp/kd5P8+wbn3Ge6+7+S/FGG79GUA7L+njY75+sy7AN99oIu+2f4+00FrgAAwA4RugIAAOwC3X1Kkg9OHHppd/demO/LSX49w2q4Hyw57L1JDuvuFy05x4eSHJ6NSyeveXuS23T3N5O8c8nrmZ/rmUluM7vGrTgtyau3Ot9F1AlZHAYu8oEkt+rud27S7+lJnr3VC+ruU5PcPsl/L9H9lCSHd/fxSd6X9WWnd4XuPjbJbyb50BaGnZrkjA3O+fIM++y+cYuX8/0s/sEHAACwBcoLAwAA7B4vzRCczHvx3ppstvfqI6rq6UnumeSIJNdPcvkkneTbGVYZvivJG7r7PduY48SqunGGksK/n6Gs8RUyrEr9eoag+SVJ3jIXLr8ryQO3Mdf7khxWVbdIcqcMIexVZvdzUJJzknwnw4raTyT5zyRv6+5ztzrXRVF3P6mqnpPkyAzP7tpJrpphJepBGVZSnpnks0k+kuTV3f2BJc/9wyQPraqXJHlQkt9IcsUMPwb/ZoZ34cMZ3oXx2JOr6gZJ7pfk95L8WpLLza7n1CSfzFBe9xXdfc5szFlVdUKSW279Sex93f3+JDetqttleFcPy1CK+5AMK4vPSvLVDHu5vinJK7t70UrWtXOelOS3q+rQJHfOEOxefXbOS2YoW3xmhnD6U0mOS/LmRSuUAQCAram98INpAAAAAAAAgJ8aygsDAAAAAAAArEDoCgAAAAAAALACoSsAAAAAAADACoSuAAAAAAAAACsQugIAAAAAAACsQOgKAAAAAAAAsAKhKwAAAAAAAMAKhK4AAAAAAAAAKxC6AgAAAAAAAKxA6AoAAAAAAACwAqErAAAAAAAAwAqErgAAAAAAAAArELoCAAAAAAAArEDoCgAAAAAAALACoSsAAAAAAADACoSuAAAAAAAAACsQugIAAAAAAACsQOgKAAAAAAAAsAKhKwAAAAAAAMAKhK4AAAAAAAAAKxC6AgAAAAAAAKxA6AoAAAAAAACwAqErAAAAAAAAwAqErgAAAAAAAAArELoCAAAAAAAArEDoCgAAAAAAALACoSsAAAAAAADACoSuAAAAAAAAACsQugIAAAAAAACsQOgKAAAAAAAAsAKhKwAAAAAAAMAKhK4AAAAAAAAAKxC6AgAAAAAAAKxA6AoAAAAAAACwAqErAAAAAAAAwAqErgAAAAAAAAArELoCAAAAAAAArEDoCgAAAAAAALACoSsAAAAAAADACv4fsWR3nSAsoe8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1920x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1920x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "time_data, memory_data = read_measurements()\n",
+    "draw_part(time_data, name='run_time', axis_title='Time (ms)', legend_position='upper left')\n",
+    "draw_part(memory_data, name='memory', axis_title='Memory (MB)', legend_position='lower left')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/run.py b/scripts/run.py
index f370d648486af03e114a800d4939fe50710ffaee..b8b2cc3ecfb600b1f9d236609fb486d687202d4e 100755
--- a/scripts/run.py
+++ b/scripts/run.py
@@ -7,7 +7,6 @@ import argparse
 import os
 import shutil
 import subprocess
-import sys
 try:
     import ConfigParser
 except ImportError:
@@ -18,6 +17,7 @@ from subprocess import CalledProcessError
 BASE_DIRECTORY = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 print("Running benchmark with root directory " + BASE_DIRECTORY)
 
+
 class JSONObject(object):
     def __init__(self, d):
         self.__dict__ = d
@@ -44,8 +44,13 @@ def benchmark(conf):
     header = os.path.join(BASE_DIRECTORY, "output", "header.csv")
     result_file = os.path.join(BASE_DIRECTORY, "output", "output.csv")
     if os.path.exists(result_file):
-        os.remove(result_file)
-    shutil.copy(header, result_file)
+        with open(result_file, "a") as file:
+            # append a separator line
+            from datetime import datetime
+            file.write('-' * 30 + ' New measurement started at ' + datetime.now().isoformat() + ' ' + '-' * 30 + '\n')
+    else:
+        shutil.copy(header, result_file)
+        # os.remove(result_file)
     os.environ['Runs'] = str(conf.Runs)
     for r in range(0, conf.Runs):
         os.environ['RunIndex'] = str(r)
@@ -103,10 +108,9 @@ if __name__ == "__main__":
                         action="store_true")
     args = parser.parse_args()
 
-
     set_working_directory("config")
     with open("config.json", "r") as config_file:
-        config = json.load(config_file, object_hook = JSONObject)
+        config = json.load(config_file, object_hook=JSONObject)
 
     if args.debug:
         os.environ['Debug'] = 'true'
@@ -119,7 +123,7 @@ if __name__ == "__main__":
 
     # if there are no args, execute a full sequence
     # with the test and the visualization/reporting
-    no_args = all(val==False for val in vars(args).values())
+    no_args = all(val is False for val in vars(args).values())
     if no_args:
         build(config, False)
         benchmark(config)