add realistic solver

208 files changed, 4046 insertions, 0 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/.gitignore
new file mode 100644
index 00000000..dc53b109
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/.gitignore
@@ -0,0 +1,10 @@
+# virtual enviroment
+venv/
+
+# python cache
+*.pyc
+.ipynb_checkpoints/
+.idea/
+
+# keep folders
+!.gitignore
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/input/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/input/.gitignore
new file mode 100644
index 00000000..b3934b01
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/input/.gitignore
@@ -0,0 +1,3 @@
+# ignore everything in this folder
+*
+!.gitignore
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/MPC.png
new file mode 100644
index 00000000..a8772bdd
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Node_Activity.png
new file mode 100644
index 00000000..9defdaff
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Node_Type.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Node_Type.png
new file mode 100644
index 00000000..e98559bb
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Node_Type.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Out_Degree.png
new file mode 100644
index 00000000..eec3f95e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/ecore/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/MPC.png
new file mode 100644
index 00000000..20df6086
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Node_Activity.png
new file mode 100644
index 00000000..c7c18186
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Node_Type.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Node_Type.png
new file mode 100644
index 00000000..6d7230db
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Node_Type.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Out_Degree.png
new file mode 100644
index 00000000..eb157b8e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/distances/yakindumm/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/MPC.png
new file mode 100644
index 00000000..b8930a7b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Node_Activity.png
new file mode 100644
index 00000000..d55a000c
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Node_Type.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Node_Type.png
new file mode 100644
index 00000000..554923f5
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Node_Type.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Out_Degree.png
new file mode 100644
index 00000000..1a605f9a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Violations.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Violations.png
new file mode 100644
index 00000000..719249d4
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/ecore/Violations.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/MPC.png
new file mode 100644
index 00000000..8f59786d
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Node_Activity.png
new file mode 100644
index 00000000..a3775f23
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Node_Type.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Node_Type.png
new file mode 100644
index 00000000..5d3f81e1
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Node_Type.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Out_Degree.png
new file mode 100644
index 00000000..a89eb552
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Violations.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Violations.png
new file mode 100644
index 00000000..50a4cba0
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/github/Violations.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/MPC.png
new file mode 100644
index 00000000..fcff0dfe
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Node_Activity.png
new file mode 100644
index 00000000..ad44025f
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Node_Type.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Node_Type.png
new file mode 100644
index 00000000..a61d6e5a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Node_Type.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Out_Degree.png
new file mode 100644
index 00000000..4749db22
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Violations.png b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Violations.png
new file mode 100644
index 00000000..ac09e580
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/output/yakindumm/Violations.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/BoxPlot.ipynb b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/BoxPlot.ipynb
new file mode 100644
index 00000000..5db66bb3
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/BoxPlot.ipynb
@@ -0,0 +1,362 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import statistics\n",
+    "import os\n",
+    "import sys\n",
+    "lib_path = os.path.abspath(os.path.join('..','..', '..', 'utils'))\n",
+    "sys.path.append(lib_path)\n",
+    "from GraphType import GraphStat\n",
+    "import readCSV as reader\n",
+    "import constants\n",
+    "import DistributionMetrics as metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getModels(folderName, numberOfModels):\n",
+    "    filenames = reader.readmultiplefiles(folderName, numberOfModels, False)\n",
+    "    graphStats = [GraphStat(filename) for filename in filenames]\n",
+    "    return graphStats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain = 'github'\n",
+    "mpc_guide = getModels('../input/{}/MPC/'.format(domain), 100)\n",
+    "na_guide = getModels('../input/{}/NodeActivity/'.format(domain), 100)\n",
+    "od_guide = getModels('../input/{}/OutDegree/'.format(domain), 100)\n",
+    "nt_guide = getModels('../input/{}/NodeType/'.format(domain), 100)\n",
+    "composite_guide = getModels('../input/{}/Composite/'.format(domain), 100)\n",
+    "composite_no_violations_guide = getModels('../input/{}/Composite_Without_Violations/'.format(domain), 100)\n",
+    "violations_guide = getModels('../input/{}/Violations/'.format(domain), 100)\n",
+    "human = getModels('../input/{}/Human/'.format(domain), 304)\n",
+    "model_types = [human, composite_guide, composite_no_violations_guide, mpc_guide, na_guide, od_guide, nt_guide, violations_guide]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if domain == 'yakindumm':\n",
+    "    type_map = {'Entry': 0.04257802080554814, 'Choice': 0.1267671379034409, 'State': 0.1596092291277674, 'Transition': 0.6138636969858629, 'Statechart': 0.010136036276340358, 'Region': 0.04467858095492131, 'Exit': 0.0018338223526273673, 'FinalState': 0.0005334755934915977}\n",
+    "elif domain == 'ecore':\n",
+    "    type_map = {'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}\n",
+    "elif domain == 'github':\n",
+    "    type_map = {'Project': 0.012636538873420432, 'Commit': 0.5525808524309276, 'User': 0.05847076461769116, 'Issue': 0.12743628185907047, 'PullRequest': 0.07560505461554937, 'IssueEvent': 0.17327050760334123}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import rep\n",
+    "mpc_rep = getModels('../input/{}/MPC_REP/'.format(domain), 1)\n",
+    "na_rep = getModels('../input/{}/NA_REP/'.format(domain), 1)\n",
+    "od_rep = getModels('../input/{}/OUT_DEGREE_REP/'.format(domain), 1)\n",
+    "rep = mpc_rep[0]\n",
+    "rep.na = na_rep[0].na\n",
+    "rep.out_d = od_rep[0].out_d\n",
+    "rep.nodeTypeStat = type_map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_ks(dist1, dist2):\n",
+    "    value, p= metrics.ks_distance(dist1, dist2)\n",
+    "    return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mkdir(path):\n",
+    "    if not os.path.exists(path):\n",
+    "        os.makedirs(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_path = '../output/{}/'.format(domain)\n",
+    "mkdir(output_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Draw diagrams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drawBoxDiagram(title, target, types, distance_func, label_position):\n",
+    "    distances = []\n",
+    "    for distributions in types:\n",
+    "        distances.append([distance_func(target, distribution) for distribution in distributions])\n",
+    "    fig, ax1 = plt.subplots()\n",
+    "    fig.set_size_inches(5, 2)\n",
+    "    result = plt.boxplot(distances)\n",
+    "    ax1.xaxis.set_ticks_position(label_position)     \n",
+    "    texts = ax1.set_xticklabels(labels, rotation=90, fontsize=12)\n",
+    "    plt.savefig('{}/{}.png'.format(output_path, title), dpi=500, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw mpc\n",
+    "mpc_types = []\n",
+    "labels = ['Hum', 'Comb+V', 'Comb-V', 'MPC', 'NA', 'OD',  'NTD', 'VIO']\n",
+    "for models in model_types:\n",
+    "        mpc_types.append([model.mpc for model in models])\n",
+    "drawBoxDiagram('MPC', rep.mpc, mpc_types, calculate_ks, 'top')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw node activity\n",
+    "na_types = []\n",
+    "labels = []\n",
+    "for models in model_types:\n",
+    "        na_types.append([model.na for model in models])\n",
+    "drawBoxDiagram('Node_Activity', rep.na, na_types, calculate_ks, 'top')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw out degree\n",
+    "out_d_types = []\n",
+    "labels = []\n",
+    "for models in model_types:\n",
+    "        out_d_types.append([model.out_d for model in models])\n",
+    "drawBoxDiagram('Out_Degree', rep.out_d, out_d_types, calculate_ks, 'top')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def manual_ks(pdf1, pdf2):\n",
+    "    result = 0\n",
+    "    sum1 = 0\n",
+    "    sum2 = 0\n",
+    "    for(a, b) in zip(pdf1, pdf2):\n",
+    "        sum1 += a\n",
+    "        sum2 += b\n",
+    "        result = max(result, abs(sum1-sum2))\n",
+    "    return result\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Commit', 'Issue', 'IssueEvent', 'Project', 'PullRequest', 'User']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw node type\n",
+    "labels = []\n",
+    "types = sorted(rep.nodeTypeStat.keys())\n",
+    "rep_type_dist = [float(rep.nodeTypeStat[key]) for key in types]\n",
+    "print(types)\n",
+    "node_type_types = []\n",
+    "for models in model_types:\n",
+    "    type_dists = []\n",
+    "    for model in models:\n",
+    "        type_dists.append([float(model.nodeTypeStat.get(key, 0)) for key in types])\n",
+    "    node_type_types.append(type_dists)\n",
+    "\n",
+    "#since we already know the pdf, we can compute the ks distance manually\n",
+    "drawBoxDiagram('Node_Type', rep_type_dist, node_type_types, manual_ks, 'top')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 360x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# draw violations\n",
+    "violation_types = []\n",
+    "labels = ['Hum', 'Comb+V', 'Comb-V', 'MPC', 'NA', 'OD',  'NTD', 'VIO']\n",
+    "for models in model_types:\n",
+    "        violation_types.append([model.violations for model in models])\n",
+    "drawBoxDiagram('Violations', None, violation_types, lambda a, b:b, 'bottom')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/DistancePlot.ipynb b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/DistancePlot.ipynb
new file mode 100644
index 00000000..44842cf4
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/Measurements/Measurement1/src/DistancePlot.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "lib_path = os.path.abspath(os.path.join('..','..', '..', 'utils'))\n",
+    "sys.path.append(lib_path)\n",
+    "import glob\n",
+    "import matplotlib.pyplot as plt\n",
+    "from GraphType import GraphCollection\n",
+    "import DistributionMetrics as metrics\n",
+    "from GraphType import GraphStat\n",
+    "import readCSV as reader\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getModels(folderName, numberOfModels):\n",
+    "    filenames = reader.readmultiplefiles(folderName, numberOfModels, False)\n",
+    "    graphStats = [GraphStat(filename) for filename in filenames]\n",
+    "    return graphStats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read models\n",
+    "domain = 'ecore'\n",
+    "rep = getModels('../input/{}/MPC_REP/'.format(domain), 1)[0]\n",
+    "na_rep = getModels('../input/{}/NA_REP/'.format(domain), 1)[0]\n",
+    "od_rep = getModels('../input/{}/OUT_DEGREE_REP/'.format(domain), 1)[0]\n",
+    "rep.na = na_rep.na\n",
+    "rep.out_d = od_rep.out_d\n",
+    "\n",
+    "human_models = getModels('../input/{}/Human/'.format(domain), 304)\n",
+    "folder = '../output/distances/{}/'.format(domain)\n",
+    "mkdir(folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if domain == 'yakindumm':\n",
+    "    rep.nodeTypeStat = {'Entry': 0.04257802080554814, 'Choice': 0.1267671379034409, 'State': 0.1596092291277674, 'Transition': 0.6138636969858629, 'Statechart': 0.010136036276340358, 'Region': 0.04467858095492131, 'Exit': 0.0018338223526273673, 'FinalState': 0.0005334755934915977}\n",
+    "elif domain == 'ecpre':\n",
+    "    rep.nodeTypeStat = {'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}\n",
+    "node_types = sorted(rep.nodeTypeStat.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_diagram(name, models, rep, metric, distance_metric):\n",
+    "    model_metrics = list(map(metric, models))\n",
+    "    rep = metric(rep)\n",
+    "    distances= list(map(lambda m: distance_metric(m,rep), model_metrics))\n",
+    "    plt.figure()\n",
+    "    plt.title('{} for {}'.format(name, domain))\n",
+    "    plt.hist(distances)\n",
+    "    plt.savefig('{}/{}.png'.format(folder, name), dpi = 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mpc(model):\n",
+    "    return model.mpc\n",
+    "def na(model):\n",
+    "    return model.na\n",
+    "def od(model):\n",
+    "    return model.out_d\n",
+    "\n",
+    "def nt(model):\n",
+    "    nd_dict = model.nodeTypeStat\n",
+    "    dist = []\n",
+    "    for key in node_types:\n",
+    "        dist.append(nd_dict.get(key, 0))\n",
+    "    return dist\n",
+    "\n",
+    "def mkdir(path):\n",
+    "    if not os.path.exists(path):\n",
+    "        os.makedirs(path)\n",
+    "\n",
+    "def ks_distance(s1, s2):\n",
+    "    value, p = metrics.ks_distance(s1, s2)\n",
+    "    return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfJ97loUfH+9ND2euUtSgwx3SWrQnIR7kkOS3JDkpiSnzEUbkqTJzfqYe5ItgQ8DzwfWA99Icn5VXTfbbUlzbVhj/ZurYb3GMcyf81w95rk4c386cFNV3VxVPwf+HThiDtqRJE1iLt4tswxYN2Z+PfCM8RslWQWs6mYfSHLDDNtbCtw1w303B/bP5OybTZv3/slp89naQGatbwZ8zPtMtmIuwj0TLKtfW1C1Glg9cGPJ2qpaMehxWmX/TM6+2TT7Z3KLoW/mYlhmPbDXmPk9gdvnoB1J0iTmIty/ASxPsm+SrYFjgPPnoB1J0iRmfVimqh5K8hrgy8CWwCeq6trZbmeMgYd2Gmf/TM6+2TT7Z3ILvm9S9WvD4ZKkRc5PqEpSgwx3SWrQogn3qS5pkORRSc7u1l+RZHT+qxyOPvrmhCTXJfl2kouTTPre2Bb1ezmMJEclqSQL+i1us62f/knyou536Nok/zbfNQ5LH39beye5NMm3ur+vQ4dR54SqasHf6L0w+z3g8cDWwNXA/uO2+Uvgo930McDZw657AfXN7wPbddPHby5902//dNs9BvgKcDmwYth1L6T+AZYD3wJ26uZ3HXbdC6hvVgPHd9P7A7cOu+5HbovlzL2fSxocAazpps8FDkoy0QeqWjNl31TVpVX1k272cnqfPdhc9Hs5jHcB7wX+dz6LWwD66Z9XAh+uqnsAqurOea5xWPrpmwJ26KYfywL6TM9iCfeJLmmwbLJtquoh4D5gl3mpbrj66ZuxjgP+Y04rWlim7J8kTwH2qqoL5rOwBaKf35/9gP2SfC3J5UkOmbfqhqufvnkH8NIk64EvAq+dn9Kmtli+iamfSxr0ddmDBvX9uJO8FFgBPGdOK1pYNtk/SbYATgeOna+CFph+fn+W0BuaeS69//q+muSAqrp3jmsbtn765sXAmVX1/iTPBD7Z9c3Dc1/epi2WM/d+Lmnwq22SLKH3L9KP5qW64errcg9JDgbeChxeVT+bp9oWgqn65zHAAcBlSW4FDgTO34xeVO33b+u8qvpFVd0C3EAv7FvXT98cB5wDUFVfB7ahd1GxoVss4d7PJQ3OB1Z200cBl1T3KkfjpuybbtjhY/SCfXMZL33EJvunqu6rqqVVNVpVo/Rekzi8qtYOp9x518/f1ufpvShPkqX0hmluntcqh6OfvrkNOAggyZPohfvGea1yEosi3Lsx9EcuaXA9cE5VXZvknUkO7zY7A9glyU3ACcBm8Q1QffbN3wPbA59OclWSzeZaP332z2arz/75MnB3kuuAS4E3VdXdw6l4/vTZNycCr0xyNXAWcOxCOan08gOS1KBFceYuSZoew12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ16P8AtZaSS8ry4ocAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_diagram('MPC', human_models, rep, mpc, ks_distance)\n",
+    "plot_diagram('Node_Activity', human_models, rep, na, ks_distance)\n",
+    "plot_diagram('Out_Degree', human_models, rep, od, ks_distance)\n",
+    "plot_diagram('Node_Type', human_models, rep, nt, metrics.manual_ks)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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/Metrics/Metrics-Calculation/metrics_plot/model comparison/input/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/model comparison/input/.gitignore
new file mode 100644
index 00000000..b3934b01
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/input/.gitignore
@@ -0,0 +1,3 @@
+# ignore everything in this folder
+*
+!.gitignore
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/MPC.png
new file mode 100644
index 00000000..ae4f5900
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/MPC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/MPC_lengend.png
new file mode 100644
index 00000000..c94b8c2c
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/MPC_lengend.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node Types.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node Types.png
new file mode 100644
index 00000000..70abdd5b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node Types.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node Types_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node Types_lengend.png
new file mode 100644
index 00000000..fe138084
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node Types_lengend.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node_Activity.png
new file mode 100644
index 00000000..a8a655f8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node_Activity_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node_Activity_lengend.png
new file mode 100644
index 00000000..bc83145d
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Node_Activity_lengend.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Out_Degree.png
new file mode 100644
index 00000000..ee97529b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Out_Degree_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Out_Degree_lengend.png
new file mode 100644
index 00000000..0f6e8538
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-BaseViatra-RealViatra-Random-rep-/Out_Degree_lengend.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/MPC.png
new file mode 100644
index 00000000..d7ed3bbb
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/Node Activity.png
new file mode 100644
index 00000000..8a20963d
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/Out Degree.png
new file mode 100644
index 00000000..4354656c
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Alloy (30 nodes)-Realistic Viatra with Node Type KS (100 nodes)-Human-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/MPC.png
new file mode 100644
index 00000000..b52aa827
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/MPC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/MPC_lengend.png
new file mode 100644
index 00000000..6adb45e2
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/MPC_lengend.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/Node Types.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/Node Types.png
new file mode 100644
index 00000000..e257f381
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/Human-Alloy-BaseViatra-RealViatra-Random-rep-/Node Types.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Node Activity.png
new file mode 100644
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Out Degree.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra consistent (100 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra consistent (100 nodes)-/MPC.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra consistent (100 nodes)-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra consistent (100 nodes)-/Node Activity.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra consistent (100 nodes)-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/Human-Viatra consistent (100 nodes)-/Out Degree.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/real vs viatra vs alloy/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/real vs viatra vs alloy/MPC.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/real vs viatra vs alloy/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/real vs viatra vs alloy/Node Activity.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/real vs viatra vs alloy/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Yakindumm/old measurements/KS_Distance/real vs viatra vs alloy/Out Degree.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/MPC.png
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index 00000000..d184e5bf
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+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/MPC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/MPC_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Activity.png
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+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png
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+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Types.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Types.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Types_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Node_Types_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Out_Degree.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Out_Degree_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/ecore/Hum-All-Rand-GS-Real-Med-/Out_Degree_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/MPC.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Activity.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Types.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Types.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Types_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Node_Types_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Out_Degree.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Out_Degree_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/github/Hum-All-Rand-GS-Real-Med-/Out_Degree_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-/TCC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-/TCC.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-/TCC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-/TCC_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-Rand-GS-Real-/TCC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-Rand-GS-Real-/TCC.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-Rand-GS-Real-/TCC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-Rand-GS-Real-/TCC_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-Real-/TCC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/test/Hum-Real-/TCC_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/MPC.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/MPC_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/MPC_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Activity.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Activity_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Types.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Types.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Types_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Node_Types_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Out_Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Out_Degree.png
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+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Out_Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Out_Degree_lengend.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/yakindu/Hum-All-Rand-GS-Real-Med-/Out_Degree_lengend.png
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py
new file mode 100644
index 00000000..75fe78eb
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py
@@ -0,0 +1,158 @@
+import os, sys
+lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))
+sys.path.append(lib_path)
+import glob
+import random 
+from sklearn.manifold import MDS
+import matplotlib.pyplot as plt
+from scipy import stats
+import numpy as np
+from GraphType import GraphCollection
+import DistributionMetrics as metrics
+
+def main():
+    domain = 'github'
+    # read models
+    alloy = GraphCollection('../input/measurement2/{}/Alloy/'.format(domain), 100, 'All')
+    human = GraphCollection('../input/measurement2/{}/Human/'.format(domain), 304, 'Hum')
+    base = GraphCollection('../input/measurement2/{}/BaseViatra/'.format(domain), 100, 'GS')
+    real = GraphCollection('../input/measurement2/{}/RealViatra/'.format(domain), 100, 'Real')
+    random = GraphCollection('../input/measurement2/{}/Random/'.format(domain), 100, 'Rand')
+    na_rep = GraphCollection('../input/measurement2/{}/Human/na_rep/'.format(domain), 1, 'Med')
+    mpc_rep = GraphCollection('../input/measurement2/{}/Human/mpc_rep/'.format(domain), 1, 'Med')
+    od_rep = GraphCollection('../input/measurement2/{}/Human/od_rep/'.format(domain), 1, 'Med')
+
+    # a hack to make the node type as the same as an exiting model
+    type_rep = GraphCollection('../input/measurement2/{}/Human/od_rep/'.format(domain), 1, 'Med')
+    if(domain == 'yakindu'):
+        type_rep.nts = [{'Entry': 0.04257802080554814, 'Choice': 0.1267671379034409, 'State': 0.1596092291277674, 'Transition': 0.6138636969858629, 'Statechart': 0.010136036276340358, 'Region': 0.04467858095492131, 'Exit': 0.0018338223526273673, 'FinalState': 0.0005334755934915977}]
+    elif (domain == 'ecore'):
+        type_rep.nts = [{'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}]
+    elif (domain == 'github'):
+        type_rep.nts = [{'Project': 0.012636538873420432, 'Commit': 0.5525808524309276, 'User': 0.05847076461769116, 'Issue': 0.12743628185907047, 'PullRequest': 0.07560505461554937, 'IssueEvent': 0.17327050760334123}]
+
+    types = sorted(type_rep.nts[0].keys())
+
+    model_collections = [human, alloy, random, base,  real]
+    for model_collection in model_collections:
+        print(model_collection.name)
+        length = len(model_collection.violations)
+        percentage = sum(map(lambda v: int(v==0), model_collection.violations)) / length
+        print(percentage)
+
+
+    models_to_compare_na = [human, alloy, random, base,  real, na_rep]
+    models_to_compare_mpc = [human, alloy, random, base,  real, mpc_rep]
+    models_to_compare_od = [human, alloy, random, base,  real, od_rep]
+    models_to_compare_nt = [human, alloy, random, base,  real, type_rep]
+    for modelCollection in models_to_compare_nt:
+        type_dists = []
+        for nt in modelCollection.nts:
+            type_dist = []
+            for key in types:
+                type_dist.append(nt.get(key, 0.0))
+            type_dists.append(type_dist)
+        modelCollection.nts = type_dists
+
+
+    # define output folder
+    outputFolder = '../output/{}/'.format(domain)
+
+    #calculate metrics
+    metricStat(models_to_compare_na, 'Node_Activity', nodeActivity, 0, outputFolder, calculateKSMatrix)
+    metricStat(models_to_compare_od, 'Out_Degree', outDegree, 1, outputFolder, calculateKSMatrix)
+    metricStat(models_to_compare_mpc, 'MPC', mpc, 2, outputFolder, calculateKSMatrix)
+    metricStat(models_to_compare_nt, 'Node_Types', nodeType, 3, outputFolder, calculateManualKSMatrix)   
+
+def calculateKSMatrix(dists):
+    dist = []
+
+    for i in range(len(dists)):
+        dist = dist + dists[i]
+    matrix = np.empty((len(dist),len(dist)))
+
+    for i in range(len(dist)):
+        matrix[i,i] = 0
+        for j in range(i+1, len(dist)):
+            value, p= metrics.ks_distance(dist[i], dist[j])
+            matrix[i, j] = value
+            matrix[j, i] = value
+    return matrix
+
+def calculateManualKSMatrix(dists):
+    dist = []
+
+    for i in range(len(dists)):
+        dist = dist + dists[i]
+    matrix = np.empty((len(dist),len(dist)))
+
+    for i in range(len(dist)):
+        matrix[i,i] = 0
+        for j in range(i+1, len(dist)):
+            value = metrics.manual_ks(dist[i], dist[j])
+            matrix[i, j] = value
+            matrix[j, i] = value
+    return matrix
+
+
+def calculateMDS(dissimilarities):
+    embedding = MDS(n_components=2, dissimilarity='precomputed')
+    trans = embedding.fit_transform(X=dissimilarities)
+    return trans
+
+def plot(graphTypes, coords, title='',index = 0, savePath = ''):
+    color = ['#377eb8' , '#e41a1c', '#4daf4a', '#984ea3', '#ff7f00', '#ffff33']
+    markers = ['o', '+', 'x', '^', 'v', '*']
+    fill_styles = ['full', 'full', 'full', 'none', 'none', 'full']
+    plt.figure(index, figsize=(5, 2))
+    # plt.title(title)
+    index = 0
+    for i in range(len(graphTypes)):
+        x = (coords[index:index+graphTypes[i].size, 0].tolist())
+        y = (coords[index:index+graphTypes[i].size, 1].tolist())
+        index += graphTypes[i].size
+        plt.plot(x, y, color=color[i], marker=markers[i], label = graphTypes[i].name, linestyle='', alpha=0.7, fillstyle = fill_styles[i])
+    plt.savefig(fname = savePath+'.png', dpi=500)
+    plt.legend(loc='upper right')
+    plt.savefig(fname = savePath+'_lengend.png', dpi=500)
+
+def mkdir_p(mypath):
+    '''Creates a directory. equivalent to using mkdir -p on the command line'''
+
+    from errno import EEXIST
+    from os import makedirs,path
+
+    try:
+        makedirs(mypath)
+    except OSError as exc: # Python >2.5
+        if exc.errno == EEXIST and path.isdir(mypath):
+            pass
+        else: raise
+
+def metricStat(graphTypes, metricName, metric, graphIndex, outputFolder, matrix_calculator):
+    metrics = []
+    for graph in graphTypes:
+        metrics.append(metric(graph))
+        outputFolder = outputFolder + graph.name + '-'
+    print('calculate' + metricName +' for ' + outputFolder)
+    mkdir_p(outputFolder)
+    out_d_coords = calculateMDS(matrix_calculator(metrics))
+    plot(graphTypes, out_d_coords, metricName, graphIndex,outputFolder + '/'+ metricName)
+
+def nodeActivity(graphType):
+    return graphType.nas
+
+def outDegree(graphType):
+    return graphType.out_ds
+
+def mpc(graphType):
+    return graphType.mpcs
+
+def nodeType(graphType):
+    return graphType.nts
+
+def tcc(graphType):
+    return graphType.tccs
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore
new file mode 100644
index 00000000..b3934b01
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore
@@ -0,0 +1,3 @@
+# ignore everything in this folder
+*
+!.gitignore
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg
new file mode 100644
index 00000000..6b987b3e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/mpc.jpg
new file mode 100644
index 00000000..ee8d5755
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/out_degree.jpg
new file mode 100644
index 00000000..25e850fe
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
new file mode 100644
index 00000000..8b3421b1
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
new file mode 100644
index 00000000..d459f5c8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
new file mode 100644
index 00000000..fdaa28e6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/Node Activity.jpg
new file mode 100644
index 00000000..08445457
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/mpc.jpg
new file mode 100644
index 00000000..74ab2449
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/out_degree.jpg
new file mode 100644
index 00000000..61c4489e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_node_activity/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/Node Activity.jpg
new file mode 100644
index 00000000..ccbca653
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/mpc.jpg
new file mode 100644
index 00000000..31965557
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/out_degree.jpg
new file mode 100644
index 00000000..d4de5301
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_out_degree/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
new file mode 100644
index 00000000..ee1d84dc
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
new file mode 100644
index 00000000..1a7e8bed
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
new file mode 100644
index 00000000..81d9b709
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
new file mode 100644
index 00000000..e26d6cf6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
new file mode 100644
index 00000000..e1b9a1cf
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
new file mode 100644
index 00000000..713ed4f4
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/mpc.jpg
new file mode 100644
index 00000000..9c26be81
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/node activity.jpg
new file mode 100644
index 00000000..3cb75479
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/out degree.jpg
new file mode 100644
index 00000000..0a869df6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_normal/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
new file mode 100644
index 00000000..b6e86773
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
new file mode 100644
index 00000000..486577a6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
new file mode 100644
index 00000000..4f338049
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/Node Activity.jpg
new file mode 100644
index 00000000..c1dba220
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/mpc.jpg
new file mode 100644
index 00000000..f51449d0
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/out_degree.jpg
new file mode 100644
index 00000000..dff91312
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_control_all_with_consistency/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
new file mode 100644
index 00000000..2dc66b35
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
new file mode 100644
index 00000000..847bad62
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
new file mode 100644
index 00000000..90ff8006
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance.ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance.ipynb
new file mode 100644
index 00000000..550e3978
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance.ipynb
@@ -0,0 +1,481 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare Metrics Distances to The Human Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from GraphType import GraphStat\n",
+    "from GraphType import GraphCollection\n",
+    "from scipy import stats\n",
+    "from ipywidgets import interact, fixed, interactive\n",
+    "import readCSV as reader\n",
+    "import ipywidgets as widgets\n",
+    "import matplotlib.pyplot as plt\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import constants\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Record the average distances of different metrics for a model to the human models "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GraphDistance:\n",
+    "    #init with a graph stat and a collection of graph stats\n",
+    "    def __init__(self, graphStat, collection):\n",
+    "        self.graph = graphStat\n",
+    "        self.collection = collection\n",
+    "        self.out_d_distance = average_ks_distance(collection.out_ds, graphStat.out_d)\n",
+    "        self.na_distance = average_ks_distance(collection.nas, graphStat.na)\n",
+    "        self.mpc_distance = average_ks_distance(collection.mpcs, graphStat.mpc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Calculate the average ks distance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def average_ks_distance(targets, sample):\n",
+    "    distance = 0.0\n",
+    "    for target in targets:\n",
+    "        value, p = stats.ks_2samp(target, sample)\n",
+    "        distance += value\n",
+    "    distance = distance / len(targets)\n",
+    "    return distance\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "* Find the median ks distance of the same number of nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def find_median(x, metric_distances):\n",
+    "    distance_dic = {}\n",
+    "    for index, num_of_nodes in enumerate(x):\n",
+    "        if num_of_nodes[0] not in distance_dic:\n",
+    "            distance_dic[num_of_nodes[0]] = []\n",
+    "        distance_dic[num_of_nodes[0]].append(metric_distances[index])\n",
+    "    median_x = []\n",
+    "    y = []\n",
+    "    for num_of_nodes, distances in distance_dic.items():\n",
+    "        median_x.append(num_of_nodes)\n",
+    "        y.append(np.median(distances))\n",
+    "    order = np.argsort(median_x)\n",
+    "    median_x = np.array(median_x)[order]\n",
+    "    median_y = np.array(y)[order]\n",
+    "    return median_x, median_y\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot Diagram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# metric_selector: GraphDistance -> float\n",
+    "def plot(infos, lines, id, metric_selector,colors, title):\n",
+    "    metric_distances = retrive_info_from_list(metric_selector, list(infos.values()))\n",
+    "    x = retrive_info_from_list(lambda a : a.graph.num_nodes, list(infos.values()))\n",
+    "    graph = plt.figure(id,figsize=(18, 10))\n",
+    "    plt.title(title)\n",
+    "    plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n",
+    "    #plot ks distance median\n",
+    "    median_x, median_y = find_median(x, metric_distances)\n",
+    "    plt.plot(median_x, median_y, color='black',marker='o')\n",
+    "    for i in range(0, len(lines)):\n",
+    "        line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n",
+    "        line_y = retrive_info_from_list(metric_selector, line_infos)\n",
+    "        line_x = retrive_info_from_list(lambda a : a.graph.num_nodes, line_infos)\n",
+    "        plt.plot(line_x, line_y, marker='o', color=colors[i])\n",
+    "    #graph.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Retrieve information from a list "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def retrive_info_from_list(selector, distances):\n",
+    "    return list(map(selector, distances))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "human = GraphCollection('../statistics/humanOutput/', 300, 'Human', True)\n",
+    "file_names = reader.readmultiplefiles('../statistics/viatraEvolve/', 1000, False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate Distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Progress Widge\n",
+    "w2 = widgets.FloatProgress(\n",
+    "    value=0,\n",
+    "    min=0,\n",
+    "    max=1.0,\n",
+    "    step=0.1,\n",
+    "    description='Loading Files...:',\n",
+    "    bar_style='info',\n",
+    "    orientation='horizontal'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca7932bce2a741afaff6b919042c42b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatProgress(value=0.0, bar_style='info', description='Loading Files...:', max=1.0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "infos = []\n",
+    "# read all files\n",
+    "counter = 0.0\n",
+    "size = len(file_names)\n",
+    "#display progress bar\n",
+    "display(w2)\n",
+    "for name in file_names:\n",
+    "    infos.append(GraphStat(name))\n",
+    "\n",
+    "info_dic = {}\n",
+    "for info in infos:\n",
+    "    w2.value = (counter/size)\n",
+    "    counter+=1\n",
+    "    info = GraphDistance(info, human)\n",
+    "    info_dic[info.graph.id] = info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Plot Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* widget for select trajectory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filenames = reader.readmultiplefiles('../statistics/trajectories/', 10, False)\n",
+    "trajectories = {}\n",
+    "for name in filenames:\n",
+    "    trajectories[name] = reader.readTrajectory(name)\n",
+    "\n",
+    "w = widgets.SelectMultiple(\n",
+    "    options = trajectories,\n",
+    "    value = [trajectories[filenames[0]]],\n",
+    "    description='Trajectory:',\n",
+    "    disabled=False,\n",
+    ")\n",
+    "\n",
+    "#generate random color for each line\n",
+    "colors = []\n",
+    "\n",
+    "for i in range(0, len(trajectories)):\n",
+    "    color = \"#%06x\" % random.randint(0, 0xFFFFFF)\n",
+    "    colors.append(color)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Out Degree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08da62cb0c3f4e6e9591c7dc811d27cc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(1,), options={'../statistics/trajectori…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(info_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Node Activity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a708f43645a24bd2b15b53ea12c7d88f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(1,), options={'../statistics/trajectori…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(info_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MPC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "124a0cb0ebfb4225bf4ced24c09032f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(1,), options={'../statistics/trajectori…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(info_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for name in file_names:\n",
+    "    contents = reader.readcsvfile(name)\n",
+    "    if(contents['State Id'][0] == 1032396643):\n",
+    "        print(name)"
+   ]
+  }
+ ],
+ "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"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
new file mode 100644
index 00000000..000822bf
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
@@ -0,0 +1,1600 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Measuremments with Representative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))\n",
+    "sys.path.append(lib_path)\n",
+    "from GraphType import GraphStat\n",
+    "from GraphType import GraphCollection\n",
+    "from scipy import stats\n",
+    "from ipywidgets import interact, fixed, interactive\n",
+    "import readCSV as reader\n",
+    "import ipywidgets as widgets\n",
+    "import matplotlib.pyplot as plt\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import constants\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Record the distances of different metrics using a representative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GraphDistanceWithRep:\n",
+    "    #init with a graph stat and a collection of graph stats\n",
+    "    def __init__(self, graphStat, rep):\n",
+    "        self.graph = graphStat\n",
+    "        self.rep = rep\n",
+    "        self.out_d_distance, _ = stats.ks_2samp(graphStat.out_d, rep.out_d)\n",
+    "        self.na_distance,_ = stats.ks_2samp(graphStat.na, rep.na)\n",
+    "        self.mpc_distance,_ = stats.ks_2samp(graphStat.mpc, rep.mpc)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Methods\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Find the median ks distance of the same number of nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_median(x, metric_distances):\n",
+    "    distance_dic = {}\n",
+    "    for index, num_of_nodes in enumerate(x):\n",
+    "        if num_of_nodes[0] not in distance_dic:\n",
+    "            distance_dic[num_of_nodes[0]] = []\n",
+    "        distance_dic[num_of_nodes[0]].append(metric_distances[index])\n",
+    "    median_x = []\n",
+    "    y = []\n",
+    "    for num_of_nodes, distances in distance_dic.items():\n",
+    "        median_x.append(num_of_nodes)\n",
+    "        y.append(np.median(distances))\n",
+    "    order = np.argsort(median_x)\n",
+    "    median_x = np.array(median_x)[order]\n",
+    "    median_y = np.array(y)[order]\n",
+    "    return median_x, median_y\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot Diagram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# metric_selector: GraphDistance -> float\n",
+    "def plot(infos, lines, id, metric_selector,colors, title, foldername):\n",
+    "    metric_distances = retrive_info_from_list(metric_selector, list(infos.values()))\n",
+    "    x = retrive_info_from_list(lambda a : a.graph.num_nodes, list(infos.values()))\n",
+    "    graph = plt.figure(id,figsize=(18, 10))\n",
+    "    plt.title(title)\n",
+    "    plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)\n",
+    "    #plot ks distance median\n",
+    "    median_x, median_y = find_median(x, metric_distances)\n",
+    "    plt.plot(median_x, median_y, color='black',marker='o')\n",
+    "    for i in range(0, len(lines)):\n",
+    "        line_infos = retrive_info_from_list(lambda a: infos[a], lines[i])\n",
+    "        line_y = retrive_info_from_list(metric_selector, line_infos)\n",
+    "        line_x = retrive_info_from_list(lambda a : a.graph.num_nodes, line_infos)\n",
+    "        plt.plot(line_x, line_y, marker='o', color=colors[i])\n",
+    "    mkdir_p(foldername)\n",
+    "    plt.savefig(fname = foldername+title+'.jpg', dpi=150)\n",
+    "    #graph.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Retrieve information from a list "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def retrive_info_from_list(selector, distances):\n",
+    "    return list(map(selector, distances))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def readStats(path, numModels):\n",
+    "    names =  reader.readmultiplefiles(path, numModels, False)\n",
+    "    stats = []\n",
+    "    for name in names:\n",
+    "        stats.append(GraphStat(name))\n",
+    "    return stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calDistanceDic(stats, rep):\n",
+    "    dic = {}\n",
+    "    for info in stats:\n",
+    "        info = GraphDistanceWithRep(info, rep)\n",
+    "        dic[info.graph.id] = info\n",
+    "    return dic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def createRandomColors(size):\n",
+    "    #generate random color for each line\n",
+    "    colors = []\n",
+    "\n",
+    "    for i in range(0, size):\n",
+    "        color = \"#%06x\" % random.randint(0, 0xFFFFFF)\n",
+    "        colors.append(color)\n",
+    "    return colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def createSelectionWidge(options):\n",
+    "    w = widgets.SelectMultiple(\n",
+    "        options = options,\n",
+    "        value = [],\n",
+    "        description='Trajectory:',\n",
+    "        disabled=False,\n",
+    "    )\n",
+    "    return w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mkdir_p(mypath):\n",
+    "    '''Creates a directory. equivalent to using mkdir -p on the command line'''\n",
+    "\n",
+    "    from errno import EEXIST\n",
+    "    from os import makedirs,path\n",
+    "\n",
+    "    try:\n",
+    "        makedirs(mypath)\n",
+    "    except OSError as exc: # Python >2.5\n",
+    "        if exc.errno == EEXIST and path.isdir(mypath):\n",
+    "            pass\n",
+    "        else: raise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metrics During GenerationPlots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read Human Representatives"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Read Models\n",
+    "#read representative\n",
+    "human_rep = GraphStat(constants.HUMAN_OUT_D_REP)\n",
+    "human_na = GraphStat(constants.HUMAN_NA_REP)\n",
+    "human_mpc = GraphStat(constants.HUMAN_MPC_REP)\n",
+    "\n",
+    "# assign rep distributions to human_rep\n",
+    "human_rep.na = human_na.na\n",
+    "human_rep.mpc = human_mpc.mpc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viatra No Constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read generated models\n",
+    "viatra_no_con_stats = readStats('../input/viatra_nocon_output/', 5000)\n",
+    "viatra_no_con_dic = calDistanceDic(viatra_no_con_stats, human_rep)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filenames = reader.readmultiplefiles('../input/viatra_nocon_output/trajectories/', 15, False)\n",
+    "trajectories = {}\n",
+    "for name in filenames:\n",
+    "    trajectories[name] = reader.readTrajectory(name)\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8471e4dd66a47ecb6abb2371be43321",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(viatra_no_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree', '../output/viatra_no_constraints/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad6e466cc3fe44d393d2c82d48244d83",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_na(lines)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_na(lines):\n",
+    "    plot(viatra_no_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity', '../output/viatra_no_constraints/')\n",
+    "interact(plot_out_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d88ebc8e4062473a96ac35fe800028ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_mpc(lines)>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_mpc(lines):\n",
+    "    plot(viatra_no_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC', '../output/viatra_no_constraints/')\n",
+    "interact(plot_out_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viatra with constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viatra_con_stats = readStats('../input/viatra_con_output/',5000)\n",
+    "viatra_con_dic = calDistanceDic(viatra_con_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1eb2ba5848a048389bca8d804fc8340a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(viatra_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree', '../output/viatra_constraints/')\n",
+    "interact(plot_out_degree, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e5840f7a5ad4515bce9080088b644f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(viatra_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity', '../output/viatra_constraints/')\n",
+    "interact(plot_na, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e30f267b092491ba1ffe8f83c5f68ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(viatra_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC', '../output/viatra_constraints/')\n",
+    "interact(plot_mpc, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlled RandomEMF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_emf_stats = readStats('../input/random_emf_output/',5000)\n",
+    "random_emf_dic = calDistanceDic(random_emf_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc1f64c92e814c32a81cd5ec5d4e50dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree', '../output/random_emf/')\n",
+    "interact(plot_out_degree, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75021f4f68db4a809ce7c86c0d25ef1b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_node_activity(lines)>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_node_activity(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity', '../output/random_emf/')\n",
+    "interact(plot_node_activity, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86f5c376905a4759a7b44ad52804424d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/random_emf/')\n",
+    "interact(plot_mpc, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlled Viatra with MPC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controled_viatra_mpc/',5000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57ba4d8443c145ad845fb862e3ef7519",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_mpc/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c020ecb466c14f3ca1bfc0fd2fe03b7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_mpc/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2165668057fd47ad92459e749ec68bad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_mpc/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## (Pseudo) Random EMF instantiator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_emf_stats = readStats('../input/real_random_output/',5000)\n",
+    "random_emf_dic = calDistanceDic(random_emf_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "907d7824033b4dfe980c391db0da63eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/random_emf_instantiator/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "08a32c21d0b64217a556715caa8db7b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/random_emf_instantiator/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9dad041ff05d46ce969cfacb07c2ba98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/random_emf_instantiator/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlled Viatra with Out Degree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controlled_viatra_out_degree/',10000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cd77560284d9419daec57192a64b75ec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_out_degree/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ab11afebf7674cebae8d7318c661cf3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_out_degree/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c20b42abcba646c18d7caa6eeb54c403",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_out_degree/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Controlled Viatra with Node Activity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controlled_viatra_out_degree_node_activity/',20000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "902b580a11fa4c8db9d03508ad629067",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_node_activity/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "851b567e745940288b577d9bd27e6f08",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_node_activity/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7de173291f394b10b5113e3312b7b2e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_node_activity/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random EMF With Normal(2,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random_emf_stats = readStats('../input/random_emf_normal/',6000)\n",
+    "random_emf_dic = calDistanceDic(random_emf_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b9ee873d9ca41649cf05f3b713d9142",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree', '../output/random_emf_normal/')\n",
+    "interact(plot_out_degree, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88f258a0b0ac4417aba320beca7508cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_node_activity(lines)>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_node_activity(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity', '../output/random_emf_normal/')\n",
+    "interact(plot_node_activity, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d71cf26018184ee6953c50b74908f52d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(random_emf_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/random_emf_normal/')\n",
+    "interact(plot_mpc, lines=[[]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/controlled_viatra_all/',20000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db15ac26aad84683b9da99fc54749850",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_all/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30bfaf8dd45d4b21b0b43afe5e9fdb8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_all/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5636d37b4416474db5441fe47e7a8a30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_all/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Viatra With Both metric and consistency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con_viatra_stats = readStats('../input/viatra_control_all_with_consistency_1/',20000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
+    "\n",
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e5c7231686544d959527cff36c1f1a5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/viatra_control_all_with_consistency_1/')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e043705333bb474e89582ea9358c57c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_na(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/viatra_control_all_with_consistency_1/')\n",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee4723b62293402e87e6a3f798019b36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/viatra_control_all_with_consistency_1/')\n",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
new file mode 100644
index 00000000..32edb00c
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
@@ -0,0 +1,392 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use K-medoid algorithm to find the suitable human model representitives"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))\n",
+    "sys.path.append(lib_path)\n",
+    "from GraphType import GraphStat\n",
+    "import readCSV as reader\n",
+    "from scipy import stats\n",
+    "from ipywidgets import interact, fixed, interactive\n",
+    "import ipywidgets as widgets\n",
+    "from pyclustering.cluster.kmedoids import kmedoids\n",
+    "from pyclustering.utils.metric import distance_metric, type_metric\n",
+    "import random\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define a new distance metric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ks_value(dest1, dest2):\n",
+    "    value, p = stats.ks_2samp(dest1, dest2)\n",
+    "    return value\n",
+    "\n",
+    "\n",
+    "ks_metric = distance_metric(type_metric.USER_DEFINED, func=ks_value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read Human Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "304"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Progress Widge\n",
+    "w = widgets.FloatProgress(\n",
+    "    value=0,\n",
+    "    min=0,\n",
+    "    max=1.0,\n",
+    "    step=0.1,\n",
+    "    description='Loading Files...:',\n",
+    "    bar_style='info',\n",
+    "    orientation='horizontal'\n",
+    ")\n",
+    "\n",
+    "humanFiles = reader.readmultiplefiles('../input/Human/', 1300, False)\n",
+    "modelToFileName = {}\n",
+    "for name in humanFiles:\n",
+    "    modelToFileName[GraphStat(name)] = name\n",
+    "\n",
+    "models = list(modelToFileName.keys())\n",
+    "len(humanFiles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find Representative by K-medroid for different dists on GraphStat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Returns the index of the representative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def findRep(graphStats, func):\n",
+    "    out_ds = list(map(func, models))\n",
+    "\n",
+    "    #choose a random starting point\n",
+    "    start_index = random.randint(0, len(out_ds))\n",
+    "\n",
+    "    # start with one initial metrid [start_index]\n",
+    "    outdegree_kmedoid = kmedoids(out_ds, [start_index], metric=ks_metric)\n",
+    "\n",
+    "    outdegree_kmedoid.process()\n",
+    "    centoids = outdegree_kmedoid.get_medoids()\n",
+    "    return centoids[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find representative for out degree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For Yakindumm\n",
+    "#### For all human models\n",
+    "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n",
+    "* the average distance between it and others is 0.05515988287586802\n",
+    "\n",
+    "#### For human models with $100 \\pm 10$ nodes\n",
+    "* the rep found is ../input/human_output_100\\R_2015225_run_1.csv\n",
+    "* the average distance between it and others is $0.046150929558524685$\n",
+    "\n",
+    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
+    "* the rep found is ../input/human_output_100\\R_2015248_run_1.csv\n",
+    "* average distance:  0.052753778714861366\n",
+    "* median:  0.0468131868131868\n",
+    "* std:  0.0246917800149673\n",
+    "* max: 0.15993907083015996\n",
+    "* min: 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../input/Human\\33_run_1.csv\n",
+      "../input/Human\\33_run_1.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "od_rep_index = findRep(models, lambda m: m.out_d)\n",
+    "print(list(modelToFileName.values())[od_rep_index])\n",
+    "od_rep_model = models[od_rep_index]\n",
+    "print(modelToFileName[od_rep_model])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average distance:  0.04615092955852465\n",
+      "median:  0.04402137483980782\n",
+      "std:  0.017305709419913242\n",
+      "max: 0.1411706837186424\n",
+      "min: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "distances = []\n",
+    "for model in models:\n",
+    "    distances.append(ks_value(od_rep_model.out_d, model.out_d))\n",
+    "print('average distance: ', np.mean(distances))\n",
+    "print('median: ', np.median(distances))\n",
+    "print('std: ', np.std(distances))\n",
+    "print('max:', max(distances))\n",
+    "print('min:', min(distances))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find Representative for node activities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For Yakindumm\n",
+    "#### For all human models\n",
+    "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n",
+    "* the average distance between it and others is 0.05275267434589047\n",
+    "\n",
+    "#### For human models with $100 \\pm 10$ nodes\n",
+    "* the rep found is ../input/human_output_100\\R_2017419_run_1.csv\n",
+    "* the average distance between it and others is $0.04679429311806747$\n",
+    "\n",
+    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
+    "* the rep found is ../input/human_output_100\\R_2017131_run_1.csv\n",
+    "* average distance:  0.024629205820449567\n",
+    "* median:  0.023787888564682946\n",
+    "* std:  0.013845547883198073\n",
+    "* max: 0.09044674910251294\n",
+    "* min: 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../input/Human\\288_run_1.csv\n",
+      "../input/Human\\288_run_1.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "na_rep_index = findRep(models, lambda m: m.na)\n",
+    "print(list(modelToFileName.values())[na_rep_index])\n",
+    "na_rep_model = models[na_rep_index]\n",
+    "print(modelToFileName[na_rep_model])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average distance:  0.046794293118067494\n",
+      "median:  0.03898868458274401\n",
+      "std:  0.02880119213919405\n",
+      "max: 0.18702970297029703\n",
+      "min: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "distances = []\n",
+    "for model in models:\n",
+    "    distances.append(ks_value(na_rep_model.na, model.na))\n",
+    "print('average distance: ', np.mean(distances))\n",
+    "print('median: ', np.median(distances))\n",
+    "print('std: ', np.std(distances))\n",
+    "print('max:', max(distances))\n",
+    "print('min:', min(distances))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find Representative for MPC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For Yakindumm\n",
+    "\n",
+    "#### For all human models\n",
+    "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n",
+    "* the average distance between it and others is 0.08556632702185384\n",
+    "\n",
+    "#### For human models with $100 \\pm 10$ nodes\n",
+    "* the rep found is ../input/human_output_100\\R_2016324_run_1.csv\n",
+    "* the average distance between it and others is $0.07028909225833631$\n",
+    "\n",
+    "#### for human model with $100 \\pm 10$ nodes and new metric\n",
+    "* average distance:  0.054782550772603904\n",
+    "* median:  0.048330503678551184\n",
+    "* std:  0.028208257424907526\n",
+    "* max: 0.21181525241675614\n",
+    "* min: 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../input/Human\\151_run_1.csv\n",
+      "../input/Human\\151_run_1.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "mpc_rep_index = findRep(models, lambda m: m.mpc)\n",
+    "print(list(modelToFileName.values())[mpc_rep_index])\n",
+    "mpc_rep_model = models[mpc_rep_index]\n",
+    "print(modelToFileName[mpc_rep_model])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average distance:  0.07028909225833632\n",
+      "median:  0.06254480286738351\n",
+      "std:  0.037281890512224164\n",
+      "max: 0.21961550993809065\n",
+      "min: 0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "distances = []\n",
+    "for model in models:\n",
+    "    distances.append(ks_value(mpc_rep_model.mpc, model.mpc))\n",
+    "print('average distance: ', np.mean(distances))\n",
+    "print('median: ', np.median(distances))\n",
+    "print('std: ', np.std(distances))\n",
+    "print('max:', max(distances))\n",
+    "print('min:', min(distances))"
+   ]
+  }
+ ],
+ "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/Metrics/Metrics-Calculation/metrics_plot/type_analysis/input/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/input/.gitignore
new file mode 100644
index 00000000..b3934b01
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/input/.gitignore
@@ -0,0 +1,3 @@
+# ignore everything in this folder
+*
+!.gitignore
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/Node Type Analysis.ipynb b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/Node Type Analysis.ipynb
new file mode 100644
index 00000000..b4e3432a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/Node Type Analysis.ipynb
@@ -0,0 +1,435 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "import glob\n",
+    "lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))\n",
+    "sys.path.append(lib_path)\n",
+    "import matplotlib.pyplot as plt\n",
+    "from GraphType import GraphStat\n",
+    "import readCSV as reader\n",
+    "import constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getModels(folderName, numberOfModels):\n",
+    "    filenames = reader.readmultiplefiles(folderName, numberOfModels, False)\n",
+    "    graphStats = [GraphStat(filename) for filename in filenames]\n",
+    "    return graphStats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drawTypeDistributions(folderName, numberOfModels):\n",
+    "    graphStats = getModels(folderName, numberOfModels)\n",
+    "    typeMap = {}\n",
+    "    keys = set()\n",
+    "    for g in graphStats:\n",
+    "        keys = keys.union(set(g.nodeTypeStat.keys()))\n",
+    "    for key in keys:\n",
+    "        typeMap[key] = [float(g.nodeTypeStat.get(key,0)) for g in graphStats]\n",
+    "    print(list(typeMap.keys()))\n",
+    "    for i, key in enumerate(typeMap.keys()):\n",
+    "        plt.figure(i)\n",
+    "        plt.hist(typeMap[key], range = (0,1), bins=50)\n",
+    "        plt.title(key)\n",
+    "        plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['EPackage', 'EAttribute', 'EEnumLiteral', 'EGenericType', 'EAnnotation', 'EOperation', 'EEnum', 'ETypeParameter', 'EParameter', 'EReference', 'EClass', 'EStringToStringMapEntry', 'EDataType']\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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45qPDpMY8ic+5zwKnDK2vB/ZOYD+SpIOYRLh/HNiY5GlJjgXOB3ZOYD+SpIMY+7RMVT2U5LXA3zD4KOTbq+rWce9nyLKndqaQYz46OOajw0TGPPY3VCVJq89zy0hShwx3SerQ1IR7kjOTfDbJ7iQXL7D9uCRXtu03Jtmw8lWO1whjfn2SzyS5Jcl1SZ66GnWO02JjHup3bpJKMvUfmxtlzEl+qD3Wtyb505WucdxG+Nv+hiTXJ/lE+/s+ezXqHJckb0+yL8mnD7I9SS5r/x63JDlt2TutqiP+wuCN2c8BTweOBT4JPHtenx8H/qAtnw9cudp1r8CYvw/42rb86qNhzK3fY4EPA7uATatd9wo8zhuBTwBPaOsnrXbdKzDm7cCr2/KzgTtXu+5ljvl7gNOATx9k+9nAXzH4ntBm4Mbl7nNajty/ekqDqvpf4MApDYZtAXa05XcDZyRZ6AtV02LRMVfV9VX1QFvdxeA7BdNslMcZ4NeA3wYeXMniJmSUMb8SeEtV3QdQVftWuMZxG2XMBTyuLT+eKf+uTFV9GLj3EF22AFfUwC7ghCQnL2ef0xLu64A9Q+uzrW3BPlX1ELAfeNKKVDcZo4x52FYGz/zTbNExJ3kOcEpVvX8lC5ugUR7nbwK+KclHk+xqZ12dZqOM+Y3Ay5LMAtcAP7Eypa2apf5/X9S0nM99lFMajHTagyky8niSvAzYBHzvRCuavEOOOcmjgEuBC1eqoBUwyuO8hsHUzOkMXp39fZJvqar7J1zbpIwy5pcC76iq30nyncA725i/MvnyVsXY82tajtxHOaXBV/skWcPgpdyhXgYd6UY6jUOSFwC/CLykqr64QrVNymJjfizwLcANSe5kMDe5c8rfVB31b/vqqvpSVf0rg5PsbVyh+iZhlDFvBa4CqKqPAcczOMFWr8Z+2pZpCfdRTmmwE7igLZ8LfKjaOxVTatExtymKP2QQ7NM+DwuLjLmq9lfViVW1oao2MHif4SVVNbM65Y7FKH/b72Pw5jlJTmQwTXPHilY5XqOM+S7gDIAkz2IQ7nMrWuXK2gm8on1qZjOwv6ruXtY9rva7yEt4t/ls4F8YvMv+i63tVxn854bBg//nwG7gH4Gnr3bNKzDmDwL3ADe3y87VrnnSY57X9wam/NMyIz7OAd7E4DcRPgWcv9o1r8CYnw18lMEnaW4GXrjaNS9zvH8G3A18icFR+lbgVcCrhh7jt7R/j0+N4+/a0w9IUoemZVpGkrQEhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nq0P8BUMBOFFP0AMIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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UM/eHgN+vqicDa4BzkzwFOA/YWlWrgK1tHeA0YFV7bAQuHHvVkqR9mjXcq+qeqvpCW/4ecCuwHFgHbG7dNgNntuV1wCU1cD2wNMkJY69ckjSjOc25J1kJPAu4ATi+qu6BwRsAcFzrthzYMbTbRGub+lobk2xLsm1ycnLulUuSZjRyuCd5DPB/gH9fVd/dV9dp2mqvhqpNVbW6qlYvW7Zs1DIkSSMYKdyTPJxBsP9VVf3f1nzv7umW9ryztU8AJw7tvgK4ezzlSpJGMcrVMgEuAm6tqncObdoCrG/L64Erh9rPaVfNrAF27Z6+kSQtjCUj9Hku8G+BLyW5ubW9FXgHcFmSDcBdwFlt29XA6cB24EHg1WOtWJI0q1nDvao+y/Tz6ABrp+lfwLkHWJck6QB4h6okdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SerQrOGe5OIkO5N8eajt6CTXJLmtPT++tSfJBUm2J7klySnzWbwkaXqjnLm/H3jxlLbzgK1VtQrY2tYBTgNWtcdG4MLxlClJmotZw72qPgPcN6V5HbC5LW8Gzhxqv6QGrgeWJjlhXMVKkkazv3Pux1fVPQDt+bjWvhzYMdRvorXtJcnGJNuSbJucnNzPMiRJ0xn3B6qZpq2m61hVm6pqdVWtXrZs2ZjLkKTD2/6G+727p1va887WPgGcONRvBXD3/pcnSdof+xvuW4D1bXk9cOVQ+zntqpk1wK7d0zeSpIWzZLYOST4EPB84NskE8J+BdwCXJdkA3AWc1bpfDZwObAceBF49DzVLkmYxa7hX1ctn2LR2mr4FnHugRUmSDox3qEpShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA7NS7gneXGSryfZnuS8+TiGJGlmS8b9gkmOAN4N/DowAXw+yZaq+uq4j7U/Vp738Rm33fmOlyxgJZI0f8Ye7sCpwPaqugMgyYeBdcCChvu+Qnyc+4yDbyqSxm0+wn05sGNofQL4l1M7JdkIbGyr30/y9f083rHAt/dz30NCzp/zLot+zPvBMR8eHPPcPGGmDfMR7pmmrfZqqNoEbDrggyXbqmr1gb7OYuKYDw+O+fAwX2Oejw9UJ4ATh9ZXAHfPw3EkSTOYj3D/PLAqyROTHAmcDWyZh+NIkmYw9mmZqnooyRuBvwaOAC6uqq+M+zhDDnhqZxFyzIcHx3x4mJcxp2qv6XBJ0iLnHaqS1CHDXZI6tGjCfbavNEjyiCSXtu03JFm58FWO1whjfnOSrya5JcnWJDNe87pYjPrVFUlelqSSLPrL5kYZc5Lfbj/rryT54ELXOG4j/G6flOTaJDe13+/TD0ad45Lk4iQ7k3x5hu1JckH787glySkHfNCqOuQfDD6YvR04GTgS+CLwlCl93gD8ZVs+G7j0YNe9AGP+NeBRbfn1h8OYW7+jgM8A1wOrD3bdC/BzXgXcBDy+rR93sOtegDFvAl7flp8C3Hmw6z7AMf8qcArw5Rm2nw58gsF9QmuAGw70mIvlzP1nX2lQVT8Cdn+lwbB1wOa2fDmwNsl0N1QtFrOOuaquraoH2+r1DO4pWMxG+TkD/Bfgz4AfLmRx82SUMb8WeHdV3Q9QVTsXuMZxG2XMBTy2LT+ORX6vTFV9BrhvH13WAZfUwPXA0iQnHMgxF0u4T/eVBstn6lNVDwG7gGMWpLr5McqYh21g8M6/mM065iTPAk6sqqsWsrB5NMrP+ZeAX0ryt0muT/LiBatufowy5rcBr0oyAVwN/O7ClHbQzPXv+6zm4+sH5sMoX2kw0tceLCIjjyfJq4DVwPPmtaL5t88xJ3kY8C7gdxaqoAUwys95CYOpmecz+NfZ3yR5WlU9MM+1zZdRxvxy4P1V9d+SPAf4QBvzT+e/vINi7Pm1WM7cR/lKg5/1SbKEwT/l9vXPoEPdSF/jkOSFwB8BZ1TVPy1QbfNltjEfBTwNuC7JnQzmJrcs8g9VR/3dvrKqflxV/wB8nUHYL1ajjHkDcBlAVX0OeCSDL9jq1di/tmWxhPsoX2mwBVjfll8GfLraJxWL1KxjblMU72EQ7It9HhZmGXNV7aqqY6tqZVWtZPA5wxlVte3glDsWo/xuf5TBh+ckOZbBNM0dC1rleI0y5ruAtQBJnswg3CcXtMqFtQU4p101swbYVVX3HNArHuxPkefwafPpwDcYfMr+R63tTxj85YbBD/8jwHbg74GTD3bNCzDmTwH3Aje3x5aDXfN8j3lK3+tY5FfLjPhzDvBOBv8nwpeAsw92zQsw5qcAf8vgSpqbgRcd7JoPcLwfAu4BfszgLH0D8DrgdUM/43e3P48vjeP32q8fkKQOLZZpGUnSHBjuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUP/HzzJnX+HTdVAAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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DFGfanOkzZczM2qtuuEfEndSeR79xhG0uAC5ooC4zM2uAP6FqZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llqMxl9mZKul3SWklrJH0ktb9a0s2Sfpp+75XaJeliSf2SVks6rNWDMDOzlytz5L4V+KuIOAg4AjhT0sHAucCtETELuDUtAxxHcd3UWcAi4NKmV21mZiOqG+4RsTEifphuPw2sBaYD84ClqdtS4IR0ex5wVRRWAFOqLqZtZmYtNqo5d0ndwKHASmCfiNgIxRMAMC11mw6sr9hsILVV72uRpD5JfYODg6Ov3MzMhlU63CXtDnwDODsifjVS1xptsV1DxJKI6ImInq6urrJlmJlZCaXCXdJOFMH+tYj4Zmp+bGi6Jf3elNoHgJkVm88ANjSnXDMzK6PM2TICLgfWRsQXK1b1AgvS7QXADRXtp6azZo4ANg9N35iZWXtMLtHnSOD9wH2SVqW2jwMXAsslLQQeAU5K624E5gL9wBbgtKZWbGZmddUN94i4k9rz6ABzavQP4MwG6zIzswb4E6pmZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWoTJXYrpC0iZJ91e0fUbSo5JWpZ+5FevOk9Qv6UFJx7SqcDMzG16ZI/crgWNrtC+OiNnp50YASQcD84E3pW3+TdKkZhVrZmbl1A33iPg+8ETJ/c0DlkXE8xHxMMWl9g5voD4zMxuDRubcz5K0Ok3b7JXapgPrK/oMpLbtSFokqU9S3+DgYANlmJlZtbGG+6XA64HZwEbgotRe61qrUWsHEbEkInoioqerq2uMZZiZWS1jCveIeCwitkXEi8BXeGnqZQCYWdF1BrChsRLNzGy0xhTukvatWHw3MHQmTS8wX9IukvYHZgF3NVaimZmN1uR6HSRdAxwFTJU0AHwaOErSbIopl3XA6QARsUbScuABYCtwZkRsa03pZmY2nLrhHhEn12i+fIT+FwAXNFKUmZk1xp9QNTPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMlQ33CVdIWmTpPsr2l4t6WZJP02/90rtknSxpH5JqyUd1srizcystjJH7lcCx1a1nQvcGhGzgFvTMsBxFNdNnQUsAi5tTplmZjYadcM9Ir4PPFHVPA9Ymm4vBU6oaL8qCiuAKVUX0zYzszYY65z7PhGxESD9npbapwPrK/oNpLbtSFokqU9S3+Dg4BjLMDOzWpr9hqpqtEWtjhGxJCJ6IqKnq6uryWWYme3Yxhrujw1Nt6Tfm1L7ADCzot8MYMPYyzMzs7EYa7j3AgvS7QXADRXtp6azZo4ANg9N35iZWftMrtdB0jXAUcBUSQPAp4ELgeWSFgKPACel7jcCc4F+YAtwWgtqNjOzOuqGe0ScPMyqOTX6BnBmo0WZmVlj/AlVM7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy1Dd73MfiaR1wNPANmBrRPRIejVwLdANrAPeGxFPNlammZmNRjOO3N8eEbMjoictnwvcGhGzgFvTspmZtVErpmXmAUvT7aXACS24DzMzG0Gj4R7A9yTdI2lRattn6KLY6fe0WhtKWiSpT1Lf4OBgg2WYmVmlhubcgSMjYoOkacDNkn5cdsOIWAIsAejp6YkG6zAzswoNHblHxIb0exNwPXA48JikfQHS702NFmlmZqMz5nCX9FuS9hi6DfwxcD/QCyxI3RYANzRapJmZjU4j0zL7ANdLGtrPv0fEdyXdDSyXtBB4BDip8TLNzGw0xhzuEfEQ8JYa7b8E5jRSlJmZNcafUDUzy5DD3cwsQw53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDLkcDczy1Cj3wppHa773P+o2b7uwne2uRIza6asw93BZWY7Kk/LmJllKOsj94nGryR2DH6cbSJwuE9gzQqJ4fbTzG0cXGYTi8O9AWMJzU6+3x1Vpx+Jd3r9NjY7ZLhPtHCcaPWMRbOO9B1EZs3RsnCXdCzwL8Ak4LKIuLBV92Wdp1lPaKN9Mhjpfls93eUnLmunloS7pEnAJcAfAQPA3ZJ6I+KBVtyf5a9ZQdrsbSaSTq/fmqtVR+6HA/3pUnxIWgbMAxzuZlUm2ns3I72SaFatzXqV5Fc9w2tVuE8H1lcsDwBvrewgaRGwKC0+I+nBMd7XVODxMW7bqTzmHcO4jFmfH9f7GNWY21FrGzTyOL9uuBWtCnfVaIuXLUQsAZY0fEdSX0T0NLqfTuIx7xg85h1Dq8bcqk+oDgAzK5ZnABtadF9mZlalVeF+NzBL0v6SdgbmA70tui8zM6vSkmmZiNgq6SzgPylOhbwiIta04r5owtROB/KYdwwe846hJWNWRNTvZWZmHcXfCmlmliGHu5lZhjom3CUdK+lBSf2Szq2xfhdJ16b1KyV1t7/K5iox5nMkPSBptaRbJQ17zmunqDfmin4nSgpJHX/aXJkxS3pveqzXSPr3dtfYbCX+bb9W0u2S7k3/vueOR53NIukKSZsk3T/Mekm6OP09Vks6rOE7jYgJ/0PxpuzPgAOAnYEfAQdX9fkQ8KV0ez5w7XjX3YYxvx14Zbr9lzvCmFO/PYDvAyuAnvGuuw2P8yzgXmCvtDxtvOtuw5iXAH+Zbh8MrBvvuhsc89uAw4D7h1k/F7iJ4jNCRwArG73PTjly/83XGUTEr4GhrzOoNA9Ymm5fB8yRVOvDVJ2i7pgj4vaI2JIWV1B8nqCTlXmcAT4LfAF4rp3FtUiZMX8QuCQingSIiE1trrHZyow5gFel23vS4Z+TiYjvA0+M0GUecFUUVgBTJO3byH12SrjX+jqD6cP1iYitwGZg77ZU1xplxlxpIcUzfyerO2ZJhwIzI+I77Syshco8zm8A3iDpB5JWpG9c7WRlxvwZ4H2SBoAbgQ+3p7RxM9r/73V1yve51/06g5J9Oknp8Uh6H9AD/GFLK2q9Eccs6RXAYuAD7SqoDco8zpMppmaOonh19t+SDomIp1pcW6uUGfPJwJURcZGk3wOuTmN+sfXljYum51enHLmX+TqD3/SRNJnipdxIL4MmulJf4SDpHcAngOMj4vk21dYq9ca8B3AIcIekdRRzk70d/qZq2X/bN0TECxHxMPAgRdh3qjJjXggsB4iI/wV2pfiCrVw1/StbOiXcy3ydQS+wIN0+Ebgt0jsVHarumNMUxZcpgr3T52GhzpgjYnNETI2I7ojopnif4fiI6BufcpuizL/tb1G8eY6kqRTTNA+1tcrmKjPmR4A5AJIOogj3wbZW2V69wKnprJkjgM0RsbGhPY73u8ijeLd5LvATinfZP5Ha/o7iPzcUD/7XgX7gLuCA8a65DWO+BXgMWJV+ese75laPuarvHXT42TIlH2cBX6S4HsJ9wPzxrrkNYz4Y+AHFmTSrgD8e75obHO81wEbgBYqj9IXAGcAZFY/xJenvcV8z/l376wfMzDLUKdMyZmY2Cg53M7MMOdzNzDLkcDczy5DD3cwsQw53M7MMOdzNzDL0/3J8TpV+4xgQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drawTypeDistributions('../input/human/', 1500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "drawTypeDistributions('../input/viatra_75/', 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract Human Models with size [90, 101]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extractModel():\n",
+    "    from shutil import copy\n",
+    "    list_of_files = reader.readmultiplefiles('../input/humanOutput/', 5000)\n",
+    "    num_nodes_list =[]\n",
+    "    human_size_dic = {}\n",
+    "    for file in list_of_files:\n",
+    "        contents = reader.readcsvfile(file)\n",
+    "        num_of_node = contents[constants.NUMBER_NODES]\n",
+    "        # human_size_dic[file] = int(num_of_node[0])\n",
+    "        # num_nodes_list.append(int(num_of_node[0]))\n",
+    "        if 90 <= num_of_node[0] <= 110:\n",
+    "            copy(file, '../input/human_output_100/')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multinomial Distribution Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9999999999999998\n",
+      "{'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.stats as stats\n",
+    "graphStats = getModels('../input/human_30_500_no_xml/', 1500)\n",
+    "totalNodes = 0\n",
+    "typeMap = {}\n",
+    "for g in graphStats:\n",
+    "    gKeys = g.nodeTypeStat.keys()\n",
+    "    size = g.numNodes[0]\n",
+    "    totalNodes += size\n",
+    "    for key in gKeys:\n",
+    "        curNum = typeMap.get(key, 0)\n",
+    "        typeMap[key] = curNum + float(g.nodeTypeStat[key]) * size\n",
+    "        \n",
+    "for key in typeMap.keys():\n",
+    "    typeMap[key] /= totalNodes\n",
+    "print(sum(typeMap.values()))\n",
+    "print(typeMap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chiSquareMultinomialTest(freq, prob):\n",
+    "    freq = np.array(freq)\n",
+    "    prob = np.array(prob)\n",
+    "    size = sum(freq)\n",
+    "    e = prob * size\n",
+    "    return stats.chisquare(freq, e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def typeDistributionTest(g):\n",
+    "    size = g.numNodes[0]\n",
+    "    freq = []\n",
+    "    prob = []\n",
+    "    for key in typeMap.keys():\n",
+    "        value = float(g.nodeTypeStat.get(key, 0))\n",
+    "        freq.append(np.round(value * size))\n",
+    "        prob.append(typeMap[key])\n",
+    "    test = chiSquareMultinomialTest(freq, prob)\n",
+    "    return test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[7.26359261e+01 6.82490355e-02]\n",
+      "[3.31848655e+01 1.59841378e-03]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test = [typeDistributionTest(graphStats[i]) for i in range(len(graphStats))]\n",
+    "print(np.mean(test, 0))\n",
+    "print(np.median(test, 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Node Counts Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from shutil import copyfile\n",
+    "import statistics \n",
+    "\n",
+    "graphStats = getModels('../input/human/', 1500)\n",
+    "sizes = []\n",
+    "filenames = reader.readmultiplefiles('../input/human/', 1500, False)\n",
+    "count = 1\n",
+    "for filename in filenames:\n",
+    "    graphStat = GraphStat(filename)\n",
+    "    size = graphStat.numNodes[0]\n",
+    "    if size >= 30 and size <= 500 and not ('EAnnotation' in graphStat.nodeTypeStat.keys() and 'EStringToStringMapEntry' in graphStat.nodeTypeStat.keys()):\n",
+    "        copyfile(filename, filename.replace('human', 'human_30_500_no_xml'))\n",
+    "        \n",
+    "        \n",
+    "# for g in graphStats:\n",
+    "#     size = g.numNodes[0]\n",
+    "#     if size >= 30 and size <= 500 and not ('EAnnotation' in g.nodeTypeStat.keys() and 'EStringToStringMapEntry' in g.nodeTypeStat.keys()):\n",
+    "#         sizes.append(g.numNodes[0])\n",
+    "# print(max(sizes))\n",
+    "# print(min(sizes))\n",
+    "# print(statistics.mean(sizes))\n",
+    "# print(len(sizes))\n",
+    "# plt.hist(sizes, bins=10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/plot_violation.py b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/plot_violation.py
new file mode 100644
index 00000000..e5e12f42
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/plot_violation.py
@@ -0,0 +1,77 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import cdf_histogram as cdf
+import readCSV as reader
+import constants
+import csv
+from shutil import copy
+
+
+# viatra_controlled = np.genfromtxt('../statistics/violations/viatra_controlled.csv', delimiter='\n')
+# viatra_controlled_mean = np.mean(viatra_controlled)
+# viatra_nocons = np.genfromtxt('../statistics/violations/viatra_no_cons_violation.csv', delimiter='\n')
+# viatra_nocons_mean = np.mean(viatra_nocons)
+# human = np.genfromtxt('../statistics/violations/human_violation.csv', delimiter='\n')
+# human_mean = np.mean(human)
+# print('viatra controlled violation mean: ' + str(viatra_controlled_mean))
+# print('viatra no cons violation mean: ' + str(viatra_nocons_mean))
+# print('human output violation mean: ' + str(human_mean))
+
+
+def read_violation_file(file_name):
+    violation = {}
+    with open(file_name) as f:
+        for i, line in enumerate(f):
+            line = int(line.rstrip())
+            if line in violation:
+                violation[line] += 1
+            else:
+                violation[line] = 1
+    f.close()
+    return violation
+
+# violation = read_violation_file('../statistics/violations/viatra_controlled.csv')
+# print(violation)
+# fig, ax = plt.subplots()
+# rects = ax.bar(violation.keys(), violation.values())
+# cdf.autolabel(rects, ax)
+# ax.set_title('viatra controlled violation distribution')
+# ax.set_xlabel('number of violations')
+# ax.set_ylabel('number of occurrence')
+# plt.show()
+# fig.savefig('../statistics/violations/viatra_controlled_violation_distribution.png')
+
+
+def plot_violation():
+    v = np.array([0,1,1,0,0])
+    print(v)
+    (n, bins) = np.histogram(v, bins=v.size, density=True)  # NumPy version (no plot)
+    # plt.plot(.5 * (bins[1:] + bins[:-1]), n)
+    plt.hist(v, bins=50, density=1)
+    plt.show()
+
+
+def human_output_size():
+    list_of_files = reader.readmultiplefiles('../statistics/humanOutput/', 5000)
+    num_nodes_list =[]
+    human_size_dic = {}
+    for file in list_of_files:
+        contents = reader.readcsvfile(file)
+        num_of_node = contents[constants.NUMBER_NODES]
+        # human_size_dic[file] = int(num_of_node[0])
+        # num_nodes_list.append(int(num_of_node[0]))
+        if 90 <= num_of_node[0] <= 110:
+            copy(file, '../statistics/human_output_100/')
+
+    # mean = np.mean(num_nodes_list)
+    # std = np.std(num_nodes_list)
+    # with open('../statistics/human_output_size.csv', 'w', newline='') as csvfile:
+    #     spamwriter = csv.writer(csvfile, delimiter=',',
+    #                             quotechar='|', quoting=csv.QUOTE_MINIMAL)
+    #     spamwriter.writerow(['mean', mean])
+    #     spamwriter.writerow(['standard deviation', std])
+    #     for key, value in human_size_dic.items():
+    #         spamwriter.writerow([key, value])
+
+
+
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/DistributionMetrics.py b/Metrics/Metrics-Calculation/metrics_plot/utils/DistributionMetrics.py
new file mode 100644
index 00000000..cf532bc5
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/DistributionMetrics.py
@@ -0,0 +1,53 @@
+from scipy import stats
+from scipy.spatial import distance
+
+def ks_distance(samples1, samples2):
+    value, p = stats.ks_2samp(samples1, samples2)
+    return (value, p)
+
+def manual_ks(pdf1, pdf2):
+    result = 0
+    sum1 = 0
+    sum2 = 0
+    for(a, b) in zip(pdf1, pdf2):
+        sum1 += a
+        sum2 += b
+        result = max(result, abs(sum1-sum2))
+    return result
+
+def js_distance(samples1, samples2):
+    map1 = fromSamples(samples1)
+    map2 = fromSamples(samples2)
+    allKeys = set(map1.keys()) | set(map2.keys())
+    dist1 = distributionFromMap(map1, allKeys)
+    dist2 = distributionFromMap(map2, allKeys)
+    return distance.jensenshannon(dist1, dist2, 2)
+
+def euclidean_distance(samples1, samples2):
+    map1 = fromSamples(samples1)
+    map2 = fromSamples(samples2)
+    allKeys = set(map1.keys()) | set(map2.keys())
+    dist1 = distributionFromMap(map1, allKeys)
+    dist2 = distributionFromMap(map2, allKeys)
+    distance = 0
+    for i in range(len(dist2)):
+        distance += pow(dist1[i] - dist2[i], 2)
+    return pow(distance, 0.5)
+
+def fromSamples(samples):
+    m = {}
+    length = len(samples)
+    for sample in samples:
+        value = m.get(sample, 0)
+        m[sample] = value + 1
+    for key in list(m.keys()):
+        m[key] /= length
+    return m
+
+def distributionFromMap(m, allKeys):
+    dist = []
+    for key in allKeys:
+        value = m.get(key, 0)
+        dist.append(value)
+    return dist
+
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/GraphType.py b/Metrics/Metrics-Calculation/metrics_plot/utils/GraphType.py
new file mode 100644
index 00000000..48d96ccc
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/GraphType.py
@@ -0,0 +1,46 @@
+import readCSV as reader
+import constants
+import numpy as np
+
+# graph stats for a collection of graphs
+class GraphCollection:
+    
+    # init with path contrain files and number of files to read reader is imported from (readCSV)
+    def __init__(self, path, number, name, shouldShuffle = True):
+        self.out_ds = []
+        self.nas = []
+        self.mpcs = []
+        self.nts = []
+        self.name = name
+        self.tccs = []
+        self.violations = []
+        models = reader.readmultiplefiles(path, number, shouldShuffle)
+        print(len(models))
+        self.size = len(models)
+        for i in range(len(models)):
+            contents, out_d, na, mpc = reader.getmetrics(models[i])
+            self.out_ds.append(out_d)
+            self.nas.append(na)
+            self.mpcs.append(mpc)
+            if(constants.Node_TYPE_KEY in contents):
+                self.nts.append(contents[constants.Node_TYPE_KEY])
+            if(constants.TCC_VALUE in contents):
+                self.tccs.append(contents[constants.TCC_VALUE])
+            if(constants.VIOLATION in contents):
+                self.violations.append(contents[constants.VIOLATION][0])
+
+#Graph stat for one graph
+class GraphStat:
+    # init with teh file name of the stat
+    def __init__(self, filename):
+        contents, self.out_d, self.na, self.mpc = reader.getmetrics(filename)
+        self.numNodes = np.array(contents[constants.NUMBER_NODES])
+        if constants.STATE_ID in contents:
+            self.id = (contents[constants.STATE_ID])[0]
+        if constants.Node_TYPE_KEY in contents:
+            self.nodeTypeStat = contents[constants.Node_TYPE_KEY]
+        if constants.VIOLATION in contents:
+            self.violations = int(contents[constants.VIOLATION][0])
+        if(constants.TCC_VALUE_KEY in contents):
+            self.tcc = contents[constants.TCC_VALUE_KEY]
+
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
new file mode 100644
index 00000000..e30cc583
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
@@ -0,0 +1,35 @@
+NUMBER_EDGE_TYPES = 'Number of Edge types'
+
+NUMBER_NODES = 'Number Of Nodes'
+
+OUT_DEGREE_COUNT = 'OutDegreeCount'
+
+OUT_DEGREE_VALUE = 'OutDegreeValue'
+
+NA_COUNT = 'NACount'
+
+NA_VALUE = 'NAValue'
+
+MPC_VALUE = 'MPCValue'
+
+MPC_COUNT = 'MPCCount'
+
+METAMODEL = 'Meta Mode'
+
+STATE_ID = 'State Id'
+
+NODE_TYPE = 'Node Type'
+
+Node_TYPE_KEY = 'NodeType'
+
+HUMAN_OUT_D_REP = '../input/humanOutput/R_2015225_run_1.csv'
+
+HUMAN_MPC_REP = '../input/humanOutput/R_2016324_run_1.csv'
+
+HUMAN_NA_REP = '../input/humanOutput/R_2017419_run_1.csv'
+
+VIOLATION = 'violations'
+
+TCC_VALUE= 'TCCValue'
+
+TCC_COUNT = 'TCCCount'
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/readCSV.py b/Metrics/Metrics-Calculation/metrics_plot/utils/readCSV.py
new file mode 100644
index 00000000..a56caf45
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/readCSV.py
@@ -0,0 +1,186 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import stats
+import glob
+import random
+import constants
+
+#
+# read csvfile returns outdegree, node activity, mpc
+# as matrix with the first row of values and second row of count
+#
+def readcsvfile(filename):
+    
+    contents = {}
+    with open(filename) as f:
+        data = list(f)
+        f.close()
+        for i, line in enumerate(data):
+            arr = line.replace('\n', '').split(',')
+            # if there is no element in the line, continue
+            if len(line) < 0: continue
+            # else check for contents
+            # if it is MPC then use float
+            if arr[0] == constants.MPC_VALUE:
+                contents[constants.MPC_VALUE] = list(map(float, arr[1:]))
+            # meta models are string
+            elif(arr[0] == constants.METAMODEL):
+                contents[constants.METAMODEL] = arr[1:]
+            # Node types
+            elif(arr[0] == constants.NODE_TYPE):
+                types = data[i+1].replace('\n', '').split(',')
+                numbers = data[i+2].replace('\n', '').split(',')
+                #convert number to floats
+                numbers = [float(n) for n in numbers]
+                contents[constants.Node_TYPE_KEY] = {t : n for t, n in zip(types, numbers)}
+            elif(arr[0] == constants.TCC_VALUE):
+                contents[constants.TCC_VALUE] = list(map(float, arr[1:]))
+            # NA and OD are integers, and store other information as string
+            else:
+                try:
+                    contents[arr[0]] = list(map(int, arr[1:]))
+                except:
+                    contents[arr[0]] = arr[1:]
+    return contents
+
+def checkAndReshape(arr):
+    if len(arr.shape) < 2:
+        arr = np.reshape(arr, (arr.shape[0],1))
+    return arr
+
+def readTrajectory(filename):
+    state_codes = []
+    with open(filename) as f:
+        for i, line in enumerate(f):
+            if(line == ''): continue
+            state_codes.append(int(line))
+    return state_codes
+#
+# take a matrix as input
+# return the sample array
+#
+def getsample(dataMatrix):
+    data = []
+    value = dataMatrix[0, :]
+    count = dataMatrix[1, :]
+    for i, v in enumerate(value):
+        for x in range(0, int(count[i])):
+            data.append(v)
+    return data
+
+def reproduceSample(values, counts):
+    arr = np.array([values, counts])
+    return getsample(arr)
+
+#
+# take an array of filenames as input
+# return the samples of outdegree, na, mpc
+#
+def getmetrics(filename):
+    contents = readcsvfile(filename)
+    outdegree_sample = reproduceSample(contents[constants.OUT_DEGREE_VALUE], contents[constants.OUT_DEGREE_COUNT])
+    na_sample = reproduceSample(contents[constants.NA_VALUE], contents[constants.NA_COUNT])
+    mpc_sample = reproduceSample(contents[constants.MPC_VALUE], contents[constants.MPC_COUNT])
+
+    if(constants.TCC_VALUE in contents):
+        contents[constants.TCC_VALUE] = reproduceSample(contents[constants.TCC_VALUE], contents[constants.TCC_COUNT])
+
+    return contents,outdegree_sample, na_sample, mpc_sample
+
+#
+# read number of files in the given path RANDOMLY
+#
+def readmultiplefiles(dirName, maxNumberOfFiles, shouldShuffle = True):
+    list_of_files = glob.glob(dirName + '*.csv')  # create the list of file
+    if shouldShuffle: 
+        random.shuffle(list_of_files)
+    #if the number of files is out of bound then just give the whole list
+    file_names =  list_of_files[:maxNumberOfFiles]
+    # print(file_names)
+    return file_names
+
+
+def plotlines(x, y, ax):
+    l1, = ax.plot(x, y)
+
+
+def testgetsamplesfromfiles():
+    files = readmultiplefiles('../statistics/viatraOutput/', 2)
+    for file in files:
+        getmetrics(file)
+
+def probability(data):
+    sum = np.sum(data)
+    probabilityList = []
+    for d in data:
+        p = d/sum
+        probabilityList.append(p)
+    a = np.array(probabilityList)
+    return a
+
+
+def cumulativeProbability(p):
+    cdf = np.cumsum(p)
+    return cdf
+
+
+def plot():
+    fig, ax = plt.subplots()
+    fig, ax1 = plt.subplots()
+    fig, ax2 = plt.subplots()
+    fig, ax3 = plt.subplots()
+    fig, ax4 = plt.subplots()
+    fig, ax5 = plt.subplots()
+    list_of_files = readmultiplefiles('../statistics/iatraOutput/')
+    for file_name in list_of_files:
+        contents = readcsvfile(file_name)
+        outdegree = [contents[constants.OUT_DEGREE_VALUE], contents[constants.OUT_DEGREE_COUNT]]
+        na = [contents[constants.NA_VALUE], contents[constants.NA_COUNT]]
+        mpc = [contents[constants.MPC_VALUE], contents[constants.MPC_COUNT]]
+        outV = outdegree[0, :]
+        outC = outdegree[1, :]
+        outP = probability(outC)
+        outCumP = cumulativeProbability(outP)
+        plotlines(outV, outP, ax)
+        naV = na[0, :]
+        naC = na[1, :]
+        naP = probability(naC)
+        naCumP = cumulativeProbability(naP)
+        plotlines(naV, naP, ax1)
+        mpcV = mpc[0, :]
+        mpcC = mpc[1, :]
+        mpcP = probability(mpcC)
+        mpcCumP = cumulativeProbability(mpcP)
+        plotlines(mpcV, mpcP, ax2)
+        plotlines(outV, outCumP, ax3)
+        plotlines(naV, naCumP, ax4)
+        plotlines(mpcV, mpcCumP, ax5)
+    ax.set_xlabel('ourdegree')
+    ax.set_ylabel('pdf')
+    ax.grid()
+
+    ax1.set_xlabel('node activity')
+    ax1.set_ylabel('pdf')
+    ax1.grid()
+
+    ax2.set_xlabel('multiplex participation coefficient')
+    ax2.set_ylabel('pdf')
+    ax2.grid()
+
+    ax3.set_xlabel('ourdegree')
+    ax3.set_ylabel('cdf')
+    ax3.grid()
+
+    ax4.set_xlabel('node activity')
+    ax4.set_ylabel('cdf')
+    ax4.grid()
+
+    ax5.set_xlabel('multiplex participation coefficient')
+    ax5.set_ylabel('cdf')
+    ax5.grid()
+
+    plt.show()
+
+
+# plot()
+