add realistic solver

34 files changed, 2476 insertions, 0 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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+++ 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
+}