add metrics data plot tool

31 files changed, 1063 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/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/MPC.png
new file mode 100644
index 00000000..cd2af5ce
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Node Activity.png
new file mode 100644
index 00000000..2ce3672b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Out Degree.png
new file mode 100644
index 00000000..d4d9afe1
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/MPC.png
new file mode 100644
index 00000000..8d76fed9
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Node Activity.png
new file mode 100644
index 00000000..daa7929a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Out Degree.png
new file mode 100644
index 00000000..e56b1b6e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/MPC.png
new file mode 100644
index 00000000..8705cee2
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Node Activity.png
new file mode 100644
index 00000000..bdab3741
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Out Degree.png
new file mode 100644
index 00000000..e4269f6e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/MPC.png
new file mode 100644
index 00000000..e4b6b527
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Node Activity.png
new file mode 100644
index 00000000..e067ad66
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Out Degree.png
new file mode 100644
index 00000000..9b89e4f9
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/MPC.png
new file mode 100644
index 00000000..d819aad5
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Node Activity.png
new file mode 100644
index 00000000..4b3b187e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Out Degree.png
new file mode 100644
index 00000000..d8e01f6e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/MPC.png
new file mode 100644
index 00000000..4f189578
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Node Activity.png
new file mode 100644
index 00000000..add3c0f8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Out Degree.png
new file mode 100644
index 00000000..f4717a1a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py b/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
new file mode 100644
index 00000000..b3c9f359
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
@@ -0,0 +1,27 @@
+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.name = name
+        models = reader.readmultiplefiles(path, number, shouldShuffle)
+        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)
+
+#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.num_nodes = np.array(contents[constants.NUMBER_NODES])
+        self.id = (contents[constants.STATE_ID])[0]
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/src/MPC.png
new file mode 100644
index 00000000..4f189578
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/MPC.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb
new file mode 100644
index 00000000..17ad1253
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/Metrics Comparison .ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metric comparison preperation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import readCSV as reader\n",
+    "import glob\n",
+    "import random \n",
+    "from sklearn.manifold import MDS\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculateKSMatrix(dists):\n",
+    "    dist = []\n",
+    "\n",
+    "    for i in range(len(dists)):\n",
+    "        dist = dist + dists[i]\n",
+    "    matrix = np.empty((len(dist),len(dist)))\n",
+    "\n",
+    "    for i in range(len(dist)):\n",
+    "        matrix[i,i] = 0\n",
+    "        for j in range(i+1, len(dist)):\n",
+    "            value, p = stats.ks_2samp(dist[i], dist[j])\n",
+    "            matrix[i, j] = value\n",
+    "            matrix[j, i] = value\n",
+    "            value, p = stats.ks_2samp(dist[j], dist[i])\n",
+    "    return matrix\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculateMDS(dissimilarities):\n",
+    "    embedding = MDS(n_components=2, dissimilarity='precomputed')\n",
+    "    trans = embedding.fit_transform(X=dissimilarities)\n",
+    "    return trans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot(names, coords, index = 0, title=''):\n",
+    "    half_length = int(coords.shape[0] / len(names))\n",
+    "    color = ['blue', 'red', 'green']\n",
+    "    graph = plt.figure(index)\n",
+    "    plt.title(title)\n",
+    "    for i in range(len(names)):\n",
+    "        x = (coords[(i*half_length):((i+1)*half_length), 0].tolist())\n",
+    "        y = (coords[(i*half_length):((i+1)*half_length), 1].tolist())\n",
+    "        plt.plot(x, y, color=color[i], marker='o', label = names[i], linestyle='', alpha=0.7)\n",
+    "    plt.legend(loc='upper right')\n",
+    "    plt.savefig(fname = title+'.png', dpi=150)\n",
+    "    #graph.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Files\n",
+    "1. define class for metric reading of each graph type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class GraphType:\n",
+    "    \n",
+    "    # init with path contrain files and number of files to read reader is imported from (readCSV)\n",
+    "    def __init__(self, path, number):\n",
+    "        self.out_ds = []\n",
+    "        self.nas = []\n",
+    "        self.mpcs = []\n",
+    "        models = reader.readmultiplefiles(path, number)\n",
+    "        for i in range(len(models)):\n",
+    "            out_d, na, mpc = reader.getmetrics(models[i])\n",
+    "            self.out_ds.append(out_d)\n",
+    "            self.nas.append(na)\n",
+    "            self.mpcs.append(mpc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. read metrics for each graph type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "human = GraphType('../statistics/humanOutput/', 300)\n",
+    "viatra30 = GraphType('../statistics/viatraOutput30/', 300)\n",
+    "viatra100 = GraphType('../statistics/viatraOutput100/', 300)\n",
+    "random = GraphType('../statistics/randomOutput/', 300)\n",
+    "alloy = GraphType('../statistics/alloyOutput/', 300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* outdegree comparison for human, Viatra30, and alloy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-11-5692e29d4679>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mout_d_coords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculateMDS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalculateKSMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mviatra30\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malloy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhuman\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mout_ds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Viatra (30 nodes)'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Alloy (30 nodes)'\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'Human'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_d_coords\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Out Degree'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-3-37c4264e6073>\u001b[0m in \u001b[0;36mcalculateKSMatrix\u001b[1;34m(dists)\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m             \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mks_2samp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py\u001b[0m in \u001b[0;36mks_2samp\u001b[1;34m(data1, data2)\u001b[0m\n\u001b[0;32m   4854\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4855\u001b[0m     \"\"\"\n\u001b[1;32m-> 4856\u001b[1;33m     \u001b[0mdata1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4857\u001b[0m     \u001b[0mdata2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4858\u001b[0m     \u001b[0mn1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m    932\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 934\u001b[1;33m     \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    935\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "out_d_coords = calculateMDS(calculateKSMatrix([viatra30.out_ds, alloy.out_ds, human.out_ds]))\n",
+    "plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Out Degree')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* outdegree comparison for human, Viatra30, and alloy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_d_coords = calculateMDS(calculateKSMatrix([viatra30.nas, alloy.nas, human.nas]))\n",
+    "plot(['Viatra (30 nodes)', 'Alloy (30 nodes)' , 'Human'], out_d_coords,0, 'Node Activity')"
+   ]
+  },
+  {
+   "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/src/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.png
new file mode 100644
index 00000000..add3c0f8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/Node Activity.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png
new file mode 100644
index 00000000..5978c7cb
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/average_ks_plot.py b/Metrics/Metrics-Calculation/metrics_plot/src/average_ks_plot.py
new file mode 100644
index 00000000..e949a729
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/average_ks_plot.py
@@ -0,0 +1,62 @@
+from GraphType import GraphStat
+from GraphType import GraphCollection
+from scipy import stats
+import readCSV as reader
+import matplotlib.pyplot as plt
+
+class GraphDistance:
+    #init with a graph stat and a collection of graph stats
+    def __init__(self, graphStat, collection):
+        self.graph = graphStat
+        self.collection = collection
+        print('calculating for %d' %(self.graph.id))
+        self.out_d_distance = average_ks_distance(collection.out_ds, graphStat.out_d)
+        self.na_distance = average_ks_distance(collection.nas, graphStat.na)
+        self.mpc_distance = average_ks_distance(collection.mpcs, graphStat.mpc)
+
+def average_ks_distance(targets, sample):
+    distance = 0.0
+    for target in targets:
+        value, p = stats.ks_2samp(target, sample)
+        distance += value
+    
+    distance = distance / len(targets)
+    return distance
+
+# metric_selector: GraphDistance -> float
+def plot(infos, lines, id, metric_selector, title):
+    metric_distances = retrive_info_from_list(metric_selector, list(infos.values()))
+    x = retrive_info_from_list(lambda a : a.graph.num_nodes, list(infos.values))
+    graph = plt.figure(id)
+    plt.title(title)
+
+    for line in lines:
+        line_infos = retrive_info_from_list(lambda a: infos[a], line)
+        line_y = retrive_info_from_list(metric_selector, line_infos)
+        line_x = retrive_info_from_list(lambda a : a.graph.num_nodes, list(infos.values))
+        plt.plot(line_x, line_y, color='blue')
+    plt.plot(x, metric_distances, color='red', linestyle='', marker='o',alpha=0.7)
+    graph.show()
+
+def retrive_info_from_list(selector, distances):
+    return list(map(selector, list(infos.values)))
+
+human = GraphCollection('../statistics/humanOutput/', 300, 'Human')
+file_names = reader.readmultiplefiles('../statistics/viatraEvolve/', 500, False)
+
+infos = []
+# read all files
+for name in file_names:
+    infos.append(GraphStat(name))
+
+infos = list(map(lambda s: GraphDistance(s, human), infos))
+
+info_dic = {}
+for info in infos:
+    info_dic[info.graph.id] = info
+
+plot(info_dic, [[1,2,3,4,5,6,7,8,9,10]], 0, lambda a: a.out_d_distance, 'out degree')
+# plot(info_dic, 1, lambda a: a.na_distance, 'node activity')
+# plot(info_dic, 2, lambda a: a.mpc_distance, 'mpc')
+
+input()
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/constants.py b/Metrics/Metrics-Calculation/metrics_plot/src/constants.py
new file mode 100644
index 00000000..4504030e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/constants.py
@@ -0,0 +1,19 @@
+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'
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
new file mode 100644
index 00000000..c7daa75e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
@@ -0,0 +1,441 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare Metrics Distances to The Human Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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"
+   ]
+  },
+  {
+   "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": 2,
+   "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": 3,
+   "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",
+    "    \n",
+    "    distance = distance / len(targets)\n",
+    "    return distance"
+   ]
+  },
+  {
+   "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):\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",
+    "    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": 5,
+   "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": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "human = GraphCollection('../statistics/humanOutput/', 300, 'Human')\n",
+    "file_names = reader.readmultiplefiles('../statistics/viatraEvolve/', 1000, False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate Distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "60e3295a164f48428bc882868e68f26f",
+       "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": 9,
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f69502eba4af40f19acc09f36681387a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 10,
+     "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0df16294cd86434b8f144ff08702d44a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_out_degree(lines):\n",
+    "    plot(info_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### MPC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b4e76d41b3d644808e47e3d1d7aaf1a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/trajectori…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 12,
+     "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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../statistics/viatraEvolve\\state_735.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name in file_names:\n",
+    "    contents = reader.readcsvfile(name)\n",
+    "    if(contents['State Id'][0] == 1032396643):\n",
+    "        print(name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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/src/plot_ks_stats.py b/Metrics/Metrics-Calculation/metrics_plot/src/plot_ks_stats.py
new file mode 100644
index 00000000..ad72a4a1
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/plot_ks_stats.py
@@ -0,0 +1,90 @@
+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
+
+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 = stats.ks_2samp(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 = ''):
+    half_length = int(coords.shape[0] / len(graphTypes))
+    color = ['blue', 'red', 'green', 'yellow']
+    lineStyle = ['', '-']
+    plt.figure(index, figsize=(7, 4))
+    plt.title(title)
+    for i in range(len(graphTypes)):
+        x = (coords[(i*half_length):((i+1)*half_length), 0].tolist())
+        y = (coords[(i*half_length):((i+1)*half_length), 1].tolist())
+        plt.plot(x, y, color=color[i], marker='o', label = graphTypes[i].name, linestyle=lineStyle[i], alpha=0.7)
+    plt.legend(loc='upper right')
+    plt.savefig(fname = savePath, dpi=150)
+    #graph.show()
+
+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):
+    metrics = []
+    foldName = '../output/'
+    for graph in graphTypes:
+        metrics.append(metric(graph))
+        foldName = foldName + graph.name + '-'
+    print('calculate' + metricName +' for ' + foldName)
+    mkdir_p(foldName)
+    out_d_coords = calculateMDS(calculateKSMatrix(metrics))
+    plot(graphTypes, out_d_coords, metricName, graphIndex,foldName + '/'+ metricName+'.png')
+
+def nodeActivity(graphType):
+    return graphType.nas
+
+def outDegree(graphType):
+    return graphType.out_ds
+
+def mpc(graphType):
+    return graphType.mpcs
+
+
+# read models
+human = GraphCollection('../statistics/humanOutput/', 500, 'Human')
+# viatra30 = GraphCollection('../statistics/viatraOutput30/', 500, 'Viatra (30 nodes)')
+# viatra60 = GraphCollection('../statistics/viatraOutput60/', 500, 'Viatra (60 nodes)')
+# viatra100 = GraphCollection('../statistics/viatraOutput100/', 500, 'Viatra (100 nodes)')
+# random = GraphCollection('../statistics/randomOutput/', 500, 'Random')
+# alloy = GraphCollection('../statistics/alloyOutput/', 500, 'Alloy (30 nodes)')
+viatraEvolve = GraphCollection('../statistics/viatraEvolve/', 130, 'viatraEvolve', shouldShuffle = False)
+
+#calculate metrics
+metricStat([human, viatraEvolve], 'Node Activity', nodeActivity, 0)
+metricStat([human, viatraEvolve], 'Out Degree', outDegree, 1)
+metricStat([human, viatraEvolve], 'MPC', mpc, 2)
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py b/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
new file mode 100644
index 00000000..8627ad4a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
@@ -0,0 +1,169 @@
+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:
+        for i, line in enumerate(f):
+            arr = line.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:]
+            # all other contants are integer
+            else:
+                contents[arr[0]] = list(map(int, arr[1:]))
+    f.close()
+    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])
+    return contents,outdegree_sample, na_sample, mpc_sample
+
+#
+# read number of files in the given path RANDOMLY
+#
+def readmultiplefiles(dirName, numberOfFiles, 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[:numberOfFiles] if numberOfFiles > len(list_of_files) else list_of_files
+    # 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()
+
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/test.py b/Metrics/Metrics-Calculation/metrics_plot/src/test.py
new file mode 100644
index 00000000..d1aae53a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/test.py
@@ -0,0 +1,35 @@
+from sklearn.datasets import load_digits
+from sklearn.manifold import MDS
+import matplotlib.pyplot as plt
+from scipy import stats
+import numpy as np
+
+dist = []
+
+for i in range(100):
+    rvs = stats.uniform.rvs(size=500, loc=0., scale=1)
+    dist.append(rvs)
+
+for i in range(100):
+    rvs2 = stats.powerlaw .rvs(1.66, size=500)
+    dist.append(rvs2)
+
+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 = stats.ks_2samp(dist[i], dist[j])
+        matrix[i, j] = value
+        matrix[j, i] = value
+
+embedding = MDS(n_components=2, dissimilarity='precomputed')
+trans = embedding.fit_transform(X=matrix)
+x = (trans[:100,0]).tolist()
+y = (trans[:100,1]).tolist()
+
+x2 = (trans[100:,0]).tolist()
+y2 = (trans[100:,1]).tolist()
+plt.plot(x, y, 'yo')
+plt.plot(x2, y2, 'ro')
+plt.show()
diff --git a/Metrics/Metrics-Calculation/metrics_plot/statistics/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/statistics/.gitignore
new file mode 100644
index 00000000..b3934b01
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/statistics/.gitignore
@@ -0,0 +1,3 @@
+# ignore everything in this folder
+*
+!.gitignore
\ No newline at end of file