clean up metric directory

44 files changed, 423 insertions, 563 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/MPC.png
deleted file mode 100644
index e660f3da..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/MPC.png
+++ /dev/null
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Node Activity.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Node Activity.png
deleted file mode 100644
index 92047e47..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Node Activity.png
+++ /dev/null
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Out Degree.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Out Degree.png
deleted file mode 100644
index dad7483f..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Out Degree.png
+++ /dev/null
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/MPC.png
index cd2af5ce..cd2af5ce 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/MPC.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Node Activity.png
index 2ce3672b..2ce3672b 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Node Activity.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Out Degree.png
index d4d9afe1..d4d9afe1 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Alloy (30 nodes)-/Out Degree.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Random-/MPC.png
index 8d76fed9..8d76fed9 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/MPC.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Random-/Node Activity.png
index daa7929a..daa7929a 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Node Activity.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Random-/Out Degree.png
index e56b1b6e..e56b1b6e 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-/Out Degree.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/MPC.png
index 8705cee2..8705cee2 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/MPC.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Node Activity.png
index bdab3741..bdab3741 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Node Activity.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Out Degree.png
index e4269f6e..e4269f6e 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-Viatra (30 nodes)-Random-Viatra (100 nodes)-/Out Degree.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/MPC.png
index e4b6b527..e4b6b527 100644
--- 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/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Node Activity.png
index e067ad66..e067ad66 100644
--- 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/model comparison/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/model comparison/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/Out Degree.png
index 9b89e4f9..9b89e4f9 100644
--- 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/model comparison/output/Human-Viatra (30 nodes)-Viatra (60 nodes)-Viatra (100 nodes)-/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/model comparison/output/real vs viatra vs alloy/MPC.png
index 4f189578..4f189578 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/MPC.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/real vs viatra vs alloy/Node Activity.png
index add3c0f8..add3c0f8 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Node Activity.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/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/model comparison/output/real vs viatra vs alloy/Out Degree.png
index f4717a1a..f4717a1a 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/output/real vs viatra vs alloy/Out Degree.png
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/real vs viatra vs alloy/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/statistics/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore
index b3934b01..b3934b01 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/statistics/.gitignore
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/input/.gitignore
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
new file mode 100644
index 00000000..d2dbd970
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
new file mode 100644
index 00000000..3ca2e0bc
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
new file mode 100644
index 00000000..5b623374
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_with_mpc/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
new file mode 100644
index 00000000..7a744ea6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
new file mode 100644
index 00000000..dfcc973e
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
new file mode 100644
index 00000000..b3f4f912
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
new file mode 100644
index 00000000..f0d09211
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/Node Activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
new file mode 100644
index 00000000..94b070d4
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/mpc.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
new file mode 100644
index 00000000..8d0151fa
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/random_emf_instantiator/out_degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
new file mode 100644
index 00000000..b10d97e6
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/MPC.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
new file mode 100644
index 00000000..e41c998b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
new file mode 100644
index 00000000..d2776bc8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_constraints/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
new file mode 100644
index 00000000..19feb5e8
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/MPC.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
new file mode 100644
index 00000000..bc7a83fc
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/node activity.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
new file mode 100644
index 00000000..13094840
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/viatra_no_constraints/out degree.jpg
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance.ipynb
index 550e3978..550e3978 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance.ipynb
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
index a0b0ad8d..4c7fecb3 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb
@@ -16,10 +16,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "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",
     "from GraphType import GraphCollection\n",
     "from scipy import stats\n",
@@ -48,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,12 +111,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
     "# metric_selector: GraphDistance -> float\n",
-    "def plot(infos, lines, id, metric_selector,colors, title):\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",
@@ -127,6 +130,8 @@
     "        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()"
    ]
   },
@@ -139,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,15 +181,74 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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": 43,
+   "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": 33,
+   "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 Models"
+    "### Read Human Representatives"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,73 +260,55 @@
     "\n",
     "# assign rep distributions to human_rep\n",
     "human_rep.na = human_na.na\n",
-    "human_rep.mpc = human_mpc.mpc\n",
-    "# Read generated models\n",
-    "viatra_no_con_stats = readStats('../statistics/viatra_nocon_output/', 5000)\n",
-    "viatra_con_stats = readStats('../statistics/viatra_con_output/',5000)\n",
-    "random_stats = readStats('../statistics/random_output/',5000)\n",
-    "con_viatra_stats = readStats('../statistics/controled_viatra/',300)"
+    "human_rep.mpc = human_mpc.mpc"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## calculate distribution distantces"
+    "## Viatra No Constraint"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "viatra_no_con_dic = calDistanceDic(viatra_no_con_stats, human_rep)\n",
-    "viatra_con_dic = calDistanceDic(viatra_con_stats, human_rep)\n",
-    "random_dic = calDistanceDic(random_stats, human_rep)\n",
-    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
+    "# 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": 11,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filenames = reader.readmultiplefiles('../statistics/viatra_nocon_output/trajectories/', 15, False)\n",
+    "filenames = reader.readmultiplefiles('../input/viatra_nocon_output/trajectories/', 15, 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)"
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "868a437468d24144926f1390cbf2acb8",
+       "model_id": "9519be563fbc41c28921c77ef6481b17",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -274,31 +320,31 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 77,
      "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')\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": 13,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8b74fe96a45445f8062468ddf2597bf",
+       "model_id": "c896725e542c4bf8a1bc76ba66819b20",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -310,31 +356,31 @@
        "<function __main__.plot_out_na(lines)>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 78,
      "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')\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": 14,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c6e7e31f454a48169dac12c8aac70eef",
+       "model_id": "880410d675624545ab73977a463bb5c9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -346,26 +392,48 @@
        "<function __main__.plot_out_mpc(lines)>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 79,
      "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')\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": 15,
+   "execution_count": 50,
+   "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": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cebc359548f74cc8b7540ecc3876c9ee",
+       "model_id": "0d04d6db770a49f4a160ff55cc7131f6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -382,26 +450,26 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 51,
      "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')\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": 16,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "682beae42eef4676b11b6fe23127a44e",
+       "model_id": "96eebad1f6274d79ad377c8c54b44615",
        "version_major": 2,
        "version_minor": 0
       },
@@ -418,26 +486,26 @@
        "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 52,
      "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')\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": 17,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6893b8c6e03441f89fc35bf784992ae9",
+       "model_id": "4fc2714a3cd3440daf5014bb4b942b9a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -454,26 +522,48 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def plot_mpc(lines):\n",
-    "    plot(viatra_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\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": 18,
+   "execution_count": 59,
+   "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": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff0e1991c69a4d77a40f57225f90295a",
+       "model_id": "4401931533b5497f864f146d7b4dcd3c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -490,26 +580,26 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def plot_out_degree(lines):\n",
-    "    plot(random_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\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": 19,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "838570f20bed4d8d9c618305984d19ef",
+       "model_id": "fb7bdedff841420bb8f817013f565020",
        "version_major": 2,
        "version_minor": 0
       },
@@ -523,29 +613,29 @@
     {
      "data": {
       "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
+       "<function __main__.plot_node_activity(lines)>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(random_dic, lines, 0, lambda a: a.na_distance, colors, 'out degree')\n",
-    "interact(plot_out_degree, lines=[[]])"
+    "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": 20,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4825f6257a74bce9dd22aac8a98effa",
+       "model_id": "6b0c349c4a3b4813825513f739ea30da",
        "version_major": 2,
        "version_minor": 0
       },
@@ -559,70 +649,56 @@
     {
      "data": {
       "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
+       "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(random_dic, lines, 0, lambda a: a.mpc_distance, colors, 'out degree')\n",
-    "interact(plot_out_degree, lines=[[]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "con_viatra_stats = readStats('../statistics/controled_viatra/',5000)\n",
-    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
+    "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": [
-    "## Trajectories for controlled viatra solver"
+    "## Controlled Viatra with MPC"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filenames = reader.readmultiplefiles('../statistics/controled_viatra/trajectories/', 25, False)\n",
-    "trajectories = {}\n",
-    "for name in filenames:\n",
-    "    trajectories[name] = reader.readTrajectory(name)\n",
+    "con_viatra_stats = readStats('../input/controled_viatra_mpc/',5000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n",
     "\n",
-    "w = widgets.SelectMultiple(\n",
-    "    options = trajectories,\n",
-    "    value = [trajectories[filenames[0]]],\n",
-    "    description='Trajectory:',\n",
-    "    disabled=False,\n",
-    ")"
+    "# trajectories and colors\n",
+    "trajectories = {}\n",
+    "w = createSelectionWidge(trajectories)\n",
+    "colors = createRandomColors(len(trajectories))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b60ae3859e343299badf29272f67d21",
+       "model_id": "b76901ba9d44433984032e0dc5679fa9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -634,31 +710,31 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 74,
      "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')\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": 58,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e7965d793a146d4bbc268554262eb58",
+       "model_id": "9e0d61e29b02467cb52618860a1bde7f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -670,31 +746,31 @@
        "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 75,
      "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')\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": 59,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55a1209d0b924a39b4729228e81ee3ab",
+       "model_id": "70074805fee44a1aa5b9ccb3770b5c0c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…"
       ]
      },
      "metadata": {},
@@ -706,30 +782,146 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def plot_mpc(lines):\n",
-    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc')\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": null,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "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": null,
+   "execution_count": 82,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "912ba2fdfd7c46848065f174aa6177e0",
+       "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": 82,
+     "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": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ba621dd0e7d4957aaff2cf209bba165",
+       "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": 83,
+     "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": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d432bbae1c6f48c3acd1767f2e2b13c7",
+       "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": 84,
+     "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": "code",
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
index 4886c215..9653b2a0 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/representative_selector .ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb
@@ -20,6 +20,9 @@
    "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",
@@ -60,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -69,7 +72,7 @@
        "1253"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,7 +90,7 @@
     ")\n",
     "\n",
     "\n",
-    "humanFiles = reader.readmultiplefiles('../statistics/humanOutput/', 1300, False)\n",
+    "humanFiles = reader.readmultiplefiles('../input/humanOutput/', 1300, False)\n",
     "modelToFileName = {}\n",
     "for name in humanFiles:\n",
     "    modelToFileName[GraphStat(name)] = name\n",
@@ -112,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,16 +141,24 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n",
+    "* the average distance between it and others is 0.05515988287586802"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../statistics/humanOutput\\R_20158_run_1.csv\n",
-      "../statistics/humanOutput\\R_20158_run_1.csv\n"
+      "../input/humanOutput\\R_20158_run_1.csv\n",
+      "../input/humanOutput\\R_20158_run_1.csv\n"
      ]
     }
    ],
@@ -159,6 +170,27 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.05515988287586802\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_distance = 0\n",
+    "count = 0\n",
+    "for model in models:\n",
+    "    total_distance += ks_value(od_rep_model.out_d, model.out_d)\n",
+    "print(total_distance / len(models))"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -166,47 +198,53 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n",
+    "* the average distance between it and others is 0.05275267434589047"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'na_rep_index' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-9-7899480190c8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mna_rp_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfindRep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mna\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodelToFileName\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mna_rep_index\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      3\u001b[0m \u001b[0mna_rep_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mna_rep_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodelToFileName\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mna_rep_model\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[1;31mNameError\u001b[0m: name 'na_rep_index' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "../input/humanOutput\\R_2016176_run_1.csv\n",
+      "../input/humanOutput\\R_2016176_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])"
+    "total_distance = 0\n",
+    "for model in models:\n",
+    "    total_distance += ks_value(od_rep_model.mpc, model.mpc)\n",
+    "print(total_distance / len(models))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../statistics/humanOutput\\R_2016176_run_1.csv\n",
-      "../statistics/humanOutput\\R_2016176_run_1.csv\n"
+      "0.05275267434589047\n"
      ]
     }
    ],
    "source": [
-    "print(list(modelToFileName.values())[na_rp_index])\n",
-    "na_rep_model = models[na_rp_index]\n",
-    "print(modelToFileName[na_rep_model])"
+    "total_distance = 0\n",
+    "count = 0\n",
+    "for model in models:\n",
+    "    total_distance += ks_value(od_rep_model.na, model.na)\n",
+    "print(total_distance / len(models))"
    ]
   },
   {
@@ -217,16 +255,24 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n",
+    "* the average distance between it and others is 0.08556632702185384"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "../statistics/humanOutput\\R_2015246_run_1.csv\n",
-      "../statistics/humanOutput\\R_2015246_run_1.csv\n"
+      "../input/humanOutput\\R_2015246_run_1.csv\n",
+      "../input/humanOutput\\R_2015246_run_1.csv\n"
      ]
     }
    ],
@@ -236,6 +282,34 @@
     "mpc_rep_model = models[mpc_rep_index]\n",
     "print(modelToFileName[mpc_rep_model])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08556632702185384\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_distance = 0\n",
+    "count = 0\n",
+    "for model in models:\n",
+    "    total_distance += ks_value(od_rep_model.mpc, model.mpc)\n",
+    "print(total_distance / len(models))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py b/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
deleted file mode 100644
index eb35aba3..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/GraphType.py
+++ /dev/null
@@ -1,28 +0,0 @@
-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])
-        if constants.STATE_ID in contents:
-            self.id = (contents[constants.STATE_ID])[0]
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/average_ks_plot.py b/Metrics/Metrics-Calculation/metrics_plot/src/average_ks_plot.py
deleted file mode 100644
index e949a729..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/average_ks_plot.py
+++ /dev/null
@@ -1,62 +0,0 @@
-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
deleted file mode 100644
index 803bae2e..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/constants.py
+++ /dev/null
@@ -1,25 +0,0 @@
-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'
-
-HUMAN_OUT_D_REP = '../statistics/humanOutput/R_20158_run_1.csv'
-
-HUMAN_MPC_REP = '../statistics/humanOutput/R_2015246_run_1.csv'
-
-HUMAN_NA_REP = '../statistics/humanOutput/R_2016176_run_1.csv'
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/plot_ks_stats.py b/Metrics/Metrics-Calculation/metrics_plot/src/plot_ks_stats.py
deleted file mode 100644
index ad72a4a1..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/plot_ks_stats.py
+++ /dev/null
@@ -1,90 +0,0 @@
-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
deleted file mode 100644
index e0402519..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/readCSV.py
+++ /dev/null
@@ -1,169 +0,0 @@
-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, maxNumberOfFiles, shouldShuffle = True):
-    list_of_files = glob.glob(dirName + '*.csv')  # create the list of file
-    if shouldShuffle: 
-        random.shuffle(list_of_files)
-    #if the number of files is out of bound then just give the whole list
-    file_names =  list_of_files[:maxNumberOfFiles]
-    # print(file_names)
-    return file_names
-
-
-def plotlines(x, y, ax):
-    l1, = ax.plot(x, y)
-
-
-def testgetsamplesfromfiles():
-    files = readmultiplefiles('../statistics/viatraOutput/', 2)
-    for file in files:
-        getmetrics(file)
-
-def probability(data):
-    sum = np.sum(data)
-    probabilityList = []
-    for d in data:
-        p = d/sum
-        probabilityList.append(p)
-    a = np.array(probabilityList)
-    return a
-
-
-def cumulativeProbability(p):
-    cdf = np.cumsum(p)
-    return cdf
-
-
-def plot():
-    fig, ax = plt.subplots()
-    fig, ax1 = plt.subplots()
-    fig, ax2 = plt.subplots()
-    fig, ax3 = plt.subplots()
-    fig, ax4 = plt.subplots()
-    fig, ax5 = plt.subplots()
-    list_of_files = readmultiplefiles('../statistics/iatraOutput/')
-    for file_name in list_of_files:
-        contents = readcsvfile(file_name)
-        outdegree = [contents[constants.OUT_DEGREE_VALUE], contents[constants.OUT_DEGREE_COUNT]]
-        na = [contents[constants.NA_VALUE], contents[constants.NA_COUNT]]
-        mpc = [contents[constants.MPC_VALUE], contents[constants.MPC_COUNT]]
-        outV = outdegree[0, :]
-        outC = outdegree[1, :]
-        outP = probability(outC)
-        outCumP = cumulativeProbability(outP)
-        plotlines(outV, outP, ax)
-        naV = na[0, :]
-        naC = na[1, :]
-        naP = probability(naC)
-        naCumP = cumulativeProbability(naP)
-        plotlines(naV, naP, ax1)
-        mpcV = mpc[0, :]
-        mpcC = mpc[1, :]
-        mpcP = probability(mpcC)
-        mpcCumP = cumulativeProbability(mpcP)
-        plotlines(mpcV, mpcP, ax2)
-        plotlines(outV, outCumP, ax3)
-        plotlines(naV, naCumP, ax4)
-        plotlines(mpcV, mpcCumP, ax5)
-    ax.set_xlabel('ourdegree')
-    ax.set_ylabel('pdf')
-    ax.grid()
-
-    ax1.set_xlabel('node activity')
-    ax1.set_ylabel('pdf')
-    ax1.grid()
-
-    ax2.set_xlabel('multiplex participation coefficient')
-    ax2.set_ylabel('pdf')
-    ax2.grid()
-
-    ax3.set_xlabel('ourdegree')
-    ax3.set_ylabel('cdf')
-    ax3.grid()
-
-    ax4.set_xlabel('node activity')
-    ax4.set_ylabel('cdf')
-    ax4.grid()
-
-    ax5.set_xlabel('multiplex participation coefficient')
-    ax5.set_ylabel('cdf')
-    ax5.grid()
-
-    plt.show()
-
-
-# plot()
-
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/test.py b/Metrics/Metrics-Calculation/metrics_plot/src/test.py
deleted file mode 100644
index 0212cc2a..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/src/test.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from pyclustering.cluster.kmedoids import kmedoids
-from pyclustering.utils import read_sample
-from pyclustering.samples.definitions import FCPS_SAMPLES
-from pyclustering.utils.metric import distance_metric, type_metric
-import matplotlib.pyplot as plt 
-
-# metric = distance_metric(type_metric.MINKOWSKI, degree=2)
-# print(metric([1,1], [2,2]))
-
-# Load list of points for cluster analysis.
-sample = [[1,1,1], [2,2,2],[3,3,3]]
-
-# Set random initial medoids.
-initial_medoids = [1, 1 ,1]
-# Create instance of K-Medoids algorithm.
-kmedoids_instance = kmedoids(sample, initial_medoids)
-# Run cluster analysis and obtain results.
-kmedoids_instance.process()
-centoids = kmedoids_instance.get_medoids()
-clusters = kmedoids_instance.get_clusters()
-# Show allocated clusters.
-for cluster_id in range(len(clusters)):
-    for index in clusters[cluster_id]:
-        if(cluster_id == 0):
-            plt.plot(sample[index][0], sample[index][1], 'ro')
-            print(sample[index][0])
-        else:
-            plt.plot(sample[index][0], sample[index][1], 'bo')
-
-plt.plot(sample[centoids[0]][0], sample[centoids[0]][1], 'bo')
-# plt.plot(sample[centoids[1]][0], sample[centoids[1]][1], 'ro')
-plt.show()
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
index 58ca7549..ce9d4255 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
@@ -18,8 +18,8 @@ METAMODEL = 'Meta Mode'
 
 STATE_ID = 'State Id'
 
-HUMAN_OUT_D_REP = '../statistics/humanOutput\R_20158_run_1.csv'
+HUMAN_OUT_D_REP = '../input/humanOutput/R_20158_run_1.csv'
 
-HUMAN_MPC_REP = '../statistics/humanOutput\R_2015246_run_1.csv'
+HUMAN_MPC_REP = '../input/humanOutput/R_2015246_run_1.csv'
 
-HUMAN_NA_REP = '../statistics/humanOutput\R_2016176_run_1.csv'
+HUMAN_NA_REP = '../input/humanOutput/R_2016176_run_1.csv'