12 files changed, 506 insertions, 22 deletions

diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/input/.gitignore b/Metrics/Metrics-Calculation/metrics_plot/model comparison/input/.gitignore
new file mode 100644
index 00000000..b3934b01
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/input/.gitignore
@@ -0,0 +1,3 @@
+# ignore everything in this folder
+*
+!.gitignore
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/MPC.png
new file mode 100644
index 00000000..e660f3da
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/MPC.png
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
new file mode 100644
index 00000000..92047e47
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Node Activity.png
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
new file mode 100644
index 00000000..dad7483f
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/output/Human-Viatra (30 nodes)-/Out Degree.png
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py
new file mode 100644
index 00000000..2f39ca93
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/model comparison/src/plot_ks_stats.py
@@ -0,0 +1,98 @@
+import os, sys
+lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))
+sys.path.append(lib_path)
+import glob
+import random 
+from sklearn.manifold import MDS
+import matplotlib.pyplot as plt
+from scipy import stats
+import numpy as np
+from GraphType import GraphCollection
+
+def main():
+    # read models
+    human = GraphCollection('../input/humanOutput/', 500, 'Human')
+    viatra30 = GraphCollection('../input/viatraOutput30/', 500, 'Viatra (30 nodes)')
+    # viatra60 = GraphCollection('../input/viatraOutput60/', 500, 'Viatra (60 nodes)')
+    # viatra100 = GraphCollection('../input/viatraOutput100/', 500, 'Viatra (100 nodes)')
+    # random = GraphCollection('../input/randomOutput/', 500, 'Random')
+    # alloy = GraphCollection('../input/alloyOutput/', 500, 'Alloy (30 nodes)')
+
+    models_to_compare = [human, viatra30]
+    
+    # define output folder
+    outputFolder = '../output/'
+
+    #calculate metrics
+    metricStat(models_to_compare, 'Node Activity', nodeActivity, 0, outputFolder)
+    metricStat(models_to_compare, 'Out Degree', outDegree, 1, outputFolder)
+    metricStat(models_to_compare, 'MPC', mpc, 2, outputFolder)
+
+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']
+    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='', 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, outputFolder):
+    metrics = []
+    for graph in graphTypes:
+        metrics.append(metric(graph))
+        outputFolder = outputFolder + graph.name + '-'
+    print('calculate' + metricName +' for ' + outputFolder)
+    mkdir_p(outputFolder)
+    out_d_coords = calculateMDS(calculateKSMatrix(metrics))
+    plot(graphTypes, out_d_coords, metricName, graphIndex,outputFolder + '/'+ metricName+'.png')
+
+def nodeActivity(graphType):
+    return graphType.nas
+
+def outDegree(graphType):
+    return graphType.out_ds
+
+def mpc(graphType):
+    return graphType.mpcs
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/MPC.png b/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/MPC.png
deleted file mode 100644
index d819aad5..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/MPC.png
+++ /dev/null
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
deleted file mode 100644
index 4b3b187e..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Node Activity.png
+++ /dev/null
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
deleted file mode 100644
index d8e01f6e..00000000
--- a/Metrics/Metrics-Calculation/metrics_plot/output/Human-viatraEvolve-/Out Degree.png
+++ /dev/null
diff --git a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
index e5868da0..a0b0ad8d 100644
--- a/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
+++ b/Metrics/Metrics-Calculation/metrics_plot/src/metrics_distance_with_selector.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,8 @@
     "# 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)"
+    "random_stats = readStats('../statistics/random_output/',5000)\n",
+    "con_viatra_stats = readStats('../statistics/controled_viatra/',300)"
    ]
   },
   {
@@ -218,7 +219,8 @@
    "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)"
+    "random_dic = calDistanceDic(random_stats, human_rep)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
    ]
   },
   {
@@ -255,7 +257,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1bdc31418d894783b36cc79c60251f00",
+       "model_id": "868a437468d24144926f1390cbf2acb8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -291,7 +293,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78565a0ec3d740908fcea753387cfc3e",
+       "model_id": "e8b74fe96a45445f8062468ddf2597bf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -327,7 +329,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4392eb19fe1844c4affeb62f9ba9163b",
+       "model_id": "c6e7e31f454a48169dac12c8aac70eef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -363,7 +365,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c667f0f9dcd5494f81d95c64ad900612",
+       "model_id": "cebc359548f74cc8b7540ecc3876c9ee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -399,7 +401,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "991d8d2bfc644c82a9b079615900dc4d",
+       "model_id": "682beae42eef4676b11b6fe23127a44e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -435,7 +437,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be65f39c2fae4c84a1d6908f3b70a86e",
+       "model_id": "6893b8c6e03441f89fc35bf784992ae9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -471,7 +473,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1de0db5b5c8d46de958f6b43144dac54",
+       "model_id": "ff0e1991c69a4d77a40f57225f90295a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -501,13 +503,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f8fa855125b4beca603abbf801412ac",
+       "model_id": "838570f20bed4d8d9c618305984d19ef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -524,7 +526,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,13 +539,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b4ed2adb29004908a3799bc91bf0662b",
+       "model_id": "f4825f6257a74bce9dd22aac8a98effa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -560,7 +562,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,6 +575,164 @@
   },
   {
    "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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trajectories for controlled viatra solver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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",
+    "\n",
+    "w = widgets.SelectMultiple(\n",
+    "    options = trajectories,\n",
+    "    value = [trajectories[filenames[0]]],\n",
+    "    description='Trajectory:',\n",
+    "    disabled=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b60ae3859e343299badf29272f67d21",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_out_degree(lines)>"
+      ]
+     },
+     "execution_count": 57,
+     "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",
+    "interact(plot_out_degree, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8e7965d793a146d4bbc268554262eb58",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_na(lines)>"
+      ]
+     },
+     "execution_count": 58,
+     "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",
+    "interact(plot_na, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "55a1209d0b924a39b4729228e81ee3ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_mpc(lines)>"
+      ]
+     },
+     "execution_count": 59,
+     "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",
+    "interact(plot_mpc, lines=w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/GraphType.py b/Metrics/Metrics-Calculation/metrics_plot/utils/GraphType.py
new file mode 100644
index 00000000..13754e80
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/GraphType.py
@@ -0,0 +1,29 @@
+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)
+        print(len(self.out_ds))
+
+#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/utils/constants.py b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
new file mode 100644
index 00000000..58ca7549
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py
@@ -0,0 +1,25 @@
+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/utils/readCSV.py b/Metrics/Metrics-Calculation/metrics_plot/utils/readCSV.py
new file mode 100644
index 00000000..e0402519
--- /dev/null
+++ b/Metrics/Metrics-Calculation/metrics_plot/utils/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, 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()
+