27 files changed, 686 insertions, 237 deletions

diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath
index 428337e5..f4f8357b 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath
@@ -4,5 +4,7 @@
         <classpathentry kind="con" path="org.eclipse.pde.core.requiredPlugins"/>
         <classpathentry kind="src" path="src"/>
         <classpathentry kind="src" path="xtend-gen"/>
+        <classpathentry kind="lib" path="C:/Users/chenp/eclipse-workspace/VIATRA-Generator/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/lib/commons-math3-3.6.1.jar"/>
+        <classpathentry kind="lib" path="C:/Users/chenp/eclipse-workspace/VIATRA-Generator/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/lib/commons-math3-3.6.1-javadoc.jar"/>
         <classpathentry kind="output" path="bin"/>
 </classpath>
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Domain.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Domain.xtend
new file mode 100644
index 00000000..8351e96b
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Domain.xtend
@@ -0,0 +1,5 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
+
+enum Domain{
+        Yakinduum
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
index 15df0dde..cf871ead 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend
@@ -1,8 +1,8 @@
 package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
 
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.EMFGraph
-import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.input.GraphReader
-import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.output.CsvFileWriter
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.CsvFileWriter
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.GraphReader
 import hu.bme.mit.inf.dslreasoner.domains.yakindu.sgraph.yakindumm.impl.YakindummPackageImpl
 import java.util.ArrayList
 
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend
index 0e505d30..cdd06027 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend
@@ -1,11 +1,12 @@
 package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
 
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.KSDistance
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.PartialInterpretationGraph
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.CsvFileWriter
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
-import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.output.CsvFileWriter
 import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
 import java.io.File
 import java.io.FileNotFoundException
@@ -17,10 +18,28 @@ import org.eclipse.viatra.dse.api.Solution
 
 class PartialInterpretationMetric {
         var static state = 0;
+        var static KSDistance ks;
         
         def static void initPaths(){
                 new File("debug/metric/").mkdir();
                 new File("debug/metric/trajectories/").mkdir();
+                ks = new KSDistance(Domain.Yakinduum);
+        }
+        
+        def static MetricDistanceGroup calculateMetricDistance(PartialInterpretation partial){
+                val metrics = new ArrayList<Metric>();
+                metrics.add(new OutDegreeMetric());
+                metrics.add(new NodeActivityMetric());
+                metrics.add(new MultiplexParticipationCoefficientMetric());
+                
+                val metricCalculator = new PartialInterpretationGraph(partial, metrics, null);          
+                var metricSamples = metricCalculator.evaluateAllMetricsToSamples();
+                
+                var mpc = ks.mpcDistance(metricSamples.mpcSamples);
+                var na = ks.naDistance(metricSamples.naSamples);
+                var outDegree = ks.outDegreeDistance(metricSamples.outDegreeSamples);
+                
+                return new MetricDistanceGroup(mpc, na, outDegree);
         }
         
         // calculate the metrics for a state
@@ -78,4 +97,28 @@ class PartialInterpretationMetric {
                         }
                 }                       
         }
+}
+
+class MetricDistanceGroup{
+        var double mpcDistance;
+        var double naDistance;
+        var double outDegreeDistance;
+        
+        new(double mpcDistance, double naDistance, double outDegreeDistance){
+                this.mpcDistance = mpcDistance;
+                this.naDistance = naDistance;
+                this.outDegreeDistance = outDegreeDistance;
+        }
+        
+        def double getMPCDistance(){
+                return this.mpcDistance
+        }
+        
+        def double getNADistance(){
+                return this.naDistance
+        }
+        
+        def double getOutDegreeDistance(){
+                return this.outDegreeDistance
+        }
 }
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend
new file mode 100644
index 00000000..1fb21529
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend
@@ -0,0 +1,47 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain
+import java.util.List
+import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader
+
+class KSDistance {
+        var static ksTester = new KolmogorovSmirnovTest();
+        var double[] mpcSamples;
+        var double[] naSamples;
+        var double[] outDegreeSamples;
+        
+        new(Domain d){
+                var metrics = RepMetricsReader.read(d);
+                mpcSamples = metrics.mpcSamples;
+                naSamples = metrics.naSamples.stream.mapToDouble([it]).toArray();
+                outDegreeSamples = metrics.outDegreeSamples.stream.mapToDouble([it]).toArray();
+        }
+        
+        def double mpcDistance(List<Double> samples){
+                // map list to array
+                var arr = samples.stream.mapToDouble([it]).toArray();
+                
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(arr.size < 2) return 1;
+                return ksTester.kolmogorovSmirnovStatistic(mpcSamples, arr);
+        }
+        
+        def double naDistance(List<Double> samples){
+                // map list to array
+                var arr = samples.stream.mapToDouble([it]).toArray();
+                
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(arr.size < 2) return 1;
+                return ksTester.kolmogorovSmirnovStatistic(naSamples as double[], arr);
+        }
+        
+        def double outDegreeDistance(List<Double> samples){
+                // map list to array
+                var arr = samples.stream.mapToDouble([it]).toArray();
+                
+                //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1
+                if(arr.size < 2) return 1;
+                return ksTester.kolmogorovSmirnovStatistic(outDegreeSamples, arr);
+        }
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend
index 6d65367f..cf4aedba 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/graph/Graph.xtend
@@ -1,8 +1,12 @@
 package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph
 
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
 import java.util.ArrayList
 import java.util.List
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
 
 abstract class Graph {
         
@@ -33,6 +37,22 @@ abstract class Graph {
                 return result;
         }       
         
+        def MetricSampleGroup evaluateAllMetricsToSamples(){
+                var sample = new MetricSampleGroup();
+                
+                for(metric : this.metrics){
+                        if(metric instanceof MultiplexParticipationCoefficientMetric){
+                                sample.mpcSamples = metric.evaluateSamples(this.statistic);                             
+                        }else if(metric instanceof NodeActivityMetric){
+                                sample.naSamples = metric.evaluateSamples(this.statistic);
+                        }else if(metric instanceof OutDegreeMetric){
+                                sample.outDegreeSamples = metric.evaluateSamples(this.statistic);
+                        }
+                }
+                
+                return sample;
+        }
+        
         def void setBasicInformation(ArrayList<ArrayList<String>> result);
         
         def GraphStatistic getStatistic();
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/input/PartialInterpretationReader.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/input/PartialInterpretationReader.xtend
deleted file mode 100644
index dbaf36b2..00000000
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/input/PartialInterpretationReader.xtend
+++ /dev/null
@@ -1,7 +0,0 @@
-package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.input
-
-import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
-
-class PartialInterpretationReader {
-        
-}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/output/CsvFileWriter.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
index 1dd204a0..bed356e9 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/output/CsvFileWriter.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
@@ -1,4 +1,4 @@
-package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.output;
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io;
 
 import java.io.File
 import java.io.FileNotFoundException
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/input/GraphReader.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/GraphReader.xtend
index 56bf95a3..fc56e142 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/input/GraphReader.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/GraphReader.xtend
@@ -1,4 +1,4 @@
-package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.input
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io;
 
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.EMFGraph
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/RepMetricsReader.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/RepMetricsReader.xtend
new file mode 100644
index 00000000..867ddd1a
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/RepMetricsReader.xtend
@@ -0,0 +1,79 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import java.io.File
+import java.util.List
+import java.util.Scanner
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MetricSampleGroup
+
+/**
+ * Read the sample of the distribution of a metric provided the csv file of the metric
+ */
+class RepMetricsReader {
+        static def read(Domain d){
+                var domainRepPath = CsvDataName.REP_PATH + d.name + '/';
+                var rep = new MetricSampleGroup()
+                rep.mpcSamples = readFile(domainRepPath + CsvDataName.MPC_REP, MultiplexParticipationCoefficientMetric.valueName,
+                                                          MultiplexParticipationCoefficientMetric.countName).map[Double.parseDouble(it)];
+                rep.naSamples = readFile(domainRepPath+CsvDataName.NA_REP, NodeActivityMetric.valueName, NodeActivityMetric.countName
+                                                        ).map[Double.parseDouble(it)];
+                rep.outDegreeSamples = readFile(domainRepPath+CsvDataName.OUT_D_REP, OutDegreeMetric.valueName, OutDegreeMetric.countName
+                                                        ).map[Double.parseDouble(it)];
+                return rep;
+        }
+        
+        /**
+         * read metric data and parse it to samples
+         */
+        private static def List<String> readFile(String filename, String valueDataName, String countDataName){
+                var s = new Scanner(new File(filename));
+                val counts = newArrayList();
+                val values = newArrayList();
+                //read data from csv
+                while(s.hasNext()){
+                        var data = s.nextLine().split(',');     
+                        
+                        if(data.size >= 1){
+                                if(data.get(0).equals(countDataName)){
+                                        //add all data with parsing them as integers
+                                        counts.addAll(data.subList(1, data.size()).map[Integer.parseInt(it)]);
+                                }else if(data.get(0).equals(valueDataName)){
+                                        //add all data without parsing (there can be either double or string, to be parsed later)
+                                        values.addAll(data.subList(1, data.size()));
+                                }
+                        }
+                }
+                
+                return createSamples(counts, values);
+        }
+        
+        // create samples from values and counts
+        private static def List<String> createSamples(List<Integer> counts, List<String> values){
+                val samples = newArrayList();
+                
+                if(counts.size() != values.size()){
+                        throw new RuntimeException("counts and values should have the same size!");
+                }
+                
+                for(var i = 0; i < counts.size(); i++){
+                        for(var j = 0; j < counts.get(i); j++){
+                                samples.add(values.get(i));
+                        }
+                }
+                
+                return samples;
+        }
+        
+        
+}
+
+class CsvDataName{
+        public static val REP_PATH = 'data/';
+        public static val MPC_REP = 'mpc_rep.csv';
+        public static val NA_REP = 'na_rep.csv';
+        public static val OUT_D_REP = 'out_d_rep.csv';
+}
+
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend
index ea52009a..38ef72f2 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/Metric.xtend
@@ -4,4 +4,5 @@ import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatis
 
 abstract class Metric {
         abstract def String[][] evaluate(GraphStatistic g);
+        abstract def double[] evaluateSamples(GraphStatistic g);
 }
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MetricSampleGroup.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MetricSampleGroup.xtend
new file mode 100644
index 00000000..8cd3daee
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MetricSampleGroup.xtend
@@ -0,0 +1,9 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
+
+import java.util.List
+
+class MetricSampleGroup{
+        public var List<Double> mpcSamples;
+        public var List<Double> naSamples;
+        public var List<Double> outDegreeSamples;
+}
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend
index 7bd48754..d9c88bb4 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/MultiplexParticipationCoefficientMetric.xtend
@@ -2,11 +2,13 @@ package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
 
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
 import java.text.DecimalFormat
+import java.util.ArrayList
 import java.util.HashMap
+import org.eclipse.emf.ecore.EObject
 
 class MultiplexParticipationCoefficientMetric extends Metric {
-        static val countName = "MPCCount";
-        static val valueName = "MPCValue";
+        public static val countName = "MPCCount";
+        public static val valueName = "MPCValue";
         
         
         override evaluate(GraphStatistic g) {
@@ -18,26 +20,7 @@ class MultiplexParticipationCoefficientMetric extends Metric {
                 val map = new HashMap<String, Integer>();
                 //calculate the metric distribution
                 g.allNodes.forEach[n|
-                        val edgeCounts = g.outDegree(n) + g.inDegree(n);
-                        
-                        var coef = 0.0;
-                        
-                        for(type : g.allTypes){
-                                val degree = g.dimentionalDegree(n, type) as double;
-                                coef += Math.pow(degree / edgeCounts, 2);
-                        }               
-                        coef = 1 - coef;                
-                        coef = coef * typeCounts / (typeCounts-1);
-                        
-                        //Consider the case that either typeCounts-1 or the edgeCounts could be 0 in some situation
-                        //in this case the metric should be evaluated to 0
-                        if(typeCounts == 1){
-                                println("bad");
-                        }
-                        
-                        if(Double.isNaN(coef)){
-                                coef = 0;
-                        }
+                        var coef = calculateMPC(n, g, typeCounts);
                         
                         //format number to String
                         val value = formatter.format(coef);
@@ -62,4 +45,36 @@ class MultiplexParticipationCoefficientMetric extends Metric {
                 
                 return datas;
         }
+        
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                val typeCounts = g.allTypes.size;
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(calculateMPC(it, g, typeCounts));
+                ]
+                
+                return samples;
+        }
+        
+        def double calculateMPC(EObject n, GraphStatistic g, int typeCounts){
+                val edgeCounts = g.outDegree(n) + g.inDegree(n);
+                        
+                var coef = 0.0;
+                
+                for(type : g.allTypes){
+                        val degree = g.dimentionalDegree(n, type) as double;
+                        coef += Math.pow(degree / edgeCounts, 2);
+                }               
+                coef = 1 - coef;                
+                coef = coef * typeCounts / (typeCounts-1);
+                
+                //Consider the case that either typeCounts-1 or the edgeCounts could be 0 in some situation
+                //in this case the metric should be evaluated to 0
+                if(Double.isNaN(coef)){
+                        coef = 0;
+                }
+                
+                return coef;
+        }
 }
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend
index 297bdd14..fbf06c47 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/NodeActivityMetric.xtend
@@ -1,11 +1,12 @@
 package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
 
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
 import java.util.HashMap
 
 class NodeActivityMetric extends Metric {
-        static val countName = "NACount";
-        static val valueName = "NAValue";
+        public static val countName = "NACount";
+        public static val valueName = "NAValue";
         
         override evaluate(GraphStatistic g) {
                 val map = new HashMap<Integer, Integer>();
@@ -34,4 +35,15 @@ class NodeActivityMetric extends Metric {
                 
                 return datas;
         }
+        
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(g.numOfEdgeTypes(it) as double);
+                ]
+                
+                return samples;
+        }
 }
\ No newline at end of file
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend
index 1ba6c8c7..55046b14 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/metrics/OutDegreeMetric.xtend
@@ -1,11 +1,12 @@
 package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics
 
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.GraphStatistic
+import java.util.ArrayList
 import java.util.HashMap
 
 class OutDegreeMetric extends Metric {
-        static val countName = "OutDegreeCount";
-        static val valueName = "OutDegreeValue";
+        public static val countName = "OutDegreeCount";
+        public static val valueName = "OutDegreeValue";
         
         override evaluate(GraphStatistic g) {
                 val map = new HashMap<Integer, Integer>();
@@ -34,5 +35,15 @@ class OutDegreeMetric extends Metric {
                 return datas;
         }
         
+        override evaluateSamples(GraphStatistic g){
+                val samples = new ArrayList<Double>();
+                
+                //calculate the metric distribution
+                g.allNodes.forEach[
+                        samples.add(g.outDegree(it) as double);
+                ]
+                
+                return samples;
+        }
         
 }
\ No newline at end of file
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 8c57a327..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
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,8 +201,7 @@
     "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",
-    "real_random_stats = readStats('../statistics/real_random_output/', 10000)\n",
-    "viatra_500_stats = readStats('../statistics/viatra500nodes/', 10000)"
+    "con_viatra_stats = readStats('../statistics/controled_viatra/',300)"
    ]
   },
   {
@@ -214,20 +213,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "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",
-    "real_random_dic = calDistanceDic(real_random_stats, human_rep)\n",
-    "viatra_500_dic = calDistanceDic(viatra_500_stats, human_rep)"
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -253,13 +251,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cba63d8050a043018a16db6f1a0440f9",
+       "model_id": "868a437468d24144926f1390cbf2acb8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -276,7 +274,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,18 +287,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c07dab0b6fd4c45b0168d206e99e4ea",
+       "model_id": "e8b74fe96a45445f8062468ddf2597bf",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(11,), options={'../statistics/viatra_no…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
       ]
      },
      "metadata": {},
@@ -312,7 +310,7 @@
        "<function __main__.plot_out_na(lines)>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,18 +323,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f3bb86160c964b5383bda7cdee81a1b7",
+       "model_id": "c6e7e31f454a48169dac12c8aac70eef",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(11,), options={'../statistics/viatra_no…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_noc…"
       ]
      },
      "metadata": {},
@@ -348,7 +346,7 @@
        "<function __main__.plot_out_mpc(lines)>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,44 +359,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filenames = reader.readmultiplefiles('../statistics/viatra_con_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)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec10c88d57f14461b160572e0a3f4e0d",
+       "model_id": "cebc359548f74cc8b7540ecc3876c9ee",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra_con…"
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
       ]
      },
      "metadata": {},
@@ -410,7 +382,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -418,23 +390,23 @@
    "source": [
     "def plot_out_degree(lines):\n",
     "    plot(viatra_con_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
-    "interact(plot_out_degree, lines=w)"
+    "interact(plot_out_degree, lines=[[]])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bde0cb7466f64699be0159a8404eb821",
+       "model_id": "682beae42eef4676b11b6fe23127a44e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(3,), options={'../statistics/viatra_con…"
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
       ]
      },
      "metadata": {},
@@ -446,7 +418,7 @@
        "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,23 +426,23 @@
    "source": [
     "def plot_na(lines):\n",
     "    plot(viatra_con_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
-    "interact(plot_na, lines=w)"
+    "interact(plot_na, lines=[[]])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fda08a154104d80aca9d6eef3404bf1",
+       "model_id": "6893b8c6e03441f89fc35bf784992ae9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(3,), options={'../statistics/viatra_con…"
+       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
       ]
      },
      "metadata": {},
@@ -482,7 +454,7 @@
        "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,18 +462,18 @@
    "source": [
     "def plot_mpc(lines):\n",
     "    plot(viatra_con_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
-    "interact(plot_mpc, lines=w)"
+    "interact(plot_mpc, lines=[[]])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f4859e7657a4080b8ee0db66ad70b04",
+       "model_id": "ff0e1991c69a4d77a40f57225f90295a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -518,7 +490,7 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -531,49 +503,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd6387994c764904b20975a54b47bf3a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(random_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
-    "interact(plot_out_degree, lines=[[]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "27e3897451694ffb8af3dd32bc0ece70",
+       "model_id": "838570f20bed4d8d9c618305984d19ef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -590,26 +526,26 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def plot_out_degree(lines):\n",
-    "    plot(random_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
+    "    plot(random_dic, lines, 0, lambda a: a.na_distance, colors, 'out degree')\n",
     "interact(plot_out_degree, lines=[[]])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f4d0964368254b599c7cb8d6b6f9a0f4",
+       "model_id": "f4825f6257a74bce9dd22aac8a98effa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -626,96 +562,41 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def plot_out_degree(lines):\n",
-    "    plot(real_random_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\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": 24,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eca026dd74a945bcbebde5cb69cfe0e2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(real_random_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\n",
-    "interact(plot_out_degree, lines=[[]])"
+    "con_viatra_stats = readStats('../statistics/controled_viatra/',5000)\n",
+    "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 27,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eff1d3cd5d1a4a85b56024362890759c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(real_random_dic, lines, 0, lambda a: a.mpc_distance, colors, 'MPC')\n",
-    "interact(plot_out_degree, lines=[[]])"
+    "## Trajectories for controlled viatra solver"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
-    "filenames = reader.readmultiplefiles('../statistics/viatra500nodes/trajectories/', 15, False)\n",
+    "filenames = reader.readmultiplefiles('../statistics/controled_viatra/trajectories/', 25, False)\n",
     "trajectories = {}\n",
     "for name in filenames:\n",
     "    trajectories[name] = reader.readTrajectory(name)\n",
@@ -725,30 +606,23 @@
     "    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": "code",
-   "execution_count": 12,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e281415e80684a85ba24ab8cb48ea3fe",
+       "model_id": "4b60ae3859e343299badf29272f67d21",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra500n…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
       ]
      },
      "metadata": {},
@@ -760,31 +634,31 @@
        "<function __main__.plot_out_degree(lines)>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "def plot_out_degree(lines):\n",
-    "    plot(viatra_500_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity')\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": 22,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d678fee844e84e2785d50945288f833d",
+       "model_id": "8e7965d793a146d4bbc268554262eb58",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra500n…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
       ]
      },
      "metadata": {},
@@ -793,34 +667,34 @@
     {
      "data": {
       "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
+       "<function __main__.plot_na(lines)>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(viatra_500_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree')\n",
-    "interact(plot_out_degree, lines=w)"
+    "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": 21,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55ca7c3f89224cce8d3a87cf23b20f92",
+       "model_id": "55a1209d0b924a39b4729228e81ee3ab",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/viatra500n…"
+       "interactive(children=(SelectMultiple(description='Trajectory:', index=(0,), options={'../statistics/controled_…"
       ]
      },
      "metadata": {},
@@ -829,18 +703,18 @@
     {
      "data": {
       "text/plain": [
-       "<function __main__.plot_out_degree(lines)>"
+       "<function __main__.plot_mpc(lines)>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def plot_out_degree(lines):\n",
-    "    plot(viatra_500_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc')\n",
-    "interact(plot_out_degree, lines=w)"
+    "def plot_mpc(lines):\n",
+    "    plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc')\n",
+    "interact(plot_mpc, lines=w)"
    ]
   },
   {
@@ -849,6 +723,20 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
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()
+