diff --git a/__pycache__/tsneGrid.cpython-37.pyc b/__pycache__/tsneGrid.cpython-37.pyc
index 6a6cdb1..4fc4056 100644
Binary files a/__pycache__/tsneGrid.cpython-37.pyc and b/__pycache__/tsneGrid.cpython-37.pyc differ
diff --git a/index.html b/index.html
index 6efae10..99f0bd9 100755
--- a/index.html
+++ b/index.html
@@ -166,7 +166,7 @@
               <div class="panel panel-default med-bottomProv" style="margin-top:0.2px; margin-bottom:+14px">
                   <div class="panel-heading">
                     <h2 class="panel-title" style="display:inline-block"  data-toggle="tooltip" data-placement="right" title="Tip: a feature of this tool that supports clusters (and points) exploration. Checking the neighborhood preservation between the entire projection's average and a selection driven by the user.">Projections Provenance</h2>
-                    [Sorting Metric:
+                    <div id="textToChange" style="display:inline-block">[Sorting Metric:</div>
                     <select id="param-SortM-view" name="param-SortM-view" onchange="ReSort(false)">
                         <option value="1" selected>Neighborhood Hit (NH)</option>                
                         <option value="2">Trustworthiness (T)</option>
diff --git a/js/tsne_vis.js b/js/tsne_vis.js
index 6c2cde7..392fe4c 100755
--- a/js/tsne_vis.js
+++ b/js/tsne_vis.js
@@ -28,13 +28,13 @@ var ArrayWithCosts = []; var Iterations = [];
 
 var VisiblePoints = []; 
 
-var sliderTrigger = false; var sliderInsideTrigger = false; var parameters; var SelProjIDS; var SelProjIDSProv; var SelectedProjections = []; var activeProjectionNumber = []; var activeProjectionNumberProv = [];
+var sliderTrigger = false; var sliderInsideTrigger = false; var parameters; var SelProjIDS; var SelProjIDSProv; var SelectedProjections = []; var activeProjectionNumber = []; var activeProjectionNumberProv = []; var globalFlagCheck = true;
 
 // This variable is for the kNN Bar Chart in order to store the first execution.
 var inside = 0; var kValuesLegend = []; var findNearestTable = []; var howManyPoints;
 var maxKNN = 0
 
-var mode = 1; var colors = ['#a6cee3','#fb9a99','#b2df8a','#33a02c','#1f78b4','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']; var projections = []; var metricsSorting = []; var dataReceivedFromServer = []; var metrics = [];
+var mode = 1; var colors = ['#a6cee3','#fb9a99','#b2df8a','#33a02c','#1f78b4','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a']; var projections = []; var metricsSorting = []; var dataReceivedFromServer = []; var dataReceivedFromServerOptimized = []; var metrics = []; var FocusedIDs = [];
 
 var Category; var target_names = []
 
@@ -96,7 +96,7 @@ function fetchVal(callback) {
 
 function OptimizePoints() {
   if (lassoFlag) {
-    varFocusedIDs = []
+    FocusedIDs = []
     for (let i = 0; i < points.length; i++) {
       if (points[i].selected) {
         FocusedIDs[i] = i
@@ -104,9 +104,9 @@ function OptimizePoints() {
     }
 
      // ajax the JSON to the server
-     $.post("http://127.0.0.1:5000/receiver", {'data': JSON.stringify(results_local), 'focus': JSON.stringify(FocusedIDs)}, function(){
-      $.get("http://127.0.0.1:5000/sender", function( data ) {
-        dataReceivedFromServer = data
+     $.post("http://127.0.0.1:5000/receiverOptimizer", JSON.stringify(FocusedIDs), function(){
+      $.get("http://127.0.0.1:5000/senderOptimizer", function( data ) {
+        dataReceivedFromServerOptimized = data
         ReSort(false)
     });
   
@@ -130,10 +130,32 @@ function ReSort(flagInitialize) {
 
   Plotly.purge(graphDiv);
 
+  var metricsSortingCopy
+  var metricsCopy
+
   projections = dataReceivedFromServer['projections']
   parameters = dataReceivedFromServer['parameters']
   metricsSorting = dataReceivedFromServer['metrics']
 
+  if (FocusedIDs.length != 0) {
+    if (globalFlagCheck) {
+      metricsSortingCopy = metricsSorting
+      metricsCopy = metrics
+    }
+    globalFlagCheck = false
+    document.getElementById("textToChange").innerHTML = "[Sorting Metric for Optimized Selection:";
+    metricsSorting = dataReceivedFromServerOptimized['metrics']
+    metrics = dataReceivedFromServerOptimized['metricsEntire']
+    if (FocusedIDs.length == points.length) {
+      document.getElementById("textToChange").innerHTML = "[Sorting Metric:";
+    }
+  } else {
+    if (!globalFlagCheck) {
+      metricsSorting = metricsSortingCopy
+      metrics = metricsCopy
+    }
+  }
+
   var traces = []
   var target_names = []
 
@@ -172,6 +194,7 @@ function ReSort(flagInitialize) {
   labelsTarget = uniqueTarget
 }
 
+
 var optionMetric = document.getElementById("param-SortM-view").value; // Get the threshold value with which the user set's the boundaries of the schema investigation
 var order = [];
 SelectedProjections = []
@@ -188,14 +211,8 @@ if (optionMetric == 1) {
   order = metricsSorting[optionMetric-1]
 }
 
-console.log(order)
-
-console.log(activeProjectionNumber)
-
 var index = order.indexOf(activeProjectionNumber);
 
-console.log(index)
-
 var arrayLineColor = []
 
 for (let k = 0; k < 8; k++) {
@@ -221,8 +238,6 @@ for (let k = 0; k < 8; k++) {
   }
 }
 
-console.log(arrayLineColor)
-
 var checkCounter = 0
 var checkCounterMetr = 0
 
@@ -555,7 +570,7 @@ if(k >= 8) {
       margin: {
         l: 10,
         r: 10,
-        b: 8,
+        b: 2,
         t: 2,
         pad: 0
       },
diff --git a/tsneGrid.py b/tsneGrid.py
index e5d333f..98b5bbe 100644
--- a/tsneGrid.py
+++ b/tsneGrid.py
@@ -145,6 +145,7 @@ def calculateGrid():
     global dataProc
     dataProc, length, labels = preprocess(data)
 
+    global D_highSpace
     D_highSpace = distance.squareform(distance.pdist(dataProc))
 
     DEFAULT_NO_DIMS = 2
@@ -180,6 +181,8 @@ def calculateGrid():
     SelectedListofParams = []
     global SelectedProjectionsReturn
     SelectedProjectionsReturn = []
+    
+    global clusterIndex
     clusterIndex = procrustesFun(projectionsAll)
 
     metricNeigh = []
@@ -187,6 +190,8 @@ def calculateGrid():
     metricCont = []
     metricStress = []
     metricShepCorr = []
+
+    global convertLabels
     convertLabels = []
     for index, label in enumerate(labels):
         if (label == 0):
@@ -206,13 +211,21 @@ def calculateGrid():
         else:
             pass
 
+    global D_lowSpaceList
+    D_lowSpaceList = []
+
+    global KeepKs
+    KeepKs = []
+
     for index in clusterIndex:
         SelectedProjectionsReturn.append(projectionsAll[index].tolist())
         SelectedListofParams.append(listofParamsAll[index])
 
         D_lowSpace = distance.squareform(distance.pdist(projectionsAll[index]))
+        D_lowSpaceList.append(D_lowSpace)
 
         k = listofParamsAll[index][0] # k = perplexity
+        KeepKs.append(k)
 
         #resultNeigh = neighborhood_hit(np.array(projectionsAll[index]), convertLabels, k)
         resultNeigh = trustworthiness(D_highSpace, D_lowSpace, k)
@@ -253,7 +266,7 @@ def calculateGrid():
         valueStress = (metricStress[index] - min_value_stress) / (max_value_stress - min_value_stress) 
         valueShep = (metricShepCorr[index] - min_value_shep) / (max_value_shep - min_value_shep) 
         metricsMatrixEntire.append([valueNeigh,valueTrust,valueCont,valueStress,valueShep])
-    
+
     sortNeigh = sorted(range(len(metricNeigh)), key=lambda k: metricNeigh[k], reverse=True)
     sortTrust = sorted(range(len(metricTrust)), key=lambda k: metricTrust[k], reverse=True)
     sortCont = sorted(range(len(metricCont)), key=lambda k: metricCont[k], reverse=True)
@@ -283,5 +296,87 @@ def background_process():
         pass
     return jsonify({ 'projections': SelectedProjectionsReturn, 'parameters': SelectedListofParams, 'metrics': metricsMatrix, 'metricsEntire': metricsMatrixEntire })
 
+@app.route('/receiverOptimizer', methods = ['POST'])
+def OptimizeSelection():
+    dataReceived= request.get_data().decode('utf8').replace("'", '"')
+    dataReceived = json.loads(dataReceived)
+    dataSelected = []
+    for data in dataReceived:
+        if data != None:
+            dataSelected.append(data)
+
+    metricNeigh = []
+    metricTrust = []
+    metricCont = []
+    metricStress = []
+    metricShepCorr = []
+
+    for index, loop in enumerate(clusterIndex):
+        #resultNeigh = neighborhood_hit(np.array(projectionsAll[index]), convertLabels, k)
+        resultNeigh = trustworthiness(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :], KeepKs[index])
+        resultTrust = trustworthiness(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :], KeepKs[index])
+        resultContinuity = continuity(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :], KeepKs[index])
+        resultStress = normalized_stress(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :])
+        resultShep = normalized_stress(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :])
+        #resultShep = shepard_diagram_correlation(D_highSpace[dataSelected, :], D_lowSpaceList[index][dataSelected, :]) 
+
+        metricNeigh.append(resultNeigh)
+        metricTrust.append(resultTrust)
+        metricCont.append(resultContinuity)
+        metricStress.append(resultStress)
+        metricShepCorr.append(resultShep)
+
+    max_value_neigh = max(metricNeigh)
+    min_value_neigh = min(metricNeigh)
+
+    max_value_trust = max(metricTrust)
+    min_value_trust = min(metricTrust)
+
+    max_value_cont = max(metricCont)
+    min_value_cont = min(metricCont)
+
+    max_value_stress = max(metricStress)
+    min_value_stress = min(metricStress)
+
+    max_value_shep = max(metricShepCorr)
+    min_value_shep = min(metricShepCorr)
+
+    global metricsMatrixEntireSel
+    metricsMatrixEntireSel = []
+
+    for index, data in enumerate(metricTrust):
+        #valueNeigh = (metricNeigh[index] - min_value_neigh) / (max_value_neigh - min_value_neigh) 
+        valueNeigh = (metricTrust[index] - min_value_trust) / (max_value_trust - min_value_trust) 
+        valueTrust = (metricTrust[index] - min_value_trust) / (max_value_trust - min_value_trust) 
+        valueCont = (metricCont[index] - min_value_cont) / (max_value_cont - min_value_cont) 
+        valueStress = (metricStress[index] - min_value_stress) / (max_value_stress - min_value_stress) 
+        valueShep = (metricShepCorr[index] - min_value_shep) / (max_value_shep - min_value_shep) 
+        metricsMatrixEntireSel.append([valueNeigh,valueTrust,valueCont,valueStress,valueShep])
+
+    sortNeigh = sorted(range(len(metricNeigh)), key=lambda k: metricNeigh[k], reverse=True)
+    sortTrust = sorted(range(len(metricTrust)), key=lambda k: metricTrust[k], reverse=True)
+    sortCont = sorted(range(len(metricCont)), key=lambda k: metricCont[k], reverse=True)
+    sortStress = sorted(range(len(metricStress)), key=lambda k: metricStress[k], reverse=True)
+    sortShepCorr = sorted(range(len(metricShepCorr)), key=lambda k: metricShepCorr[k], reverse=True)
+
+    global metricsMatrixSel
+    metricsMatrixSel = []
+
+    metricsMatrixSel.append(sortNeigh)
+    metricsMatrixSel.append(sortTrust)
+    metricsMatrixSel.append(sortCont)
+    metricsMatrixSel.append(sortStress)
+    metricsMatrixSel.append(sortShepCorr)
+
+    return 'OK'
+
+@app.route('/senderOptimizer')
+def SendOptimizedProjections():
+    global metricsMatrixSel
+    global metricsMatrixEntireSel
+
+    return jsonify({'metrics': metricsMatrixSel, 'metricsEntire': metricsMatrixEntireSel })
+
 if __name__ == '__main__':
-    app.run("0.0.0.0", "5000")
\ No newline at end of file
+    app.run("0.0.0.0", "5000")
+