diff --git a/DRAMSys/analyzer/scripts/plots.py b/DRAMSys/analyzer/scripts/plots.py
index 8d92fd5f..8b7621a9 100755
--- a/DRAMSys/analyzer/scripts/plots.py
+++ b/DRAMSys/analyzer/scripts/plots.py
@@ -15,12 +15,17 @@ def plot(function):
 
 @plot
 def memory_utilisation_window(connection, tracePath, steps):
-    # This function determines the average memory bandwidth over time in percentage and in Gbit/s.
-    # The average bandwidth over time is done dividing the time into windows of the same length and getting the average bandwidth in each window.
-    # Through data from the database, DataStrobeEnd and DataStrobeBegin, it is possible to assess when a data transfer begins or ends.
-    # Hence, it is achievable to ckeck when a data transfer happens and if it occupies or is inside a time window. Then, it is attainable to determine the average bandwidth in percentage.
-    # Besides, extracting the data from the memory specs, it is feasible to calculate the maximum data rate of the memory and then determine the bandwidth in Gbit/s.
-    # The bandwidth data are then plotted in two graphics.
+    # This function determines the average memory bandwidth over time in
+    # percentage and in Gbit/s. The average bandwidth over time is done
+    # dividing the time into windows of the same length and getting the average
+    # bandwidth in each window. Through data from the database, DataStrobeEnd
+    # and DataStrobeBegin, it is possible to access when a data transfer begins
+    # or ends. Hence, it is achievable to check when a data transfer happens
+    # and if it occupies or is inside a time window. Then, it is attainable to
+    # determine the average bandwidth in percentage. Besides, extracting the
+    # data from the memory specs, it is feasible to calculate the maximum data
+    # rate of the memory and then determine the bandwidth in Gbit/s. The
+    # bandwidth data are then plotted in two graphics.
 
     cursor = connection.cursor()
     cursor.execute(" SELECT WindowSize FROM GeneralInfo ")
@@ -64,9 +69,9 @@ def memory_utilisation_window(connection, tracePath, steps):
     name = ntpath.basename(tracePath)
     basename, extension = os.path.splitext(name)
 
-    OUTPUT_FILE = 'memory_utilization_' + basename + '.pdf'
-    outputFile = "Output file is {0}".format(OUTPUT_FILE)
-
+    outputFileNameGBPS    = 'memory_utilization_gbps_' + basename + '.pdf'
+    outputFileNamePercent = 'memory_utilization_percent_' + basename + '.pdf'
+    outputFiles = "Output files are {0},{1}".format(outputFileNameGBPS,outputFileNamePercent)
 
     import matplotlib.pyplot as plt
     import numpy as np
@@ -74,31 +79,35 @@ def memory_utilisation_window(connection, tracePath, steps):
 
     #windowSize/1000: picoseconds to nanoseconds conversion
     time = np.arange(0, (steps+1)*windowSize/1000, windowSize/1000)
+    maxBandwidth = [maxDataRate/1024] * (steps+1)
 
     plt.figure()
 
-    subplotIndex = 211
-    plt.subplot(subplotIndex)
+    #Plot Bandwidth in Percent
     plt.plot(time, bandwidthPercentage)
     plt.xlabel('Time [ns]')
     plt.ylabel('Bandwidth [%]')
     plt.ylim(-1, maximumPercentage + (10 - maximumPercentage%10))
     plt.grid(True)
 
-    subplotIndex += 1
-    plt.subplot(subplotIndex)
-    plt.plot(time, bandwidth)
-    plt.xlabel('Time [ns]')
-    plt.ylabel('Bandwidth [Gibit/s]')
-    plt.ylim((-0.01)*float(maxDataRate)/1024, ((maximumPercentage + (10 - maximumPercentage%10))/100)*float(maxDataRate)/1024)
-    plt.grid(True)
-
-    pdf = PdfPages(OUTPUT_FILE)
+    pdf = PdfPages(outputFileNamePercent)
     pdf.savefig()
     pdf.close()
     plt.close()
-    return outputFile
 
+    #Plot absolute bandwidth
+    plt.plot(time, bandwidth)
+    plt.plot(time, maxBandwidth)
+    plt.xlabel('Time [ns]')
+    plt.ylabel('Bandwidth [Gibit/s]')
+    #plt.ylim((-0.01)*float(maxDataRate)/1024, ((maximumPercentage + (10 - maximumPercentage%10))/100)*float(maxDataRate)/1024)
+    plt.grid(True)
+
+    pdf = PdfPages(outputFileNameGBPS)
+    pdf.savefig()
+    pdf.close()
+    plt.close()
+    return outputFiles
 
 @plot
 def power_window(connection, tracePath, steps):
@@ -123,8 +132,8 @@ def power_window(connection, tracePath, steps):
         name = ntpath.basename(tracePath)
         basename, extension = os.path.splitext(name)
 
-        OUTPUT_FILE = 'power_' + basename + '.pdf'
-        outputFile = "\n" + "Output file is {0}".format(OUTPUT_FILE)
+        outputFileName = 'power_' + basename + '.pdf'
+        outputFile = "\n" + "Output file is {0}".format(outputFileName)
 
         import matplotlib.pyplot as plt
         from matplotlib.backends.backend_pdf import PdfPages
@@ -133,7 +142,7 @@ def power_window(connection, tracePath, steps):
         plt.xlabel('Time [ns]')
         plt.ylabel('Power [mW]')
         plt.grid(True)
-        pdf = PdfPages(OUTPUT_FILE)
+        pdf = PdfPages(outputFileName)
         pdf.savefig()
         pdf.close()
         plt.close()
@@ -144,14 +153,14 @@ def latency_histogram(connection, tracePath, steps):
     # This function plots an histogram with access latencys
     outputFile = ""
     cursor = connection.cursor()
-    cursor.execute("SELECT ((p2.PhaseEnd - p1.PhaseEnd)/1000) FROM Transactions t, Phases p1, Phases p2 WHERE t.id = p1.Transact and t.id = p2.Transact and p1.PhaseName = \"REQ\" and p2.PhaseName = \"RESP\" ")
+    cursor.execute("SELECT ((p2.PhaseEnd - p1.PhaseBegin)/1000) FROM Transactions t, Phases p1, Phases p2 WHERE t.id = p1.Transact and t.id = p2.Transact and p1.PhaseName = \"REQ\" and p2.PhaseName = \"RESP\" ")
     result = cursor.fetchall()
 
     name = ntpath.basename(tracePath)
     basename, extension = os.path.splitext(name)
 
-    OUTPUT_FILE = 'hist_' + basename + '.pdf'
-    outputFile = "\n" + "Output file is {0}".format(OUTPUT_FILE)
+    outputFileName = 'hist_' + basename + '.pdf'
+    outputFile = "\n" + "Output file is {0}".format(outputFileName)
 
     numberOfBins=50
 
@@ -161,7 +170,7 @@ def latency_histogram(connection, tracePath, steps):
     plt.grid(True)
     plt.xlabel("Access Time [ns]")
     plt.ylabel("Number (Frequency)")
-    pdf = PdfPages(OUTPUT_FILE)
+    pdf = PdfPages(outputFileName)
     pdf.savefig()
     pdf.close()
     plt.close()