changed from a constant window size to a constant step size

DRAMSys/analyzer/scripts/plots.py

@@ -20,11 +20,11 @@ def memory_utilisation_window(connection):
     # Besides, extracting the data from the memory specs, it is possible 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.
 
-    windowSize = 1000000
+    steps = 1000
     cursor = connection.cursor()
     cursor.execute(""" SELECT max(DataStrobeEnd) FROM Transactions """)
     total = cursor.fetchone()
-    steps = ceil(float(total[0])/float(windowSize))
+    windowSize = ceil(float(total[0])/float(steps))
     # print(steps)
     # All possible cases of data transfers inside a time window
     queryFull = """ SELECT sum(DataStrobeEnd - DataStrobeBegin) FROM transactions Where DataStrobeBegin > ? and DataStrobeEnd < ?"""  # The data transfer begins and ends inside the time window
@@ -38,7 +38,7 @@ def memory_utilisation_window(connection):
     bandwidthPercentage = [0] * steps
     bandwidth = [0] * steps
     for i in range(steps):
-        # print(i)
+        #print(i)
         bandwidthPercentage[i] = 0
         cursor.execute(queryPart, (i*windowSize, (i+1)*windowSize))
         result = cursor.fetchone()
@@ -90,18 +90,18 @@ def memory_utilisation_window(connection):
     window.savefig()
     window.close()
 
-@plot
-def latency_histogram(connection):
-    # This function plots an histogram with access latencys
-    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\" ")
-    result = cursor.fetchall()
-    #result.sort()
-    #print(max(result)[0])
-    import matplotlib.pyplot as plt
-    plt.hist(result, bins=max(result)[0], histtype='barstacked')
-    plt.savefig('hist.png')
-    return "Saved as hist.png"
+#@plot
+#def latency_histogram(connection):
+#    # This function plots an histogram with access latencys
+#    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\" ")
+#    result = cursor.fetchall()
+#    #result.sort()
+#    #print(max(result)[0])
+#    import matplotlib.pyplot as plt
+#    plt.hist(result, bins=max(result)[0], histtype='barstacked')
+#    plt.savefig('hist.png')
+#    return "Saved as hist.png"
 
 
 def generatePlots(pathToTrace):