One output PDF file per channel with 2 subplots.

DRAMSys/analyzer/scripts/memUtil.py

@@ -1,5 +1,3 @@
-import sys
-import sqlite3
 import xml.etree.ElementTree as ET
 
 

DRAMSys/analyzer/scripts/plots.py

@@ -2,6 +2,8 @@ import sys
 import sqlite3
 from memUtil import *
 from math import *
+import ntpath
+import os
 
 plots = []
 
@@ -12,7 +14,7 @@ def plot(function):
 
 
 @plot
-def memory_utilisation_window(connection):
+def memory_utilisation_window(connection, tracePath):
     # 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 the data from the database, DataStrobeEnd and DataStrobeBegin, it is possible to assess when a data transfer begins or ends.
@@ -25,6 +27,8 @@ def memory_utilisation_window(connection):
     cursor.execute(""" SELECT max(DataStrobeEnd) FROM Transactions """)
     total = cursor.fetchone()
     windowSize = ceil(float(total[0])/float(steps))
+    if (windowSize == 0):
+        windowSize = 1
     # 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 +42,7 @@ 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()
@@ -65,43 +69,52 @@ def memory_utilisation_window(connection):
         bandwidth[i] = float(bandwidthPercentage[i])*float(maxDataRate)/1024
         bandwidthPercentage[i] *= 100
 
+    name = ntpath.basename(tracePath)
+    basename, extension = os.path.splitext(name)
+
+    OUTPUT_FILE = 'memory_utilization_' + basename + '.pdf'
+    print("Output file is {0}".format(OUTPUT_FILE))
+
     import matplotlib.pyplot as plt
     import numpy as np
     from matplotlib.backends.backend_pdf import PdfPages
 
     time = np.arange(0, steps*windowSize, windowSize)
 
-    plt.figure(1)
+    plt.figure()
+
+    subplotIndex = 211
+    plt.subplot(subplotIndex)
     plt.plot(time/1000, bandwidthPercentage)
     plt.xlabel('Time (ns)')
     plt.ylabel('Bandwidth (%)')
     plt.ylim(0, 120)
     plt.grid(True)
-    windowPercentage = PdfPages('windowPercentage.pdf')
-    windowPercentage.savefig()
-    windowPercentage.close()
 
-    plt.figure(2)
+    subplotIndex += 1
+    plt.subplot(subplotIndex)
     plt.plot(time/1000, bandwidth)
     plt.xlabel('Time (ns)')
     plt.ylabel('Bandwidth (Gibit/s)')
     plt.grid(True)
-    window = PdfPages('window.pdf')
-    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"
+    pdf = PdfPages(OUTPUT_FILE)
+    pdf.savefig()
+    pdf.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"
 
 
 def generatePlots(pathToTrace):
@@ -111,7 +124,7 @@ def generatePlots(pathToTrace):
     print("Generating plots for {0}".format(pathToTrace))
 
     for p in plots:
-        p(connection)
+        p(connection, pathToTrace)
 
     connection.close()