break
last_time = -1
- index=0
+ index=-1
perfs=[]
- for line in current_line:
- time, perf, x, block_size = line
+ for line in enumerate(current_line):
+ # Index will be used to remember what file was featuring what value
+ index=index+1
+
+ time, perf, x, block_size = line[1]
if (blk_size == 0):
try:
blk_size=int(block_size)
# We ignore the first 500msec as it doesn't seems to be part of the real benchmark
# Time < 500 usually reports BW=0 breaking the min computing
- if (((int(time)) > 500) or (int(time)==-1)):
- # Now it's time to estimate if the data we got is part of the time range we want to plot
- if ((int(time)>(int(min_time)*1000)) and ((int(time) < (int(max_time)*1000)) or max_time=="-1")):
+ if (min_time == 0):
+ min_time==0.5
+
+ # Then we estimate if the data we got is part of the time range we want to plot
+ if ((float(time)>(float(min_time)*1000)) and ((int(time) < (int(max_time)*1000)) or max_time==-1)):
disk_perf[index].append(int(perf))
- perfs.append("%s %s"% (time, perf))
- index = index + 1
+ perfs.append("%d %s %s"% (index, time, perf))
# If we reach this point, it means that all the traces are coherent
for p in enumerate(perfs):
- perf_time,perf = p[1].split()
- if (perf_time != "-1"):
- temp_outfile[p[0]].write("%s %.2f %s\n" % (p[0], float(float(perf_time)/1000), perf))
+ index, perf_time,perf = p[1].split()
+ temp_outfile[int(index)].write("%s %.2f %s\n" % (index, float(float(perf_time)/1000), perf))
for file in files: