--- /dev/null
+#!/usr/bin/env python
+
+import sys
+from pylab import figure, plot, show, subplots_adjust, title
+
+def get_closet_index(time, timelist, current_index):
+ #print current_index
+ best_difference = float('inf')
+ best_index = current_index
+ for i in xrange(best_index, len(timelist)):
+ difference = abs(time - timelist[i])
+ if difference < best_difference:
+ best_difference = difference
+ best_index = i
+ if difference > best_difference:
+ return best_index
+
+ return best_index
+
+def sum_best_time(current_indicies, time, timelists, datalists, type):
+ #For the given time, search each of the time lists and get the best index.
+ #By best, we mean with time closet to the specified time.
+ #Use that index in the corresponding datalist and add the value to the sum.
+
+ sum = type(0)
+
+ for i in xrange(len(timelists)):
+ current_indicies[i] = get_closet_index(time, timelists[i], current_indicies[i])
+ sum = sum + datalists[i][current_indicies[i]]
+
+ return sum
+
+def get_sum_with_times(timelists, datalists, type):
+ current_indicies = [0 for l in datalists]
+ target_length = min([len(l) for l in datalists])
+ result_timelist = []
+ result_datalist = []
+ for t in xrange(len(timelists)):
+ if len(timelists[t]) == target_length: #
+ result_timelist = timelists[t]
+
+ for i in xrange(len(result_timelist)):
+ result_datalist.append(sum_best_time(current_indicies, result_timelist[i], timelists, datalists, type))
+
+ return (result_timelist, result_datalist)
+
+def get_smallest_list(lists):
+ minlength = min([len(l) for l in lists])
+ for l in lists:
+ if len(l) == minlength:
+ return l
+
+def add_plot(timelists, datalists, index, count, title, figure, type=float, add_sum=False, ymax=None, hline=None):
+ subplot = figure.add_subplot(count, 1, index)
+ subplot.set_title(title)
+
+ for i in xrange(len(datalists)):
+ subplot.plot(timelists[i], datalists[i])
+
+ if add_sum and len(datalists) > 1:
+ #subplot.plot(get_smallest_list(timelists), get_sum(datalists, type))
+ sum_tuple = get_sum_with_times(timelists, datalists, type)
+ subplot.plot(sum_tuple[0], sum_tuple[1])
+
+ if hline != None:
+ subplot.axhline(y = hline)
+ subplot.set_ylim(ymax=ymax)
+
+ return subplot
+
+# File Objects
+files = []
+
+# Data points
+times = []
+time_begin = float('inf')
+local_rates = []
+ideal_weights = []
+local_weights = []
+total_weights = []
+flows = []
+flows_5k = []
+flows_10k = []
+flows_20k = []
+flows_50k = []
+flows_avg = []
+max_flow_rates = []
+max_flow_hashs = []
+local_limits = []
+total_over_max_weights = []
+
+# On/off points
+on_times = []
+off_times = []
+
+# Open all the files.
+for i in xrange(1, len(sys.argv)):
+ files.append(open(sys.argv[i], "r"))
+
+for file in files:
+ time = []
+ local_rate = []
+ ideal_weight = []
+ local_weight = []
+ total_weight = []
+ flow = []
+ flow_5k = []
+ flow_10k = []
+ flow_20k = []
+ flow_50k = []
+ flow_avg = []
+ max_flow_rate = []
+ max_flow_hash = []
+ local_limit = []
+ total_over_max_weight = []
+
+ last_time = 0.0
+
+ for line in file:
+ if line == "--Switching enforcement on.--\n":
+ on_times.append(last_time)
+ if line == "--Switching enforcement off.--\n":
+ off_times.append(last_time)
+ splitline = line.split(" ")
+ if len(splitline) == 12:
+ # It's a data line.
+ time.append(float(splitline[0]))
+ local_rate.append(int(splitline[1]))
+ ideal_weight.append(float(splitline[2]))
+ local_weight.append(float(splitline[3]))
+ total_weight.append(float(splitline[4]))
+ flow.append(int(splitline[5]))
+ flow_5k.append(int(splitline[6]))
+ flow_10k.append(int(splitline[7]))
+ flow_20k.append(int(splitline[8]))
+ flow_50k.append(int(splitline[9]))
+ flow_avg.append(int(splitline[10]))
+ local_limit.append(int(splitline[11]))
+ last_time = float(splitline[0])
+ if len(splitline) == 14 or len(splitline) == 15:
+ # It's a data line.
+ time.append(float(splitline[0]))
+ local_rate.append(int(splitline[1]))
+ ideal_weight.append(float(splitline[2]))
+ local_weight.append(float(splitline[3]))
+ total_weight.append(float(splitline[4]))
+ flow.append(int(splitline[5]))
+ flow_5k.append(int(splitline[6]))
+ flow_10k.append(int(splitline[7]))
+ flow_20k.append(int(splitline[8]))
+ flow_50k.append(int(splitline[9]))
+ flow_avg.append(int(splitline[10]))
+ max_flow_rate.append(int(splitline[11]))
+ max_flow_hash.append(int(splitline[12]))
+ local_limit.append(int(splitline[13]))
+ last_time = float(splitline[0])
+ if len(splitline) == 16:
+ try:
+ # It's a data line.
+ time.append(float(splitline[0]))
+ local_rate.append(int(splitline[1]))
+ ideal_weight.append(float(splitline[2]))
+ local_weight.append(float(splitline[3]))
+ total_weight.append(float(splitline[4]))
+ flow.append(int(splitline[5]))
+ flow_5k.append(int(splitline[6]))
+ flow_10k.append(int(splitline[7]))
+ flow_20k.append(int(splitline[8]))
+ flow_50k.append(int(splitline[9]))
+ flow_avg.append(int(splitline[10]))
+ max_flow_rate.append(int(splitline[11]))
+ max_flow_hash.append(int(splitline[12]))
+ local_limit.append(int(splitline[13]))
+ total_over_max_weight.append(float(splitline[15]))
+ last_time = float(splitline[0])
+ except ValueError:
+ print "Warning: Caught ValueError on line: " + line
+
+ file.close()
+
+ times.append(time)
+ local_rates.append(local_rate)
+ ideal_weights.append(ideal_weight)
+ local_weights.append(local_weight)
+ total_weights.append(total_weight)
+ flows.append(flow)
+ flows_5k.append(flow_5k)
+ flows_10k.append(flow_10k)
+ flows_20k.append(flow_20k)
+ flows_50k.append(flow_50k)
+ flows_avg.append(flow_avg)
+ max_flow_rates.append(max_flow_rate)
+ max_flow_hashs.append(max_flow_hash)
+ local_limits.append(local_limit)
+ total_over_max_weights.append(total_over_max_weight)
+
+for t in xrange(len(times)):
+ mintime = min(times[t])
+ if mintime < time_begin:
+ time_begin = mintime
+
+for t in xrange(len(times)):
+ for i in xrange(len(times[t])):
+ times[t][i] -= time_begin
+
+print time_begin
+
+fig = figure()
+subplots = []
+subplots_adjust(left = 0.12, right = 0.94, bottom = 0.05, top = 0.94)
+
+graph_count = 5
+
+subplots.append(add_plot(times, local_rates, 1, graph_count, "Local Rate", fig, int, True))
+
+subplots.append(add_plot(times, local_limits, 2, graph_count, "Local Limit", fig, int, True))
+
+subplots.append(add_plot(times, local_weights, 3, graph_count, "Weight", fig, float, False))
+
+#subplots.append(add_plot(times, ideal_weights, 4, graph_count, "Ideal Weight", fig, float, False))
+
+#subplots.append(add_plot(times, total_over_max_weights, 5, graph_count, "Ideal Weight", fig, float, False))
+
+subplots.append(add_plot(times, flows, 4, graph_count, "# of flows", fig, int, False))
+
+#add_plot(times, flows_5k, 6, graph_count, "# of flows > 5KB/s", fig, int, False)
+#add_plot(times, flows_20k, 7, graph_count, "# of flows > 20KB/s", fig, int, False)
+#add_plot(times, flows_50k, 6, graph_count, "# of flows > 50KB/s", fig, int, False)
+
+subplots.append(add_plot(times, flows_avg, 5, graph_count, "Average flow rate", fig, int, False))
+
+#subplots.append(add_plot(times, max_flow_rates, 6, graph_count, "Max flow rate", fig, int, False, ymax=160000))
+#subplots.append(add_plot(times, max_flow_hashs, 7, graph_count, "Max flow hash", fig, int, False))
+
+xlimits = subplots[0].get_xlim()
+
+for sub in subplots:
+ for on in on_times:
+ if on < time_begin:
+ on = time_begin
+ sub.axvline(x = (on - time_begin), color = 'green')
+
+ for off in off_times:
+ if off < time_begin:
+ off = time_begin
+ sub.axvline(x = (off - time_begin), color = 'red')
+
+ sub.set_xlim(xmin = xlimits[0], xmax = xlimits[1])
+
+show()
git://git.onelab.eu / distributedratelimiting.git / blobdiff