DRL-graphResults-FPS.py

   1 #!/usr/bin/env python
   2
   3 import sys
   4 from pylab import figure, plot, show, subplots_adjust, title
   5
   6 def get_closet_index(time, timelist, current_index):
   7     #print current_index
   8     best_difference = float('inf')
   9     best_index = current_index
  10     for i in xrange(best_index, len(timelist)):
  11         difference = abs(time - timelist[i])
  12         if difference < best_difference:
  13             best_difference = difference
  14             best_index = i
  15         if difference > best_difference:
  16             return best_index
  17
  18     return best_index
  19
  20 def sum_best_time(current_indicies, time, timelists, datalists, type):
  21     #For the given time, search each of the time lists and get the best index.
  22     #By best, we mean with time closet to the specified time.
  23     #Use that index in the corresponding datalist and add the value to the sum.
  24
  25     sum = type(0)
  26
  27     for i in xrange(len(timelists)):
  28         current_indicies[i] = get_closet_index(time, timelists[i], current_indicies[i])
  29         sum = sum + datalists[i][current_indicies[i]]
  30
  31     return sum
  32
  33 def get_sum_with_times(timelists, datalists, type):
  34     current_indicies = [0 for l in datalists]
  35     target_length = min([len(l) for l in datalists])
  36     result_timelist = []
  37     result_datalist = []
  38     for t in xrange(len(timelists)):
  39         if len(timelists[t]) == target_length: #
  40             result_timelist = timelists[t]
  41
  42     for i in xrange(len(result_timelist)):
  43         result_datalist.append(sum_best_time(current_indicies, result_timelist[i], timelists, datalists, type))
  44
  45     return (result_timelist, result_datalist)
  46
  47 def get_smallest_list(lists):
  48     minlength = min([len(l) for l in lists])
  49     for l in lists:
  50         if len(l) == minlength:
  51             return l
  52
  53 def add_plot(timelists, datalists, index, count, title, figure, type=float, add_sum=False, ymax=None, hline=None):
  54     subplot = figure.add_subplot(count, 1, index)
  55     subplot.set_title(title)
  56
  57     for i in xrange(len(datalists)):
  58         subplot.plot(timelists[i], datalists[i])
  59
  60     if add_sum and len(datalists) > 1:
  61         #subplot.plot(get_smallest_list(timelists), get_sum(datalists, type))
  62         sum_tuple = get_sum_with_times(timelists, datalists, type)
  63         subplot.plot(sum_tuple[0], sum_tuple[1])
  64
  65     if hline != None:
  66         subplot.axhline(y = hline)
  67     subplot.set_ylim(ymax=ymax)
  68
  69     return subplot
  70
  71 # File Objects
  72 files = []
  73
  74 # Data points
  75 times = []
  76 time_begin = float('inf')
  77 local_rates = []
  78 ideal_weights = []
  79 local_weights = []
  80 total_weights = []
  81 flows = []
  82 flows_5k = []
  83 flows_10k = []
  84 flows_20k = []
  85 flows_50k = []
  86 flows_avg = []
  87 max_flow_rates = []
  88 max_flow_hashs = []
  89 local_limits = []
  90 total_over_max_weights = []
  91
  92 # On/off points
  93 on_times = []
  94 off_times = []
  95
  96 # Open all the files.
  97 for i in xrange(1, len(sys.argv)):
  98     files.append(open(sys.argv[i], "r"))
  99
 100 for file in files:
 101     time = []
 102     local_rate = []
 103     ideal_weight = []
 104     local_weight = []
 105     total_weight = []
 106     flow = []
 107     flow_5k = []
 108     flow_10k = []
 109     flow_20k = []
 110     flow_50k = []
 111     flow_avg = []
 112     max_flow_rate = []
 113     max_flow_hash = []
 114     local_limit = []
 115     total_over_max_weight = []
 116
 117     last_time = 0.0
 118
 119     for line in file:
 120         if line == "--Switching enforcement on.--\n":
 121             on_times.append(last_time)
 122         if line == "--Switching enforcement off.--\n":
 123             off_times.append(last_time)
 124         splitline = line.split(" ")
 125         if len(splitline) == 12:
 126             # It's a data line.
 127             time.append(float(splitline[0]))
 128             local_rate.append(int(splitline[1]))
 129             ideal_weight.append(float(splitline[2]))
 130             local_weight.append(float(splitline[3]))
 131             total_weight.append(float(splitline[4]))
 132             flow.append(int(splitline[5]))
 133             flow_5k.append(int(splitline[6]))
 134             flow_10k.append(int(splitline[7]))
 135             flow_20k.append(int(splitline[8]))
 136             flow_50k.append(int(splitline[9]))
 137             flow_avg.append(int(splitline[10]))
 138             local_limit.append(int(splitline[11]))
 139             last_time = float(splitline[0])
 140         if len(splitline) == 14 or len(splitline) == 15:
 141             # It's a data line.
 142             time.append(float(splitline[0]))
 143             local_rate.append(int(splitline[1]))
 144             ideal_weight.append(float(splitline[2]))
 145             local_weight.append(float(splitline[3]))
 146             total_weight.append(float(splitline[4]))
 147             flow.append(int(splitline[5]))
 148             flow_5k.append(int(splitline[6]))
 149             flow_10k.append(int(splitline[7]))
 150             flow_20k.append(int(splitline[8]))
 151             flow_50k.append(int(splitline[9]))
 152             flow_avg.append(int(splitline[10]))
 153             max_flow_rate.append(int(splitline[11]))
 154             max_flow_hash.append(int(splitline[12]))
 155             local_limit.append(int(splitline[13]))
 156             last_time = float(splitline[0])
 157         if len(splitline) == 16:
 158             try:
 159                 # It's a data line.
 160                 time.append(float(splitline[0]))
 161                 local_rate.append(int(splitline[1]))
 162                 ideal_weight.append(float(splitline[2]))
 163                 local_weight.append(float(splitline[3]))
 164                 total_weight.append(float(splitline[4]))
 165                 flow.append(int(splitline[5]))
 166                 flow_5k.append(int(splitline[6]))
 167                 flow_10k.append(int(splitline[7]))
 168                 flow_20k.append(int(splitline[8]))
 169                 flow_50k.append(int(splitline[9]))
 170                 flow_avg.append(int(splitline[10]))
 171                 max_flow_rate.append(int(splitline[11]))
 172                 max_flow_hash.append(int(splitline[12]))
 173                 local_limit.append(int(splitline[13]))
 174                 total_over_max_weight.append(float(splitline[15]))
 175                 last_time = float(splitline[0])
 176             except ValueError:
 177                 print "Warning: Caught ValueError on line: " + line
 178
 179     file.close()
 180
 181     times.append(time)
 182     local_rates.append(local_rate)
 183     ideal_weights.append(ideal_weight)
 184     local_weights.append(local_weight)
 185     total_weights.append(total_weight)
 186     flows.append(flow)
 187     flows_5k.append(flow_5k)
 188     flows_10k.append(flow_10k)
 189     flows_20k.append(flow_20k)
 190     flows_50k.append(flow_50k)
 191     flows_avg.append(flow_avg)
 192     max_flow_rates.append(max_flow_rate)
 193     max_flow_hashs.append(max_flow_hash)
 194     local_limits.append(local_limit)
 195     total_over_max_weights.append(total_over_max_weight)
 196
 197 for t in xrange(len(times)):
 198     mintime = min(times[t])
 199     if mintime < time_begin:
 200         time_begin = mintime
 201
 202 for t in xrange(len(times)):
 203     for i in xrange(len(times[t])):
 204         times[t][i] -= time_begin
 205
 206 print time_begin
 207
 208 fig = figure()
 209 subplots = []
 210 subplots_adjust(left = 0.12, right = 0.94, bottom = 0.05, top = 0.94)
 211
 212 graph_count = 5
 213
 214 subplots.append(add_plot(times, local_rates, 1, graph_count, "Local Rate", fig, int, True))
 215
 216 subplots.append(add_plot(times, local_limits, 2, graph_count, "Local Limit", fig, int, True))
 217
 218 subplots.append(add_plot(times, local_weights, 3, graph_count, "Weight", fig, float, False))
 219
 220 #subplots.append(add_plot(times, ideal_weights, 4, graph_count, "Ideal Weight", fig, float, False))
 221
 222 #subplots.append(add_plot(times, total_over_max_weights, 5, graph_count, "Ideal Weight", fig, float, False))
 223
 224 subplots.append(add_plot(times, flows, 4, graph_count, "# of flows", fig, int, False))
 225
 226 #add_plot(times, flows_5k, 6, graph_count, "# of flows > 5KB/s", fig, int, False)
 227 #add_plot(times, flows_20k, 7, graph_count, "# of flows > 20KB/s", fig, int, False)
 228 #add_plot(times, flows_50k, 6, graph_count, "# of flows > 50KB/s", fig, int, False)
 229
 230 subplots.append(add_plot(times, flows_avg, 5, graph_count, "Average flow rate", fig, int, False))
 231
 232 #subplots.append(add_plot(times, max_flow_rates, 6, graph_count, "Max flow rate", fig, int, False, ymax=160000))
 233 #subplots.append(add_plot(times, max_flow_hashs, 7, graph_count, "Max flow hash", fig, int, False))
 234
 235 xlimits = subplots[0].get_xlim()
 236
 237 for sub in subplots:
 238     for on in on_times:
 239         if on < time_begin:
 240             on = time_begin
 241         sub.axvline(x = (on - time_begin), color = 'green')
 242
 243     for off in off_times:
 244         if off < time_begin:
 245             off = time_begin
 246         sub.axvline(x = (off - time_begin), color = 'red')
 247
 248     sub.set_xlim(xmin = xlimits[0], xmax = xlimits[1])
 249
 250 show()