133 lines
3.9 KiB
Python
Executable File
133 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import sys
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import csv
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import fileinput
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import subprocess
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def parse_onionperf_file(file_name):
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res = {}
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# fetch sources
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sources = fetch_sources(file_name)
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# collect circuit building times for source
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for s in sources:
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with open(os.path.dirname(__file__) + "/datasets/" + file_name, 'r') as f:
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reader = csv.DictReader(filter(lambda row: row[0]!='#', f), delimiter=',')
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s_counter = 0
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s_total = 0
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for row in reader:
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if row['source'] == s:
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# we have completed a circuit after reaching the exit relay
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if int(row['position']) == 3:
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s_counter += 1
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s_total += float(row['md'])
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# calculate average
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if s_counter != 0:
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res[s] = s_total/s_counter
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return res
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def fetch_sources(file_name):
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sources = []
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with open(os.path.dirname(__file__) + "/datasets/" + file_name, 'r') as f:
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# open the file rows as dicts, skip the comments
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reader = csv.DictReader(filter(lambda row: row[0]!='#', f), delimiter=',')
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# fetch sources
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for row in reader:
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if row['source'] != '' and row['source'] not in sources:
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sources.append(row['source'])
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f.close()
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return sources
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# Plot the relative circuit building times using gnuplot
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if __name__ == '__main__':
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# set some defaults
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file_name = os.getenv('CB_FILE')
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# fetch average times from onion performance file
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avg_times = parse_onionperf_file(file_name)
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#print(avg_times)
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# fetch simulator times
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sim_times = {}
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dats = []
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dats += [each for each in os.listdir('.') if each.endswith('.dat') and 'client_c_relative' not in each]
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for dat in dats:
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with open(dat, 'r') as f:
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for line in f.readlines():
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sim_times.update({dat: float(line.split(" ")[15])})
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#print(sim_times)
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# vanilla is base
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base = sim_times['vanilla_none.dat']
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# calculate ratios
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ratios = {
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'tele-thresh': sim_times['telescoping_threshsig.dat']/base,
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'tele-merkle': sim_times['telescoping_merkle.dat']/base,
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'singlep-thresh': sim_times['singlepass_threshsig.dat']/base,
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'singlep-merkle': sim_times['singlepass_merkle.dat']/base
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}
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#print(ratios)
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# calculate relative times
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relative_times = {}
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for avg in avg_times.keys():
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for ratio in ratios.keys():
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relative_times.update({
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avg + "-vanilla": avg_times[avg]
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})
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relative_times.update({
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avg + "-" + ratio: avg_times[avg] * ratios[ratio]
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})
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#print(relative_times)
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# Create dat file
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for source in fetch_sources(file_name):
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with open("client_c_relative-%s.dat" % source, "w") as datout:
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for k in relative_times.keys():
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if source in k:
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datout.write("%s " % \
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(relative_times[k]))
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datout.close()
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# Build plot
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for source in fetch_sources(file_name):
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output_file = "client_c_relative-%s.pdf" % source
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gpcode = """set terminal pdf font "DejaVuSans,14" size 5,2.25
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set output '%s'
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set title 'Relative circuit building times'
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set arrow from %s
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set grid y
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set key out
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unset xtics
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set ylabel 'Relative times (ms)'
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plot """ % (output_file, max(relative_times.values()))
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col = 1
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firstplot = True
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for k in relative_times.keys():
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if source in k and source:
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if firstplot is False:
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gpcode += ", "
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gpcode += "'client_c_relative-%s.dat' using 0:%d with points title '%s'" % (source, col, k)
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col += 1
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firstplot = False
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gp = subprocess.Popen(['gnuplot', '-'], stdin=subprocess.PIPE)
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gp.communicate(gpcode.encode('ascii'))
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