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Commit 36c9120d authored by Karsten Loesing's avatar Karsten Loesing
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Remove existing Tor control log visualizations. 

These visualizations are not really useful to us, so we can as well
remove them and all remaining pylab code with it.

As part of this change, output files are being renamed from
tgen.onionperf.viz.yyyy-mm-dd.{csv,pdf} to just
onionperf.viz.yyyy-mm-dd.{csv,pdf}.

Implements #34214.
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onionperf/onionperf
+ 2
− 18
View file @ 36c9120d
......@@ -334,13 +334,6 @@ files generated by this script will be written""",
action="store", dest="prefix",
default=None)
visualize_parser.add_argument('-f', '--format',
help="""A comma-separated LIST of color/line format strings to cycle to
matplotlib's plot command (see matplotlib.pyplot.plot)""",
metavar="LIST", type=str,
action="store", dest="lineformats",
default=util.LINEFORMATS)
# get args and call the command handler for the chosen mode
if len(sys.argv) == 1:
main_parser.print_help()
......@@ -429,25 +422,16 @@ def analyze(args):
logging.error("Given paths were an unrecognized mix of file and directory paths, nothing will be analyzed")
def visualize(args):
from onionperf.visualization import TGenVisualization, TorVisualization
from onionperf.visualization import TGenVisualization
from onionperf.analysis import Analysis
lflist = args.lineformats.strip().split(",")
lfcycle = cycle(lflist)
tgen_viz = TGenVisualization()
tor_viz = TorVisualization()
for (path, label) in args.datasets:
nextformat = next(lfcycle)
anal = Analysis.load(filename=path)
if anal is not None:
tgen_viz.add_dataset(anal, label, nextformat)
tor_viz.add_dataset(anal, label, nextformat)
tgen_viz.add_dataset(anal, label)
tgen_viz.plot_all(args.prefix)
tor_viz.plot_all(args.prefix)
def type_nonnegative_integer(value):
i = int(value)
......
onionperf/visualization.py
+ 6
− 144
View file @ 36c9120d
......@@ -6,138 +6,25 @@
import matplotlib; matplotlib.use('Agg') # for systems without X11
from matplotlib.backends.backend_pdf import PdfPages
import pylab, numpy, time
import time
from abc import abstractmethod, ABCMeta
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import datetime
'''
pylab.rcParams.update({
'backend': 'PDF',
'font.size': 16,
'figure.max_num_figures' : 50,
'figure.figsize': (6, 4.5),
'figure.dpi': 100.0,
'figure.subplot.left': 0.10,
'figure.subplot.right': 0.95,
'figure.subplot.bottom': 0.13,
'figure.subplot.top': 0.92,
'grid.color': '0.1',
'axes.grid' : True,
'axes.titlesize' : 'small',
'axes.labelsize' : 'small',
'axes.formatter.limits': (-4, 4),
'xtick.labelsize' : 'small',
'ytick.labelsize' : 'small',
'lines.linewidth' : 2.0,
'lines.markeredgewidth' : 0.5,
'lines.markersize' : 10,
'legend.fontsize' : 'x-small',
'legend.fancybox' : False,
'legend.shadow' : False,
'legend.ncol' : 1.0,
'legend.borderaxespad' : 0.5,
'legend.numpoints' : 1,
'legend.handletextpad' : 0.5,
'legend.handlelength' : 1.6,
'legend.labelspacing' : .75,
'legend.markerscale' : 1.0,
'ps.useafm' : True,
'pdf.use14corefonts' : True,
'text.usetex' : True,
})
'''
class Visualization(object, metaclass=ABCMeta):
def __init__(self):
self.datasets = []
def add_dataset(self, analysis, label, lineformat):
self.datasets.append((analysis, label, lineformat))
def add_dataset(self, analysis, label):
self.datasets.append((analysis, label))
@abstractmethod
def plot_all(self, output_prefix):
pass
class TorVisualization(Visualization):
def plot_all(self, output_prefix, relays_only=False):
self.relays_only = relays_only
if len(self.datasets) > 0:
prefix = output_prefix + '.' if output_prefix is not None else ''
ts = time.strftime("%Y-%m-%d_%H:%M:%S")
self.page = PdfPages("{0}tor.onionperf.viz.{1}.pdf".format(prefix, ts))
self.__plot_bytes(direction="bytes_read")
self.__plot_bytes(direction="bytes_written")
self.page.close()
def __plot_bytes(self, direction="bytes_written"):
mafig = pylab.figure()
allcdffig = pylab.figure()
eachcdffig = pylab.figure()
for (anal, label, lineformat) in self.datasets:
tput = {}
pertput = []
for node in anal.get_nodes():
if self.relays_only and 'relay' not in node and 'thority' not in node: continue
d = anal.get_tor_bandwidth_summary(node, direction)
if d is None: continue
for tstr in d:
mib = d[tstr] / 1048576.0
t = int(tstr)
if t not in tput: tput[t] = 0
tput[t] += mib
pertput.append(mib)
pylab.figure(mafig.number)
x = sorted(tput.keys())
y = [tput[t] for t in x]
y_ma = movingaverage(y, 60)
pylab.scatter(x, y, s=0.1)
pylab.plot(x, y_ma, lineformat, label=label)
pylab.figure(allcdffig.number)
x, y = getcdf(y)
pylab.plot(x, y, lineformat, label=label)
pylab.figure(eachcdffig.number)
x, y = getcdf(pertput)
pylab.plot(x, y, lineformat, label=label)
pylab.figure(mafig.number)
pylab.xlabel("Tick (s)")
pylab.ylabel("Throughput (MiB/s)")
pylab.xlim(xmin=0.0)
pylab.ylim(ymin=0.0)
pylab.title("60 second moving average throughput, {0}, all relays".format("write" if direction == "bytes_written" else "read"))
pylab.legend(loc="lower right")
self.page.savefig()
pylab.close()
del(mafig)
pylab.figure(allcdffig.number)
pylab.xlabel("Throughput (MiB/s)")
pylab.ylabel("Cumulative Fraction")
pylab.title("1 second throughput, {0}, all relays".format("write" if direction == "bytes_written" else "read"))
pylab.legend(loc="lower right")
self.page.savefig()
pylab.close()
del(allcdffig)
pylab.figure(eachcdffig.number)
# pylab.xscale('log')
pylab.xlabel("Throughput (MiB/s)")
pylab.ylabel("Cumulative Fraction")
pylab.title("1 second throughput, {0}, each relay".format("write" if direction == "bytes_written" else "read"))
pylab.legend(loc="lower right")
self.page.savefig()
pylab.close()
del(eachcdffig)
class TGenVisualization(Visualization):
def plot_all(self, output_prefix):
......@@ -145,9 +32,9 @@ class TGenVisualization(Visualization):
prefix = output_prefix + '.' if output_prefix is not None else ''
ts = time.strftime("%Y-%m-%d_%H:%M:%S")
self.__extract_data_frame()
self.data.to_csv("{0}tgen.onionperf.viz.{1}.csv".format(prefix, ts))
self.data.to_csv("{0}onionperf.viz.{1}.csv".format(prefix, ts))
sns.set_context("paper")
self.page = PdfPages("{0}tgen.onionperf.viz.{1}.pdf".format(prefix, ts))
self.page = PdfPages("{0}onionperf.viz.{1}.pdf".format(prefix, ts))
self.__plot_firstbyte_ecdf()
self.__plot_firstbyte_time()
self.__plot_lastbyte_ecdf()
......@@ -161,7 +48,7 @@ class TGenVisualization(Visualization):
def __extract_data_frame(self):
transfers = []
for (analysis, label, lineformat) in self.datasets:
for (analysis, label) in self.datasets:
for client in analysis.get_nodes():
tgen_transfers = analysis.get_tgen_transfers(client)
for transfer_id, transfer_data in tgen_transfers.items():
......@@ -321,28 +208,3 @@ class TGenVisualization(Visualization):
sns.despine()
self.page.savefig()
plt.close()
# helper - compute the window_size moving average over the data in interval
def movingaverage(interval, window_size):
window = numpy.ones(int(window_size)) / float(window_size)
return numpy.convolve(interval, window, 'same')
# # helper - cumulative fraction for y axis
def cf(d): return pylab.arange(1.0, float(len(d)) + 1.0) / float(len(d))
# # helper - return step-based CDF x and y values
# # only show to the 99th percentile by default
def getcdf(data, shownpercentile=0.99, maxpoints=10000.0):
data.sort()
frac = cf(data)
k = len(data) / maxpoints
x, y, lasty = [], [], 0.0
for i in range(int(round(len(data) * shownpercentile))):
if i % k > 1.0: continue
assert not numpy.isnan(data[i])
x.append(data[i])
y.append(lasty)
x.append(data[i])
y.append(frac[i])
lasty = frac[i]
return x, y
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