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#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
#
# Script for plotting ECTester key generation results.
#
# Example usage:
#
# > java -jar ECTesterReader.jar -g 10000 -b 192 -fp -o gen.csv
# ...
# > ./plot_gen.py gen.csv
# ...
#
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.colors as colors
from operator import itemgetter
from copy import deepcopy
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Plot results of ECTester key generation timing.")
parser.add_argument("-o", "--output", dest="output", type=argparse.FileType("wb"), help="Write image to [file], do not display.", metavar="file")
parser.add_argument("--pub", dest="pub", action="store_true", help="Show public key scatter plot.")
parser.add_argument("--priv", dest="priv", action="store_true", help="Show private key scatter plot.")
parser.add_argument("--hist", dest="hist", action="store_true", help="Show histogram.")
parser.add_argument("--hw-hist", dest="hw_hist", action="store_true", help="Show Hamming weight 2D histogram (private key Hamming weight and generation time).")
parser.add_argument("--skip-first", dest="skip_first", action="store_true", help="Skip first entry, as it's usually a large outlier.")
parser.add_argument("-t", "--title", dest="title", type=str, nargs="?", default="", help="What title to give the figure.")
parser.add_argument("file", type=str, help="The file to plot(csv).")
opts = parser.parse_args()
with open(opts.file, "r") as f:
header = f.readline()
header_names = header.split(";")
plots = [opts.priv, opts.pub, opts.hist, opts.hw_hist]
n_plots = sum(plots)
if n_plots == 0:
n_plots = 4
plots = [True, True, True, True]
hx = lambda x: int(x, 16)
data = np.genfromtxt(opts.file, delimiter=";", skip_header=1, converters={2: hx, 3: hx}, dtype=np.dtype([("index","u4"), ("time","u4"), ("pub", "O"), ("priv", "O")]))
if opts.skip_first:
data = data[1:]
if "nano" in header_names[1]:
unit = r"$\mu s$"
time_data = map(lambda x: x[1]//1000, data)
else:
unit = r"ms"
time_data = map(itemgetter(1), data)
time_data = list(time_data)
priv_data = list(map(itemgetter(2), data))
pub_data = list(map(itemgetter(3), data))
plt.style.use("ggplot")
fig = plt.figure()
layout_kwargs = {}
if opts.title is None:
fig.suptitle(opts.file)
layout_kwargs["rect"] = [0, 0.02, 1, 0.98]
elif opts.title:
fig.suptitle(opts.title)
layout_kwargs["rect"] = [0, 0.02, 1, 0.98]
fig.tight_layout(**layout_kwargs)
plot_i = 1
if plots[0]:
axe_private = fig.add_subplot(n_plots, 1, plot_i)
axe_private.scatter(time_data, priv_data, marker="x", s=10)
axe_private.set_ylabel("private key value\n(big endian)")
axe_private.set_xlabel("time ({})".format(unit))
plot_i += 1
if plots[1]:
axe_public = fig.add_subplot(n_plots, 1, plot_i)
axe_public.scatter(time_data, pub_data, marker="x", s=10)
axe_public.set_ylabel("public key value\n(big endian)")
axe_public.set_xlabel("time ({})".format(unit))
plot_i += 1
if plots[2]:
axe_hist = fig.add_subplot(n_plots, 1, plot_i)
time_max = max(time_data)
time_avg = np.average(time_data)
time_median = np.median(time_data)
axe_hist.hist(time_data, bins=time_max//3, log=True)
axe_hist.axvline(x=time_avg, alpha=0.7, linestyle="dotted", color="red", label="avg = {}".format(time_avg))
axe_hist.axvline(x=time_median, alpha=0.7, linestyle="dotted", color="green", label="median = {}".format(time_median))
axe_hist.set_ylabel("count\n(log)")
axe_hist.set_xlabel("time ({})".format(unit))
axe_hist.xaxis.set_major_locator(ticker.MaxNLocator())
axe_hist.legend(loc="best")
plot_i += 1
if plots[3]:
priv_bit_bins = {}
for i in range(len(data)):
skey = priv_data[i]
time = time_data[i]
skey_hw = 0
while skey:
skey_hw += 1
skey &= skey - 1
if skey_hw in priv_bit_bins:
priv_bit_bins[skey_hw].append(time)
else:
priv_bit_bins[skey_hw] = [time]
priv_bit_x = []
priv_bit_y = []
for k,v in priv_bit_bins.items():
priv_bit_x.extend([k] * len(v))
priv_bit_y.extend(v)
axe_priv_hist = fig.add_subplot(n_plots, 1, plot_i)
h, xe, ye = np.histogram2d(priv_bit_x, priv_bit_y, bins=[max(priv_bit_bins) - min(priv_bit_bins), (max(time_data) - min(time_data))//5])
cmap = deepcopy(plt.cm.plasma)
cmap.set_bad("black")
im = axe_priv_hist.imshow(h.T, origin="low", cmap=cmap, aspect="auto", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=colors.LogNorm())
axe_priv_hist.set_xlabel("private key Hamming weight")
axe_priv_hist.set_ylabel("time ({})".format(unit))
fig.colorbar(im, ax=axe_priv_hist)
if plot_i > 2:
fig.text(0.01, 0.02, "Data size: {}".format(len(time_data)), size="small")
if opts.output is None:
plt.show()
else:
fig.set_size_inches(12, 10)
ext = opts.output.name.split(".")[-1]
plt.savefig(opts.output, format=ext, dpi=400, bbox_inches='tight')
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