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#!/usr/bin/env python
#
# 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
from operator import itemgetter
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("file", type=str, help="The file to plot(csv).")
opts = parser.parse_args()
plots = [opts.priv, opts.pub, opts.hist]
n_plots = sum(plots)
if n_plots == 0:
n_plots = 3
plots = [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")]))
time_data = map(itemgetter(1), data)
priv_data = map(itemgetter(2), data)
pub_data = map(itemgetter(3), data)
fig = plt.figure(tight_layout=True)
fig.suptitle(opts.file)
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 (ms)")
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 (ms)")
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 (ms)")
axe_hist.xaxis.set_major_locator(ticker.MaxNLocator())
axe_hist.legend(loc="best")
if opts.output is None:
plt.show()
else:
plt.savefig(opts.output, dpi=400)
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