diff options
Diffstat (limited to 'util/plot_gen.py')
| -rwxr-xr-x | util/plot_gen.py | 66 |
1 files changed, 54 insertions, 12 deletions
diff --git a/util/plot_gen.py b/util/plot_gen.py index 12f7089..98d8261 100755 --- a/util/plot_gen.py +++ b/util/plot_gen.py @@ -1,4 +1,4 @@ -#!/usr/bin/env python +#!/usr/bin/env python3 # -*- coding: UTF-8 -*- # # Script for plotting ECTester key generation results. @@ -14,7 +14,9 @@ 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__": @@ -23,7 +25,9 @@ if __name__ == "__main__": 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() @@ -32,11 +36,11 @@ if __name__ == "__main__": header = f.readline() header_names = header.split(";") - plots = [opts.priv, opts.pub, opts.hist] + plots = [opts.priv, opts.pub, opts.hist, opts.hw_hist] n_plots = sum(plots) if n_plots == 0: - n_plots = 3 - plots = [True, True, True] + 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")])) @@ -45,16 +49,24 @@ if __name__ == "__main__": if "nano" in header_names[1]: unit = r"$\mu s$" - time_data = map(lambda x: x[1]/1000, data) + time_data = map(lambda x: x[1]//1000, data) else: unit = r"ms" time_data = map(itemgetter(1), data) - priv_data = map(itemgetter(2), data) - pub_data = map(itemgetter(3), 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(tight_layout=True) - fig.suptitle(opts.file) + 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]: @@ -76,18 +88,48 @@ if __name__ == "__main__": 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.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) - fig.text(0.01, 0.02, "Data size: {}".format(len(time_data)), size="small") + 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) - plt.savefig(opts.output, dpi=400) + ext = opts.output.name.split(".")[-1] + plt.savefig(opts.output, format=ext, dpi=400, bbox_inches='tight') |