diff options
Diffstat (limited to 'util/plot_dh.py')
| -rwxr-xr-x | util/plot_dh.py | 76 |
1 files changed, 38 insertions, 38 deletions
diff --git a/util/plot_dh.py b/util/plot_dh.py index 468e73a..60e20ae 100755 --- a/util/plot_dh.py +++ b/util/plot_dh.py @@ -18,10 +18,17 @@ import argparse from copy import deepcopy from operator import itemgetter +from utils import hw, moving_average, plot_hist + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Plot ECTester ECDH 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("--skip-first", dest="skip_first", action="store_true", help="Skip first entry, as it's usually a large outlier.") + parser.add_argument("--priv", dest="priv", action="store_true", help="Show private key MSB heatmap plot.") + parser.add_argument("--hist", dest="hist", action="store_true", help="Show time histogram.") + parser.add_argument("--hw-hist", dest="hw_hist", action="store_true", help="Show Hamming weight heatmap (private key Hamming weight and time).") + parser.add_argument("--avg", dest="avg", action="store_true", help="Show moving average of time.") + parser.add_argument("--log", dest="log", action="store_true", help="Use logarithmic scale.") + parser.add_argument("--skip-first", dest="skip_first", nargs="?", const=1, type=int, help="Skip first entry, as it's usually a large outlier.") parser.add_argument("-t", "--title", dest="title", nargs="?", default="", type=str, help="What title to give the figure.") parser.add_argument("file", type=str, help="The file to plot(csv).") @@ -34,18 +41,17 @@ if __name__ == "__main__": hx = lambda x: int(x, 16) data = np.genfromtxt(opts.file, delimiter=";", skip_header=1, converters={2: hx, 3: hx, 4: hx}, dtype=np.dtype([("index","u4"), ("time","u4"), ("pub", "O"), ("priv", "O"), ("secret","O")])) if opts.skip_first: - data = data[1:] + data = data[opts.skip_first:] + time_data = data["time"] if "nano" in header_names[1]: unit = r"$\mu s$" - time_data = map(lambda x: x[1]//1000, data) + time_data = np.array(list(map(lambda x: x//1000, time_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)) - secret_data = list(map(itemgetter(4), data)) + priv_data = data["priv"] + pub_data = data["pub"] + secret_data = data["secret"] plt.style.use("ggplot") fig = plt.figure() @@ -58,43 +64,37 @@ if __name__ == "__main__": layout_kwargs["rect"] = [0, 0.02, 1, 0.98] fig.tight_layout(**layout_kwargs) - axe_hist = fig.add_subplot(2,1,1) 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") + time_min = min(time_data) + bit_size = len(bin(max(priv_data))) - 2 - 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(2,1,2) - h, xe, ye = np.histogram2d(priv_bit_x, priv_bit_y, bins=[max(priv_bit_bins) - min(priv_bit_bins), (time_max - min(time_data))//5]) cmap = deepcopy(plt.cm.plasma) cmap.set_bad("black") + + norm = colors.Normalize() + if opts.log: + norm = colors.LogNorm() + + axe_private = fig.add_subplot(3,1,1) + priv_msb = np.array(list(map(lambda x: x >> (bit_size - 8), priv_data)), dtype=np.dtype("u1")) + heatmap, xedges, yedges = np.histogram2d(priv_msb, time_data, bins=[128, time_max - time_min]) + extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] + axe_private.imshow(heatmap.T, extent=extent, aspect="auto", cmap=cmap, origin="low", interpolation="nearest", norm=norm) + axe_private.set_xlabel("private key MSB value") + axe_private.set_ylabel("ECDH time ({})".format(unit)) + + axe_hist = fig.add_subplot(3,1,2) + plot_hist(axe_hist, time_data, "ECDH time ({})".format(unit), opts.log) + axe_hist.legend(loc="best") + + axe_priv_hist = fig.add_subplot(3,1,3) + priv_hw = np.array(list(map(hw, priv_data)), dtype=np.dtype("u2")) + h, xe, ye = np.histogram2d(priv_hw, time_data, bins=[max(priv_hw) - min(priv_hw), time_max - time_min]) 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.axvline(x=bit_size//2, alpha=0.7, linestyle="dotted", color="white", label=str(bit_size//2) + " bits") axe_priv_hist.set_xlabel("private key Hamming weight") axe_priv_hist.set_ylabel("time ({})".format(unit)) + axe_priv_hist.legend(loc="best") fig.colorbar(im, ax=axe_priv_hist) fig.text(0.01, 0.02, "Data size: {}".format(len(time_data)), size="small") |