diff options
Diffstat (limited to 'util/plot_gen.py')
| -rwxr-xr-x | util/plot_gen.py | 186 |
1 files changed, 0 insertions, 186 deletions
diff --git a/util/plot_gen.py b/util/plot_gen.py deleted file mode 100755 index 4ee1ddc..0000000 --- a/util/plot_gen.py +++ /dev/null @@ -1,186 +0,0 @@ -#!/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 -from scipy.stats import entropy -import matplotlib.pyplot as plt -from matplotlib import ticker, colors -from copy import deepcopy -import argparse - -from utils import hw, moving_average, plot_hist, miller_correction - -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("--priv", dest="priv", action="store_true", help="Show private key MSB heatmap plot.") - parser.add_argument("--entropy", dest="entropy", action="store_true", help="Show estimated entropy of private key MSB conditioned on time of generation.") - parser.add_argument("--hist", dest="hist", action="store_true", help="Show keygen time histogram.") - parser.add_argument("--export-hist", dest="export_hist", action="store_true", help="Show export time histogram.") - parser.add_argument("--avg", dest="avg", action="store_true", help="Show moving average of keygen time.") - parser.add_argument("--hw-hist", dest="hw_hist", action="store_true", help="Show Hamming weight heatmap (private key Hamming weight and keygen 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", 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(";") - if len(header_names) not in (4, 5): - print("Bad data?") - exit(1) - - plots = [opts.priv, opts.hist, opts.export_hist, opts.avg, opts.hw_hist] - n_plots = sum(plots) - if n_plots == 0: - plots = [True for _ in range(5)] - if len(header_names) == 4: - n_plots = 4 - plots[2] = False - else: - n_plots = 5 - - - if plots[2] and len(header_names) != 5: - n_plots = n_plots - 1 - if n_plots == 0: - print("Nothing to plot.") - exit(1) - plots[2] = False - - hx = lambda x: int(x, 16) - if len(header_names) == 4: - data = np.genfromtxt(opts.file, delimiter=";", skip_header=1, converters={2: hx, 3: hx}, dtype=np.dtype([("index", "u4"), ("gen_time", "u4"), ("pub", "O"), ("priv", "O")])) - else: - data = np.genfromtxt(opts.file, delimiter=";", skip_header=1, converters={3: hx, 4: hx}, dtype=np.dtype([("index", "u4"), ("gen_time", "u4"), ("export_time", "u4"), ("pub", "O"), ("priv", "O")])) - - if opts.skip_first: - data = data[opts.skip_first:] - - - gen_time_data = data["gen_time"] - export_time_data = None - if "export_time" in data.dtype.names: - export_time_data = data["export_time"] - pub_data = data["pub"] - priv_data = data["priv"] - - gen_unit = "ms" - if header_names[1].endswith("[nano]"): - gen_unit = r"$\mu s$" - np.floor_divide(gen_time_data, 1000, out=gen_time_data) - export_unit = "ms" - if len(header_names) == 5 and header_names[2].endswith("[nano]"): - export_unit = r"$\mu s$" - np.floor_divide(export_time_data, 1000, out=export_time_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) - - max_gen_time = max(gen_time_data) - min_gen_time = min(gen_time_data) - bit_size = len(bin(max(priv_data))) - 2 - - sorted_data = np.sort(data, order="gen_time") - - cmap = deepcopy(plt.cm.plasma) - cmap.set_bad("black") - - norm = colors.Normalize() - if opts.log: - norm = colors.LogNorm() - - plot_i = 1 - if plots[0]: - axe_private = fig.add_subplot(n_plots, 1, plot_i) - priv_msb = np.array(list(map(lambda x: x >> (bit_size - 8), priv_data)), dtype=np.dtype("u1")) - max_msb = max(priv_msb) - min_msb = min(priv_msb) - heatmap, xedges, yedges = np.histogram2d(priv_msb, gen_time_data, bins=[max_msb - min_msb, max_gen_time - min_gen_time]) - extent = [min_msb, max_msb, 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("keygen time ({})".format(gen_unit)) - plot_i += 1 - - if plots[1]: - axe_hist = fig.add_subplot(n_plots, 1, plot_i) - plot_hist(axe_hist, gen_time_data, "keygen time ({})".format(gen_unit), opts.log) - plot_i += 1 - - if plots[2]: - axe_hist = fig.add_subplot(n_plots, 1, plot_i) - plot_hist(axe_hist, export_time_data, "export time ({})".format(export_unit), opts.log) - plot_i += 1 - - if plots[3]: - axe_avg = fig.add_subplot(n_plots, 1, plot_i) - #if len(header_names) == 5: - # axe_other = axe_avg.twinx() - # axe_other.plot(moving_average(export_time_data, 100), color="green", alpha=0.6, label="export, window = 100") - # axe_other.plot(moving_average(export_time_data, 1000), color="yellow", alpha=0.6, label="export, window = 1000") - # axe_other.legend(loc="lower right") - axe_avg.plot(moving_average(gen_time_data, 100), label="window = 100") - axe_avg.plot(moving_average(gen_time_data, 1000), label="window = 1000") - axe_avg.set_ylabel("keygen time ({})".format(gen_unit)) - axe_avg.set_xlabel("index") - axe_avg.legend(loc="best") - plot_i += 1 - - if plots[4]: - axe_priv_hist = fig.add_subplot(n_plots, 1, plot_i) - priv_hw = np.array(list(map(hw, priv_data)), dtype=np.dtype("u2")) - h, xe, ye = np.histogram2d(priv_hw, gen_time_data, bins=[max(priv_hw) - min(priv_hw), max_gen_time - min_gen_time]) - im = axe_priv_hist.imshow(h.T, origin="low", cmap=cmap, aspect="auto", extent=[xe[0], xe[-1], ye[0], ye[-1]], norm=norm) - 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("keygen time ({})".format(gen_unit)) - axe_priv_hist.legend(loc="best") - fig.colorbar(im, ax=axe_priv_hist) - - fig.text(0.01, 0.02, "Data size: {}".format(len(gen_time_data)), size="small") - - if opts.entropy: - i = 0 - entropies = {} - while i < len(data): - time_val = sorted_data["gen_time"][i] - j = i - msbs = [0 for _ in range(256)] - while j < len(data) and sorted_data["gen_time"][j] == time_val: - msbs[(sorted_data["priv"][j] >> (bit_size - 8)) & 0xff] += 1 - j += 1 - if j - 100 > i: - entropies[time_val] = miller_correction(entropy(msbs, base=2), j - i, 256) - i = j - - entropy = sum(entropies.values())/len(entropies) - fig.text(0.01, 0.04, "Entropy of privkey MSB(estimated): {:.2f} b".format(entropy), 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') |