#!/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 from matplotlib import ticker, colors from copy import deepcopy import argparse def hw(i): res = 0 while i: res += 1 i &= i - 1 return res def moving_average(a, n) : ret = np.cumsum(a, dtype=float) ret[n:] = ret[n:] - ret[:-n] return ret[n - 1:] / n 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("--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"$\mu s$" gen_time_data = list(map(lambda x: x//1000, gen_time_data)) export_unit = "ms" if len(header_names) == 5 and header_names[2].endswith("[nano]"): export_unit = r"$\mu s$" export_time_data = list(map(lambda x: x//1000, 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 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")) heatmap, xedges, yedges = np.histogram2d(priv_msb, gen_time_data, bins=[256, max_gen_time - min_gen_time]) 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\n(big endian)") 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) time_avg = np.average(gen_time_data) time_median = np.median(gen_time_data) axe_hist.hist(gen_time_data, bins=max_gen_time - min_gen_time, log=opts.log) axe_hist.axvline(x=time_avg, alpha=0.7, linestyle="dotted", color="blue", 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)" if opts.log else "")) axe_hist.set_xlabel("keygen time ({})".format(gen_unit)) axe_hist.xaxis.set_major_locator(ticker.MaxNLocator()) axe_hist.legend(loc="best") plot_i += 1 if plots[2]: axe_hist = fig.add_subplot(n_plots, 1, plot_i) time_max = max(export_time_data) time_min = min(export_time_data) time_avg = np.average(export_time_data) time_median = np.median(export_time_data) axe_hist.hist(export_time_data, bins=time_max - time_min, log=opts.log) axe_hist.axvline(x=time_avg, alpha=0.7, linestyle="dotted", color="blue", 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)" if opts.log else "")) axe_hist.set_xlabel("export time ({})".format(export_unit)) axe_hist.xaxis.set_major_locator(ticker.MaxNLocator()) axe_hist.legend(loc="best") 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.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')