2 files changed, 46 insertions, 73 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")
diff --git a/util/plot_gen.py b/util/plot_gen.py
index c07fc91..9d4863f 100755
--- a/util/plot_gen.py
+++ b/util/plot_gen.py
@@ -17,17 +17,7 @@ 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
+from utils import hw, moving_average, plot_hist
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Plot results of ECTester key generation timing.")
@@ -85,7 +75,6 @@ if __name__ == "__main__":
     pub_data = data["pub"]
     priv_data = data["priv"]
 
-
     gen_unit = "ms"
     if header_names[1].endswith("[nano]"):
         gen_unit = r"$\mu s$"
@@ -121,39 +110,23 @@ if __name__ == "__main__":
     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]]
+        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\n(big endian)")
+        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)
-        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_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)
-        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_hist(axe_hist, export_time_data, "export time ({})".format(export_unit), opts.log)
         plot_i += 1
 
     if plots[3]: