KiHoLee
669737e83d
exp_learned.py completes the learned side of the result stages, so every figure can carry both realizations of keyed masking. real() holds the structured artifacts aside and restores them, since exp_real_sec writes fixed file names. replot_security.py ranks legend handles from one declared order at all three ax.legend call sites, so entries no longer follow plot-call order and drift between figures.
572 lines
24 KiB
Python
572 lines
24 KiB
Python
"""Canonical replot script for paper 11: regenerates every result figure
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from ../data/*.csv and writes paper-ready PDFs to ../fig/. No experiment
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is rerun. All result plots share one canvas and axes rectangle (8:6 box).
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Label dictionary is fixed here and copied verbatim into tables and prose.
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fig_sec_snr.pdf : legitimate and eavesdropper SER vs SNR (Fig. 2)
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fig_sec_keylen.pdf : SER vs key length L (Fig. 3)
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fig_sec_jam.pdf : target-user SER vs JSR, four cases (Fig. 4)
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fig_sec_sens.pdf : eavesdropper SER vs fraction of key held (Fig. 5)
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fig_sec_brute.pdf : eavesdropper SER vs number of key guesses (Fig. 6)
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fig_sec_kpa.pdf : eavesdropper SER vs known-plaintext frames (Fig. 7)
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fig_sec_real.pdf : token error rate on real streams (Fig. 8)
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Curves that coincide by construction are drawn deliberately layered: the
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lower one wide and semi-transparent, the upper one narrow with open
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markers, and their markers staggered to different sample points through
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markevery offsets. Marker size is uniform across every figure, so the
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stagger, not the size, is what keeps each legend entry visible.
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"""
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from __future__ import annotations
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from pathlib import Path
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import csv
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import math
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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ROOT = Path(__file__).resolve().parents[1]
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DATA = ROOT / "data"
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FIG = ROOT / "fig"
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FIG.mkdir(exist_ok=True)
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plt.rcParams.update({
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"font.family": "serif",
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"font.serif": ["DejaVu Serif", "Times New Roman"],
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# The manuscript includes each result figure at 0.74 of a 3.455 in
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# column while the canvas is 3.15 in, a printed scale of 0.812. Every
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# size below is therefore pre-divided by that scale so the PRINTED
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# sizes are 8 pt labels, 7.6 pt ticks and a 6 pt legend at the
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# smallest rung. Change the include width and these must change
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# with it.
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"font.size": 9.9,
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"axes.labelsize": 9.9,
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"legend.fontsize": 9.2,
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"xtick.labelsize": 9.4,
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"ytick.labelsize": 9.4,
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"axes.grid": True,
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"grid.linestyle": "--",
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"grid.linewidth": 0.4,
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"grid.alpha": 0.6,
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"lines.linewidth": 1.5,
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"lines.markersize": 5.2,
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"figure.figsize": (3.15, 2.25), # shorter canvas: same printed width and font size, less page height
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"pdf.fonttype": 42,
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})
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AXES_RECT = dict(left=0.215, right=0.970, top=0.955, bottom=0.225)
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C_LEGIT = "#c0392b" # KM, structured keys
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C_LEARN = "#d98c00" # KM, learned keys
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C_OMA = "#7f8c8d" # orthogonal multiple access
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C_PUB = "#16a085" # public masks
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C_PERM = "#8e44ad" # permutation key
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C_PAD = "#a0522d" # index cipher
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C_EVE = "#2c5fa8" # an adversary of KM
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C_CH = "#95a5a6" # chance and reference levels
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C_MATCH = C_PUB # the matched jammer is what public masks admit
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# One entry per curve the figures draw. Colour identifies the scheme and
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# line style the role: solid for a legitimate rate, dashed for an
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# adversary, dash-dot for a comparison scheme, dotted for a reference.
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# Every figure reads its curves from here, so a reader who learns a
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# curve in one figure reads the same curve in the next.
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STY = {
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"km_str": dict(color=C_LEGIT, marker="o", ls="-"),
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"km_lrn": dict(color=C_LEARN, marker="d", ls="-"),
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"oma": dict(color=C_OMA, marker="^", ls=":"),
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"pub": dict(color=C_PUB, marker="v", ls="-."),
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"perm": dict(color=C_PERM, marker="X", ls="--"),
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"pad": dict(color=C_PAD, marker="P", ls="-."),
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"eve": dict(color=C_EVE, marker="s", ls="--"),
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"insider": dict(color=C_EVE, marker="v", ls="-."),
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}
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# fixed label dictionary: tables and prose copy these strings verbatim
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LBL = {
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"legit": "KM (str.)",
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"legit_learned": "KM (lrn.)",
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"oma": "OMA",
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"eve_pub": "Eavesdropper, public masks",
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"eve_key": "Eavesdropper", # the wrong-key condition is in the caption
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"chance": "Random guess",
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"nojam": "No jammer",
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"mask": "KM (str.)",
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"perm": "Permutation key",
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"pad": "Index cipher",
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"insider": "Insider",
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"outsider": "Outsider",
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}
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# deliberate-layering style for the LOWER of two coinciding curves
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UNDER = dict(lw=2.6, alpha=0.85) # thick filled line, layered under
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# and for the curve riding on top of it
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OVER = dict(lw=1.2, mfc="none") # thin open marker, rides on top
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def load(name):
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with open(DATA / name) as f:
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return list(csv.DictReader(f))
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def col(rows, k, f=float):
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return [f(r[k]) for r in rows]
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def _inflate(box, fig):
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"""Grow a bounding box by the marker radius plus the line width, in
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pixels, so a marker whose CENTER clears the box cannot still touch
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its frame."""
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pad = (plt.rcParams["lines.markersize"] / 2.0
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+ plt.rcParams["lines.linewidth"]) * fig.dpi / 72.0
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from matplotlib.transforms import Bbox
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return Bbox.from_extents(box.x0 - pad, box.y0 - pad,
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box.x1 + pad, box.y1 + pad)
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def save(fig, name, insets=()):
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"""Write the figure and assert that no axis label is clipped.
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A long y label, or wide minor tick labels such as 6x10^-1 on a log
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axis that spans less than a decade, silently pushes the label off
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the canvas under the fixed axes rectangle. Reading the plotting code
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cannot reveal this, so the check is made on the rendered geometry.
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"""
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fig.subplots_adjust(**AXES_RECT)
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fig.canvas.draw()
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fbox = fig.get_window_extent()
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for ax in fig.axes:
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for lbl in (ax.yaxis.label, ax.xaxis.label):
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if not lbl.get_text():
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continue
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b = lbl.get_window_extent()
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if (b.x0 < fbox.x0 or b.y0 < fbox.y0
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or b.x1 > fbox.x1 or b.y1 > fbox.y1):
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raise RuntimeError(
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f"{name}: axis label '{lbl.get_text()}' is clipped "
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f"(label {b} outside figure {fbox}); shorten the "
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f"label or widen the margin")
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# No curve may pass under the legend box. Reading the code cannot
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# reveal this, so the check is made on the rendered geometry, the
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# same discipline as the clipping guard above.
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leg = ax.get_legend()
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if leg is not None:
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raw = leg.get_window_extent()
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if (raw.x0 < fbox.x0 or raw.y0 < fbox.y0
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or raw.x1 > fbox.x1 or raw.y1 > fbox.y1):
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raise RuntimeError(
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f"{name}: the legend box leaves the canvas "
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f"({raw} outside {fbox}); narrow or move it")
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lb = _inflate(raw, fig)
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for line in ax.get_lines():
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# full-span reference lines (axhline/axvline) carry axes-
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# fraction endpoints [0,1]; they are not data curves and,
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# spanning the whole axis, would forbid any bottom legend
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xd = list(line.get_xdata())
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if xd == [0, 1] or list(line.get_ydata()) == [0, 1]:
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continue
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xy = line.get_xydata()
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if len(xy) == 0:
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continue
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for px, py in ax.transData.transform(xy):
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if lb.x0 <= px <= lb.x1 and lb.y0 <= py <= lb.y1:
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raise RuntimeError(
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f"{name}: a data curve passes under the legend "
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f"box; move the legend or shrink it")
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for t in ax.texts:
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tb = t.get_window_extent()
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if (lb.x0 < tb.x1 and tb.x0 < lb.x1
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and lb.y0 < tb.y1 and tb.y0 < lb.y1):
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raise RuntimeError(
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f"{name}: the annotation {t.get_text()!r} sits under "
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f"the legend box; move one of them")
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for t in ax.texts:
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tb = t.get_window_extent()
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for line in ax.get_lines():
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xy = line.get_xydata()
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if len(xy) == 0:
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continue
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for px, py in ax.transData.transform(xy):
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if tb.x0 <= px <= tb.x1 and tb.y0 <= py <= tb.y1:
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raise RuntimeError(
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f"{name}: a curve is drawn through the "
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f"annotation {t.get_text()!r}; move it")
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for ins in insets:
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ib = ins.get_window_extent()
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for a in fig.axes:
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if a is ins:
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continue
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for line in a.get_lines():
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xy = line.get_xydata()
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if len(xy) == 0:
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continue
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for px, py in a.transData.transform(xy):
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if ib.x0 <= px <= ib.x1 and ib.y0 <= py <= ib.y1:
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raise RuntimeError(
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f"{name}: a data curve passes under the inset "
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f"panel; move or shrink the inset")
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fig.savefig(FIG / f"{name}.pdf")
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plt.close(fig)
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print("[OK]", name)
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PL_CHOSEN = [] # sizes the sweep settled on, one per figure
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PL_FORCED = None # set by main() on its second pass
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def main_legit(snr_db="10"):
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"""The legitimate SER of the main configuration, read from the curve
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the main configuration produced rather than looked up by key length."""
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for r in load("sec_snr.csv"):
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if float(r["snr_db"]) == float(snr_db):
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return float(r["legit"])
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raise KeyError("no %s dB row in sec_snr.csv" % snr_db)
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# Legend order, applied by place_legend to whatever subset a figure
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# draws: the proposal first, then the comparison schemes in the order of
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# Table IV, then adversaries, then reference levels. Entries not listed
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# keep their plot order after the ranked ones.
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LEGEND_ORDER = [
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"KM (str.)", "KM (lrn.)",
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"Public masks", "Permutation key", "Index cipher", "OMA",
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"Eavesdropper", "Eavesdropper, keyed", "Eavesdropper, public masks",
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"Outsider", "Insider",
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"No jammer", "Random guess",
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]
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def _rank(label):
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"""Rank a legend label, matching the collection-SNR variants of
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Fig. 7 on their scheme prefix so they stay together and in order."""
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for i, name in enumerate(LEGEND_ORDER):
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if label == name or label.startswith(name + ","):
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return i
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return len(LEGEND_ORDER)
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def place_legend(ax, cands=("lower left", "upper left", "center left",
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"center right", "lower center", "upper right",
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"upper center", "center", "lower right"),
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sizes=(9.2, 8.8, 8.4, 8.0, 7.6, 7.2, 6.8, 6.4), ncol=1):
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"""Choose the location and font size whose box the fewest curve points
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fall inside, scored on rendered geometry rather than guessed from the
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data. The size sweep is what makes a long label set placeable: a
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five-entry legend of full scheme names has no clear corner at the
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default size on every figure.
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The axes rectangle is applied first, because save() enforces the same
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test after applying it. Scoring the default layout and then checking
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a different one is how a placement that looked clear here failed
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there.
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When PL_FORCED is set, only that size is tried: the driver runs every
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figure once to learn the smallest size any of them needs, then reruns
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them all at that one size so the legends print uniformly."""
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ax.figure.subplots_adjust(**AXES_RECT)
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if PL_FORCED is not None:
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sizes = (PL_FORCED,)
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best = None
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for size in sizes:
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for loc in cands:
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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leg = ax.legend(h, l, loc=loc, prop={"size": size}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9,
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handletextpad=0.5, borderaxespad=0.55,
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framealpha=1.0)
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ax.figure.canvas.draw()
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lb = _inflate(leg.get_window_extent(), ax.figure)
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hits = 0
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for line in ax.get_lines():
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xy = line.get_xydata()
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if len(xy) == 0:
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continue
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for px, py in ax.transData.transform(xy):
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if lb.x0 <= px <= lb.x1 and lb.y0 <= py <= lb.y1:
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hits += 1
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for t in ax.texts:
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tb = t.get_window_extent()
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if (lb.x0 < tb.x1 and tb.x0 < lb.x1
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and lb.y0 < tb.y1 and tb.y0 < lb.y1):
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hits += 50 # an annotation hidden is worse than a
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# few curve points clipped
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if best is None or hits < best[2]:
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best = (loc, size, hits)
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if hits == 0:
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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ax.legend(h, l, loc=loc, prop={"size": size}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9,
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handletextpad=0.5, borderaxespad=0.55,
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framealpha=1.0)
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PL_CHOSEN.append(size)
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return best
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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ax.legend(h, l, loc=best[0], prop={"size": best[1]}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9, handletextpad=0.5,
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borderaxespad=0.55, framealpha=1.0)
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PL_CHOSEN.append(best[1])
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return best
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def fig_snr():
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r = load("sec_snr.csv")
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x = col(r, "snr_db")
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fig, ax = plt.subplots()
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# legitimate and the public-mask eavesdropper coincide by
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# construction (same physical layer, public masks decode alike), so
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# the pair is deliberately layered; OMA is separate at this frame
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ax.semilogy(x, col(r, "legit"), **STY["km_str"],
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markevery=(0, 3), label=LBL["legit"], **UNDER)
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# the learned family is the other end of the key-space trade-off,
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# so the figure carries what it costs at every SNR
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rl = load("sec_snr_learned.csv")
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ax.semilogy(col(rl, "snr_db"), col(rl, "legit"), **STY["km_lrn"],
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markevery=(2, 3), label=LBL["legit_learned"])
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ax.semilogy(x, col(r, "oma"), **STY["oma"],
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markevery=(1, 3), label=LBL["oma"], **OVER)
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ax.semilogy(x, col(r, "eve_public"), color=STY["pub"]["color"], marker=STY["pub"]["marker"],
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ls="none", markevery=(2, 3), markerfacecolor="none",
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label=LBL["eve_pub"])
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# this figure carries two eavesdroppers, so the bare label of the
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# key-length figure would not tell them apart
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ax.semilogy(x, col(r, "eve_wrong"), **STY["eve"], label="Eavesdropper, keyed")
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# the chance level lies within 3.5e-4 of the wrong-key curve, so it is
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# drawn for reference but left out of the legend, which the caption
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# names instead; five long entries leave this figure no clear corner
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ax.plot(x, col(r, "chance"), color=C_CH, ls="-.", lw=0.9)
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ax.set_xlabel("SNR (dB)")
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ax.set_ylabel("SER")
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ax.set_xlim(min(x), max(x))
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# most of a decade below the data leaves the lower-left genuinely
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# empty, which is what gives the legend a clear berth
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ax.set_ylim(bottom=2e-4)
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place_legend(ax)
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save(fig, "fig_sec_snr")
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def fig_keylen():
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"""The OMA reference is the resource-matched one of oma_ser_keylen,
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which is undefined below L=16 unless 16/L is an integer; those
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rows carry nan and are skipped."""
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r = load("sec_keylen.csv")
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x = col(r, "L", int)
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fig, ax = plt.subplots()
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ax.semilogy(x, col(r, "legit_ser"), **STY["km_str"], label=LBL["legit"])
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rl = load("sec_keylen_learned.csv")
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ax.semilogy(col(rl, "L", int), col(rl, "legit_ser"), **STY["km_lrn"], label=LBL["legit_learned"])
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op = [(l, v) for l, v in zip(x, col(r, "oma")) if not math.isnan(v)]
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ax.semilogy([p[0] for p in op], [p[1] for p in op], **STY["oma"], label=LBL["oma"])
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ax.semilogy(x, col(r, "eve_ser"), **STY["eve"], label=LBL["eve_key"])
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ax.set_ylim(top=22.0) # headroom above the flat eavesdropper curve
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# an error rate cannot exceed one, and the room below the data holds
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# the legend, since every curve decays to the right
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ax.set_ylim(top=1.4, bottom=1.2e-2)
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ax.set_xlabel("Key length $L$")
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ax.set_ylabel("SER")
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ax.set_xscale("log", base=2)
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place_legend(ax)
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save(fig, "fig_sec_keylen")
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def fig_jam():
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"""Target-user SER against JSR in four cases. A linear axis is used
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because the range spans less than one decade, where a log axis would
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print wide minor tick labels that crowd out the y label. The
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no-jammer reference is named in the caption rather than in the
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legend, which keeps the legend four rows tall."""
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r = load("sec_jam_cmp.csv")
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x = col(r, "jsr_db")
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me = max(1, len(x) // 8)
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fig, ax = plt.subplots()
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rj = load("sec_jam_learned.csv")
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ax.plot(col(rj, "jsr_db"), col(rj, "blind"), **STY["km_lrn"],
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markevery=(1, me), label=LBL["legit_learned"])
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ax.plot(x, col(r, "matched"), **STY["pub"],
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markevery=me, label="Public masks")
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ax.plot(x, col(r, "oma_targeted"), **STY["oma"],
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markevery=me, label=LBL["oma"])
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# the two blind curves agree to 0.002; deliberate layering
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ax.plot(x, col(r, "blind"), **STY["km_str"],
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markevery=(0, me), label=LBL["mask"], **UNDER)
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ax.plot(x, col(r, "perm_blind"), **STY["perm"],
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markevery=(me // 2, me), label=LBL["perm"], **OVER)
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|
nojam = float(load("sec_jam.csv")[0]["nojam"])
|
|
# the unjammed reference is named in the caption rather than in the
|
|
# legend, which keeps the folded legend two rows tall
|
|
# Behind the legend rather than through it. The placement guard skips
|
|
# axis-spanning lines, so it cannot move the legend off this one, and
|
|
# a reference drawn along the legend frame reads as part of the box.
|
|
ax.axhline(nojam, color=C_OMA, ls=(0, (1, 3)), lw=0.9, zorder=0)
|
|
ax.set_ylim(-0.42, 1.05)
|
|
ax.set_yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
|
|
ax.set_xlabel("JSR (dB)")
|
|
ax.set_ylabel("SER")
|
|
ax.set_xlim(min(x), max(x))
|
|
place_legend(ax)
|
|
save(fig, "fig_sec_jam")
|
|
|
|
|
|
def fig_sens():
|
|
"""Key sensitivity of three schemes on one axis, the fraction of the
|
|
key the attacker holds. All three ride the random-guess level over
|
|
most of the range, so the flat region is deliberately layered."""
|
|
r = load("sec_sens_cmp.csv")
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|
x = col(r, "frac")
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|
fig, ax = plt.subplots()
|
|
ax.plot(x, col(r, "ser_mask"), **STY["km_str"],
|
|
markevery=(0, 3), label=LBL["mask"], **UNDER)
|
|
rs = load("sec_sens_learned.csv")
|
|
ax.plot(col(rs, "frac"), col(rs, "ser_mask"), **STY["km_lrn"],
|
|
markevery=(1, 3), label=LBL["legit_learned"])
|
|
ax.plot(x, col(r, "ser_perm"), **STY["perm"],
|
|
markevery=(1, 3), label=LBL["perm"], **OVER)
|
|
ax.plot(x, col(r, "ser_pad"), **STY["pad"],
|
|
markevery=(2, 3), lw=1.2, mfc="none", label=LBL["pad"])
|
|
# the chance level comes from the stored curve, not from a second
|
|
# copy of the configuration constants
|
|
chance = float(load("sec_snr.csv")[0]["chance"])
|
|
ax.axhline(chance, color=C_CH, ls=":", lw=0.9, label=LBL["chance"])
|
|
ax.set_xlabel("Fraction of the key recovered")
|
|
ax.set_ylabel("Eavesdropper SER")
|
|
ax.set_xlim(0, 1)
|
|
place_legend(ax)
|
|
save(fig, "fig_sec_sens")
|
|
|
|
|
|
def fig_brute():
|
|
"""Brute-force search against the three keyed schemes at the same
|
|
key length, each mapped through its own sensitivity curve."""
|
|
r = load("sec_brute_cmp.csv")
|
|
x = col(r, "K")
|
|
fig, ax = plt.subplots()
|
|
ax.semilogx(x, col(r, "ser_perm"), **STY["perm"],
|
|
markevery=(0, 3), label=LBL["perm"], **UNDER)
|
|
ax.semilogx(x, col(r, "ser_pad"), **STY["pad"],
|
|
markevery=(1, 3), label=LBL["pad"], **OVER)
|
|
ax.semilogx(x, col(r, "ser_mask"), **STY["km_str"],
|
|
markevery=(2, 3), label=LBL["mask"])
|
|
rb = load("sec_brute_learned.csv")
|
|
ax.semilogx(col(rb, "K"), col(rb, "ser_mask"), **STY["km_lrn"],
|
|
markevery=(1, 3), label=LBL["legit_learned"])
|
|
ax.set_xlabel("Number of key guesses $K$")
|
|
ax.set_ylabel("Eavesdropper SER")
|
|
ax.set_ylim(0.0, 1.05) # keep the reference line off the spine
|
|
place_legend(ax)
|
|
save(fig, "fig_sec_brute")
|
|
|
|
|
|
def fig_real():
|
|
r = load("real_sec_ter.csv")
|
|
x = col(r, "snr_db")
|
|
fig, ax = plt.subplots()
|
|
# insider and outsider still nearly coincide and are layered; the
|
|
# legitimate and OMA curves are separate at this frame
|
|
ax.semilogy(x, col(r, "ter_legit"), **STY["km_str"],
|
|
markevery=(0, 2), label=LBL["legit"], **UNDER)
|
|
rt = load("real_sec_ter_learned.csv")
|
|
ax.semilogy(col(rt, "snr_db"), col(rt, "ter_legit"),
|
|
**STY["km_lrn"], markevery=(1, 2),
|
|
label=LBL["legit_learned"])
|
|
ax.semilogy(x, col(r, "ter_oma"), **STY["oma"],
|
|
markevery=(1, 2), label=LBL["oma"], **OVER)
|
|
ax.semilogy(x, col(r, "ter_insider"), **STY["insider"],
|
|
markevery=(0, 2), label=LBL["insider"], **UNDER)
|
|
ax.semilogy(x, col(r, "ter_eve"), **STY["eve"],
|
|
markevery=(1, 2), label=LBL["outsider"], **OVER)
|
|
ax.set_xlabel("SNR (dB)")
|
|
ax.set_ylabel("TER")
|
|
ax.set_xlim(min(x), max(x))
|
|
place_legend(ax)
|
|
save(fig, "fig_sec_real")
|
|
|
|
|
|
def fig_kpa():
|
|
"""Known-plaintext recovery of the keyed masks at three collection
|
|
SNRs, with the permutation key under the same attack as the linear
|
|
comparison scheme."""
|
|
r = load("kpa.csv")
|
|
fig, ax = plt.subplots()
|
|
sty = {0.0: (C_LEGIT, "o"), 10.0: (C_EVE, "s"),
|
|
20.0: (C_PUB, "v")}
|
|
for off, (snr, (c, mk)) in enumerate(sty.items()):
|
|
rows = [row for row in r if float(row["snr_db"]) == snr]
|
|
n = [float(row["n_frames"]) for row in rows]
|
|
ser = [float(row["eve_ser"]) for row in rows]
|
|
ax.semilogx(n, ser, color=c, marker=mk, ls="-",
|
|
markevery=(off, 4), markerfacecolor="none" if off else c,
|
|
label=f"KM (str.), {int(snr)} dB")
|
|
if snr == 10.0:
|
|
kl = [q for q in load("kpa_learned.csv")
|
|
if float(q["snr_db"]) == snr]
|
|
ax.semilogx([float(q["n_frames"]) for q in kl],
|
|
[float(q["eve_ser"]) for q in kl], **STY["km_lrn"],
|
|
markevery=(2, 4),
|
|
label=f"KM (lrn.), {int(snr)} dB")
|
|
try:
|
|
p = load("pkpa.csv")
|
|
ax.semilogx(col(p, "n_frames"), col(p, "eve_ser"), color=C_MATCH,
|
|
marker="P", ls="--", markevery=(3, 4),
|
|
label=LBL["perm"] + ", 20 dB")
|
|
except FileNotFoundError:
|
|
print("[skip] pkpa.csv not present yet")
|
|
# legitimate reference measured with the SAME estimator as the
|
|
# eavesdropper curves, namely the four-user average of eval_ser_sse
|
|
# in the main configuration, rather than the user-1 convention of the
|
|
# scheme-comparison table
|
|
ax.set_xlabel("Known-plaintext frames $N$")
|
|
ax.set_ylabel("Eavesdropper SER")
|
|
ax.set_xscale("log", base=2)
|
|
# the 0 dB curve sweeps the upper-right, so anchor the legend at the
|
|
# top edge past the steep drops, above every curve at large N
|
|
ax.set_ylim(top=1.18)
|
|
place_legend(ax)
|
|
save(fig, "fig_sec_kpa")
|
|
|
|
|
|
def run_all():
|
|
fig_snr()
|
|
fig_keylen()
|
|
fig_jam()
|
|
try:
|
|
fig_sens()
|
|
fig_brute()
|
|
except FileNotFoundError:
|
|
print("[skip] attack-difficulty CSVs not present yet")
|
|
try:
|
|
fig_real()
|
|
except FileNotFoundError:
|
|
print("[skip] real-token CSV not present yet")
|
|
try:
|
|
fig_kpa()
|
|
except FileNotFoundError:
|
|
print("[skip] known-plaintext CSV not present yet")
|
|
|
|
|
|
def main():
|
|
"""Two passes: the first learns the smallest legend size any figure
|
|
needs, the second forces that one size everywhere so the legends
|
|
print uniformly, which the figure standard requires."""
|
|
global PL_FORCED
|
|
PL_FORCED = None
|
|
PL_CHOSEN.clear()
|
|
run_all()
|
|
if PL_CHOSEN:
|
|
PL_FORCED = min(PL_CHOSEN)
|
|
print("[uniform] legend size %.1f pt on every figure" % PL_FORCED)
|
|
PL_CHOSEN.clear()
|
|
run_all()
|
|
print("[done] figures in", FIG)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|