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Fig. 4 on a log ordinate

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The unjammed reference puts the range at 0.053 to 0.998, over a decade,
so the linear axis and its below-zero legend band are no longer needed.
Minor tick labels are suppressed to keep the left margin clear.
This commit is contained in:
KiHoLee
2026-08-28 20:34:39 +09:00
parent 669737e83d
commit cbdb068219
8 changed files with 19 additions and 12 deletions
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code/replot_security.py
+19 -12
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@@ -25,6 +25,7 @@ import math
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
ROOT = Path(__file__).resolve().parents[1]
DATA = ROOT / "data"
@@ -378,26 +379,29 @@ def fig_keylen():
def fig_jam():
"""Target-user SER against JSR in four cases. A linear axis is used
because the range spans less than one decade, where a log axis would
print wide minor tick labels that crowd out the y label. The
no-jammer reference is named in the caption rather than in the
legend, which keeps the legend four rows tall."""
"""Target-user SER against JSR in five cases, on a log ordinate.
With the unjammed reference the range spans 0.053 to 0.998, over a
decade, so the axis carries two major ticks and the minor tick
labels that crowd a sub-decade log axis are suppressed. The room
below the data holds the legend, which is why the earlier linear
version reserved a band below zero instead. The no-jammer reference
is named in the caption rather than in the legend."""
r = load("sec_jam_cmp.csv")
x = col(r, "jsr_db")
me = max(1, len(x) // 8)
fig, ax = plt.subplots()
rj = load("sec_jam_learned.csv")
ax.plot(col(rj, "jsr_db"), col(rj, "blind"), **STY["km_lrn"],
ax.semilogy(col(rj, "jsr_db"), col(rj, "blind"), **STY["km_lrn"],
markevery=(1, me), label=LBL["legit_learned"])
ax.plot(x, col(r, "matched"), **STY["pub"],
ax.semilogy(x, col(r, "matched"), **STY["pub"],
markevery=me, label="Public masks")
ax.plot(x, col(r, "oma_targeted"), **STY["oma"],
ax.semilogy(x, col(r, "oma_targeted"), **STY["oma"],
markevery=me, label=LBL["oma"])
# the two blind curves agree to 0.002; deliberate layering
ax.plot(x, col(r, "blind"), **STY["km_str"],
ax.semilogy(x, col(r, "blind"), **STY["km_str"],
markevery=(0, me), label=LBL["mask"], **UNDER)
ax.plot(x, col(r, "perm_blind"), **STY["perm"],
ax.semilogy(x, col(r, "perm_blind"), **STY["perm"],
markevery=(me // 2, me), label=LBL["perm"], **OVER)
nojam = float(load("sec_jam.csv")[0]["nojam"])
# the unjammed reference is named in the caption rather than in the
@@ -406,8 +410,11 @@ def fig_jam():
# 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_ylim(6e-3, 1.4)
# a log axis spanning little more than a decade prints minor labels
# like 6x10^-1 that consume the left margin, so only the decades are
# labelled
ax.yaxis.set_minor_formatter(mticker.NullFormatter())
ax.set_xlabel("JSR (dB)")
ax.set_ylabel("SER")
ax.set_xlim(min(x), max(x))