From 3fcdda4516dc233e0e778533fd50e64e940ae202 Mon Sep 17 00:00:00 2001
From: wassname <1103714+wassname@users.noreply.github.com>
Date: Fri, 26 Jun 2026 04:13:12 +0800
Subject: [PATCH] plots: drop redundant foundation_dcontrast dumbbell

The range plot already shows the per-foundation steer (human cloud + AI base
dot + +C/-C arrows) with the human anchor the dumbbell lacks; dcontrast plotted
the same delta on the C-nats scale and failed the eraser test. C stays the
measurement in the CSV/foundations table, just not a separate panel.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
---
 scripts/plot_steer_showcase.py | 41 ----------------------------------
 1 file changed, 41 deletions(-)

diff --git a/scripts/plot_steer_showcase.py b/scripts/plot_steer_showcase.py
index e672393..3531404 100644
--- a/scripts/plot_steer_showcase.py
+++ b/scripts/plot_steer_showcase.py
@@ -106,43 +106,6 @@ def read_profiles(run_dir: Path, name: str, dims: list[str], value_col: str = "m
     return {c: np.array([d[f] for f in dims]) for c, d in by_c.items()}, pmass
 
 
-def plot_ordinal_steer(run_dir: Path, out: Path, name: str, vec_label: str, C: float) -> Path:
-    """The steer-effect plot: per-factor change in the logit contrast C (steered minus base), +C (red)
-    vs -C (blue). This is the ordinal twin of the MFV dlogit dumbbell, and the figure that actually
-    shows what we steered for -- the E range saturates and hides it, the contrast does not. C_sd in the
-    CSV gives a per-factor SE over the 18 items for the error bars (uncertainty, not just the mean)."""
-    instr = get_instrument(name)
-    dims = instr.dimensions
-    cprof, pmass = read_profiles(run_dir, name, dims, value_col="C")
-    sdprof, _ = read_profiles(run_dir, name, dims, value_col="C_sd")
-    cs = sorted(cprof)
-    base = cprof[0.0]
-    pos_c = 1.0 if 1.0 in cprof else max(cs)
-    neg_c = -1.0 if -1.0 in cprof else min(cs)
-    n_items = 18  # mfq2/factor; SE = sd / sqrt(n). (big5/16pf/humor differ but this is a rough band.)
-    dpos, dneg = cprof[pos_c] - base, cprof[neg_c] - base
-    se = sdprof[0.0] / n_items ** 0.5
-    y = np.arange(len(dims))[::-1]
-    fig, ax = plt.subplots(figsize=(6.4, 4.2))
-    ax.axvline(0, color="0.6", lw=0.8, zorder=1)
-    POS, NEG = T.maps.POS_COL, T.maps.NEG_COL
-    for fi, yi in zip(range(len(dims)), y):
-        ax.plot([dneg[fi], dpos[fi]], [yi, yi], color="0.8", lw=1.0, zorder=2)
-        ax.errorbar(dpos[fi], yi, xerr=1.96 * se[fi], fmt="o", color=POS, ms=5, capsize=2, zorder=3)
-        ax.errorbar(dneg[fi], yi, xerr=1.96 * se[fi], fmt="o", color=NEG, ms=5, capsize=2, zorder=3)
-    ax.set_yticks(y); ax.set_yticklabels([d.capitalize() for d in dims])
-    ax.set_xlabel("Delta contrast C vs base  (nats; agree-minus-disagree, rank-weighted)")
-    ax.set_title(f"Steered {instr.display}: {vec_label}", fontsize=11)
-    ax.scatter([], [], color=POS, label=f"+C={C:+.2f}")
-    ax.scatter([], [], color=NEG, label=f"-C={-C:+.2f}")
-    ax.legend(fontsize=8, loc="best")
-    ax.spines[["top", "right"]].set_visible(False)
-    fig.tight_layout()
-    path = T.maps.save_both(fig, out / name, "foundation_dcontrast")
-    plt.close(fig)
-    return path
-
-
 def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float) -> list[Path]:
     instr = get_instrument(name)
     dims = instr.dimensions
@@ -202,10 +165,6 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float)
     figz = T.maps.plot_range_zoom(instr, dims, coh_cs, prof_coh, humans, vec_label)
     paths.append(T.maps.save_both(figz, out / name, "range_zoom"))
     plt.close(figz)
-
-    # steer-effect plot in the sensitive contrast readout (the E map/range above are for human
-    # comparison; this is "did the steer move it"). Parallels the MFV dlogit dumbbell.
-    paths.append(plot_ordinal_steer(run_dir, out, name, vec_label, C))
     return paths