1 files changed, 133 insertions, 0 deletions
diff --git a/research/flossing/make_trm_multi4_meeting_artifacts.py b/research/flossing/make_trm_multi4_meeting_artifacts.py
new file mode 100644
index 0000000..5f964ed
--- /dev/null
+++ b/research/flossing/make_trm_multi4_meeting_artifacts.py
@@ -0,0 +1,133 @@
+from __future__ import annotations
+
+from pathlib import Path
+import csv
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+ROOT = Path("research/flossing")
+SPECTRUM_DIR = ROOT / "official_gbs768_spectrum"
+PTRM_DIR = ROOT / "ptrm_same_subset"
+PLOT_DIR = ROOT / "meeting_artifacts"
+
+
+def _read_csv_rows(path: Path) -> list[dict[str, str]]:
+    with path.open(newline="") as fh:
+        return list(csv.DictReader(fh))
+
+
+def _load_spectrum_rows() -> list[tuple[str, int, np.lib.npyio.NpzFile]]:
+    return [
+        (
+            "TRM baseline best",
+            58590,
+            np.load(SPECTRUM_DIR / "trm_gbs768_base_step58590_n512_k8_seed20260602.npz"),
+        ),
+        (
+            "multi4 best",
+            35805,
+            np.load(SPECTRUM_DIR / "trm_gbs768_multi4_step35805_n512_k8_seed20260602.npz"),
+        ),
+        (
+            "multi4 final",
+            65100,
+            np.load(SPECTRUM_DIR / "trm_gbs768_multi4_step65100_n512_k8_seed20260602.npz"),
+        ),
+    ]
+
+
+def plot_spectra() -> None:
+    rows = _load_spectrum_rows()
+    fig, axes = plt.subplots(1, 3, figsize=(13.5, 4.2), sharey=True)
+    for ax, (name, step, data) in zip(axes, rows):
+        spec = data["lyap_spec"].astype(float)
+        exact = data["exact_correct"].astype(bool)
+        x = np.arange(1, spec.shape[1] + 1)
+        for mask, color, label in [
+            (exact, "#1f77b4", "success"),
+            (~exact, "#d62728", "failure"),
+        ]:
+            mean = spec[mask].mean(axis=0)
+            se = spec[mask].std(axis=0) / max(mask.sum(), 1) ** 0.5
+            ax.plot(x, mean, marker="o", color=color, label=label)
+            ax.fill_between(x, mean - se, mean + se, color=color, alpha=0.16, linewidth=0)
+        ax.axhline(0, color="black", linewidth=0.8, alpha=0.55)
+        ax.set_title(f"{name}\nstep {step}, acc={exact.mean():.3f}")
+        ax.set_xlabel("Lyapunov spectrum rank")
+        ax.grid(alpha=0.22)
+    axes[0].set_ylabel("finite-time exponent")
+    axes[0].legend(frameon=False)
+    fig.suptitle("TRM official GBS768: spectrum separates success/failure; multi4 best suppresses successful spectrum")
+    fig.tight_layout()
+    fig.savefig(PLOT_DIR / "trm_gbs768_spectrum_success_failure_n512.png", dpi=220)
+    plt.close(fig)
+
+
+def plot_headline() -> None:
+    rows = _read_csv_rows(SPECTRUM_DIR / "headline_trm_multi4_dynamics_table.csv")
+    labels = ["baseline\nbest", "multi4\nbest", "multi4\nfinal"]
+    fig, axes = plt.subplots(1, 3, figsize=(12, 3.8))
+    metrics = [
+        ("full_exact", "Full test exact acc", "#2f6f4e"),
+        ("mean8_all", "Mean top-8 exponent", "#8a4b2a"),
+        ("pos_count_all", "Positive exponents / 8", "#4d5f8a"),
+    ]
+    for ax, (col, title, color) in zip(axes, metrics):
+        values = np.asarray([float(row[col]) for row in rows])
+        ax.bar(labels, values, color=color, alpha=0.86)
+        ax.set_title(title)
+        ax.grid(axis="y", alpha=0.22)
+        for i, v in enumerate(values):
+            ax.text(i, v, f"{v:.3f}" if col != "pos_count_all" else f"{v:.2f}", ha="center", va="bottom", fontsize=9)
+    fig.suptitle("Accuracy improves at multi4 best while chaotic volume drops; final collapse restores positive spectrum")
+    fig.tight_layout()
+    fig.savefig(PLOT_DIR / "trm_gbs768_headline_dynamics_bars.png", dpi=220)
+    plt.close(fig)
+
+
+def plot_ptrm() -> None:
+    row = _read_csv_rows(PTRM_DIR / "paired_ptrm_k100_n1000_seed0_summary.csv")[0]
+    labels = ["deterministic", "mean rollout", "Q-selected", "oracle"]
+    base = [
+        float(row["base_det"]),
+        float(row["base_mean_rollout"]),
+        float(row["base_qmax"]),
+        float(row["base_oracle"]),
+    ]
+    multi = [
+        float(row["multi4_det"]),
+        float(row["multi4_mean_rollout"]),
+        float(row["multi4_qmax"]),
+        float(row["multi4_oracle"]),
+    ]
+    x = np.arange(len(labels))
+    width = 0.36
+    fig, ax = plt.subplots(figsize=(8.4, 4.2))
+    ax.bar(x - width / 2, base, width, label="baseline best", color="#6b7280")
+    ax.bar(x + width / 2, multi, width, label="multi4 best", color="#2563eb")
+    ax.set_ylim(0.86, 1.0)
+    ax.set_xticks(x, labels)
+    ax.set_ylabel("exact accuracy")
+    ax.set_title("PTRM same-subset comparison (n=1000, K=100, D=64, sigma=0.3, L-only)")
+    ax.grid(axis="y", alpha=0.22)
+    ax.legend(frameon=False)
+    for xpos, vals in [(x - width / 2, base), (x + width / 2, multi)]:
+        for xi, v in zip(xpos, vals):
+            ax.text(xi, v + 0.002, f"{v:.3f}", ha="center", va="bottom", fontsize=8)
+    fig.tight_layout()
+    fig.savefig(PLOT_DIR / "ptrm_same_subset_k100_n1000_comparison.png", dpi=220)
+    plt.close(fig)
+
+
+def main() -> None:
+    PLOT_DIR.mkdir(parents=True, exist_ok=True)
+    plot_spectra()
+    plot_headline()
+    plot_ptrm()
+    print(f"wrote plots to {PLOT_DIR}")
+
+
+if __name__ == "__main__":
+    main()