"""Phase diagram of frozen-band recurrence: dynamics dial vs fidelity.

x: spectral radius rho(A) of the trained state map (log scale)
y: substrate fidelity = easy-bucket pass@1 at k=4 (free-running generation)
label: hard-bucket pass@1 at k=4 (the gain every regime buys)
All points: same frozen E2B band, same data, same 250-item eval, e400.
"""
from pathlib import Path

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

OUT = Path(__file__).resolve().parent.parent / "results-loop"

#            name                     rho    easy   hard   color
#            name                  rho(plot x)  easy   hard   color  dy
PTS = [
    ("untrained 14%", 0.22, 0.885, 0.143, "#8a8f98", -26),
    ("merge ★ 46%", 0.26, 0.885, 0.464, "#2b6cb0", -40),
    ("per-depth 36%", 0.30, 0.893, 0.357, "#805ad5", 26),
    ("tied-α 43%", 0.35, 0.910, 0.429, "#0987a0", -32),
    ("rand-k 36%", 0.41, 0.918, 0.357, "#3182ce", 22),
    ("noise-s₀ 43%*", 0.48, 0.904, 0.429, "#b83280", -34),
    ("4×MLP 54%†", 0.56, 0.902, 0.536, "#5f6b7a", 20),
    ("Parcae rec 41%*\n(ρ<1 enforced, learned B)", 0.292, 0.722, 0.411, "#2f855a", -50),
    ("unconstrained rec 39%\n(learned A,B)", 4.5, 0.697, 0.393, "#c53030", 16),
]
# x jittered around true rho=0.3 for the three anchored arms (visibility)
# untrained merge x offset for visibility (true rho = 0.30)

fig, ax = plt.subplots(figsize=(7.6, 5.2))
ax.set_xscale("log")
ax.axvspan(1.0, 20, color="#fff5f5", zorder=0)
ax.axvline(1.0, color="#c53030", lw=1.2, ls="--")
ax.text(1.06, 0.99, "ρ = 1  stability boundary", rotation=90, fontsize=8,
        color="#c53030", va="top")
ax.text(3.1, 0.615, "norm projection converts\nexplosion → stationary churn\n"
        "(training survives, substrate pays)", fontsize=8, color="#c53030",
        ha="center")

for name, rho, easy, hard, c, dy in PTS:
    ax.scatter(rho, easy, s=90 + 900 * hard, color=c, alpha=0.85, zorder=3,
               edgecolor="white", linewidth=1.5)
    lbl = name
    dx = 0
    ax.annotate(lbl,
                (rho, easy), textcoords="offset points", xytext=(dx, dy),
                ha="center", fontsize=7.5, color=c)

ax.annotate("", xy=(0.285, 0.740), xytext=(0.29, 0.868),
            arrowprops=dict(arrowstyle="->", color="#2f855a", lw=1.3))
ax.text(0.55, 0.79, "learned free B:\nfixed point leaves the\n"
        "substrate manifold\n(curriculum, s₀, ρ, capacity\nall causally cleared)",
        fontsize=8, color="#2f855a", ha="left")
ax.text(0.34, 0.972, "the anchored family — B tied to the anchor (labels: hard-bucket at k=4; * seed mean, † single seed)",
        fontsize=7.8, color="#2b6cb0", ha="center", style="italic")


ax.set_xlim(0.17, 12)
ax.set_ylim(0.58, 1.0)
ax.set_xlabel("ρ(A) — spectral radius of the trained state map (dynamics dial)")
ax.set_ylabel("substrate fidelity — easy-bucket pass@1 at k=4")
ax.set_title("Frozen-band recurrence phase diagram: stability ≠ fidelity\n"
             "(marker size ∝ hard-bucket gain at k=4; every regime buys "
             "36–54% within seed noise; only anchored B keeps the substrate)",
             fontsize=10.5)
ax.grid(True, color="#ececec", lw=0.7, which="both")
ax.set_axisbelow(True)
for s in ("top", "right"):
    ax.spines[s].set_visible(False)

fig.text(0.13, -0.06,
         "Same frozen gemma-4-E2B band (L14–30), same MBPP data, same "
         "250-item eval, e400 checkpoints. ρ from checkpoints (power "
         "iteration / diag max). Dynamics (x) and fixed-point location "
         "(y) are independent dials: contraction guarantees convergence "
         "and certified tail gradients, but only anchored content — fixed "
         "B=(1−α)I, zero-init correction, difficulty→depth curriculum — "
         "keeps the fixed point substrate-preserving.", fontsize=7.5,
         color="#555", wrap=True)
fig.tight_layout()
fig.savefig(OUT / "fig_phase.png", dpi=140, facecolor="white",
            bbox_inches="tight")
print("wrote", OUT / "fig_phase.png")