"""Large-label figures from a mechanically normalized phase-2 result file."""
import argparse
import json
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np

BLUE="#0F4D92"
RED="#B64342"
TEAL="#42949E"
VIOLET="#9A4D8E"


def style():
    # Visual reference: github.com/ChenLiu-1996/figures4papers (2026-09-16).
    plt.rcParams.update({"font.family":"DejaVu Sans","font.size":19,
        "axes.labelsize":22,"axes.titlesize":20,"xtick.labelsize":18,
        "ytick.labelsize":18,"legend.fontsize":18,"legend.frameon":False,
        "axes.spines.top":False,"axes.spines.right":False,"axes.linewidth":1.5,
        "lines.linewidth":2.8,"lines.markersize":8,"axes.titlepad":14,
        "pdf.fonttype":42,"ps.fonttype":42,"savefig.facecolor":"white"})


def save(fig,folder,name):
    folder.mkdir(parents=True,exist_ok=True)
    for ext in ("pdf","png"):
        fig.savefig(folder/(name+"."+ext),dpi=300,bbox_inches="tight",pad_inches=.12)
    plt.close(fig)


def main(folder):
    data=json.loads((folder/"plot_data.json").read_text())
    rows=data["rows"]
    out=folder/"figures"
    style()
    n=max(r["n"] for r in rows)
    budgets=sorted({r["budget"] for r in rows})[-2:]
    fig,axs=plt.subplots(1,2,figsize=(13.4,5.8))
    fig.subplots_adjust(left=.10,right=.985,bottom=.28,top=.86,wspace=.33)
    for ax,B in zip(axs,budgets):
        selected=[r for r in rows if r["n"]==n and abs(r["budget"]-B)<1e-10]
        if len(selected)!=1:
            raise ValueError("Expected one frozen finalist per n,budget")
        row=selected[0]
        cases=sorted(row["cases"],key=lambda c:c["K"])
        K=np.array([c["K"] for c in cases])
        lo=np.array([c["L"] for c in cases]);hi=np.array([c["U"] for c in cases])
        if np.any(hi<lo):
            raise ValueError("Invalid certified interval")
        middle=(lo+hi)/2
        ax.errorbar(K,middle,yerr=np.stack([middle-lo,hi-middle]),fmt="o-",
                    color=BLUE,capsize=6,capthick=2,label="Certified optimum interval")
        ax.axhline(row["model_tv"],color=RED,ls="--",label="Model TV (serial baseline)")
        ax.fill_between(K,row["model_tv"],lo,where=lo>row["model_tv"],
                        color=TEAL,alpha=.14,label="Unavoidable excess")
        ax.set(xlabel="Allowed rounds $K$",ylabel="Total variation",xticks=[2,3,4],
               title=rf"$n={n},\ B={B:g}$")
        ax.set_ylim(bottom=0)
        kl_note=f"Certified KL upper ≈ {row['actual_kl']:.5f}"
        if not row["budget_matched"]:
            kl_note+="\nBudget inactive (weight cap)"
        ax.text(.04,.05,kl_note,transform=ax.transAxes,
                fontsize=18,color="#4D4D4D",va="bottom",linespacing=1.2)
    handles,labels=axs[0].get_legend_handles_labels()
    fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.54,.01),ncol=2,fontsize=18)
    save(fig,out,"certified_depth_cost")

    fig,ax=plt.subplots(figsize=(8.8,6.3))
    fig.subplots_adjust(left=.18,right=.98,bottom=.19,top=.88)
    for (n,color,marker) in zip(sorted({r["n"] for r in rows}),[BLUE,RED,TEAL],["o","s","^"]):
        group=sorted([r for r in rows if r["n"]==n],key=lambda r:r["budget"])
        gaps=[next(c["gap_lower"] for c in r["cases"] if c["K"]==3) for r in group]
        ax.plot([r["budget"] for r in group],gaps,color=color,marker=marker,label=rf"$n={n}$")
    ax.axhline(0,color="#767676",lw=1,ls=":")
    ax.set(xlabel="Joint-KL budget $B$",ylabel="Excess TV lower bound",
           title="Selected finite-depth witnesses ($K=3$)")
    ax.set_xticks(sorted({r["budget"] for r in rows}))
    ax.legend(loc="best",fontsize=20)
    save(fig,out,"certified_execution_gap")

    candidates=data["candidates"]
    fig,axs=plt.subplots(1,2,figsize=(13.4,6.2))
    fig.subplots_adjust(left=.10,right=.985,bottom=.34,top=.85,wspace=.32)
    specs=[("single","Single alternative",RED,"^"),
           ("manuscript","Theorem ladder",BLUE,"D"),
           ("relaxed","Relaxed two-level",TEAL,"o")]
    for ax,n in zip(axs,[min(r["n"] for r in rows),max(r["n"] for r in rows)]):
        for family,label,color,marker in specs:
            group=[r for r in candidates if r["n"]==n and r["family"]==family]
            ax.scatter([r["actual_kl"] for r in group],[r["gap"] for r in group],
                       c=color,marker=marker,s=90,label=label,alpha=.9,
                       edgecolors="white",linewidths=.5)
        ax.axhline(0,color="#767676",ls=":",lw=1)
        ax.set(xlabel="Actual joint KL",ylabel="Excess TV (LP screening)",
               title=rf"Candidate search: $n={n},\ K=3$")
    handles,labels=axs[0].get_legend_handles_labels()
    fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.54,.105),ncol=3,fontsize=18)
    fig.text(.54,.012,
             "All candidates: floating-point screening; only selected witnesses independently certified.\n"
             "Finite candidate search; no claim of an outer optimum.",
             ha="center",va="bottom",fontsize=17,color="#4D4D4D",linespacing=1.2)
    save(fig,out,"candidate_search_diagnostics")
    print("Exported three PDF/PNG figure pairs.",flush=True)


if __name__=="__main__":
    ap=argparse.ArgumentParser()
    ap.add_argument("--run-dir",type=Path,required=True)
    main(ap.parse_args().run_dir)