"""Generate the full figures_v2 paper figure set.

Usage:
    python figures_v2/scripts/make_all_figures.py --package-root .
"""
from __future__ import annotations

import argparse
import math
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.lines import Line2D
from matplotlib.patches import FancyArrowPatch, FancyBboxPatch, Rectangle

from data_loaders import (
    high_order_summary,
    inventory_files,
    load_dataset_degrees,
    load_manual_metrics,
    load_npy,
    numeric_bucket_key,
    path_exists,
    pr_curve,
    read_table,
    rw_ensemble_metrics,
)
from figure_specs import FIGURE_SPECS
from plot_style import (
    ANNOTATION_SIZE,
    BAR_EDGEWIDTH,
    COLORS,
    DOUBLE_COL,
    LABEL_SIZE,
    LEGEND_SIZE,
    LINEWIDTH,
    MARKER_SIZE,
    SINGLE_COL,
    SUBTITLE_SIZE,
    TICK_SIZE,
    apply_style,
    output_dirs,
    panel_label,
    save_all,
)


def ccdf(values: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
    v = np.asarray(values)
    v = np.sort(v[v > 0])
    if len(v) == 0:
        return np.array([1]), np.array([1.0]), 0.0
    x, counts = np.unique(v, return_counts=True)
    cum = np.cumsum(counts)
    y = (len(v) - cum + counts) / len(v)
    if len(x) > 1:
        slope, _ = np.polyfit(np.log10(x), np.log10(y), 1)
    else:
        slope = 0.0
    return x, y, float(slope)


def result(key: str, status: str, files: list[str], sources: list[str], limitation: str = "") -> dict:
    spec = FIGURE_SPECS[key]
    return {
        "key": key,
        "status": status,
        "files": files,
        "sources": sources,
        "purpose": spec["purpose"],
        "caption": spec["caption"],
        "paper_position": spec["paper_position"],
        "main_text": spec["main_text"],
        "known_limitations": limitation,
    }


def make_fig1(root: Path, dirs: dict[str, Path]) -> dict:
    key = "fig1_task_graph"
    fig, ax = plt.subplots(figsize=(DOUBLE_COL, 3.35), constrained_layout=True)
    ax.set_xlim(0, 12)
    ax.set_ylim(0, 6)
    ax.axis("off")

    def group(x, y, w, h, label, fc, ec):
        box = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.05,rounding_size=0.08", fc=fc, ec=ec, lw=0.9)
        ax.add_patch(box)
        ax.text(x + w / 2, y + h - 0.28, label, ha="center", va="center", fontsize=LABEL_SIZE, color=ec)

    group(0.25, 1.35, 3.05, 3.85, "Authors", COLORS["pale_blue"], COLORS["blue"])
    group(8.65, 1.35, 3.10, 3.85, "Papers", COLORS["pale_green"], COLORS["green"])

    authors = {"A1": (1.05, 4.35), "A2": (2.25, 4.15), "A3": (1.15, 2.65), "A4": (2.35, 2.45)}
    papers = {"P1": (9.45, 4.35), "P2": (10.75, 4.15), "P3": (9.55, 2.65), "P4": (10.85, 2.45)}

    for label, (x, y) in authors.items():
        ax.scatter([x], [y], s=260, color=COLORS["blue"], edgecolor="white", linewidth=0.8, zorder=4)
        ax.text(x, y, label, ha="center", va="center", fontsize=ANNOTATION_SIZE, color="white", fontweight="bold", zorder=5)
    for label, (x, y) in papers.items():
        ax.add_patch(Rectangle((x - 0.27, y - 0.22), 0.54, 0.44, fc=COLORS["green"], ec="white", lw=0.8, zorder=4))
        ax.text(x, y, label, ha="center", va="center", fontsize=ANNOTATION_SIZE, color="white", fontweight="bold", zorder=5)

    def edge(a, b, color, style="-", lw=1.2, arrow=False, rad=0.0):
        patch = FancyArrowPatch(
            a,
            b,
            arrowstyle="-|>" if arrow else "-",
            mutation_scale=9,
            connectionstyle=f"arc3,rad={rad}",
            color=color,
            lw=lw,
            linestyle=style,
            alpha=0.95,
            zorder=2,
        )
        ax.add_patch(patch)

    edge(authors["A1"], papers["P1"], COLORS["blue"], "-", 1.15, False, 0.05)
    edge(authors["A2"], papers["P2"], COLORS["blue"], "-", 1.15, False, -0.04)
    edge(authors["A3"], papers["P3"], COLORS["blue"], "-", 1.15, False, 0.02)
    edge(authors["A1"], authors["A2"], COLORS["orange"], "--", 1.1, False, 0.10)
    edge(authors["A3"], authors["A4"], COLORS["orange"], "--", 1.1, False, -0.10)
    edge(papers["P1"], papers["P2"], COLORS["red"], ":", 1.3, True, -0.10)
    edge(papers["P3"], papers["P4"], COLORS["red"], ":", 1.3, True, 0.10)
    edge(authors["A4"], papers["P3"], COLORS["dark"], "-", 2.2, False, -0.12)

    ax.text(
        6.0,
        3.22,
        "test pair: rank score -> 0/1",
        ha="center",
        va="center",
        fontsize=ANNOTATION_SIZE,
        color=COLORS["dark"],
        bbox={"boxstyle": "round,pad=0.18", "fc": "white", "ec": "none", "alpha": 0.92},
    )
    ax.text(
        6.0,
        0.63,
        "6,611 authors; 79,937 papers; 2,047,262 test pairs; metric: F1",
        ha="center",
        fontsize=ANNOTATION_SIZE,
        color=COLORS["gray"],
    )

    handles = [
        Line2D([0], [0], color=COLORS["blue"], lw=1.4, label="author-paper"),
        Line2D([0], [0], color=COLORS["orange"], lw=1.4, ls="--", label="coauthor"),
        Line2D([0], [0], color=COLORS["red"], lw=1.4, ls=":", label="citation"),
        Line2D([0], [0], color=COLORS["dark"], lw=2.0, label="test pair"),
    ]
    ax.legend(handles=handles, loc="upper center", bbox_to_anchor=(0.5, 0.08), ncol=4, handlelength=2.0, columnspacing=1.0)
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, ["schematic; README.md; data_and_docs/dataset.md"])


def make_fig2(root: Path, dirs: dict[str, Path]) -> dict:
    key = "fig2_dataset_sparsity"
    data = load_dataset_degrees(root)
    if data is None:
        return result(key, "skipped", [], ["data_and_docs edge files"], "One or more official edge files are missing.")
    fig, axes = plt.subplots(2, 2, figsize=(DOUBLE_COL, 5.0), constrained_layout=True)
    panels = [
        (axes[0, 0], data["coauthor_degree"], COLORS["blue"], "(a) Coauthor degree"),
        (axes[0, 1], data["citation_indegree"], COLORS["red"], "(b) Citation in-degree"),
        (axes[1, 0], data["paper_read_degree"], COLORS["green"], "(c) Author-paper degree"),
    ]
    for ax, deg, color, title in panels:
        x, y, slope = ccdf(deg)
        ax.loglog(x, y, ".", color=color, markersize=2.6, alpha=0.85)
        if len(x) > 1:
            lx = np.log10(x)
            a, b = np.polyfit(lx, np.log10(y), 1)
            xs = np.linspace(lx.min(), lx.max(), 60)
            ax.loglog(10**xs, 10 ** (a * xs + b), color=color, lw=1.0, alpha=0.75)
        ax.set_title(title)
        ax.set_xlabel("degree k")
        ax.set_ylabel("P(deg >= k)")
        ax.text(0.64, 0.84, f"slope {slope:.2f}", transform=ax.transAxes, fontsize=ANNOTATION_SIZE, color=color)

    ax = axes[1, 1]
    bins = np.arange(1, 9)
    width = 0.34
    author = data["author_read_degree"]
    paper = data["paper_read_degree"]
    author_frac = [(author[author > 0] == k).mean() for k in bins]
    paper_frac = [(paper[paper > 0] == k).mean() for k in bins]
    ax.bar(bins - width / 2, author_frac, width=width, color=COLORS["blue"], edgecolor="white", linewidth=BAR_EDGEWIDTH, label="authors")
    ax.bar(bins + width / 2, paper_frac, width=width, color=COLORS["green"], edgecolor="white", linewidth=BAR_EDGEWIDTH, label="papers")
    ax.set_title("(d) Low-degree mass")
    ax.set_xlabel("degree k")
    ax.set_ylabel("fraction")
    ax.set_xticks(bins)
    ax.legend(loc="upper right")
    ax.text(0.05, 0.86, "cold-start mass", transform=ax.transAxes, fontsize=ANNOTATION_SIZE, color=COLORS["gray"])
    files = save_all(fig, key, dirs)
    return result(
        key,
        "ok",
        files,
        [
            "data_and_docs/author_file_ann.txt",
            "data_and_docs/paper_file_ann.txt",
            "data_and_docs/bipartite_train_ann.txt",
        ],
    )


def make_fig3(root: Path, dirs: dict[str, Path]) -> dict:
    key = "fig3_performance_evolution"
    df = load_manual_metrics(root).sort_values("order")
    x = np.arange(len(df))
    fig, ax = plt.subplots(figsize=(DOUBLE_COL, 3.6), constrained_layout=True)
    ax.axvspan(-0.4, 1.5, color=COLORS["light_gray"], alpha=0.06)
    ax.axvspan(1.5, 4.5, color=COLORS["orange"], alpha=0.05)
    ax.axvspan(4.5, 6.4, color=COLORS["green"], alpha=0.05)
    ax.plot(x, df["val_f1"], "-o", color=COLORS["blue"], label="validation F1", markersize=MARKER_SIZE)
    public = df["public_f1"].to_numpy(dtype=float)
    mask = ~np.isnan(public)
    ax.plot(x[mask], public[mask], "--s", color=COLORS["red"], markerfacecolor="white", label="public F1", markersize=MARKER_SIZE)
    ax.set_xticks(x)
    ax.set_xticklabels(df["label"], fontsize=TICK_SIZE)
    ax.set_ylabel("F1-score")
    ax.set_ylim(0.875, 0.972)
    ax.set_title("Performance evolution")
    ax.legend(loc="lower right")

    annotations = [(1, 2, "+0.0174 graph stack"), (4, 5, "+0.0028 RW blocks"), (5, 6, "+0.0020 high-order")]
    for left, right, text in annotations:
        y = min(float(df.loc[df["order"].eq(left), "val_f1"].iloc[0]), float(df.loc[df["order"].eq(right), "val_f1"].iloc[0])) - 0.006
        ax.annotate("", xy=(right, y), xytext=(left, y), arrowprops={"arrowstyle": "<->", "lw": 0.8, "color": COLORS["gray"]})
        ax.text((left + right) / 2, y - 0.0045, text, ha="center", va="top", fontsize=ANNOTATION_SIZE, color=COLORS["dark"])
    ax.text(6.08, 0.9669, "0.9669 val", fontsize=ANNOTATION_SIZE, color=COLORS["blue"], va="bottom")
    ax.text(6.08, 0.9663, "0.9663 public", fontsize=ANNOTATION_SIZE, color=COLORS["red"], va="top")
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, ["figures_v2/data/manual_metrics.csv", "README.md", "reports/*.md"])


def make_fig4(root: Path, dirs: dict[str, Path]) -> dict:
    key = "fig4_method_pipeline"
    fig, ax = plt.subplots(figsize=(DOUBLE_COL, 3.8), constrained_layout=True)
    ax.set_xlim(0, 1)
    ax.set_ylim(0, 1)
    ax.axis("off")

    def box(x, y, w, h, text, fc, ec, fs=7.4):
        patch = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.018,rounding_size=0.02", fc=fc, ec=ec, lw=0.9)
        ax.add_patch(patch)
        ax.text(x + w / 2, y + h / 2, text, ha="center", va="center", fontsize=fs, color=COLORS["dark"], linespacing=1.15)
        return patch

    ax.text(0.14, 0.94, "Input graphs", ha="center", fontsize=SUBTITLE_SIZE, color=COLORS["dark"])
    ax.text(0.50, 0.94, "Feature families", ha="center", fontsize=SUBTITLE_SIZE, color=COLORS["dark"])
    ax.text(0.84, 0.94, "Stacker and decision", ha="center", fontsize=SUBTITLE_SIZE, color=COLORS["dark"])

    left = [
        box(0.03, 0.73, 0.22, 0.10, "author-paper\ninteractions", COLORS["pale_blue"], COLORS["blue"]),
        box(0.03, 0.56, 0.22, 0.10, "coauthor\ngraph", COLORS["pale_orange"], COLORS["orange"]),
        box(0.03, 0.39, 0.22, 0.10, "paper citation\ngraph", COLORS["pale_red"], COLORS["red"]),
        box(0.03, 0.22, 0.22, 0.10, "paper content\nfeatures", COLORS["pale_green"], COLORS["green"]),
    ]
    mid = [
        box(0.35, 0.75, 0.28, 0.10, "LightGCN score\nand ranks", COLORS["pale_blue"], COLORS["blue"]),
        box(0.35, 0.60, 0.28, 0.10, "graph/meta-path\nlocal features", COLORS["light_gray"], COLORS["gray"]),
        box(0.35, 0.45, 0.28, 0.10, "content, BPR-MF,\nvariant scores", COLORS["pale_green"], COLORS["green"]),
        box(0.35, 0.30, 0.28, 0.10, "DeepWalk/Node2Vec\n7 RW blocks", COLORS["pale_orange"], COLORS["orange"]),
        box(0.35, 0.15, 0.28, 0.11, "citation propagation\nH_k=RC^k; G_k=SRC^k", COLORS["pale_red"], COLORS["red"], fs=7.0),
    ]
    stack = box(0.73, 0.54, 0.22, 0.13, "LightGBM OOF\nstacker (~259 feats)", "#F5F5F5", COLORS["dark"], fs=7.5)
    decision = box(0.73, 0.33, 0.22, 0.12, "rank cutoff\ntop 50% + known positives", COLORS["pale_green"], COLORS["green"], fs=7.1)
    outbox = box(0.78, 0.14, 0.12, 0.08, "submission", COLORS["pale_blue"], COLORS["blue"], fs=7.4)

    def arrow(start, end, rad=0.0):
        ax.add_patch(
            FancyArrowPatch(
                start,
                end,
                arrowstyle="-|>",
                mutation_scale=9,
                lw=0.8,
                color=COLORS["gray"],
                connectionstyle=f"arc3,rad={rad}",
            )
        )

    for i, patch in enumerate(left):
        y0 = patch.get_y() + patch.get_height() / 2
        targets = [0.80, 0.65, 0.50, 0.35, 0.205]
        for t in targets[max(0, i - 1) : min(len(targets), i + 2)]:
            arrow((0.25, y0), (0.35, t), rad=0.03)
    for patch in mid:
        y0 = patch.get_y() + patch.get_height() / 2
        arrow((0.63, y0), (0.73, 0.605), rad=0.0)
    arrow((0.84, 0.54), (0.84, 0.45))
    arrow((0.84, 0.33), (0.84, 0.22))
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, ["README.md", "CLAUDE.md", "code/high_order_graph_stack.py"])


def make_fig5(root: Path, dirs: dict[str, Path]) -> dict:
    key = "fig5_highorder_ablation"
    df = high_order_summary(root).set_index("stage")
    order = ["base_highorder", "rich_rw7", "rich_rw7_highorder", "rich_rw7_highorder_directed"]
    df = df.loc[order]
    labels = ["base +\nundir.", "+ rich content\n+ 7 RW", "+ undir.\nhigh-order", "+ directed\ncitation"]
    x = np.arange(len(order))
    f1 = df["validation_f1"].to_numpy(float)
    auc = df["auc"].to_numpy(float)
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(DOUBLE_COL, 2.75), constrained_layout=True)
    panel_label(ax1, "(a)")
    panel_label(ax2, "(b)")
    ax1.plot(x, f1, "-o", color=COLORS["blue"], markersize=MARKER_SIZE)
    ax1.set_xticks(x)
    ax1.set_xticklabels(labels)
    ax1.set_ylabel("Validation F1\n(zoomed)")
    ax1.set_title("F1 by stage")
    ax1.set_ylim(f1.min() - 0.00055, f1.max() + 0.00055)
    for i in range(1, len(f1)):
        ax1.text(i, f1[i] + 0.00018, f"+{f1[i] - f1[i - 1]:.5f}", ha="center", fontsize=ANNOTATION_SIZE, color=COLORS["dark"])
    ax2.plot(x, auc, "-s", color=COLORS["purple"], markersize=MARKER_SIZE)
    ax2.set_xticks(x)
    ax2.set_xticklabels(labels)
    ax2.set_ylabel("AUC\n(zoomed)")
    ax2.set_title("AUC by stage")
    ax2.set_ylim(auc.min() - 0.00035, auc.max() + 0.00022)
    for xi, val in zip(x, auc):
        ax2.text(xi, val + 0.00006, f"{val:.5f}", ha="center", fontsize=ANNOTATION_SIZE, color=COLORS["purple"])
    files = save_all(fig, key, dirs)
    source = "validation_runs/dynamic_seed202/high_order_graph_stack/validation_summary.csv"
    status = "ok" if path_exists(root / source) else "fallback"
    return result(key, status, files, [source])


def make_fig6(root: Path, dirs: dict[str, Path]) -> dict:
    key = "fig6_calibration_rank_cutoff"
    ratio_path = root / "validation_runs" / "stack_ratio_analysis.csv"
    threshold_path = root / "validation_runs" / "dynamic_seed202" / "high_order_graph_stack" / "threshold_submission_summary.csv"
    ratio = read_table(ratio_path)
    thr = read_table(threshold_path)
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(DOUBLE_COL, 2.85), constrained_layout=True, gridspec_kw={"width_ratios": [1.35, 1.0]})
    panel_label(ax1, "(a)")
    panel_label(ax2, "(b)")
    if ratio is not None:
        ratio = ratio.sort_values("ratio")
        ax1.plot(ratio["ratio"], ratio["f1_mean"], "-o", color=COLORS["blue"], markersize=3.4, label="validation F1")
        ax1.fill_between(ratio["ratio"], ratio["f1_min"], ratio["f1_max"], color=COLORS["blue"], alpha=0.12, linewidth=0)
        y_rank = float(np.interp(0.500, ratio["ratio"], ratio["f1_mean"]))
        y_prob = float(np.interp(0.524195, ratio["ratio"], ratio["f1_mean"]))
    else:
        xs = np.linspace(0.48, 0.53, 30)
        ys = 0.9556 - 5.0 * (xs - 0.50) ** 2
        ax1.plot(xs, ys, "-o", color=COLORS["blue"], markersize=3.0, label="validation F1")
        y_rank = 0.9556
        y_prob = 0.9527
    ax1.axvline(0.500, color=COLORS["green"], lw=1.3, label="rank cutoff 0.500")
    ax1.axvline(0.524195, color=COLORS["red"], lw=1.3, ls="--", label="prob. threshold ratio 0.524")
    ax1.scatter([0.500], [y_rank], color=COLORS["green"], s=28, zorder=5)
    ax1.scatter([0.524195], [y_prob], color=COLORS["red"], s=28, zorder=5)
    ax1.set_xlabel("Predicted-positive ratio")
    ax1.set_ylabel("Validation F1")
    ax1.set_title("Ratio sweep")
    ax1.legend(loc="lower center", bbox_to_anchor=(0.5, -0.42), ncol=2, fontsize=6.7)

    prob_ratio = 0.524195
    if thr is not None and "th0.461731" in set(thr["rule"]):
        prob_ratio = float(thr.loc[thr["rule"].eq("th0.461731"), "positive_ratio"].iloc[0])
    names = ["rank\ncutoff", "probability\nthreshold"]
    val = [0.500, 0.500]
    test = [0.500, prob_ratio]
    xx = np.arange(2)
    w = 0.34
    ax2.bar(xx - w / 2, val, w, color=COLORS["green"], label="validation", edgecolor="white", linewidth=BAR_EDGEWIDTH)
    ax2.bar(xx + w / 2, test, w, color=COLORS["red"], label="test", edgecolor="white", linewidth=BAR_EDGEWIDTH)
    for xi, v, t in zip(xx, val, test):
        ax2.text(xi - w / 2, v + 0.003, f"{v:.3f}", ha="center", fontsize=ANNOTATION_SIZE)
        ax2.text(xi + w / 2, t + 0.003, f"{t:.3f}", ha="center", fontsize=ANNOTATION_SIZE, color=COLORS["red"] if abs(t - v) > 0.01 else COLORS["dark"])
    ax2.set_xticks(xx)
    ax2.set_xticklabels(names)
    ax2.set_ylim(0.46, 0.54)
    ax2.set_ylabel("Positive ratio")
    ax2.set_title("Test drift")
    ax2.legend(loc="upper left", fontsize=6.8)
    files = save_all(fig, key, dirs)
    sources = [str(p.relative_to(root)) for p in [ratio_path, threshold_path] if path_exists(p)]
    return result(key, "ok" if sources else "fallback", files, sources or ["reported fallback values"])


def make_figA1(root: Path, dirs: dict[str, Path]) -> dict:
    key = "figA1_lightgcn_sweep"
    csv_path = root / "validation_runs" / "dynamic_summary.csv"
    df = read_table(csv_path)
    if df is None:
        return result(key, "skipped", [], [str(csv_path.relative_to(root))], "dynamic_summary.csv is missing.")
    work = df[df["split"].eq("dynamic_seed202")].copy()
    parsed = work["run"].str.extract(r"l(?P<layers>\d+)d(?P<dim>\d+)").astype(float)
    work = pd.concat([work, parsed], axis=1).dropna(subset=["layers", "dim"])
    work["layers"] = work["layers"].astype(int)
    work["dim"] = work["dim"].astype(int)
    piv = work.groupby(["dim", "layers"])["f1"].max().unstack()
    fig, ax = plt.subplots(figsize=(SINGLE_COL * 1.35, 2.85), constrained_layout=True)
    data = piv.to_numpy(float)
    cmap = plt.cm.viridis.copy()
    cmap.set_bad(COLORS["light_gray"])
    img = ax.imshow(np.ma.masked_invalid(data), cmap=cmap, aspect="auto")
    ax.set_xticks(np.arange(len(piv.columns)))
    ax.set_xticklabels([str(c) for c in piv.columns])
    ax.set_yticks(np.arange(len(piv.index)))
    ax.set_yticklabels([str(i) for i in piv.index])
    for i in range(data.shape[0]):
        for j in range(data.shape[1]):
            val = data[i, j]
            ax.text(j, i, "" if math.isnan(val) else f"{val:.5f}", ha="center", va="center", fontsize=6.5, color="white" if not math.isnan(val) and val > np.nanmedian(data) else COLORS["dark"])
    if 512 in piv.index and 2 in piv.columns:
        ax.add_patch(Rectangle((list(piv.columns).index(2) - 0.5, list(piv.index).index(512) - 0.5), 1, 1, fill=False, ec=COLORS["red"], lw=1.0))
    ax.set_xlabel("Layers")
    ax.set_ylabel("Embedding dim")
    ax.set_title("LightGCN validation sweep")
    cbar = fig.colorbar(img, ax=ax, shrink=0.78)
    cbar.set_label("Validation F1")
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, [str(csv_path.relative_to(root))])


def make_figA2(root: Path, dirs: dict[str, Path]) -> dict:
    key = "figA2_rw_ensemble"
    sizes, f1s, sources = rw_ensemble_metrics(root)
    fig, ax = plt.subplots(figsize=(SINGLE_COL, 2.3), constrained_layout=True)
    ax.plot(sizes, f1s, "-o", color=COLORS["green"], markersize=MARKER_SIZE)
    ax.set_xticks(sizes)
    ax.set_xlabel("# random-walk blocks")
    ax.set_ylabel("Validation F1")
    ax.set_title("RW ensemble size")
    ax.set_ylim(min(f1s) - 0.00035, max(f1s) + 0.00035)
    for s, f in zip(sizes, f1s):
        ax.text(s, f + 0.00012, f"{f:.5f}", ha="center", fontsize=ANNOTATION_SIZE)
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, sources)


def make_figA3(root: Path, dirs: dict[str, Path]) -> dict:
    key = "figA3_feature_group_contribution"
    groups = [
        ("Graph/meta-path", 0.0174, COLORS["blue"]),
        ("7 RW blocks", 0.0028, COLORS["orange"]),
        ("DeepWalk/Node2Vec", 0.0022, COLORS["green"]),
        ("High-order", 0.0020, COLORS["red"]),
        ("BPR-MF", 0.0017, COLORS["purple"]),
        ("Variant scores", 0.0011, COLORS["gray"]),
        ("Rich content", 0.0006, COLORS["green"]),
        ("Mean-cos content", 0.0005, COLORS["blue"]),
    ]
    labels, gains, colors = zip(*groups)
    y = np.arange(len(groups))
    fig, ax = plt.subplots(figsize=(SINGLE_COL * 1.45, 3.0), constrained_layout=True)
    ax.barh(y, gains, color=colors, edgecolor="white", linewidth=BAR_EDGEWIDTH)
    ax.set_yticks(y)
    ax.set_yticklabels(labels)
    ax.invert_yaxis()
    ax.set_xlim(0, 0.0185)
    ax.set_xlabel("Incremental validation F1 gain")
    ax.set_title("Feature-group contribution")
    for yi, gain in zip(y, gains):
        if gain > 0.010:
            ax.text(gain - 0.00045, yi, f"+{gain:.4f}", ha="right", va="center", fontsize=ANNOTATION_SIZE, color="white")
        else:
            ax.text(gain + 0.00025, yi, f"+{gain:.4f}", ha="left", va="center", fontsize=ANNOTATION_SIZE)
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, ["figures_v2/data/manual_metrics.csv", "reports/*.md"])


def small_heatmap(ax, sub: pd.DataFrame, title: str) -> None:
    sub = sub.copy().sort_values("f1").head(8)
    mat = sub[["precision", "recall", "f1"]].to_numpy(float)
    labels = [str(v).replace("_", " ").replace("[", " [").replace("degree", "deg") for v in sub["bucket"]]
    img = ax.imshow(mat, cmap="RdYlGn", vmin=0.0, vmax=1.0, aspect="auto")
    ax.set_title(title)
    ax.set_xticks([0, 1, 2])
    ax.set_xticklabels(["prec.", "rec.", "F1"])
    ax.set_yticks(np.arange(len(labels)))
    ax.set_yticklabels(labels, fontsize=6.3)
    for i in range(mat.shape[0]):
        for j in range(mat.shape[1]):
            ax.text(j, i, f"{mat[i, j]:.2f}", ha="center", va="center", fontsize=6.0, color=COLORS["dark"])
        if mat[i, 2] < 0.90:
            ax.add_patch(Rectangle((-0.5, i - 0.5), 3, 1, fill=False, ec=COLORS["red"], lw=0.8))
    return img


def make_figA4(root: Path, dirs: dict[str, Path]) -> dict:
    key = "figA4_error_buckets"
    csv_path = root / "validation_runs" / "dynamic_seed202" / "error_group_calibration" / "error_analysis_buckets.csv"
    df = read_table(csv_path)
    if df is None:
        return result(key, "skipped", [], [str(csv_path.relative_to(root))], "error_analysis_buckets.csv is missing.")
    df["lo"] = df["bucket"].map(numeric_bucket_key)
    degree = df[df["bucket_type"].isin(["author_degree", "paper_degree", "paper_citation_in"])].sort_values(["bucket_type", "lo"])
    score = df[df["bucket_type"].isin(["final_score", "LightGCN_score", "author_internal_rank"])].copy()
    local = df[df["bucket_type"].isin(["has_local_evidence", "BPR-MF_score", "content_score", "DeepWalk_score", "Node2Vec_score"])].copy()
    fig, axes = plt.subplots(1, 3, figsize=(DOUBLE_COL, 3.9), constrained_layout=True)
    imgs = [
        small_heatmap(axes[0], degree, "(a) Degree buckets"),
        small_heatmap(axes[1], score, "(b) Rank / score buckets"),
        small_heatmap(axes[2], local, "(c) Local evidence buckets"),
    ]
    cbar = fig.colorbar(imgs[-1], ax=axes.ravel().tolist(), shrink=0.72, pad=0.02)
    cbar.set_label("Score")
    files = save_all(fig, key, dirs)
    return result(key, "ok", files, [str(csv_path.relative_to(root))])


def ecdf(vals: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    vals = np.sort(vals)
    y = np.arange(1, len(vals) + 1) / len(vals)
    return vals, y


def make_figA5(root: Path, dirs: dict[str, Path]) -> dict:
    key = "figA5_oof_pr_score"
    vr = root / "validation_runs" / "dynamic_seed202"
    y = load_npy(vr / "val_labels_seed202.npy")
    if y is None:
        return result(key, "skipped", [], ["validation_runs/dynamic_seed202/val_labels_seed202.npy"], "Validation labels are missing.")
    models = [
        ("LightGCN", vr / "dyn202_l2d512_bpr_bigbatch_more" / "scores" / "val_vanilla_ensemble_mean.npy", COLORS["gray"]),
        ("Graph stack", vr / "post95_ablation" / "ensemble_lgcn_oof.npy", COLORS["orange"]),
        ("DeepWalk/Node2Vec", vr / "node2vec_deepwalk" / "node2vec_stack_oof.npy", COLORS["green"]),
        ("High-order final", vr / "high_order_graph_stack" / "rich_rw7_highorder_directed_oof.npy", COLORS["red"]),
    ]
    loaded = []
    for name, path, color in models:
        scores = load_npy(path)
        if scores is not None and len(scores) == len(y):
            loaded.append((name, scores.astype(float), color, path))
    if not loaded:
        return result(key, "skipped", [], ["OOF score arrays"], "No aligned OOF score arrays were found.")
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(DOUBLE_COL, 2.9), constrained_layout=True, gridspec_kw={"width_ratios": [1.25, 1.0]})
    panel_label(ax1, "(a)")
    panel_label(ax2, "(b)")
    y = y.astype(int)
    for name, scores, color, _ in loaded:
        recall, precision, ap = pr_curve(y, scores)
        ax1.plot(recall, precision, color=color, lw=LINEWIDTH, label=f"{name} AP={ap:.4f}")
    ax1.set_xlabel("Recall")
    ax1.set_ylabel("Precision")
    ax1.set_ylim(0.88, 1.005)
    ax1.set_title("OOF PR curves")
    ax1.legend(loc="lower left", fontsize=6.5)
    final_name, final_scores, _, _ = loaded[-1]
    pos_x, pos_y = ecdf(final_scores[y == 1])
    neg_x, neg_y = ecdf(final_scores[y == 0])
    lo, hi = np.percentile(final_scores, [1, 99])
    ax2.plot(neg_x, neg_y, color=COLORS["blue"], label="negative")
    ax2.plot(pos_x, pos_y, color=COLORS["red"], label="positive")
    ax2.set_xlim(lo, hi)
    ax2.set_xlabel("Final OOF score (1st-99th pct.)")
    ax2.set_ylabel("ECDF")
    ax2.set_title("Final score ECDF")
    ax2.legend(loc="lower right", fontsize=LEGEND_SIZE)
    files = save_all(fig, key, dirs)
    sources = [str((vr / "val_labels_seed202.npy").relative_to(root))] + [str(p.relative_to(root)) for _, _, _, p in loaded]
    status = "ok" if len(loaded) == len(models) else "partial"
    return result(key, status, files, sources, "Only aligned OOF arrays are plotted.")


def make_figA6(root: Path, dirs: dict[str, Path]) -> dict:
    key = "figA6_feature_importance"
    return result(
        key,
        "skipped",
        [],
        ["cached_scores/lgb_model.pkl", "cached_scores/lgb_v2_model.pkl"],
        "LightGBM is not importable in this environment and reliable feature names are not available; no importance plot was generated.",
    )


FIGURE_BUILDERS = [
    make_fig1,
    make_fig2,
    make_fig3,
    make_fig4,
    make_fig5,
    make_fig6,
    make_figA1,
    make_figA2,
    make_figA3,
    make_figA4,
    make_figA5,
    make_figA6,
]

BUILDER_KEYS = {
    make_fig1: "fig1_task_graph",
    make_fig2: "fig2_dataset_sparsity",
    make_fig3: "fig3_performance_evolution",
    make_fig4: "fig4_method_pipeline",
    make_fig5: "fig5_highorder_ablation",
    make_fig6: "fig6_calibration_rank_cutoff",
    make_figA1: "figA1_lightgcn_sweep",
    make_figA2: "figA2_rw_ensemble",
    make_figA3: "figA3_feature_group_contribution",
    make_figA4: "figA4_error_buckets",
    make_figA5: "figA5_oof_pr_score",
    make_figA6: "figA6_feature_importance",
}


def write_readme(root: Path, results: list[dict]) -> None:
    lines = [
        "# figures_v2 outputs",
        "",
        "Generated by `figures_v2/scripts/make_all_figures.py`.",
        "",
        "Run:",
        "",
        "```bash",
        "python figures_v2/scripts/make_all_figures.py --package-root .",
        "```",
        "",
        "Use PDF files in ACM LaTeX for vector output; PNG files are 300 dpi previews.",
        "",
    ]
    for r in results:
        lines.extend(
            [
                f"## {r['key']}",
                "",
                f"Figure ID: `{r['key']}`",
                f"Output files: {', '.join('`' + str(Path(f).relative_to(root)).replace(chr(92), '/') + '`' for f in r['files']) if r['files'] else 'skipped'}",
                f"Data source: {'; '.join(r['sources'])}",
                f"Purpose in paper: {r['purpose']}",
                f"Caption draft: {r['caption']}",
                f"Known limitations: {r['known_limitations'] or 'None.'}",
                "",
            ]
        )
    lines.extend(
        [
            "## LaTeX insertion",
            "",
            "Single-column figure:",
            "",
            "```latex",
            "\\begin{figure}",
            "  \\centering",
            "  \\includegraphics[width=\\columnwidth]{figures_v2/pdf/fig2_dataset_sparsity.pdf}",
            "  \\caption{...}",
            "\\end{figure}",
            "```",
            "",
            "Double-column figure:",
            "",
            "```latex",
            "\\begin{figure*}",
            "  \\centering",
            "  \\includegraphics[width=\\textwidth]{figures_v2/pdf/fig4_method_pipeline.pdf}",
            "  \\caption{...}",
            "\\end{figure*}",
            "```",
            "",
        ]
    )
    (root / "figures_v2" / "README_FIGURES.md").write_text("\n".join(lines), encoding="utf-8")


def validate_outputs(root: Path, results: list[dict]) -> pd.DataFrame:
    rows = []
    for r in results:
        for file in r["files"]:
            p = Path(file)
            rows.append(
                {
                    "figure": r["key"],
                    "path": p.relative_to(root).as_posix(),
                    "exists": p.exists(),
                    "size_bytes": p.stat().st_size if p.exists() else 0,
                    "nonempty": p.exists() and p.stat().st_size > 1000,
                }
            )
    df = pd.DataFrame(rows)
    out = root / "figures_v2" / "data" / "output_validation.csv"
    df.to_csv(out, index=False)
    return df


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--package-root", default=".", help="Repository root.")
    args = parser.parse_args()
    root = Path(args.package_root).resolve()
    apply_style()
    dirs = output_dirs(root)
    inventory_files(root, root / "figures_v2" / "data" / "audit_file_inventory.csv")

    results = []
    for builder in FIGURE_BUILDERS:
        try:
            r = builder(root, dirs)
        except Exception as exc:  # noqa: BLE001
            key = BUILDER_KEYS.get(builder, builder.__name__.replace("make_", ""))
            r = result(key, "error", [], [], f"{type(exc).__name__}: {exc}")
        results.append(r)
        print(f"[{r['status']:<7}] {r['key']}")

    write_readme(root, results)
    validation = validate_outputs(root, results)
    print("\nGenerated files:")
    if validation.empty:
        print("  none")
    else:
        for _, row in validation.iterrows():
            print(f"  {row['path']} ({row['size_bytes']} bytes)")
    print("\nREADME:", (root / "figures_v2" / "README_FIGURES.md").as_posix())
    print("Audit inventory:", (root / "figures_v2" / "data" / "audit_file_inventory.csv").as_posix())


if __name__ == "__main__":
    main()