"""
Visualize fingerprint images at representative score milestones.
Shows one sample per score bucket (e.g. every 10 points) with quality
score and all 6 concept signals.

Usage:
    python sifq/visualize_score_milestones.py \
        --scores sifq/eval_results/sifq_scores_v13.jsonl \
        --output sifq/eval_results/v13/milestone_samples.png \
        [--n_buckets 8] [--sensor R_1000_slap] [--seed 42]
"""

from __future__ import annotations

import argparse
import json
import random
from pathlib import Path

import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image

CONCEPT_NAMES = [
    "orientation_coherence",
    "ridge_valley_clarity",
    "continuity",
    "noise_level",
    "contrast_uniformity",
    "minutiae_reliability",
]
CONCEPT_COLORS = [
    "#4c72b0", "#dd8452", "#55a868", "#c44e52", "#8172b3", "#937860"
]


def load_scores(path: str) -> list[dict]:
    records = []
    with open(path) as f:
        for line in f:
            line = line.strip()
            if line:
                records.append(json.loads(line))
    return records


def pick_milestone_samples(
    records: list[dict],
    n_buckets: int,
    sensor: str | None,
    seed: int,
    excluded_sensors: set[str] | None = None,
) -> list[dict]:
    rng = random.Random(seed)

    if excluded_sensors:
        records = [r for r in records if r["sensor_id"] not in excluded_sensors]

    if sensor:
        pool = [r for r in records if r["sensor_id"] == sensor]
        if not pool:
            raise ValueError(
                f"No records for sensor '{sensor}'. "
                f"Available: {sorted(set(r['sensor_id'] for r in records))}"
            )
    else:
        pool = records

    scores = [r["q_score"] for r in pool]
    q_min, q_max = min(scores), max(scores)
    edges = np.linspace(q_min, q_max, n_buckets + 1)

    samples = []
    for i in range(n_buckets):
        lo, hi = edges[i], edges[i + 1]
        bucket = [r for r in pool if lo <= r["q_score"] < hi]
        if i == n_buckets - 1:  # include right edge on last bucket
            bucket = [r for r in pool if lo <= r["q_score"] <= hi]
        if not bucket:
            continue
        # Pick sample closest to bucket midpoint
        mid = (lo + hi) / 2
        bucket.sort(key=lambda r: abs(r["q_score"] - mid))
        samples.append(bucket[0])

    return samples


def draw_concept_bar(ax, concepts: list[float]):
    """Draw a horizontal bar chart of concept values."""
    y = np.arange(len(CONCEPT_NAMES))
    bars = ax.barh(
        y,
        concepts,
        color=CONCEPT_COLORS,
        height=0.6,
        edgecolor="none",
    )
    ax.set_xlim(0, 1)
    ax.set_yticks(y)
    ax.set_yticklabels(
        [n.replace("_", "\n") for n in CONCEPT_NAMES],
        fontsize=6,
    )
    ax.set_xticks([0, 0.5, 1.0])
    ax.tick_params(axis="x", labelsize=6)
    ax.spines[["top", "right"]].set_visible(False)
    ax.set_xlabel("activation", fontsize=6)
    # Annotate values
    for bar, v in zip(bars, concepts):
        ax.text(
            min(v + 0.03, 0.97),
            bar.get_y() + bar.get_height() / 2,
            f"{v:.2f}",
            va="center",
            fontsize=5.5,
            color="#333333",
        )


def make_score_colormap(q_min: float, q_max: float):
    cmap = plt.cm.RdYlGn
    norm = plt.Normalize(vmin=q_min, vmax=q_max)
    return cmap, norm


def visualize(
    scores_path: str,
    output_path: str,
    n_buckets: int = 8,
    sensor: str | None = None,
    excluded_sensors: set[str] | None = None,
    seed: int = 42,
    version: str = "",
):
    records = load_scores(scores_path)
    samples = pick_milestone_samples(records, n_buckets, sensor, seed, excluded_sensors)

    n = len(samples)
    all_scores = [r["q_score"] for r in records]
    q_min, q_max = min(all_scores), max(all_scores)
    cmap, norm = make_score_colormap(q_min, q_max)

    # Layout: each sample = 1 column with [image | concept bar]
    # Top row: images, bottom row: concept bars
    fig = plt.figure(figsize=(n * 2.8, 7))
    fig.patch.set_facecolor("#1a1a2e")

    title_sensor = sensor if sensor else "all sensors"
    ver_tag = f"SIFQ {version} — " if version else "SIFQ — "
    fig.suptitle(
        f"{ver_tag}Score Milestones  ({title_sensor})\n"
        f"Score range: {q_min:.1f} – {q_max:.1f}  |  "
        f"n_buckets={n_buckets}  |  total={len(records):,} samples",
        color="white",
        fontsize=11,
        y=0.99,
    )

    outer = gridspec.GridSpec(
        2, n,
        figure=fig,
        hspace=0.08,
        wspace=0.35,
        top=0.92,
        bottom=0.04,
        left=0.04,
        right=0.97,
        height_ratios=[3, 2],
    )

    for col, rec in enumerate(samples):
        q = rec["q_score"]
        color = cmap(norm(q))

        # ---- Image ----
        ax_img = fig.add_subplot(outer[0, col])
        img_path = rec["image_path"]
        try:
            img = Image.open(img_path).convert("L")
            ax_img.imshow(img, cmap="gray", aspect="auto")
        except Exception:
            ax_img.set_facecolor("#333")
            ax_img.text(
                0.5, 0.5, "N/A",
                ha="center", va="center",
                color="white", fontsize=8,
                transform=ax_img.transAxes,
            )

        # Score badge
        ax_img.set_title(
            f"Q = {q:.1f}",
            color="white",
            fontsize=9,
            fontweight="bold",
            pad=3,
        )
        # Colored border around image matching score
        for spine in ax_img.spines.values():
            spine.set_edgecolor(color)
            spine.set_linewidth(3)
        ax_img.set_xticks([])
        ax_img.set_yticks([])
        ax_img.set_facecolor("#111")

        # Sensor + identity label below image
        sensor_lbl = rec.get("sensor_id", "")
        identity_lbl = rec.get("identity_id", "")[:8]
        ax_img.set_xlabel(
            f"{sensor_lbl}\n{identity_lbl}",
            color="#aaaaaa",
            fontsize=6,
            labelpad=2,
        )

        # ---- Concept bar ----
        ax_bar = fig.add_subplot(outer[1, col])
        ax_bar.set_facecolor("#0d0d1a")
        for spine in ax_bar.spines.values():
            spine.set_edgecolor("#444")
        ax_bar.tick_params(colors="white")
        ax_bar.yaxis.label.set_color("white")
        ax_bar.xaxis.label.set_color("#aaaaaa")
        ax_bar.set_xlabel("activation", fontsize=6, color="#aaaaaa")

        concepts = rec.get("concepts", [0.0] * 6)
        draw_concept_bar(ax_bar, concepts)

        # Style ticks for dark bg
        for lbl in ax_bar.get_yticklabels():
            lbl.set_color("white")
        for lbl in ax_bar.get_xticklabels():
            lbl.set_color("#aaaaaa")
        ax_bar.tick_params(axis="both", colors="#aaaaaa")

    # Colorbar legend
    sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
    sm.set_array([])
    cbar_ax = fig.add_axes([0.15, 0.005, 0.70, 0.012])
    cb = fig.colorbar(sm, cax=cbar_ax, orientation="horizontal")
    cb.set_label("Quality Score", color="white", fontsize=8)
    cb.ax.xaxis.set_tick_params(color="white")
    plt.setp(cb.ax.xaxis.get_ticklabels(), color="white", fontsize=7)

    out = Path(output_path)
    out.parent.mkdir(parents=True, exist_ok=True)
    plt.savefig(out, dpi=150, bbox_inches="tight", facecolor=fig.get_facecolor())
    plt.close()
    print(f"Saved → {out}")


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--scores",
        default="sifq/eval_results/sifq_scores_v13.jsonl",
        help="Path to sifq_scores_*.jsonl",
    )
    parser.add_argument(
        "--output",
        default="sifq/eval_results/v13/milestone_samples.png",
        help="Output PNG path",
    )
    parser.add_argument(
        "--n_buckets",
        type=int,
        default=8,
        help="Number of score buckets / columns",
    )
    parser.add_argument(
        "--sensor",
        default=None,
        help="Filter to a specific sensor_id (e.g. R_1000_slap)",
    )
    parser.add_argument(
        "--exclude-sensor",
        default="",
        help="Comma-separated sensor_ids to exclude. "
             "E.g. 'R_1000_slap,R_500_slap,S_500_slap'",
    )
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--version", type=str, default="", help="Version tag shown in plot title (e.g. v17)")
    args = parser.parse_args()

    excluded: set[str] = set()
    if args.exclude_sensor:
        excluded = {s.strip() for s in args.exclude_sensor.split(",") if s.strip()}

    visualize(
        scores_path=args.scores,
        output_path=args.output,
        n_buckets=args.n_buckets,
        sensor=args.sensor,
        excluded_sensors=excluded,
        seed=args.seed,
        version=args.version,
    )


if __name__ == "__main__":
    main()