"""
band_edge_detector/cli.py
=========================
Command-line interface.

Usage:
    band-edge-detector --input scores.csv --output report/
    band-edge-detector --input scores.csv --method pca --n-bins 7
    band-edge-detector --input long_scores.csv --format long
"""

from __future__ import annotations

import argparse
import sys
from pathlib import Path

from .data import BenchmarkMatrix
from .report import analyze


def main(argv=None):
    parser = argparse.ArgumentParser(
        prog="band-edge-detector",
        description="Detect band-edge compression in benchmark scores.",
    )
    parser.add_argument("--input", required=True,
                        help="CSV with models as rows, benchmarks as columns.")
    parser.add_argument("--format", choices=["wide", "long"], default="wide",
                        help="'wide' = models × benchmarks; "
                             "'long' = (model, benchmark, score).")
    parser.add_argument("--output", default="band_edge_report",
                        help="Directory for figures.")
    parser.add_argument("--method", choices=["mean_z", "pca", "irt"],
                        default="mean_z",
                        help="Capability proxy method.")
    parser.add_argument("--n-bins", type=int, default=5,
                        help="Quantile bins.")
    parser.add_argument("--threshold", type=float, default=0.5,
                        help="Compression threshold.")
    parser.add_argument("--min-coverage", type=float, default=0.5,
                        help="Drop benchmarks with less coverage than this.")
    parser.add_argument("--min-benchmarks", type=int, default=5,
                        help="Drop models with fewer than this many scores.")

    args = parser.parse_args(argv)

    if args.format == "wide":
        matrix = BenchmarkMatrix.from_csv(args.input)
    else:
        matrix = BenchmarkMatrix.from_long_csv(args.input)

    matrix = matrix.filter_benchmarks(min_coverage=args.min_coverage)
    matrix = matrix.filter_models(min_benchmarks=args.min_benchmarks)

    print(f"Loaded {matrix.n_models} models × {matrix.n_benchmarks} benchmarks")

    report = analyze(
        matrix,
        method=args.method,
        n_bins=args.n_bins,
        compression_threshold=args.threshold,
        output_dir=args.output,
    )

    print(report.summary())
    print(f"\nFigures written to: {args.output}/")

    return 0


if __name__ == "__main__":
    sys.exit(main())