"""
Debug script to understand threshold computation.

Shows:
1. Distribution of sampled GLS scores
2. What percentile values are computed
3. What threshold values correspond to each percentile
"""

import pickle
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from tqdm import tqdm

def analyze_threshold_computation(results_dir):
    """Analyze threshold computation from sampled scores."""
    results_dir = Path(results_dir)
    pkl_files = sorted(results_dir.glob("prompt_*.pkl"))

    print(f"Found {len(pkl_files)} pickle files")

    # Collect sampled scores
    sampled_scores = []
    for pkl_file in tqdm(pkl_files, desc="Collecting sampled scores"):
        with open(pkl_file, "rb") as f:
            result = pickle.load(f)

        if "sampled_gumbel_scores" in result:
            sampled_scores.extend(result["sampled_gumbel_scores"])

    sampled_scores = np.array(sampled_scores)

    print(f"\nCollected {len(sampled_scores)} sampled scores")
    print(f"Score range: [{sampled_scores.min():.3f}, {sampled_scores.max():.3f}]")
    print(f"Score mean: {sampled_scores.mean():.3f}")
    print(f"Score median: {np.median(sampled_scores):.3f}")
    print(f"Score std: {sampled_scores.std():.3f}")

    # Compute percentiles
    percentiles = np.concatenate([
        np.linspace(0, 1, 100),           # 0-1%: 100 points
        np.linspace(1, 10, 100),          # 1-10%: 100 points
        np.linspace(10, 50, 200),         # 10-50%: 200 points
        np.linspace(50, 100, 600),        # 50-100%: 600 points
    ])
    percentiles = np.unique(percentiles)

    # Compute thresholds
    thresholds = np.percentile(sampled_scores, percentiles)

    print(f"\nComputed {len(thresholds)} thresholds")
    print(f"Threshold range: [{thresholds.min():.3f}, {thresholds.max():.3f}]")

    # Show some examples
    print("\nExamples of percentile -> threshold mapping:")
    for p in [0, 0.1, 0.5, 1, 5, 10, 50, 90, 95, 99, 99.5, 99.9, 100]:
        if p in percentiles:
            idx = np.where(percentiles == p)[0][0]
            t = thresholds[idx]
            print(f"  {p:6.1f}th percentile -> threshold = {t:8.3f}")

    # Create visualizations
    fig, axes = plt.subplots(2, 2, figsize=(14, 10))

    # Plot 1: Histogram of sampled scores
    ax = axes[0, 0]
    ax.hist(sampled_scores, bins=100, edgecolor='black', alpha=0.7)
    ax.set_xlabel('Sampled GLS Score', fontsize=12)
    ax.set_ylabel('Count (log scale)', fontsize=12)
    ax.set_yscale('log')
    ax.set_title('Distribution of Sampled GLS Scores', fontsize=14, fontweight='bold')
    ax.axvline(np.median(sampled_scores), color='red', linestyle='--',
               linewidth=2, label=f'Median = {np.median(sampled_scores):.3f}')
    ax.axvline(sampled_scores.mean(), color='orange', linestyle='--',
               linewidth=2, label=f'Mean = {sampled_scores.mean():.3f}')
    ax.legend()
    ax.grid(alpha=0.3)

    # Plot 2: Thresholds vs percentiles
    ax = axes[0, 1]
    ax.plot(percentiles, thresholds, linewidth=2)
    ax.set_xlabel('Percentile (%)', fontsize=12)
    ax.set_ylabel('Threshold Value', fontsize=12)
    ax.set_title('Threshold vs Percentile', fontsize=14, fontweight='bold')
    ax.grid(alpha=0.3)

    # Plot 3: Zoom on low percentiles (0-10%)
    ax = axes[1, 0]
    mask = percentiles <= 10
    ax.plot(percentiles[mask], thresholds[mask], linewidth=2, marker='o', markersize=3)
    ax.set_xlabel('Percentile (%)', fontsize=12)
    ax.set_ylabel('Threshold Value', fontsize=12)
    ax.set_title('Threshold vs Percentile (Zoom: 0-10%)', fontsize=14, fontweight='bold')
    ax.grid(alpha=0.3)

    # Plot 4: Show what FPR each threshold would give
    # For each threshold, count how many scores are < threshold
    fprs = []
    for t in thresholds:
        fpr = np.mean(sampled_scores < t) * 100  # Convert to %
        fprs.append(fpr)
    fprs = np.array(fprs)

    ax = axes[1, 1]
    ax.plot(percentiles, fprs, linewidth=2, label='Actual FPR')
    ax.plot(percentiles, percentiles, linewidth=2, linestyle='--',
            color='red', alpha=0.5, label='Expected FPR = Percentile')
    ax.set_xlabel('Percentile (%)', fontsize=12)
    ax.set_ylabel('FPR (%)', fontsize=12)
    ax.set_title('Expected vs Actual FPR', fontsize=14, fontweight='bold')
    ax.legend()
    ax.grid(alpha=0.3)

    plt.tight_layout()

    output_file = Path(results_dir).parent / f"debug_thresholds_{Path(results_dir).name}.pdf"
    plt.savefig(output_file, dpi=150, bbox_inches='tight')
    print(f"\nSaved plot to {output_file}")

    # Check if FPR matches percentile
    print("\nChecking FPR vs Percentile match:")
    print("(Should be identical if logic is correct)")
    for p in [0, 1, 5, 10, 50, 90, 95, 99, 100]:
        if p in percentiles:
            idx = np.where(percentiles == p)[0][0]
            t = thresholds[idx]
            actual_fpr = fprs[idx]
            print(f"  {p:6.1f}th percentile: threshold={t:8.3f}, expected FPR={p:6.2f}%, actual FPR={actual_fpr:6.2f}%")


if __name__ == "__main__":
    import sys

    if len(sys.argv) < 2:
        print("Usage: python debug_threshold_computation.py <results_dir>")
        print("Example: python debug_threshold_computation.py gumbel_cgs_analysis_results/sweep_20251012_211529/sigma0.01_n5000")
        sys.exit(1)

    results_dir = sys.argv[1]
    analyze_threshold_computation(results_dir)