"""Genomics-specific QC metrics library.

Common quality control metrics for genomics workflows including:
- Ti/Tv ratio (transition/transversion)
- Het/hom ratio (heterozygous/homozygous)
- Depth of coverage statistics
- dbSNP concordance
- Mendelian violation rate (for trio data)
"""

from __future__ import annotations

import pandas as pd


def compute_ti_tv_ratio(variants: pd.DataFrame) -> float:
    """Calculate transition/transversion ratio for SNVs.

    Ti/Tv ratio is a key QC metric:
    - Whole genome: ~2.0-2.1
    - Exome: ~3.0-3.3
    - Low quality data: <2.0

    Args:
        variants: DataFrame with columns 'ref_allele', 'alt_allele', 'variant_type'

    Returns:
        Ti/Tv ratio
    """
    # Filter to SNVs only
    snvs = variants[variants["variant_type"] == "SNV"].copy()

    if len(snvs) == 0:
        return 0.0

    # Define transitions and transversions
    transitions = [("A", "G"), ("G", "A"), ("C", "T"), ("T", "C")]
    transversions = [
        ("A", "C"),
        ("A", "T"),
        ("C", "A"),
        ("C", "G"),
        ("G", "C"),
        ("G", "T"),
        ("T", "A"),
        ("T", "G"),
    ]

    # Count each type
    ti_count = sum(
        1 for _, row in snvs.iterrows() if (row["ref_allele"], row["alt_allele"]) in transitions
    )
    tv_count = sum(
        1
        for _, row in snvs.iterrows()
        if (row["ref_allele"], row["alt_allele"]) in transversions
    )

    return ti_count / tv_count if tv_count > 0 else 0.0


def compute_het_hom_ratio(variants: pd.DataFrame) -> float:
    """Calculate heterozygous/homozygous variant ratio.

    Het/Hom ratio indicates data quality:
    - Human genome: ~1.5-2.0
    - Higher ratio: possible contamination
    - Lower ratio: possible consanguinity

    Args:
        variants: DataFrame with 'genotype' column

    Returns:
        Het/Hom ratio
    """
    het_count = len(variants[variants["genotype"] == "heterozygous"])
    hom_count = len(variants[variants["genotype"] == "homozygous"])

    return het_count / hom_count if hom_count > 0 else 0.0


def compute_depth_statistics(variants: pd.DataFrame) -> dict[str, float]:
    """Compute sequencing depth statistics.

    Args:
        variants: DataFrame with 'depth' column

    Returns:
        Dictionary with mean, median, min, max, std depth
    """
    return {
        "mean_depth": float(variants["depth"].mean()),
        "median_depth": float(variants["depth"].median()),
        "min_depth": float(variants["depth"].min()),
        "max_depth": float(variants["depth"].max()),
        "std_depth": float(variants["depth"].std()),
    }


def compute_quality_statistics(variants: pd.DataFrame) -> dict[str, float]:
    """Compute variant quality score statistics.

    Args:
        variants: DataFrame with 'quality_score' column

    Returns:
        Dictionary with quality score statistics
    """
    return {
        "mean_quality": float(variants["quality_score"].mean()),
        "median_quality": float(variants["quality_score"].median()),
        "min_quality": float(variants["quality_score"].min()),
        "max_quality": float(variants["quality_score"].max()),
        "q1_quality": float(variants["quality_score"].quantile(0.25)),
        "q3_quality": float(variants["quality_score"].quantile(0.75)),
    }


def compute_dbsnp_concordance(
    variants: pd.DataFrame, dbsnp_column: str = "in_dbsnp"
) -> float:
    """Calculate fraction of variants found in dbSNP.

    High concordance (>90%) indicates good quality for common variants.

    Args:
        variants: DataFrame with boolean column indicating dbSNP presence
        dbsnp_column: Name of column with dbSNP status

    Returns:
        Fraction of variants in dbSNP
    """
    if dbsnp_column not in variants.columns:
        return 0.0

    return float(variants[dbsnp_column].sum() / len(variants))


def compute_mendelian_violations(
    child_variants: pd.DataFrame,
    mother_variants: pd.DataFrame,
    father_variants: pd.DataFrame,
) -> dict[str, int | float | str]:
    """Calculate Mendelian violation rate for trio data.

    Low violation rate (<2%) indicates good quality.

    Args:
        child_variants: Child variants DataFrame
        mother_variants: Mother variants DataFrame (not yet implemented)
        father_variants: Father variants DataFrame (not yet implemented)

    Returns:
        Dictionary with violation counts by type

    Note:
        This is a placeholder implementation. Full Mendelian inheritance logic
        would check parent genotypes against child genotype for each variant.
    """
    # Simplified implementation - real version would check inheritance patterns
    # TODO: Implement full trio analysis using mother_variants and father_variants
    _ = (mother_variants, father_variants)  # Acknowledge unused for now

    violations: dict[str, int | float | str] = {
        "total_sites": len(child_variants),
        "violations": 0,
        "violation_rate": 0.0,
        "note": "Placeholder - implement full Mendelian inheritance logic",
    }

    return violations


def compute_all_qc_metrics(variants: pd.DataFrame) -> dict[str, any]:
    """Compute all standard genomics QC metrics.

    Args:
        variants: DataFrame with standard variant columns

    Returns:
        Dictionary with all QC metrics
    """
    metrics = {
        "total_variants": len(variants),
        "ti_tv_ratio": compute_ti_tv_ratio(variants),
        "het_hom_ratio": compute_het_hom_ratio(variants),
    }

    # Add depth statistics
    metrics.update(compute_depth_statistics(variants))

    # Add quality statistics
    metrics.update(compute_quality_statistics(variants))

    # Variant type distribution
    metrics["variant_type_counts"] = variants["variant_type"].value_counts().to_dict()

    # Genotype distribution
    metrics["genotype_counts"] = variants["genotype"].value_counts().to_dict()

    return metrics


# Example usage
if __name__ == "__main__":
    # Create example variants
    variants = pd.DataFrame(
        {
            "variant_type": ["SNV"] * 20 + ["INDEL"] * 5,
            "ref_allele": ["A", "G", "C", "T"] * 6 + ["A"],
            "alt_allele": ["G", "A", "T", "C"] * 6 + ["T"],
            "genotype": ["heterozygous"] * 18 + ["homozygous"] * 7,
            "depth": [30, 40, 35, 45] * 6 + [25],
            "quality_score": [50, 60, 55, 65] * 6 + [45],
        }
    )

    # Compute metrics
    metrics = compute_all_qc_metrics(variants)

    print("Genomics QC Metrics:")
    print(f"Ti/Tv ratio: {metrics['ti_tv_ratio']:.2f}")
    print(f"Het/Hom ratio: {metrics['het_hom_ratio']:.2f}")
    print(f"Mean depth: {metrics['mean_depth']:.1f}x")
    print(f"Mean quality: {metrics['mean_quality']:.1f}")
