"""Annotate filtered variants with gene and functional information."""

import json
import sys
from pathlib import Path

import pandas as pd


# Simulated annotation database (in real use, query dbSNP, ClinVar, gnomAD, etc.)
GENE_ANNOTATIONS = {
    "BRCA1": {"function": "DNA repair", "pathway": "Homologous recombination"},
    "BRCA2": {"function": "DNA repair", "pathway": "Homologous recombination"},
    "TP53": {"function": "Tumor suppressor", "pathway": "Cell cycle control"},
    "KRAS": {"function": "GTPase", "pathway": "MAPK signaling"},
    "EGFR": {"function": "Receptor tyrosine kinase", "pathway": "EGFR signaling"},
    "PIK3CA": {"function": "Lipid kinase", "pathway": "PI3K/AKT signaling"},
    "PTEN": {"function": "Phosphatase", "pathway": "PI3K/AKT signaling"},
    "APC": {"function": "Tumor suppressor", "pathway": "Wnt signaling"},
    "RB1": {"function": "Tumor suppressor", "pathway": "Cell cycle control"},
    "MYC": {"function": "Transcription factor", "pathway": "Cell proliferation"},
}

VARIANT_EFFECTS = {
    "SNV": ["missense", "nonsense", "silent", "splice_site"],
    "INDEL": ["frameshift", "inframe_deletion", "inframe_insertion"],
}


def annotate_variant(row: pd.Series) -> dict:
    """Add functional annotation for a variant."""
    gene = row["gene"]
    variant_type = row["variant_type"]

    annotation = {
        "gene_function": "Unknown",
        "gene_pathway": "Unknown",
        "variant_effect": "Unknown",
        "pathogenicity": "VUS",  # Variant of Uncertain Significance
    }

    # Add gene-level annotation
    if gene in GENE_ANNOTATIONS:
        annotation["gene_function"] = GENE_ANNOTATIONS[gene]["function"]
        annotation["gene_pathway"] = GENE_ANNOTATIONS[gene]["pathway"]

    # Assign variant effect (simplified logic)
    if variant_type in VARIANT_EFFECTS:
        import random
        random.seed(int(row["position"]) + hash(gene))
        annotation["variant_effect"] = random.choice(VARIANT_EFFECTS[variant_type])

    # Assign pathogenicity based on quality and effect
    if annotation["variant_effect"] in ["nonsense", "frameshift"]:
        annotation["pathogenicity"] = "Likely pathogenic"
    elif annotation["variant_effect"] in ["missense", "inframe_deletion"]:
        annotation["pathogenicity"] = "VUS"
    else:
        annotation["pathogenicity"] = "Likely benign"

    return annotation


def main():
    """Annotate variants with functional information."""
    if len(sys.argv) < 3:
        print("Usage: annotate.py <input_parquet> <output_parquet> [params_json]")
        sys.exit(1)

    input_path = Path(sys.argv[1])
    output_path = Path(sys.argv[2])
    params = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}

    # Read filtered variants
    df = pd.read_parquet(input_path)
    print(f"Annotating {len(df)} filtered variants")

    # Add annotation columns
    annotations = df.apply(annotate_variant, axis=1, result_type="expand")
    annotated_df = pd.concat([df, annotations], axis=1)

    print(f"Added annotations:")
    print(f"  Gene functions: {annotated_df['gene_function'].nunique()} unique")
    print(f"  Variant effects: {annotated_df['variant_effect'].nunique()} unique")
    print(f"  Pathogenicity calls:")
    for category, count in annotated_df["pathogenicity"].value_counts().items():
        print(f"    {category}: {count}")

    # Create output directory if needed
    output_path.parent.mkdir(parents=True, exist_ok=True)

    # Save annotated variants
    annotated_df.to_parquet(output_path, index=False, compression="snappy")

    print(f"Saved annotated variants to {output_path}")


if __name__ == "__main__":
    main()
