"""Inspect the E-MTAB-5009 count table and sample metadata."""

from pathlib import Path

import pandas as pd


PROJECT_ROOT = Path(__file__).resolve().parents[1]
DATA_DIR = PROJECT_ROOT / "data" / "raw" / "E-MTAB-5009"
COUNTS_PATH = DATA_DIR / "WP2_100acc.htseq.txt"
METADATA_PATH = DATA_DIR / "E-MTAB-5009.sdrf.txt"


def inspect_counts() -> None:
    # The first field in each data row is a gene ID. Its header cell is absent,
    # so index_col=0 prevents the gene IDs from being mistaken for counts.
    counts = pd.read_csv(COUNTS_PATH, sep="\t", index_col=0)
    counts.index.name = "gene_id"

    library_sizes = counts.sum(axis=0)

    print("COUNT TABLE")
    print(f"Path: {COUNTS_PATH}")
    print(f"Shape: {counts.shape[0]:,} genes x {counts.shape[1]:,} count columns")
    print(f"Duplicate gene IDs: {counts.index.duplicated().sum():,}")
    print(f"Duplicate column names: {counts.columns.duplicated().sum():,}")
    print(f"Missing values: {counts.isna().sum().sum():,}")
    print(f"Smallest count: {counts.min().min():,}")
    print(f"Largest count: {counts.max().max():,}")
    print(f"Genes with zero counts in every column: {(counts.sum(axis=1) == 0).sum():,}")
    print(
        "Library sizes (min / median / max): "
        f"{library_sizes.min():,.0f} / "
        f"{library_sizes.median():,.0f} / "
        f"{library_sizes.max():,.0f}"
    )
    print("\nFirst 5 genes and first 5 count columns:")
    print(counts.iloc[:5, :5].to_string())


def inspect_metadata() -> None:
    metadata = pd.read_csv(METADATA_PATH, sep="\t")

    # One assay can occupy multiple SDRF rows because paired-end reads and
    # sequencing lanes are described separately. Deduplicate before counting
    # biological assays and conditions.
    assays = metadata.drop_duplicates(subset="Assay Name")

    print("\nMETADATA")
    print(f"Path: {METADATA_PATH}")
    print(f"Raw metadata shape: {metadata.shape[0]:,} rows x {metadata.shape[1]:,} columns")
    print(f"Unique assays: {assays['Assay Name'].nunique():,}")
    print(f"Unique ecotypes: {assays['Factor Value[ecotype]'].nunique():,}")
    print("Unique assays by condition:")
    print(assays["Factor Value[environmental stress]"].value_counts().to_string())
    print(f"Missing cells in metadata: {metadata.isna().sum().sum():,}")
    print("\nSelected metadata columns for the first 5 unique assays:")
    selected = [
        "Assay Name",
        "Factor Value[ecotype]",
        "Factor Value[environmental stress]",
        "Factor Value[block]",
    ]
    print(assays[selected].head().to_string(index=False))


def main() -> None:
    for required_path in (COUNTS_PATH, METADATA_PATH):
        if not required_path.exists():
            raise FileNotFoundError(
                f"Missing {required_path}. Run scripts/download_data.ps1 first."
            )

    inspect_counts()
    inspect_metadata()


if __name__ == "__main__":
    main()