#!/usr/bin/env python3
"""Convert the Steps tree from a ROOT file to Parquet.

Usage:
    uv run python steps_to_parquet.py input.root
    uv run python steps_to_parquet.py input.root -o output.parquet
    uv run python steps_to_parquet.py input.root --batch-size "200 MB" --tree Hits
"""

import argparse
from pathlib import Path

import awkward as ak
import polars as pl
import uproot


def _batch_to_polars(batch: ak.Array) -> pl.DataFrame:
    """Convert one awkward-array batch to a Polars DataFrame.

    Flat numeric/string fields are converted via numpy; variable-length fields
    (like child_track_ids) fall back to Python lists so polars stores them as
    List columns — a type parquet understands natively.
    """
    col_dict: dict = {}
    for field in ak.fields(batch):
        arr = batch[field]
        if arr.ndim == 1 and not isinstance(arr.layout, ak.contents.ListOffsetArray):
            col_dict[field] = ak.to_numpy(arr)
        else:
            col_dict[field] = ak.to_list(arr)
    return pl.DataFrame(col_dict)


def convert_steps_to_parquet(
    root_path: str | Path,
    output_path: str | Path | None = None,
    batch_size: str = "100 MB",
    tree_name: str = "Steps",
    compression: str = "snappy",
) -> Path:
    """Read *tree_name* from *root_path* and write it to a Parquet file.

    Reads in batches of *batch_size* so that peak ROOT-deserialization memory
    stays bounded. All batches are collected as Polars DataFrames and written
    in a single pass at the end (Polars' parquet writer does not support
    row-group appending without pyarrow).

    Parameters
    ----------
    root_path:   Input ROOT file.
    output_path: Output Parquet file. Defaults to *root_path* with .parquet suffix.
    batch_size:  Uproot read batch size — an uproot size string ("100 MB") or
                 integer row count (500_000).
    tree_name:   Name of the TTree inside the ROOT file.
    compression: Parquet compression codec (snappy | lz4 | zstd | gzip | none).
    """
    root_path = Path(root_path)
    if output_path is None:
        output_path = root_path.with_suffix(".parquet")
    else:
        output_path = Path(output_path)

    with uproot.open(root_path) as f:
        tree = f[tree_name]
        n_entries = tree.num_entries
        print(f"Reading '{tree_name}' from {root_path.name}  ({n_entries} entries)")

        batches: list[pl.DataFrame] = []
        rows_done = 0
        for batch in tree.iterate(library="ak", step_size=batch_size):
            batches.append(_batch_to_polars(batch))
            rows_done += len(batch)
            print(f"  {rows_done:,} / {n_entries:,} rows read", end="\r", flush=True)

    print(f"\nWriting {output_path} …", end=" ", flush=True)
    pl.concat(batches).write_parquet(output_path, compression=compression)
    print(f"done  ({output_path.stat().st_size / 1e6:.1f} MB)")
    return output_path


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Convert a Steps (or any flat+jagged) tree in a ROOT file to Parquet."
    )
    parser.add_argument("root_file", help="Input ROOT file")
    parser.add_argument(
        "-o", "--output", help="Output Parquet file (default: <input>.parquet)"
    )
    parser.add_argument(
        "--batch-size",
        default="100 MB",
        help="Uproot read batch size (default: '100 MB').  E.g. '50 MB', '500000' (rows).",
    )
    parser.add_argument(
        "--tree", default="Steps", help="Tree name inside the ROOT file (default: Steps)"
    )
    parser.add_argument(
        "--compression",
        default="snappy",
        choices=["snappy", "lz4", "zstd", "gzip", "none"],
        help="Parquet compression codec (default: snappy)",
    )
    args = parser.parse_args()

    convert_steps_to_parquet(
        args.root_file,
        output_path=args.output,
        batch_size=args.batch_size,
        tree_name=args.tree,
        compression=None if args.compression == "none" else args.compression,
    )


if __name__ == "__main__":
    main()