FlowCache/FlowCache4MAGI-1-dev3-motion/tools/plot_residual_norms.py

#!/usr/bin/env python3

import argparse
import json
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Set, Tuple

import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt


def parse_int_list(value: Optional[str]) -> Optional[Set[int]]:
    if not value:
        return None
    return {int(item.strip()) for item in value.split(",") if item.strip()}


def load_records(json_path: Path) -> List[dict]:
    with json_path.open("r") as f:
        payload = json.load(f)
    if isinstance(payload, list):
        return payload
    if isinstance(payload, dict) and isinstance(payload.get("records"), list):
        return payload["records"]
    raise ValueError(f"Cannot find records in {json_path}")


def group_records(records: List[dict], chunk_ids: Optional[Set[int]], max_chunks: Optional[int]) -> Dict[int, List[dict]]:
    grouped = defaultdict(list)
    for record in records:
        chunk_idx = int(record["chunk_idx"])
        if chunk_ids is not None and chunk_idx not in chunk_ids:
            continue
        grouped[chunk_idx].append(record)

    grouped = dict(sorted(grouped.items()))
    if max_chunks is not None:
        grouped = dict(list(grouped.items())[:max_chunks])
    return grouped


def build_plot(
    grouped_records: Dict[int, List[dict]],
    output_path: Path,
    x_field: str,
    y_field: str,
    title: Optional[str],
    reverse_x: bool,
    figsize: Tuple[float, float],
    dpi: int,
) -> None:
    fig, ax = plt.subplots(figsize=figsize)

    for chunk_idx, records in grouped_records.items():
        points = []
        for record in records:
            if x_field not in record or y_field not in record:
                continue
            if record[x_field] is None or record[y_field] is None:
                continue
            points.append((float(record[x_field]), float(record[y_field])))
        if not points:
            continue

        points.sort(key=lambda item: item[0])
        xs, ys = zip(*points)
        ax.plot(xs, ys, marker="o", linewidth=1.6, markersize=3, label=f"chunk {chunk_idx}")

    ax.set_xlabel(x_field)
    ax.set_ylabel(y_field)
    ax.set_title(title or f"{y_field} by timestep")
    ax.grid(True, alpha=0.3)
    if reverse_x:
        ax.invert_xaxis()
    ax.legend(loc="best", fontsize="small", ncols=2)
    fig.tight_layout()

    output_path.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output_path, dpi=dpi)
    plt.close(fig)


def parse_arguments():
    parser = argparse.ArgumentParser(description="Plot per-chunk residual norm curves from MAGI residual stats JSON.")
    parser.add_argument("json_path", type=Path, help="Path to residual stats JSON saved by --residual_stats_path.")
    parser.add_argument(
        "-o",
        "--output",
        type=Path,
        help="Output image path. Defaults to <json_path stem>_residual_norms.png.",
    )
    parser.add_argument("--chunks", type=str, help="Comma-separated chunk_idx list to plot, for example: 0,1,2.")
    parser.add_argument("--max-chunks", type=int, help="Plot at most this many chunks after filtering.")
    parser.add_argument(
        "--x-field",
        choices=["timestep", "cur_denoise_step", "denoise_idx"],
        default="timestep",
        help="Record field used for the x axis.",
    )
    parser.add_argument(
        "--y-field",
        choices=["residual_norm", "residual_diff_norm"],
        default="residual_norm",
        help="Record field used for the y axis.",
    )
    parser.add_argument("--reverse-x", action="store_true", help="Reverse the x axis.")
    parser.add_argument("--title", type=str, help="Figure title.")
    parser.add_argument("--figsize", type=str, default="10,6", help="Figure size as width,height.")
    parser.add_argument("--dpi", type=int, default=160, help="Output image DPI.")
    return parser.parse_args()


def main():
    args = parse_arguments()
    output_path = args.output or args.json_path.with_name(f"{args.json_path.stem}_residual_norms.png")
    figsize_parts = [float(part.strip()) for part in args.figsize.split(",")]
    if len(figsize_parts) != 2:
        raise ValueError("--figsize must be formatted as width,height")

    records = load_records(args.json_path)
    grouped_records = group_records(records, parse_int_list(args.chunks), args.max_chunks)
    if not grouped_records:
        raise ValueError("No records matched the requested chunks.")

    build_plot(
        grouped_records=grouped_records,
        output_path=output_path,
        x_field=args.x_field,
        y_field=args.y_field,
        title=args.title,
        reverse_x=args.reverse_x,
        figsize=(figsize_parts[0], figsize_parts[1]),
        dpi=args.dpi,
    )
    print(f"Saved plot to {output_path}")


if __name__ == "__main__":
    main()