#!/usr/bin/env python3
"""
refactor_and_label.py

One-shot pipeline: refactor raw SPH experiment folders into the public release
layout AND apply auto-labels using a per-experiment median-zmin threshold.

LABELING RULE (per-frame):
  Initial Emptiness  if the corresponding monitor row has any |value| > 1e30
  Keyhole            if zmin < 1.5 * per-experiment-median-zmin
                     (pool bottom is deeper than 1.5x the typical pool bottom)
  Conduction         otherwise

The per-experiment median zmin is computed over valid (non-IE) rows of
monitor/position-bounds_melt.dat for that experiment only --- no cross-
experiment comparison. Both median_zmin and per-frame zmin are negative
(pool extends below substrate surface at z=0); "zmin < 1.5*median_zmin"
is equivalent to "|zmin| > 1.5*|median_zmin|".

EXCLUSIONS (whole experiment skipped, label column left empty):
  - fewer than 10 valid (non-IE) rows
  - per-experiment median zmin >= 0 (pool not subsurface)

ASSUMPTION:
  monitor/position-bounds_melt.dat has one row per simulation timestep,
  starting at timestep 0. PNG filenames use the simulation timestep number
  (e.g. ss_100069_*.png), so the monitor row at index i corresponds to
  simulation timestep i. PNG frames are mapped by:
      monitor_row = rows[png_timestep]
  PNG frames whose timestep exceeds the available monitor rows are left
  unlabeled (counted in n_frames_unmapped).

USAGE:
    # Single experiment (testing or one-off):
    python refactor_and_label.py --mode single <P-...folder> <dest>

    # Whole dataset (batch):
    python refactor_and_label.py --mode set <parent_dir_of_P-folders> <output_parent>

In set mode: source is a directory containing many P-...VX-...LS-... experiment
folders; output receives sim_00001/, sim_00002/, ... in sorted order.
In single mode: source is one P-...VX-... folder; output receives sim_00001/.
"""

import argparse
import csv
import json
import re
import shutil
import statistics
import sys
import traceback
from datetime import datetime, timezone
from pathlib import Path

# ---- Constants -------------------------------------------------------------
PARAM_MAP = {
    "P":  ("laser_power",           "W"),
    "VX": ("scan_speed_x",          "m/s"),
    "LS": ("laser_spot_size",       "m"),
    "ST": ("substrate_temperature", "K"),
}
SENTINEL_THRESHOLD   = 1e30
MIN_VALID_ROWS       = 10
KEYHOLE_RATIO        = 1.5
SCRIPT_VERSION       = "refactor+label v0.2"


# ---- Folder-name parsing ---------------------------------------------------
def decode_value(raw):
    if re.fullmatch(r"[A-Z0-9]+", raw):
        return raw
    return float(raw.replace("p", "."))


def parse_folder_name(name):
    out = {}
    for token in name.split("_"):
        if "-" not in token:
            continue
        key, _, value = token.partition("-")
        out[key] = value
    return out


# ---- Monitor-file parsing --------------------------------------------------
def parse_monitor_dat(path):
    rows = []
    with open(path) as f:
        for line in f:
            line = line.strip()
            if not line or line.startswith("#"):
                continue
            parts = [p.strip() for p in line.split(",")]
            try:
                values = [float(p) for p in parts[:6]]
            except ValueError:
                continue
            if len(values) < 6:
                continue
            rows.append(values)
    return rows


def is_initial_emptiness_row(row):
    return any(abs(v) > SENTINEL_THRESHOLD for v in row)


# ---- Per-experiment processing --------------------------------------------
def process_one(src, dst, sim_id):
    """Refactor + label a single experiment. Returns a summary dict."""
    raw = parse_folder_name(src.name)

    # 1) Parameters
    parameters = {}
    for letter, (canonical, unit) in PARAM_MAP.items():
        if letter not in raw:
            return {"sim_id": sim_id, "src_name": src.name,
                    "fatal_error": f"missing parameter '{letter}' in folder name"}
        parameters[canonical] = {"value": decode_value(raw[letter]), "unit": unit}
    material = raw.get("M", None)

    # 2) Locate inputs
    final_results = src / "final_results"
    if not final_results.is_dir():
        return {"sim_id": sim_id, "src_name": src.name, "fatal_error": "missing final_results/"}

    details_candidates = list(final_results.glob("*.json"))
    if len(details_candidates) != 1:
        return {"sim_id": sim_id, "src_name": src.name,
                "fatal_error": f"expected 1 JSON in final_results/, found {len(details_candidates)}"}
    details_src = details_candidates[0]

    monitor_src = final_results / "monitor"
    if not monitor_src.is_dir():
        return {"sim_id": sim_id, "src_name": src.name, "fatal_error": "missing final_results/monitor/"}

    png_dir = src / "png_files"
    if not png_dir.is_dir():
        return {"sim_id": sim_id, "src_name": src.name, "fatal_error": "missing png_files/"}

    # 3) Build dest
    dst.mkdir(parents=True)
    for view in ("front", "side", "top"):
        (dst / "frames" / view).mkdir(parents=True)

    # 4) Copy details + monitor
    shutil.copy2(details_src, dst / "experiment_details.json")
    shutil.copytree(monitor_src, dst / "monitor")

    # 5) Process PNG frames
    pattern = re.compile(r"ss_(\d+)_(front|side|top)\.png")
    by_timestep = {}
    for p in png_dir.iterdir():
        m = pattern.match(p.name)
        if not m:
            continue
        ts, view = int(m.group(1)), m.group(2)
        by_timestep.setdefault(ts, {})[view] = p

    complete = {ts: views for ts, views in by_timestep.items() if len(views) == 3}
    skipped = len(by_timestep) - len(complete)
    sorted_timesteps = sorted(complete.keys())
    n_frames = len(sorted_timesteps)

    for idx, ts in enumerate(sorted_timesteps):
        new_name = f"frame_{idx:05d}.png"
        for view, src_path in complete[ts].items():
            shutil.copy2(src_path, dst / "frames" / view / new_name)

    # 6) Auto-label using monitor file (per-experiment median zmin, 1.5x threshold)
    monitor_dat = dst / "monitor" / "position-bounds_melt.dat"
    rows = parse_monitor_dat(monitor_dat) if monitor_dat.is_file() else []

    label_status     = "labeled"
    exclusion_reason = None
    median_zmin      = None
    n_ie_rows        = 0
    n_valid_rows     = 0
    n_unmapped       = 0
    labels           = [""] * n_frames

    if not rows:
        label_status, exclusion_reason = "excluded", "missing or empty position-bounds_melt.dat"
    else:
        is_ie       = [is_initial_emptiness_row(r) for r in rows]
        n_ie_rows   = sum(is_ie)
        valid       = [r for r, ie in zip(rows, is_ie) if not ie]
        n_valid_rows = len(valid)

        if n_valid_rows < MIN_VALID_ROWS:
            label_status     = "excluded"
            exclusion_reason = f"only {n_valid_rows} valid rows (need >= {MIN_VALID_ROWS})"
        else:
            median_zmin = statistics.median(r[4] for r in valid)
            if median_zmin >= 0:
                label_status     = "excluded"
                exclusion_reason = (f"per-experiment median zmin >= 0 "
                                    f"({median_zmin:.3e} m); pool not subsurface")
            else:
                # Map each PNG frame to its monitor row by simulation timestep:
                # the .dat file has one row per simulation timestep, starting at 0.
                # PNG filenames use the simulation timestep number (e.g. ss_100069_*.png),
                # so row index = png timestep.
                #
                # Both median_zmin and per-frame zmin are negative (pool extends
                # below substrate surface at z=0); keyhole = pool bottom deeper
                # than 1.5x the typical bottom, i.e. zmin < 1.5*median_zmin
                # (more negative than the threshold).
                threshold   = KEYHOLE_RATIO * median_zmin   # negative
                for frame_idx, ts in enumerate(sorted_timesteps):
                    if ts >= len(rows):
                        n_unmapped += 1
                        continue  # leave label empty
                    if is_ie[ts]:
                        labels[frame_idx] = "Initial Emptiness"
                    else:
                        zmin = rows[ts][4]
                        labels[frame_idx] = "Keyhole" if zmin < threshold else "Conduction"

    # 7) Write parameters.json
    with open(dst / "parameters.json", "w") as f:
        json.dump(parameters, f, indent=2)

    # 8) Write frames.csv (with labels)
    with open(dst / "frames.csv", "w", newline="") as f:
        writer = csv.writer(f)
        writer.writerow(["frame_idx", "timestep", "label",
                         "front_filename", "side_filename", "top_filename"])
        for idx, ts in enumerate(sorted_timesteps):
            new_name = f"frame_{idx:05d}.png"
            writer.writerow([idx, ts, labels[idx],
                             f"frames/front/{new_name}",
                             f"frames/side/{new_name}",
                             f"frames/top/{new_name}"])

    # 9) Write metadata.json
    with open(dst / "metadata.json", "w") as f:
        json.dump({
            "sim_id":                sim_id,
            "original_folder_name":  src.name,
            "original_hash":         raw.get("H", None),
            "material":              material,
            "n_frames":              n_frames,
            "n_skipped_incomplete":  skipped,
        }, f, indent=2)

    # 10) Write labeling_provenance.json
    n_keyhole    = sum(1 for l in labels if l == "Keyhole")
    n_conduction = sum(1 for l in labels if l == "Conduction")
    n_ie         = sum(1 for l in labels if l == "Initial Emptiness")
    now          = datetime.now(timezone.utc).isoformat()

    if label_status == "labeled":
        provenance = {
            "method":                  "automatic",
            "automatic_method":        "per-experiment-median-zmin, threshold = 1.5x median",
            "n_frames_total":          n_frames,
            "n_frames_auto_labeled":   n_frames - n_unmapped,
            "n_frames_unmapped":       n_unmapped,
            "n_frames_human_verified": 0,
            "n_keyhole":               n_keyhole,
            "n_conduction":            n_conduction,
            "n_initial_emptiness":     n_ie,
            "median_zmin_m":           median_zmin,
            "keyhole_threshold_m":     KEYHOLE_RATIO * median_zmin,
            "n_monitor_rows_total":    len(rows),
            "n_monitor_rows_ie":       n_ie_rows,
            "n_monitor_rows_valid":    n_valid_rows,
            "labeler_id":              None,
            "labeled_at":              now,
            "tool_version":            SCRIPT_VERSION,
        }
    else:
        provenance = {
            "method":                  "excluded",
            "automatic_method":        "per-experiment-median-zmin, threshold = 1.5x median",
            "n_frames_total":          n_frames,
            "n_frames_auto_labeled":   0,
            "n_frames_human_verified": 0,
            "exclusion_reason":        exclusion_reason,
            "n_monitor_rows_total":    len(rows),
            "n_monitor_rows_ie":       n_ie_rows,
            "n_monitor_rows_valid":    n_valid_rows,
            "labeler_id":              None,
            "labeled_at":              now,
            "tool_version":            SCRIPT_VERSION,
        }

    with open(dst / "labeling_provenance.json", "w") as f:
        json.dump(provenance, f, indent=2)

    return {
        "sim_id":            sim_id,
        "src_name":          src.name,
        "dst_name":          dst.name,
        "n_frames":          n_frames,
        "n_unmapped":        n_unmapped,
        "label_status":      label_status,
        "exclusion_reason":  exclusion_reason,
        "median_zmin":       median_zmin,
        "n_keyhole":         n_keyhole,
        "n_conduction":      n_conduction,
        "n_initial_emptiness": n_ie,
    }


# ---- Driver ----------------------------------------------------------------
def main():
    ap = argparse.ArgumentParser(description=__doc__,
                                 formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("source", type=Path,
                    help="Source path. In --mode single: a single P-...VX-... folder. "
                         "In --mode set: a parent directory containing many P-...VX-... folders.")
    ap.add_argument("dest", type=Path,
                    help="Destination path. In --mode single: dest folder for sim_NNNNN/. "
                         "In --mode set: parent directory; sim_NNNNN/ folders will be created here.")
    ap.add_argument("--mode", choices=["single", "set"], required=True,
                    help="single: process one experiment folder. "
                         "set: iterate over all P-* folders in source.")
    ap.add_argument("--start", type=int, default=1,
                    help="Starting sim number (default: 1)")
    ap.add_argument("--prefix", default="P-",
                    help="Source folder prefix to match in --mode set (default: 'P-')")
    args = ap.parse_args()

    src_root = args.source.resolve()
    dst_root = args.dest.resolve()
    if not src_root.is_dir():
        sys.exit(f"ERROR: source not found: {src_root}")

    if args.mode == "single":
        if not src_root.name.startswith(args.prefix):
            sys.exit(f"ERROR: --mode single expects a folder whose name starts with "
                     f"'{args.prefix}'. Got: {src_root.name}")
        raw = [src_root]
        print(f"Single-experiment mode: {src_root.name}")
    else:  # set
        if src_root.name.startswith(args.prefix):
            sys.exit(f"ERROR: --mode set expects a parent directory, but you pointed at "
                     f"a single experiment folder ({src_root.name}). "
                     f"Either use --mode single, or pass the parent directory.")
        raw = sorted(p for p in src_root.iterdir()
                     if p.is_dir() and p.name.startswith(args.prefix))
        if not raw:
            sys.exit(f"ERROR: no '{args.prefix}*/' folders found in {src_root}")
        print(f"Batch mode: {len(raw)} raw experiments in {src_root}")

    dst_root.mkdir(parents=True, exist_ok=True)
    print(f"Output: {dst_root}\n")

    results = []
    for i, src_folder in enumerate(raw):
        sim_id   = args.start + i
        sim_name = f"sim_{sim_id:05d}"
        dst_folder = dst_root / sim_name

        if dst_folder.exists():
            print(f"  [SKIP]  {sim_name}: destination already exists")
            continue

        try:
            r = process_one(src_folder, dst_folder, sim_id)
        except Exception as e:
            print(f"  [ERROR] {sim_name}: {type(e).__name__}: {e}")
            traceback.print_exc()
            if dst_folder.exists():
                shutil.rmtree(dst_folder)
            continue

        results.append(r)
        if "fatal_error" in r:
            print(f"  [FATAL] {sim_name}: {r['fatal_error']}")
            if dst_folder.exists():
                shutil.rmtree(dst_folder)
        elif r["label_status"] == "excluded":
            print(f"  [EXCL]  {sim_name}: {r['exclusion_reason']}")
        else:
            unmap_note = f" (UNMAPPED: {r.get('n_unmapped', 0)})" if r.get('n_unmapped', 0) else ""
            print(f"  [OK]    {sim_name}: {r['n_frames']:4d} frames | "
                  f"K={r['n_keyhole']:4d} C={r['n_conduction']:4d} IE={r['n_initial_emptiness']:3d} | "
                  f"median_zmin={r['median_zmin']:.3e} m{unmap_note}")

    # Summary
    n_ok       = sum(1 for r in results if r.get("label_status") == "labeled")
    n_exc      = sum(1 for r in results if r.get("label_status") == "excluded")
    n_fatal    = sum(1 for r in results if "fatal_error" in r)
    tot_K      = sum(r.get("n_keyhole", 0) for r in results)
    tot_C      = sum(r.get("n_conduction", 0) for r in results)
    tot_IE     = sum(r.get("n_initial_emptiness", 0) for r in results)
    tot_frames = tot_K + tot_C + tot_IE

    print("\n" + "=" * 70)
    print("SUMMARY")
    print("=" * 70)
    print(f"Experiments:     {len(results)}")
    print(f"  labeled:       {n_ok}")
    print(f"  excluded:      {n_exc}")
    if n_fatal:
        print(f"  fatal errors:  {n_fatal}")
    print(f"\nLabeled frames:  {tot_frames}")
    print(f"  Keyhole:           {tot_K}")
    print(f"  Conduction:        {tot_C}")
    print(f"  Initial Emptiness: {tot_IE}")


if __name__ == "__main__":
    main()