"""PyTorch Dataset over lucky9-cyou/mimic-iv-aligned-ppg-ecg.

For v1 we:
  - Keep only segments where ECG lead II is present (93.7% of data)
  - Extract lead II ECG and PPG Pleth
  - Window: 10 s slices at 5 s stride
  - Native rates: ECG 250 Hz, PPG 125 Hz -> ECG window 2500 samples, PPG 1250

Each item returns {ecg: [1, 2500], ppg: [1, 1250], subject_id, segment_start,
measured_ptt_ms (per-window estimate, may be NaN), delta_t_seconds (sampled per
step outside the dataset)}.

The caller handles delta_t sampling (60% log-uniform + 40% from measured_ptt).
"""
from __future__ import annotations

import json
import os
import re
from pathlib import Path
from typing import Iterable

import numpy as np
import torch
from scipy.signal import butter, filtfilt, find_peaks
from torch.utils.data import Dataset

from datasets import load_from_disk

ECG_FS = 250.0
PPG_FS = 125.0
WINDOW_SEC = 10.0
STRIDE_SEC = 5.0
ECG_WIN = int(ECG_FS * WINDOW_SEC)  # 2500
PPG_WIN = int(PPG_FS * WINDOW_SEC)  # 1250
ECG_STRIDE = int(ECG_FS * STRIDE_SEC)
PPG_STRIDE = int(PPG_FS * STRIDE_SEC)


def _parse_subject(record_name: str) -> str:
    m = re.match(r"p\d+/(p\d+)/", record_name)
    return m.group(1) if m else record_name.split("/")[0]


def _bandpass(x: np.ndarray, fs: float, lo: float, hi: float, order: int = 3) -> np.ndarray:
    ny = 0.5 * fs
    b, a = butter(order, [lo / ny, min(hi / ny, 0.99)], btype="band")
    return filtfilt(b, a, x, method="gust").astype(np.float32)


def _zscore(x: np.ndarray, eps: float = 1e-6) -> np.ndarray:
    m = x.mean()
    s = x.std() + eps
    return ((x - m) / s).astype(np.float32)


def _r_peaks(ecg: np.ndarray, fs: float) -> np.ndarray:
    x = _bandpass(ecg, fs, 5.0, 15.0)
    s = np.diff(x, prepend=x[:1]) ** 2
    w = max(int(0.12 * fs), 1)
    mwa = np.convolve(s, np.ones(w) / w, mode="same")
    thr = mwa.mean() + 0.5 * mwa.std()
    p, _ = find_peaks(mwa, height=thr, distance=int(0.3 * fs))
    return p


def _ppg_peaks(ppg: np.ndarray, fs: float) -> np.ndarray:
    x = _bandpass(ppg, fs, 0.5, 8.0)
    p, _ = find_peaks(x, distance=int(0.3 * fs),
                     height=x.mean() + 0.3 * x.std(),
                     prominence=0.1 * x.std())
    return p


def _window_ptt_ms(ecg_win: np.ndarray, ppg_win: np.ndarray) -> float:
    """Median PTT across beats in one window; np.nan if <3 clean beats."""
    r = _r_peaks(ecg_win, ECG_FS)
    p = _ppg_peaks(ppg_win, PPG_FS)
    if len(r) < 3 or len(p) < 3:
        return float("nan")
    r_t = r / ECG_FS
    p_t = p / PPG_FS
    ptts = []
    for rt in r_t:
        cand = p_t[(p_t >= rt + 0.050) & (p_t <= rt + 0.500)]
        if len(cand) == 1:
            ptts.append((cand[0] - rt) * 1000.0)
    if len(ptts) < 3:
        return float("nan")
    return float(np.median(ptts))


class MIMICAlignedDataset(Dataset):
    """Indexes windows across a set of cached shard directories.

    Args:
        shard_roots: list of "<snapshot_root>/shard_XXXXX" paths (pre-downloaded)
        build_index: if True, scan and build/save the window index; if False,
            load existing index_path
        index_path: where to cache the index (JSON: list[{shard_idx, row_idx,
            win_start_ecg, win_start_ppg, subject_id, ptt_ms}])
        normalise: if True, apply bandpass + zscore per window
    """

    def __init__(
        self,
        shard_roots: list[Path],
        index_path: Path,
        build_index: bool = True,
        normalise: bool = True,
        subjects_allow: set[str] | None = None,
        subset_frac: float = 1.0,
        subset_seed: int = 0,
    ):
        self.shard_roots = [Path(p) for p in shard_roots]
        self.index_path = Path(index_path)
        self.normalise = normalise
        self.subjects_allow = subjects_allow
        if build_index or not self.index_path.exists():
            self._build_index()
        self.index = json.loads(self.index_path.read_text())
        if subjects_allow is not None:
            self.index = [r for r in self.index if r["subject_id"] in subjects_allow]
        if subset_frac < 1.0:
            rng = np.random.default_rng(subset_seed)
            n_keep = max(1, int(len(self.index) * subset_frac))
            keep = rng.choice(len(self.index), size=n_keep, replace=False)
            self.index = [self.index[i] for i in sorted(keep)]
        self._shard_cache: dict[int, object] = {}

    def _build_index(self) -> None:
        records = []
        for s_path in self.shard_roots:
            sidx = int(s_path.name.split("_")[1])
            ds = load_from_disk(str(s_path))
            for row_idx in range(len(ds)):
                row = ds[row_idx]
                names = list(row["ecg_names"])
                if "II" not in names:
                    continue
                subject_id = _parse_subject(row["record_name"])
                ecg_siglen = int(row["ecg_siglen"])
                ppg_siglen = int(row["ppg_siglen"])
                # require full windows only
                n_win = min(
                    (ecg_siglen - ECG_WIN) // ECG_STRIDE + 1,
                    (ppg_siglen - PPG_WIN) // PPG_STRIDE + 1,
                )
                if n_win <= 0:
                    continue
                for w in range(n_win):
                    records.append({
                        "shard_idx": sidx,
                        "row_idx": row_idx,
                        "subject_id": subject_id,
                        "win_start_ecg": w * ECG_STRIDE,
                        "win_start_ppg": w * PPG_STRIDE,
                    })
        self.index_path.parent.mkdir(parents=True, exist_ok=True)
        self.index_path.write_text(json.dumps(records))

    def _load_shard(self, sidx: int):
        if sidx not in self._shard_cache:
            for p in self.shard_roots:
                if int(p.name.split("_")[1]) == sidx:
                    self._shard_cache[sidx] = load_from_disk(str(p))
                    break
        return self._shard_cache[sidx]

    def __len__(self) -> int:
        return len(self.index)

    def __getitem__(self, idx: int) -> dict:
        rec = self.index[idx]
        ds = self._load_shard(rec["shard_idx"])
        row = ds[rec["row_idx"]]
        ecg_full = np.asarray(row["ecg"], dtype=np.float32)
        ppg_full = np.asarray(row["ppg"], dtype=np.float32)[0]
        names = list(row["ecg_names"])
        ecg_lead = ecg_full[names.index("II")]
        se = rec["win_start_ecg"]
        sp = rec["win_start_ppg"]
        ecg_win = ecg_lead[se : se + ECG_WIN].copy()
        ppg_win = ppg_full[sp : sp + PPG_WIN].copy()
        if ecg_win.shape[0] != ECG_WIN or ppg_win.shape[0] != PPG_WIN:
            raise RuntimeError(f"bad window at idx {idx}: {ecg_win.shape}, {ppg_win.shape}")

        # PTT is computed ONLY at index-build time (cached in the index dict).
        # __getitem__ stays cheap so the GPU isn't waiting on peak detection.
        ptt_ms = float(rec.get("ptt_ms", float("nan")))
        if self.normalise:
            ecg_win = _zscore(_bandpass(ecg_win, ECG_FS, 0.5, 40.0))
            ppg_win = _zscore(_bandpass(ppg_win, PPG_FS, 0.5, 8.0))
        return {
            "ecg": torch.from_numpy(ecg_win).unsqueeze(0),  # [1, 2500]
            "ppg": torch.from_numpy(ppg_win).unsqueeze(0),  # [1, 1250]
            "subject_id": rec["subject_id"],
            "ptt_ms": float(ptt_ms) if np.isfinite(ptt_ms) else float("nan"),
        }


def split_by_subject(
    subjects: Iterable[str], frac: float = 0.9, seed: int = 0
) -> tuple[set[str], set[str]]:
    subjects = sorted(set(subjects))
    rng = np.random.default_rng(seed)
    perm = rng.permutation(len(subjects))
    cut = int(len(subjects) * frac)
    train = {subjects[i] for i in perm[:cut]}
    test = {subjects[i] for i in perm[cut:]}
    return train, test


def collate_with_dt(
    items: list[dict],
    log_uniform_frac: float = 0.6,
    dt_min_ms: float = 50.0,
    dt_max_ms: float = 500.0,
    rng: np.random.Generator | None = None,
) -> dict:
    """Stack a batch and sample Δt. 60% log-uniform, 40% measured PTT where available."""
    rng = rng if rng is not None else np.random.default_rng()
    ecg = torch.stack([b["ecg"] for b in items])
    ppg = torch.stack([b["ppg"] for b in items])
    ptts = np.array([b["ptt_ms"] for b in items], dtype=np.float32)
    b = len(items)
    dt_ms = np.empty(b, dtype=np.float32)
    use_log = rng.random(b) < log_uniform_frac
    log_lo, log_hi = np.log(dt_min_ms), np.log(dt_max_ms)
    dt_ms[use_log] = np.exp(rng.uniform(log_lo, log_hi, size=int(use_log.sum())))
    # for the 40% branch: measured PTT when finite, else fallback to log-uniform
    rest = ~use_log
    for i in np.nonzero(rest)[0]:
        if np.isfinite(ptts[i]):
            dt_ms[i] = ptts[i]
        else:
            dt_ms[i] = np.exp(rng.uniform(log_lo, log_hi))
    return {
        "ecg": ecg,
        "ppg": ppg,
        "dt_seconds": torch.from_numpy(dt_ms / 1000.0),
        "ptt_ms": torch.from_numpy(ptts),
        "subject_id": [b["subject_id"] for b in items],
    }