"""Convert SPE-1 paired patch / Neuropixels recordings to Arrow

For each cell, this writes:
    <output_root>/<cell_id>/
        train/, test/                  (HuggingFace ``DatasetDict`` Arrow shards)
        dataset_dict.json
        conversion_metadata.json       (GT electrode + bad channels + spike counts)
        templates.npz                  (peak-aligned mean template, Zhao et al. 2026)

Usage:
    python scripts/convert_to_arrow.py \\
        --data-dir <SPE1_RAW>/Recordings \\
        --chan-map <SPE1_RAW>/chanMap.mat \\
        --summary "<SPE1_RAW>/Data Summary.xlsx" \\
        --output  .                                 \\
        [--cells c14 c26 ...]   [--duration 300]
"""

from __future__ import annotations

import argparse
import gc
import json
import os
import sys

import numpy as np
import pandas as pd
import scipy.io
import scipy.signal
import spikeinterface.core as sc
import spikeinterface.extractors as se
import spikeinterface.preprocessing as spre
from datasets import Dataset


ZHAO_CELLS: list[str] = [
    "c14",
    "c15",
    "c16",
    "c19",
    "c24",
    "c26",
    "c28",
    "c29",
    "c37",
    "c45",
    "c46",
]
NPX_SAMPLE_RATE: float = 30_000.0  # Neuropixels sampling rate
PATCH_SAMPLE_RATE: float = 50_023.0  # Patch amplifier sampling rate (approx.)
SKIP_SECONDS: float = 10.0  # Burn-in to discard


def detect_spikes_from_patch(
    patch_sig: np.ndarray,
    patch_sample_rate: float = PATCH_SAMPLE_RATE,
) -> np.ndarray:
    """Detect APs via 200 Hz HP + 12·MAD threshold (Garcia, SI blog 2020)."""
    sos = scipy.signal.iirfilter(
        5,
        200.0 / patch_sample_rate * 2,
        analog=False,
        btype="highpass",
        ftype="butter",
        output="sos",
    )
    patch_sig_f = scipy.signal.sosfiltfilt(sos, patch_sig, axis=0)
    med = np.median(patch_sig_f)
    mad = np.median(np.abs(patch_sig_f - med)) * 1.4826
    thresh = med - 12 * mad
    refractory = int(patch_sample_rate * 0.001)  # 1 ms
    idx, _ = scipy.signal.find_peaks(-patch_sig_f, height=-thresh, distance=refractory)
    return idx.astype(np.int64)


def extract_peak_aligned_templates(
    recording: sc.BaseRecording,
    spike_samples: np.ndarray,
    ms_before: float = 1.0,
    ms_after: float = 2.0,
    min_spikes: int = 2,
) -> tuple[np.ndarray, dict]:
    """Mean spike template, no per-spike realignment (Zhao et al. 2026)."""
    fs = recording.get_sampling_frequency()
    n_channels = recording.get_num_channels()
    total_samples = recording.get_num_samples()
    n_before = int(ms_before * fs / 1000.0)
    n_after = int(ms_after * fs / 1000.0)
    win_len = n_before + n_after

    running_sum: np.ndarray | None = None
    n_used = 0
    n_oob = 0
    for sample_idx in spike_samples:
        start = int(sample_idx) - n_before
        end = int(sample_idx) + n_after
        if start < 0 or end > total_samples:
            n_oob += 1
            continue
        snippet = recording.get_traces(start_frame=start, end_frame=end).astype(
            np.float32
        )
        running_sum = snippet if running_sum is None else running_sum + snippet
        n_used += 1

    if running_sum is not None and n_used >= min_spikes:
        mean_template = running_sum / n_used
    else:
        mean_template = np.zeros((win_len, n_channels), dtype=np.float32)

    return (
        mean_template[np.newaxis, ...],  # [1, win_len, n_ch]
        {
            "extraction_method": "zhao2026_raw_mean_template",
            "ms_before": float(ms_before),
            "ms_after": float(ms_after),
            "nbefore": int(n_before),
            "nafter": int(n_after),
            "n_spikes_total": int(len(spike_samples)),
            "n_spikes_used": int(n_used),
            "n_out_of_bounds_discarded": int(n_oob),
        },
    )


def convert_cell(
    cell_id: str,
    cell_dir: str,
    channel_locations: np.ndarray,
    output_root: str,
    summary_row: dict | None = None,
    duration_s: float = 300.0,
    segment_duration_s: float = 3.0,
    train_fraction: float = 0.8,
    ms_before: float = 1.0,
    ms_after: float = 2.0,
    skip_existing: bool = True,
) -> str:
    output_dir = os.path.join(output_root, cell_id)
    if skip_existing and os.path.isfile(
        os.path.join(output_dir, "conversion_metadata.json")
    ):
        print(f"  [{cell_id}] Already converted, skipping.")
        return output_dir

    # Locate per-cell binaries
    files: dict[str, str] = {}
    for fname in os.listdir(cell_dir):
        for suffix, key in [
            ("npx_raw.bin", "npx_raw"),
            ("patch_ch1.bin", "patch_v"),
            ("wc_spike_samples.npy", "wc_spike"),
        ]:
            if fname.endswith(suffix):
                files[key] = os.path.join(cell_dir, fname)
    for k in ("npx_raw", "patch_v"):
        if k not in files:
            raise FileNotFoundError(f"[{cell_id}] missing {k} in {cell_dir}")

    print(f"  [{cell_id}] Loading recordings …")
    npx_raw = np.memmap(files["npx_raw"], dtype="int16", mode="r").reshape(-1, 384)
    n_npx_samples = npx_raw.shape[0]

    patch_v = np.fromfile(files["patch_v"], dtype="float64")
    if "wc_spike" in files:
        spike_idx_patch = np.load(files["wc_spike"]).astype(np.int64)
        spike_source = "wc_spike_samples.npy (authoritative GT)"
    else:
        spike_idx_patch = detect_spikes_from_patch(patch_v, PATCH_SAMPLE_RATE)
        spike_source = "detect_spikes_from_patch (fallback)"
    print(f"  [{cell_id}] Spike source: {spike_source}")

    # Re-express patch spike indices in NPX clock & restrict to analysis window
    time_factor = n_npx_samples / len(patch_v)
    spike_idx_npx = (spike_idx_patch * time_factor).astype(np.int64)
    start_frame = int(SKIP_SECONDS * NPX_SAMPLE_RATE)
    end_frame = min(start_frame + int(duration_s * NPX_SAMPLE_RATE), n_npx_samples)
    spike_idx_npx = spike_idx_npx[
        (spike_idx_npx >= start_frame) & (spike_idx_npx < end_frame)
    ]
    spike_idx_relative = spike_idx_npx - start_frame
    n_analysis_samples = end_frame - start_frame

    print(
        f"  [{cell_id}] {len(spike_idx_npx)} spikes in "
        f"[{SKIP_SECONDS:.0f}s, {SKIP_SECONDS + duration_s:.0f}s]"
    )

    rec = se.BinaryRecordingExtractor(
        file_paths=files["npx_raw"],
        sampling_frequency=NPX_SAMPLE_RATE,
        num_channels=384,
        dtype="int16",
        time_axis=0,
    )
    rec.set_channel_locations(channel_locations)
    rec = rec.frame_slice(start_frame, end_frame)
    rec.set_property("gain_to_uV", np.full(384, 2.34375, dtype=np.float32))
    rec.set_property("offset_to_uV", np.zeros(384, dtype=np.float32))

    # Preprocessing: bandpass 300–3000 Hz + global CMR
    rec_bp = spre.bandpass_filter(rec, freq_min=300.0, freq_max=3000.0)
    rec_prep = spre.common_reference(rec_bp, operator="median", reference="global")

    # Bad channel detection on bandpass (NOT on CMR'd traces as CMR collapses
    # the inter-channel coherence the default detector relies on).
    bad_ids, _ = spre.detect_bad_channels(recording=rec_bp)
    rec_ids = list(rec_bp.channel_ids)
    bad_idx = sorted(int(rec_ids.index(c)) for c in bad_ids)
    print(f"  [{cell_id}] {len(bad_idx)} bad channels: {bad_idx}")

    # Templates
    print(f"  [{cell_id}] Extracting peak-aligned templates …")
    templates_array, tpl_meta = extract_peak_aligned_templates(
        rec_prep,
        spike_idx_relative,
        ms_before=ms_before,
        ms_after=ms_after,
    )
    n_before = tpl_meta["nbefore"]
    n_after = tpl_meta["nafter"]
    template = templates_array[0]  # [win_len, n_channels]
    peak_ch = int(np.argmax(np.max(np.abs(template), axis=0)))

    # Arrow segments
    print(f"  [{cell_id}] Writing Arrow segments …")
    seg_len = int(segment_duration_s * NPX_SAMPLE_RATE)
    segments = [
        (i, min(i + seg_len, n_analysis_samples))
        for i in range(0, n_analysis_samples, seg_len)
    ]
    n_train = int(len(segments) * train_fraction)

    for split, seg_range in [
        ("train", segments[:n_train]),
        ("test", segments[n_train:]),
    ]:

        def _gen(sr=seg_range):
            for s0, s1 in sr:
                traces = rec_prep.get_traces(start_frame=s0, end_frame=s1)
                cp = traces.T.astype(np.float32)  # [n_channels, T]
                cit: list[list[int]] = [[] for _ in range(384)]
                ctt: list[list[float]] = [[] for _ in range(384)]
                in_seg = spike_idx_relative[
                    (spike_idx_relative >= s0) & (spike_idx_relative < s1)
                ]
                t_ms = (in_seg - s0).astype(np.float64) / NPX_SAMPLE_RATE * 1000.0
                cit[peak_ch].extend([0] * len(t_ms))
                ctt[peak_ch].extend(t_ms.tolist())
                yield {
                    "sample_id": f"{s0:010d}",
                    "cit": [np.array(c, dtype=np.int32) for c in cit],
                    "ctt": [np.array(c, dtype=np.float64) for c in ctt],
                    "cp": [cp[ch] for ch in range(384)],
                }
                del cp, traces
                gc.collect()

        split_dir = os.path.join(output_dir, split)
        os.makedirs(split_dir, exist_ok=True)
        ds = Dataset.from_generator(_gen, writer_batch_size=1)
        ds.save_to_disk(split_dir)
        del ds
        gc.collect()
        print(f"  [{cell_id}] Saved {split}: {len(seg_range)} segments")

    with open(os.path.join(output_dir, "dataset_dict.json"), "w") as f:
        json.dump({"splits": {"train": {"name": "train"}, "test": {"name": "test"}}}, f)

    # Ground-truth electrode location from Data Summary.xlsx
    gt_chan_idx: int | None = None
    unit_xy: list[float] | None = None
    if summary_row is not None:
        gt_chan_idx = int(summary_row.get("chan_predicted", -1))
        if 0 <= gt_chan_idx < len(channel_locations):
            xy = channel_locations[gt_chan_idx].tolist()
            unit_xy = [float(xy[0]), float(xy[1]), 0.0]

    metadata = {
        "source_metadata": {
            "mode": "spe1_paired_recording",
            "dataset": "Marques-Smith et al. (2018) — CRCNS spe-1",
            "cell_id": cell_id,
            "skip_seconds": float(SKIP_SECONDS),
            "duration_s": float(duration_s),
            "patch_sample_rate_hz": float(PATCH_SAMPLE_RATE),
            "preprocessing": "bandpass_300_3000Hz + global_CMR",
            "spike_source": spike_source,
        },
        "template_metadata": tpl_meta,
        "sampling_frequency": float(NPX_SAMPLE_RATE),
        "num_units": 1,
        "num_channels": 384,
        "unit_peak_channel": {"0": peak_ch},
        "unit_locations_um": ({"0": unit_xy} if unit_xy is not None else None),
        "gt_electrode_chan_idx": gt_chan_idx,
        "bad_channel_ids": bad_idx,
        "n_spikes_total": int(len(spike_idx_npx)),
        "n_spikes_used_for_templates": int(tpl_meta["n_spikes_used"]),
        "segment_duration_s": float(segment_duration_s),
        "train_fraction": float(train_fraction),
    }
    with open(os.path.join(output_dir, "conversion_metadata.json"), "w") as f:
        json.dump(metadata, f, indent=2)

    np.savez(
        os.path.join(output_dir, "templates.npz"),
        templates=templates_array,
        sampling_frequency=float(NPX_SAMPLE_RATE),
        probe_positions=channel_locations,
        nbefore=n_before,
        nafter=n_after,
    )

    print(
        f"  [{cell_id}] Done — peak_ch={peak_ch}, "
        f"spikes_used={tpl_meta['n_spikes_used']}"
    )
    del rec_prep, rec_bp, rec, npx_raw, patch_v
    del spike_idx_patch, spike_idx_npx, spike_idx_relative, templates_array
    gc.collect()
    return output_dir


def main():
    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
    p.add_argument(
        "--data-dir",
        required=True,
        help="Path to the SPE-1 Recordings/ folder (per-cell subdirs).",
    )
    p.add_argument(
        "--chan-map",
        default=None,
        help="chanMap.mat (default: <data-dir>/../chanMap.mat).",
    )
    p.add_argument(
        "--summary",
        default=None,
        help="Data Summary.xlsx (default: <data-dir>/../Data Summary.xlsx).",
    )
    p.add_argument("--output", required=True, help="Root output directory.")
    p.add_argument(
        "--cells",
        nargs="+",
        default=None,
        help="Cell IDs (default: all 11 Hao et al. cells).",
    )
    p.add_argument(
        "--duration",
        type=float,
        default=300.0,
        help="Seconds of recording to use after burn-in (default: 300).",
    )
    p.add_argument(
        "--segment-duration",
        type=float,
        default=3.0,
        help="Arrow segment duration in seconds (default: 3.0).",
    )
    p.add_argument("--train-fraction", type=float, default=0.8)
    p.add_argument("--ms-before", type=float, default=1.0)
    p.add_argument("--ms-after", type=float, default=2.0)
    p.add_argument("--no-skip-existing", action="store_true")
    args = p.parse_args()

    spe1_root = os.path.dirname(args.data_dir.rstrip("/"))
    chan_map = args.chan_map or os.path.join(spe1_root, "chanMap.mat")
    if not os.path.isfile(chan_map):
        sys.exit(f"chanMap.mat not found at {chan_map}")

    summary_path = args.summary
    if summary_path is None:
        for candidate in ("Data Summary.xlsx", "Data_Summary.xlsx"):
            cand = os.path.join(spe1_root, candidate)
            if os.path.isfile(cand):
                summary_path = cand
                break

    d = scipy.io.loadmat(chan_map)
    channel_locations = np.zeros((384, 2), dtype=np.float32)
    channel_locations[:, 0] = d["xcoords"][:, 0]
    channel_locations[:, 1] = d["ycoords"][:, 0]
    print(f"Loaded probe geometry: 384 channels from {chan_map}")

    summary: dict[str, dict] = {}
    if summary_path and os.path.isfile(summary_path):
        df = pd.read_excel(summary_path)
        for _, row in df.iterrows():
            if "Cell" in row and pd.notna(row["Cell"]):
                summary[str(row["Cell"])] = row.to_dict()
        print(f"Loaded Data Summary: {len(summary)} entries from {summary_path}")
    else:
        print("WARNING: Data Summary.xlsx not found — unit locations will be unset.")

    cells = args.cells if args.cells is not None else ZHAO_CELLS
    os.makedirs(args.output, exist_ok=True)

    for cell_id in cells:
        cell_dir = os.path.join(args.data_dir, cell_id)
        if not os.path.isdir(cell_dir):
            print(f"  [{cell_id}] not found at {cell_dir} — skipping")
            continue
        try:
            convert_cell(
                cell_id=cell_id,
                cell_dir=cell_dir,
                channel_locations=channel_locations,
                output_root=args.output,
                summary_row=summary.get(cell_id),
                duration_s=args.duration,
                segment_duration_s=args.segment_duration,
                train_fraction=args.train_fraction,
                ms_before=args.ms_before,
                ms_after=args.ms_after,
                skip_existing=not args.no_skip_existing,
            )
        except Exception as e:
            print(f"  [{cell_id}] ERROR: {e}")
            raise


if __name__ == "__main__":
    main()