"""Waveform-shape metrics for false-trigger detection.

A false trigger is an isolated impulse (quiet → spike → quiet); a real event
rings for many cycles. Two numbers separate them: crest factor (how far the
peak stands above the typical sample) and how many samples sit near the peak.
"""
from __future__ import annotations
import numpy as np

_GEO_CHANNELS = ("Tran", "Vert", "Long")
NEAR_PEAK_FRACTION = 0.5   # a sample "near the peak" is >= this * peak amplitude


def channel_shape(x) -> dict | None:
    x = np.asarray(x, dtype=float)
    if x.size < 2:
        return None
    peak = float(np.max(np.abs(x)))
    if peak <= 0:
        return None
    rms = float(np.sqrt(np.mean(x ** 2)))
    if rms <= 0:
        return None
    near = int(np.sum(np.abs(x) >= NEAR_PEAK_FRACTION * peak))
    return {"crest_factor": peak / rms, "near_peak_count": near,
            "sample_count": int(x.size)}


def shape_from_samples(chans: dict) -> dict | None:
    best_axis, best_peak, best_x = None, -1.0, None
    for ax in _GEO_CHANNELS:
        x = chans.get(ax)
        if x is None:
            continue
        x = np.asarray(x, dtype=float)
        if x.size < 2:
            continue
        p = float(np.max(np.abs(x)))
        if p > best_peak:
            best_axis, best_peak, best_x = ax, p, x
    if best_axis is None:
        return None
    s = channel_shape(best_x)
    if s is None:
        return None
    s["axis"] = best_axis
    return s


def shape_from_h5(path) -> dict | None:
    import h5py
    try:
        with h5py.File(path, "r") as f:
            chans = {ax: f[f"samples/{ax}"][:] for ax in _GEO_CHANNELS
                     if f"samples/{ax}" in f}
    except Exception:
        return None
    return shape_from_samples(chans)