import numpy as np, h5py
from sfm.shape_metrics import shape_from_h5

def _write_h5(path, chans):
    with h5py.File(path, "w") as f:
        g = f.create_group("samples")
        for k, v in chans.items():
            g.create_dataset(k, data=np.asarray(v, dtype="float32"))

def test_shape_from_h5_reads_dominant_axis(tmp_path):
    p = tmp_path / "ev.h5"
    long = np.zeros(1024, dtype="float32"); long[100] = 0.48
    _write_h5(p, {"Tran": np.zeros(1024), "Vert": np.zeros(1024), "Long": long,
                  "MicL": np.ones(1024)})
    s = shape_from_h5(str(p))
    assert s["axis"] == "Long" and s["near_peak_count"] <= 3

def test_shape_from_h5_none_on_missing_or_degenerate(tmp_path):
    assert shape_from_h5(str(tmp_path / "nope.h5")) is None
    p = tmp_path / "degen.h5"
    _write_h5(p, {"Tran": np.zeros(1), "Vert": np.zeros(1), "Long": np.zeros(1)})
    assert shape_from_h5(str(p)) is None