def test_accumulate_normals():
    # set up comparison
    n_pts = int(1.6e5)  # approx number in sample source space
    n_tris = int(3.2e5)
    # use all positive to make a worst-case for cumulative summation
    # (real "nn" vectors will have both positive and negative values)
    tris = (rng.rand(n_tris, 1) * (n_pts - 2)).astype(int)
    tris = np.c_[tris, tris + 1, tris + 2]
    tri_nn = rng.rand(n_tris, 3)
    this = dict(tris=tris, np=n_pts, ntri=n_tris, tri_nn=tri_nn)

    # cut-and-paste from original code in surface.py:
    #    Find neighboring triangles and accumulate vertex normals
    this['nn'] = np.zeros((this['np'], 3))
    for p in range(this['ntri']):
        # vertex normals
        verts = this['tris'][p]
        this['nn'][verts, :] += this['tri_nn'][p, :]
    nn = _accumulate_normals(this['tris'], this['tri_nn'], this['np'])

    # the moment of truth (or reckoning)
    assert_allclose(nn, this['nn'], rtol=1e-7, atol=1e-7)
Test efficient normal accumulation for surfaces
dataset/ETHPy150Open mne-tools/mne-python/mne/tests/test_source_space.py/test_accumulate_normals