def test_accumulate_normals():
"""Test efficient normal accumulation for surfaces."""
# 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)
def test_accumulate_normals():
"""Test efficient normal accumulation for surfaces."""
# 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)
