def test_ensure_spacing_batch_processing(p, size):
coord = np.random.randn(100, 2)
# --- Consider the average distance btween the point as spacing
spacing = np.median(pdist(coord, metric=minkowski, p=p))
expected = ensure_spacing(coord, spacing=spacing, p_norm=p)
assert np.array_equal(
ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size),
expected)
def test_ensure_spacing_trivial(p):
# --- Empty input
assert ensure_spacing([], p_norm=p) == []
# --- Verified spacing
coord = np.random.randn(100, 2)
# --- 0 spacing
assert np.array_equal(coord, ensure_spacing(coord, spacing=0, p_norm=p))
# Spacing is chosen to be half the minimum distance
spacing = pdist(coord, minkowski, p=p).min() * 0.5
out = ensure_spacing(coord, spacing=spacing, p_norm=p)
assert np.array_equal(coord, out)
def test_ensure_spacing_p_norm(p, size):
coord = np.random.randn(100, 2)
# --- Consider the average distance btween the point as spacing
spacing = np.median(pdist(coord, metric=minkowski, p=p))
out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
assert pdist(out, metric=minkowski, p=p).min() > spacing
def test_max_batch_size():
"""Small batches are slow, large batches -> large allocations -> also slow.
https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
"""
coords = np.random.randint(low=0, high=1848, size=(40000, 2))
tstart = time.time()
ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=2000)
dur1 = time.time() - tstart
tstart = time.time()
ensure_spacing(coords,
spacing=100,
min_split_size=50,
max_split_size=20000)
dur2 = time.time() - tstart
assert dur1 < dur2
def test_max_batch_size():
"""Small batches are slow, large batches -> large allocations -> also slow.
https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
"""
coords = np.random.randint(low=0, high=1848, size=(40000, 2))
tstart = time.time()
ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=2000)
dur1 = time.time() - tstart
tstart = time.time()
ensure_spacing(coords,
spacing=100,
min_split_size=50,
max_split_size=20000)
dur2 = time.time() - tstart
# Originally checked dur1 < dur2 to assert that the default batch size was
# faster than a much larger batch size. However, on rare occasion a CI test
# case would fail with dur1 ~5% larger than dur2. To be more robust to
# variable load or differences across architectures, we relax this here.
assert dur1 < 1.33 * dur2
def test_ensure_spacing_nD(ndim, size):
coord = np.ones((5, ndim))
expected = np.ones((1, ndim))
assert np.array_equal(ensure_spacing(coord, min_split_size=size), expected)
def test_ensure_spacing_nD(ndim):
coord = np.ones((5, ndim))
expected = np.ones((1, ndim))
assert np.array_equal(ensure_spacing(coord), expected)
