def test_orientation(self):
"""
The array returned by `unit_gridpoints()` should have the correct
ordering.
"""
for shape in itertools.product((3, 5, 8), repeat=2):
with self.subTest(shape=shape):
n, m = shape
a = 0.5 * float(n - 1)
b = 0.5 * float(m - 1)
ji = unit_gridpoints(shape, mode="ji")
xy = unit_gridpoints(shape, mode="xy")
# `out[0]` is the top-left corner
numpy.testing.assert_array_almost_equal(ji[0], (-a, -b))
numpy.testing.assert_array_almost_equal(xy[0], (-b, a))
# `out[m - 1]` is the top-right corner
numpy.testing.assert_array_almost_equal(ji[m - 1], (-a, b))
numpy.testing.assert_array_almost_equal(xy[m - 1], (b, a))
# `out[(n - 1) * m]` is the bottom-left corner
numpy.testing.assert_array_almost_equal(
ji[(n - 1) * m], (a, -b))
numpy.testing.assert_array_almost_equal(
xy[(n - 1) * m], (-b, -a))
# `out[-1]` is the bottom right corner
numpy.testing.assert_array_almost_equal(ji[-1], (a, b))
numpy.testing.assert_array_almost_equal(xy[-1], (b, -a))
def test_known_values(self):
"""`unit_gridpoints()` should return known result with known input."""
shape = (3, 5)
ji = unit_gridpoints(shape, mode="ji")
xy = unit_gridpoints(shape, mode="xy")
_ji = numpy.column_stack((
[-1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[-2, -1, 0, 1, 2, -2, -1, 0, 1, 2, -2, -1, 0, 1, 2],
))
_xy = numpy.column_stack((
[-2, -1, 0, 1, 2, -2, -1, 0, 1, 2, -2, -1, 0, 1, 2],
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, -1, -1, -1, -1, -1],
))
numpy.testing.assert_array_almost_equal(xy, _xy)
numpy.testing.assert_array_almost_equal(ji, _ji)
def test_zero_mean(self):
"""The array returned by `unit_gridpoints()` should have zero mean."""
for shape in itertools.product((3, 5, 8), repeat=2):
for mode in ("ji", "xy"):
with self.subTest(shape=shape, mode=mode):
pts = unit_gridpoints(shape, mode=mode)
numpy.testing.assert_array_almost_equal(
numpy.mean(pts, axis=0), 0)
def test_shape(self):
"""
The array returned by `unit_gridpoints()` should have the correct shape.
"""
for shape in itertools.product((3, 5, 8), repeat=2):
for mode in ("ji", "xy"):
with self.subTest(shape=shape, mode=mode):
n, m = shape
pts = unit_gridpoints(shape, mode=mode)
self.assertEqual(pts.ndim, 2)
self.assertEqual(pts.shape[0], n * m)
self.assertEqual(pts.shape[1], 2)
def test_incomplete_grid(self):
"""
`estimate_grid_orientation()` should return the expected
AffineTransform for a grid with one point missing.
"""
for shape in [(5, 5), (8, 8)]:
for i in range(50):
with self.subTest(shape=shape, i=i):
n, m = shape
shuffle = self._rng.permutation(n * m)
tform = random_affine_transform(self._rng)
xy = tform.apply(
unit_gridpoints(shape, mode="xy")[shuffle])[:-1]
out = estimate_grid_orientation(xy, shape, AffineTransform)
numpy.testing.assert_array_almost_equal(
tform.matrix, out.matrix)
numpy.testing.assert_array_almost_equal(
tform.translation, out.translation)
def test_full_grid(self):
"""
`nearest_neighbor_graph()` should return the expected graph for a full
grid.
"""
for shape in itertools.product((5, 8), repeat=2):
for i in range(50):
with self.subTest(shape=shape, i=i):
n, m = shape
shuffle = self._rng.permutation(n * m)
index = numpy.argsort(shuffle)
tform = random_affine_transform(self._rng)
xy = tform.apply(
unit_gridpoints(shape, mode="xy")[shuffle])
graph = unit_gridpoints_graph(shape, tform, index)
out = nearest_neighbor_graph(xy)
numpy.testing.assert_array_almost_equal(
graph.adjacency_matrix(), out.adjacency_matrix())
def test_incomplete_grid(self):
"""
`nearest_neighbor_graph()` should return the expected graph for a grid
with one point missing.
"""
for shape in itertools.product((5, 8), repeat=2):
for i in range(50):
with self.subTest(shape=shape, i=i):
n, m = shape
shuffle = self._rng.permutation(n * m)
index = numpy.argsort(shuffle)
tform = random_affine_transform(self._rng)
xy = tform.apply(
unit_gridpoints(shape, mode="xy")[shuffle])[:-1]
_graph = unit_gridpoints_graph(shape, tform, index)
vertex = n * m - 1
# To prevent a RuntimeError loop over a copy
for neighbor in _graph[vertex].copy():
_graph.remove_edge((vertex, neighbor))
graph = _graph[:-1]
out = nearest_neighbor_graph(xy)
numpy.testing.assert_array_almost_equal(
graph.adjacency_matrix(), out.adjacency_matrix())