def test_n_closest_points():
labels = cle.push(
np.asarray([[1, 1, 1, 3, 3, 3], [1, 1, 1, 3, 3, 3], [1, 1, 1, 3, 3, 3],
[0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2,
2]]))
reference = cle.push(np.asarray([[0, 3, 3, 1]]))
centroids = cle.centroids_of_labels(labels)
distance_matrix = cle.generate_distance_matrix(centroids, centroids)
max_float = np.finfo(np.float).max
cle.set_where_x_equals_y(distance_matrix, max_float)
cle.set_row(distance_matrix, 0, max_float)
cle.set_column(distance_matrix, 0, max_float)
n_closest_points = cle.n_closest_points(distance_matrix,
n=1,
ignore_background=False)
a = cle.pull(n_closest_points)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.01))
def test_centroids_of_labels_3d():
labels = cle.push(np.asarray([[
[1, 1, 2],
], [
[1, 2, 2],
], [[3, 3, 3]]]))
reference = cle.push(
np.asarray([
# CENTROID_X(25),
[0.33333334, 1.6666666, 1.],
# CENTROID_Y(26),
[0., 0., 0.],
# CENTROID_Z(27),
[0.33333334, 0.6666667, 2.],
]))
result = cle.centroids_of_labels(labels)
a = cle.pull(result)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.0001))
def _make_distance_matrix(self, labels, distance_matrix=None):
if distance_matrix is None:
import pyclesperanto_prototype as cle
centroids = cle.centroids_of_labels(labels)
distance_matrix = cle.generate_distance_matrix(
centroids, centroids)
cle.set_column(distance_matrix, 0, 0)
cle.set_row(distance_matrix, 0, 0)
return distance_matrix
def test_to_networkx():
import pyclesperanto_prototype as cle
labels = cle.push(np.asarray([[0, 1], [2, 3]]))
# extract centroid positions
centroids = cle.centroids_of_labels(labels)
# determine a distance matrix
distance_matrix = cle.generate_distance_matrix(centroids, centroids)
# threshold the distance matrix
adjacency_matrix = cle.generate_proximal_neighbors_matrix(distance_matrix,
max_distance=50)
nx_graph = cle.to_networkx(adjacency_matrix, centroids)
assert len(nx_graph.nodes) == 3
def test_maximum_distance_of_n_shortest_distances():
labels = cle.push(
np.asarray([[1, 1, 1, 3, 3, 3], [1, 1, 1, 3, 3, 3], [1, 1, 1, 3, 3, 3],
[0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2,
2]]))
reference = cle.push(np.asarray([[0, 3, 3, 3]]))
centroids = cle.centroids_of_labels(labels)
distance_matrix = cle.generate_distance_matrix(centroids, centroids)
maximum_distance_of_n_far_off_distances = cle.maximum_distance_of_n_shortest_distances(
distance_matrix, n=1)
a = cle.pull(maximum_distance_of_n_far_off_distances)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.01))
def test_average_distance_of_touching_neighbors():
labels = cle.push(
np.asarray([[1, 1, 1, 3, 3, 3], [1, 1, 1, 3, 3, 3], [1, 1, 1, 3, 3, 3],
[0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2, 2], [0, 0, 0, 2, 2,
2]]))
reference = cle.push(np.asarray([[0, 3, 3, 3]]))
centroids = cle.centroids_of_labels(labels)
distance_matrix = cle.generate_distance_matrix(centroids, centroids)
touch_matrix = cle.generate_touch_matrix(labels)
average_distance_of_touching_neighbors = cle.average_distance_of_touching_neighbors(
distance_matrix, touch_matrix)
a = cle.pull(average_distance_of_touching_neighbors)
b = cle.pull(reference)
print(a)
print(b)
assert (np.allclose(a, b, 0.01))
