def test_sorted():
np.random.seed(42)
for dim in [2, 3]:
points = np.random.random((1000, dim))
f = diode.fill_alpha_shapes(points)
assert (is_sorted(f, key=lambda x: (x[1], len(x[0]))))
def get_alpha_shapes(coords, exact=True):
"""
Args:
coords: matrix with xyz data
Returns:
simplices
"""
return diode.fill_alpha_shapes(coords[:, :3], exact=exact)
def test_square():
points = np.array([[0., 0.], [1., 0.], [0., 1.]])
f = diode.fill_alpha_shapes(points)
expected_f = [([0], 0.0), ([1], 0.0), ([2], 0.0), ([0, 1], 0.25),
([0, 2], 0.25), ([1, 2], 0.5), ([0, 1, 2], 0.5)]
for s1, s2 in zip(sorted(f), sorted(expected_f)):
assert (s1 == s2)
plt.draw()
return pc
def L2RankDist(rankA, rankB, weightfun):
diff = {
key: get_rankval(rankA, key) - get_rankval(rankB, key)
for key in weightfun
}
return sum(math.pow(diff[key], 2) * weightfun[key] for key in weightfun)
for i in range(num_sam1 + num_sam2):
filename = './samples/samples{:d}.csv'.format(i)
samples.append(np.genfromtxt(filename, delimiter=','))
simplices = diode.fill_alpha_shapes(samples[-1])
f = d.Filtration(simplices)
m = d.homology_persistence(f)
dgms = d.init_diagrams(m, f)
dgms_list.append(dgms[homology_dimension])
local_birth_max = np.max([pt.birth for pt in dgms[homology_dimension]])
birth_max = max(birth_max, local_birth_max)
local_death_max = np.max([pt.death for pt in dgms[homology_dimension]])
death_max = max(death_max, local_death_max)
grid = (birth_max + death_max) / 100
rankfuns = []
for dgm in dgms_list:
print(dgm)
bvals = np.arange(0.0, np.max([pt.birth for pt in dgm]), grid)
distances = [get_distance(other_point, point) for other_point in trees]
trees = trees[np.argpartition(distances, num_points)]
samples.append(trees[:num_points]) # closest num_points
samples = np.asarray(samples)
for i in range(len(samples)):
np.savetxt('./samples/samples{:d}.csv'.format(i),
samples[i],
delimiter=',')
# Constructing the persistent homology diagrams and distance matrix
dgms_list = []
for i in range(len(samples)):
sample_set = samples[i]
simplices = diode.fill_alpha_shapes(sample_set)
f = d.Filtration(simplices)
m = d.homology_persistence(f)
dgms = d.init_diagrams(m, f)
dgms_list.append(dgms[homology_dimension])
distance_matrix = np.zeros((len(dgms_list), len(dgms_list)))
for i in range(len(dgms_list)):
for j in range(i):
# Chose Wasserstein, q=1 to emphasise many near-diagonal points
dist = d.wasserstein_distance(dgms_list[i], dgms_list[j], q=1)
distance_matrix[i, j] = dist
distance_matrix[j, i] = distance_matrix[i, j]
np.savetxt('./processed_data/dist_matrix.csv', distance_matrix, delimiter=',')