def get_upper_star(pcpds_obj):
f = d.fill_freudenthal(pcpds_obj.get_point_cloud().astype('f4'), False)
m = d.homology_persistence(f)
diagram = d.init_diagrams(m, f)
pcpds_obj.set_persistance_diagram(diagram)
pcpds_obj.set_filtration_used(Filtration.get_upper_star, "upper_star")
return pcpds_obj
def compare_diagrams(n=16, negate=False, n_threads=4, seed=1, top_dim=2):
# no wrap in Dionysus
wrap = False
# generate random grid data
np.random.seed(seed)
a = np.random.randn(n**3).reshape((n, n, n))
# compute diagrams with Oineus
oin_dgms = oin.compute_diagrams_ls(a, negate, wrap, top_dim, n_threads)
# compute diagrams with Dionysis
fil_us = dion.fill_freudenthal(a, reverse=negate)
p = dion.homology_persistence(fil_us)
dion_dgms = dion.init_diagrams(p, fil_us)
dist = 0.0
for dim in range(top_dim):
# convert Oineus diagram to Dionysus format
oin_dgm = dion.Diagram(oin_dgms[dim])
dion_dgm = dion_dgms[dim]
dist += dion.bottleneck_distance(oin_dgm, dion_dgm)
print("total dist: ", dist)
assert (dist < 0.001)
def persistent(self):
"""
This method is used to create dionysus persistent homology
implementation of given vectors
:return: dionysus diagrams
"""
vectors = self._get_vectors()
f_lower_star = dn.fill_freudenthal(vectors)
p = dn.homology_persistence(f_lower_star)
dgms = dn.init_diagrams(p, f_lower_star)
return dgms
def persistent(self):
"""
This method is used to create dionysus persistent homology
implementation of given vectors
:return: dionysus diagrams
"""
vectors = self._get_vectors()
f_lower_star = dn.fill_freudenthal(vectors)
p = dn.homology_persistence(f_lower_star)
dgms = dn.init_diagrams(p, f_lower_star)
return dgms
def compute_persistence(diagram):
f_lower_star = d.fill_freudenthal(diagram, reverse = False)
p = d.homology_persistence(f_lower_star)
dgms_lower = d.init_diagrams(p, f_lower_star)
return dgms_lower
def compute_persistence(directional_transform):
f_lower_star = d.fill_freudenthal(directional_transform, reverse = True)
p = d.homology_persistence(f_lower_star)
dgms_lower = d.init_diagrams(p, f_lower_star)
return dgms_lower
elif i == 1:
h1.append(np.array((p.birth, p.death)))
h0 = np.asarray(h0)
h1 = np.asarray(h1)
h0 = h0.reshape((-1, 2))
h1 = h1.reshape((-1, 2))
return [h0, h1]
PI_null_images = []
PI_one_images = []
for i in range(images.shape[0]):
print(i)
f_lower_star = d.fill_freudenthal(images[i, :, :, 0].astype(float))
f_upper_star = d.fill_freudenthal(images[i, :, :, 0].astype(float),
reverse=True)
p = d.homology_persistence(f_lower_star)
dgms_temp = d.init_diagrams(p, f_lower_star)
h0_temp = homology_persistent_diagrams(dgms_temp)[0]
h1_temp = homology_persistent_diagrams(dgms_temp)[1]
pim = PersImage(pixels=[20, 20], spread=1)
PI_0_temp = pim.transform(h0_temp[1:, :])
PI_1_temp = pim.transform(h1_temp)
PI_null_images.append(PI_0_temp)
PI_one_images.append(PI_1_temp)
PI_null_images = np.array(PI_null_images, dtype=np.float32)
PI_null_images = PI_null_images.reshape((-1, 20, 20, 1)) # reshape
PI_null_images = PI_null_images / (PI_null_images.max() / 255.0) # normalize