def cubical_persistence(images,
title,
plot_diagrams=False,
betti_curves=False,
scaled=False):
homology_dimensions = (0, 1, 2)
cp = CubicalPersistence(
homology_dimensions=homology_dimensions,
coeff=2,
periodic_dimensions=None,
infinity_values=None,
reduced_homology=True,
n_jobs=N_JOBS,
)
diagrams_cubical_persistence = cp.fit_transform(images)
if scaled:
sc = Scaler(metric="bottleneck")
diagrams_cubical_persistence = sc.fit_transform(
diagrams_cubical_persistence)
else:
scaled_diagrams_cubical_persistence = diagrams_cubical_persistence
if plot_diagrams:
fig = cp.plot(diagrams_cubical_persistence)
fig.update_layout(title=title)
fig.show()
if betti_curves:
BC = BettiCurve()
X_betti_curves = BC.fit_transform(diagrams_cubical_persistence)
fig = BC.plot(X_betti_curves)
fig.update_layout(title=title)
fig.show()
if title is not None:
print(f"Computed CP for {title}")
return diagrams_cubical_persistence
def generate_sample_representations(paths_to_patches, labels):
sample_rep_dir = DOTENV_KEY2VAL["GEN_FIGURES_DIR"] + "/sample_rep/"
try:
os.mkdir(sample_rep_dir)
except OSError:
print("Creation of the directory %s failed" % sample_rep_dir)
else:
print("Successfully created the directory %s " % sample_rep_dir)
for i, path in enumerate(paths_to_patches):
patch = np.load(path)
cp = CubicalPersistence(
homology_dimensions=(0, 1, 2),
coeff=2,
periodic_dimensions=None,
infinity_values=None,
reduced_homology=True,
n_jobs=N_JOBS,
)
diagrams_cubical_persistence = cp.fit_transform(
patch.reshape(1, 30, 36, 30)
)
for h_dim in HOMOLOGY_DIMENSIONS:
cp.plot(
diagrams_cubical_persistence,
homology_dimensions=[h_dim],
).update_traces(
marker=dict(size=10, color=HOMOLOGY_CMAP[h_dim]),
).write_image(
sample_rep_dir
+ f"persistence_diagram_{labels[i]}_H_{h_dim}.png",
scale=SCALE,
)
representation_names = [
"Persistence landscape",
"Betti curve",
"Persistence image",
"Heat kernel",
"Silhouette",
]
for j, rep in enumerate(representation_names):
# Have not found a better way of doing this yet.
if rep == "Persistence landscape":
rep = PersistenceLandscape(
n_layers=N_LAYERS, n_bins=VEC_SIZE, n_jobs=N_JOBS
)
elif rep == "Betti curve":
rep = BettiCurve()
elif rep == "Persistence image":
rep = PersistenceImage(
sigma=0.001, n_bins=VEC_SIZE, n_jobs=N_JOBS
)
elif rep == "Heat kernel":
rep = HeatKernel(sigma=0.001, n_bins=VEC_SIZE, n_jobs=N_JOBS)
elif rep == "Silhouette":
rep = Silhouette(power=1.0, n_bins=VEC_SIZE, n_jobs=N_JOBS)
vectorial_representation = rep.fit_transform(
diagrams_cubical_persistence
)
if representation_names[j] in ["Persistence image", "Heat kernel"]:
for h_dim in range(vectorial_representation.shape[1]):
plt.imshow(
vectorial_representation[0:, h_dim, :, :].reshape(
VEC_SIZE, VEC_SIZE
),
cmap=(HOMOLOGY_CMAP[h_dim] + "s").capitalize(),
)
# plt.title(
# f"{representation_names[j]} representation of a "
# f"{labels[i]} patient in h_{image}"
# )
plt.savefig(
sample_rep_dir
+ f"{representation_names[j].replace(' ', '_')}"
f"_{labels[i]}_h_{h_dim}.png",
bbox_inches="tight",
)
else:
rep.plot(vectorial_representation).update_layout(
title=None,
margin=dict(l=0, r=0, b=0, t=0, pad=4),
).write_image(
sample_rep_dir
+ f"{representation_names[j].replace(' ', '_')}"
f"_{labels[i]}.png",
scale=SCALE,
)
print(f"Done plotting {labels[i]} sample")
im_filtration = radial_filtration.fit_transform(im_binarized)
radplot = radial_filtration.plot(im_filtration, colorscale="jet")
radplot.update_layout(template='plotly_dark')
# PLOTS
st.subheader("")
st.write(binplot)
st.subheader("Figure 1. Binarized Plot")
st.subheader("")
st.subheader("")
st.write(radplot)
st.subheader("Figure 2. Radial Filtration Plot")
st.subheader("")
# CUBICAL SIMPLICIAL COMPLEXES
cubical_persistence = CubicalPersistence(n_jobs=-1)
im_cubical = cubical_persistence.fit_transform(im_filtration)
cubplot = cubical_persistence.plot(im_cubical)
cubplot.update_layout(template='plotly_dark')
st.subheader("")
st.write(cubplot)
st.subheader("Figure 3. Cubical Simplicial Complex Persistence Diagram")
st.subheader("")