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 test_sc_transform_shape(X, metric, metric_params):
sc = Scaler(metric=metric, metric_params=metric_params, n_jobs=1)
X_res = sc.fit_transform(X)
assert X_res.shape == X.shape
X_inv_res = sc.inverse_transform(X_res)
assert_almost_equal(X_inv_res, X)
def test_dd_transform(X, metric, metric_params):
dsc = Scaler(metric=metric, metric_params=metric_params, n_jobs=1)
X_res = dsc.fit_transform(X)
assert X_res.shape == X.shape
dsc = Scaler(metric=metric, metric_params=metric_params, n_jobs=1)
X_inv_res = dsc.fit(X_res).inverse_transform(X_res)
assert X_inv_res.shape == X.shape
def parallel_embed_(self, embedding):
vr = VietorisRipsPersistence(
metric='euclidean',
homology_dimensions=self.homology_dimensions_,
n_jobs=self.n_job)
diagram_scaler = Scaler(n_jobs=self.n_job)
persistence_diagrams = diagram_scaler.fit_transform(
vr.fit_transform([embedding]))
if self.filtering_:
diagram_filter = Filtering(
epsilon=0.1, homology_dimensions=self.filtering_dimensions_)
persistence_diagrams = diagram_filter.fit_transform(
persistence_diagrams)
return persistence_diagrams[0]
def cubical_persistence(patch):
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(patch)
sc = Scaler(metric="bottleneck")
scaled_diagrams_cubical_persistence = sc.fit_transform(
diagrams_cubical_persistence)
print("Computed cubical persistence")
return scaled_diagrams_cubical_persistence
