API reference

Auto-generated from the installed oineus package. Entries are grouped thematically below; full alphabetical indices live at the end.

Note

Many public functions currently have minimal docstrings. Signatures and types are accurate; prose will improve over time.

Filtration construction

freudenthal_filtration(data[, negate, wrap, ...])
vr_filtration(data[, from_pwdists, max_dim, ...])	Build a Vietoris-Rips filtration from points or pairwise distances.
cube_filtration(data[, negate, wrap, ...])
alpha_filtration(points[, weights, exact, ...])	Build an alpha-shape filtration from a 2D or 3D point cloud.
compute_diagrams_alpha(points[, weights, ...])	Compute alpha-shape persistence diagrams.
list_to_filtration(data)
mapping_cylinder(fil_domain, fil_codomain, ...)	Build the mapping cylinder of the inclusion fil_domain -> fil_codomain.
min_filtration(fil_1, fil_2[, with_indices])
multiply_filtration(fil, sigma[, sigma_value])	Multiply every cell in fil by the auxiliary simplex sigma.

Persistence computation

reduce	oineus._oineus.ReductionParams = Params(n_threads = 1, chunk_size = 128, write_dgms = 0, sort_dgms = 0, clearing_opt = 1, acq_rel = 0, print_time = 0, compute_v = 0, compute_u = 0, col_repr = BitTree, do_sanity_check = 0, elapsed = 0, timings = ReductionTimings(total = 0s, prepare = 0, reduce = 0, restore_elz = 0, copy_back = 0, copy_pivots = 0), verbose = 0), dualize: bool = False) -> oineus._oineus.Decomposition reduce(filtration: oineus._oineus.ProdFiltration, params: oineus._oineus.ReductionParams = Params(n_threads = 1, chunk_size = 128, write_dgms = 0, sort_dgms = 0, clearing_opt = 1, acq_rel = 0, print_time = 0, compute_v = 0, compute_u = 0, col_repr = BitTree, do_sanity_check = 0, elapsed = 0, timings = ReductionTimings(total = 0s, prepare = 0, reduce = 0, restore_elz = 0, copy_back = 0, copy_pivots = 0), verbose = 0), dualize: bool = False) -> oineus._oineus.Decomposition reduce(filtration: oineus._oineus.CubeFiltration_1D, params: oineus._oineus.ReductionParams = Params(n_threads = 1, chunk_size = 128, write_dgms = 0, sort_dgms = 0, clearing_opt = 1, acq_rel = 0, print_time = 0, compute_v = 0, compute_u = 0, col_repr = BitTree, do_sanity_check = 0, elapsed = 0, timings = ReductionTimings(total = 0s, prepare = 0, reduce = 0, restore_elz = 0, copy_back = 0, copy_pivots = 0), verbose = 0), dualize: bool = False) -> oineus._oineus.Decomposition reduce(filtration: oineus._oineus.CubeFiltration_2D, params: oineus._oineus.ReductionParams = Params(n_threads = 1, chunk_size = 128, write_dgms = 0, sort_dgms = 0, clearing_opt = 1, acq_rel = 0, print_time = 0, compute_v = 0, compute_u = 0, col_repr = BitTree, do_sanity_check = 0, elapsed = 0, timings = ReductionTimings(total = 0s, prepare = 0, reduce = 0, restore_elz = 0, copy_back = 0, copy_pivots = 0), verbose = 0), dualize: bool = False) -> oineus._oineus.Decomposition reduce(filtration: oineus._oineus.CubeFiltration_3D, params: oineus._oineus.ReductionParams = Params(n_threads = 1, chunk_size = 128, write_dgms = 0, sort_dgms = 0, clearing_opt = 1, acq_rel = 0, print_time = 0, compute_v = 0, compute_u = 0, col_repr = BitTree, do_sanity_check = 0, elapsed = 0, timings = ReductionTimings(total = 0s, prepare = 0, reduce = 0, restore_elz = 0, copy_back = 0, copy_pivots = 0), verbose = 0), dualize: bool = False) -> oineus._oineus.Decomposition
compute_diagrams_ls(data[, negate, wrap, ...])
compute_diagrams_vr(data[, from_pwdists, ...])
compute_relative_diagrams	oineus._oineus.Filtration, include_inf_points: bool = True) -> oineus._oineus.Diagrams compute_relative_diagrams(fil: oineus._oineus.ProdFiltration, rel: oineus._oineus.ProdFiltration, include_inf_points: bool = True) -> oineus._oineus.Diagrams
get_boundary_matrix	bool, arg2: bool, arg3: int, arg4: int, /) -> list[list[int]]
is_reduced(a)	Check whether a Z_2 boundary matrix is reduced.

Diagrams

Diagrams(*args, **kwargs)
DiagramPoint(*args, **kwargs)
IndexDiagramPoint(*args, **kwargs)
DiagramMatching	Optimal Wasserstein matching: bucketed pair indices, cost, distance.

Distances and matchings

bottleneck_distance(dgm_1, dgm_2[, delta])	Compute the bottleneck distance between two persistence diagrams.
wasserstein_distance(dgm_1, dgm_2[, q, ...])	Compute the q-Wasserstein distance between two persistence diagrams.
sliced_wasserstein_distance(dgm_1, dgm_2[, ...])	Sliced Wasserstein distance between two single-dimension diagrams.
sliced_wasserstein_distance_diag_corrected(...)	Diagonal-corrected sliced Wasserstein distance.
wasserstein_matching(dgm_1, dgm_2, *[, q, ...])	Compute the optimal q-Wasserstein matching between two persistence diagrams.
bottleneck_matching(dgm_1, dgm_2, *[, ...])	Compute the optimal bottleneck matching between two persistence diagrams.
point_to_diagonal(dgm[, indices])	Get diagonal projection coordinates for points in a persistence diagram.
get_permutation_dtv	oineus._oineus.Filtration, /) -> dict[int, int]

Fréchet means

frechet_mean(diagrams, *[, weights, ...])
frechet_mean_multistart(diagrams, *[, ...])
progressive_frechet_mean(diagrams, *[, ...])
progressive_frechet_mean_multistart(diagrams, *)
frechet_mean_objective(diagrams, barycenter, *)
init_frechet_mean_first_diagram(diagrams)
init_frechet_mean_random_diagram(diagrams, *)
init_frechet_mean_medoid_diagram(diagrams, *)
init_frechet_mean_diagonal_grid(diagrams, *)

Core classes

Simplex(*args, **kwargs)
CombinatorialSimplex(*args, **kwargs)
ProdSimplex(*args, **kwargs)
Filtration(*args, **kwargs)
ProdFiltration(*args, **kwargs)
Decomposition(*args, **kwargs)
ReductionParams(*args, **kwargs)
ReductionTimings(*args, **kwargs)	Per-phase wall-clock breakdown (seconds) of the last reduce() call.

Kernel / image / cokernel

KICRParams(*args, **kwargs)
KerImCokReduced(*args, **kwargs)
KerImCokReducedProd(*args, **kwargs)

compute_kernel_image_cokernel_reduction(K, L)
compute_ker_cok_reduction_cyl(fil_2, fil_3)
get_induced_matching	oineus._oineus.Filtration, dim: int = 18446744073709551615, n_threads: int = 1) -> dict[int, dict[oineus._oineus.DiagramPoint, oineus._oineus.DiagramPoint]] get_induced_matching(included_filtration: oineus._oineus.ProdFiltration, containing_filtration: oineus._oineus.ProdFiltration, dim: int = 18446744073709551615, n_threads: int = 1) -> dict[int, dict[oineus._oineus.DiagramPoint, oineus._oineus.DiagramPoint]] get_induced_matching(included_filtration: oineus._oineus.CubeFiltration_1D, containing_filtration: oineus._oineus.CubeFiltration_1D, dim: int = 18446744073709551615, n_threads: int = 1) -> dict[int, dict[oineus._oineus.DiagramPoint, oineus._oineus.DiagramPoint]] get_induced_matching(included_filtration: oineus._oineus.CubeFiltration_2D, containing_filtration: oineus._oineus.CubeFiltration_2D, dim: int = 18446744073709551615, n_threads: int = 1) -> dict[int, dict[oineus._oineus.DiagramPoint, oineus._oineus.DiagramPoint]] get_induced_matching(included_filtration: oineus._oineus.CubeFiltration_3D, containing_filtration: oineus._oineus.CubeFiltration_3D, dim: int = 18446744073709551615, n_threads: int = 1) -> dict[int, dict[oineus._oineus.DiagramPoint, oineus._oineus.DiagramPoint]]

Topology optimization

TopologyOptimizer(*args, **kwargs)
TopologyOptimizerProd(*args, **kwargs)
IndicesValues

get_denoise_target	oineus._oineus.Filtration, arg2: oineus._oineus.Decomposition, arg3: float, arg4: oineus._oineus.DenoiseStrategy, /) -> dict[oineus._oineus.DiagramPoint, oineus._oineus.DiagramPoint]
get_nth_persistence	oineus._oineus.Decomposition, arg2: int, arg3: int, /) -> float
get_ls_wasserstein_matching_target_values(...)

Enums

ConflictStrategy(value)
DenoiseStrategy(value)
DiagramPlaneDomain(value)
FrechetMeanInit(value)
VREdge(*args, **kwargs)

Utilities

to_scipy_matrix(sparse_cols[, shape])
max_distance(data[, from_pwdists])
plot_diagram(diagrams[, ax, color, cmap, ...])	Plot one or more persistence diagrams.
plot_diagram_gradient(diagram[, gradient, ...])	Plot a gradient vector field on top of a persistence diagram.
plot_matching(dgm_a, dgm_b, matching[, ax, ...])	Plot a matching between two persistence diagrams.
plot_chain(source, filtration, chain, *[, ...])	Render a chain of cells over its underlying source.

Differentiable (oineus.diff)

freudenthal_filtration(data[, negate, wrap, ...])
vr_filtration(data[, from_pwdists, max_dim, ...])
cube_filtration(data[, negate, wrap, ...])	Differentiable cubical filtration from tensor-valued grid data.
alpha_filtration(points[, eps, exact, ...])	Build a differentiable alpha filtration from a point cloud.
weak_alpha_filtration(points[, print_time])	Build a differentiable weak-alpha filtration from a point cloud.
cech_delaunay_filtration(points[, eps, ...])	Build a differentiable Cech-Delaunay filtration from a point cloud.
mapping_cylinder_filtration(fil_domain, ...)
min_filtration(fil_1, fil_2)
persistence_diagram(fil[, dualize, ...])	Compute differentiable persistence diagrams from a DiffFiltration.
sliced_wasserstein_distance(dgm1, dgm2[, ...])	Sliced Wasserstein distance between two persistence diagrams.
sliced_wasserstein_distance_diag_corrected(...)	Diagonal-corrected sliced Wasserstein distance.
wasserstein_cost(dgm_a, dgm_b[, ...])	Differentiable Wasserstein cost between two persistence diagrams.

DiffFiltration(fil, values)
TopologyOptimizer(fil, *[, with_crit_sets, ...])	Thin Python wrapper that picks the right C++ TopologyOptimizer instantiation for the filtration's cell type and forwards calls to it.
PersistenceDiagrams(fil, *, dualize, ...)	Container for differentiable persistence diagrams in all dimensions.

Indices

• Index

• Module Index

• Search Page