Package 'SimplicialComplex'

Description

Construct an Alpha Complex

Usage

AlphaComplex(points, epsilon = Inf)

Arguments

Value

A list of class "alpha_complex" with elements:

simplices

A list of integer vectors, every included simplex (all dimensions, not just the maximal ones - membership in the alpha complex is not simply generated by cliques).

filtration

A numeric vector, the alpha value of each simplex in simplices, in the same order.

Pass the result to as_filtration for the same list(simplex=, t=) format used by build_filtration and every persistence function, or just call build_filtration(points, method = "Alpha", eps_max = epsilon).

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
alpha_complex <- AlphaComplex(points, epsilon = 1)

Description

Convert a flood_complex object to a filtration list

Usage

as_filtration(fc)

Arguments

Value

A filtration list compatible with boundary_info.

Description

The bottleneck distance between the points of two persistence diagrams in a given homological dimension, allowing points to be matched to the diagonal: the infimum, over all matchings, of the largest single point-to-point distance (Cohen-Steiner, Edelsbrunner and Harer (2007), "Stability of Persistence Diagrams") - the $p \to \infty$ limit of wasserstein_distance. Essential (death = Inf) classes are kept rather than dropped - see wasserstein_distance's Details for why and how. Computed exactly by binary search over the (finitely many) candidate distance values for the smallest one admitting a perfect matching, checked with a standard augmenting-path bipartite matcher, on the same augmented assignment problem wasserstein_distance uses (see augmented_cost_matrix).

Usage

bottleneck_distance(df1, df2, dimension = 0, ground = c("Linf", "L2"))

Arguments

Value

A single non-negative number, the bottleneck distance.

Examples

df1 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 2))
df2 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 3))
bottleneck_distance(df1, df2, dimension = 0)

Description

Compute the boundary operator for a simplicial complex

Usage

boundary(simplices, bound_dim)

Arguments

Details

$\partial_k \sigma = \sum_i (-1)^i [v_0 v_1 \ldots \hat{v}_i \ldots v_k]$

Value

A sparse matrix representing $\partial_{k}$.

Examples

simplices <- list(c(1, 2), c(3, 4), c(2, 1, 3), c(4, 2))
boundary(simplices, 0)

Description

Get the boundary matrix and its reduction information in matrix form

Usage

boundary_info(filist, max_dimension = NULL)

Arguments

Value

A list containing the boundary matrix, the last boundary row, the pivot owner for persistence extraction, and filist - the exact (possibly max_dimension-restricted) filtration list the other three elements were computed from, re-tagged with the same "max_dimension" attribute so extract_persistence_pairs can auto-detect it too. Always pass THIS filist back into extract_persistence_pairs, not your original one - see its Details for why.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
res <- boundary_info(filtration)

Description

Build a cubical (grid) filtration from an image

Usage

build_cubical_filtration(image, superlevel = FALSE)

Arguments

Value

A filtration list: one list(simplex = <integer vector of pixel ids>, t = <numeric>) per simplex, sorted by (t, dimension, lexicographic order) - the same format as build_filtration.

Examples

# a ring (value 1) around a hole (value 5) around a background (value 9):
# the hole is born once the ring closes and dies once its center fills in
image <- matrix(c(
  9, 9, 9, 9, 9,
  9, 1, 1, 1, 9,
  9, 1, 5, 1, 9,
  9, 1, 1, 1, 9,
  9, 9, 9, 9, 9
), nrow = 5, byrow = TRUE)
filtration <- build_cubical_filtration(image)
pairs <- persistence_pairs(filtration)
pairs[pairs$dim == 1, ] # one H1 bar: birth = 1 (ring closes), death = 5
plot_persistence(pairs)

Description

Build a filtration from a point cloud, for any of five complex types

Usage

build_filtration(
  points,
  method = c("VR", "Delaunay", "Alpha", "Cech", "Witness"),
  eps_max = NULL,
  landmarks = NULL,
  nu = 1,
  max_dimension = NULL
)

Arguments

Value

A filtration list, as described above. When max_dimension is set, the list carries it as a "max_dimension" attribute (see above); otherwise the attribute is absent, exactly as before this parameter existed.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
vr_filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
alpha_filtration <- build_filtration(points, method = "Alpha")
delaunay_filtration <- build_filtration(points, method = "Delaunay")
cech_filtration <- build_filtration(points, method = "Cech", eps_max = 0.8)
# cap at H1: persistence_pairs()/etc. need no max_dimension of their own
vr_capped <- build_filtration(points, method = "VR", eps_max = 1.2,
                               max_dimension = 1)
pairs <- persistence_pairs(vr_capped)

Description

Wraps flood_complex and returns the filtration in the same format as build_filtration: a list of list(simplex = <integer vector>, t = <numeric>), sorted by (time, dimension, lexicographic order). The result plugs directly into boundary_info() / extract_persistence_pairs() / plot_persistence(), as well as into the faster flood_persistence.

Usage

build_flood_filtration(points, landmarks, ...)

Arguments

Value

A filtration list compatible with boundary_info.

Description

Construct a Cech Complex

Usage

CechComplex(points, epsilon)

Arguments

Value

A list with:

network

An igraph object: the 1-skeleton of the candidate graph (edges where distance $\le 2\epsilon$).

simplices

A list of integer vectors, each the vertex indices of a maximal clique of network.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
cech_complex <- CechComplex(points, epsilon = 0.8)

Description

Implements Algorithm 21 (CollectG): for every critical edge $e = (u, v)$ (i.e. every edge not used as a DMVF matching/tree edge - by construction these all have persistence greater than dmvf$delta, so unlike the book's pseudocode no extra filter is needed here), unions $e$ with the unique tree paths from $u$ and from $v$ up to their respective roots.

Usage

collect_g(dmvf)

Arguments

Value

A data.frame(u, v) of the (deduplicated, undirected) edges of the reconstructed graph.

Description

Builds the same point cloud's filtration under several build_filtration methods and summarizes, side by side, how expensive each one was to build and what persistent homology it found - useful for seeing directly how Cech's dimension blow-up, Alpha's Delaunay-bounded dimension, VR's cheap-but-approximate cliques, and Witness's landmark subsampling trade off against each other on the same data (see Otter et al. (2017), Section 5.2, for the underlying trade-offs).

Usage

compare_complexes(
  points,
  methods = c("VR", "Delaunay", "Alpha", "Cech", "Witness"),
  eps_max = NULL,
  landmarks = NULL,
  nu = 1
)

Arguments

Value

A list with:

summary

A data frame, one row per method, with the number of vertices and simplices, the highest simplex dimension reached, build and persistence-computation time in seconds, and the number of finite/essential persistence pairs found.

diagrams

A named list of persistence diagram data frames (one per method, from persistence_pairs), for further comparison (e.g. with wasserstein_distance/ bottleneck_distance, or plot_persistence).

filtrations

A named list of the raw filtration lists.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1, 0.5, 0.5), ncol = 2, byrow = TRUE)
cmp <- compare_complexes(points, methods = c("VR", "Alpha", "Delaunay"), eps_max = 1.2)
cmp$summary

Description

Picks a single build_filtration method, builds the persistence diagram of each point cloud with it, and compares the two diagrams with either wasserstein_distance or bottleneck_distance. Useful for asking "how different are these two datasets topologically", holding the complex construction fixed so the comparison is apples-to-apples.

Usage

complex_distance(
  points1,
  points2,
  method = c("VR", "Delaunay", "Alpha", "Cech", "Witness"),
  distance = c("wasserstein", "bottleneck"),
  dimension = 0,
  p = 2,
  ground = NULL,
  eps_max = NULL,
  landmarks = NULL,
  nu = 1
)

Arguments

Value

A list with:

distance

The computed distance (a single number).

method, distance_type, dimension

Echoed back for reference.

diagram1, diagram2

The two persistence diagrams (data frames) the distance was computed from.

Examples

set.seed(1)
cloud_a <- matrix(rnorm(24), ncol = 2)
cloud_b <- matrix(rnorm(24), ncol = 2) + 0.1
complex_distance(cloud_a, cloud_b, method = "VR", distance = "bottleneck",
                  dimension = 0, eps_max = 0.6)

Description

CROCKER = "Contour Realization Of Computed k-dimensional hole Evolution in the Rips complex". Treats the $k$-th Betti number as a function of two parameters at once.

Usage

crocker(
  point_clouds,
  dim,
  method = "VR",
  eps_max = NULL,
  n_eps = 50,
  max_dimension = NULL,
  n_cores = 1
)

Arguments

Value

An object of class "crocker": a list with

matrix

An n_eps x n numeric matrix; entry [j, i] is $b_k$ of frame $i$ at eps_grid[j].

eps_grid

The sampled scale values (length n_eps).

time

Frame indices, seq_along(point_clouds).

dim

The homology dimension tracked.

long

A tidy data frame with columns t, epsilon, betti, ready for plot_crocker.

Description

Construct a Delaunay Complex

Usage

DelaunayComplex(points)

Arguments

Value

A list of class c("delaunay_complex", "alpha_complex"); see AlphaComplex for the element description.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
delaunay_complex <- DelaunayComplex(points)

Description

Computes the same optimal matching that wasserstein_distance/bottleneck_distance reduce to internally, and returns it decoded into a readable table instead of just the distance - what plot_matching draws. Every point of both diagrams appears in exactly one row: matched to a point of the other diagram (type = "real-real"), or matched to the diagonal, i.e. effectively unmatched (type = "x-diagonal" for a point of df1, "y-diagonal" for a point of df2).

Usage

diagram_matching(
  df1,
  df2,
  dimension,
  distance = c("wasserstein", "bottleneck"),
  p = 2,
  ground = NULL
)

Arguments

Value

A list with distance (the matching's cost, equal to what wasserstein_distance/bottleneck_distance would return), matches (a data frame with columns x_birth, x_death, x_essential, y_birth, y_death, y_essential, type; a NA pair on one side means that row's point matched the diagonal), and X, Y, essential_X, essential_Y (the two diagrams' points after the same essential-pair capping wasserstein_distance uses - see its Details - and which of them were essential before capping).

Examples

df1 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 2))
df2 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 3))
diagram_matching(df1, df2, dimension = 0, distance = "wasserstein", p = 2)

Description

Compute the Euler characteristic $\chi$ of a simplicial complex

Usage

euler_characteristic(simplices, tol)

Arguments

Details

The Euler characteristic is computed as:

$\chi = \sum_{k=0}^{k_{\max}} (-1)^k \beta_k$

where $\beta_k$ is the $k$th Betti number, and $k_{\max}$ is the highest dimension of any simplex in the complex.

Interpretation of values:

• $\chi = 2$: Sphere-like surfaces

• $\chi = 1$: Disk-like spaces

• $\chi = 0$: Torus-like or circle-like spaces

• $\chi < 0$: Surfaces with multiple handles or genus

Value

An integer representing the Euler characteristic $\chi$.

See Also

betti_number

Examples

simplices <- list(c(1, 2), c(3, 4), c(2, 1, 3), c(4, 2))
euler_characteristic(simplices, tol=0.1)

Description

This function extracts the persistence from combining the boundary matrix and its filtration

Usage

extract_persistence_pairs(filist, last_1, pivot_owner, max_dimension = NULL)

Arguments

Details

boundary_info() and extract_persistence_pairs() are two halves of one computation - last_1/pivot_owner only mean anything relative to the exact filist boundary_info() used internally. Whenever max_dimension is involved, always call as:

res <- boundary_info(filtration, max_dimension = k)
pairs <- extract_persistence_pairs(res$filist, res$last_1, res$pivot_owner,
                                    max_dimension = k)

A length mismatch between filist and last_1/pivot_owner is refused with an error rather than silently producing wrong pairs - see persistence_pairs for a one-call alternative that cannot run into this.

Value

A data frame with columns: dimension, birth, and death.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
res <- boundary_info(filtration)
pairs <- extract_persistence_pairs(filtration, res$last_1, res$pivot_owner)

Description

Generate all unique faces of a given dimension from simplices

Usage

faces(simplices, target_dim)

Arguments

Details

The function generates all possible subsets (combinations) of each simplex, removes duplicates, and filters them to only include those of length target_dim + 1.

For example, a 2-simplex c(1, 2, 3) has three 1-dimensional faces (edges): c(1,2), c(1,3), and c(2,3), and three 0-dimensional faces (vertices): 1, 2, and 3.

Value

A list of faces (each a numeric vector) of dimension target_dim.

Examples

simplices <- list(c(1, 2), c(3, 4), c(2, 1, 3), c(4, 2))
faces(simplices, target_dim=0)

Description

Builds the Flood complex of a point cloud: a Delaunay complex on a set of landmarks whose simplices are filtered by their covering radius with respect to the full point cloud ("flood time").

Usage

flood_complex(
  points,
  landmarks,
  max_dimension = NULL,
  points_per_edge = 30,
  backend = c("auto", "cpu", "torch"),
  batch_points = 2^22,
  delaunay = NULL
)

Arguments

Value

A list of class "flood_complex" with elements simplices (list of integer vectors, landmark indices), filtration (numeric vector of flood times), landmarks (matrix), landmark_indices (or NULL).

Description

Computes persistence pairs from a filtration list with the standard column-reduction algorithm, but on sparse columns (integer index vectors over GF(2)) instead of a dense matrix. Produces the same output format as extract_persistence_pairs(filist, res$last_1, res$pivot_owner) while scaling to the much larger complexes produced by build_flood_filtration.

Usage

flood_persistence(filist, max_dimension = NULL)

Arguments

Value

A data frame with columns dim, birth, death.

Description

Row-reduce (partial pivoting) a matrix

Usage

gauss_jordan_eliminate(M, tol = 1e-08)

Arguments

Details

Shared elimination step behind ker and im (and, internally, the zigzag module's own linear solves): partial-pivoting Gauss-Jordan elimination, stopping early once every row has a pivot.

Value

A list with R (the row-reduced matrix) and pivots (the column indices where a pivot was found, in row order).

Description

Selects n_lms landmarks from a point cloud via (exact) Farthest-Point Sampling. Equivalent to flooder::generate_landmarks (which uses an approximate bucket-FPS; the exact version below gives the same qualitative coverage).

Usage

generate_landmarks(points, n_lms, start_idx = 1)

Arguments

Value

A list with landmarks (n_lms x d matrix) and indices (row indices into points).

Description

The classical graph-theory Laplacian - degree matrix minus adjacency matrix - built directly from a complex's 1-skeleton (vertices + edges).

Usage

graph_laplacian(simplices)

Arguments

Value

A list with the Laplacian L, the degree matrix D, the adjacency matrix A, and the vertex basis (row/column order).

Description

Computes $L_k(K) = B_k^T B_k + B_{k+1} B_{k+1}^T$.

Usage

hodge_laplacian(K_simplices, k)

Arguments

Value

A list with the full Laplacian, its down/up pieces, and the k-simplex basis (row/column order) everything is expressed in.

Description

Compute a homology basis from a cycle basis and a boundary basis

Usage

homology(Z, B = matrix(numeric(0), nrow = nrow(Z), ncol = 0), tol = NULL)

Arguments

Details

$H_k = \ker(\partial_k) / \mathrm{im}(\partial_{k+1})$: two cycles represent the same homology class exactly when they differ by a boundary. This walks the columns of Z in order, greedily keeping any column that increases the rank of the span accumulated so far (starting from B's span) - i.e. any cycle that is not already a linear combination of B and the cycles kept before it. The kept columns are one representative chain per homology class.

betti_number computes $\dim H_k$ directly from ranks (rank-nullity), without ever materializing Z, B, or a homology basis - that is cheaper when only the count is needed. Use homology() when the actual representative cycles matter (e.g. to visualize or track a specific hole), not to recompute a Betti number.

As with ker and im, the specific representative chosen for each class depends on the order cycles in Z are tested against the growing boundary span, and is not unique.

Value

A matrix with nrow(Z) rows, one column per representative of a basis of the quotient $H_k = Z_k / B_k$. If $H_k = 0$, the result has 0 columns; ncol() of the result is the Betti number $\beta_k = \dim H_k$.

Description

Compute a basis for the image (column space) of a matrix

Usage

im(M, tol = 1e-08)

Arguments

Details

$\mathrm{im}(M) = \{ Mx : x \in \mathbb{R}^{\mathrm{ncol}(M)} \}$, the column space of M. The basis returned is a subset of M's own columns, specifically, the pivot columns found by Gauss-Jordan elimination rather than synthetic linear combinations, so each basis vector is directly interpretable as one of the original columns of M (e.g. the boundary of one specific simplex).

Value

A matrix with nrow(M) rows, one column per basis vector of $\mathrm{im}(M)$. If the image is trivial ($\{0\}$), the result has 0 columns.

Description

Compute a basis for the kernel (null space) of a matrix

Usage

ker(M, tol = 1e-08)

Arguments

Details

$\ker(M) = \{ x \in \mathbb{R}^{\mathrm{ncol}(M)} : Mx = 0 \}$. The basis is obtained from Gauss-Jordan elimination of $M$: one basis vector per free (non-pivot) column, in the usual parametric-solution construction. If M has 0 rows (the zero map), every standard basis vector of the domain is in the kernel, so the identity matrix is returned.

As with any basis, the specific vectors returned are not unique, they depend on the pivoting order of the elimination, only the number of columns ($\dim \ker(M)$) is an invariant of M.

Value

A matrix with ncol(M) rows, one column per basis vector of $\ker(M)$. If the kernel is trivial ($\{0\}$), the result has 0 columns.

Generalizes build_cubical_filtration to any triangulation: given the maximal simplices of a simplicial complex and a function defined at its vertices, builds the simplex-wise lower-star filtration $\mathcal{F}_f$.

Description

Generalizes build_cubical_filtration to any triangulation: given the maximal simplices of a simplicial complex and a function defined at its vertices, builds the simplex-wise lower-star filtration $\mathcal{F}_f$.

Usage

lower_star_filtration(top_simplices, f)

Arguments

Value

A filtration list: one list(simplex =integer vector of vertex ids, t = numeric) per simplex (every face of every maximal simplex), sorted by (t, dimension, lexicographic order).

Examples

# two triangles sharing an edge, function increasing away from vertex 1
triangles <- list(c(1, 2, 3), c(2, 3, 4))
f <- c(0, 1, 1, 2)
filtration <- lower_star_filtration(triangles, f)
length(filtration) # 4 vertices + 5 edges + 2 triangles

Description

Implements Algorithm 20 (MorseRecon): given a triangulated domain and a density function rho that concentrates around a hidden geometric graph $G$, computes the "mountain ridges" of $f = -\rho$ - the 1-unstable manifolds of the discrete gradient field after cancelling vertex-edge persistence pairs with persistence at most delta - as an approximation $\hat G$ of $G$.

Usage

morse_recon(top_simplices, rho, delta = 0, vertex_coords = NULL)

Arguments

Value

An object of class "morse_recon": a list with

Description

Implements Algorithm 19 (SimplePersDMVF) together with the threshold-$\delta$ simplification: vertex-edge persistence pairs with persistence at most delta are cancelled, leaving a discrete Morse vector field where every non-critical vertex $v$ is matched with the tree edge connecting it to its component's root, and every root is a critical vertex.

Usage

partial_pers_dmvf(filist1, delta = 0)

Arguments

Value

A list with components:

Description

Converts a persistence diagram data frame (the direct output of extract_persistence_pairs(), persistence_pairs(), or flood_persistence()) into its persistence landscape: a collection of continuous, piecewise-linear functions $\lambda(k, \cdot)$, obtained by overlaying the "tent" function of every birth-death pair and, at each time t, taking the $k$th largest tent value (Bubenik, 2015; Chazal and Michel, 2021, Section 5.4).

Usage

persistence_landscape(
  df,
  dimension = 0,
  k_max = NULL,
  resolution = 500,
  t_range = NULL
)

Arguments

Value

A data frame with columns t, k, value: the discretized landscape functions, one row per (grid point, level) pair.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
res <- boundary_info(filtration)
pairs <- extract_persistence_pairs(filtration, res$last_1, res$pivot_owner)
landscape <- persistence_landscape(pairs, dimension = 0)

Description

Same standard column-reduction algorithm as boundary_info() + extract_persistence_pairs(), but each column is stored as a sorted integer vector over GF(2) instead of a row of a dense n x n matrix. Memory drops from O(n^2) to O(total number of non-zeros), which is what makes filtrations with thousands to hundreds of thousands of simplices (e.g. Flood or large Vietoris-Rips complexes) feasible. Output is identical to the dense pipeline.

Usage

persistence_pairs(filist, max_dimension = NULL)

Arguments

Value

A data frame with columns: dim, birth, and death.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
pairs <- persistence_pairs(filtration)
pairs_h0_only <- persistence_pairs(filtration, max_dimension = 0)
# or bake the cap into the filtration itself, and drop the argument here:
capped <- build_filtration(points, method = "VR", eps_max = 1.2,
                            max_dimension = 0)
pairs_h0_only2 <- persistence_pairs(capped)

Description

Computes the persistent Laplacian $\Delta_q^{X,Y}$.

Usage

persistent_laplacian(X_simplices, Y_simplices, q)

Arguments

Value

A list with the full Laplacian, its upper/down pieces, and the q-simplex basis (in row/column order) everything is expressed in.

Description

Plot a CROCKER matrix as a filled contour plot

Usage

plot_crocker(cr)

Arguments

Value

A ggplot2 object: a tile plot of the Betti number over time and scale.

Description

Plot a Persistence Landscape

Usage

plot_landscape(landscape_df)

Arguments

Value

A ggplot2 object with one line per landscape level $\lambda(k, \cdot)$.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
res <- boundary_info(filtration)
pairs <- extract_persistence_pairs(filtration, res$last_1, res$pivot_owner)
landscape <- persistence_landscape(pairs, dimension = 0)
plot_landscape(landscape)

Description

Visualizes the point correspondence that realizes the Wasserstein or bottleneck distance between two persistence diagrams in a given homological dimension: both diagrams' points, overlaid on the same axes, joined by dashed lines to their matched partner - either a point of the other diagram, or (for a point left unmatched) its own projection onto the diagonal birth = death.

Usage

plot_matching(
  df1,
  df2,
  dimension,
  distance = c("wasserstein", "bottleneck"),
  p = 2,
  ground = NULL,
  labels = c("Diagram 1", "Diagram 2")
)

Arguments

Details

Essential (death = Inf) points are capped exactly the way wasserstein_distance does (see its Details) so they take part in the matching instead of being dropped, and are drawn as triangles at their capped height - marked "essential" in the legend - so they stay visually distinguishable from genuine finite points that happen to reach that height. Matches to the diagonal are always drawn to the point's perpendicular projection ((birth+death)/2, (birth+death)/2) regardless of ground, since that is the standard, readable way to depict an unmatched point whichever ground metric produced the matching.

Value

A ggplot2 object; the title reports the resulting distance.

Examples

df1 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 2))
df2 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 3))
plot_matching(df1, df2, dimension = 0, distance = "wasserstein", p = 2)

Description

A 3D companion to plot_morse_recon: renders the input density rho as an actual terrain surface over the triangulated domain (using the triangles stored in mr$filtration), and overlays the discrete Morse critical structure on it with the rgl package, producing a mouse-rotatable 3D scene rather than a flat projection:

• the root (critical) vertices of mr$dmvf, the surviving density peaks after persistence simplification - as labelled spheres ("M1", "M2", ...);

• the endpoints of mr$dmvf$critical_edges, the surviving "saddle-like" edges of Section 10.4.1 as smaller spheres;

• mr$graph_edges itself, i.e. the collected 1-unstable manifolds from collect_g, literally the vertex-to- saddle connections along the reconstructed ridge lines as raised line segments on the surface.

Usage

plot_morse_landscape(
  mr,
  z_scale = NULL,
  show_critical_edges = TRUE,
  label_roots = TRUE,
  surface_col = c("#f7fbff", "#6baed6", "#08306b"),
  root_col = "red",
  saddle_col = "blue",
  edge_col = "black",
  point_radius = NULL,
  edge_lwd = 3,
  window_size = c(1400, 1000),
  title_cex = 1.4
)

Arguments

Value

A interactive 3D rgl scene.

Display

On macOS, opening a native rgl window requires XQuartz; Windows and Linux do not need it. To avoid a native window altogether, render the scene as a WebGL widget in the RStudio Viewer or a browser:

options(rgl.useNULL = TRUE)
plot_morse_landscape(mr)
rgl::rglwidget()

Description

Draws the reconstructed graph $\hat G$ (the union of 1-unstable manifolds of the surviving high-persistence critical edges) over the input vertices, optionally coloured by the input density rho.

Usage

plot_morse_recon(mr, show_density = TRUE, point_size = 0.6, edge_size = 0.9)

Arguments

Value

A ggplot2 object.

Description

Figure zooms into a small neighbourhood of the mesh (as most DMT papers illustrate the vector field, since drawing it over the whole domain is unreadable) and draws the actual computed structure on top of the local triangulation:

• every triangulation edge with both endpoints in the window, in light grey, for context;

• the surviving critical edges of mr$dmvf$critical_edges that fall inside the window, as thick red segments. These are the "saddles" that did not get matched to a vertex;

• for every non-critical vertex $v$ in the window whose DMVF match mr$dmvf$parent[[v]] is also in the window, a black arrow from $v$ to the midpoint of its matched edge $(v, \mathrm{parent}(v))$, the vertex-edge V-path arrows themselves.

Usage

plot_morse_vpath(
  mr,
  center,
  radius,
  vertex_size = 1.2,
  arrow_size = 0.12,
  critical_size = 1.3
)

Arguments

Value

A ggplot2 object.

Description

Plot Persistence Diagram

Usage

plot_persistence(df)

Arguments

Value

A ggplot2 object representing the persistence diagram.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
res <- boundary_info(filtration)
pairs <- extract_persistence_pairs(filtration, res$last_1, res$pivot_owner)
plot_persistence(pairs)

Description

Drops every simplex of dimension greater than max_dimension from a filtration list; everything else (order, ties, t values) is left untouched.

Usage

restrict_filtration(filist, max_dimension)

Arguments

Value

A filtration list containing only the entries with dimension <= max_dimension, in the same relative order.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
filtration <- build_filtration(points, method = "VR", eps_max = 1.2)
restrict_filtration(filtration, max_dimension = 1) # vertices + edges only

Description

Construct a Vietoris–Rips Complex (1-skeleton + maximal simplices)

Usage

VietorisRipsComplex(points, epsilon)

Arguments

Details

The Vietoris–Rips complex at scale $\epsilon$ includes a simplex for every finite set of points with pairwise distances < $\epsilon$. This function constructs the 1-skeleton (edges only) and then uses maximal cliques in that graph as the maximal simplices.

Value

A list with:

network

An igraph object representing the 1-skeleton.

simplices

A list of integer vectors, each the vertex indices of a maximal simplex.

Examples

points <- matrix(c(0, 1, 1, 0, 0, 0, 1, 1), ncol = 2)
epsilon <- 1.5
vr_complex <- VietorisRipsComplex(points, epsilon)

Description

The $p$-Wasserstein distance between the points of two persistence diagrams in a given homological dimension, allowing points to be matched to the diagonal (Cohen-Steiner, Edelsbrunner, Harer and Mileyko (2010), "Lipschitz Functions Have $L_p$-Stable Persistence"). Essential (death = Inf) classes are kept rather than dropped - see Details. Computed exactly via the Hungarian algorithm (clue::solve_LSAP) on the augmented assignment problem described in augmented_cost_matrix.

Usage

wasserstein_distance(df1, df2, dimension = 0, p = 2, ground = c("L2", "Linf"))

Arguments

Value

A single non-negative number, the $p$-Wasserstein distance.

Examples

df1 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 2))
df2 <- data.frame(dim = c(0, 0), birth = c(0, 0), death = c(1, 3))
wasserstein_distance(df1, df2, dimension = 0, p = 2)

Description

Construct a Witness Complex

Usage

WitnessComplex(points, landmarks, epsilon, nu = 1)

Arguments

Value

A list with:

network

An igraph object: the 1-skeleton on the landmark vertex set.

simplices

A list of integer vectors (indices into landmarks / landmark_indices), each the vertices of a maximal simplex.

landmarks

The landmark coordinate matrix.

landmark_indices

Row indices into points, or NULL if landmarks was given as coordinates rather than indices.

edge_birth

A symmetric matrix giving, for every pair of landmarks, the exact scale at which their edge is witnessed (used by build_filtration to time every simplex exactly, rather than only checking membership at the single scale epsilon).

Examples

points <- matrix(rnorm(200), ncol = 2)
witness_complex <- WitnessComplex(points, landmarks = 15, epsilon = 0.5)

Description

Internally, each $K_i \to K_{i+1}$ step is expanded into a sequence of single simplex insertions (faces before cofaces) or single simplex deletions (cofaces before faces), exactly as boundary_info/persistence_pairs assume for ordinary filtrations. At every elementary insertion or deletion, the representative-cycle basis of each affected homology dimension is updated directly via linear algebra (reusing boundary and faces for every boundary-matrix computation), an insertion either creates a new cycle (birth in dimension $q$) or turns an existing cycle into a boundary (death in dimension $q-1$); a deletion either destroys an existing cycle (death in dimension $q$) or frees a previously-trivial cycle from being a boundary (birth in dimension $q-1$), where $q$ is the dimension of the simplex being inserted/deleted.

Usage

zigzag_persistence(complexes, max_dimension = NULL)

Arguments

Value

A data frame with columns dim, birth, death (integer indices into 0, ..., n, i.e. into complexes), one row per bar. Every bar uses a closed interval: death is the last index at which the class is still present, and a class still alive at $K_n$ is reported with death = n, since a zigzag filtration, unlike an ordinary one, has no canonical "infinity" to extend to. This differs by one from persistence_pairs's convention, where death is the index of the killing simplex and the interval is half-open (birth <= i < death); on a purely-growing filtration the two agree after death_here = death_there - 1.