gridDiag: Persistence Diagram of a function over a Grid

In TDA: Statistical Tools for Topological Data Analysis

Persistence Diagram of a function over a Grid

Description

The function gridDiag computes the Persistence Diagram of a filtration of sublevel sets (or superlevel sets) of a function evaluated over a grid of points in arbitrary dimension d.

Usage

gridDiag(
    X = NULL, FUN = NULL, lim = NULL, by = NULL, FUNvalues = NULL,
    maxdimension = max(NCOL(X), length(dim(FUNvalues))) - 1,
    sublevel = TRUE, library = "GUDHI", location = FALSE,
    printProgress = FALSE, diagLimit = NULL, ...)

Arguments

X	an n by d matrix of coordinates, used by the function FUN, where n is the number of points stored in X and d is the dimension of the space. NULL if this option is not used. The default value is NULL.
FUN	a function whose inputs are 1) an n by d matrix of coordinates X, 2) an m by d matrix of coordinates Grid, 3) an optional smoothing parameter, and returns a numeric vector of length m. For example see distFct, kde, and dtm which compute the distance function, the kernel density estimator and the distance to measure, over a grid of points using the input X. Note that Grid is not an input of gridDiag, but is automatically computed by the function using lim, and by. NULL if this option is not used. The default value is NULL.
lim	a 2 by d matrix, where each column specifying the range of each dimension of the grid, over which the function FUN is evaluated. NULL if this option is not used. The default value is NULL.
by	either a number or a vector of length d specifying space between points of the grid in each dimension. If a number is given, then same space is used in each dimension. NULL if this option is not used. The default value is NULL.
FUNvalues	an m1 * m2 * ... * md array of function values over m1 * m2 * ... * md grid, where mi is the number of scales of grid on ith dimension. NULL if this option is not used. The default value is NULL.
maxdimension	a number that indicates the maximum dimension of the homological features to compute: 0 for connected components, 1 for loops, 2 for voids and so on. The default value is d - 1, which is (dimension of embedding space - 1).
sublevel	a logical variable indicating if the Persistence Diagram should be computed for sublevel sets (TRUE) or superlevel sets (FALSE) of the function. The default value is TRUE.
library	a string specifying which library to compute the persistence diagram. The user can choose either the library "GUDHI", "Dionysus", or "PHAT". The default value is "GUDHI".
location	if TRUE and if "Dionysus" or "PHAT" is used for computing the persistence diagram, location of birth point and death point of each homological feature is returned. Additionaly if library="Dionysus", location of representative cycles of each homological feature is also returned. The default value is FALSE.
printProgress	if TRUE a progress bar is printed. The default value is FALSE.
diagLimit	a number that replaces Inf (if sublevel is TRUE) or -Inf (if sublevel is FALSE) in the Death value of the most persistent connected component. The default value is NULL and the max/min of the function is used.
...	additional parameters for the function FUN.

Details

If the values of X, FUN are set, then FUNvalues should be NULL. In this case, gridDiag evaluates the function FUN over a grid. If the value of FUNvalues is set, then X, FUN should be NULL. In this case, FUNvalues is used as function values over the grid. If location=TRUE, then lim, and by should be set.

Once function values are either computed or given, gridDiag constructs a filtration by triangulating the grid and considering the simplices determined by the values of the function of dimension up to maxdimension+1.

Value

The function gridDiag returns a list with the following components:

diagram	an object of class diagram, a P by 3 matrix, where P is the number of points in the resulting persistence diagram. The first column stores the dimension of each feature (0 for components, 1 for loops, 2 for voids, etc). Second and third columns are Birth and Death of the features, in case of a filtration constructed using sublevel sets (from -Inf to Inf), or Death and Birth of features, in case of a filtration constructed using superlevel sets (from Inf to -Inf).
birthLocation	only if location=TRUE and if "Dionysus" or "PHAT" is used for computing the persistence diagram: a P by d matrix, where P is the number of points in the resulting persistence diagram. Each row represents the location of the grid point completing the simplex that gives birth to an homological feature.
deathLocation	only if location=TRUE and if "Dionysus" or "PHAT" is used for computing the persistence diagram: a P by d matrix, where P is the number of points in the resulting persistence diagram. Each row represents the location of the grid point completing the simplex that kills an homological feature.
cycleLocation	only if location=TRUE and if "Dionysus" is used for computing the persistence diagram: a list of length P, where P is the number of points in the resulting persistence diagram. Each element is a P_i by h_i +1 by d array for h_i dimensional homological feature. It represents location of h_i +1 vertices of P_i simplices, where P_i simplices constitutes the h_i dimensional homological feature.

Note

The user can decide to use either the C++ library GUDHI, Dionysus, or PHAT. See references.

Since dimension of simplicial complex from grid points in R^d is up to d, homology of dimension ≥ d is trivial. Hence setting maxdimension with values ≥ d is equivalent to maxdimension=d-1.

Author(s)

Brittany T. Fasy, Jisu Kim, and Fabrizio Lecci

References

Fasy B, Lecci F, Rinaldo A, Wasserman L, Balakrishnan S, Singh A (2013). "Statistical Inference For Persistent Homology." (arXiv:1303.7117). Annals of Statistics.

Morozov D (2007). "Dionysus, a C++ library for computing persistent homology." https://www.mrzv.org/software/dionysus/

Bauer U, Kerber M, Reininghaus J (2012). "PHAT, a software library for persistent homology." https://bitbucket.org/phat-code/phat/

See Also

summary.diagram, plot.diagram, distFct, kde, kernelDist, dtm, alphaComplexDiag, alphaComplexDiag, ripsDiag

Examples

## Distance Function Diagram and Kernel Density Diagram

# input data
n <- 300
XX <- circleUnif(n)

## Ranges of the grid
Xlim <- c(-1.8, 1.8)
Ylim <- c(-1.6, 1.6)
lim <- cbind(Xlim, Ylim)
by <- 0.05

h <- .3  #bandwidth for the function kde

#Distance Function Diagram of the sublevel sets
Diag1 <- gridDiag(XX, distFct, lim = lim, by = by, sublevel = TRUE,
                  printProgress = TRUE) 

#Kernel Density Diagram of the superlevel sets
Diag2 <- gridDiag(XX, kde, lim = lim, by = by, sublevel = FALSE,
    library = "Dionysus", location = TRUE, printProgress = TRUE, h = h)
#plot
par(mfrow = c(2, 2))
plot(XX, cex = 0.5, pch = 19)
title(main = "Data")
plot(Diag1[["diagram"]])
title(main = "Distance Function Diagram")
plot(Diag2[["diagram"]])
title(main = "Density Persistence Diagram")
one <- which(Diag2[["diagram"]][, 1] == 1)
plot(XX, col = 2, main = "Representative loop of grid points")
for (i in seq(along = one)) {
  points(Diag2[["birthLocation"]][one[i], , drop = FALSE], pch = 15, cex = 3,
      col = i)
  points(Diag2[["deathLocation"]][one[i], , drop = FALSE], pch = 17, cex = 3,
      col = i)
  for (j in seq_len(dim(Diag2[["cycleLocation"]][[one[i]]])[1])) {
    lines(Diag2[["cycleLocation"]][[one[i]]][j, , ], pch = 19, cex = 1, col = i)
  }
}

TDA documentation built on Feb. 16, 2023, 6:35 p.m.