View source: R/filtrationDiag.R
filtrationDiag | R Documentation |
The function filtrationDiag
computes the persistence diagram of the filtration.
filtrationDiag(
filtration, maxdimension, library = "GUDHI", location = FALSE,
printProgress = FALSE, diagLimit = NULL)
filtration |
a list representing the input filtration. This list consists of three components: |
maxdimension |
integer: max dimension of the homological features to be computed. (e.g. 0 for connected components, 1 for connected components and loops, 2 for connected components, loops, voids, etc.) |
library |
a string specifying which library to compute the persistence diagram. The user can choose either the library |
location |
if |
printProgress |
logical: if |
diagLimit |
a number that replaces |
The user can decide to use either the C++ library GUDHI or Dionysus. See refereneces.
The function filtrationDiag
returns a list with the following elements:
diagram |
an object of class |
birthLocation |
only if |
deathLocation |
only if |
cycleLocation |
only if |
Jisu Kim
Maria C (2014). "GUDHI, Simplicial Complexes and Persistent Homology Packages." https://project.inria.fr/gudhi/software/.
Morozov D (2007). "Dionysus, a C++ library for computing persistent homology". https://www.mrzv.org/software/dionysus/
Edelsbrunner H, Harer J (2010). "Computational topology: an introduction." American Mathematical Society.
Fasy B, Lecci F, Rinaldo A, Wasserman L, Balakrishnan S, Singh A (2013). "Statistical Inference For Persistent Homology." (arXiv:1303.7117). Annals of Statistics.
summary.diagram
, plot.diagram
n <- 5
X <- cbind(cos(2*pi*seq_len(n)/n), sin(2*pi*seq_len(n)/n))
maxdimension <- 1
maxscale <- 1.5
dist <- "euclidean"
library <- "Dionysus"
FltRips <- ripsFiltration(X = X, maxdimension = maxdimension,
maxscale = maxscale, dist = "euclidean", library = "Dionysus",
printProgress = TRUE)
DiagFltRips <- filtrationDiag(filtration = FltRips, maxdimension = maxdimension,
library = "Dionysus", location = TRUE, printProgress = TRUE)
plot(DiagFltRips[["diagram"]])
FUNvalues <- X[, 1] + X[, 2]
FltFun <- funFiltration(FUNvalues = FUNvalues, cmplx = FltRips[["cmplx"]])
DiagFltFun <- filtrationDiag(filtration = FltFun, maxdimension = maxdimension,
library = "Dionysus", location = TRUE, printProgress = TRUE)
plot(DiagFltFun[["diagram"]], diagLim = c(-2, 5))
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