visualize_sgwt_kernels: Visualize SGWT kernels and scaling functions

In BioGSP: Biological Graph Signal Processing for Spatial Data Analysis

Visualize SGWT kernels and scaling functions

Description

Visualize the scaling function and wavelet kernels used in SGWT based on the eigenvalue spectrum and selected parameters

Usage

visualize_sgwt_kernels(
  eigenvalues,
  scales = NULL,
  J = 4,
  scaling_factor = 2,
  kernel_type = "heat",
  lmax = NULL,
  eigenvalue_range = NULL,
  resolution = 1000
)

Arguments

eigenvalues	Vector of eigenvalues from graph Laplacian
scales	Vector of scales for the wavelets (if NULL, auto-generated)
J	Number of scales to generate if scales is NULL (default: 4)
scaling_factor	Scaling factor between consecutive scales (default: 2)
kernel_type	Type of wavelet kernel ("mexican_hat" or "meyer", default: "mexican_hat")
lmax	Maximum eigenvalue (optional, computed if NULL)
eigenvalue_range	Range of eigenvalues to plot (default: full range)
resolution	Number of points for smooth curve plotting (default: 1000)

Value

List containing the filter visualization plot and filter values

Examples

# Generate some example eigenvalues
eigenvals <- seq(0, 2, length.out = 100)

# Visualize kernels with specific parameters
viz_result <- visualize_sgwt_kernels(
  eigenvalues = eigenvals,
  J = 4,
  scaling_factor = 2,
  kernel_type = "heat"
)
print(viz_result$plot)

BioGSP documentation built on Feb. 2, 2026, 5:06 p.m.