`lacunr` Quick-start guide

In lacunr: Fast 3D Lacunarity for Voxel Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  dev = "png", dev.args = list(type = "cairo-png"),
  fig.retina=2,
  pngquant = "--speed=1 --quality=50"
)

The standard workflow for lacunr is fairly simple:

1. Convert point cloud data to voxels using voxelize()
2. Arrange the voxels into a 3-dimensional array using bounding_box()
3. Calculate a lacunarity curve using lacunarity()

library(lacunr)
# create a data.frame of simulated point cloud data
set.seed(5678)
pc <- data.frame(X = rnorm(1000, 10), Y = rnorm(1000, 50), Z = rnorm(1000, 25))
# convert to voxels of size 0.5
vox <- voxelize(pc, edge_length = c(0.5, 0.5, 0.5))
# generate 3D array
box <- bounding_box(vox)
# calculate lacunarity curve
lac_curve <- lacunarity(box)

Lacunarity and H(r) curves can be plotted using lac_plot(), lacnorm_plot(), or hr_plot():

# plot lacunarity curve
plot <- lac_plot(lac_curve)
print(plot)

3D arrays generated by bounding_box() can have their dimensions selectively increased using pad_array():

# add two layers of empty space to the Z axis of the array
box_pad1 <- pad_array(box, z = 2)
# add two layers of occupied space to the Y axis of the array
box_pad2 <- pad_array(box, y = 2, fill = 1)

For more extensive explanation on these functions and their use, please see the package documentation (available by typing ?lacunr into your console), or the other vignettes via browseVignettes("lacunr").

lacunr documentation built on June 22, 2024, 10:49 a.m.