lacunr Quick-start guide

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").