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knitr::opts_chunk$set(collapse = TRUE, comment = "#>") options(knitr.table.format = "html", rmarkdown.html_vignette.check_title = FALSE) library(eRTG3D) library(raster) library(ggplot2) set.seed(123)
First some example trajectories are created in form of Correlated Random Walks (CRWs):
crws <- lapply(X=seq(1:100), FUN = function(X) { sim.crw.3d(nStep = 100, rTurn = 0.99, rLift = 0.99, meanStep = 0.1) }) plot2d(crws)
Count points per voxel and plot counts as raster stack:
points <- do.call("rbind", crws) extent <- extent(c(-10, 10, -10, 10)) ud <- voxelCount(points, extent, xyRes=5, zMin=-10, zMax=10) plotRaster(ud)
By calculating Chi maps, the over- and underrepresentation of points in the voxel space can be interpreted statistically:
chi <- chiMaps(ud) plotRaster(chi, centerColorBar=TRUE)
The voxel dataCube of type rasterStack can be exported as Tiff image sequence. Image sequences are a common structure to represent voxel data and most of the specific software to visualize voxel data is able to read it (e.g. blender)
saveImageSlices(ud, filename = "utilization-distribution", dir="folder/path") saveImageSlices(chi, filename = "chi-map-cube", dir="folder/path")
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