Computes event-features
This function computes event features of 3D events.
get_features_3d(dat.xyz, res.cluster, normal.stats, win_size, tt)
dat.xyz: The data in a cluster friendly format. The first three columns have t,x and y positions with the fourth column having the pixel value of that position.res.cluster: Cluster details from dbscan.normal.stats: The background statistics, output from stats_3d.win_size: The window length of the moving window model.tt: Related to event ages. For example if tt=10 then the event ages are 10, 20, 30 and 40.An Nx22x4 array is returned. Here N is the total number of events extracted in all windows. The second dimension has 30 features and the class label for the supervised setting. The third dimension has 4 different event ages : tt, 2tt, 3tt, 4tt. For example, the element at [10,6,3] has the 6th feature, of the 10th extracted event when the age of the event is 3tt. The features are listed below: - cluster_id: An identification number for each event.
pixels: The number of pixels of each event.
length: The length of the event.
width: The width of the event.
total_value: The total value of the pixels.
l2w_ratio: Length to width ratio of event.
centroid_x: x coordinate of event centroid.
centroid_y: y coordinate of event centroid.
centroid_z: z coordinate of event centroid.
mean: Mean value of event pixels.
std_dev: Standard deviation of event pixels.
slope: Slope of a linear model fitted to the event.
quad1: First coefficient of a quadratic model fitted to the event.
quad2: Second coefficient of a quadratic model fitted to the event.
sd_from_mean: Let us denote the 80th percentile of the event pixels value by x. How many standard deviations is x is away from the mean?
set.seed(1) arr <- array(rnorm(12000),dim=c(40,25,30)) arr[25:33,12:20, 20:23] <- 10 # getting events out <- get_clusters_3d(arr, thres=0.985) mean_sd <- stats_3d(arr[1:20,1:6,1:8]) ftrs <- get_features_3d(out$data, out$cluster$cluster, mean_sd, win_size=40, tt=2 )