Filter lines by endpoint cluster membership

Filters trajectory data by checking the cluster labels at each trajectory's endpoints. For every `trajectory_id`, the start endpoint is defined as the spot with the smallest projection parameter `pos_on_seg`, and the end endpoint as the spot with the largest `pos_on_seg`. Only trajectories whose start cluster is in `allowed_start_clusters` *and* whose end cluster is in `allowed_end_clusters` are kept.

Usage

filter_out_by_endpoint_clusters(
  out,
  allowed_start_clusters,
  allowed_end_clusters
)

Arguments

out

A data frame containing selected spots with columns `trajectory_id`, `cluster`, and `pos_on_seg`, typically the output of `build_similar_trajectories()`.

allowed_start_clusters

Vector of allowed cluster labels for the start endpoint.

allowed_end_clusters

Vector of allowed cluster labels for the end endpoint.

Value

The same data frame as `out`, filtered to keep only the lines matching the allowed endpoint cluster constraints.

Details

The endpoint clusters are computed per `trajectory_id`:

• `start_cluster = cluster[which.min(pos_on_seg)]`

• `end_cluster = cluster[which.max(pos_on_seg)]`

If multiple spots share the same minimum/maximum `pos_on_seg`, the first is taken (as per `which.min()` / `which.max()`).

This function uses `dplyr` (`group_by`, `summarise`, `filter`, `pull`) and the base R pipe `|>`.

Examples

# Minimal example
out <- data.frame(
  trajectory_id = c("L1","L1","L2","L2"),
  pos_on_seg = c(0.0, 1.0, 0.0, 1.0),
  cluster  = c("A", "B", "A", "C"),
  x = 1:4, y = 1:4
)

# Keep only trajectories starting in A and ending in B
filter_out_by_endpoint_clusters(out, allowed_start_clusters = "A",
allowed_end_clusters = "B")
#>   trajectory_id pos_on_seg cluster x y
#> 1            L1          0       A 1 1
#> 2            L1          1       B 2 2