Schedule sampling events at specified times and, optionally, a given set of locations on a landscape

`schedule_sampling(model, times, ..., locations = NULL, strict = FALSE)`

- model
Object of the class

`slendr_model`

- times
Integer vector of times (in model time units) at which to schedule remembering of individuals in the tree-sequence

- ...
Lists of two elements (

`slendr_pop`

population object-<number of individuals to sample), representing from which populations should how many individuals be remembered at times given by`times`

- locations
List of vector pairs, defining two-dimensional coordinates of locations at which the closest number of individuals from given populations should be sampled. If

`NULL`

(the default), individuals will be sampled randomly throughout their spatial boundary.- strict
Should any occurence of a population not being present at a given time result in an error? Default is

`FALSE`

, meaning that invalid sampling times for any populations will be quietly ignored.

Data frame with three columns: time of sampling, population to sample from, how many individuals to sample

If both times and locations are given, the the sampling will be scheduled on each specified location in each given time-point. Note that for the time-being, in the interest of simplicity, no sanity checks are performed on the locations given except the restriction that the sampling points must fall within the bounding box around the simulated world map. Other than that, slendr will simply instruct its SLiM backend script to sample individuals as close to the sampling points given as possible, regardless of whether those points lie within a population spatial boundary at that particular moment of time.

```
check_dependencies(python = TRUE) # make sure dependencies are present
# load an example model with an already simulated tree sequence
path <- system.file("extdata/models/introgression", package = "slendr")
model <- read_model(path)
# afr and eur objects would normally be created before slendr model compilation,
# but here we take them out of the model object already compiled for this
# example (in a standard slendr simulation pipeline, this wouldn't be necessary)
afr <- model$populations[["AFR"]]
eur <- model$populations[["EUR"]]
# schedule the recording of 10 African and 100 European individuals from a
# given model at 20 ky, 10 ky, 5ky ago and at present-day (time 0)
schedule <- schedule_sampling(
model, times = c(20000, 10000, 5000, 0),
list(afr, 10), list(eur, 100)
)
# the result of `schedule_sampling` is a simple data frame (note that the locations
# of sampling locations have `NA` values because the model is non-spatial)
schedule
#> # A tibble: 8 × 7
#> time pop n y_orig x_orig y x
#> <int> <chr> <int> <lgl> <lgl> <lgl> <lgl>
#> 1 0 AFR 10 NA NA NA NA
#> 2 0 EUR 100 NA NA NA NA
#> 3 5000 AFR 10 NA NA NA NA
#> 4 5000 EUR 100 NA NA NA NA
#> 5 10000 AFR 10 NA NA NA NA
#> 6 10000 EUR 100 NA NA NA NA
#> 7 20000 AFR 10 NA NA NA NA
#> 8 20000 EUR 100 NA NA NA NA
```