/
utils.R
747 lines (638 loc) Β· 27.6 KB
/
utils.R
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#' Check that the required dependencies are available for slendr to work
#'
#' @param python Is the slendr Python environment required?
#' @param slim Is SLiM required?
#' @param quit Should the R interpreter quit if required slendr dependencies are
#' missing? This option (which is not turned on by default, being set to
#' \code{FALSE}) is used mainly in avoiding running slendr man page examples on
#' machines which lack dependencies. If set to \code{TRUE}, a logical value
#' is returned.
#'
#' @return If \code{quit = TRUE}, no values is returned, if \code{quit = FALSE},
#' a scalar logical value is returned indicating whether or not the dependencies
#' are present.
#'
#' @export
check_dependencies <- function(python = FALSE, slim = FALSE, quit = FALSE) {
# check whether SLiM and Python are present (only if needed!)
missing_slim <- if (slim) !all(Sys.which("slim") != "") else FALSE
missing_python <- if (python) !is_slendr_env_present() else FALSE
fail <- missing_slim || missing_python
if (fail) {
if (interactive()) {
error_slim <- if (missing_slim) "SLiM" else ""
error_python <- if (missing_python) "slendr Python environment" else ""
stop(sprintf("Missing requirements for this example: %s",
paste(error_slim, error_python, sep = ", ")), call. = FALSE)
} else {
if (quit)
q()
else
return(FALSE)
}
} else {
return(invisible(TRUE))
}
}
# Internal implementation of expand_range() and shrink_range() functions
shrink_or_expand <- function(pop, by, end, start, overlap, snapshots, polygon,
lock, verbose) {
check_event_time(c(start, end), pop)
check_removal_time(start, pop)
check_removal_time(end, pop)
map <- attr(pop, "map")
# get the last active population size
prev_N <- sapply(attr(pop, "history"), function(event) event$N) %>%
Filter(Negate(is.null), .) %>%
unlist %>%
utils::tail(1)
# get the last available population boundary
region_start <- pop[nrow(pop), ]
sf::st_agr(region_start) <- "constant"
if (is.null(snapshots)) {
n <- 1
message("Iterative search for the minimum sufficient number of intermediate
spatial snapshots, starting at ", n, ". This should only take a couple of
seconds, but if you don't want to wait, you can set `snapshots = N` manually.")
} else
n <- snapshots
# iterate through the number of intermediate spatial boundaries to reach
# the required overlap between subsequent spatial maps
repeat {
times <- seq(start, end, length.out = n + 1)[-1]
# generate intermediate spatial maps, starting from the last one
inter_regions <- list()
inter_regions[[1]] <- region_start
for (i in seq_along(times)) {
exp_region <- sf::st_buffer(inter_regions[[1]], dist = i * (by / n))
exp_region$time <- times[i]
sf::st_agr(exp_region) <- "constant"
# restrict the next spatial boundary to the region of interest
if (!is.null(polygon)) {
if (!inherits(polygon, "slendr_region"))
polygon <- region(polygon = polygon, map = map)
exp_region <- sf::st_intersection(exp_region, polygon)
exp_region$region <- NULL
sf::st_agr(exp_region) <- "constant"
}
inter_regions[[i + 1]] <- exp_region
}
if (!is.null(snapshots)) break
# if the boundary is supposed to be shrinking, the order of spatial maps
# must be reversed in order to check the amount of overlap
direction <- ifelse(by < 0, rev, identity)
overlaps <- compute_overlaps(do.call(rbind, direction(inter_regions)))
if (all(overlaps >= overlap)) {
message("The required ", sprintf("%.1f%%", 100 * overlap),
" overlap between subsequent spatial maps has been met")
break
} else {
n <- n + 1
if (verbose)
message("- Increasing to ", n, " snapshots")
}
}
all_maps <- do.call(rbind, inter_regions[-1]) %>% rbind(pop, .)
sf::st_agr(all_maps) <- "constant"
result <- copy_attributes(
all_maps, pop,
c("map", "parent", "remove", "intersect", "aquatic", "history")
)
start_area <- sf::st_area(utils::head(inter_regions, 1)[[1]])
end_area <- sf::st_area(utils::tail(inter_regions, 1)[[1]])
action <- ifelse(start_area < end_area, "expand", "contract")
attr(result, "history") <- append(attr(result, "history"), list(data.frame(
pop = unique(region_start$pop),
event = action,
tstart = start,
tend = end
)))
if (lock) {
areas <- as.numeric(sapply(inter_regions, sf::st_area))
area_changes <- areas[-1] / areas[-length(areas)]
new_N <- as.integer(round(cumprod(area_changes) * prev_N))
prev_N <- c(prev_N, new_N[-length(new_N)])
times <- sapply(inter_regions[-1], `[[`, "time")
changes <- lapply(seq_len(length(new_N)), function(i) {
data.frame(
pop = unique(pop$pop),
event = "resize",
how = "step",
N = new_N[i],
prev_N = prev_N[i],
tresize = times[i],
tend = NA
)
})
attr(result, "history") <- append(attr(result, "history"), changes)
# for (i in seq_along(inter_regions)[-1]) {
# time <- inter_regions[[i]]$time
# result <- resize(result, N = new_N[i - 1], time = time, how = "step")
# }
}
result
}
# Get path to an appropriate SLiM binary
get_binary <- function(method) {
if (method == "gui") {
if (Sys.info()["sysname"] == "Darwin")
binary <- "open -a SLiMgui"
else
binary <- "SLiMgui"
} else
binary <- "slim"
binary
}
# Check whether given population region has not yet been intersected
check_not_intersected <- function(pop) {
if (!is.null(attr(pop, "intersected")))
stop("An already intersected population range object was provided.
Please provide a range object before it was intersected against a map.",
call. = FALSE)
}
# Check whether the given value makes sense given the map dimensions
check_resolution <- function(map, val) {
xrange <- sf::st_bbox(map)[c("xmin", "xmax")]
yrange <- sf::st_bbox(map)[c("ymin", "ymax")]
if (diff(xrange) < val | diff(yrange) < val)
stop(sprintf("Value of %s = %s larger than the overall world size",
deparse(substitute(val)), val),
call. = FALSE)
}
# Set a bounding box of a given object, and return that object again
# (for some reason there's no builtin way to set a bounding box in
# sf <https://twitter.com/TimSalabim3/status/1063099774977667072>)
set_bbox <- function(x, xmin, xmax, ymin, ymax) {
bbox <- c(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax)
attr(bbox, "class") <- "bbox"
attr(sf::st_geometry(x), "bbox") <- bbox
x
}
# Does the object have a Coordinate Reference System assigned to it?
has_crs <- function(x) {
!is.na(sf::st_crs(x)$epsg)
}
# Set slendr classes (or fix their priorities if already present)
set_class <- function(x, type) {
other_classes <- class(x) %>% .[!grepl("^slendr", .)]
c("slendr", paste0("slendr_", type), other_classes)
}
# Get a data frame of geneflow events active at a given time point
get_geneflows <- function(model, time) {
if (is.null(model$geneflow)) return(NULL)
pop_names <- unique(unlist(sapply(model$populations, `[[`, "pop")))
direction <- time_direction(model)
if (direction == "forward") {
before_op <- `<=`
after_op <- `>=`
} else {
before_op <- `>=`
after_op <- `<=`
}
if (!is.null(time))
geneflows <- subset(model$geneflow, before_op(tstart_orig, time) & after_op(tend_orig, time))
else
geneflows <- model$geneflow
geneflows$from <- factor(geneflows$from, levels = pop_names)
geneflows$to <- factor(geneflows$to, levels = pop_names)
migr_coords <- lapply(seq_len(nrow(geneflows)), function(row_i) {
# if time point was not provided, simply take the midpoint of the current
# gene-flow event
if (is.null(time)) {
time <- geneflows[row_i, c("tstart_orig", "tend_orig")] %>% as.numeric() %>% mean()
}
from <- model$populations[pop_names == geneflows[row_i, ]$from][[1]] %>%
.[before_op(.$time, time), ] %>%
.[nrow(.), ]
to <- model$populations[pop_names == geneflows[row_i, ]$to][[1]] %>%
.[before_op(.$time, time), ] %>%
.[nrow(.), ]
from_center <- sf::st_centroid(from) %>% sf::st_coordinates()
to_center <- sf::st_centroid(to) %>% sf::st_coordinates()
coords <- as.data.frame(cbind(from_center, to_center), stringsAsFactors = FALSE)
colnames(coords) <- c("from_x", "from_y", "to_x", "to_y")
coords
}) %>%
do.call(rbind, .)
cbind(geneflows, migr_coords)
}
# Transfer given set of attributes from one object to another
copy_attributes <- function(to, from, which) {
for (i in which)
attr(to, i) <- attr(from, i)
class(to) <- class(from)
to
}
# Get split times of populations in the lineage of the given population
get_lineage_splits <- function(x) {
parent <- attr(x, "parent")
time <- attr(x, "history")[[1]]$time
if (is.character(parent))
return(time)
else
return(c(time, get_lineage_splits(parent)))
}
# Get direction of time implied by the history of the population
#
# @param x Object of the class \code{slendr_pop} or \code{slendr_model}
#
# @return Either "forward", "backward", or "unknown"
time_direction <- function(x) {
if (inherits(x, "slendr_model")) return(x$direction)
split_times <- get_lineage_splits(x)
if (length(split_times) == 1) {
event_times <- attr(x, "history") %>%
sapply(function(event) c(event$time, event$tresize, event$tend,
event$start, event$end)) %>%
unlist %>%
unique %>%
stats::na.omit()
if (length(event_times) == 1) {
removal_time <- attr(x, "remove")
if (removal_time == -1)
"unknown"
else if (all(event_times > removal_time))
"backward"
else if (all(event_times < removal_time))
"forward"
else
stop("Inconsistent time direction of population events", call. = FALSE)
} else if (all(diff(event_times) < 0))
"backward"
else
"forward"
} else if (all(diff(split_times) > 0))
"backward"
else
"forward"
}
# Check the consistency of the given split time to the parent population
check_split_time <- function(time, parent) {
parent_time <- attr(parent, "history")[[1]]$time
direction <- time_direction(parent)
if (direction == "forward" & time <= parent_time) {
stop(sprintf("The model implies forward time direction but the specified split
time (%d) is lower than the parent's (%s)",
time, parent_time),
call. = FALSE)
} else if (direction == "backward" & time >= parent_time) {
stop(sprintf("The model implies backward time direction but the specified split
time (%s) is higher than the parent's (%s)",
time, parent_time),
call. = FALSE)
} else if (time == parent_time) {
stop("Population can be only created after its parent is already present in the simulation", call. = FALSE)
}
}
# Get time of the very last event currently specified
get_previous_time <- function(pop) {
sapply(attr(pop, "history"), function(event) {
c(event$time, event$tresize, event$tstart, event$tend)
}) %>%
unlist %>%
Filter(Negate(is.null), .) %>%
Filter(Negate(is.na), .) %>%
utils::tail(1)
}
check_event_time <- function(time, pop) {
if (length(time) > 1 & time[1] == time[2])
stop("Start time of the event is equal to the end time of the event", call. = FALSE)
direction <- time_direction(pop)
previous_time <- get_previous_time(pop)
# testing for consistent for ancestral populations (unknown time direction)
if (direction == "unknown") {
# there is no information about time direction from ancestral populations,
# but we can test that all times of the current event are before/after that
# population's split time
if (!(all(time >= previous_time) | all(time <= previous_time)))
stop(sprintf("The new event (time %s) falls both before and after the last active time (%s) for the population",
paste(time, collapse = "-"), previous_time), call. = FALSE)
# if a start-end time window was specified for the event, we can lock in
# a time direction even for ancestral populations for which the flow of
# time is otherwise unknown
if (length(time) > 1) {
# the first time point implies time going backwards, but the event window
# implies a forward direction
if (time[1] < previous_time & time[2] > time[1])
stop(sprintf("The new event (time %s) implies a forward time direction but the population split time (%d) is higher (indicating backward time direction)",
paste(time, collapse = "-"), previous_time), call. = FALSE)
else if (time[1] > previous_time & time[2] < time[1])
stop(sprintf("The new event (time %s) implies a backward time direction but the population split time (%d) is lower (indicating forward time direction)",
paste(time, collapse = "-"), previous_time), call. = FALSE)
}
} else if (direction %in% c("backward", "forward")) {
# direction of an event follows the already established time direction
if ((direction == "backward" & length(time) > 1 & time[1] < time[2]) |
(direction == "forward" & length(time) > 1 & time[1] > time[2])) {
event_direction <- if (time[1] < time[2]) "forward" else "backward"
stop(sprintf("The new %s event (time %s) is inconsistent with the %s time direction assumed by the model",
event_direction, paste0(time, collapse = "-"), direction), call. = FALSE)
}
# time of event is consistent with the already established time direction
if ((direction == "backward" & !all(previous_time >= time)) |
(direction == "forward" & !all(previous_time <= time))) {
stop(sprintf("The new event (time %s) pre-dates the last specified active event (%s) which is incompatible with the assumed %s time direction of the model",
paste(time, collapse = "-"), previous_time, direction),
call. = FALSE)
}
} else
stop(sprintf("Unknown time direction %s", direction), call. = FALSE)
}
# Check whether a given population will be present for sampling
# (used exclusively in the schedule_sampling() function to avoid situations when
# a user would sample from a population in the same generation that it
# would be created)
check_present_time <- function(time, pop, offset, direction = NULL) {
if (is.null(direction))
direction <- time_direction(pop)
split_time <- get_lineage_splits(pop)[1]
if (time == split_time ||
(direction == "backward" && time > split_time - offset) ||
(direction == "forward" && time < split_time + offset))
stop("Population ", pop$pop[1], " is not present at a time ", time, call. = FALSE)
}
check_removal_time <- function(time, pop, direction = NULL) {
if (is.null(direction))
direction <- time_direction(pop)
removal_time <- attr(pop, "remove")
if (removal_time != -1 & direction == "forward" & any(time > removal_time)) {
stop(sprintf("The specified event time (%d) is not consistent with the scheduled removal of %s (%s) given the assumed %s time direction",
time, pop$pop[1], removal_time, direction),
call. = FALSE)
}
if (removal_time != -1 & direction == "backward" & any(time < removal_time)) {
stop(sprintf("The specified event time (%d) is not consistent with the scheduled removal of %s (%s) given the assumed %s time direction",
time, pop$pop[1], removal_time, direction),
call. = FALSE)
}
}
# Calculate overlap between subsequent spatial maps
compute_overlaps <- function(x) {
sf::st_agr(x) <- "constant"
sapply(
seq_len(nrow(x))[-1], function(i) {
a <- x[i - 1, ]
b <- x[i, ]
intersection <- sf::st_intersection(a, b)
if (nrow(intersection) == 0) return(0)
sf::st_area(intersection) / sf::st_area(b)
}
)
}
# Take care of missing interactions and offspring distances
set_distances <- function(dispersal_table, resolution,
competition, mating, dispersal) {
if (is.null(competition)) {
if (all(is.na(dispersal_table$competition))) {
pop_names <- paste(unique(dispersal_table[is.na(dispersal_table$competition), ]$pop), collapse = ", ")
stop("Parameter 'competition' missing for ", pop_names, " and a general
value of this parameter was not provided to the compile() function", call. = FALSE)
} else
competition <- utils::tail(dispersal_table$competition[which(!is.na(dispersal_table$competition))], 1)
}
# replace all NA values with the last specified competition distance
dispersal_table$competition[is.na(dispersal_table$competition)] <- competition
if (is.null(mating)) {
if (all(is.na(dispersal_table$mating))) {
pop_names <- paste(unique(dispersal_table[is.na(dispersal_table$mating), ]$pop), collapse = ", ")
stop("Parameter 'mating' missing for ", pop_names, " and a general
value of this parameter was not provided to the compile() function", call. = FALSE)
} else
mating <- utils::tail(dispersal_table$mating[which(!is.na(dispersal_table$mating))], 1)
}
# replace all NA values with the last specified mate choice distance
dispersal_table$mating[is.na(dispersal_table$mating)] <- mating
if (is.null(dispersal)) {
if (all(is.na(dispersal_table$dispersal))) {
pop_names <- paste(unique(dispersal_table[is.na(dispersal_table$dispersal), ]$pop), collapse = ", ")
stop("Parameter 'dispersal' missing for ", pop_names, " and a general
value of this parameter was not provided to the compile() function", call. = FALSE)
} else
dispersal <- utils::tail(dispersal_table$dispersal[which(!is.na(dispersal_table$dispersal))], 1)
}
# replace all NA values with the last specified dispersalchoice distance
dispersal_table$dispersal[is.na(dispersal_table$dispersal)] <- dispersal
dispersal_table[, c("competition", "mating", "dispersal")] <-
dispersal_table[, c("competition", "mating", "dispersal")] / resolution
dispersal_table
}
# Return the map attribute of a slendr object
get_map <- function(x) {
if (!inherits(x, "slendr"))
stop("Can't access a map attribute of a non-slendr type object", call. = FALSE)
map <- attr(x, "map")
if (inherits(map, "slendr_map"))
map
else
NULL
}
# Does a given object have an attribute map?
has_map <- function(x) {
inherits(get_map(x), "slendr_map")
}
# Process the sampling schedule which was provided by the user or -- if no
# sampling schedule was specified -- generate one automatically for populations
# which survive to the end of the simulation.
process_sampling <- function(samples, model, verbose = FALSE) {
# if no explicit sampling schedule was given, try to generate it at least
# for the populations which survive to the present
if (is.null(samples)) {
if (verbose)
message("Tree-sequence recording is on but no sampling schedule was given. ",
"Generating one for all individuals surviving to the end of the simulation.")
# get a list of populations surviving to the end of the simulation
surviving_pops <- purrr::keep(model$populations, ~ attr(.x, "remove") == -1)
# find out at which time is the simulation supposed to end ...
start_times <- purrr::map_int(model$populations,
~ attr(.x, "history")[[1]]$time)
if (model$direction == "backward") {
start_time <- max(start_times)
# modulo for length isn't divisible by generation time
sampling_time <- start_time - model$orig_length +
model$orig_length %% model$generation_time
} else {
start_time <- min(start_times)
sampling_time <- start_time + model$generation_time * round(model$orig_length / model$generation_time)
}
# ... and then call
# schedule_sampling(model, end_time, <all surviving populations>)
# which is what would be called normally by the user, manually
samples <- do.call(
schedule_sampling,
c(list(model = model, times = sampling_time),
purrr::map(surviving_pops, ~ list(.x, Inf)))
)
# take care of the edge in which no populations are expected to survive
# until the end of the simulation, leaving no samples to be remembered
# TODO: is this actually allowed to happen in SLiM?
if (is.null(samples)) {
warning("No populations survive to the end of the simulations which means ",
"that no individuals will be remembered.", call. = FALSE)
return(NULL)
} else {
# default sampling schedules are not spatial
samples$x <- samples$y <- samples$x_orig <- samples$y_orig <- NA
}
}
# sum up all individual `n` counts for each pop/time/location in case of
# multiples
df <- dplyr::group_by(samples, time, pop, x, y, x_orig, y_orig) %>%
dplyr::summarise(n = sum(n), .groups = "drop") %>%
dplyr::arrange(time)
# samples must either all have a sampling location defined, or none
pop_consistent <- split(df, df$pop) %>% vapply(function(pop) {
all(is.na(pop$x_orig) | is.na(pop$y_orig)) ||
all(!is.na(pop$x_orig) | !is.na(pop$y_orig))
}, logical(1))
if (!all(pop_consistent)) {
stop("For each population, samples must be all spatial or all non-spatial.\n",
"This is not true for the following populations: ",
paste(names(pop_consistent)[!pop_consistent], collapse = ", "),
call. = FALSE)
}
# in case a backwards time model is not to be simulated all the way to the
# present (i.e. time 0), the conversion of sampling times from absolute model
# time units into SLiM's forward time units needs to be adjusted relative
# to the actual start of the population (the oldest split in the model), not
# to the `orig_length` which would be the duration time of the simulation
# (if not, `convert_to_forward` would translate into negative SLiM generations)
oldest_time <- get_oldest_time(model$populations, model$direction)
if (model$direction == "backward" && oldest_time != model$orig_length) {
end_time <- oldest_time
} else if (model$direction == "forward" && oldest_time > 1) {
# same for forward models starting not in generation 1
time_orig <- df$time
df$time <- df$time - oldest_time + model$generation_time
end_time <- model$orig_length
} else
end_time <- model$orig_length
processed_schedule <- df %>%
convert_to_forward(
direction = model$direction,
columns = "time",
generation_time = model$generation_time,
end_time = end_time
) %>%
dplyr::arrange(time_gen) %>%
dplyr::select(pop, n, time_gen, x, y, time_orig, x_orig, y_orig)
# if the original times should be replaced, do it
if (model$direction == "forward" && oldest_time > 1)
processed_schedule$time_orig <- time_orig
# if locations are missing, replace NA with -1 values for SLiM to understand
processed_schedule <- replace(processed_schedule, is.na(processed_schedule), -1)
processed_schedule %>% dplyr::mutate(n = ifelse(is.infinite(n), "INF", n))
}
# Make sure all given locations fall within world bounding box
check_location_bounds <- function(locations, map) {
xrange <- attr(map, "xrange")
yrange <- attr(map, "yrange")
checks <- sapply(locations, function(loc) {
loc[1] >= xrange[1] & loc[1] <= xrange[2] &
loc[2] >= yrange[1] & loc[2] <= yrange[2]
})
if (!all(checks))
stop("The following locations fall outside of the world map: ",
paste(locations[!checks], collapse = ", "), call. = FALSE)
}
# Convert SLiM time units as they are saved in the tree-sequence output
# (and also other slendr output formats such as the locations of individuals
# or ancestry proportions over time) back to user-specified time units
# (either forward or backward)
convert_slim_time <- function(times, model) {
ancestors <- dplyr::filter(model$splits, parent == "__pop_is_ancestor")
if (model$direction == "backward") {
oldest_time <- max(ancestors[, ]$tsplit_orig)
result <- times * model$generation_time +
(max(ancestors[, ]$tsplit_orig) - model$orig_length)
# does the backward simulation model terminate sooner than "present-day"? if
# so, shift the times to start at the original time specified by user (also
# check for the situation where the simulation wouldn't end at 0 because the
# length of the simulation is not divisible by generation time)
shortened <- oldest_time != model$orig_length
indivisible <- model$orig_length %% model$generation_time != 0
if (shortened && !indivisible) {
result <- result + model$orig_length %% model$generation_time
} else if (indivisible) {
result <- result + model$orig_length -
round(model$orig_length / model$generation_time) * model$generation_time
}
} else {
result <- (model$length - times + 1) * model$generation_time
# did the simulation start at a later time than "generation 1"?
# if it did, shift the time appropriately
if (min(round(ancestors[, ]$tsplit_orig / model$generation_time) != 1))
result <- result + ancestors[1, ]$tsplit_orig - model$generation_time
}
as.numeric(result)
}
# Convert msprime node time units into the user-specified time units
convert_msprime_time <- function(time, model) {
if (model$direction == "forward")
as.numeric(model$orig_length - (time - 1) * model$generation_time)
else {
as.numeric(time * model$generation_time)
}
}
get_oldest_time <- function(populations, direction) {
times <- sapply(populations, function(pop) attr(pop, "history")[[1]]$time)
if (direction == "forward")
min(times)
else
max(times)
}
kernel_fun <- function(fun = c("normal", "uniform", "cauchy", "exponential",
"brownian")) {
match.arg(fun)
}
ask_install <- function(module) {
answer <- utils::menu(c("No", "Yes"),
title = paste("Python module", module,
"is missing in the environment. Install?"))
answer == 2
}
is_slendr_env_present <- function() {
tryCatch({
PYTHON_ENV %in% reticulate::conda_list()$name
}, error = function(cond) FALSE
)
}
order_pops <- function(populations, direction) {
pop_names <- purrr::map_chr(populations, ~ .x$pop[1])
split_times <- purrr::map_int(populations, ~ attr(.x, "history")[[1]]$time)
names(split_times) <- pop_names
if (length(direction) > 0 && direction == "backward") {
split_times <- sort(split_times, decreasing = TRUE)
} else if (length(direction) > 0 && direction == "forward") {
split_times <- sort(split_times)
}
names(split_times)
}
#' Pipe operator
#'
#' @return See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
NULL
utils::globalVariables(
names = c(
".", "node_id", "location", "name", "level", "child_id", "child_time",
"parent_id", "parent_time", "child_pop", "parent_pop", "child_location",
"parent_location", "connection", "left", "right", "parent", "tsplit_gen",
"tstart_orig", "tend_orig", "time_gen", "n", "pop", "time_orig", "time",
"pop_is", "time.x", "time.y", "ind_id", "sampled", "remembered", "retained",
"alive", "pedigree_id", "from_x", "from_y", "from", "to_x", "to_y", "to",
"node1.name", "node2.name", "type", "label", "rate", "pop_id", "vcf_file",
"gen", "newx", "newy", "child", "time", "node_label", "chr_name", "pos",
"pyslim", "tskit", "msprime", "x", "y", "x_orig", "y_orig",
"orig_x", "orig_y", "phylo_id", "raster_x", "raster_y",
"pop.y", "pop_id.y", "time_tskit", "time_tskit.x", "time_tskit.y",
"N", "center", "child_node_id", "child_phylo_id", "geometry", "parent_node_id",
"parent_phylo_id", "set_boundary", "xend", "xmax", "xmin", "yend",
"arc_degree", "node1", "node2", "mrca", "node1_time", "node2_time", "tmrca", "count", "total"
), package = "slendr"
)