Mass rename of step to time in implementation

DESCRIPTION

@@ -1,8 +1,9 @@
 Package: dust
 Title: Iterate Multiple Realisations of Stochastic Models
-Version: 0.11.35
+Version: 0.12.0
 Authors@R: c(person("Rich", "FitzJohn", role = c("aut", "cre"),
                     email = "rich.fitzjohn@gmail.com"),
+             person("Alex", "Hill", role = "aut"),
              person("John", "Lees", role = "aut"),
              person("Imperial College of Science, Technology and Medicine",
                     role = "cph"))

NEWS.md

@@ -1,3 +1,9 @@
+# dust 0.12.0
+
+* Breaking change, with `step` (and so on with `step_end`) changing to `time` everywhere, in order to smooth the inclusion of continuous time models. This affects quite a few methods:
+  * `step()` and `set_step()` become `time()` and `set_time()`
+  * `update_state()`'s argument changes from `step` to `time`
+
 # dust 0.11.24
 
 * Add `min_log_likelihood` support to `$filter()` (#361)

R/compile.R

@@ -129,7 +129,7 @@ glue_whisker <- function(template, data) {
 dust_template_data <- function(model, config, cuda, reload_data) {
   methods <- function(target) {
     nms <- c("alloc", "run", "simulate", "set_index", "n_state",
-             "update_state", "state", "step", "reorder", "resample",
+             "update_state", "state", "time", "reorder", "resample",
              "rng_state", "set_rng_state", "set_n_threads",
              "set_data", "compare_data", "filter")
     m <- sprintf("%s = dust_%s_%s_%s", nms, target, config$name, nms)

R/data.R

@@ -1,21 +1,26 @@
 ##' Prepare data for use with the `$set_data()` method. This is not
 ##' required for use but tries to simplify the most common use case
-##' where you have a [data.frame] with some column indicating "model
-##' step" (`name_step`), and other columns that might be use in your
-##' `data_compare` function. Each row will be turned into a named R
-##' list, which your `dust_data` function can then work with to get
+##' where you have a [data.frame] with some column indicating "dust
+##' time step" (`name_time`), and other columns that might be use in
+##' your `data_compare` function. Each row will be turned into a named
+##' R list, which your `dust_data` function can then work with to get
 ##' this time-steps values. See Details for use with multi-pars
 ##' objects.
 ##'
+##' Note that here "dust time step" (`name_time`) refers to the *dust*
+##' time step (which will be a non-negative integer) and not the
+##' rescaled value of time that you probably use within the model. See
+##' [dust_generator] for more information.
+##'
 ##' The data object as accepted by `data_set` must be a [list] and
-##' each element must itself be a list with two elements; the model
-##' `step` at which the data applies and any R object that corresponds
+##' each element must itself be a list with two elements; the dust
+##' `time` at which the data applies and any R object that corresponds
 ##' to data at that point. We expect that most of the time this second
 ##' element will be a key-value list with scalar keys, but more
 ##' flexibility may be required.
 ##'
 ##' For multi-data objects, the final format is a bit more awkward;
-##' each time step we have a list with elements `step`, `data_1`,
+##' each time step we have a list with elements `time`, `data_1`,
 ##' `data_2`, ..., `data_n` for `n` parameters. There are two ways of
 ##' creating this that might be useful: *sharing* the data across all
 ##' parameters and using some column as a grouping value.
@@ -38,7 +43,7 @@
 ##'
 ##' @param object An object, at this point must be a [data.frame]
 ##'
-##' @param name_step The name of the data column within `object`; this
+##' @param name_time The name of the data column within `object`; this
 ##'   column must be integer-like and every integer must be
 ##'   nonnegative and unique
 ##'
@@ -52,28 +57,28 @@
 ##'
 ##' @export
 ##' @examples
-##' d <- data.frame(step = seq(0, 50, by = 10), a = runif(6), b = runif(6))
+##' d <- data.frame(time = seq(0, 50, by = 10), a = runif(6), b = runif(6))
 ##' dust::dust_data(d)
-dust_data <- function(object, name_step = "step", multi = NULL) {
+dust_data <- function(object, name_time = "time", multi = NULL) {
   assert_is(object, "data.frame")
-  steps <- object[[name_step]]
-  if (is.null(steps)) {
+  times <- object[[name_time]]
+  if (is.null(times)) {
     stop(sprintf("'%s' is not a column in %s",
-                 name_step, deparse(substitute(object))))
+                 name_time, deparse(substitute(object))))
   }
-  isteps <- as.integer(round(steps))
-  if (any(isteps < 0)) {
-    stop(sprintf("All elements in column '%s' must be nonnegative", name_step))
+  itimes <- as.integer(round(times))
+  if (any(itimes < 0)) {
+    stop(sprintf("All elements in column '%s' must be nonnegative", name_time))
   }
-  if (any(abs(steps - isteps) > sqrt(.Machine$double.eps))) {
-    stop(sprintf("All elements in column '%s' must be integer-like", name_step))
+  if (any(abs(times - itimes) > sqrt(.Machine$double.eps))) {
+    stop(sprintf("All elements in column '%s' must be integer-like", name_time))
   }
 
   rows <- lapply(seq_len(nrow(object)), function(i) as.list(object[i, ]))
   if (is.null(multi)) {
-    ret <- Map(list, isteps, rows)
+    ret <- Map(list, itimes, rows)
   } else if (is_integer_like(multi)) {
-    ret <- Map(function(i, d) c(list(i), rep(list(d), multi)), isteps, rows)
+    ret <- Map(function(i, d) c(list(i), rep(list(d), multi)), itimes, rows)
   } else if (is.character(multi)) {
     group <- object[[multi]]
     if (is.null(group)) {
@@ -83,20 +88,21 @@ dust_data <- function(object, name_step = "step", multi = NULL) {
     if (!is.factor(group)) {
       stop(sprintf("Column '%s' must be a factor", multi))
     }
-    isteps <- unname(split(isteps, group))
-    if (length(unique(isteps)) != 1L) {
+    itimes <- unname(split(itimes, group))
+    if (length(unique(itimes)) != 1L) {
       stop("All groups must have the same time steps, in the same order")
     }
-    isteps <- isteps[[1L]]
+    itimes <- itimes[[1L]]
     rows_grouped <- unname(split(rows, group))
-    ret <- lapply(seq_along(isteps), function(i)
-      c(list(isteps[[i]]), lapply(rows_grouped, "[[", i)))
+    ret <- lapply(seq_along(itimes), function(i) {
+      c(list(itimes[[i]]), lapply(rows_grouped, "[[", i))
+    })
   } else {
     stop("Invalid option for 'multi'; must be NULL, integer or character")
   }
 
-  if (any(duplicated(isteps))) {
-    stop(sprintf("All elements in column '%s' must be unique", name_step))
+  if (any(duplicated(itimes))) {
+    stop(sprintf("All elements in column '%s' must be unique", name_time))
   }
 
   ret