R/generate_r.R

Defines functions generate_r_include generate_r_dim generate_r_run generate_r_set_initial generate_r_interpolate_t generate_r_metadata generate_r_rhs generate_r_set_user generate_r_ic generate_r_create generate_r_core generate_r

generate_r <- function(dat, options) {
  if (dat$features$has_delay) {
    ## We're going to need an additional bit of internal data here,
    ## but this sits outside the core odin ir
    dat$meta$use_dde <- "odin_use_dde"
    dat$data$elements[[dat$meta$use_dde]] <- list(name = dat$meta$use_dde,
                                                  location = "internal",
                                                  storage_type = "bool",
                                                  rank = 0L,
                                                  dimnames = NULL)
  }

  dat$meta$support <-
    list(get_user_double = "_get_user_double",
         get_user_dim = "_get_user_dim",
         check_user = "_check_user",
         check_interpolate_y = "_check_interpolate_y",
         check_interpolate_t = "_check_interpolate_t")

  ## This is our little rewriter - we'll tidy this up later
  rewrite <- function(x) {
    generate_r_sexp(x, dat$data, dat$meta)
  }

  eqs <- generate_r_equations(dat, rewrite)

  ## Then start putting together the initial conditions
  env <- new.env(parent = odin_base_env())

  generate_r_include(dat, env)

  ## Support functions will come in this way:
  if (dat$features$has_user) {
    env[[dat$meta$support$get_user_double]] <- support_get_user_double
    env[[dat$meta$support$get_user_dim]] <- support_get_user_dim
  }
  env[[dat$meta$support$check_user]] <- support_check_user
  if (dat$features$has_interpolate) {
    env[[dat$meta$support$check_interpolate_y]] <- support_check_interpolate_y
    env[[dat$meta$support$check_interpolate_t]] <- support_check_interpolate_t
  }

  core <- generate_r_core(eqs, dat, env, rewrite)
  ret <- generate_r_class(core, dat, env)
  attr(ret, "generator") <- env[[dat$config$base]]
  ret
}


generate_r_core <- function(eqs, dat, env, rewrite) {
  core <- list(
    create = generate_r_create(eqs, dat, env, rewrite),
    ic = generate_r_ic(eqs, dat, env, rewrite),
    set_user = generate_r_set_user(eqs, dat, env),
    rhs_desolve = generate_r_rhs(eqs, dat, env, rewrite, "desolve"),
    rhs_dde = generate_r_rhs(eqs, dat, env, rewrite, "dde"),
    output = generate_r_rhs(eqs, dat, env, rewrite, "output"),
    interpolate_t = generate_r_interpolate_t(dat, env, rewrite),
    set_initial = generate_r_set_initial(dat, env, rewrite),
    run = generate_r_run(dat, env, rewrite),
    metadata = generate_r_metadata(dat, rewrite))
  list2env(core, env)
  core
}


generate_r_create <- function(eqs, dat, env, rewrite) {
  alloc <- call("<-", as.name(dat$meta$internal),
                quote(new.env(parent = emptyenv())))
  eqs_create <- r_flatten_eqs(eqs[dat$components$create$equations])
  if (dat$features$has_delay && !dat$features$discrete) {
    initial_time <- call("<-", rewrite(dat$meta$initial_time), NA_real_)
  } else {
    initial_time <- NULL
  }
  ret <- as.name(dat$meta$internal)
  body <- r_expr_block(list(alloc, eqs_create, initial_time, ret))
  as_function(alist(), body, env)
}


## TODO: 'ic' ==> 'initial'
generate_r_ic <- function(eqs, dat, env, rewrite) {
  ## Equations to run before initial conditions are computed:
  eqs_initial <- r_flatten_eqs(eqs[dat$components$initial$equations])

  ## We need a little fiction here because any use of a variable must
  ## use its "initial" name at this point.  We could filter through
  ## dependencies and work out if this is necessary, but this should
  ## be fairly harmless, and at the moment we don't report this well.
  subs <- lapply(dat$data$variable$contents, function(x) rewrite(x$initial))
  eqs_initial <- lapply(eqs_initial, substitute_, as.environment(subs))

  ## Allocate space for the state vector
  state <- as.name(dat$meta$state)
  var_length <- rewrite(dat$data$variable$length)
  alloc <- call("<-", state, call("numeric", var_length))

  ## Assign into the state vector
  f <- function(x) {
    d <- dat$data$elements[[x$name]]
    if (d$rank == 0L) {
      target <- call("[[", state, r_offset_to_position(x$offset))
    } else {
      offset <- rewrite(x$offset)
      seq <- call("seq_len", rewrite(d$dimnames$length))
      target <- call("[", state, call("+", offset, seq))
    }
    call("<-", target, rewrite(x$initial))
  }
  assign <- lapply(dat$data$variable$contents, f)

  ## Build the function:
  body <- as.call(c(list(quote(`{`)), eqs_initial, alloc, assign, state))
  args <- alist(time = , internal = ) # nolint
  names(args)[[1]] <- dat$meta$time
  names(args)[[2]] <- dat$meta$internal
  as_function(args, body, env)
}


generate_r_set_user <- function(eqs, dat, env) {
  eqs_user <- r_flatten_eqs(eqs[dat$components$user$equations])
  args <- alist(user = , internal = , unused_user_action = ) # nolint
  names(args)[[1]] <- dat$meta$user
  names(args)[[2]] <- dat$meta$internal

  user <- vcapply(dat$user, "[[", "name", USE.NAMES = FALSE)
  user_args <- if (length(user) == 1L) user else as.call(c(quote(c), user))
  check <- call(dat$meta$support$check_user,
                as.name(dat$meta$user), user_args, quote(unused_user_action))

  body <- r_expr_block(c(list(check, eqs_user)))
  as_function(args, body, env)
}


generate_r_rhs <- function(eqs, dat, env, rewrite, rhs_type) {
  discrete <- dat$features$discrete
  has_output <- dat$features$has_output

  if (rhs_type == "output" && !has_output) {
    return(NULL)
  }
  if (discrete && rhs_type != "dde") {
    return(NULL)
  }

  ## This bit is surprisingly hard:
  if (discrete || rhs_type == "desolve") {
    use <- c("rhs", "output")
  } else {
    use <- if (rhs_type == "dde") "rhs" else "output"
  }
  join <- function(x, nm) {
    if (length(x) == 1L) x[[1]][[nm]] else union(x[[1]][[nm]], x[[2]][[nm]])
  }
  use_vars <- join(dat$components[use], "variables")
  use_eqs <- join(dat$components[use], "equations")

  ## NOTE: this is really similar to code in ic but that goes the
  ## other way, into the state vector.
  f <- function(x) {
    d <- dat$data$elements[[x$name]]
    state <- as.name(dat$meta$state)
    if (d$rank == 0L) {
      extract <- call("[[", state, r_offset_to_position(x$offset))
    } else {
      seq <- call("seq_len", rewrite(d$dimnames$length))
      extract <- call("[", state, call("+", rewrite(x$offset), seq))
      if (d$rank > 1L) {
        extract <- call("array", extract, generate_r_dim(d, rewrite))
      }
    }
    call("<-", as.name(x$name), extract)
  }

  vars <- unname(drop_null(lapply(dat$data$variable$contents[use_vars], f)))

  ## NOTE: There are two reasonable things to do here - we can look up
  ## the length of the variable (dat$data$variable$length) or we can
  ## just make this a vector the same length as the incoming state (as
  ## dydt is always the same length as y).  Neither seems much better
  ## than the other, so going with the same length approach here as it
  ## is less logic and will work for variable-length cases.
  alloc_result <- call("<-", as.name(dat$meta$result),
                       call("numeric", call("length", as.name(dat$meta$state))))

  ## For output_length we have no real choice but to look up the
  ## length each time.
  alloc_output <- call("<-", as.name(dat$meta$output),
                       call("numeric", rewrite(dat$data$output$length)))
  alloc <- list(rhs = alloc_result, output = alloc_output)[use]

  result <- as.name(dat$meta$result)
  output <- as.name(dat$meta$output)

  if (rhs_type == "desolve") {
    if (has_output) {
      ret <- call("list", result, output)
    } else {
      ret <- call("list", result)
    }
  } else if (rhs_type == "output") {
    ret <- output
  } else {
    if (discrete && has_output) {
      ret <- list(
        call("<-", call("attr", result, "output"), output),
        result)
    } else {
      ret <- result
    }
  }

  eqs_include <- r_flatten_eqs(eqs[use_eqs])
  body <- as.call(c(list(quote(`{`)), c(vars, alloc, eqs_include, ret)))
  args <- alist(t = , y = , parms = ) # nolint
  names(args)[[1]] <- dat$meta$time
  names(args)[[2]] <- dat$meta$state
  names(args)[[3]] <- dat$meta$internal
  as_function(args, body, env)
}


generate_r_metadata <- function(dat, rewrite) {
  ord <- function(location) {
    contents <- dat$data$elements[names(dat$data[[location]]$contents)]
    len <- lapply(contents, generate_r_dim, rewrite)
    as.call(c(list(quote(list)), len))
  }

  ynames <- call(
    "make_names",
    quote(private$variable_order), quote(private$output_order),
    dat$features$discrete)
  n_out <- quote(support_n_out(private$output_order))

  env <- new.env(parent = environment(odin))

  body <- list(
    call("<-", as.name(dat$meta$internal), quote(private$data)),
    call("<-", quote(private$variable_order), ord("variable")),
    call("<-", quote(private$output_order), ord("output")),
    call("<-", call("$", quote(private), quote(ynames)), ynames),
    call("<-", call("$", quote(private), quote(n_out)), n_out))

  if (dat$features$has_interpolate) {
    body <- c(
      body,
      quote(private$interpolate_t <- private$core$interpolate_t(private$data)))
  }

  args <- alist(self = , private = ) # nolint
  body <- as.call(c(list(as.name("{")), body))

  as_function(args, body, env)
}


generate_r_interpolate_t <- function(dat, env, rewrite) {
  if (!dat$features$has_interpolate) {
    return(function(...) NULL)
  }

  args_min <- lapply(dat$interpolate$min, function(x)
    call("[[", rewrite(x), 1L))
  if (length(args_min) == 1L) {
    min <- args_min[[1L]]
  } else {
    min <- as.call(c(list(quote(max)), args_min))
  }

  args_max <- lapply(dat$interpolate$max, function(x)
    call("[[", rewrite(x), call("length", rewrite(x))))
  if (length(args_max) == 0L) {
    max <- Inf
  } else if (length(args_max) == 1L) {
    max <- args_max[[1L]]
  } else {
    max <- as.call(c(list(quote(min)), args_max))
  }

  args_critical <- lapply(dat$interpolate$critical, rewrite)
  if (length(args_critical) == 0L) {
    critical <- numeric(0)
  } else if (length(args_critical) == 1L) {
    critical <- args_critical[[1L]]
  } else {
    critical <-
      call("sort", call("unique", as.call(c(list(quote(c)), args_critical))))
  }

  args <- set_names(alist(internal = ), dat$meta$internal) # nolint
  body <- call("{",
               call("list",
                    min = min, max = max, critical = critical))
  as_function(args, body, env)
}


generate_r_set_initial <- function(dat, env, rewrite) {
  if (!dat$features$has_delay) {
    return(function(...) NULL)
  }

  set_y <- call(
    "if", call("!", call("is.null", as.name(dat$meta$state))),
    r_expr_block(lapply(dat$data$variable$contents, function(x)
      call("<-", rewrite(x$initial),
           r_extract_variable(x, dat$data$elements, as.name(dat$meta$state),
                              rewrite)))))
  set_t <- call("<-", rewrite(dat$meta$initial_time), as.name(dat$meta$time))

  body <- list(set_y, set_t)
  args <- set_names(
    alist(, , ),
    c(dat$meta$time, dat$meta$state, dat$meta$internal))

  if (!dat$features$discrete) {
    args <- c(args, set_names(alist(x = ), dat$meta$use_dde)) # nolint
    body <- c(body, list(call("<-", rewrite(dat$meta$use_dde),
                              as.name(dat$meta$use_dde))))
  }

  as_function(args, r_expr_block(body), env)
}


## Thos feels pretty messy, but I think we can clean it up later.
## It's quite likely that we'd be better off with two separate
## generators - one for discrete and one for continuous models.
generate_r_run <- function(dat, env, rewrite) {
  args <- alist(data = , t = , y = , n_out = , ynames = , ... = , # nolint
                  ## These are only used in some cases!
                  interpolate_t = ) # nolint
  if (dat$features$discrete) {
    args <- c(args, alist(replicate = )) # nolint
  } else {
    args <- c(args, alist(use_dde = , tcrit = )) # nolint
  }
  if (!dat$features$discrete && dat$features$has_delay) {
    args <- c(args, list(n_history = DEFAULT_HISTORY_SIZE))
  }

  names(args)[[1L]] <- dat$meta$internal
  names(args)[[2L]] <- dat$meta$time
  names(args)[[3L]] <- dat$meta$state
  ## for now at least, quote these - we might have to fix this up
  ## later by having a second (non-ir driven) meta list.
  n_out <- quote(n_out)
  ynames <- quote(ynames)
  tcrit <- if (dat$features$discrete) NULL else quote(tcrit)

  replicate <- quote(replicate)
  use_dde <- quote(use_dde)
  n_history <- quote(n_history)
  interpolate_t <- quote(interpolate_t)

  ret <- quote(ret)

  t0 <- call("[[", as.name(dat$meta$time), 1L)

  if (dat$features$has_delay) {
    set_initial <- list(quote(set_initial), t0, as.name(dat$meta$state),
                        as.name(dat$meta$internal))
    if (!dat$features$discrete) {
      set_initial <- c(set_initial, list(use_dde))
    }
    set_initial <- as.call(set_initial)
  } else {
    set_initial <- NULL
  }

  if (dat$features$has_interpolate) {
    check_interpolate_t <-
      call(dat$meta$support$check_interpolate_t,
           as.name(dat$meta$time), interpolate_t, tcrit)
    if (!dat$features$discrete) {
      check_interpolate_t <- call("<-", tcrit, check_interpolate_t)
    }
  } else {
    check_interpolate_t <- NULL
  }

  compute_initial <-
    call("if", call("is.null", as.name(dat$meta$state)),
         r_expr_block(call("<-", as.name(dat$meta$state),
                         call("ic", t0, as.name(dat$meta$internal)))))

  output <- if (dat$features$has_output) quote(output) else NULL

  run_args_dde <- list(as.name(dat$meta$state), as.name(dat$meta$time),
                       quote(rhs_dde), as.name(dat$meta$internal),
                       ynames = FALSE, quote(`...`))
  run_args_desolve <- list(as.name(dat$meta$state), as.name(dat$meta$time),
                           quote(rhs_desolve), as.name(dat$meta$internal),
                           quote(`...`))
  if (!dat$features$discrete) {
    run_args_desolve <- c(run_args_desolve, tcrit = tcrit)
  }
  if (dat$features$has_output) {
    run_args_dde <- c(run_args_dde, list(n_out = n_out))
    if (!dat$features$discrete) {
      run_args_dde <- c(run_args_dde, list(output = quote(output)))
    }
  }
  if (!dat$features$discrete && dat$features$has_delay) {
    run_args_dde <- c(run_args_dde, list(n_history = n_history))
    run_args_desolve <- c(run_args_desolve,
                          list(control = call("list", mxhist = n_history)))
  }

  if (dat$features$discrete) {
    run_replicate <-
      as.call(c(list(quote(dde::difeq_replicate), replicate), run_args_dde))
    run_single <-
      as.call(c(list(quote(dde::difeq)), run_args_dde))
    run <- r_expr_if(
      call("is.null", replicate),
      call("<-", ret, run_single),
      call("<-", ret, run_replicate))
  } else {
    run_fn_dde <- quote(dde::dopri)
    if (dat$features$has_delay) {
      run_fn_desolve <- quote(deSolve::dede)
    } else {
      run_fn_desolve <- quote(deSolve::ode)
    }
    run <- r_expr_if(
      use_dde,
      call("<-", ret, as.call(c(list(run_fn_dde), run_args_dde))),
      call("<-", ret, as.call(c(list(run_fn_desolve), run_args_desolve))))
  }

  set_names <- r_expr_if(
    call("is.null", ynames),
    list(call("<-", call("colnames", ret), NULL),
         call("<-", call("class", ret), "matrix")),
    call("<-", call("colnames", ret), ynames))


  body <- drop_null(list(set_initial, check_interpolate_t, compute_initial, run,
                         set_names, ret))

  as_function(args, r_expr_block(body), env)
}


generate_r_dim <- function(data_info, rewrite) {
  if (data_info$rank == 0L) {
    NULL
  } else if (data_info$rank == 1L) {
    rewrite(data_info$dimnames$length)
  } else {
    as.call(c(list(quote(c)), lapply(data_info$dimnames$dim, rewrite)))
  }
}


generate_r_include <- function(dat, env) {
  include <- dat$config$include$data
  if (length(include) > 0) {
    src <- unlist(lapply(include, function(x) list_to_character(x$source)))
    tmp <- tempfile()
    on.exit(unlink(tmp))
    writeLines(src, tmp)
    sys.source(tmp, env)
  }
}

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odin documentation built on April 28, 2022, 9:05 a.m.