# NOT RUN {
if (interactive()) {
library(igraph) # use %>% in a few examples
library(raster)
tmpdir <- file.path(tempdir(), "examples")
# Create a default simList object for use through these examples
mySim <- simInit(
times = list(start = 0.0, end = 2.0, timeunit = "year"),
params = list(
.globals = list(stackName = "landscape", burnStats = "nPixelsBurned"),
# Turn off interactive plotting
fireSpread = list(.plotInitialTime = NA),
caribouMovement = list(.plotInitialTime = NA),
randomLandscapes = list(.plotInitialTime = NA)
),
modules = list("randomLandscapes", "fireSpread", "caribouMovement"),
paths = list(modulePath = system.file("sampleModules", package = "SpaDES"),
outputPath = tmpdir),
# Save final state of landscape and caribou
outputs = data.frame(objectName = c("landscape", "caribou"), stringsAsFactors = FALSE)
)
# Example 1 - test alternative parameter values
# Create an experiment - here, 2 x 2 x 2 (2 levels of 2 params in fireSpread,
# and 2 levels of 1 param in caribouMovement)
# Here is a list of alternative values for each parameter. They are length one
# numerics here -- e.g., list(0.2, 0.23) for spreadprob in fireSpread module,
# but they can be anything, as long as it is a list.
experimentParams <- list(fireSpread = list(spreadprob = list(0.2, 0.23),
nFires = list(20, 10)),
caribouMovement = list(N = list(100, 1000)))
sims <- experiment(mySim, params = experimentParams)
# see experiment:
attr(sims, "experiment")
# Read in outputs from sims object
FireMaps <- do.call(stack, lapply(1:NROW(attr(sims, "experiment")$expDesign),
function(x) sims[[x]]$landscape$Fires))
if (interactive()) Plot(FireMaps, new = TRUE)
# Or reload objects from files, useful if sim objects too large to store in RAM
caribouMaps <- lapply(sims, function(sim) {
caribou <- readRDS(outputs(sim)$file[outputs(sim)$objectName == "caribou"])
})
names(caribouMaps) <- paste0("caribou", 1:8)
# Plot whole named list
if (interactive()) Plot(caribouMaps, size = 0.1)
# Example 2 - test alternative modules
# Example of changing modules, i.e., caribou with and without fires
# Create an experiment - here, 2 x 2 x 2 (2 levels of 2 params in fireSpread,
# and 2 levels of 1 param in caribouMovement)
experimentModules <- list(
c("randomLandscapes", "fireSpread", "caribouMovement"),
c("randomLandscapes", "caribouMovement"))
sims <- experiment(mySim, modules = experimentModules)
attr(sims, "experiment")$expVals # shows 2 alternative experiment levels
# Example 3 - test alternative parameter values and modules
# Note, this isn't fully factorial because all parameters are not
# defined inside smaller module list
sims <- experiment(mySim, modules = experimentModules, params = experimentParams)
attr(sims, "experiment")$expVals # shows 10 alternative experiment levels
# Example 4 - manipulate manipulate directory names -
# "simNum" is special value for dirPrefix, it is converted to 1, 2, ...
sims <- experiment(mySim, params = experimentParams, dirPrefix = c("expt", "simNum"))
attr(sims, "experiment")$expVals # shows 8 alternative experiment levels, 24 unique
# parameter values
# Example 5 - doing replicate runs -
sims <- experiment(mySim, replicates = 2)
attr(sims, "experiment")$expDesign # shows 2 replicates of same experiment
# Example 6 - doing replicate runs, but within a sub-directory
sims <- experiment(mySim, replicates = 2, dirPrefix = c("expt"))
lapply(sims, outputPath) # shows 2 replicates of same experiment, within a sub directory
# Example 7 - doing replicate runs, of a complex, non factorial experiment.
# Here we do replication, parameter variation, and module variation all together.
# This creates 20 combinations.
# The experiment function tries to make fully factorial, but won't
# if all the levels don't make sense. Here, changing parameter values
# in the fireSpread module won't affect the simulation when the fireSpread
# module is not loaded:
# library(raster)
# beginCluster(20) # if you have multiple clusters available, use them here to save time
sims <- experiment(mySim, replicates = 2, params = experimentParams,
modules = experimentModules,
dirPrefix = c("expt", "simNum"))
# endCluster() # end the clusters
attr(sims, "experiment")
# Example 8 - Use replication to build a probability map.
# For this to be meaningful, we need to provide a fixed input landscape,
# not a randomLandscape for each experiment level. So requires 2 steps.
# Step 1 - run randomLandscapes module twice to get 2 randomly
# generated landscape maps. We will use 1 right away, and we will
# use the two further below
mySimRL <- simInit(
times = list(start = 0.0, end = 0.1, timeunit = "year"),
params = list(
.globals = list(stackName = "landscape"),
# Turn off interactive plotting
randomLandscapes = list(.plotInitialTime = NA)
),
modules = list("randomLandscapes"),
paths = list(modulePath = system.file("sampleModules", package = "SpaDES"),
outputPath = file.path(tmpdir, "landscapeMaps1")),
outputs = data.frame(objectName = "landscape", saveTime = 0, stringsAsFactors = FALSE)
)
# Run it twice to get two copies of the randomly generated landscape
mySimRLOut <- experiment(mySimRL, replicate = 2)
#extract one of the random landscapes, which will be passed into next as an object
landscape <- mySimRLOut[[1]]$landscape
# here we don't run the randomLandscapes module; instead we pass in a landscape
# as an object, i.e., a fixed input
mySimNoRL <- simInit(
times = list(start = 0.0, end = 1, timeunit = "year"), # only 1 year to save time
params = list(
.globals = list(stackName = "landscape", burnStats = "nPixelsBurned"),
# Turn off interactive plotting
fireSpread = list(.plotInitialTime = NA),
caribouMovement = list(.plotInitialTime = NA)
),
modules = list("fireSpread", "caribouMovement"), # No randomLandscapes modules
paths = list(modulePath = system.file("sampleModules", package = "SpaDES"),
outputPath = tmpdir),
objects = c("landscape"), # Pass in the object here
# Save final state (the default if saveTime is not specified) of landscape and caribou
outputs = data.frame(objectName = c("landscape", "caribou"), stringsAsFactors = FALSE)
)
# Put outputs into a specific folder to keep them easy to find
outputPath(mySimNoRL) <- file.path(tmpdir, "example8")
sims <- experiment(mySimNoRL, replicates = 8) # Run experiment
attr(sims, "experiment") # shows the experiment, which in this case is just replicates
# list all files that were saved called 'landscape'
landscapeFiles <- dir(outputPath(mySimNoRL), recursive = TRUE, pattern = "landscape",
full.names = TRUE)
# Can read in Fires layers from disk since they were saved, or from the sims
# object
# Fires <- lapply(sims, function(x) x$landscape$Fires) %>% stack
Fires <- lapply(landscapeFiles, function(x) readRDS(x)$Fires) %>% stack()
Fires[Fires > 0] <- 1 # convert to 1s and 0s
fireProb <- sum(Fires)/nlayers(Fires) # sum them and convert to probability
if (interactive()) Plot(fireProb, new = TRUE)
# Example 9 - Pass in inputs, i.e., input data objects taken from disk
# Here, we, again, don't provide randomLandscapes module, so we need to
# provide an input stack called lanscape. We point to the 2 that we have
# saved to disk in Example 8
mySimInputs <- simInit(
times = list(start = 0.0, end = 2.0, timeunit = "year"),
params = list(
.globals = list(stackName = "landscape", burnStats = "nPixelsBurned"),
# Turn off interactive plotting
fireSpread = list(.plotInitialTime = NA),
caribouMovement = list(.plotInitialTime = NA)
),
modules = list("fireSpread", "caribouMovement"),
paths = list(modulePath = system.file("sampleModules", package = "SpaDES"),
outputPath = tmpdir),
# Save final state of landscape and caribou
outputs = data.frame(objectName = c("landscape", "caribou"), stringsAsFactors = FALSE)
)
landscapeFiles <- dir(tmpdir, pattern = "landscape_year0", recursive = TRUE, full.names = TRUE)
# Varying inputs files - This could be combined with params, modules, replicates also
outputPath(mySimInputs) <- file.path(tmpdir, "example9")
sims <- experiment(mySimInputs,
inputs = lapply(landscapeFiles,function(filenames) {
data.frame(file = filenames, loadTime = 0,
objectName = "landscape",
stringsAsFactors = FALSE) })
)
# load in experimental design object
experiment <- load(file = file.path(tmpdir, "example9", "experiment.RData")) %>% get()
print(experiment) # shows input files and details
# Example 10 - Use a very simple output dir name using substrLength = 0,
# i.e., just the simNum is used for outputPath of each spades call
outputPath(mySim) <- file.path(tmpdir, "example10")
sims <- experiment(mySim, modules = experimentModules, replicates = 2,
substrLength = 0)
lapply(sims, outputPath) # shows that the path is just the simNum
experiment <- load(file = file.path(tmpdir, "example10", "experiment.RData")) %>% get()
print(experiment) # shows input files and details
# Example 11 - use clearSimEnv = TRUE to remove objects from simList
# This will shrink size of return object, which may be useful because the
# return from experiment function may be a large object (it is a list of
# simLists). To see size of a simList, you have to look at the objects
# contained in the envir(simList). These can be obtained via objs(sim)
sapply(sims, function(x) object.size(objs(x))) %>% sum + object.size(sims)
# around 3 MB
# rerun with clearSimEnv = TRUE
sims <- experiment(mySim, modules = experimentModules, replicates = 2,
substrLength = 0, clearSimEnv = TRUE)
sapply(sims, function(x) object.size(objs(x))) %>% sum + object.size(sims)
# around 250 kB, i.e., all the simList contents except the objects.
# Example 12 - pass in objects
experimentObj <- list(landscape = lapply(landscapeFiles, readRDS) %>%
setNames(paste0("landscape",1:2)))
# Pass in this list of landscape objects
sims <- experiment(mySimNoRL, objects = experimentObj)
# Remove all temp files
unlink(tmpdir, recursive = TRUE)
}
# }
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