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xo30xoqa authored
Previously, a global logger was used, which would have given problems on a parallel run. Also split up the `setupdatadir()` function to improve code structure.
xo30xoqa authoredPreviously, a global logger was used, which would have given problems on a parallel run. Also split up the `setupdatadir()` function to improve code structure.
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simulation.jl 6.15 KiB
### Persephone - a socio-economic-ecological model of European agricultural landscapes.
###
### This file includes the core functions for initialising and running simulations.
###
#XXX With the parameter scanning, code execution has become rather difficult to follow.
# Can I refactor this into two clear, separate paths - one for the normal case (single
# run) and one for parameter scanning?
"""
initmodel(settings)
Initialise a model object using a ready-made settings dict. This is
a helper function for `initialise()` and `initmodelsparallel()`.
"""
function initmodel(settings::Dict{String, Any})
@debug "Initialising model object."
createdatadir(settings["core.outdir"], settings["core.overwrite"])
logger = modellogger(settings["core.loglevel"], settings["core.outdir"])
with_logger(logger) do
events = Vector{FarmEvent}()
dataoutputs = Vector{DataOutput}()
landscape = initlandscape(settings["core.landcovermap"], settings["core.farmfieldsmap"])
space = GridSpace(size(landscape), periodic=false)
properties = Dict{Symbol,Any}(:settings=>settings,
:logger=>logger,
:date=>settings["core.startdate"],
:landscape=>landscape,
:dataoutputs=>dataoutputs,
:events=>events)
model = AgentBasedModel(Union{Farmer,Animal,FarmPlot}, space, properties=properties,
rng=StableRNG(settings["core.seed"]), warn=false)
saveinputfiles(model)
initfarms!(model)
initfields!(model)
initnature!(model)
model
end
end
"""
initmodelsparallel(settings)
Initialise multiple model objects using ready-made settings dicts. This is
a helper function for `initialise()`.
"""
function initmodelsparallel(settingsdicts::Vector{Dict{String, Any}})
#TODO parallelise model initialisation
@debug "Beginning to initialise model objects."
models = Vector{AgentBasedModel}()
for settings in settingsdicts
push!(models, initmodel(settings))
end
models
end
"""
paramscan(settings)
Initialise a list of model objects, covering all possible parameter combinations
given by the settings (i.e. a full-factorial experiment). This is a helper function
for `initialise()`.
"""
function paramscan(settings::Dict{String,Any}, scanparams::Vector{String})
# recursively generate a set of settings dicts covering all combinations
function generatecombinations(params::Vector{String})
(length(params) == 0) && return [settings]
param = pop!(params)
combinations = Vector{Dict{String,Any}}()
for comb in generatecombinations(params)
for value in settings[param]
newcombination = deepcopy(comb)
newcombination[param] = value
if comb["core.outdir"] == settings["core.outdir"]
outdir = joinpath(comb["core.outdir"], "$(split(param, ".")[2])_$(value)")
else
outdir = "$(comb["core.outdir"])_$(split(param, ".")[2])_$(value)"
end
newcombination["core.outdir"] = outdir
push!(combinations, newcombination)
end
end
combinations
end
generatecombinations(scanparams)
end
"""
initialise(config=PARAMFILE, seed=nothing)
Initialise the model: read in parameters, create the output data directory,
and instantiate the AgentBasedModel object(s). Optionally allows specifying the
configuration file and overriding the `seed` parameter. This returns a single
model object unless the config file contains multiple values for one or more
parameters, in which case it creates a full-factorial simulation experiment
and returns a vector of model objects.
"""
function initialise(config::String=PARAMFILE, seed::Union{Int64,Nothing}=nothing)
@info "Simulation run started at $(Dates.now())."
settings = getsettings(config, seed)
scanparams = settings["internal.scanparams"]
delete!(settings, "internal.scanparams")
isempty(scanparams) ? initmodel(settings) : initmodelsparallel(settings, scanparameters)
end
"""
stepsimulation!(model)
Execute one update of the model.
"""
function stepsimulation!(model::AgentBasedModel)
with_logger(model.logger) do
@info "Simulating day $(model.date)."
for a in Schedulers.ByType((Farmer,FarmPlot,Animal), true)(model)
try #The animal may have been killed
stepagent!(model[a], model)
catch exc
# check if the KeyError comes from the `model[a]` or the function call
isa(exc, KeyError) && isa(exc.key, Int) ? continue : throw(exc)
end
end
updateevents!(model)
outputdata(model)
model.date += Day(1)
model
end
end
"""
finalise!(model)
Wrap up the simulation. Currently doesn't do anything except print some information.
"""
function finalise!(model::AgentBasedModel)
with_logger(model.logger) do
@info "Simulated $(model.date-@param(core.startdate))."
@info "Simulation run completed at $(Dates.now()),\nwrote output to $(@param(core.outdir))."
#XXX is there anything to do here?
model
end
end
"""
simulate!(model)
Carry out a complete simulation run using a pre-initialised model object.
"""
function simulate!(model::AgentBasedModel)
runtime = Dates.value(@param(core.enddate)-@param(core.startdate))+1
step!(model, dummystep, stepsimulation!, runtime)
finalise!(model)
end
"""
simulate(config=PARAMFILE, seed=nothing)
Initialise one or more model objects and carry out a full simulation experiment,
optionally specifying a configuration file and a seed for the RNG.
This is the default way to run a Persephone simulation.
"""
function simulate(config::String=PARAMFILE, seed::Union{Int64,Nothing}=nothing)
models = initialise(config, seed)
if isa(models, Vector)
for m in models
@info "Executing run $(m.settings["core.outdir"])"
simulate!(m) #TODO parallelise
end
else
simulate!(models)
end
end