sim.inner.tree() modifies its input #119
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I couldn't reproduce this issue because a different exception gets thrown: > settings <- yaml.load_file('~/git/twt/working/issue119.yaml')
> model <- Model$new(settings)
Error in FUN(X[[i]], ...) :
ID7316 of Lineage 16 is not a specified Compartment object.
Available compartments:
ID18_1ID277_1ID165_1ID221_1ID190_1ID105_1ID188_1ID406_1ID192_1ID97_1ID207_1ID399_1ID56_1ID57_1ID218_1ID228_1ID335_1ID149_1ID235_1ID281_1ID288_1ID233_1ID152_1ID260_1ID418_1ID84_1ID237_1ID1_1ID212_1ID463_1ID389_1ID93_1ID372_1ID214_1ID474_1ID295_1ID100_1ID21_1ID225_1ID529_1ID210_1ID426_1ID183_1ID316_1ID61_1ID553_1ID102_1ID229_1ID274_1ID191_1ID104_1ID430_1ID113_1ID117_1ID337_1ID33_1ID560_1ID73_1 |
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Never mind - this is a copy-paste error of the MRE |
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Ran into a different problem: > set.seed(1234)
> outer.tree <- load.outer.tree(model)
> plot(outer.tree)
Error in max(sapply(comp$get.lineages(), function(line) line$get.sampling.time())) :
invalid 'type' (list) of argumentThis occurs because the > node
[1] "ID316_1"
> comp
<Compartment>
Public:
add.lineage: function (new.lineage)
clone: function (deep = FALSE)
copy: function (deep = FALSE)
get.branching.time: function ()
get.lineages: function ()
get.name: function ()
get.sampling.time: function ()
get.source: function ()
get.type: function ()
initialize: function (name = NA, type = NA, source = NA, branching.time = NA,
is.unsampled: function ()
remove.lineage: function (ex.lineage)
set.branching.time: function (new.branching.time)
set.sampling.time: function ()
set.source: function (new.source)
set.type: function (new.type)
set.unsampled: function (is.unsampled)
Private:
branching.time: 519
deep_clone: function (name, value)
lineages: list
name: ID316_1
sampling.time: NA
source: Compartment, R6
type: CompartmentType, R6
unsampled: FALSE
> comp$get.lineages()
list() |
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Reproduced the problem: > library(twt)
> set.seed(1234)
> #settings <- yaml.load_file('../data/sampled-hosts-1.yaml')
> settings <- yaml.load_file('issue119.yaml')
> m <- Model$new(settings)
>
> run <- twt::load.outer.tree(model = m)
> plot(run) # <-- OK
>
> events <- run$get.eventlog()$get.all.events()
> dim(events)
[1] 57 8
>
> inner <- sim.inner.tree(run)
>
> plot(run) # <-- fails
Error in .plot.outer.tree(run, ...) :
Detected multiple roots in Run object: ID73_1, ID529_1
>
> events2 <- run$get.eventlog()$get.events('transmission')
> dim(events2)
[1] 38 8Very strange - |
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@ewong347 ran into the same problem mentioned above in PoonLab/simclone#4 (private repo) |
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Another Yaml specification MRE #Modelling from initial infection
InitialConditions:
originTime: 10.0 #
size:
Active: 1000 # number of susceptible at the origin time of the simulation for Type `active`
latent: 100
indexType: Active
CompartmentTypes:
'Active':
branching.rates: (Active=0.5, latent=0.05)
transition.rates: () #
coalescent.rate: 1.5
migration.rates: ()
generation.time: 1
bottleneck.size: 1 # bottleneck size when transmission event occurs
effective.size : 2
'latent':
branching.rates: (Active=0.0005, latent= 0.00005)
transition.rates: ()
migration.rates: () # no migration (assume no superinfection)
generation.time: 1
bottleneck.size: 1 # bottleneck size when transmission event occurs
effective.size : 2
Compartments:
'Active':
type: Active # CompartmentType
replicates: 1
source: NA
branching.time: 0
'Latent':
type: latent
replicates: 1
source: NA #try dropping me
branching.time: 0
Lineages:
'A':
type: virus
sampling.time: 0
location: Active
replicates: 1 # Starts in the Active compartment |
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I think this is related to #92. We should probably be cloning the Run object produced by |
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This is a follow-up of #118 where host "ID73" was the index case of the entire epidemic. My output from the ABM did not identify this host, as spotted by @ArtPoon , now the code is fixed.
I have noticed a strange behaviour, that is probably unintentional. It seems that
sim.inner.tree()modifies its input. Below is the MRE. I would not expect the second callplot(outer.tree)to fail -- likely thatouter.treewas changed on the way... Thanks!and the associated YAML file:
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