profiling.R

# https://adv-r.hadley.nz/vectors-chap.html
# https://gallery.rcpp.org/articles/handling-R6-objects-in-rcpp/

#package is outdates, so instead we install from github
# install.packages("pkgapi") 

# library(devtools)
# install_github("r-lib/pkgapi")

# library(pkgapi)
# pkgapi::map_package("/Users/Trinity/Library/R/x86_64/4.1/library/base")
# .libPaths()
# 
# debugonce(lapply)
# lapply(c(1:10),x)
# undebug()
# 
# ?base::lapply
# showMethods("lapply")
# selectMethod("lapply", "ANY") 
# showMethods("sapply")


start_time <- Sys.time()

# please inlcude all libraries here and not in other scripts
library(R6)
# library(Rcpp)
library(ggplot2)

print("Sourcing Driver.R ... ")
{
  source("globalEnvironment.R")
  source("person.R")
  source("population.R")
  source("rHelperFunctions.R")
  source("rCoreFunctions.R")
  source("plots.R")

}
# sourceCpp(file = "cCoreFunctions.cpp",env = globalenv())
################################################################################################

library(profvis)
profvis({
  {
    bDebugMode=F
    set.seed(1)
    pop<-initialize.simulation(id = 1,n = POP.SIZE)
    while(pop$params$CYNOW<= END.YEAR)
          pop<-run.one.year(pop)
  }
})


# create.initial.population <- function( id=0,
#                                        n=0 # number of people if not specified as in mc
# ){
#   # 1- create an empty population
#   pop<-POPULATION$new(id = id,
#                       members = list(),
#                       params = generate.new.modelParameter(),
#                       stats =  generate.new.stat())
#   
#   # 2- create member list of persons for this population 
#   #subset the first n row to create n persons
#   sim.pop=pop$params$step.dataset
#   if (n>nrow(sim.pop)) stop ("Requested size of initial population is greater than simpop data in 2015")
#   sim.pop=sim.pop[1:n,]
#   #set up attributes for each row
#   sim.pop$age[sim.pop$age==0] = 0.5 #we need this so that the first agegroups is set to 1
#   sim.pop$age[sim.pop$age>85] = 85 
#   sim.pop$sex[sim.pop$sex=="MALE"] = MALE 
#   sim.pop$sex[sim.pop$sex=="FEMALE"] = FEMALE
#   sim.pop$ncd[sim.pop$hypertension==0 & sim.pop$diabetes==0] = NCD.NEG #1, neither 
#   sim.pop$ncd[sim.pop$hypertension==0 & sim.pop$diabetes==1] = NCD.DIAB #2, diabetic
#   sim.pop$ncd[sim.pop$hypertension==1 & sim.pop$diabetes==0] = NCD.HYP #3, hypertensive
#   sim.pop$ncd[sim.pop$hypertension==1 & sim.pop$diabetes==1] = NCD.DIAB_HYP #4, both
#   #vector of attributes for agents
#   vIds = c(1:n) #@MS: the person id is a unique number that we use to differentiate people. 
#   # we need to start this from 0 and add it over time. 
#   # otherwise, the future values may overlap with ones you're reading from the step dataset sim.pop$X
#   vAges = sim.pop$age
#   vSexes = as.numeric(sim.pop$sex)
#   vNcdState = sim.pop$ncd
#   vHivState = rep(HIV.NEG,n)
#   vtDiabInc= c(sim.pop$ncd%in%c(NCD.DIAB))*0
#   vtHypInc= c(sim.pop$ncd%in%c(NCD.HYP))*0
#   vtDiabHypInc= c(sim.pop$ncd%in%c(NCD.DIAB_HYP))*0
#   tborn=pop$params$TNOW
#   #### option1: R
#   # memberList = (mapply(PERSON$new,
#   #                      tborn,
#   #                      vIds,
#   #                      vSexes,
#   #                      vAges,
#   #                      vHivState,
#   #                      vNcdState,
#   #                      vtDiabInc,
#   #                      vtHypInc,
#   #                      vtDiabHypInc
#   # ))
#   # memberList = unlist(memberList)
#   # pop$members<-memberList
#   
#   #### option2: RCPP
#   memberList=rcpp_initialize_population(
#     tborn,
#     vIds,
#     vSexes,
#     vAges,
#     vHivState,
#     vNcdState,
#     vtDiabInc,
#     vtHypInc,
#     vtDiabHypInc
#   )
#   memberList = unlist(memberList)
#   pop$members<-memberList
#   
#   pop$greet()
#   
#   pop
# }



# total time
# 2590 R
# 2640 Rcpp