scripts/09_predict_R0_from_fluctuating_T.R

In lorecatta/DENVclimate:

# calculate R0 with daily ((nocturnal or diurnal) fluctuating temp


devtools::load_all()


options(didehpc.cluster = "fi--didemrchnb")

CLUSTER <- TRUE

my_resources <- c(
  file.path("R", "temp_functions_all.R"),
  file.path("R", "R0_function.R"),
  file.path("R", "calculate_annual_traits_R0_FT.R"),
  file.path("R", "utilities.R"))

if (CLUSTER) {

  # running out of place

  workdir <- "Q:/DENVclimate"
  didehpc::didehpc_config_global(workdir = workdir)
  root <- file.path(workdir, "context")

} else {

  root <- "context"

}

context::context_log_start()
ctx <- context::context_save(path = root,
                             sources = my_resources)


# define parameters -----------------------------------------------------------


in_path <- file.path("output", "termal_response_fits", "informative")

dir_save <- file.path("figures", "trait_R0_relationships")

TS_file_names <- c("TS2_DayTemp.rds", "TS2_NightTemp.rds")


# define variables ------------------------------------------------------------


TS_file_name <- TS_file_names[2]


# are you using the cluster? --------------------------------------------------


if (CLUSTER) {

  config <- didehpc::didehpc_config(template = "24Core")
  obj <- didehpc::queue_didehpc(ctx, config = config)

} else {

  context::context_load(ctx)
  context::parallel_cluster_start(8, ctx)

}


# load data -------------------------------------------------------------------


a_samps <- readRDS(file.path(in_path, "a_samps.rds"))
b_samps <- readRDS(file.path(in_path, "b_samps.rds"))
c_samps <- readRDS(file.path(in_path, "c_samps.rds"))
MDR_samps <- readRDS(file.path(in_path, "MDR_samps.rds"))
EFD_samps <- readRDS(file.path(in_path, "EFD_samps.rds"))
e2a_samps <- readRDS(file.path(in_path, "e2a_samps.rds"))
PDR_samps <- readRDS(file.path(in_path, "PDR_samps.rds"))
lf_samps <- readRDS(file.path(in_path, "lf_DENV_samps.rds"))

foi_covariates <- readRDS(file.path("output", "foi_data_cov_rescaled.rds"))

TS2 <- readRDS(file.path("output", "trait_R0_relationships", TS_file_name))


# pre processing --------------------------------------------------------------


no_data <- nrow(foi_covariates)
n <- dim(a_samps)[1]
thinned <- seq(1, n, by = 5)
lthin <- length(thinned)


# submit all jobs -------------------------------------------------------------


if (CLUSTER) {

  annual_R0_all_data_points <- queuer::qlapply(
    seq_len(no_data),
    calculate_annual_traits_R0,
    obj,
    lthin,
    TS2,
    thinned,
    a_samps,
    PDR_samps,
    MDR_samps,
    e2a_samps,
    b_samps,
    c_samps,
    lf_samps,
    EFD_samps)

} else {

  annual_R0_all_data_points <- loop(seq_len(no_data),
                                    calculate_annual_traits_R0,
                                    lthin,
                                    TS2,
                                    thinned,
                                    a_samps,
                                    PDR_samps,
                                    MDR_samps,
                                    e2a_samps,
                                    b_samps,
                                    c_samps,
                                    lf_samps,
                                    EFD_samps,
                                    parallel = TRUE)

}