Run optimizeAgReactivity on every nth optimisation

reactivity-adjust-nth-optim.R

library(Racmacs)
library(progress)


# Make a map with 1000 optimisations
n_optim <- 1000
titers <- read.titerTable("hi.csv")
map <- make.acmap(titer_table = titers, number_of_dimensions = 2, number_of_optimizations = n_optim)


# Run reactivity optimisation on every 500th optimisation
# I.e. this will generate 1000/500 = 2 maps
skip <- 500
n_iter <- n_optim / skip


# Make sure the optimisations are sorted, so that skipping samples diverse maps
map <- sortOptimizations(map)


# Set up a progress bar
pb <- progress_bar$new(
    format = "(:spin) [:bar] :percent [Elapsed time: :elapsedfull || Estimated time remaining: :eta]",
    total = n_iter,
)


# For storing results
reactivity_adjusted <- list()

for (i in 1:n_iter) {
    # Increment progress bar
    pb$tick()

    # Run reactivity adjustment
    reactivity_adjusted[[i]] <- optimizeAgReactivity(map, optimization_number = i * skip)
}