load(file = '/tmp/maps_nested_job')
n.row <- nrow(maps_nested_job)
# Run the search code with the estimated optimal gain maps in parallel
ptm <- proc.time()
model_search_lst <- parallel::mclapply(1:n.row, FUN = function(x) {try({
if(maps_nested_job$prior_type[x] == "polar") {
prior_type <- 2
} else if(maps_nested_job$prior_type[x] == "uniform") {
prior_type <- 1
}
paste("Currently processing prior type:", prior_type)
bgScale <- maps_nested_job$scale.dist[x]
n_trials <- maps_nested_job$n_trials[x]
# call matlab code from the wrapper function with the supplied parameters
single_search <- searchR::run_single_search(map_path = maps_nested_job$file_id[x],
seed_val = maps_nested_job$seed_val[x],
contrast = maps_nested_job$contrast[x],
efficiency = maps_nested_job$efficiency[x],
n_trials = n_trials,
radius = 8 / bgScale,
priorType = prior_type,
search_params = maps_nested_job$params[x])
})}, mc.cores = 16)
proc.time() - ptm
maps_nested_job$raw_search <- model_search_lst
maps_nested_job$radius <- 8 / maps_nested_job$scale.dist
save(file = '/tmp/maps_nested_job', maps_nested_job)
print(paste("Saved... ", '/tmp/maps_nested_job'))
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