library(tidyverse)
load('/tmp/efficiency_map_1D') # load efficiency estimated maps
maps_nested <- efficiency_map_1D %>%
group_by(n_rings,sp_const, prior.scale, scale.dist, prior_type,
map_prior, subject, params, contrast, efficiency, seed_val, label, n_trials) %>%
nest()
maps_nested$file_id <- paste0('/main/calen/fitmaps/', stringi::stri_rand_strings(nrow(maps_nested), 16), '.mat')
# interpolate to estimated gain maps to the full retina. to be used in actual search.
#source('~/Dropbox/Calen/Work/search/modeling/optimal_dp_scale_map/_code/_estimate_maps/fit_maps.R', echo=FALSE)
system("rm -rf /main/calen/fitmaps/*")
split_maps <- split(maps_nested, rep(1:(length(maps_nested$data)/16), length.out = length(maps_nested$data)))
file_id <- map(split_maps, function(x) {
maps <- searchR::fit_maps(maps_1d = x$data)
searchR::store_maps_disk(x$file_id, maps)
})
maps_nested_job <- maps_nested
save(file = '/tmp/maps_nested_job', maps_nested_job)
print(paste("Saved... ", '/tmp/maps_nested_job'))
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