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# Function for parallel timeindex conversion
parallel_posfix <- function(df) {
chunk_size = 100000
num_chunks = ceiling(nrow(df)/chunk_size)
chunks=split(df$timeindex, ceiling(seq_along(df$timeindex)/chunk_size))
print(paste0("Parallel posfix | Chunks: ", num_chunks))
cl = parallel::makeCluster(4)
parallel::clusterEvalQ(cl, library(fasttime))
print(paste0("Parallel posfix | Cluster ", cl, " of ", detectCores()))
flush.console()
result <- parallel::clusterApply(cl, chunks, function(chunk) {
cat("Processing chunk\n")
flush.console()
#fasttime::fastPOSIXct(chunk, format = "%Y-%m-%d %H:%M:%S")
as.POSIXct(chunk)
})
stopCluster(cl)
print(" Reactive tsdf | Make conversion -->")
print(" Reactive tsdf | Make conversion ")
flush.console()
return(unlist(result))
}
# Get next index for the projection plot
set_plot_id <- function(prj_plot_id)({
prj_plot_id(prj_plot_id()+1)
})
# Get projection plot name
get_prjs_plot_name <- function(dataset_name, encoder_name, selected, cluster, prj_plot_id, input){
set_plot_id()
plt_name <- paste0(
execution_id, "_",
prj_plot_id(), "_",
dataset_name, "_",
encoder_name, "_",
input$cpu_flag, "_",
input$dr_method, "_",
input$clustering_options, "_",
"zoom", "_",
input$zoom_btn, "_",
"point_alpha_",
input$point_alpha, "_",
"show_lines_",
input$show_lines, "_",
"prjs.png"
)
print(paste0("embeddings plot name", plt_name))
plt_name
}
get_ts_plot_name <- function(dataset_name, encoder_name, prj_plot_id, input){
print("Getting timeserie plot name")
plt_name <- paste0(dataset_name, "_", encoder_name, input$dr_method, "_ts.html")
print(paste0("ts plot name: ", plt_name))
plt_name
} |