121 lines
3.0 KiB
R
121 lines
3.0 KiB
R
library(ggplot2)
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library(dplyr)
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library(readr)
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library(viridisLite)
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# Usage: Rscript combined_instr_fault_correlation_heatmap.r exp_abspath1 ... [faults_file]
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args <- commandArgs(trailingOnly = TRUE)
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if (length(args) < 1) {
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stop("Need at least 1 experiment")
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}
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csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
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args[length(args)]
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} else {
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"faults.csv"
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}
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exp_args <- if (grepl("\\.csv$", args[length(args)])) {
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args[-length(args)]
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} else {
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args
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}
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all_data <- data.frame()
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all_mnem <- data.frame()
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for (arg in exp_args) {
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csv_file <- file.path(arg, csv_suffix)
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if (!file.exists(csv_file)) {
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warning(paste("Missing:", csv_file))
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next
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}
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df <- read_csv(csv_file, col_types = cols())
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df$experiment <- basename(arg)
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all_data <- bind_rows(all_data, df)
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# TODO: This is ignoring any filters currently
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mnem_file <- file.path(arg, "mnemonics.csv")
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if (!file.exists(mnem_file)) {
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warning(paste("Missing:", mnem_file))
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next
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}
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mdf <- read_csv(mnem_file, col_types = cols())
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all_mnem <- bind_rows(all_mnem, mdf)
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}
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if (nrow(all_data) == 0) {
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stop("No faults.csv data loaded")
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}
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if (nrow(all_mnem) == 0) {
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stop("No mnemonics.csv data loaded")
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}
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# no OK_MARKER, sum GROUP1 + TRAP.
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all_data <- all_data |>
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filter(!is.na(mnemonic), mnemonic != "NULL") |>
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filter(resulttype != "OK_MARKER") |>
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mutate(
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resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype)
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)
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if (nrow(all_data) == 0) {
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stop("No failure-marker data to plot")
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}
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# Sum faults per (mnemonic, marker) pair for all experiments
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heat <- all_data |>
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group_by(mnemonic, resulttype) |>
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summarise(faults = sum(faults, na.rm = TRUE), .groups = "drop")
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# Sum mnemonic counts for all experiments
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mnem_counts <- all_mnem |>
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filter(!is.na(mnemonic), mnemonic != "NULL") |>
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group_by(mnemonic) |>
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summarise(count = sum(count, na.rm = TRUE), .groups = "drop")
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# Normalize by mnemonic count
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heat <- heat |>
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left_join(mnem_counts, by = "mnemonic") |>
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filter(!is.na(count), count > 0) |>
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mutate(fault_rate = faults / count)
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if (nrow(heat) == 0) {
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stop("Heat join failed")
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}
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# Order by fault rate
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mnem_order <- heat |>
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group_by(mnemonic) |>
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summarise(total = sum(fault_rate), .groups = "drop") |>
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arrange(desc(total)) |>
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pull(mnemonic)
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heat <- heat |>
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mutate(mnemonic = factor(mnemonic, levels = mnem_order))
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plot <- ggplot(
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heat,
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aes(x = mnemonic, y = resulttype, fill = fault_rate)
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) +
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geom_tile(colour = "white") +
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scale_fill_viridis_c(name = "Fault rate", trans = "log10") +
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labs(
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title = "Instruction / Fault Rate Heatmap (Normalized)",
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x = "Instruction",
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y = "Fault Type"
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) +
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theme_minimal() +
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theme(
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axis.text.x = element_text(angle = 90, hjust = 1),
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panel.grid = element_blank(),
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plot.title = element_text(size = 14, face = "bold")
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)
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suffix <- gsub("^faults|\\.csv$", "", csv_suffix)
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outfile <- paste0("injections/instr_fault_rate_heatmap", suffix, ".svg")
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ggsave(outfile, plot = plot, width = 12, height = 6)
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print(paste("Saved", outfile))
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