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study.org

@@ -435,14 +435,7 @@ specifies the compression level to use.
 
 #+NAME: benchmarks-csv
 #+begin_src csv :eval never :tangle benchmarks.csv
-5000,high
-5000,low
-10000,high
-10000,low
-15000,high
-15000,low
-20000,high
-20000,low
+
 #+end_src
 
 We remind the reader that to rerun the benchmarks presented in the study, one
@@ -655,7 +648,7 @@ with increasing size of the target linear system.
 #+NAME: get-ram-plot
 #+HEADER: :noweb yes :exports results :results silent
 #+begin_src R
-<<code-ram>>
+
 #+end_src
 
 #+CAPTION: RAM usage peaks of sequential runs of =minisolver= on linear systems
@@ -676,34 +669,17 @@ is defined in this section. However, it is possible to run it in its entirety by
 evaluating Linsting [[get-ram-plot]].
 
 #+begin_src R 
-library(svglite)
-library(ggplot2)
-data <- read.csv(file = "results.csv", header = FALSE)
-colnames(data) <- c("size", "compression", "time", "ram", "epsilon")
+
 #+end_src
 
 Except that here, the Y-axis represents the RAM usage in mibibytes (MiB).
 
 #+begin_src R
-plot <- ggplot(
-  data = data,
-  mapping = aes(x = size, y = ram, color = compression)
-) +
-geom_line() +
-geom_point(size = 2.5) +
-scale_x_continuous(name = "# Unknowns (N)") +
-scale_y_continuous(name = "RAM usage peaks [MiB]") +
-labs(color = "Compression level") +
-scale_color_manual(
-  values = c("high" = "#F07E26", "low" = "#9B004F")
-) +
-theme_bw()
-#+end_src
 
 The destination file name changes too, of course.
 
 #+begin_src R
-ggsave(file = "figures/results-ram.pdf", plot = plot, width = 5, height = 3)
+
 #+end_src
 
 * Conclusion