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Path density plots

Tobias Stephan

2026-03-08

Source: vignettes/path_density.Rmd
path_density.Rmd

This document provides step-by-step instructions for producing density plots derived from HeFTy inverse thermal history models as seen in Padgett et al. (2025) and Johns-Buss et al. (2025).

Load Input Data

Open R and install and load the necessary packages. You can install the packages by running the following code:

install.packages("ggplot2")
remotes::install_github("tobiste/thermoclustr")

Next, install the {thermoclustr} package by running the following code:

Define the path to your Hefty output (.txt file). For example:

path2myfile <- "inst/112-73_30_H1_50-inv.txt"

Be aware that R uses forward-slashes (/) to separate folders.

Next, you import the .txt file into R by using the function read_hefty():

Part II

Plot the path density

To plot the density of the paths, you simply use the function plot_path_density():

# set `theme_classic()` as the default ggplot theme
theme_set(theme_classic())

plot1 <- plot_path_density_filled(tT_paths, show.legend = FALSE)
print(plot1)

This uses the package’s default values for smoothing and binning and creates a ggplot type graphic.

You can customize the smoothing and density binning by changing the parameters - bins - the number of filled contours.

  • GOF_rank - Selects only the n highest GOF ranked paths.

  • densify - Should extra points be added along the individual paths to avoid that only the vertices of the path are evaluated?

  • n - How many equally-spaced extra points should be added along between the vertices of the path (if densify=TRUE).

  • samples - Size of a random subsample of all the paths to reduce the computation time.

plot2 <- plot_path_density_filled(tT_paths, bins = 25, GOF_rank = 5, densify = TRUE, n = 100, max_distance = 1, samples = 100, show.legend = FALSE)
print(plot2)

Finally, you can customize your ggplot, such as axes labels, change colors, and reverse the axes:

plot2 +
  labs(
    title = "Kernel density of t-T paths",
    caption = "data from Fontes Pinto et al. (in prep.)",
    x = "Time (Ma)",
    y = bquote("Temperature (" * degree * "C)")
  ) +
  coord_cartesian(expand = FALSE) +
  scale_x_continuous(transform = "reverse", position = "top") +
  scale_y_continuous(transform = "reverse") +
  guides(fill = "none")