color_dissimilarity

commit 4a64535d7641b8076c37dcba7a4e34160d6f1b7e
parent 708361796bcd0dc533d51bba10dffc232ebcc0dd
Author: Eamon Caddigan <eamon.caddigan@gmail.com>
Date:   Wed,  8 Sep 2021 06:20:36 -0400

A simpler approach to choosing dissimilar colors, with advantages and disadvantages

Diffstat:
M
demo.Rmd
 | 
11
++++++-----
A
seq_dissim_colors.R
 | 
35
+++++++++++++++++++++++++++++++++++

2 files changed, 41 insertions(+), 5 deletions(-)
diff --git a/demo.Rmd b/demo.Rmd
@@ -17,7 +17,7 @@ library(grDevices)
 ```
 ```{r}
 source('dissimilarity.R')
-source('max_dissim.R')
+source('seq_dissim_colors.R')
 source('color_utils.R')
 ```
 ```{r}
@@ -33,6 +33,8 @@ Next let's grab some random colors and show them.
 some_colors <- c('red', 'black', 'white', 'khaki', 'gray50',
                  '#a6cee3', '#1f78b4', '#b2df8a', '#33a02c')
 some_colors <- paste0("gray", round(seq(10, 90, length.out = 13)))
+some_colors <- colorRampPalette(c("gray10", "gray90"), space = "Lab")(6)
+some_colors <- sample(some_colors)
 compare_colors(some_colors)
 ```
 
@@ -45,14 +47,13 @@ compare_colors(some_colors, function(x) colorspace::deutan(x, 0.5))
 And here's the dissimilarity matrix of the colors.
 
 ```{r}
-dissim_mat <- cvd_dissimilarity(some_colors)
+dissim_mat <- cvd_dissimilarity(some_colors, "none")
 image(dissim_mat)
 ```
 
 Let's look at sets of dissimilar colors from the palette.
 
 ```{r}
-lapply(max_dissim_colors(dissim_mat), unlist)
+colorspace::swatchplot(some_colors,
+                       some_colors[seq_dissim_colors(dissim_mat)])
 ```
-
-No, this isn't doing the right thing! :(
diff --git a/seq_dissim_colors.R b/seq_dissim_colors.R
@@ -0,0 +1,35 @@
+#' Choose maximally dissimilar colors sequentially
+#'
+#' This function chooses colors greedily, staring with the most dissimilar pair
+#' and then choosing the color that is most dissimilar from the previously
+#' chosen ones.
+#'
+#' @param dissim_mat A dissimilarity matrix
+#'
+#' @return The element IDs in decreasing similarity
+#' @export
+seq_dissim_colors <- function(dissim_mat) {
+  color_sequence <- rep(NA, ncol(dissim_mat))
+
+  # First, we'll find the column that has the pair of colors with the maximum
+  # dissimilarity and save that column as the "first" color we picked and store
+  # this column as a vector.
+  color_sequence[1] <- which.max(dissim_mat) %/% nrow(dissim_mat)
+  dissim_vec <- dissim_mat[, color_sequence[1]]
+
+  for (i in seq(2, length(dissim_vec))) {
+    # Find the index of the maximum dissimilarity of the current vector and save
+    # it in the list of colors.
+    color_sequence[i] <- which.max(dissim_vec)
+
+    # Replace each element in this vector with the minimum of that element and the
+    # element with the same index in the newly selected color.
+    dissim_vec <- pmin(dissim_vec, dissim_mat[, color_sequence[i]])
+  }
+
+  # Add the color names if they're present in the dissimilarity matrix
+  if (!is.null(colnames(dissim_mat)))
+    names(color_sequence) <- colnames(dissim_mat)[color_sequence]
+
+  color_sequence
+}