Revert "in plotImage, add arguments to control contrast limits an…

…d `sat`uration"

R/plotImage.R

@@ -12,12 +12,9 @@
 #'   800 x 800px; use Inf to plot the lowest resolution available.
 #' @param ch image channel(s) to be used for plotting (defaults to 
 #'   the first channel(s) available); use \code{channels()} to see 
-#'   which channels are available for a given \code{ImageArray}
-#' @param c character vector; colors to use for each channel. 
-#' @param cl list of length-2 numeric vectors (non-negative, increasing); 
-#'   specifies channel-wise contrast limits - defaults to [0, 1] for all 
-#'   (ignored when \code{image(x, i)} is an RGB image; 
-#'   for convenience, any NULL = [0, 1], and n = [0, n]).
+#'   which channels are available for a given \code{ImageArray} 
+#'   
+#' @param c plotting aesthetics; color 
 #'
 #' @return ggplot
 #'
@@ -31,14 +28,6 @@
 #'   plotImage(x, i=2, k=.))
 #' patchwork::wrap_plots(ms)
 #' 
-#' # custom colors
-#' cmy <- c("cyan", "magenta", "yellow")
-#' plotSpatialData() + plotImage(x, c=cmy)
-#' 
-#' # contrast limits
-#' plotSpatialData() + plotImage(x, c=cmy, 
-#'   cl=list(c(0.2,1), c(0,0.8), c(0,1)))
-#' 
 #' @import SpatialData
 NULL
 
@@ -47,53 +36,22 @@ NULL
 #' @export
 plotSpatialData <- \() ggplot() + scale_y_reverse() + .theme 
 
-.check_cl <- \(cl, d) {
-    if (is.null(cl)) {
-        # default to [0, 1] for all channels
-        cl <- replicate(d, c(0, 1), FALSE)
-    } else {
-        # should be a list with as many elements as channels
-        if (!is.list(cl)) stop("'cl' should be a list")
-        if (length(cl) != d) stop("'cl' should be of length ", d)
-        for (. in seq_len(d)) {
-            # replace NULL by [0, 1] & n by [0, n]
-            if (is.null(cl[[.]])) cl[[.]] <- c(0, 1)
-            if (length(cl[[.]]) == 1) {
-                if (cl[[.]] < 0) stop("scalar 'cl' can't be < 0")
-                cl[[.]] <- c(0, cl[[.]])
-            }
-        }
-        # elements should be length-2, numeric, non-negative, increasing
-        .f <- \(.) length(.) == 2 && is.numeric(.) && all(. >= 0) && .[2] > .[1]
-        if (!all(vapply(cl, .f, logical(1))))
-            stop("elements of 'cl' should be length-2,",
-                " non-negative, increasing numeric vectors")
-    }
-    return(cl)
-}
-
 # merge/manage image channels
 # if no colors and channels defined, return the first channel
 #' @importFrom grDevices col2rgb
 #' @noRd
-.chs2rgb <- \(a, ch, c=NULL, cl=NULL) {
-    cl <- .check_cl(cl, d <- dim(a)[1])
+.manage_channels <- \(a, ch, c=NULL){
     if (length(ch) > (n <- length(.DEFAULT_COLORS)) && is.null(c))
         stop("Only ", n, " default colors available, but",
             length(ch), " are needed; please specify 'c'")
     if (!is.null(c) || (is.null(c) && length(ch) > 1)) {
         if (is.null(c)) c <- .DEFAULT_COLORS[seq_along(ch)] 
         c <- col2rgb(c)/255
         b <- array(0, dim=c(3, dim(a)[-1]))
-        for (i in seq_len(d)) {
-            for (j in seq_len(3)) {
-                rgb <- a[i,,,drop=FALSE]*c[j,i]
-                # apply upper contrast lim.
-                rgb <- rgb*(1/cl[[i]][2]) 
-                b[j,,] <- b[j,,,drop=FALSE] + rgb
-                # apply lower contrast lim.
-                b[j,,][b[j,,] < cl[[i]][1]] <- 0
-            }
+        for (i in seq_len(dim(a)[1])) {
+            b[1,,] <- b[1,,,drop=FALSE] + a[i,,,drop=FALSE]*c[1,i]
+            b[2,,] <- b[2,,,drop=FALSE] + a[i,,,drop=FALSE]*c[2,i]
+            b[3,,] <- b[3,,,drop=FALSE] + a[i,,,drop=FALSE]*c[3,i]
         }
         a <- pmin(b, 1)
     } else {
@@ -106,22 +64,21 @@ plotSpatialData <- \() ggplot() + scale_y_reverse() + .theme
 #' @importFrom SpatialData getZarrArrayPath
 #' @importFrom Rarr zarr_overview
 #' @noRd
-.get_img_dt <- \(a) {
+.get_image_dtype <- \(a) {
     pa <- getZarrArrayPath(a)
     df <- zarr_overview(pa, as_data_frame=TRUE)
     if (!is.null(dt <- df$data_type)) return(dt)
 }
 
 # normalize the image data given its data type
 #' @noRd
-.norm_ia <- \(a, dt) {
-    d <- dim(a)[1]
-    if (dt %in% names(.DTYPE_MAX_VALUES)) {
-        a <- a / .DTYPE_MAX_VALUES[dt]
-    } else if (max(a) > 1) {
-        for (i in seq_len(d))
-            a[i,,] <- a[i,,] / max(a[i,,])
-    }
+.normalize_image_array <- \(a, dt){
+  if (dt %in% names(.DTYPE_MAX_VALUES)) {
+      a <- a/.DTYPE_MAX_VALUES[dt]
+  } else if (max(a) > 1) {
+      for (i in seq_len(dim(a)[1])) 
+          a[i,,] <- a[i,,]/max(a[i,,])
+  }
   return(a)
 }
 
@@ -167,26 +124,24 @@ plotSpatialData <- \() ggplot() + scale_y_reverse() + .theme
     which.min(d)
 }
 
-.get_img_data <- \(x, k=NULL, w=800, h=800) {
+.get_plot_data <- \(x, k=NULL, w=800, h=800) {
     if (!is.null(k)) return(data(x, k))
     data(x, .guess_scale(x, w, h))
 }
 
 #' @importFrom methods as
 #' @importFrom grDevices rgb
 #' @importFrom DelayedArray realize
-.df_i <- \(x, k=NULL, ch=NULL, c=NULL, cl=NULL) {
-    a <- .get_img_data(x, k)
-    ch <- .ch_idx(x, ch)
+.df_i <- \(x, k=NULL, ch=NULL, c=NULL) {
+    a <- .get_plot_data(x, k)
+    ch_i <- .ch_idx(x, ch)
+    if (!.is_rgb(x))
+        a <- a[ch_i, , , drop=FALSE]
+    dt <- .get_image_dtype(a)
+    a <- realize(as(a, "DelayedArray"))
+    a <- .normalize_image_array(a, dt)
     if (!.is_rgb(x))
-        a <- a[ch, , , drop=FALSE]
-    dt <- .get_img_dt(a)
-    a <- as(a, "DelayedArray")
-    a <- .norm_ia(realize(a), dt)
-    # enter when image isn't RGB already, either
-    # custom colors or contrasts are specified
-    if (!.is_rgb(x) || !is.null(c) || !is.null(cl))
-        a <- .chs2rgb(a, ch, c, cl)
+        a <- .manage_channels(a, ch_i, c)
     apply(a, c(2, 3), \(.) do.call(rgb, as.list(.))) 
 }
 
@@ -208,13 +163,13 @@ plotSpatialData <- \() ggplot() + scale_y_reverse() + .theme
 
 #' @rdname plotImage
 #' @export
-setMethod("plotImage", "SpatialData", \(x, i=1, j=1, k=NULL, ch=NULL, c=NULL, cl=NULL) {
+setMethod("plotImage", "SpatialData", \(x, i=1, j=1, k=NULL, ch=NULL, c=NULL) {
     if (is.numeric(i))
         i <- imageNames(x)[i]
     y <- image(x, i)
     if (is.numeric(j))
         j <- CTname(y)[j]
-    df <- .df_i(y, k, ch, c, cl)
+    df <- .df_i(y, k, ch, c)
     wh <- .get_wh(x, i, j)
     .gg_i(df, wh$w, wh$h)
 })

inst/NEWS

@@ -1,7 +1,6 @@
 changes in version 0.99.2
 
-- in 'plotImage', added support to visualize channels of choice,
-  as well as 'cl' argument to control constrast limits
+- in 'plotImage', added support to visualize channels of choice
 - updated vignette to include corresponding examples
 
 changes in version 0.99.1

tests/testthat/test-plotArray.R

@@ -22,11 +22,11 @@ test_that(".guess_scale", {
             dim <- lapply(c(6, 3), \(.) c(3, rep(., 2))), \(.) 
             array(sample(seq_len(255), prod(.), replace=TRUE), dim=.)))
     # manual scale
-    expect_identical(.get_img_data(img, k=1), lys[[1]]) 
-    expect_identical(.get_img_data(img, k=2), lys[[2]])
+    expect_identical(.get_plot_data(img, k=1), lys[[1]]) 
+    expect_identical(.get_plot_data(img, k=2), lys[[2]])
     # automatic scale
-    expect_identical(.get_img_data(img, k=NULL, w=5, h=7), lys[[1]]) 
-    expect_identical(.get_img_data(img, k=NULL, w=2, h=2), lys[[2]])
+    expect_identical(.get_plot_data(img, k=NULL, w=5, h=7), lys[[1]]) 
+    expect_identical(.get_plot_data(img, k=NULL, w=2, h=2), lys[[2]])
 })
 
 test_that("plotImage()", {

tests/testthat/test-plotImage.R

@@ -24,84 +24,7 @@ test_that(".ch_idx()", {
   expect_warning(expect_equal(.ch_idx(image(x,1), ch=99), 1)) 
 })
 
-test_that(".check_cl", {
-    # valid
-    n <- sample(seq(3, 9), 1)
-    v <- replicate(n, sort(runif(2)), FALSE)
-    expect_identical(.check_cl(v, n), v)
-    # single NULL
-    n <- sample(seq(3, 9), 1)
-    l <- .check_cl(NULL, n)
-    expect_is(l, "list")
-    expect_identical(l, replicate(n, c(0, 1), FALSE))
-    # one NULL, rest scalar
-    n <- sample(seq(3, 9), 1)
-    i <- sample(n, 1)
-    . <- replicate(n, NULL, FALSE)
-    .[[i]] <- v <- c(0.2, 0.8)
-    l <- .check_cl(., n)
-    expect_is(l, "list")
-    expect_identical(l[[i]], v)
-    expect_identical(l[-i], replicate(n-1, c(0, 1), FALSE))
-    # invalid
-    expect_error(.check_cl(c(0.2, 0.4, 0.6), 3)) # non-list
-    expect_error(.check_cl(as.list(seq_len(4)), 3)) # wrong length
-    expect_error(.check_cl(list(NULL, NULL, c(-1, 1)), 3)) # negative entry
-    expect_error(.check_cl(as.list(letters[seq_len(3)]), 3)) # non-numeric
-    expect_error(.check_cl(list(NULL, NULL, c(1, 0)), 3)) # decreasing
-    expect_error(.check_cl(list(NULL, NULL, -1), 3)) # negative scalar
-    expect_error(.check_cl(list(NULL, NULL, 0), 3)) # zero scalar
+# TODO: any tests for image array normalization ? 
+test_that(".normalize_image_array", {
+    skip()
 })
-
-dir.create(td <- tempfile())
-pa <- unzip_spd_demo(zipname="steinbock_io.zip", dest=td, source="biocOSN")
-x <- readSpatialData(pa, images=1, labels=FALSE, points=FALSE, shapes=FALSE, tables=FALSE)
-a <- data(image(x)[seq_len(3), seq_len(100), seq_len(100)], 1)
-
-test_that(".get_img_dt", {
-    da <- (za <- data(image(x)))[1,,]
-    dt_za <- .get_img_dt(za) # from 'ZarrArray'
-    dt_da <- .get_img_dt(da) # from 'DelayedArray'
-    expect_is(dt_za, "character")
-    expect_identical(dt_za, dt_da)
-})
-
-test_that(".norm_ia", {
-    # valid data type
-    dt <- .get_img_dt(a)
-    b <- .norm_ia(realize(a), dt)
-    expect_equal(
-        apply(b, 1, range), 
-        replicate(3, c(0, 1)))
-    # invalid data type
-    b <- .norm_ia(realize(a), "")
-    expect_equal(
-        apply(b, 1, range), 
-        replicate(3, c(0, 1)))
-})
-
-test_that(".chs2rgb", {
-    dt <- .get_img_dt(a)
-    ch <- seq_len(d <- dim(a)[1])
-    a <- .norm_ia(realize(a), dt)
-    # no colors, no contrasts
-    b <- .chs2rgb(a, ch)
-    expect_identical(a, b)
-    # colors
-    cmy <- c("cyan", "magenta", "yellow")
-    b <- .chs2rgb(a, ch, c=cmy)
-    expect_equal(dim(a), dim(b))
-    expect_equal(
-        apply(b, 1, range), 
-        replicate(d, c(0, 1)))
-    # lower contrast lim.
-    lim <- list(c(0.5, 1), NULL, NULL)
-    b <- .chs2rgb(a, ch, cl=lim)
-    expect_identical(b[-1,,], a[-1,,])
-    expect_true(sum(b[1,,] == 0) > sum(a[1,,] == 0))
-    # upper contrast lim.
-    lim <- list(c(0, 0.5), NULL, NULL)
-    b <- .chs2rgb(a, ch, cl=lim)
-    fac <- mean(b[1,,]/a[1,,], na.rm=TRUE)
-    expect_equal(fac, 2, tolerance=0.05)
-})

vignettes/SpatialData.plot.Rmd

@@ -276,30 +276,15 @@ pa <- unzip_spd_demo(
 x <- readSpatialData(pa, anndataR=FALSE)
 ```
 
-### channels
+Plotting with multiple image channels.
 
-```{r steinbock-ch}
-plotSpatialData() + plotImage(x,
+```{r steinbock-plot}
+plotSpatialData() + plotImage(x, 
     i="Patient3_003_image",
     ch=c(6, 22, 39),
     c=c("blue", "cyan", "yellow"))
 ```
 
-### contrasts
-
-```{r steinbock-cl, fig.width=9, fig.height=3}
-i <- image(x, "Patient3_003_image")
-image(x, "crop") <- i[, 200:400, 200:400]
-lapply(list(c(0.2, 1), c(0, 0.8), c(0, 1.2)), \(.) {
-    plotSpatialData() + plotImage(x, 
-        i="crop", 
-        ch=c(6, 22, 39), 
-        cl=list(1, 1, .),
-        c=c("blue", "cyan", "yellow")) +
-        ggtitle(sprintf("[%s, %s]", .[1], .[2]))
-}) |> wrap_plots(nrow=1)
-```
-
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