Prep for new ijtiff::display().

.github/workflows/R-CMD-check.yaml

@@ -39,10 +39,14 @@ jobs:
           http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
 
-      - name: Install system dependencies
+      - name: Install Linux system dependencies
         if: runner.os == 'Linux'
         run: sudo apt-get install -y libwebp-dev libzstd-dev
 
+      - name: Install MacOS system dependencies
+        if: runner.os == 'macOS'
+        run: brew install --cask xquartz
+
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: any::rcmdcheck

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: detrendr
 Title: Detrend Images
-Version: 0.6.16
+Version: 0.6.17
 Authors@R: c(
     person("Rory", "Nolan", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-5239-4043")),
@@ -52,6 +52,7 @@ Suggests:
     knitr,
     lattice,
     MASS,
+    magick,
     matrixStats,
     mgcv,
     pacman,
@@ -70,5 +71,5 @@ Config/testthat/parallel: true
 Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 SystemRequirements: GNU make

NEWS.md

@@ -1,3 +1,9 @@
+# `detrendr` 0.6.17
+
+## BUG FIXES
+* Prep for new `ijtiff::display()`.
+
+
 # `detrendr` 0.6.16
 
 ## BUG FIXES

R/detrendr.R

@@ -20,12 +20,11 @@ if (getRversion() >= "2.15.1") {
 #' for preprocessing in fluorescence fluctuation and correlation spectroscopy
 #' (FFS and FCS).
 #'
-#' @docType package
 #' @name detrendr
 #' @aliases detrendr-package
 #' @references Rory Nolan, Luis A. J. Alvarez, Jonathan Elegheert, Maro
 #'   Iliopoulou, G. Maria Jakobsdottir, Marina Rodriguez-Muñoz, A. Radu
 #'   Aricescu, Sergi Padilla-Parra; nandb—number and brightness in R with a
 #'   novel automatic detrending algorithm, Bioinformatics,
 #'   https://doi.org/10.1093/bioinformatics/btx434.
-NULL
+"_PACKAGE"

codemeta.json

@@ -8,7 +8,7 @@
   "codeRepository": "https://github.com/rorynolan/detrendr",
   "issueTracker": "https://github.com/rorynolan/detrendr/issues",
   "license": "https://spdx.org/licenses/BSD-3-Clause",
-  "version": "0.6.16",
+  "version": "0.6.17",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -181,6 +181,18 @@
       },
       "sameAs": "https://CRAN.R-project.org/package=MASS"
     },
+    {
+      "@type": "SoftwareApplication",
+      "identifier": "magick",
+      "name": "magick",
+      "provider": {
+        "@id": "https://cran.r-project.org",
+        "@type": "Organization",
+        "name": "Comprehensive R Archive Network (CRAN)",
+        "url": "https://cran.r-project.org"
+      },
+      "sameAs": "https://CRAN.R-project.org/package=magick"
+    },
     {
       "@type": "SoftwareApplication",
       "identifier": "matrixStats",
@@ -513,7 +525,7 @@
     },
     "SystemRequirements": "GNU make"
   },
-  "fileSize": "813.008KB",
+  "fileSize": "10268.038KB",
   "citation": [
     {
       "@type": "ScholarlyArticle",

detrendr.Rproj

@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: a7e7ab1f-4608-47c2-861d-78fd6e12dbe3
 
 RestoreWorkspace: Default
 SaveWorkspace: Default

vignettes/single-images.Rmd

@@ -14,8 +14,10 @@ In this vignette, we will look at detrending single images interactively in an R
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(
   echo = TRUE, comment = "#>",
-  fig.width = 7, fig.height = 6
+  fig.width = 7, fig.height = 6,
+  crop = TRUE
 )
+knitr::knit_hooks$set(crop = knitr::hook_pdfcrop)
 pacman::p_load(dplyr, tidyr, MASS, mgcv, ggplot2, gridExtra)
 set.seed(1)
 ```
@@ -46,7 +48,7 @@ dim(my_img)
 
 Plotting the mean intensities of the frames in the two channels, we can see that the second channel has more obvious bleaching.
 
-```{r plot-mean-profile, echo=FALSE, fig.height=3, message=FALSE}
+```{r plot-mean-profile, echo=FALSE, out.width='100%', message=FALSE}
 plot_tbl <- tibble(
   frame = seq_len(dim(my_img)[4]),
   channel1 = apply(autothresholdr::mean_stack_thresh(my_img[, , 1, ], "h"), 4,
@@ -92,7 +94,7 @@ display(mean_pillars(my_img[, , 2, ]))
 
 You can see here that this is an image of part of a cell, with the edge of the cell across the top left and hence the top left corner of the image is not cell, just background. It's important to _threshold_ away this background part: the detrending routine assumes that all parts of the image are part of the region of interest (the cell), so we need to set the background parts to `NA` beforehand to tell the detrending routine that this area should be excluded. `detrendr` has all of the thresholding functionality of the _ImageJ Auto Threshold_ plugin. You can read more about this at https://imagej.net/plugins/auto-threshold. My favourite method is _Huang_. Let's look at both of these channels with _Huang_ thresholding.
 
-```{r display-thresholded-mean, echo=FALSE, fig.height=4}
+```{r display-thresholded-mean, echo=FALSE, out.width='100%'}
 graphics::par(mfrow = c(1, 2))
 display(mean_pillars(autothresholdr::mean_stack_thresh(my_img[, , 1, ], "h")))
 display(mean_pillars(autothresholdr::mean_stack_thresh(my_img[, , 2, ], "h")))
@@ -120,7 +122,7 @@ my_detrended_img <- img_detrend_robinhood(my_img_threshed)
 
 Let's check out the mean intensity profiles of this detrended image.
 
-```{r plot-detrended-mean-profile, echo=FALSE, fig.height=3, message=FALSE}
+```{r plot-detrended-mean-profile, echo=FALSE, out.width='100%', message=FALSE}
 plot_tbl <- tibble(
   frame = seq_len(dim(my_detrended_img)[4]),
   channel1 = apply(my_detrended_img[, , 1, ], 3, mean, na.rm = TRUE),