only_end-to-end_nosavingobject

.github/workflows/build_deploy.yaml

@@ -1,8 +1,13 @@
 name: pkgdown
 
 on:
-  repository_dispatch:
-    types: [trigger-workflow-2]
+  push:
+    branches:
+    - phili
+
+# on:
+#   repository_dispatch:
+#     types: [trigger-workflow-2]
 
 jobs:
   build-and-deploy:

.github/workflows/docker-push.yaml

@@ -6,8 +6,6 @@ on:
   push:
     branches:
     - main
-    - phili
-    - jomain
 
 jobs:
   build-docker-and-push:

vignettes/a-end-to-end-untargeted-metabolomics.qmd

@@ -115,17 +115,15 @@ the code below we use a *fork-based* parallel processing on unix system, and a
 *socket-based* parallel processing on the Windows operating system.
 
 ```{r par-process, eval = FALSE}
-register(SerialParam())
 #' Set up parallel processing using 2 cores
-## if (.Platform$OS.type == "unix") {
-##     register(MulticoreParam(2))
-## } else {
-##     register(SnowParam(2))
-## }
+if (.Platform$OS.type == "unix") {
+    register(MulticoreParam(2))
+} else {
+    register(SnowParam(2))
+}
 
 ```
 
-
 # Data organisation
 
 The experimental data is now represented by a `MsExperiment` object from the
@@ -1713,7 +1711,7 @@ Javascript as well as loaded easily back into R.
 # d <- file.path(tempdir(), "objects/lcms1")
 # saveMsObject(lcms1, AlabasterParam(path = d))
 #for now let's do R object because the previous method is not implemented yet.
-save(lcms1, file = "preprocessed_lcms1.RData")
+#save(lcms1, file = "preprocessed_lcms1.RData")
 ```
 
 # Data normalization