Fix remove baseline, add documentation

R/baseline-GCIMSDataset.R

@@ -6,6 +6,7 @@
 #' @param dt_peak_fwhm_ms Full Width at Half Maximum in milliseconds. Used to
 #' determine the length of the regions where local minima are searched.
 #' @param dt_region_multiplier A multiplier to calculate the region
+#' @param remove A boolean, if TRUE it removes the baseline from the intensity
 #' @return The modified [GCIMSDataset]
 #' @export
 setMethod(

R/baseline-GCIMSSample.R

@@ -11,11 +11,12 @@
 #' @param dt_peak_fwhm_ms Full Width at Half Maximum in milliseconds. Used to
 #' determine the length of the regions where local minima are searched.
 #' @param dt_region_multiplier A multiplier to calculate the region
+#' @param remove A boolean, if TRUE it removes the baseline from the intensity
 #' @return The modified [GCIMSSample]
 #' @export
 methods::setMethod(
   "estimateBaseline", "GCIMSSample",
-  function(object, dt_peak_fwhm_ms, dt_region_multiplier, rt_length_s, remove) {
+  function(object, dt_peak_fwhm_ms, dt_region_multiplier, rt_length_s, remove = TRUE) {
     rt <- rtime(object)
     dt <- dtime(object)
     int <- intensity(object)

vignettes/creating-a-workflow-step.Rmd

@@ -83,7 +83,7 @@ You can use this template for now:
 
 ```{r}
 cutDrift_Dataset <- function(x, dt_from, dt_to) {
-    delayed_op <- GCIMS:::GCIMSDelayedOp(
+    delayed_op <- DelayedOperation(
       name = "cutDrift", # Give a human-readable name
       fun = cutDrift_Sample, # The function that is applied to the sample
       params = list(dt_from = dt_from, dt_to = dt_to) # The arguments that function needs
@@ -205,7 +205,7 @@ We use the same function for the dataset:
 
 ```{r}
 cutDrift2_Dataset <- function(x, dt_from, dt_to) {
-    delayed_op <- GCIMS:::GCIMSDelayedOp(
+    delayed_op <- DelayedOperation(
       name = "cutDrift2", # Give a human-readable name
       fun = cutDrift2_Sample, # The function that is applied to the sample
       params = list(dt_from = dt_from, dt_to = dt_to) # The arguments that function needs
@@ -351,7 +351,7 @@ We now pass those extraction and aggregation functions as well:
 
 ```{r}
 cutDrift3_Dataset <- function(x, dt_from, dt_to) {
-    delayed_op <- GCIMS:::GCIMSDelayedOp(
+    delayed_op <- DelayedOperation(
       name = "cutDrift3", # Give a human-readable name
       fun = cutDrift2_Sample, # The function that is applied to the sample
       params = list(dt_from = dt_from, dt_to = dt_to), # The arguments that function needs

vignettes/introduction-to-gcims.Rmd

@@ -46,7 +46,7 @@ show_progress_bar <- interactive() && is.null(getOption("knitr.in.progress"))
 # disable parallellization: (Useful for better error reporting)
 #register(SerialParam(progressbar = show_progress_bar), default = TRUE)
 # enable parallellization with 3 workers (you can use more if you have them):
-register(SnowParam(workers = 3, progressbar = show_progress_bar, exportglobals = FALSE), default = TRUE)
+register(SnowParam(workers = 2, progressbar = show_progress_bar, exportglobals = FALSE), default = TRUE)
 ```
 
 
@@ -546,27 +546,26 @@ ggplot(to_plot) +
 You can also apply it to a single sample:
 
 ```{r}
-ket1 <- estimateBaseline(
+ket1_no_basline <- estimateBaseline(
   ket1,
   dt_peak_fwhm_ms = 0.2, 
   dt_region_multiplier = 12,
-  rt_length_s = 200
+  rt_length_s = 200,
+  remove = TRUE
+)
+ket1_basline <- estimateBaseline(
+  ket1,
+  dt_peak_fwhm_ms = 0.2, 
+  dt_region_multiplier = 12,
+  rt_length_s = 200,
+  remove = FALSE
 )
-```
-
-
-```{r}
-plot(ket1)
-```
-
-```{r}
-plot(ket1, remove_baseline = TRUE)
 ```
 
 ```{r}
 cowplot::plot_grid(
-  plot(ket1, rt_range = c(0, 500), dt_range = c(6, 16)),
-  plot(ket1, rt_range = c(0, 500), dt_range = c(6, 16), remove_baseline = TRUE),
+  plot(ket1_no_basline, rt_range = c(0, 500), dt_range = c(6, 16)),
+  plot(x = ket1_basline, rt_range = c(0, 500), dt_range = c(6, 16)),
   ncol = 2
 )
 ```