From 846c9aabfa0cc202049ea6f706427f9c761a0495 Mon Sep 17 00:00:00 2001
From: Lukas Schneiderbauer <lukas.schneiderbauer@gmail.com>
Date: Sun, 5 Jan 2025 18:23:02 +0100
Subject: [PATCH] `fcwt()`: change argument name: remove_coi -> rm_coi

---
 R/fcwt.R                   | 10 +++++-----
 R/fcwt_batch.R             |  2 +-
 man/fcwt.Rd                |  8 ++++----
 tests/testthat/test-fcwt.R |  2 +-
 4 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/R/fcwt.R b/R/fcwt.R
index ff2b58b..6f06cf6 100644
--- a/R/fcwt.R
+++ b/R/fcwt.R
@@ -76,15 +76,15 @@
 #'  sigma, it depends on the use case. So the default choice can very well be
 #'  quite a bad choice (it probably is for audio data).
 #'
-#' @param remove_coi ( [TRUE] | [FALSE] )
-#'  Boundary effects can result in nonphysical artifacts. If `remove_coi = TRUE`,
+#' @param rm_coi ( [TRUE] | [FALSE] )
+#'  Boundary effects can result in nonphysical artifacts. If `rm_coi = TRUE`,
 #'  those are effectively removed by setting corresponding values to [NA].
 #'  We define the essential support of the
 #'  (Gaussian) wavelet to be four times its standard deviation,
 #'  \eqn{\Delta t = \frac{\sigma}{f}}, and so a wavelet touches
 #'  the boundary if the distance of the center of the wavelet to the boundary
 #'  is less then \eqn{\Delta t /2}. Values that fall into that range are removed
-#'  if `remove_coi = TRUE`.
+#'  if `rm_coi = TRUE`.
 #'
 #' @param n_threads
 #'  Number of threads used by the computation, if supported by your platform.
@@ -138,7 +138,7 @@ fcwt <- function(x,
                    ),
                  freq_scale = c("log", "linear"),
                  # abs = FALSE,
-                 remove_coi = TRUE,
+                 rm_coi = TRUE,
                  n_threads = 2L) {
   stopifnot(is.numeric(x))
   stopifnot(is.numeric(n_freqs), n_freqs > 0)
@@ -193,7 +193,7 @@ fcwt <- function(x,
       freq_scale, sigma
     )
 
-  if (remove_coi) {
+  if (rm_coi) {
     sc_set_coi_na(sc)
   } else {
     sc
diff --git a/R/fcwt_batch.R b/R/fcwt_batch.R
index 1ed68ab..af58f47 100644
--- a/R/fcwt_batch.R
+++ b/R/fcwt_batch.R
@@ -184,7 +184,7 @@ fcwt_batch <- function(x,
         n_freqs = n_freqs,
         freq_scale = freq_scale,
         sigma = sigma,
-        remove_coi = FALSE,
+        rm_coi = FALSE,
         n_threads = n_threads
       ) |>
         # we fully remove COI infected time slices
diff --git a/man/fcwt.Rd b/man/fcwt.Rd
index 14aff21..9896b67 100644
--- a/man/fcwt.Rd
+++ b/man/fcwt.Rd
@@ -14,7 +14,7 @@ fcwt(
   n_freqs = 3 * ceiling(log(du(freq_end/freq_begin), base = 1 +
     sigma_freq_res_rel(sigma))),
   freq_scale = c("log", "linear"),
-  remove_coi = TRUE,
+  rm_coi = TRUE,
   n_threads = 2L
 )
 }
@@ -72,15 +72,15 @@ are proportional to the frequency uncertainties.
 In this sense, the logarithmic frequency scale is actually the natural
 scale for the continuous wavelet transform.}
 
-\item{remove_coi}{( \link{TRUE} | \link{FALSE} )
-Boundary effects can result in nonphysical artifacts. If \code{remove_coi = TRUE},
+\item{rm_coi}{( \link{TRUE} | \link{FALSE} )
+Boundary effects can result in nonphysical artifacts. If \code{rm_coi = TRUE},
 those are effectively removed by setting corresponding values to \link{NA}.
 We define the essential support of the
 (Gaussian) wavelet to be four times its standard deviation,
 \eqn{\Delta t = \frac{\sigma}{f}}, and so a wavelet touches
 the boundary if the distance of the center of the wavelet to the boundary
 is less then \eqn{\Delta t /2}. Values that fall into that range are removed
-if \code{remove_coi = TRUE}.}
+if \code{rm_coi = TRUE}.}
 
 \item{n_threads}{Number of threads used by the computation, if supported by your platform.
 Defaults to 2 threads (to accommodate CRAN requirements).
diff --git a/tests/testthat/test-fcwt.R b/tests/testthat/test-fcwt.R
index 9bbe691..0477671 100644
--- a/tests/testthat/test-fcwt.R
+++ b/tests/testthat/test-fcwt.R
@@ -61,7 +61,7 @@ test_that("fcwt() removing coi works", {
       freq_end = 1000,
       n_freqs = 10,
       sigma = 1,
-      remove_coi = TRUE
+      rm_coi = TRUE
     )
 
   # we have a time series that is 1000 / 44100 s long