more doc updates

R/fcwt.R

@@ -11,10 +11,12 @@
 #'  the argument `sigma`.
 #'
 #' @param signal
-#'  Real-valued time series. The time step gaps are assumed to be evenly spaced.
+#'  Real-valued time series. The time steps are assumed to be evenly spaced.
 #'
 #' @param sample_freq
-#'  Sampling rate of input time series in physical units.
+#'  Sampling rate of input time series. This number primarily establishes
+#'  a connection to physical units which is used in other frequency definitions
+#'  as well as the units of the output data.
 #'
 #' @param freq_begin
 #'  Sets the minimal frequency (`> 0`), in same units as `sample_freq`,
@@ -26,8 +28,8 @@
 #'
 #' @param n_freqs
 #'  Number of frequency bins generated by the CWT. The frequencies
-#'  are linearly distributed. Computation time increases
-#'  when raising the number of frequency bins.
+#'  are linearly distributed. Computation time increases when raising the
+#'  number of frequency bins.
 #'
 #' @param sigma
 #'  Sets a dimensionless parameter modifying the wavelet spread which in the
@@ -38,8 +40,8 @@
 #'  resolution and a worse (better) time resolution.
 #'
 #' @param remove_coi
-#'  Boundary effects can result in nonphysical artifacts. Remove those by
-#'  settings corresponding values to NA.
+#'  Boundary effects can result in nonphysical artifacts. If `TRUE`, the function
+#'  removes those by settings corresponding values to NA.
 #'  We define the essential support of the
 #'  (Gaußian) wavelet to be four times its standard deviation,
 #'  \eqn{4 \Sigma_t = 2 * \sqrt{2} \frac{\sigma}{f}}, and so a wavelet touches
@@ -48,10 +50,11 @@
 #'  if `remove_coi = TRUE`.
 #'
 #' @param nthreads
-#'  Number of threads used by the computation, if applicable.
+#'  Number of threads used by the computation, if supported by your platform.
 #'
 #' @return
-#'  A numeric real-valued matrix with `dim = c(length(signal), n_freqs)`.
+#'  The spectogram, a numeric real-valued matrix with dimensions
+#'  `dim = c(length(signal), n_freqs)`.
 #'
 #' @examples
 #' ts_sin_440 <- sin((1:44100) * 2 * pi * 440 / 44100)

R/fcwt_batch.R

@@ -1,8 +1,12 @@
-#' Automatic batching of fcwt calculations on long sequences
+#' Fcwt batch processing
 #'
-#' Performs a fast continuous wavelet transform on long sequences. It should
-#' not be used on short sequences since boundary artefacts are discarded (and
-#' those potentially dominate for short sequences.)
+#' Performs a fast continuous wavelet transform on long sequences by sequentially
+#' processing junks of the input signal and keeping only low-resolution output
+#' data to preserve memory.
+#' This is only useful for very long signals whose output does not fit into
+#' memory as a whole.
+#' It should not be used on short signals since boundary artefacts are discarded
+#' (and those potentially dominate for short sequences.)
 #'
 #' @details
 #' In case of input sequences that exceed the a certain size, the output
@@ -37,7 +41,7 @@
 #'  Can not be higher than the time resolution of the input signal.
 #'
 #' @inheritParams fcwt
-#' @seealso fcwt
+#' @seealso [fcwt()]
 #' @importFrom utils txtProgressBar setTxtProgressBar
 #' @export
 #' @examples