R/util.R

In cardinalR: Collection of Data Structures

#' Generate Random Noise Dimensions
#'
#' This function generates random noise dimensions to be added to the coordinates of a sphere.
#'
#' @param n The number of observations for which to generate noise dimensions.
#' @param num_noise The number of noise dimensions to generate.
#' @param min_n The minimum value for the random noise.
#' @param max_n The maximum value for the random noise.
#'
#' @return A matrix containing the generated random noise dimensions.
#'
#' @examples
#' # Generate random noise dimensions with 3 dimensions, minimum value -1, and maximum value 1
#' set.seed(20240412)
#' gen_noise_dims(n = 50, num_noise = 3, min_n = -0.01, max_n = 0.01)
#'
#' @export
gen_noise_dims <- function(n, num_noise, min_n, max_n) {
  if (n <= 0) {
    stop("Number of points should be a positive number.")
  }

  if (num_noise < 0) {
    stop("Number of noise dimensions should be a positive number.")
  }

  if (missing(n)) {
    stop("Missing n.")
  }

  if (missing(num_noise)) {
    stop("Missing num_noise.")
  }

  ## Is there are noise dimensions?
  if (num_noise != 0) {
    if (missing(min_n)) {
      stop("Missing min_n.")
    }

    if (missing(max_n)) {
      stop("Missing max_n.")
    }

    # Initialize an empty list to store the vectors
    noise_dim_val_list <- list()

    for (j in 1:num_noise) {
      if ((j %% 2) == 0) {
        noise_dim_val_list[[j]] <- stats::runif(n, min = min_n, max = max_n)
      } else {
        noise_dim_val_list[[j]] <- (-1) * stats::runif(n, min = min_n, max = max_n)
      }
    }

    noise_mat <- matrix(unlist(noise_dim_val_list), ncol = num_noise)
  } else {
    noise_mat <- NULL
  }

  return(noise_mat)
}

#' Generate Background Noise Data
#'
#' This function generates background noise data with specified parameters such as
#' the number of samples, number of dimensions, mean, and standard deviation.
#'
#' @param n Number of samples to generate.
#' @param num_dims Number of dimensions (columns) of the data.
#' @param mean Mean of the normal distribution used to generate noise (default is 0).
#' @param sd Standard deviation of the normal distribution used to generate noise (default is 1).
#'
#' @return A matrix containing the generated background noise data, with
#' \code{n} rows and \code{num_dims} columns.
#'
#' @examples
#'
#' # Generate background noise with custom mean and standard deviation
#' set.seed(20240412)
#' gen_bkg_noise(n = 50, num_dims = 3, mean = 5, sd = 2)
#'
#' @export
gen_bkg_noise <- function(n, num_dims, mean, sd) {
  if (n <= 0) {
    stop("Number of points should be a positive number.")
  }

  if (num_dims < 0) {
    stop("Number of dimensions should be a positive number.")
  }

  if (missing(n)) {
    stop("Missing n.")
  }

  if (missing(num_dims)) {
    stop("Missing num_noise.")
  }

  if (num_dims != 0) {
    if (missing(mean)) {
      stop("Missing mean.")
    }

    if (missing(sd)) {
      stop("Missing sd.")
    }

    # Initialize an empty list to store the vectors
    noise_bkg_val_list <- list()

    for (j in 1:num_dims) {
      noise_bkg_val_list[[j]] <- stats::rnorm(n, mean = mean, sd = sd)
    }

    bkg_mat <- matrix(unlist(noise_bkg_val_list), ncol = length(noise_bkg_val_list))
  } else {
    bkg_mat <- NULL
  }

  return(bkg_mat)
}

utils::globalVariables(c("n"))