R/log_parser.R

In chz31/SlicerMorphR: Import SlicerMorph data into R

#' Parse the GPA log output
#'
#' This function parse log output (analysis.log file) from the SlicerMorph's GPA
#' (General Procrustes analysis) module output.Parser saves everything necessary,
#' including the output files as well as raw coordinates, in a large R object.
#' These output files are shown in the analysis.log file.
#' For details: please see https://github.com/SlicerMorph/Tutorials/blob/main/GPA_3/README.md
#' Note: if the coordinate system of the fcsv files are not LPS (i.e., "0"), the signs of the x and y coordinates will be flipped
#' @param  file An object that points to the location of the analysis.log file, either by hard coding the path or interactively via the function "file.choose()"
#' @param  forceLPS The forceLPS determines if the landmark coordinates stored in .fcsv files should be converted into LPS format in case if they are not.
#' For details of this parameter, please refer to the help file of read.markups.fcsv().
#' @return A list that contains every output of the GPA module.\cr
#' The output contains: \cr
#' \itemize{
#'   \item $input.path = Unix style path to input folder with landmark files
#'   \item $output.path = Unix stype path to the output folder created by GPA
#'   \item $files = files included in the analysis
#'   \item $format = format of landmark files ("fcsv" or "mrk.json")
#'   \item $no.LM = number of landmarks original
#'   \item $skipped = If any landmark is omitted in GPA (TRUE/FALSE)
#'   \item $skippedLM = Indices of skipped LMs (created only if $skipped=TRUE)
#'   \item $scale = are data scaled by centroid sizes (TRUE/FALSE)
#'   \item $MeanShape = filename that contains mean shape coordinates calculated by GPA (csv format)
#'   \item $eigenvalues = filename that contains eigenvalues as calculated by PCA in the SlicerMorph GPA (csv format)
#'   \item $eigenvectors = filename that contains eigenvectors as calculated by PCA in the SlicerMorph GPA (csv format)
#'   \item $OutputData = filename that contains procrustes distances, centroid sizes and procrustes aligned coordinates as calculated by the SlicerMorph GPA (csv format)
#'   \item $$pcScores = filename that contains individal PC scores of specimens as calculated by PCA in the SlicerMorph GPA (csv format)
#'   \item $ID = list of specimen identifiers (obtained from file names)
#'   \item $LM = 3D landmark array that contains the 3D raw coordinates as inputed to the SlicerMorph GPA module
#'   \item $semi = If any landmarks are tagged as semi-landmarks (TRUE/FALSE)
#'   \item $semiLM = indices of LMs tagged as semi-landmarks (created only if $semi==TRUE)
#'}
#'
#' @examples
#' SM.log.file <- file.choose()
#' SM.log <- parser(SM.log.file. forceLPS = TRUE)
#' head(SM.log)
#' @export

parser = function(file=NULL, forceLPS = FALSE){
  cut=function(x) return(strsplit(x,"=")[[1]][2])
  temp = unlist(lapply(X=readLines(file), FUN=cut))
  log = list()

  log$input.path = temp[3]
  log$output.path = temp[4]
  log$files = unlist(strsplit(temp[5],','))
  #log$format =  substr(temp[6], start=2, stop = nchar(temp[6]))
  log$format =  temp[6]
  log$no.LM = as.numeric(temp[7])

  # checking if any LMs are skipped
  if (is.na(temp[8])) log$skipped=FALSE else {
    log$skipped=TRUE
    log$skipped.LM=unlist(strsplit(temp[8],","))
  }

  # parsing if any landmarks are tagged as semiLMs

  if (is.na(temp[15])) log$semi=FALSE else {
    log$semi=TRUE
    log$semiLMs=unlist(strsplit(temp[15],","))
  }

  # checking if scaling to centroid size is disabled during GPA
  if (as.logical(temp[9])) log$scale=TRUE else log$scale=FALSE

  log$MeanShape=temp[10]
  log$eigenvalues=temp[11]
  log$eigenvectors=temp[12]
  log$OutputData=temp[13]
  log$pcScores=temp[14]
  log$ID=gsub(log$format, "", fixed=T, log$files )

  if (!log$skipped) {
    log$LM = array(dim =c (log$no.LM, 3, length(log$files)),
                   dimnames = list(1:log$no.LM, c("x", "y", "z"), log$ID))
    if (log$format==".fcsv") for (i in 1:length(log$files)) log$LM[,,i] = read.markups.fcsv(paste(log$input.path,log$files[i],sep = "/"), forceLPS) else
      for (i in 1:length(log$files))  log$LM[,,i] = read.markups.json(paste(log$input.path,log$files[i],sep = "/"))
  } else {
    log$LM = array(dim = c(log$no.LM - length(log$skipped.LM), 3, length(log$files)),
                   dimnames = list((1:log$no.LM)[-as.numeric(log$skipped.LM)], c("x", "y", "z"), log$ID))
    if (log$format==".fcsv") for (i in 1:length(log$files)) log$LM[,,i] = read.markups.fcsv(paste(log$input.path,log$files[i],sep = "/"), forceLPS)[-c(as.numeric(log$skipped.LM) ), ] else
      for (i in 1:length(log$files)) log$LM[,,i] = read.markups.json(paste(log$input.path,log$files[i],sep = "/"))[-c(as.numeric(log$skipped.LM) ), ]

  }

  return(log)

}