R/tune.edp.baseline.dm.R

In jrminter/rEDP: Process electron diffaction patterns

#' Tune the baseline for the pattern
#'
#' Plots background with parameters
#'
#' @param dat.path Data directory (teminate with "/")
#' @param edp.base Base name
#' @param r.min minimum radius [px]
#' @param r.max maximum radius [px]
#' @param sb.win Window size for spectrum background
#' window=c("3","5","7","9","11","13","15")
#' @param sb.ord polynomial order for the spline
#' order=c("2","4","6","8")
#' @param do.log TRUE if y axis as a log
#' @param ... other graphical parameters
#'
#' @return a list
#' \item{r.min}{min radius used}
#' \item{r.max}{max radius used}
#' \item{sb.win}{window used for bkg}
#' \item{sb.ord}{background spline order used }
#' \item{plt.df}{dataframe with r, y.obs, y.bkg}
#'
#' @import rPeaks
#'
#' @export
#'
#' @examples
#' # not run
tune.edp.baseline.dm <-
function(dat.path='./',
         edp.base='Al',
         r.min=200,
         r.max=800,
         sb.win='15',
         sb.ord='4',
         do.log=TRUE,
         ...){
  # tune the background for a radially-averaged EDP
  # assumes radial dist'n data from V. Hou/J. Minter DigitalMicrograph
  # plug-in 'EDP'
  edp.file <- paste(dat.path, edp.base,
                    '-dc-ra.csv', sep='')
  df.dat <- read.csv(edp.file, header = T, sep=",")
  names(df.dat) <-c("r.px","int.gross")
  df.dat <- subset(df.dat, df.dat[,1] > r.min)
  df.dat <- subset(df.dat, df.dat[,1] < r.max)
  if(do.log==TRUE) {
    plot(df.dat[,1], df.dat[,2], type='l', col='black',
         xlab='r [px]', ylab='gross cts', log='y', ...)
  } else {
    plot(df.dat[,1], df.dat[,2], type='l', col='black',
         xlab='r [px]', ylab='gross cts', ...)
  }
  #
  # SpectrumBackground(y, iterations=100, decreasing=FALSE,
  #                    order=c("2","4","6","8"),
  #                    smoothing=FALSE,
  #                    window=c("3","5","7","9","11","13","15"),
  #                    compton=FALSE)

  y.bkg <- SpectrumBackground(df.dat[,2], order=sb.ord,
                              window=sb.win)
  lines(df.dat[,1], y.bkg, col='red')

  plt.df <- data.frame(r=df.dat[,1], y.obs=df.dat[,2], y.bkg=y.bkg)

  edp.bkg.par=list(r.min, r.max, sb.win, sb.ord, plt.df )
  edp.bkg.par
}
# for testing
# tune.edp.baseline.dm( do.log=TRUE)