R/DVCSSigma.R

In rcarcasses/HQCD-P: An R package for the Holographic Pomeron

#' @export
DVCSSigma <- function() Sigma('DVCS', tmin = -1.0, tmax = 0.0)

getExternalStateFactor.DVCSSigma <- getExternalStateFactor.DVCSDSigma

#' @export
enlargeData.DVCSSigma <- function(dvcss, ...) {
  # interesting values of x for producing a plot, for instace.
  Wvals <- seq(20, 160, len = 50)
  Q2s <- sort(unique(dvcss$data$Q2))
  # for each one of the 34 previous values combine them with the xvals
  Reduce(rbind, lapply(Q2s, function(Q2) data.frame(Q2 = rep(Q2, length(Wvals)), W = Wvals)))
}

#' @export
plot.DVCSSigma <- function(dvcss, predicted = NULL) {
  # get all different Q2s
  allQ2s <- sort(unique(dvcss$data$Q2))
  data <- dvcss$data
  values <- data$dsigma
  plot.new()
  par(mar = c(4, 5, 2, 2))
  # prepare the plot
  plot(1e-10, 1,
       log = 'y',
       las = 1,
       xlim = c(30, 165),
       ylim = c(0.07, 40),
       xlab = expression(W ~ italic('(GeV)')), ylab = expression(sigma[t] ~ italic('(nb)')),
       xaxt = 'n', yaxt = 'n')
  hTicks <- 10^(-1:1)
  vTicks <- c(60, 100, 140)
  abline(h = hTicks, v = vTicks, col = 'gray90', lty = 3)
  axis(2, at = hTicks, las = 1, labels = c(expression(10^-1), expression(1), expression(10)))
  axis(1, at = vTicks)
  numCols <- 4
  i <- 1
  cols <- c('black', 'red', 'green', 'blue')
  lapply(allQ2s, function(Q2) {
    # get the subset of data for the given Q2
    dataForQ2 <- data[data$Q2 == Q2,]
    colIndex <- mod(i, numCols) + 1
    with(dataForQ2,{
      # draw the experimental points
      lines(W, sigma, type = 'p', pch = 19, col = cols[colIndex], cex = 0.7)
      # draw the experimental error bars
      arrows(W, sigma - deltaSigma, W, sigma + deltaSigma, length = 0.02, angle = 90, code = 3, col = cols[colIndex])
    })
    # find the predicted values and plot them
    predictedQ2 <- predicted[predicted$Q2 == Q2,]
    # order the entries
    predictedQ2 <- predictedQ2[order(predictedQ2$W),]
    # plot the predicted lines
    lines(predictedQ2$W, predictedQ2$predicted, col = cols[colIndex])
    # put the values of Q2 in each line
    textPosX <- c(60, 120, 140, 160)[i %% 4 + 1]
    textPosY <- splinefun(predictedQ2$W, predictedQ2$predicted)(textPosX)
    #textPosX <- 1.1 * textPosX
    boxed.labels(textPosX, textPosY, labels = paste(Q2), col = cols[colIndex],
                 cex = 0.8, xpad = 1.3, ypad = 1.3, border = FALSE, bg = 'white', ylog = TRUE)
    i <<- i + 1
  })
  par()
  invisible(predicted)
}