R/plotevents.R

In TED: Turbulence Time Series Event Detection and Classification

#' Plot the detected events
#' 
#' This function plots the detected events from a time series.

#' 
#' @param events an object of class `events'.
#' @param cluster logical, if TRUE then the detected events are highlighted using different colors for different clusters
#' @param mycl a vector specifying which cluster each event belongs to
#' @param ... other arguments that can be passed to plot
#' @return ...
#' @references Yanfei Kang, Danijel Belusic and Kate Smith-Miles (2014). Detecting and Classifying Events in Noisy Time Series.
#'  \emph{J. Atmos. Sci.}, \bold{71}, 1090-1104.
#' \url{http://dx.doi.org/10.1175/JAS-D-13-0182.1}.
#' @seealso \code{\link{noiseTests}}, \code{\link{eventExtraction}}, \code{\link{eventDetection}}

#' @export
#' @examples
#' ##################################
#' #   1st art eg (white noise)
#' ##################################
#' set.seed(123)
#' n=128
#' types=c('box','rc','cr','sine')
#' shapes=matrix(NA,20,n)
#' for (i in 1:20){
#'   shapes[i,]=cbfs(type=types[sample(1:4,1)])
#' }
#' whitenoise=ts2mat(rnorm(128*20),128)
#' # generate x which randomly combine the four types of events with each two of them 
#' # separated by noise
#' x=c(rnorm(128),t(cbind(shapes,whitenoise)))
#' plot(x,ty='l')
#' w=128; alpha=0.05
#' # event detection
#' \dontrun{
#' events=eventDetection(x,w,'white',FALSE,alpha,'art')
#' clustering events
#' cc=eventCluster(events,4)
#' myclkm=cc$cl
#' # plot the clustered events
#' plotevents(events,cluster=TRUE, myclkm)
#' }
#' ##################################
#' #   2nd art eg (red noise)
#' ##################################
#' set.seed(123)
#' # generate a time series with red noise; this is the same with the one used
#' # in Kang et al. (2014)
#' coeff=0.5;s=1
#' x=c(arima.sim(list(order = c(1,0,0),ar=coeff),n=500,sd=s),
#'     cbfs_red('rc'),arima.sim(list(order = c(1,0,0),ar=coeff),n=400,sd=s),
#'     cbfs_red('cr'),arima.sim(list(order = c(1,0,0),ar=coeff),n=400,sd=s),
#'     cbfs_red('box'),arima.sim(list(order = c(1,0,0),ar=coeff),n=400,sd=s),
#'     cbfs_red('sine'),arima.sim(list(order = c(1,0,0),ar=coeff),n=1000,sd=s),
#'     arima.sim(list(order = c(1,0,0),ar=0.8),n=1100,sd=4))
#' w=128; alpha=0.05
#' # event detection
#' \dontrun{
#' events=eventDetection(x,w,'red',parallel=FALSE,alpha,'art')
#' # plot events without clustering
#' plotevents(events)
#' }
plotevents <- function(events, cluster = FALSE, mycl, ...) {
    x = events$x
    a = events$start
    b = events$end
    # plot the time series
    plot(x, main = "Events detected", type = "l", xlab = "t", ylab = "x", ...)
    if (cluster) {
        # highlight the detected events
        for (i in 1:length(a)) {
            lines(c(a[i]:b[i]), x[a[i]:b[i]], col = mycl[i] + 1)
        }
    } else {
        for (i in 1:length(a)) {
            lines(c(a[i]:b[i]), x[a[i]:b[i]], col = 2)
        }
    }
}