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#' S3 class for storing input-output data.
#'
#' \code{idframe} is an S3 class for storing and manipulating input-ouput data. It supports discrete time and frequency domain data.
#'
#' @param output dataframe/matrix/vector containing the outputs
#' @param input dataframe/matrix/vector containing the inputs
#' @param Ts sampling interval (Default: 1)
#' @param start Time of the first observation
#' @param end Time of the last observation Optional Argument
#' @param unit Time Unit (Default: "seconds")
#'
#' @return an idframe object
#'
#' @seealso \code{\link{plot.idframe}}, the plot method for idframe objects,
#' \code{\link{summary.idframe}}, the summary method for idrame objects
#'
#' @examples
#'
#' dataMatrix <- matrix(rnorm(1000),ncol=5)
#' data <- idframe(output=dataMatrix[,3:5],input=dataMatrix[,1:2],Ts=1)
#'
#' @export
idframe <- function(output=NULL,input=NULL,Ts = 1,start=0,end=NULL,
unit = c("seconds","minutes","hours",
"days")[1]){
l <- list(output,input)
l2 <- lapply(l,data.frame)
n <- dim(l2[[1]])
if(!is.null(end)){
start <- end - Ts*(n-1)
}
l3 <- lapply(l,ts,start=start,deltat=1/Ts)
# Object Constructor
dat <- list(output=l3[[1]],input=l3[[1]],Ts=Ts,unit=unit)
class(dat) <- "idframe"
return(dat)
}
#' Plotting idframe objects
#'
#' Plotting method for objects inherting from class \code{idframe}
#'
#' @param object an object of class \code{idframe}
#' @param par a list of arguments passed to par() before plotting.
#' @param col line color, to be passed to plot.(Default=\code{"steelblue"})
#' @param ... additional arguments to be passed to the \code{tfplot} function
#'
#' @seealso \code{\link[tfplot]{tfplot}}
#' @examples
#' data(cstr)
#' plot(cstr,col="blue")
#'
#' @export
plot.idframe <- function(object,par=list(mar=c(3,4,2,2)),
col="steelblue",...){
require(tfplot)
if(nrow(object$output)==0){
data <- object$input
} else if(nrow(object$input)==0){
data <- object$output
} else{
data <- cbind(object$output,object$input)
}
tfplot(data,Xaxis=NULL,par=par,col=col,...)
}
#' @export
summary.idframe <- function(object){
out_sum <- summary(object$output)
in_sum <- summary(object$input)
out <- list(out_sum=out_sum,in_sum=in_sum,Ts=object$Ts,
unit=object$unit,nsample = dim(object$output)[1])
class(out) <- "summary.idframe"
return(out)
}
#' @export
print.summary.idframe <- function(object,...){
cat("\t\t Number of samples:");cat(object$nsample)
cat("\nSampling time: ")
cat(object$Ts);cat(" ");cat(object$unit)
cat("\n\n")
cat("Outputs \n")
print(object$out_sum)
cat("\n")
cat("Inputs \n")
print(object$in_sum)
}
#' @export
time.idframe <- function(data){
time(data$output)
}
#' S3 class for storing frequency response data
#'
#' @param response complex vector/matrix containing the response
#' @param freq the frequencies at which the response is observed/estimated
#' @param Ts sampling time of data
#'
#' @return an idfrd object
#'
#' @note
#' The class can currently store only SISO Responses. Future versions will
#' have support for multivariate data
#'
#' @seealso
#' \code{\link{plot.idfrd}} for generating bode plots; \code{\link{spa}} and
#' \code{\link{etfe}} for estimating the frequency response given input/output data
#'
#' @export
idfrd <- function(response,freq,Ts){
out <- list(response=response,freq=freq,Ts=Ts)
class(out) <- "idfrd"
return(out)
}
#' Plotting idfrd objects
#'
#' Generates the bode plot of the given frequency response data. It uses the
#' ggplot2 plotting engine
#'
#' @param object An object of class \code{idframe}
#'
#' @seealso \code{\link[ggplot2]{ggplot}}
#'
#' @examples
#' data(frf)
#' frf <- spa(data) # Estimates the frequency response from data
#' plot(frf)
#'
#' @export
plot.idfrd <- function(object){
require(ggplot2);require(reshape2);require(signal)
mag <- 20*log10(Mod(object$resp))
phase <- -360/2/pi*unwrap(Arg(object$resp))
sys_df <- data.frame(Frequency = object$freq,Gain = mag,Phase = phase)
melted_sys_df <- melt(sys_df, id.var = c("Frequency"))
bode <- ggplot(sys_df, aes(x = Frequency)) +
geom_line(colour="steelblue") + scale_x_log10() + theme_bw() +
geom_vline(xintercept=max(object$freq),size=1.2)
bode_gain <- bode + aes(y = Gain)
bode_phase <- bode + aes(y = Phase)
multiplot(bode_gain,bode_phase)
}
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
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