--- /dev/null
+#' Plot curves
+#'
+#' Plot a range of curves in data
+#'
+#' @param data Object of class Data
+#' @param indices Range of indices (integers or dates)
+#'
+#' @export
+plotCurves <- function(data, indices=seq_len(data$getSize()))
+{
+ series = data$getSeries(indices)
+ yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ for (i in seq_along(indices))
+ {
+ plot(series[,i], type="l", ylim=yrange,
+ xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
+ if (i < length(indices))
+ par(new=TRUE)
+ }
+}
+
+#' Plot error
+#'
+#' Draw error graphs, potentially from several runs of \code{computeForecast}
+#'
+#' @param err Error as returned by \code{computeError}
+#' @param cols Colors for each error (default: 1,2,3,...)
+#'
+#' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
+#' \code{\link{plotSimils}}, \code{\link{plotFbox}},
+#' \code{\link{computeFilaments}, }\code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
+#'
+#' @export
+plotError <- function(err, cols=seq_along(err))
+{
+ if (!is.null(err$abs))
+ err = list(err)
+ par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
+ L = length(err)
+ yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
+ for (i in seq_len(L))
+ {
+ plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
+ ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
+ if (i < L)
+ par(new=TRUE)
+ }
+ yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
+ for (i in seq_len(L))
+ {
+ plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
+ ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
+ if (i < L)
+ par(new=TRUE)
+ }
+ yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
+ for (i in seq_len(L))
+ {
+ plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
+ ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
+ if (i < L)
+ par(new=TRUE)
+ }
+ yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
+ for (i in seq_len(L))
+ {
+ plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
+ ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
+ if (i < L)
+ par(new=TRUE)
+ }
+}
+
+#' Plot measured / predicted
+#'
+#' Plot measured curve (in black) and predicted curve (in blue)
+#'
+#' @param data Object return by \code{getData}
+#' @param pred Object as returned by \code{computeForecast}
+#' @param index Index in forecasts (integer or date)
+#'
+#' @export
+plotPredReal <- function(data, pred, index)
+{
+ horizon = length(pred$getSerie(1))
+ measure = data$getSerie( pred$getIndexInData(index)+1 )[1:horizon]
+ prediction = pred$getSerie(index)
+ yrange = range(measure, prediction)
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
+ plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10")
+ par(new=TRUE)
+ plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
+}
+
+#' Plot similarities
+#'
+#' Plot histogram of similarities (weights)
+#'
+#' @param pred Object as returned by \code{computeForecast}
+#' @param index Index in forecasts (integer or date)
+#'
+#' @export
+plotSimils <- function(pred, index)
+{
+ weights = pred$getParams(index)$weights
+ if (is.null(weights))
+ stop("plotSimils only works on 'Neighbors' forecasts")
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
+}
+
+#' Functional boxplot
+#'
+#' Draw the functional boxplot on the left, and bivariate plot on the right
+#'
+#' @param data Object return by \code{getData}
+#' @param indices integer or date indices to process
+#' @param plot_bivariate Should the bivariate plot appear?
+#'
+#' @export
+plotFbox <- function(data, indices=seq_len(data$getSize()))
+{
+ if (!requireNamespace("rainbow", quietly=TRUE))
+ stop("Functional boxplot requires the rainbow package")
+
+ series_matrix = data$getSeries(indices)
+ # Remove series with NAs
+ no_NAs_indices = sapply( 1:ncol(series_matrix),
+ function(i) all(!is.na(series_matrix[,i])) )
+ series_matrix = series_matrix[,no_NAs_indices]
+
+ series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
+ plotlegend=FALSE, lwd=2)
+ rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
+}
+
+#' Compute filaments
+#'
+#' Get similar days in the past, as black as distances are small
+#'
+#' @param data Object as returned by \code{getData}
+#' @param pred Object of class Forecast
+#' @param index Index in forecast (integer or date)
+#' @param limit Number of neighbors to consider
+#' @param plot Should the result be plotted?
+#'
+#' @return A list with
+#' \itemize{
+#' \item index : index of the current serie ('today')
+#' \item neighb_indices : indices of its neighbors
+#' \item colors : colors of neighbors curves (shades of gray)
+#' }
+#'
+#' @export
+computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
+{
+ ref_serie = data$getCenteredSerie( pred$getIndexInData(index) )
+ if (any(is.na(ref_serie)))
+ stop("computeFilaments requires a serie without NAs")
+
+ # Compute colors for each neighbor (from darkest to lightest)
+ sorted_dists = sort(-log(pred$getParams(index)$weights), index.return=TRUE)
+ nn = min(limit, length(sorted_dists$x))
+ min_dist = min(sorted_dists$x[1:nn])
+ max_dist = max(sorted_dists$x[1:nn])
+ color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
+ colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
+
+ if (plot)
+ {
+ # Complete series with (past and present) tomorrows
+ ref_serie = c(ref_serie, data$getCenteredSerie( pred$getIndexInData(index)+1 ))
+ centered_series = rbind(
+ data$getCenteredSeries( pred$getParams(index)$indices ),
+ data$getCenteredSeries( pred$getParams(index)$indices+1 ) )
+ yrange = range( ref_serie, quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
+ for (i in nn:1)
+ {
+ plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
+ xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
+ par(new=TRUE)
+ }
+ # Also plot ref curve, in red
+ plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
+ abline(v=24, lty=2, col=colors()[56], lwd=1)
+ }
+
+ list(
+ "index"=pred$getIndexInData(index),
+ "neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
+ "colors"=colors)
+}
+
+#' Functional boxplot on filaments
+#'
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#'
+#' @param data Object return by \code{getData}
+#' @param fil Output of \code{computeFilaments}
+#'
+#' @export
+plotFilamentsBox = function(data, fil, ...)
+{
+ if (!requireNamespace("rainbow", quietly=TRUE))
+ stop("Functional boxplot requires the rainbow package")
+
+ series_matrix = rbind(
+ data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
+ series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
+ plotlegend=FALSE, lwd=2)
+
+ # "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
+ usr <- par("usr")
+ yr <- (usr[4] - usr[3]) / 27
+ par(new=TRUE)
+ plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
+ ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
+ abline(v=24, lty=2, col=colors()[56])
+}
+
+#' Plot relative conditional variability / absolute variability
+#'
+#' Draw the relative conditional variability / absolute variability based on filaments
+#' obtained by \code{computeFilaments}
+#'
+#' @param data Object return by \code{getData}
+#' @param fil Output of \code{computeFilaments}
+#'
+#' @export
+plotRelVar = function(data, fil, ...)
+{
+ ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
+ apply(data$getSeries(fil$neighb_indices+1),1,sd) )
+ fdays = getNoNA2(data, 1, fil$index-1)
+ global_var = c( apply(data$getSeries(fdays),1,sd), apply(data$getSeries(fdays+1),1,sd) )
+
+ yrange = range(ref_var, global_var)
+ par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
+ xlab="Temps (heures)", ylab="Écart-type")
+ par(new=TRUE)
+ plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
+ abline(v=24, lty=2, col=colors()[56])
+}
projects / talweg.git / blobdiff