X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;fp=pkg%2FR%2Fplot.R;h=eb0c81afe7c49a2772c35404ef7108e87dadde91;hb=ff5df8e310b73883565761ab4b1aa5a0672e9f27;hp=0000000000000000000000000000000000000000;hpb=63ff1ecbd80adfe347faa0d954f526d15f033c22;p=talweg.git

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+#' 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])
+}