[talweg.git] / pkg / R / plot.R

#' 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 index Index in data (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, index, limit=60, plot=TRUE)
{
	ref_serie = data$getCenteredSerie(index)
	if (any(is.na(ref_serie)))
		stop("computeFilaments requires a serie without NAs")

	# Determine indices of no-NAs days followed by no-NAs tomorrows
	fdays = getNoNA2(data, 1, dateIndexToInteger(index,data)-1)
	# Series + tomorrows in columns, ref_serie first
	centered_series = data$getCenteredSeries(fdays)

	# Obtain neighbors (closest for euclidian norm)
	L = length(ref_serie)
	distances = sqrt( colSums( (centered_series - ref_serie)^2 / L ) )
	sorted_distances = sort(distances, index.return=TRUE)

	# Compute colors for each neighbor (from darkest to lightest)
	nn = min(limit, length(distances))
	min_dist = min(sorted_distances$x[1:nn])
	max_dist = max(sorted_distances$x[1:nn])
	color_values = floor( 19.5 * (sorted_distances$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$getCenteredSerie(index+1))
		centered_series = rbind( centered_series, data$getCenteredSeries(fdays+1) )
		yrange = quantile(cbind(ref_serie,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_distances$ix[i]], ylim=yrange, type="l", col=colors[i],
				xlab=ifelse(i==nn,"Temps (en heures)",""), ylab=ifelse(i==nn,"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"=index,"neighb_indices"=fdays[sorted_distances$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])
}
Commit	Line	Data
98e958ca	1	#' Plot curves
e030a6e3	2	#'
af3b84f4	3	#' Plot a range of curves in data
e030a6e3 BA	4	#'
	5	#' @param data Object of class Data
	6	#' @param indices Range of indices (integers or dates)
	7	#'
	8	#' @export
1e20780e	9	plotCurves <- function(data, indices=seq_len(data$getSize()))
e030a6e3	10	{
98e958ca	11	series = data$getSeries(indices)
a5a3a294	12	yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
e030a6e3 BA	13	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	14	for (i in seq_along(indices))
	15	{
98e958ca	16	plot(series[,i], type="l", ylim=yrange,
e030a6e3	17	xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
1e20780e	18	if (i < length(indices))
e030a6e3 BA	19	par(new=TRUE)
	20	}
	21	}
	22
af3b84f4	23	#' Plot error
3d69ff21	24	#'
af3b84f4	25	#' Draw error graphs, potentially from several runs of \code{computeForecast}
3d69ff21	26	#'
99f83c9a	27	#' @param err Error as returned by \code{computeError}
09cf9c19	28	#' @param cols Colors for each error (default: 1,2,3,...)
3d69ff21	29	#'
98e958ca BA	30	#' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
	31	#' \code{\link{plotSimils}}, \code{\link{plotFbox}},
	32	#' \code{\link{computeFilaments}, }\code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
3d69ff21 BA	33	#'
3d69ff21 BA	34	#' @export
09cf9c19	35	plotError <- function(err, cols=seq_along(err))
3d69ff21	36	{
09cf9c19 BA	37	if (!is.null(err$abs))
09cf9c19 BA	38	err = list(err)
4e95ec8f	39	par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
3d69ff21	40	L = length(err)
98e958ca	41	yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
3d69ff21 BA	42	for (i in seq_len(L))
	43	{
	44	plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
09cf9c19	45	ylab=ifelse(i==1,"Moyenne \|y - y_hat\|",""), ylim=yrange, col=cols[i])
3d69ff21 BA	46	if (i < L)
	47	par(new=TRUE)
	48	}
98e958ca	49	yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
3d69ff21 BA	50	for (i in seq_len(L))
	51	{
	52	plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
09cf9c19	53	ylab=ifelse(i==1,"Moyenne \|y - y_hat\|",""), ylim=yrange, col=cols[i])
3d69ff21 BA	54	if (i < L)
	55	par(new=TRUE)
	56	}
98e958ca	57	yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
3d69ff21 BA	58	for (i in seq_len(L))
	59	{
	60	plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
09cf9c19	61	ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
3d69ff21 BA	62	if (i < L)
	63	par(new=TRUE)
	64	}
98e958ca	65	yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
3d69ff21 BA	66	for (i in seq_len(L))
	67	{
	68	plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
09cf9c19	69	ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
3d69ff21 BA	70	if (i < L)
	71	par(new=TRUE)
	72	}
	73	}
	74
98e958ca BA	75	#' Plot measured / predicted
	76	#'
	77	#' Plot measured curve (in black) and predicted curve (in blue)
	78	#'
	79	#' @param data Object return by \code{getData}
	80	#' @param pred Object as returned by \code{computeForecast}
	81	#' @param index Index in forecasts (integer or date)
	82	#'
	83	#' @export
	84	plotPredReal <- function(data, pred, index)
	85	{
	86	horizon = length(pred$getSerie(1))
	87	measure = data$getSerie( pred$getIndexInData(index)+1 )[1:horizon]
	88	prediction = pred$getSerie(index)
	89	yrange = range(measure, prediction)
	90	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
	91	plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10")
	92	par(new=TRUE)
	93	plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
	94	}
	95
	96	#' Plot similarities
	97	#'
	98	#' Plot histogram of similarities (weights)
	99	#'
	100	#' @param pred Object as returned by \code{computeForecast}
	101	#' @param index Index in forecasts (integer or date)
	102	#'
	103	#' @export
	104	plotSimils <- function(pred, index)
	105	{
	106	weights = pred$getParams(index)$weights
	107	if (is.null(weights))
	108	stop("plotSimils only works on 'Neighbors' forecasts")
	109	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	110	hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
	111	}
	112
af3b84f4	113	#' Functional boxplot
3d69ff21	114	#'
af3b84f4	115	#' Draw the functional boxplot on the left, and bivariate plot on the right
3d69ff21 BA	116	#'
3d69ff21 BA	117	#' @param data Object return by \code{getData}
98e958ca	118	#' @param indices integer or date indices to process
fa8078f9	119	#' @param plot_bivariate Should the bivariate plot appear?
3d69ff21 BA	120	#'
3d69ff21 BA	121	#' @export
98e958ca	122	plotFbox <- function(data, indices=seq_len(data$getSize()))
3d69ff21 BA	123	{
	124	if (!requireNamespace("rainbow", quietly=TRUE))
	125	stop("Functional boxplot requires the rainbow package")
	126
98e958ca BA	127	series_matrix = data$getSeries(indices)
98e958ca BA	128	# Remove series with NAs
af3b84f4 BA	129	no_NAs_indices = sapply( 1:ncol(series_matrix),
af3b84f4 BA	130	function(i) all(!is.na(series_matrix[,i])) )
99f83c9a	131	series_matrix = series_matrix[,no_NAs_indices]
3d69ff21 BA	132
3d69ff21 BA	133	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
fa8078f9	134	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
3d69ff21 BA	135	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
3d69ff21 BA	136	plotlegend=FALSE, lwd=2)
98e958ca BA	137	rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
	138	}
	139
	140	#' Compute filaments
	141	#'
	142	#' Get similar days in the past, as black as distances are small
	143	#'
	144	#' @param data Object as returned by \code{getData}
	145	#' @param index Index in data (integer or date)
	146	#' @param limit Number of neighbors to consider
	147	#' @param plot Should the result be plotted?
	148	#'
	149	#' @return A list with
	150	#' \itemize{
	151	#' \item index : index of the current serie ('today')
	152	#' \item neighb_indices : indices of its neighbors
	153	#' \item colors : colors of neighbors curves (shades of gray)
	154	#' }
	155	#'
	156	#' @export
	157	computeFilaments <- function(data, index, limit=60, plot=TRUE)
	158	{
	159	ref_serie = data$getCenteredSerie(index)
	160	if (any(is.na(ref_serie)))
	161	stop("computeFilaments requires a serie without NAs")
	162
	163	# Determine indices of no-NAs days followed by no-NAs tomorrows
	164	fdays = getNoNA2(data, 1, dateIndexToInteger(index,data)-1)
	165	# Series + tomorrows in columns, ref_serie first
	166	centered_series = data$getCenteredSeries(fdays)
	167
	168	# Obtain neighbors (closest for euclidian norm)
	169	L = length(ref_serie)
	170	distances = sqrt( colSums( (centered_series - ref_serie)^2 / L ) )
	171	sorted_distances = sort(distances, index.return=TRUE)
	172
	173	# Compute colors for each neighbor (from darkest to lightest)
	174	nn = min(limit, length(distances))
	175	min_dist = min(sorted_distances$x[1:nn])
	176	max_dist = max(sorted_distances$x[1:nn])
	177	color_values = floor( 19.5 * (sorted_distances$x[1:nn]-min_dist) / (max_dist-min_dist) ) + 1
	178	colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
	179
	180	if (plot)
	181	{
	182	# Complete series with (past and present) tomorrows
	183	ref_serie = c(ref_serie,data$getCenteredSerie(index+1))
	184	centered_series = rbind( centered_series, data$getCenteredSeries(fdays+1) )
a5a3a294	185	yrange = quantile(cbind(ref_serie,centered_series), probs=c(0.025,0.975), na.rm=TRUE)
98e958ca BA	186	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
	187	for (i in nn:1)
	188	{
	189	plot(centered_series[,sorted_distances$ix[i]], ylim=yrange, type="l", col=colors[i],
	190	xlab=ifelse(i==nn,"Temps (en heures)",""), ylab=ifelse(i==nn,"PM10 centré",""))
	191	par(new=TRUE)
	192	}
	193	# Also plot ref curve, in red
	194	plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
6d50a76f	195	abline(v=24, lty=2, col=colors()[56], lwd=1)
98e958ca BA	196	}
	197
	198	list("index"=index,"neighb_indices"=fdays[sorted_distances$ix[1:nn]],"colors"=colors)
fa8078f9 BA	199	}
fa8078f9 BA	200
af3b84f4	201	#' Functional boxplot on filaments
fa8078f9	202	#'
af3b84f4	203	#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
fa8078f9 BA	204	#'
fa8078f9 BA	205	#' @param data Object return by \code{getData}
98e958ca	206	#' @param fil Output of \code{computeFilaments}
fa8078f9 BA	207	#'
fa8078f9 BA	208	#' @export
98e958ca	209	plotFilamentsBox = function(data, fil, ...)
fa8078f9	210	{
98e958ca BA	211	if (!requireNamespace("rainbow", quietly=TRUE))
	212	stop("Functional boxplot requires the rainbow package")
	213
	214	series_matrix = rbind(
	215	data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
	216	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
	217	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	218	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
	219	plotlegend=FALSE, lwd=2)
	220
fa8078f9 BA	221	# "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
	222	usr <- par("usr")
	223	yr <- (usr[4] - usr[3]) / 27
98e958ca	224	par(new=TRUE)
a5a3a294	225	plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
fa8078f9	226	ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
d5fe1f30	227	abline(v=24, lty=2, col=colors()[56])
3d69ff21	228	}
16b1c049	229
af3b84f4	230	#' Plot relative conditional variability / absolute variability
16b1c049	231	#'
af3b84f4 BA	232	#' Draw the relative conditional variability / absolute variability based on filaments
af3b84f4 BA	233	#' obtained by \code{computeFilaments}
16b1c049 BA	234	#'
16b1c049 BA	235	#' @param data Object return by \code{getData}
98e958ca	236	#' @param fil Output of \code{computeFilaments}
16b1c049 BA	237	#'
16b1c049 BA	238	#' @export
98e958ca	239	plotRelVar = function(data, fil, ...)
16b1c049	240	{
98e958ca BA	241	ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
	242	apply(data$getSeries(fil$neighb_indices+1),1,sd) )
	243	fdays = getNoNA2(data, 1, fil$index-1)
	244	global_var = c( apply(data$getSeries(fdays),1,sd), apply(data$getSeries(fdays+1),1,sd) )
16b1c049	245
af3b84f4	246	yrange = range(ref_var, global_var)
16b1c049	247	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
af3b84f4 BA	248	plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
af3b84f4 BA	249	xlab="Temps (heures)", ylab="Écart-type")
16b1c049	250	par(new=TRUE)
98e958ca	251	plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
16b1c049 BA	252	abline(v=24, lty=2, col=colors()[56])
16b1c049 BA	253	}