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

#' @title plot curves
#'
#' @description 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$getSize()))
{
	yrange = quantile( sapply( indices, function(i) {
		serie = c(data$getCenteredSerie(i))
		if (!all(is.na(serie)))
			range(serie, na.rm=TRUE)
		c()
	}), probs=c(0.05,0.95) )
	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	for (i in seq_along(indices))
	{
		plot(data$getSerie(indices[i]), type="l", ylim=yrange,
			xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
		if (i < length(indices))
			par(new=TRUE)
	}
}

#' @title plot measured / predicted
#'
#' @description Plot measured curve (in black) and predicted curve (in red)
#'
#' @param data Object return by \code{getData}
#' @param pred Object as returned by \code{computeForecast}
#' @param index Index in forecasts
#'
#' @export
plotPredReal <- function(data, pred, index)
{
	horizon = length(pred$getSerie(1))
	measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon]
	yrange = range( pred$getSerie(index), measure )
	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(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
}

#' @title Compute filaments
#'
#' @description Get similar days in the past + "past tomorrow", as black as distances are small
#'
#' @param data Object as returned by \code{getData}
#' @param index Index in data
#' @param limit Number of neighbors to consider
#' @param plot Should the result be plotted?
#'
#' @export
computeFilaments <- function(data, index, limit=60, plot=TRUE)
{
	index = dateIndexToInteger(index, data)
	ref_serie = data$getCenteredSerie(index)
	if (any(is.na(ref_serie)))
		stop("computeFilaments requires a serie without NAs")
	L = length(ref_serie)

	# Determine indices of no-NAs days followed by no-NAs tomorrows
	first_day = ifelse(length(data$getCenteredSerie(1))<L, 2, 1)
	fdays_indices = c()
	for (i in first_day:(index-1))
	{
		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
			fdays_indices = c(fdays_indices, i)
	}

	distances = sapply(fdays_indices, function(i) {
		sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
	})
	indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))]
	yrange = quantile( c(ref_serie, sapply( indices, function(i) {
		ii = fdays_indices[i]
		serie = c(data$getCenteredSerie(ii), data$getCenteredSerie(ii+1))
		if (!all(is.na(serie)))
			return (range(serie, na.rm=TRUE))
		c()
	}) ), probs=c(0.05,0.95) )
	grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
	min_dist = min(distances[indices])
	max_dist = max(distances[indices])
	color_values = floor( 19.5 * (distances[indices]-min_dist) / (max_dist-min_dist) ) + 1
	plot_order = sort(color_values, index.return=TRUE, decreasing=TRUE)$ix
	colors = c(grays[ color_values[plot_order] ], "#FF0000")
	if (plot)
	{
		par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
		for ( i in seq_len(length(indices)+1) )
		{
			ii = ifelse(i<=length(indices), fdays_indices[ indices[plot_order[i]] ], index)
			plot(c(data$getCenteredSerie(ii),data$getCenteredSerie(ii+1)),
				ylim=yrange, type="l", col=colors[i],
				xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
			if (i <= length(indices))
				par(new=TRUE)
		}
		abline(v=24, lty=2, col=colors()[56])
	}
	list("indices"=c(fdays_indices[ indices[plot_order] ],index), "colors"=colors)
}

#' @title Plot similarities
#'
#' @description Plot histogram of similarities (weights)
#'
#' @param pred Object as returned by \code{computeForecast}
#' @param index Index in forecasts (not in data)
#'
#' @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")
}

#' @title Plot error
#'
#' @description 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{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
#'   \code{\link{plotFbox}}
#'
#' @export
plotError <- function(err, cols=seq_along(err))
{
	if (!is.null(err$abs))
		err = list(err)
	par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
	L = length(err)
	yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
			ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
	yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
			ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
	yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
			ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
	yrange = range( sapply(1:L, function(index) ( err[[index]]$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)
	}
}

#' @title Functional boxplot
#'
#' @description Draw the functional boxplot on the left, and bivariate plot on the right
#'
#' @param data Object return by \code{getData}
#' @param fiter Optional filter: return TRUE on indices to process
#' @param plot_bivariate Should the bivariate plot appear?
#'
#' @export
plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE)
{
	if (!requireNamespace("rainbow", quietly=TRUE))
		stop("Functional boxplot requires the rainbow package")

	start_index = 1
	end_index = data$getSize()
	if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
	{
		# Shifted start (7am, or 1pm, or...)
		start_index = 2
		end_index = data$getSize() - 1
	}

	L = length(data$getCenteredSerie(2))
	series_matrix = sapply(start_index:end_index, function(index) {
		if (filter(index))
			as.matrix(data$getSerie(index))
		else
			rep(NA,L)
	})
	# TODO: merge with previous step: only one pass should be required
	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)
	if (plot_bivariate)
		par(mfrow=c(1,2))
	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)
	if (plot_bivariate)
		rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
}

#' @title Functional boxplot on filaments
#'
#' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments}
#'
#' @param data Object return by \code{getData}
#' @param indices Indices as output by \code{computeFilaments}
#'
#' @export
plotFilamentsBox = function(data, indices, ...)
{
	past_neighbs_indices = head(indices,-1)
	plotFbox(data, function(i) i %in% past_neighbs_indices, plot_bivariate=FALSE)
	par(new=TRUE)
	# "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
	usr <- par("usr")
	yr <- (usr[4] - usr[3]) / 27
	plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2,
		ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
}

#' @title Plot relative conditional variability / absolute variability
#'
#' @description Draw the relative conditional variability / absolute variability based on on
#'   filaments obtained by \code{computeFilaments}
#'
#' @param data Object return by \code{getData}
#' @param indices Indices as output by \code{computeFilaments}
#'
#' @export
plotRelativeVariability = function(data, indices, ...)
{
	#plot left / right separated by vertical line brown dotted
	#median of 3 runs for random length(indices) series
	ref_series = t( sapply(indices, function(i) {
		c( data$getSerie(i), data$getSerie(i+1) )
	}) )
	ref_var = apply(ref_series, 2, sd)

	# Determine indices of no-NAs days followed by no-NAs tomorrows
	first_day = ifelse(length(data$getCenteredSerie(1))<length(ref_series[1,]), 2, 1)
	fdays_indices = c()
	for (i in first_day:(tail(indices,1)-1))
	{
		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
			fdays_indices = c(fdays_indices, i)
	}

	# TODO: 3 == magic number
	random_var = matrix(nrow=3, ncol=48)
	for (mc in seq_len(nrow(random_var)))
	{
		random_indices = sample(fdays_indices, length(indices))
		random_series = t( sapply(random_indices, function(i) {
			c( data$getSerie(i), data$getSerie(i+1) )
		}) )
		random_var[mc,] = apply(random_series, 2, sd)
	}
	random_var = apply(random_var, 2, median)

	yrange = range(ref_var, random_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(random_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
	abline(v=24, lty=2, col=colors()[56])
}
Commit	Line	Data
e030a6e3 BA	1	#' @title plot curves
	2	#'
	3	#' @description Plot a range of curves in data
	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$getSize()))
e030a6e3	10	{
841b7f5a	11	yrange = quantile( sapply( indices, function(i) {
e030a6e3 BA	12	serie = c(data$getCenteredSerie(i))
	13	if (!all(is.na(serie)))
	14	range(serie, na.rm=TRUE)
	15	c()
841b7f5a	16	}), probs=c(0.05,0.95) )
e030a6e3 BA	17	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	18	for (i in seq_along(indices))
	19	{
	20	plot(data$getSerie(indices[i]), type="l", ylim=yrange,
	21	xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
1e20780e	22	if (i < length(indices))
e030a6e3 BA	23	par(new=TRUE)
	24	}
	25	}
	26
3d69ff21 BA	27	#' @title plot measured / predicted
	28	#'
	29	#' @description Plot measured curve (in black) and predicted curve (in red)
	30	#'
	31	#' @param data Object return by \code{getData}
99f83c9a	32	#' @param pred Object as returned by \code{computeForecast}
3d69ff21 BA	33	#' @param index Index in forecasts
	34	#'
	35	#' @export
	36	plotPredReal <- function(data, pred, index)
	37	{
	38	horizon = length(pred$getSerie(1))
3d69ff21 BA	39	measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon]
3d69ff21 BA	40	yrange = range( pred$getSerie(index), measure )
1e20780e	41	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
4e95ec8f	42	plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10")
3d69ff21	43	par(new=TRUE)
4e95ec8f	44	plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
3d69ff21 BA	45	}
3d69ff21 BA	46
fa8078f9	47	#' @title Compute filaments
3d69ff21	48	#'
fa8078f9	49	#' @description Get similar days in the past + "past tomorrow", as black as distances are small
3d69ff21 BA	50	#'
	51	#' @param data Object as returned by \code{getData}
	52	#' @param index Index in data
	53	#' @param limit Number of neighbors to consider
fa8078f9	54	#' @param plot Should the result be plotted?
3d69ff21 BA	55	#'
3d69ff21 BA	56	#' @export
fa8078f9	57	computeFilaments <- function(data, index, limit=60, plot=TRUE)
3d69ff21 BA	58	{
	59	index = dateIndexToInteger(index, data)
	60	ref_serie = data$getCenteredSerie(index)
	61	if (any(is.na(ref_serie)))
fa8078f9	62	stop("computeFilaments requires a serie without NAs")
3d69ff21	63	L = length(ref_serie)
1e20780e	64
16b1c049 BA	65	# Determine indices of no-NAs days followed by no-NAs tomorrows
	66	first_day = ifelse(length(data$getCenteredSerie(1))<L, 2, 1)
	67	fdays_indices = c()
	68	for (i in first_day:(index-1))
	69	{
	70	if ( !any(is.na(data$getSerie(i)) \| is.na(data$getSerie(i+1))) )
	71	fdays_indices = c(fdays_indices, i)
	72	}
1e20780e	73
16b1c049 BA	74	distances = sapply(fdays_indices, function(i) {
	75	sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
	76	})
1e20780e	77	indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))]
841b7f5a	78	yrange = quantile( c(ref_serie, sapply( indices, function(i) {
16b1c049	79	ii = fdays_indices[i]
1e20780e	80	serie = c(data$getCenteredSerie(ii), data$getCenteredSerie(ii+1))
3d69ff21	81	if (!all(is.na(serie)))
e5aa669a	82	return (range(serie, na.rm=TRUE))
e030a6e3	83	c()
1e20780e	84	}) ), probs=c(0.05,0.95) )
3d69ff21	85	grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
16b1c049 BA	86	min_dist = min(distances[indices])
	87	max_dist = max(distances[indices])
	88	color_values = floor( 19.5 * (distances[indices]-min_dist) / (max_dist-min_dist) ) + 1
	89	plot_order = sort(color_values, index.return=TRUE, decreasing=TRUE)$ix
fa8078f9 BA	90	colors = c(grays[ color_values[plot_order] ], "#FF0000")
fa8078f9 BA	91	if (plot)
3d69ff21	92	{
fa8078f9	93	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
16b1c049	94	for ( i in seq_len(length(indices)+1) )
fa8078f9	95	{
16b1c049 BA	96	ii = ifelse(i<=length(indices), fdays_indices[ indices[plot_order[i]] ], index)
16b1c049 BA	97	plot(c(data$getCenteredSerie(ii),data$getCenteredSerie(ii+1)),
fa8078f9 BA	98	ylim=yrange, type="l", col=colors[i],
	99	xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
	100	if (i <= length(indices))
	101	par(new=TRUE)
	102	}
16b1c049	103	abline(v=24, lty=2, col=colors()[56])
3d69ff21	104	}
16b1c049	105	list("indices"=c(fdays_indices[ indices[plot_order] ],index), "colors"=colors)
3d69ff21 BA	106	}
	107
	108	#' @title Plot similarities
	109	#'
	110	#' @description Plot histogram of similarities (weights)
	111	#'
99f83c9a	112	#' @param pred Object as returned by \code{computeForecast}
3d69ff21 BA	113	#' @param index Index in forecasts (not in data)
	114	#'
	115	#' @export
	116	plotSimils <- function(pred, index)
	117	{
	118	weights = pred$getParams(index)$weights
	119	if (is.null(weights))
	120	stop("plotSimils only works on 'Neighbors' forecasts")
4e95ec8f BA	121	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
4e95ec8f BA	122	hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
3d69ff21 BA	123	}
	124
	125	#' @title Plot error
	126	#'
99f83c9a	127	#' @description Draw error graphs, potentially from several runs of \code{computeForecast}
3d69ff21	128	#'
99f83c9a	129	#' @param err Error as returned by \code{computeError}
09cf9c19	130	#' @param cols Colors for each error (default: 1,2,3,...)
3d69ff21 BA	131	#'
	132	#' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
	133	#' \code{\link{plotFbox}}
	134	#'
	135	#' @export
09cf9c19	136	plotError <- function(err, cols=seq_along(err))
3d69ff21	137	{
09cf9c19 BA	138	if (!is.null(err$abs))
09cf9c19 BA	139	err = list(err)
4e95ec8f	140	par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
3d69ff21 BA	141	L = length(err)
	142	yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
	143	for (i in seq_len(L))
	144	{
	145	plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
09cf9c19	146	ylab=ifelse(i==1,"Moyenne \|y - y_hat\|",""), ylim=yrange, col=cols[i])
3d69ff21 BA	147	if (i < L)
	148	par(new=TRUE)
	149	}
	150	yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE )
	151	for (i in seq_len(L))
	152	{
	153	plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
09cf9c19	154	ylab=ifelse(i==1,"Moyenne \|y - y_hat\|",""), ylim=yrange, col=cols[i])
3d69ff21 BA	155	if (i < L)
	156	par(new=TRUE)
	157	}
	158	yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE )
	159	for (i in seq_len(L))
	160	{
	161	plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
09cf9c19	162	ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
3d69ff21 BA	163	if (i < L)
	164	par(new=TRUE)
	165	}
	166	yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE )
	167	for (i in seq_len(L))
	168	{
	169	plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
09cf9c19	170	ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
3d69ff21 BA	171	if (i < L)
	172	par(new=TRUE)
	173	}
	174	}
	175
	176	#' @title Functional boxplot
	177	#'
	178	#' @description Draw the functional boxplot on the left, and bivariate plot on the right
	179	#'
	180	#' @param data Object return by \code{getData}
	181	#' @param fiter Optional filter: return TRUE on indices to process
fa8078f9	182	#' @param plot_bivariate Should the bivariate plot appear?
3d69ff21 BA	183	#'
3d69ff21 BA	184	#' @export
fa8078f9	185	plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE)
3d69ff21 BA	186	{
	187	if (!requireNamespace("rainbow", quietly=TRUE))
	188	stop("Functional boxplot requires the rainbow package")
	189
	190	start_index = 1
	191	end_index = data$getSize()
	192	if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
	193	{
	194	# Shifted start (7am, or 1pm, or...)
	195	start_index = 2
	196	end_index = data$getSize() - 1
	197	}
	198
99f83c9a	199	L = length(data$getCenteredSerie(2))
3d69ff21	200	series_matrix = sapply(start_index:end_index, function(index) {
99f83c9a BA	201	if (filter(index))
	202	as.matrix(data$getSerie(index))
	203	else
	204	rep(NA,L)
3d69ff21	205	})
99f83c9a BA	206	# TODO: merge with previous step: only one pass should be required
	207	no_NAs_indices = sapply( 1:ncol(series_matrix), function(i) all(!is.na(series_matrix[,i])) )
	208	series_matrix = series_matrix[,no_NAs_indices]
3d69ff21 BA	209
3d69ff21 BA	210	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
fa8078f9 BA	211	if (plot_bivariate)
	212	par(mfrow=c(1,2))
	213	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
3d69ff21 BA	214	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
3d69ff21 BA	215	plotlegend=FALSE, lwd=2)
fa8078f9 BA	216	if (plot_bivariate)
	217	rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
	218	}
	219
	220	#' @title Functional boxplot on filaments
	221	#'
	222	#' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments}
	223	#'
	224	#' @param data Object return by \code{getData}
	225	#' @param indices Indices as output by \code{computeFilaments}
	226	#'
	227	#' @export
	228	plotFilamentsBox = function(data, indices, ...)
	229	{
	230	past_neighbs_indices = head(indices,-1)
	231	plotFbox(data, function(i) i %in% past_neighbs_indices, plot_bivariate=FALSE)
	232	par(new=TRUE)
	233	# "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
	234	usr <- par("usr")
	235	yr <- (usr[4] - usr[3]) / 27
	236	plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2,
	237	ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
3d69ff21	238	}
16b1c049 BA	239
	240	#' @title Plot relative conditional variability / absolute variability
	241	#'
	242	#' @description Draw the relative conditional variability / absolute variability based on on
	243	#' filaments obtained by \code{computeFilaments}
	244	#'
	245	#' @param data Object return by \code{getData}
	246	#' @param indices Indices as output by \code{computeFilaments}
	247	#'
	248	#' @export
	249	plotRelativeVariability = function(data, indices, ...)
	250	{
	251	#plot left / right separated by vertical line brown dotted
	252	#median of 3 runs for random length(indices) series
	253	ref_series = t( sapply(indices, function(i) {
	254	c( data$getSerie(i), data$getSerie(i+1) )
	255	}) )
	256	ref_var = apply(ref_series, 2, sd)
	257
	258	# Determine indices of no-NAs days followed by no-NAs tomorrows
	259	first_day = ifelse(length(data$getCenteredSerie(1))<length(ref_series[1,]), 2, 1)
	260	fdays_indices = c()
	261	for (i in first_day:(tail(indices,1)-1))
	262	{
	263	if ( !any(is.na(data$getSerie(i)) \| is.na(data$getSerie(i+1))) )
	264	fdays_indices = c(fdays_indices, i)
	265	}
	266
	267	# TODO: 3 == magic number
	268	random_var = matrix(nrow=3, ncol=48)
	269	for (mc in seq_len(nrow(random_var)))
	270	{
	271	random_indices = sample(fdays_indices, length(indices))
	272	random_series = t( sapply(random_indices, function(i) {
	273	c( data$getSerie(i), data$getSerie(i+1) )
	274	}) )
	275	random_var[mc,] = apply(random_series, 2, sd)
	276	}
	277	random_var = apply(random_var, 2, median)
	278
	279	yrange = range(ref_var, random_var)
	280	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	281	plot(ref_var, type="l", col=1, lwd=3, ylim=yrange, xlab="Temps (heures)", ylab="Écart-type")
	282	par(new=TRUE)
	283	plot(random_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
	284	abline(v=24, lty=2, col=colors()[56])
	285	}