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diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index f4fc3bf..7b5f2b5 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -8,12 +8,12 @@
 #' @export
 plotCurves <- function(data, indices=seq_len(data$getSize()))
 {
-	yrange = quantile( range( sapply( indices, function(i) {
+	yrange = quantile( sapply( indices, function(i) {
 		serie = c(data$getCenteredSerie(i))
 		if (!all(is.na(serie)))
 			range(serie, na.rm=TRUE)
 		c()
-	}) ), probs=c(0.05,0.95) )
+	}), 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))
 	{
@@ -61,48 +61,48 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE)
 	if (any(is.na(ref_serie)))
 		stop("computeFilaments requires a serie without NAs")
 	L = length(ref_serie)
-	first_day = ifelse(length(data$getCenteredSerie(1)<L), 2, 1)
-	distances = sapply(first_day:(index-1), function(i) {
-		sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
-	})
-	# HACK to suppress NA effect while keeping indexation
-	
-
-
-
-
-##TOCONTINUE
-
-
 
+	# 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[is.na(distances)] = max(distances,na.rm=TRUE) + 1
+	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( range( ref_serie, sapply( indices, function(i) {
-		ii = i - 1 + first_day
+	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
-	color_values = floor( 20.5 * distances[indices] / (1+max(distances[indices])) )
-	plot_order = sort(color_values, index.return=TRUE)$ix
+	min_dist = min(distances[indices])
+	max_dist = max(distances[indices])
+	color_values = floor( 19.5 * (distances[indices]-min_dist) / (max_dist-min_dist) ) + 1
+	plot_order = sort(color_values, index.return=TRUE, decreasing=TRUE)$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 c(plot_order,length(indices)+1) )
+		for ( i in seq_len(length(indices)+1) )
 		{
-			ind = ifelse(i<=length(indices), indices[i] - first_day + 1, index)
-			plot(c(data$getCenteredSerie(ind),data$getCenteredSerie(ind+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(indices[plot_order]-first_day+1,index), "colors"=colors)
+	list("indices"=c(fdays_indices[ indices[plot_order] ],index), "colors"=colors)
 }
 
 #' @title Plot similarities
@@ -233,3 +233,50 @@ plotFilamentsBox = function(data, indices, ...)
 	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])
+}