X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=9a7a1ee55181c14704a1cf59f587d22a7ca6944b;hb=25b75559e2d9bf84e2de35b851d93fefdae36e17;hp=c4f9cda76abef4d47187d36a498d53235b89fb85;hpb=fa8078f9658dccadc0f9bcc7b54c13ecda621816;p=talweg.git

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index c4f9cda..9a7a1ee 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -6,20 +6,20 @@
 #' @param indices Range of indices (integers or dates)
 #'
 #' @export
-plotCurves <- function(data, indices)
+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))
 	{
 		plot(data$getSerie(indices[i]), type="l", ylim=yrange,
 			xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
-		if (ii < length(indices))
+		if (i < length(indices))
 			par(new=TRUE)
 	}
 }
@@ -29,16 +29,16 @@ plotCurves <- function(data, indices)
 #' @description Plot measured curve (in black) and predicted curve (in red)
 #'
 #' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{getForecast}
+#' @param pred Object as returned by \code{computeForecast}
 #' @param index Index in forecasts
 #'
 #' @export
 plotPredReal <- function(data, pred, index)
 {
 	horizon = length(pred$getSerie(1))
-	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
 	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="")
@@ -61,45 +61,53 @@ 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$getCenteredSerie(1)<L), 2, 1)
-	distances = sapply(first_day:(index-1), function(i) {
+
+	# Determine indices of no-NAs days followed by no-NAs tomorrows
+	fdays = c()
+	for (i in 1:(index-1))
+	{
+		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
+			fdays = c(fdays, i)
+	}
+
+	distances = sapply(fdays, function(i) {
 		sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
 	})
-	# HACK to suppress NA effect while keeping indexation
-	distances[is.na(distances)] = max(distances,na.rm=TRUE) + 1
-	indices = sort(distances, index.return=TRUE)$ix[1:min(limit,index-first_day)]
-	yrange = quantile( range( ref_serie, sapply( indices, function(i) {
-		index = i - first_day + 1
-		serie = c(data$getCenteredSerie(index), data$getCenteredSerie(index+1))
+	indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))]
+	yrange = quantile( c(ref_serie, sapply( indices, function(i) {
+		serie = c(data$getCenteredSerie(fdays[i]), data$getCenteredSerie(fdays[i]+1))
 		if (!all(is.na(serie)))
 			return (range(serie, na.rm=TRUE))
 		c()
-	}) ), probs=c(0.1,0.9) )
+	}) ), 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[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[plot_order] ],index), "colors"=colors)
 }
 
 #' @title Plot similarities
 #'
 #' @description Plot histogram of similarities (weights)
 #'
-#' @param pred Object as returned by \code{getForecast}
+#' @param pred Object as returned by \code{computeForecast}
 #' @param index Index in forecasts (not in data)
 #'
 #' @export
@@ -114,9 +122,9 @@ plotSimils <- function(pred, index)
 
 #' @title Plot error
 #'
-#' @description Draw error graphs, potentially from several runs of \code{getForecast}
+#' @description Draw error graphs, potentially from several runs of \code{computeForecast}
 #'
-#' @param err Error as returned by \code{getError}
+#' @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}}
@@ -177,22 +185,16 @@ 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
-	}
-
-	series_matrix = sapply(start_index:end_index, function(index) {
-		as.matrix(data$getSerie(index))
+	L = length(data$getCenteredSerie(2))
+	series_matrix = sapply(1:data$getSize(), function(index) {
+		if (filter(index))
+			as.matrix(data$getSerie(index))
+		else
+			rep(NA,L)
 	})
-	# Remove NAs. + filter TODO: merge with previous step: only one pass required...
-	nas_indices = seq_len(ncol(series_matrix))[ sapply( 1:ncol(series_matrix),
-		function(index) ( !filter(index) || any(is.na(series_matrix[,index])) ) ) ]
-	series_matrix = series_matrix[,-nas_indices]
+	# 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)
@@ -223,3 +225,49 @@ 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$getSerie(i+1) )
+	}) )
+	ref_var = apply(ref_series, 2, sd)
+
+	# Determine indices of no-NAs days followed by no-NAs tomorrows
+	fdays = c()
+	for (i in 1:(tail(indices,1)-1))
+	{
+		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
+			fdays = c(fdays, i)
+	}
+
+	# TODO: 3 == magic number
+	random_var = matrix(nrow=3, ncol=48)
+	for (mc in seq_len(nrow(random_var)))
+	{
+		random_indices = sample(fdays, length(indices))
+		random_series = t( sapply(random_indices, function(i) {
+			c( data$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])
+}