X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=9a7a1ee55181c14704a1cf59f587d22a7ca6944b;hb=25b75559e2d9bf84e2de35b851d93fefdae36e17;hp=7b5f2b5d599f9556a32b1ddbf68ee3f304990fcc;hpb=841b7f5a10742afa28707ebf7dca273b38d8b8e4;p=talweg.git

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 7b5f2b5..9a7a1ee 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -29,7 +29,7 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 #' @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
@@ -63,21 +63,19 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE)
 	L = length(ref_serie)
 
 	# Determine indices of no-NAs days followed by no-NAs tomorrows
-	first_day = ifelse(length(data$getCenteredSerie(1))<L, 2, 1)
-	fdays_indices = c()
-	for (i in first_day:(index-1))
+	fdays = c()
+	for (i in 1:(index-1))
 	{
 		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
-			fdays_indices = c(fdays_indices, i)
+			fdays = c(fdays, i)
 	}
 
-	distances = sapply(fdays_indices, function(i) {
+	distances = sapply(fdays, 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))
+		serie = c(data$getCenteredSerie(fdays[i]), data$getCenteredSerie(fdays[i]+1))
 		if (!all(is.na(serie)))
 			return (range(serie, na.rm=TRUE))
 		c()
@@ -93,7 +91,7 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE)
 		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)
+			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",""))
@@ -102,14 +100,14 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE)
 		}
 		abline(v=24, lty=2, col=colors()[56])
 	}
-	list("indices"=c(fdays_indices[ indices[plot_order] ],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
@@ -124,9 +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}}
@@ -187,22 +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)
@@ -253,19 +245,18 @@ plotRelativeVariability = function(data, indices, ...)
 	ref_var = apply(ref_series, 2, sd)
 
 	# Determine indices of no-NAs days followed by no-NAs tomorrows
-	first_day = ifelse(length(data$getCenteredSerie(1))<length(ref_series[1,]), 2, 1)
-	fdays_indices = c()
-	for (i in first_day:(tail(indices,1)-1))
+	fdays = c()
+	for (i in 1:(tail(indices,1)-1))
 	{
 		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
-			fdays_indices = c(fdays_indices, i)
+			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_indices, length(indices))
+		random_indices = sample(fdays, length(indices))
 		random_series = t( sapply(random_indices, function(i) {
 			c( data$getSerie(i), data$getSerie(i+1) )
 		}) )