X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=5cb55b0099f4878edb3da7ec44a9004bcc858711;hb=99f83c9af27492f6fb9b10f51fb8704ed588f5c1;hp=e4147986d6432dae8733be59cf01a947f44b3cea;hpb=16b1c049a9c402544d7076cacfe6b00a3785b51f;p=talweg.git

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index e414798..5cb55b0 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))
 	{
@@ -29,7 +29,7 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 #' @description Plot measured curve (in black) and predicted curve (in red)
 #'
 #' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{getForecast}
+#' @param pred Object as returned by \code{computeForecast}
 #' @param index Index in forecasts
 #'
 #' @export
@@ -75,7 +75,7 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE)
 		sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
 	})
 	indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))]
-	yrange = quantile( range( ref_serie, sapply( indices, function(i) {
+	yrange = quantile( c(ref_serie, sapply( indices, function(i) {
 		ii = fdays_indices[i]
 		serie = c(data$getCenteredSerie(ii), data$getCenteredSerie(ii+1))
 		if (!all(is.na(serie)))
@@ -109,7 +109,7 @@ computeFilaments <- function(data, index, limit=60, plot=TRUE)
 #'
 #' @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 +124,9 @@ 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}}
@@ -196,13 +196,16 @@ plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE)
 		end_index = data$getSize() - 1
 	}
 
+	L = length(data$getCenteredSerie(2))
 	series_matrix = sapply(start_index:end_index, function(index) {
-		as.matrix(data$getSerie(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)