X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=2b7411763b21dfe0877cc16268c63131265fd7ee;hb=e7bc2af6e930ac5ecaac0e9b4207a49e463edddc;hp=52b077bea6cf71eff9d816cf0be4fea2f5a50530;hpb=4e25de2cf40b946ac6e8c2abc824637a249941d1;p=talweg.git

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 52b077b..2b74117 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -1,8 +1,8 @@
 #' Plot curves
 #'
-#' Plot a range of curves in data
+#' Plot a range of curves in data.
 #'
-#' @param data Object of class Data
+#' @inheritParams computeError
 #' @param indices Range of indices (integers or dates)
 #'
 #' @export
@@ -22,14 +22,14 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 
 #' Plot error
 #'
-#' Draw error graphs, potentially from several runs of \code{computeForecast}
+#' Draw error graphs, potentially from several runs of \code{computeForecast()}.
 #'
-#' @param err Error as returned by \code{computeError}
+#' @param err Error as returned by \code{computeError()}
 #' @param cols Colors for each error (default: 1,2,3,...)
 #'
 #' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
-#'   \code{\link{plotSimils}}, \code{\link{plotFbox}},
-#'   \code{\link{computeFilaments}, }\code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
+#'   \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
+#'   \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
 #'
 #' @export
 plotError <- function(err, cols=seq_along(err))
@@ -74,18 +74,16 @@ plotError <- function(err, cols=seq_along(err))
 
 #' Plot measured / predicted
 #'
-#' Plot measured curve (in black) and predicted curve (in blue)
+#' Plot measured curve (in black) and predicted curve (in blue).
 #'
-#' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
 #' @param index Index in forecasts (integer or date)
 #'
 #' @export
 plotPredReal <- function(data, pred, index)
 {
-	horizon = length(pred$getSerie(1))
-	measure = data$getSerie( pred$getIndexInData(index)+1 )[1:horizon]
-	prediction = pred$getSerie(index)
+	prediction = pred$getForecast(index)
+	measure = data$getSerie( pred$getIndexInData(index) )[1:length(prediction)]
 	yrange = range(measure, prediction)
 	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="Time (hours)", ylab="PM10")
@@ -95,9 +93,9 @@ plotPredReal <- function(data, pred, index)
 
 #' Plot similarities
 #'
-#' Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights), for 'Neighbors' method.
 #'
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
 #' @param index Index in forecasts (integer or date)
 #'
 #' @export
@@ -112,10 +110,10 @@ plotSimils <- function(pred, index)
 
 #' Functional boxplot
 #'
-#' Draw the functional boxplot on the left, and bivariate plot on the right
+#' Draw the functional boxplot on the left, and bivariate plot on the right.
 #'
-#' @param data Object return by \code{getData}
-#' @param indices integer or date indices to process
+#' @inheritParams computeError
+#' @inheritParams plotCurves
 #'
 #' @export
 plotFbox <- function(data, indices=seq_len(data$getSize()))
@@ -138,13 +136,14 @@ plotFbox <- function(data, indices=seq_len(data$getSize()))
 
 #' Compute filaments
 #'
-#' Get similar days in the past, as black as distances are small
+#' Obtain similar days in the past, and (optionally) plot them -- as black as distances
+#' are small.
 #'
-#' @param data Object as returned by \code{getData}
-#' @param pred Object of class Forecast
+#' @inheritParams computeError
 #' @param index Index in forecast (integer or date)
 #' @param limit Number of neighbors to consider
 #' @param plot Should the result be plotted?
+#' @param predict_from First prediction instant
 #'
 #' @return A list with
 #' \itemize{
@@ -154,10 +153,9 @@ plotFbox <- function(data, indices=seq_len(data$getSize()))
 #' }
 #'
 #' @export
-computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
+computeFilaments <- function(data, pred, index, predict_from, limit=60, plot=TRUE)
 {
-	ref_serie = data$getCenteredSerie( pred$getIndexInData(index) )
-	if (any(is.na(ref_serie)))
+	if (is.null(pred$getParams(index)$weights) || is.na(pred$getParams(index)$weights[1]))
 		stop("computeFilaments requires a serie without NAs")
 
 	# Compute colors for each neighbor (from darkest to lightest)
@@ -171,11 +169,13 @@ computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 	if (plot)
 	{
 		# Complete series with (past and present) tomorrows
-		ref_serie = c(ref_serie, data$getCenteredSerie( pred$getIndexInData(index)+1 ))
+		ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
+			data$getCenteredSerie( pred$getIndexInData(index) ) )
 		centered_series = rbind(
 			data$getCenteredSeries( pred$getParams(index)$indices ),
 			data$getCenteredSeries( pred$getParams(index)$indices+1 ) )
-		yrange = range( ref_serie, quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
+		yrange = range( ref_serie,
+			quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
 		par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
 		for (i in nn:1)
 		{
@@ -185,7 +185,7 @@ computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 		}
 		# Also plot ref curve, in red
 		plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
-		abline(v=24, lty=2, col=colors()[56], lwd=1)
+		abline(v=24+predict_from-0.5, lty=2, col=colors()[56], lwd=1)
 	}
 
 	list(
@@ -196,48 +196,51 @@ computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 
 #' Functional boxplot on filaments
 #'
-#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
 #'
-#' @param data Object return by \code{getData}
+#' @inheritParams computeError
 #' @param fil Output of \code{computeFilaments}
 #'
 #' @export
-plotFilamentsBox = function(data, fil)
+plotFilamentsBox = function(data, fil, predict_from)
 {
 	if (!requireNamespace("rainbow", quietly=TRUE))
 		stop("Functional boxplot requires the rainbow package")
 
 	series_matrix = rbind(
-		data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
+		data$getSeries(fil$neighb_indices-1), data$getSeries(fil$neighb_indices) )
 	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
+
 	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 		plotlegend=FALSE, lwd=2)
 
-	# "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
+	# "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
 	usr <- par("usr")
 	yr <- (usr[4] - usr[3]) / 27
 	par(new=TRUE)
 	plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
 		ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
-	abline(v=24, lty=2, col=colors()[56])
+	abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
 }
 
 #' Plot relative conditional variability / absolute variability
 #'
 #' Draw the relative conditional variability / absolute variability based on filaments
-#' obtained by \code{computeFilaments}
+#' obtained by \code{computeFilaments()}.
 #'
-#' @param data Object return by \code{getData}
-#' @param fil Output of \code{computeFilaments}
+#' @inheritParams computeError
+#' @inheritParams plotFilamentsBox
 #'
 #' @export
-plotRelVar = function(data, fil)
+plotRelVar = function(data, fil, predict_from)
 {
-	ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
-		apply(data$getSeries(fil$neighb_indices+1),1,sd) )
-	fdays = getNoNA2(data, 1, fil$index-1)
-	global_var = c( apply(data$getSeries(fdays),1,sd), apply(data$getSeries(fdays+1),1,sd) )
+	ref_var = c( apply(data$getSeries(fil$neighb_indices-1),1,sd),
+		apply(data$getSeries(fil$neighb_indices),1,sd) )
+	tdays = .getNoNA2(data, 2, fil$index)
+	global_var = c(
+		apply(data$getSeries(tdays-1),1,sd),
+		apply(data$getSeries(tdays),1,sd) )
 
 	yrange = range(ref_var, global_var)
 	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
@@ -245,5 +248,5 @@ plotRelVar = function(data, fil)
 		xlab="Time (hours)", ylab="Standard deviation")
 	par(new=TRUE)
 	plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
-	abline(v=24, lty=2, col=colors()[56])
+	abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
 }