X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=c8f7792cb05be2c4215bd93da8fac016b0b6e1ed;hb=af3b84f4cacade7d83221ca0249b546c50ddf340;hp=5cb55b0099f4878edb3da7ec44a9004bcc858711;hpb=99f83c9af27492f6fb9b10f51fb8704ed588f5c1;p=talweg.git

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 5cb55b0..c8f7792 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -1,6 +1,6 @@
-#' @title plot curves
+#' plot curves
 #'
-#' @description Plot a range of curves in data
+#' Plot a range of curves in data
 #'
 #' @param data Object of class Data
 #' @param indices Range of indices (integers or dates)
@@ -24,9 +24,9 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 	}
 }
 
-#' @title plot measured / predicted
+#' plot measured / predicted
 #'
-#' @description Plot measured curve (in black) and predicted curve (in red)
+#' Plot measured curve (in black) and predicted curve (in red)
 #'
 #' @param data Object return by \code{getData}
 #' @param pred Object as returned by \code{computeForecast}
@@ -44,9 +44,9 @@ plotPredReal <- function(data, pred, index)
 	plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
 }
 
-#' @title Compute filaments
+#' Compute filaments
 #'
-#' @description Get similar days in the past + "past tomorrow", as black as distances are small
+#' Get similar days in the past + "past tomorrow", as black as distances are small
 #'
 #' @param data Object as returned by \code{getData}
 #' @param index Index in data
@@ -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",""))
@@ -102,12 +100,12 @@ 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
+#' Plot similarities
 #'
-#' @description Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights)
 #'
 #' @param pred Object as returned by \code{computeForecast}
 #' @param index Index in forecasts (not in data)
@@ -122,15 +120,15 @@ plotSimils <- function(pred, index)
 	hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
 }
 
-#' @title Plot error
+#' Plot error
 #'
-#' @description 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 cols Colors for each error (default: 1,2,3,...)
 #'
-#' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
-#'   \code{\link{plotFbox}}
+#' @seealso \code{\link{plotPredReal}},\code{\link{plotFilaments}}
+#'   \code{\link{plotSimils}},\code{\link{plotFbox}},\code{\link{plotRelativeVariability}}
 #'
 #' @export
 plotError <- function(err, cols=seq_along(err))
@@ -173,9 +171,9 @@ plotError <- function(err, cols=seq_along(err))
 	}
 }
 
-#' @title Functional boxplot
+#' Functional boxplot
 #'
-#' @description 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 fiter Optional filter: return TRUE on indices to process
@@ -187,24 +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
-	}
-
 	L = length(data$getCenteredSerie(2))
-	series_matrix = sapply(start_index:end_index, function(index) {
+	series_matrix = sapply(1:data$getSize(), function(index) {
 		if (filter(index))
 			as.matrix(data$getSerie(index))
 		else
 			rep(NA,L)
 	})
 	# 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])) )
+	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)
@@ -217,9 +207,9 @@ plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE)
 		rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 }
 
-#' @title Functional boxplot on filaments
+#' Functional boxplot on filaments
 #'
-#' @description 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}
 #' @param indices Indices as output by \code{computeFilaments}
@@ -237,10 +227,10 @@ plotFilamentsBox = function(data, indices, ...)
 		ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
 }
 
-#' @title Plot relative conditional variability / absolute variability
+#' Plot relative conditional variability / absolute variability
 #'
-#' @description Draw the relative conditional variability / absolute variability based on on
-#'   filaments obtained by \code{computeFilaments}
+#' Draw the relative conditional variability / absolute variability based on filaments
+#' obtained by \code{computeFilaments}
 #'
 #' @param data Object return by \code{getData}
 #' @param indices Indices as output by \code{computeFilaments}
@@ -248,37 +238,24 @@ plotFilamentsBox = function(data, indices, ...)
 #' @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$getCenteredSerie(1))<length(ref_series[1,]), 2, 1)
-	fdays_indices = c()
-	for (i in first_day:(tail(indices,1)-1))
+	fdays = c()
+	for (i in 1:(tail(indices,1)-1))
 	{
 		if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
-			fdays_indices = c(fdays_indices, i)
-	}
-
-	# TODO: 3 == magic number
-	random_var = matrix(nrow=3, ncol=48)
-	for (mc in seq_len(nrow(random_var)))
-	{
-		random_indices = sample(fdays_indices, length(indices))
-		random_series = t( sapply(random_indices, function(i) {
-			c( data$getSerie(i), data$getSerie(i+1) )
-		}) )
-		random_var[mc,] = apply(random_series, 2, sd)
+			fdays = c(fdays, i)
 	}
-	random_var = apply(random_var, 2, median)
+	global_var = c( apply(data$getSerie(fdays),2,sd), apply(data$getSerie(fdays+1),2,sd) )
 
-	yrange = range(ref_var, random_var)
+	yrange = range(ref_var, global_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")
+	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])