-#' @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)
#'
#' @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$getSerie(indices[i]), type="l", ylim=yrange,
xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
- if (ii < length(indices))
+ if (i < length(indices))
par(new=TRUE)
}
}
-#' @title plot measured / predicted
+#' plot measured / predicted
#'
-#' @description Plot measured curve (in black) and predicted curve (in red)
+#' Plot measured curve (in black) and predicted curve (in red)
#'
#' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{getForecast}
+#' @param pred Object as returned by \code{computeForecast}
#' @param index Index in forecasts
#'
#' @export
plotPredReal <- function(data, pred, index)
{
horizon = length(pred$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="")
}
-#' @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
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
+#' Plot similarities
#'
-#' @description Plot histogram of similarities (weights)
+#' 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
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{getForecast}
+#' 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}}
-#' \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))
}
}
-#' @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
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)
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}
plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2,
ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
}
+
+#' Plot relative conditional variability / absolute variability
+#'
+#' Draw the relative conditional variability / absolute variability based on filaments
+#' obtained by \code{computeFilaments}
+#'
+#' @param data Object return by \code{getData}
+#' @param indices Indices as output by \code{computeFilaments}
+#'
+#' @export
+plotRelativeVariability = function(data, indices, ...)
+{
+ ref_series = t( sapply(indices, function(i) {
+ c( data$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$getSerie(i+1))) )
+ fdays = c(fdays, i)
+ }
+ global_var = c( apply(data$getSerie(fdays),2,sd), apply(data$getSerie(fdays+1),2,sd) )
+
+ 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")
+ par(new=TRUE)
+ plot(random_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
+ abline(v=24, lty=2, col=colors()[56])
+}
projects / talweg.git / blobdiff