#' @param indices Range of indices (integers or dates)
#'
#' @export
-plotCurves <- function(data, indices)
+plotCurves <- function(data, indices=seq_len(data$getSize()))
{
yrange = quantile( range( sapply( indices, function(i) {
serie = c(data$getCenteredSerie(i))
{
plot(data$getSerie(indices[i]), type="l", ylim=yrange,
xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
- if (ii < length(indices))
+ if (i < length(indices))
par(new=TRUE)
}
}
plotPredReal <- function(data, pred, index)
{
horizon = length(pred$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="")
if (any(is.na(ref_serie)))
stop("computeFilaments requires a serie without NAs")
L = length(ref_serie)
- first_day = ifelse(length(data$getCenteredSerie(1)<L), 2, 1)
- distances = sapply(first_day:(index-1), function(i) {
+
+ # Determine indices of no-NAs days followed by no-NAs tomorrows
+ first_day = ifelse(length(data$getCenteredSerie(1))<L, 2, 1)
+ fdays_indices = c()
+ for (i in first_day:(index-1))
+ {
+ if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
+ fdays_indices = c(fdays_indices, i)
+ }
+
+ distances = sapply(fdays_indices, 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)]
+ indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))]
yrange = quantile( range( ref_serie, sapply( indices, function(i) {
- index = i - first_day + 1
- serie = c(data$getCenteredSerie(index), data$getCenteredSerie(index+1))
+ ii = fdays_indices[i]
+ serie = c(data$getCenteredSerie(ii), data$getCenteredSerie(ii+1))
if (!all(is.na(serie)))
return (range(serie, na.rm=TRUE))
c()
- }) ), probs=c(0.1,0.9) )
+ }) ), probs=c(0.05,0.95) )
grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
- color_values = floor( 20.5 * distances[indices] / (1+max(distances[indices])) )
- plot_order = sort(color_values, index.return=TRUE)$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$getCenteredSerie(ind+1)),
+ ii = ifelse(i<=length(indices), fdays_indices[ indices[plot_order[i]] ], index)
+ plot(c(data$getCenteredSerie(ii),data$getCenteredSerie(ii+1)),
ylim=yrange, type="l", col=colors[i],
xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
if (i <= length(indices))
par(new=TRUE)
}
+ abline(v=24, lty=2, col=colors()[56])
}
- list("indices"=c(indices[plot_order]-first_day+1,index), "colors"=colors)
+ list("indices"=c(fdays_indices[ indices[plot_order] ],index), "colors"=colors)
}
#' @title Plot similarities
plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2,
ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
}
+
+#' @title Plot relative conditional variability / absolute variability
+#'
+#' @description Draw the relative conditional variability / absolute variability based on 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, ...)
+{
+ #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$getCenteredSerie(1))<length(ref_series[1,]), 2, 1)
+ fdays_indices = c()
+ for (i in first_day:(tail(indices,1)-1))
+ {
+ if ( !any(is.na(data$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)
+ }
+ random_var = apply(random_var, 2, median)
+
+ yrange = range(ref_var, random_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