-#' plot measured / predicted
-#'
-#' 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}
-#' @param index Index in forecasts
-#'
-#' @export
-plotPredReal <- function(data, pred, index)
-{
- horizon = length(pred$getSerie(1))
- measure = data$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$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
-}
-
-#' Compute filaments
-#'
-#' Get similar days in the past + "past tomorrow", as black as distances are small
-#'
-#' @param data Object as returned by \code{getData}
-#' @param index Index in data
-#' @param limit Number of neighbors to consider
-#' @param plot Should the result be plotted?
-#'
-#' @export
-computeFilaments <- function(data, index, limit=60, plot=TRUE)
-{
- index = dateIndexToInteger(index, data)
- ref_serie = data$getCenteredSerie(index)
- if (any(is.na(ref_serie)))
- stop("computeFilaments requires a serie without NAs")
- L = length(ref_serie)
-
- # 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$getCenteredSerie(i))^2 ) / L )
- })
- indices = sort(distances, index.return=TRUE)$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(0.05,0.95) )
- grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
- 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)$ix
- colors = c(grays[ color_values[plot_order] ], "#FF0000")
- if (plot)
- {
- par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
- for ( i in seq_len(length(indices)+1) )
- {
- ii = ifelse(i<=length(indices), fdays[ indices[plot_order[i]] ], index)
- plot(c(data$getCenteredSerie(ii),data$getCenteredSerie(ii+1)),
- ylim=yrange, type="l", col=colors[i],
- xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
- if (i <= length(indices))
- par(new=TRUE)
- }
- abline(v=24, lty=2, col=colors()[56])
- }
- list("indices"=c(fdays[ indices[plot_order] ],index), "colors"=colors)
-}
-
-#' Plot similarities
-#'
-#' Plot histogram of similarities (weights)
-#'
-#' @param pred Object as returned by \code{computeForecast}
-#' @param index Index in forecasts (not in data)
-#'
-#' @export
-plotSimils <- function(pred, index)
-{
- weights = pred$getParams(index)$weights
- if (is.null(weights))
- stop("plotSimils only works on 'Neighbors' forecasts")
- par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
-}
-