pkg/R/plot.R

   1 #' @title plot curves
   2 #'
   3 #' @description Plot a range of curves in data
   4 #'
   5 #' @param data Object of class Data
   6 #' @param indices Range of indices (integers or dates)
   7 #'
   8 #' @export
   9 plotCurves <- function(data, indices)
  10 {
  11     yrange = range( sapply( indices, function(i) {
  12         serie = c(data$getCenteredSerie(i))
  13         if (!all(is.na(serie)))
  14             range(serie, na.rm=TRUE)
  15         c()
  16     }) )
  17     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
  18     for (i in seq_along(indices))
  19     {
  20         plot(data$getSerie(indices[i]), type="l", ylim=yrange,
  21             xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
  22         if (ii < length(indices))
  23             par(new=TRUE)
  24     }
  25 }
  26
  27 #' @title plot measured / predicted
  28 #'
  29 #' @description Plot measured curve (in black) and predicted curve (in red)
  30 #'
  31 #' @param data Object return by \code{getData}
  32 #' @param pred Object as returned by \code{getForecast}
  33 #' @param index Index in forecasts
  34 #'
  35 #' @export
  36 plotPredReal <- function(data, pred, index)
  37 {
  38     horizon = length(pred$getSerie(1))
  39     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
  40     measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon]
  41     yrange = range( pred$getSerie(index), measure )
  42     plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10")
  43     par(new=TRUE)
  44     plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
  45 }
  46
  47 #' @title Plot filaments
  48 #'
  49 #' @description Plot similar days in the past + "past tomorrow", as black as distances are small
  50 #'
  51 #' @param data Object as returned by \code{getData}
  52 #' @param index Index in data
  53 #' @param limit Number of neighbors to consider
  54 #'
  55 #' @export
  56 plotFilaments <- function(data, index, limit=60)
  57 {
  58     index = dateIndexToInteger(index, data)
  59     ref_serie = data$getCenteredSerie(index)
  60     if (any(is.na(ref_serie)))
  61         stop("plotFilaments requires a serie without NAs")
  62     L = length(ref_serie)
  63     first_day = ifelse(length(data$getCenteredSerie(1)<L), 2, 1)
  64     distances = sapply(first_day:(index-1), function(i) {
  65         sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
  66     })
  67     # HACK to suppress NA effect while keeping indexation
  68     distances[is.na(distances)] = max(distances,na.rm=TRUE) + 1
  69     indices = sort(distances, index.return=TRUE)$ix[1:min(limit,index-first_day)]
  70     yrange = range( ref_serie, sapply( indices, function(i) {
  71         index = i - first_day + 1
  72         serie = c(data$getCenteredSerie(index), data$getCenteredSerie(index+1))
  73         if (!all(is.na(serie)))
  74             return (range(serie, na.rm=TRUE))
  75         c()
  76     }) )
  77     grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
  78     colors = c(
  79         grays[ floor( 20.5 * distances[indices] / (1+max(distances[indices])) ) ], "#FF0000")
  80     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
  81     for (i in seq_len(length(indices)+1))
  82     {
  83         ind = ifelse(i<=length(indices), indices[i] - first_day + 1, index)
  84         plot(c(data$getCenteredSerie(ind),data$getCenteredSerie(ind+1)),
  85             ylim=yrange, type="l", col=colors[i],
  86             xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
  87         if (i <= length(indices))
  88             par(new=TRUE)
  89     }
  90 }
  91
  92 #' @title Plot similarities
  93 #'
  94 #' @description Plot histogram of similarities (weights)
  95 #'
  96 #' @param pred Object as returned by \code{getForecast}
  97 #' @param index Index in forecasts (not in data)
  98 #'
  99 #' @export
 100 plotSimils <- function(pred, index)
 101 {
 102     weights = pred$getParams(index)$weights
 103     if (is.null(weights))
 104         stop("plotSimils only works on 'Neighbors' forecasts")
 105     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 106     hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
 107 }
 108
 109 #' @title Plot error
 110 #'
 111 #' @description Draw error graphs, potentially from several runs of \code{getForecast}
 112 #'
 113 #' @param err Error as returned by \code{getError}
 114 #' @param cols Colors for each error (default: 1,2,3,...)
 115 #'
 116 #' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
 117 #'   \code{\link{plotFbox}}
 118 #'
 119 #' @export
 120 plotError <- function(err, cols=seq_along(err))
 121 {
 122     if (!is.null(err$abs))
 123         err = list(err)
 124     par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
 125     L = length(err)
 126     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
 127     for (i in seq_len(L))
 128     {
 129         plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 130             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
 131         if (i < L)
 132             par(new=TRUE)
 133     }
 134     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE )
 135     for (i in seq_len(L))
 136     {
 137         plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 138             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
 139         if (i < L)
 140             par(new=TRUE)
 141     }
 142     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE )
 143     for (i in seq_len(L))
 144     {
 145         plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 146             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
 147         if (i < L)
 148             par(new=TRUE)
 149     }
 150     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE )
 151     for (i in seq_len(L))
 152     {
 153         plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 154             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
 155         if (i < L)
 156             par(new=TRUE)
 157     }
 158 }
 159
 160 #' @title Functional boxplot
 161 #'
 162 #' @description Draw the functional boxplot on the left, and bivariate plot on the right
 163 #'
 164 #' @param data Object return by \code{getData}
 165 #' @param fiter Optional filter: return TRUE on indices to process
 166 #'
 167 #' @export
 168 plotFbox <- function(data, filter=function(index) TRUE)
 169 {
 170     if (!requireNamespace("rainbow", quietly=TRUE))
 171         stop("Functional boxplot requires the rainbow package")
 172
 173     start_index = 1
 174     end_index = data$getSize()
 175     if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
 176     {
 177         # Shifted start (7am, or 1pm, or...)
 178         start_index = 2
 179         end_index = data$getSize() - 1
 180     }
 181
 182     series_matrix = sapply(start_index:end_index, function(index) {
 183         as.matrix(data$getSerie(index))
 184     })
 185     # Remove NAs. + filter TODO: merge with previous step: only one pass required...
 186     nas_indices = seq_len(ncol(series_matrix))[ sapply( 1:ncol(series_matrix),
 187         function(index) ( !filter(index) || any(is.na(series_matrix[,index])) ) ) ]
 188     series_matrix = series_matrix[,-nas_indices]
 189
 190     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 191     par(mfrow=c(1,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 192     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
 193         plotlegend=FALSE, lwd=2)
 194     rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 195 }