R/plot.R

   1 #' @title plot measured / predicted
   2 #'
   3 #' @description Plot measured curve (in black) and predicted curve (in red)
   4 #'
   5 #' @param data Object return by \code{getData}
   6 #' @param pred Object as returned by \code{getForecast}
   7 #' @param index Index in forecasts
   8 #'
   9 #' @export
  10 plotPredReal <- function(data, pred, index)
  11 {
  12     horizon = length(pred$getSerie(1))
  13     par(mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2, lwd=2)
  14     measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon]
  15     yrange = range( pred$getSerie(index), measure )
  16     plot(measure, type="l", ylim=yrange, lwd=3)
  17     par(new=TRUE)
  18     plot(pred$getSerie(index), type="l", col=2, ylim=yrange, lwd=3)
  19 }
  20
  21 #' @title Plot filaments
  22 #'
  23 #' @description Plot similar days in the past + "past tomorrow", as black as distances are small
  24 #'
  25 #' @param data Object as returned by \code{getData}
  26 #' @param index Index in data
  27 #' @param limit Number of neighbors to consider
  28 #'
  29 #' @export
  30 plotFilaments <- function(data, index, limit=60)
  31 {
  32     index = dateIndexToInteger(index, data)
  33     ref_serie = data$getCenteredSerie(index)
  34     if (any(is.na(ref_serie)))
  35         stop("plotFilaments requires a serie without NAs")
  36     L = length(ref_serie)
  37     first_day = ifelse(length(data$getCenteredSerie(1)<L), 2, 1)
  38     distances = sapply(first_day:(index-1), function(i) {
  39         sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
  40     })
  41     # HACK to suppress NA effect while keeping indexation
  42     distances[is.na(distances)] = max(distances,na.rm=TRUE) + 1
  43     indices = sort(distances, index.return=TRUE)$ix[1:min(limit,index-first_day)]
  44     yrange = range( ref_serie, sapply( indices, function(i) {
  45         index = i - first_day + 1
  46         serie = c(data$getCenteredSerie(index), data$getCenteredSerie(index+1))
  47         if (!all(is.na(serie)))
  48             return ( range(serie, na.rm=TRUE) )
  49         return (0)
  50     }) )
  51     grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
  52     colors = c(
  53         grays[ floor( 20.5 * distances[indices] / (1+max(distances[indices])) ) ], "#FF0000")
  54     par(mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2, lwd=2)
  55     for (i in seq_len(length(indices)+1))
  56     {
  57         ind = ifelse(i<=length(indices), indices[i] - first_day + 1, index)
  58         plot(c(data$getCenteredSerie(ind),data$getCenteredSerie(ind+1)),
  59             ylim=yrange, type="l", col=colors[i],
  60             xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
  61         if (i <= length(indices))
  62             par(new=TRUE)
  63     }
  64 }
  65
  66 #' @title Plot similarities
  67 #'
  68 #' @description Plot histogram of similarities (weights)
  69 #'
  70 #' @param pred Object as returned by \code{getForecast}
  71 #' @param index Index in forecasts (not in data)
  72 #'
  73 #' @export
  74 plotSimils <- function(pred, index)
  75 {
  76     weights = pred$getParams(index)$weights
  77     if (is.null(weights))
  78         stop("plotSimils only works on 'Neighbors' forecasts")
  79     par(mar=c(4.7,5,1,1))
  80     hist(pred$getParams(index)$weights, nclass=20, xlab="Weight", ylab="Frequency")
  81 }
  82
  83 #' @title Plot error
  84 #'
  85 #' @description Draw error graphs, potentially from several runs of \code{getForecast}
  86 #'
  87 #' @param err Error as returned by \code{getError}
  88 #'
  89 #' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
  90 #'   \code{\link{plotFbox}}
  91 #'
  92 #' @export
  93 plotError <- function(err)
  94 {
  95     par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2, lwd=2)
  96     L = length(err)
  97     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
  98     for (i in seq_len(L))
  99     {
 100         plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 101             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=i)
 102         if (i < L)
 103             par(new=TRUE)
 104     }
 105     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE )
 106     for (i in seq_len(L))
 107     {
 108         plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 109             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=i)
 110         if (i < L)
 111             par(new=TRUE)
 112     }
 113     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE )
 114     for (i in seq_len(L))
 115     {
 116         plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 117             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=i)
 118         if (i < L)
 119             par(new=TRUE)
 120     }
 121     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE )
 122     for (i in seq_len(L))
 123     {
 124         plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 125             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=i)
 126         if (i < L)
 127             par(new=TRUE)
 128     }
 129 }
 130
 131 #' @title Functional boxplot
 132 #'
 133 #' @description Draw the functional boxplot on the left, and bivariate plot on the right
 134 #'
 135 #' @param data Object return by \code{getData}
 136 #' @param fiter Optional filter: return TRUE on indices to process
 137 #'
 138 #' @export
 139 plotFbox <- function(data, filter=function(index) (TRUE))
 140 {
 141     if (!requireNamespace("rainbow", quietly=TRUE))
 142         stop("Functional boxplot requires the rainbow package")
 143
 144     start_index = 1
 145     end_index = data$getSize()
 146     if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
 147     {
 148         # Shifted start (7am, or 1pm, or...)
 149         start_index = 2
 150         end_index = data$getSize() - 1
 151     }
 152
 153     series_matrix = sapply(start_index:end_index, function(index) {
 154         as.matrix(data$getSerie(index))
 155     })
 156     # Remove NAs. + filter TODO: merge with previous step: only one pass required...
 157     nas_indices = seq_len(ncol(series_matrix))[ sapply( 1:ncol(series_matrix),
 158         function(index) ( !filter(index) || any(is.na(series_matrix[,index])) ) ) ]
 159     series_matrix = series_matrix[,-nas_indices]
 160
 161     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 162     par(mfrow=c(1,2), mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2)
 163     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
 164         plotlegend=FALSE, lwd=2)
 165     rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 166 }