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, xlab="Temps (en heures)", ylab="PM10")
  17     par(new=TRUE)
  18     plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, lwd=3, xlab="", ylab="")
  19 }
  20
  21 #' @title Plot filaments
  22 #'
  23 #' @description Plot similar days in the past + "past tomorrow", as black as distances are small
  24 #'
  25 #' @param data Object as returned by \code{getData}
  26 #' @param index Index in data
  27 #' @param limit Number of neighbors to consider
  28 #'
  29 #' @export
  30 plotFilaments <- function(data, index, limit=60)
  31 {
  32     index = dateIndexToInteger(index, data)
  33     ref_serie = data$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)$weights, nclass=20, xlab="Weight", ylab="Frequency")
  81 }
  82
  83 #' @title Plot error
  84 #'
  85 #' @description Draw error graphs, potentially from several runs of \code{getForecast}
  86 #'
  87 #' @param err Error as returned by \code{getError}
  88 #' @param cols Colors for each error (default: 1,2,3,...)
  89 #'
  90 #' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
  91 #'   \code{\link{plotFbox}}
  92 #'
  93 #' @export
  94 plotError <- function(err, cols=seq_along(err))
  95 {
  96     if (!is.null(err$abs))
  97         err = list(err)
  98     par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2, lwd=2)
  99     L = length(err)
 100     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
 101     for (i in seq_len(L))
 102     {
 103         plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 104             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
 105         if (i < L)
 106             par(new=TRUE)
 107     }
 108     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE )
 109     for (i in seq_len(L))
 110     {
 111         plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 112             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
 113         if (i < L)
 114             par(new=TRUE)
 115     }
 116     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE )
 117     for (i in seq_len(L))
 118     {
 119         plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 120             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
 121         if (i < L)
 122             par(new=TRUE)
 123     }
 124     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE )
 125     for (i in seq_len(L))
 126     {
 127         plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 128             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
 129         if (i < L)
 130             par(new=TRUE)
 131     }
 132 }
 133
 134 #' @title Functional boxplot
 135 #'
 136 #' @description Draw the functional boxplot on the left, and bivariate plot on the right
 137 #'
 138 #' @param data Object return by \code{getData}
 139 #' @param fiter Optional filter: return TRUE on indices to process
 140 #'
 141 #' @export
 142 plotFbox <- function(data, filter=function(index) TRUE)
 143 {
 144     if (!requireNamespace("rainbow", quietly=TRUE))
 145         stop("Functional boxplot requires the rainbow package")
 146
 147     start_index = 1
 148     end_index = data$getSize()
 149     if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
 150     {
 151         # Shifted start (7am, or 1pm, or...)
 152         start_index = 2
 153         end_index = data$getSize() - 1
 154     }
 155
 156     series_matrix = sapply(start_index:end_index, function(index) {
 157         as.matrix(data$getSerie(index))
 158     })
 159     # Remove NAs. + filter TODO: merge with previous step: only one pass required...
 160     nas_indices = seq_len(ncol(series_matrix))[ sapply( 1:ncol(series_matrix),
 161         function(index) ( !filter(index) || any(is.na(series_matrix[,index])) ) ) ]
 162     series_matrix = series_matrix[,-nas_indices]
 163
 164     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 165     par(mfrow=c(1,2), mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2)
 166     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
 167         plotlegend=FALSE, lwd=2)
 168     rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 169 }