pkg/R/plot.R

   1 #' Plot curves
   2 #'
   3 #' Plot a range of curves in data.
   4 #'
   5 #' @inheritParams computeError
   6 #' @param indices Range of indices (integers or dates)
   7 #'
   8 #' @export
   9 plotCurves <- function(data, indices=seq_len(data$getSize()))
  10 {
  11     series = data$getSeries(indices)
  12     yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
  13     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
  14     for (i in seq_along(indices))
  15     {
  16         plot(series[,i], type="l", ylim=yrange,
  17             xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"PM10",""))
  18         if (i < length(indices))
  19             par(new=TRUE)
  20     }
  21 }
  22
  23 #' Plot error
  24 #'
  25 #' Draw error graphs, potentially from several runs of \code{computeForecast()}.
  26 #'
  27 #' @param err Error as returned by \code{computeError()}
  28 #' @param cols Colors for each error (default: 1,2,3,...)
  29 #'
  30 #' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
  31 #'   \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
  32 #'   \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
  33 #'
  34 #' @export
  35 plotError <- function(err, cols=seq_along(err))
  36 {
  37     if (!is.null(err$abs))
  38         err = list(err)
  39     par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
  40     L = length(err)
  41     yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
  42     for (i in seq_len(L))
  43     {
  44         plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Time (hours)",""),
  45             ylab=ifelse(i==1,"Mean |y - y_hat|",""), ylim=yrange, col=cols[i])
  46         if (i < L)
  47             par(new=TRUE)
  48     }
  49     yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
  50     for (i in seq_len(L))
  51     {
  52         plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
  53             ylab=ifelse(i==1,"Mean |y - y_hat|",""), ylim=yrange, col=cols[i])
  54         if (i < L)
  55             par(new=TRUE)
  56     }
  57     yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
  58     for (i in seq_len(L))
  59     {
  60         plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Time (hours)",""),
  61             ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
  62         if (i < L)
  63             par(new=TRUE)
  64     }
  65     yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
  66     for (i in seq_len(L))
  67     {
  68         plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
  69             ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
  70         if (i < L)
  71             par(new=TRUE)
  72     }
  73 }
  74
  75 #' Plot measured / predicted
  76 #'
  77 #' Plot measured curve (in black) and predicted curve (in blue).
  78 #'
  79 #' @inheritParams computeError
  80 #' @param index Index in forecasts (integer or date)
  81 #'
  82 #' @export
  83 plotPredReal <- function(data, pred, index)
  84 {
  85     prediction = pred$getForecast(index)
  86     measure = data$getSerie( pred$getIndexInData(index) )[length(prediction)]
  87     yrange = range(measure, prediction)
  88     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
  89     plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
  90     par(new=TRUE)
  91     plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
  92 }
  93
  94 #' Plot similarities
  95 #'
  96 #' Plot histogram of similarities (weights), for 'Neighbors' method.
  97 #'
  98 #' @inheritParams computeError
  99 #' @param index Index in forecasts (integer or date)
 100 #'
 101 #' @export
 102 plotSimils <- function(pred, index)
 103 {
 104     weights = pred$getParams(index)$weights
 105     if (is.null(weights))
 106         stop("plotSimils only works on 'Neighbors' forecasts")
 107     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 108     hist(pred$getParams(index)$weights, nclass=20, main="", xlab="Weight", ylab="Count")
 109 }
 110
 111 #' Functional boxplot
 112 #'
 113 #' Draw the functional boxplot on the left, and bivariate plot on the right.
 114 #'
 115 #' @inheritParams computeError
 116 #' @inheritParams plotCurves
 117 #'
 118 #' @export
 119 plotFbox <- function(data, indices=seq_len(data$getSize()))
 120 {
 121     if (!requireNamespace("rainbow", quietly=TRUE))
 122         stop("Functional boxplot requires the rainbow package")
 123
 124     series_matrix = data$getSeries(indices)
 125     # Remove series with NAs
 126     no_NAs_indices = sapply( 1:ncol(series_matrix),
 127         function(i) all(!is.na(series_matrix[,i])) )
 128     series_matrix = series_matrix[,no_NAs_indices]
 129
 130     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 131     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 132     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 133         plotlegend=FALSE, lwd=2)
 134     rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 135 }
 136
 137 #' Compute filaments
 138 #'
 139 #' Obtain similar days in the past, and (optionally) plot them -- as black as distances
 140 #' are small.
 141 #'
 142 #' @inheritParams computeError
 143 #' @param index Index in forecast (integer or date)
 144 #' @param limit Number of neighbors to consider
 145 #' @param plot Should the result be plotted?
 146 #' @param predict_from First prediction instant
 147 #'
 148 #' @return A list with
 149 #' \itemize{
 150 #'   \item index : index of the current serie ('today')
 151 #'   \item neighb_indices : indices of its neighbors
 152 #'   \item colors : colors of neighbors curves (shades of gray)
 153 #' }
 154 #'
 155 #' @export
 156 computeFilaments <- function(data, pred, index, predict_from, limit=60, plot=TRUE)
 157 {
 158     if (is.null(pred$getParams(index)$weights) || is.na(pred$getParams(index)$weights[1]))
 159         stop("computeFilaments requires a serie without NAs")
 160
 161     # Compute colors for each neighbor (from darkest to lightest)
 162     sorted_dists = sort(-log(pred$getParams(index)$weights), index.return=TRUE)
 163     nn = min(limit, length(sorted_dists$x))
 164     min_dist = min(sorted_dists$x[1:nn])
 165     max_dist = max(sorted_dists$x[1:nn])
 166     color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
 167     colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
 168
 169     if (plot)
 170     {
 171         # Complete series with (past and present) tomorrows
 172         ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
 173             data$getCenteredSerie( pred$getIndexInData(index) ) )
 174         centered_series = rbind(
 175             data$getCenteredSeries( pred$getParams(index)$indices ),
 176             data$getCenteredSeries( pred$getParams(index)$indices+1 ) )
 177         yrange = range( ref_serie,
 178             quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
 179         par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
 180         for (i in nn:1)
 181         {
 182             plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
 183                 xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"Centered PM10",""))
 184             par(new=TRUE)
 185         }
 186         # Also plot ref curve, in red
 187         plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
 188         abline(v=24+predict_from-0.5, lty=2, col=colors()[56], lwd=1)
 189     }
 190
 191     list(
 192         "index"=pred$getIndexInData(index),
 193         "neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
 194         "colors"=colors)
 195 }
 196
 197 #' Functional boxplot on filaments
 198 #'
 199 #' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
 200 #'
 201 #' @inheritParams computeError
 202 #' @param fil Output of \code{computeFilaments}
 203 #'
 204 #' @export
 205 plotFilamentsBox = function(data, fil, predict_from)
 206 {
 207     if (!requireNamespace("rainbow", quietly=TRUE))
 208         stop("Functional boxplot requires the rainbow package")
 209
 210     series_matrix = rbind(
 211         data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
 212     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 213     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 214     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 215         plotlegend=FALSE, lwd=2)
 216
 217     # "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
 218     usr <- par("usr")
 219     yr <- (usr[4] - usr[3]) / 27
 220     par(new=TRUE)
 221     plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
 222         ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
 223     abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
 224 }
 225
 226 #' Plot relative conditional variability / absolute variability
 227 #'
 228 #' Draw the relative conditional variability / absolute variability based on filaments
 229 #' obtained by \code{computeFilaments()}.
 230 #'
 231 #' @inheritParams computeError
 232 #' @inheritParams plotFilamentsBox
 233 #'
 234 #' @export
 235 plotRelVar = function(data, fil, predict_from)
 236 {
 237     ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
 238         apply(data$getSeries(fil$neighb_indices+1),1,sd) )
 239     tdays = .getNoNA2(data, 2, fil$index)
 240     global_var = c(
 241         apply(data$getSeries(tdays-1),1,sd),
 242         apply(data$getSeries(tdays),1,sd) )
 243
 244     yrange = range(ref_var, global_var)
 245     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 246     plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
 247         xlab="Time (hours)", ylab="Standard deviation")
 248     par(new=TRUE)
 249     plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
 250     abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
 251 }