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     matplot(series, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
  15 }
  16
  17 #' Plot error
  18 #'
  19 #' Draw error graphs, potentially from several runs of \code{computeForecast()}.
  20 #'
  21 #' @param err Error as returned by \code{computeError()}
  22 #' @param cols Colors for each error (default: 1,2,3,...)
  23 #'
  24 #' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
  25 #'   \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
  26 #'   \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
  27 #'
  28 #' @export
  29 plotError <- function(err, cols=seq_along(err))
  30 {
  31     if (!is.null(err$abs))
  32         err = list(err)
  33     par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lty=1)
  34     L = length(err)
  35
  36     yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
  37     matplot( sapply( seq_len(L), function(i) err[[i]]$abs$day ), type="l",
  38         xlab="Time (hours)", ylab="Mean |y - y_hat|", ylim=yrange, col=cols, lwd=2 )
  39
  40     yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
  41     matplot( sapply( seq_len(L), function(i) err[[i]]$abs$indices ), type="l",
  42         xlab="Time (days)", ylab="Mean |y - y_hat|", ylim=yrange, col=cols, lwd=2 )
  43
  44     yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
  45     matplot( sapply( seq_len(L), function(i) err[[i]]$MAPE$day ), type="l",
  46         xlab="Time (hours)", ylab="Mean MAPE", ylim=yrange, col=cols, lwd=2 )
  47
  48     yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
  49     matplot( sapply( seq_len(L), function(i) err[[i]]$MAPE$indices ), type="l",
  50         xlab="Time (days)", ylab="Mean MAPE", ylim=yrange, col=cols, lwd=2 )
  51 }
  52
  53 #' Plot measured / predicted
  54 #'
  55 #' Plot measured curve (in black) and predicted curve (in blue).
  56 #'
  57 #' @inheritParams computeError
  58 #' @param index Index in forecasts (integer or date)
  59 #'
  60 #' @export
  61 plotPredReal <- function(data, pred, index)
  62 {
  63     prediction = pred$getForecast(index)
  64     measure = data$getSerie( pred$getIndexInData(index) )[1:length(pred$getForecast(1))]
  65
  66     # Remove the common part, where prediction == measure
  67     dot_mark <- ifelse(prediction[1]==measure[1], which.max(prediction==measure), 0)
  68     prediction = prediction[(dot_mark+1):length(prediction)]
  69     measure = measure[(dot_mark+1):length(measure)]
  70
  71     yrange = range(measure, prediction)
  72     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
  73     plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
  74     par(new=TRUE)
  75     plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
  76 }
  77
  78 #' Plot similarities
  79 #'
  80 #' Plot histogram of similarities (weights), for 'Neighbors' method.
  81 #'
  82 #' @inheritParams computeError
  83 #' @param index Index in forecasts (integer or date)
  84 #'
  85 #' @export
  86 plotSimils <- function(pred, index)
  87 {
  88     weights = pred$getParams(index)$weights
  89     if (is.null(weights))
  90         stop("plotSimils only works on 'Neighbors' forecasts")
  91     par(mfrow=c(1,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
  92     small_weights = weights[ weights < 1/length(weights) ]
  93     large_weights = weights[ weights >= 1/length(weights) ]
  94     hist(small_weights, nclass=25, main="", xlab="Weight < 1/N", ylab="Count")
  95     hist(large_weights, nclass=25, main="", xlab="Weight >= 1/N", ylab="Count")
  96 }
  97
  98 #' Functional boxplot
  99 #'
 100 #' Draw the functional boxplot on the left, and bivariate plot on the right.
 101 #'
 102 #' @inheritParams computeError
 103 #' @inheritParams plotCurves
 104 #'
 105 #' @export
 106 plotFbox <- function(data, indices=seq_len(data$getSize()))
 107 {
 108     if (!requireNamespace("rainbow", quietly=TRUE))
 109         stop("Functional boxplot requires the rainbow package")
 110
 111     series_matrix = data$getSeries(indices)
 112     # Remove series with NAs
 113     no_NAs_indices = sapply( 1:ncol(series_matrix),
 114         function(i) all(!is.na(series_matrix[,i])) )
 115     series_matrix = series_matrix[,no_NAs_indices]
 116
 117     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 118     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 119     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 120         plotlegend=FALSE, lwd=2)
 121     rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 122 }
 123
 124 #' Compute filaments
 125 #'
 126 #' Obtain similar days in the past, and (optionally) plot them -- as black as distances
 127 #' are small.
 128 #'
 129 #' @inheritParams computeError
 130 #' @param index Index in forecast (integer or date)
 131 #' @param limit Number of neighbors to consider
 132 #' @param plot Should the result be plotted?
 133 #'
 134 #' @return A list with
 135 #' \itemize{
 136 #'   \item index : index of the current serie ('today')
 137 #'   \item neighb_indices : indices of its neighbors
 138 #'   \item colors : colors of neighbors curves (shades of gray)
 139 #' }
 140 #'
 141 #' @export
 142 computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 143 {
 144     weights <- pred$getParams(index)$weights
 145     if (is.null(weights) || is.na(pred$getParams(index)$weights[1]))
 146         stop("computeFilaments requires a serie without NAs")
 147
 148     nn <- min(limit, length(weights))
 149     sorted_dists = sort(-log(weights), index.return=TRUE)
 150     # Compute colors for each neighbor (from darkest to lightest), if weights differ
 151     if ( any( weights != weights[1] ) )
 152     {
 153         min_dist = min(sorted_dists$x[1:nn])
 154         max_dist = max(sorted_dists$x[1:nn])
 155         color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
 156         colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
 157     }
 158     else
 159         colors <- rep(colors()[17], length(weights))
 160
 161     if (plot)
 162     {
 163         # Complete series with (past and present) tomorrows
 164         ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
 165             data$getCenteredSerie( pred$getIndexInData(index) ) )
 166         centered_series = rbind(
 167             data$getCenteredSeries( pred$getParams(index)$indices-1 ),
 168             data$getCenteredSeries( pred$getParams(index)$indices ) )
 169         yrange = range( ref_serie,
 170             quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
 171         par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
 172         for (i in nn:1)
 173         {
 174             plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
 175                 xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"Centered PM10",""))
 176             par(new=TRUE)
 177         }
 178         # Also plot ref curve, in red
 179         plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
 180         dot_mark <- 0.5 + which.max( pred$getForecast(1) ==
 181             data$getSerie( pred$getIndexInData(1) )[1:length(pred$getForecast(1))] )
 182         abline(v=24+dot_mark, lty=2, col=colors()[56], lwd=1)
 183     }
 184
 185     list(
 186         "index"=pred$getIndexInData(index),
 187         "neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
 188         "colors"=colors)
 189 }
 190
 191 #' Functional boxplot on filaments
 192 #'
 193 #' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
 194 #'
 195 #' @inheritParams computeError
 196 #' @param fil Output of \code{computeFilaments}
 197 #' @param predict_from First predicted time step
 198 #'
 199 #' @export
 200 plotFilamentsBox = function(data, fil, predict_from)
 201 {
 202     if (!requireNamespace("rainbow", quietly=TRUE))
 203         stop("Functional boxplot requires the rainbow package")
 204
 205     series_matrix = rbind(
 206         data$getSeries(fil$neighb_indices-1), data$getSeries(fil$neighb_indices) )
 207     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 208
 209     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 210     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 211         plotlegend=FALSE, lwd=2)
 212
 213     # "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
 214     usr <- par("usr")
 215     yr <- (usr[4] - usr[3]) / 27
 216     par(new=TRUE)
 217     plot(c(data$getSerie(fil$index-1),data$getSerie(fil$index)), type="l", lwd=2, lty=2,
 218         ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
 219     abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
 220 }
 221
 222 #' Plot relative conditional variability / absolute variability
 223 #'
 224 #' Draw the relative conditional variability / absolute variability based on filaments
 225 #' obtained by \code{computeFilaments()}.
 226 #'
 227 #' @inheritParams computeError
 228 #' @inheritParams plotFilamentsBox
 229 #'
 230 #' @export
 231 plotRelVar = function(data, fil, predict_from)
 232 {
 233     ref_var = c( apply(data$getSeries(fil$neighb_indices-1),1,sd),
 234         apply(data$getSeries(fil$neighb_indices),1,sd) )
 235     tdays = .getNoNA2(data, 2, fil$index)
 236     global_var = c(
 237         apply(data$getSeries(tdays-1),1,sd),
 238         apply(data$getSeries(tdays),1,sd) )
 239
 240     yrange = range(ref_var, global_var)
 241     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 242     plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
 243         xlab="Time (hours)", ylab="Standard deviation")
 244     par(new=TRUE)
 245     plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
 246     abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
 247 }