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