pkg/R/plot.R

   1 #' Plot curves
   2 #'
   3 #' Plot a range of curves in data
   4 #'
   5 #' @param data Object of class Data
   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$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
  69             ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
  70         if (i < L)
  71             par(new=TRUE)
  72     }
  73 }
  74
  75 #' Plot measured / predicted
  76 #'
  77 #' Plot measured curve (in black) and predicted curve (in blue)
  78 #'
  79 #' @param data Object return by \code{getData}
  80 #' @param pred Object as returned by \code{computeForecast}
  81 #' @param index Index in forecasts (integer or date)
  82 #'
  83 #' @export
  84 plotPredReal <- function(data, pred, index)
  85 {
  86     horizon = length(pred$getForecast(1))
  87     measure = data$getSerie( pred$getIndexInData(index)+1 )[1:horizon]
  88     prediction = pred$getForecast(index)
  89     yrange = range(measure, prediction)
  90     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
  91     plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
  92     par(new=TRUE)
  93     plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
  94 }
  95
  96 #' Plot similarities
  97 #'
  98 #' Plot histogram of similarities (weights)
  99 #'
 100 #' @param pred Object as returned by \code{computeForecast}
 101 #' @param index Index in forecasts (integer or date)
 102 #'
 103 #' @export
 104 plotSimils <- function(pred, index)
 105 {
 106     weights = pred$getParams(index)$weights
 107     if (is.null(weights))
 108         stop("plotSimils only works on 'Neighbors' forecasts")
 109     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 110     hist(pred$getParams(index)$weights, nclass=20, main="", xlab="Weight", ylab="Count")
 111 }
 112
 113 #' Functional boxplot
 114 #'
 115 #' Draw the functional boxplot on the left, and bivariate plot on the right
 116 #'
 117 #' @param data Object return by \code{getData}
 118 #' @param indices integer or date indices to process
 119 #'
 120 #' @export
 121 plotFbox <- function(data, indices=seq_len(data$getSize()))
 122 {
 123     if (!requireNamespace("rainbow", quietly=TRUE))
 124         stop("Functional boxplot requires the rainbow package")
 125
 126     series_matrix = data$getSeries(indices)
 127     # Remove series with NAs
 128     no_NAs_indices = sapply( 1:ncol(series_matrix),
 129         function(i) all(!is.na(series_matrix[,i])) )
 130     series_matrix = series_matrix[,no_NAs_indices]
 131
 132     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 133     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 134     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 135         plotlegend=FALSE, lwd=2)
 136     rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 137 }
 138
 139 #' Compute filaments
 140 #'
 141 #' Get similar days in the past, as black as distances are small
 142 #'
 143 #' @param data Object as returned by \code{getData}
 144 #' @param pred Object of class Forecast
 145 #' @param index Index in forecast (integer or date)
 146 #' @param limit Number of neighbors to consider
 147 #' @param plot Should the result be plotted?
 148 #'
 149 #' @return A list with
 150 #' \itemize{
 151 #'   \item index : index of the current serie ('today')
 152 #'   \item neighb_indices : indices of its neighbors
 153 #'   \item colors : colors of neighbors curves (shades of gray)
 154 #' }
 155 #'
 156 #' @export
 157 computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 158 {
 159     ref_serie = data$getCenteredSerie( pred$getIndexInData(index) )
 160     if (any(is.na(ref_serie)))
 161         stop("computeFilaments requires a serie without NAs")
 162
 163     # Compute colors for each neighbor (from darkest to lightest)
 164     sorted_dists = sort(-log(pred$getParams(index)$weights), index.return=TRUE)
 165     nn = min(limit, length(sorted_dists$x))
 166     min_dist = min(sorted_dists$x[1:nn])
 167     max_dist = max(sorted_dists$x[1:nn])
 168     color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
 169     colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
 170
 171     if (plot)
 172     {
 173         # Complete series with (past and present) tomorrows
 174         ref_serie = c(ref_serie, data$getCenteredSerie( pred$getIndexInData(index)+1 ))
 175         centered_series = rbind(
 176             data$getCenteredSeries( pred$getParams(index)$indices ),
 177             data$getCenteredSeries( pred$getParams(index)$indices+1 ) )
 178         yrange = range( ref_serie,
 179             quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
 180         par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
 181         for (i in nn:1)
 182         {
 183             plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
 184                 xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"Centered PM10",""))
 185             par(new=TRUE)
 186         }
 187         # Also plot ref curve, in red
 188         plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
 189         abline(v=24, lty=2, col=colors()[56], lwd=1)
 190     }
 191
 192     list(
 193         "index"=pred$getIndexInData(index),
 194         "neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
 195         "colors"=colors)
 196 }
 197
 198 #' Functional boxplot on filaments
 199 #'
 200 #' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
 201 #'
 202 #' @param data Object return by \code{getData}
 203 #' @param fil Output of \code{computeFilaments}
 204 #'
 205 #' @export
 206 plotFilamentsBox = function(data, fil)
 207 {
 208     if (!requireNamespace("rainbow", quietly=TRUE))
 209         stop("Functional boxplot requires the rainbow package")
 210
 211     series_matrix = rbind(
 212         data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
 213     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 214     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 215     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
 216         plotlegend=FALSE, lwd=2)
 217
 218     # "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
 219     usr <- par("usr")
 220     yr <- (usr[4] - usr[3]) / 27
 221     par(new=TRUE)
 222     plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
 223         ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
 224     abline(v=24, lty=2, col=colors()[56])
 225 }
 226
 227 #' Plot relative conditional variability / absolute variability
 228 #'
 229 #' Draw the relative conditional variability / absolute variability based on filaments
 230 #' obtained by \code{computeFilaments}
 231 #'
 232 #' @param data Object return by \code{getData}
 233 #' @param fil Output of \code{computeFilaments}
 234 #'
 235 #' @export
 236 plotRelVar = function(data, fil)
 237 {
 238     ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
 239         apply(data$getSeries(fil$neighb_indices+1),1,sd) )
 240     fdays = getNoNA2(data, 1, fil$index-1)
 241     global_var = c(
 242         apply(data$getSeries(fdays),1,sd),
 243         apply(data$getSeries(fdays+1),1,sd) )
 244
 245     yrange = range(ref_var, global_var)
 246     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 247     plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
 248         xlab="Time (hours)", ylab="Standard deviation")
 249     par(new=TRUE)
 250     plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
 251     abline(v=24, lty=2, col=colors()[56])
 252 }