pkg/R/plot.R

   1 #' @title plot curves
   2 #'
   3 #' @description 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)
  10 {
  11     yrange = quantile( range( sapply( indices, function(i) {
  12         serie = c(data$getCenteredSerie(i))
  13         if (!all(is.na(serie)))
  14             range(serie, na.rm=TRUE)
  15         c()
  16     }) ), probs=c(0.05,0.95) )
  17     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
  18     for (i in seq_along(indices))
  19     {
  20         plot(data$getSerie(indices[i]), type="l", ylim=yrange,
  21             xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
  22         if (ii < length(indices))
  23             par(new=TRUE)
  24     }
  25 }
  26
  27 #' @title plot measured / predicted
  28 #'
  29 #' @description Plot measured curve (in black) and predicted curve (in red)
  30 #'
  31 #' @param data Object return by \code{getData}
  32 #' @param pred Object as returned by \code{getForecast}
  33 #' @param index Index in forecasts
  34 #'
  35 #' @export
  36 plotPredReal <- function(data, pred, index)
  37 {
  38     horizon = length(pred$getSerie(1))
  39     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
  40     measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon]
  41     yrange = range( pred$getSerie(index), measure )
  42     plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10")
  43     par(new=TRUE)
  44     plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
  45 }
  46
  47 #' @title Compute filaments
  48 #'
  49 #' @description Get similar days in the past + "past tomorrow", as black as distances are small
  50 #'
  51 #' @param data Object as returned by \code{getData}
  52 #' @param index Index in data
  53 #' @param limit Number of neighbors to consider
  54 #' @param plot Should the result be plotted?
  55 #'
  56 #' @export
  57 computeFilaments <- function(data, index, limit=60, plot=TRUE)
  58 {
  59     index = dateIndexToInteger(index, data)
  60     ref_serie = data$getCenteredSerie(index)
  61     if (any(is.na(ref_serie)))
  62         stop("computeFilaments requires a serie without NAs")
  63     L = length(ref_serie)
  64     first_day = ifelse(length(data$getCenteredSerie(1)<L), 2, 1)
  65     distances = sapply(first_day:(index-1), function(i) {
  66         sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
  67     })
  68     # HACK to suppress NA effect while keeping indexation
  69     distances[is.na(distances)] = max(distances,na.rm=TRUE) + 1
  70     indices = sort(distances, index.return=TRUE)$ix[1:min(limit,index-first_day)]
  71     yrange = quantile( range( ref_serie, sapply( indices, function(i) {
  72         index = i - first_day + 1
  73         serie = c(data$getCenteredSerie(index), data$getCenteredSerie(index+1))
  74         if (!all(is.na(serie)))
  75             return (range(serie, na.rm=TRUE))
  76         c()
  77     }) ), probs=c(0.1,0.9) )
  78     grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number
  79     color_values = floor( 20.5 * distances[indices] / (1+max(distances[indices])) )
  80     plot_order = sort(color_values, index.return=TRUE)$ix
  81     colors = c(grays[ color_values[plot_order] ], "#FF0000")
  82     if (plot)
  83     {
  84         par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
  85         for ( i in c(plot_order,length(indices)+1) )
  86         {
  87             ind = ifelse(i<=length(indices), indices[i] - first_day + 1, index)
  88             plot(c(data$getCenteredSerie(ind),data$getCenteredSerie(ind+1)),
  89                 ylim=yrange, type="l", col=colors[i],
  90                 xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
  91             if (i <= length(indices))
  92                 par(new=TRUE)
  93         }
  94     }
  95     list("indices"=c(indices[plot_order]-first_day+1,index), "colors"=colors)
  96 }
  97
  98 #' @title Plot similarities
  99 #'
 100 #' @description Plot histogram of similarities (weights)
 101 #'
 102 #' @param pred Object as returned by \code{getForecast}
 103 #' @param index Index in forecasts (not in data)
 104 #'
 105 #' @export
 106 plotSimils <- function(pred, index)
 107 {
 108     weights = pred$getParams(index)$weights
 109     if (is.null(weights))
 110         stop("plotSimils only works on 'Neighbors' forecasts")
 111     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 112     hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
 113 }
 114
 115 #' @title Plot error
 116 #'
 117 #' @description Draw error graphs, potentially from several runs of \code{getForecast}
 118 #'
 119 #' @param err Error as returned by \code{getError}
 120 #' @param cols Colors for each error (default: 1,2,3,...)
 121 #'
 122 #' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
 123 #'   \code{\link{plotFbox}}
 124 #'
 125 #' @export
 126 plotError <- function(err, cols=seq_along(err))
 127 {
 128     if (!is.null(err$abs))
 129         err = list(err)
 130     par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
 131     L = length(err)
 132     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
 133     for (i in seq_len(L))
 134     {
 135         plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 136             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
 137         if (i < L)
 138             par(new=TRUE)
 139     }
 140     yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE )
 141     for (i in seq_len(L))
 142     {
 143         plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 144             ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
 145         if (i < L)
 146             par(new=TRUE)
 147     }
 148     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE )
 149     for (i in seq_len(L))
 150     {
 151         plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
 152             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
 153         if (i < L)
 154             par(new=TRUE)
 155     }
 156     yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE )
 157     for (i in seq_len(L))
 158     {
 159         plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
 160             ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
 161         if (i < L)
 162             par(new=TRUE)
 163     }
 164 }
 165
 166 #' @title Functional boxplot
 167 #'
 168 #' @description Draw the functional boxplot on the left, and bivariate plot on the right
 169 #'
 170 #' @param data Object return by \code{getData}
 171 #' @param fiter Optional filter: return TRUE on indices to process
 172 #' @param plot_bivariate Should the bivariate plot appear?
 173 #'
 174 #' @export
 175 plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE)
 176 {
 177     if (!requireNamespace("rainbow", quietly=TRUE))
 178         stop("Functional boxplot requires the rainbow package")
 179
 180     start_index = 1
 181     end_index = data$getSize()
 182     if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
 183     {
 184         # Shifted start (7am, or 1pm, or...)
 185         start_index = 2
 186         end_index = data$getSize() - 1
 187     }
 188
 189     series_matrix = sapply(start_index:end_index, function(index) {
 190         as.matrix(data$getSerie(index))
 191     })
 192     # Remove NAs. + filter TODO: merge with previous step: only one pass required...
 193     nas_indices = seq_len(ncol(series_matrix))[ sapply( 1:ncol(series_matrix),
 194         function(index) ( !filter(index) || any(is.na(series_matrix[,index])) ) ) ]
 195     series_matrix = series_matrix[,-nas_indices]
 196
 197     series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
 198     if (plot_bivariate)
 199         par(mfrow=c(1,2))
 200     par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
 201     rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
 202         plotlegend=FALSE, lwd=2)
 203     if (plot_bivariate)
 204         rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
 205 }
 206
 207 #' @title Functional boxplot on filaments
 208 #'
 209 #' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments}
 210 #'
 211 #' @param data Object return by \code{getData}
 212 #' @param indices Indices as output by \code{computeFilaments}
 213 #'
 214 #' @export
 215 plotFilamentsBox = function(data, indices, ...)
 216 {
 217     past_neighbs_indices = head(indices,-1)
 218     plotFbox(data, function(i) i %in% past_neighbs_indices, plot_bivariate=FALSE)
 219     par(new=TRUE)
 220     # "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
 221     usr <- par("usr")
 222     yr <- (usr[4] - usr[3]) / 27
 223     plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2,
 224         ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
 225 }