| 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=seq_len(data$getSize())) |
| 10 | { |
| 11 | yrange = quantile( sapply( indices, function(i) { |
| 12 | serie = c(data$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$getSerie(indices[i]), type="l", ylim=yrange, |
| 21 | xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10","")) |
| 22 | if (i < length(indices)) |
| 23 | par(new=TRUE) |
| 24 | } |
| 25 | } |
| 26 | |
| 27 | #' @title plot measured / predicted |
| 28 | #' |
| 29 | #' @description Plot measured curve (in black) and predicted curve (in red) |
| 30 | #' |
| 31 | #' @param data Object return by \code{getData} |
| 32 | #' @param pred Object as returned by \code{computeForecast} |
| 33 | #' @param index Index in forecasts |
| 34 | #' |
| 35 | #' @export |
| 36 | plotPredReal <- function(data, pred, index) |
| 37 | { |
| 38 | horizon = length(pred$getSerie(1)) |
| 39 | measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon] |
| 40 | yrange = range( pred$getSerie(index), measure ) |
| 41 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3) |
| 42 | plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10") |
| 43 | par(new=TRUE) |
| 44 | plot(pred$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$getCenteredSerie(index) |
| 61 | if (any(is.na(ref_serie))) |
| 62 | stop("computeFilaments requires a serie without NAs") |
| 63 | L = length(ref_serie) |
| 64 | |
| 65 | # Determine indices of no-NAs days followed by no-NAs tomorrows |
| 66 | fdays = c() |
| 67 | for (i in 1:(index-1)) |
| 68 | { |
| 69 | if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) ) |
| 70 | fdays = c(fdays, i) |
| 71 | } |
| 72 | |
| 73 | distances = sapply(fdays, function(i) { |
| 74 | sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L ) |
| 75 | }) |
| 76 | indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))] |
| 77 | yrange = quantile( c(ref_serie, sapply( indices, function(i) { |
| 78 | serie = c(data$getCenteredSerie(fdays[i]), data$getCenteredSerie(fdays[i]+1)) |
| 79 | if (!all(is.na(serie))) |
| 80 | return (range(serie, na.rm=TRUE)) |
| 81 | c() |
| 82 | }) ), probs=c(0.05,0.95) ) |
| 83 | grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number |
| 84 | min_dist = min(distances[indices]) |
| 85 | max_dist = max(distances[indices]) |
| 86 | color_values = floor( 19.5 * (distances[indices]-min_dist) / (max_dist-min_dist) ) + 1 |
| 87 | plot_order = sort(color_values, index.return=TRUE, decreasing=TRUE)$ix |
| 88 | colors = c(grays[ color_values[plot_order] ], "#FF0000") |
| 89 | if (plot) |
| 90 | { |
| 91 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2) |
| 92 | for ( i in seq_len(length(indices)+1) ) |
| 93 | { |
| 94 | ii = ifelse(i<=length(indices), fdays[ indices[plot_order[i]] ], index) |
| 95 | plot(c(data$getCenteredSerie(ii),data$getCenteredSerie(ii+1)), |
| 96 | ylim=yrange, type="l", col=colors[i], |
| 97 | xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré","")) |
| 98 | if (i <= length(indices)) |
| 99 | par(new=TRUE) |
| 100 | } |
| 101 | abline(v=24, lty=2, col=colors()[56]) |
| 102 | } |
| 103 | list("indices"=c(fdays[ indices[plot_order] ],index), "colors"=colors) |
| 104 | } |
| 105 | |
| 106 | #' @title Plot similarities |
| 107 | #' |
| 108 | #' @description Plot histogram of similarities (weights) |
| 109 | #' |
| 110 | #' @param pred Object as returned by \code{computeForecast} |
| 111 | #' @param index Index in forecasts (not in data) |
| 112 | #' |
| 113 | #' @export |
| 114 | plotSimils <- function(pred, index) |
| 115 | { |
| 116 | weights = pred$getParams(index)$weights |
| 117 | if (is.null(weights)) |
| 118 | stop("plotSimils only works on 'Neighbors' forecasts") |
| 119 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5) |
| 120 | hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif") |
| 121 | } |
| 122 | |
| 123 | #' @title Plot error |
| 124 | #' |
| 125 | #' @description Draw error graphs, potentially from several runs of \code{computeForecast} |
| 126 | #' |
| 127 | #' @param err Error as returned by \code{computeError} |
| 128 | #' @param cols Colors for each error (default: 1,2,3,...) |
| 129 | #' |
| 130 | #' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}} |
| 131 | #' \code{\link{plotFbox}} |
| 132 | #' |
| 133 | #' @export |
| 134 | plotError <- function(err, cols=seq_along(err)) |
| 135 | { |
| 136 | if (!is.null(err$abs)) |
| 137 | err = list(err) |
| 138 | par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2) |
| 139 | L = length(err) |
| 140 | yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE ) |
| 141 | for (i in seq_len(L)) |
| 142 | { |
| 143 | plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""), |
| 144 | ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i]) |
| 145 | if (i < L) |
| 146 | par(new=TRUE) |
| 147 | } |
| 148 | yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE ) |
| 149 | for (i in seq_len(L)) |
| 150 | { |
| 151 | plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""), |
| 152 | ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i]) |
| 153 | if (i < L) |
| 154 | par(new=TRUE) |
| 155 | } |
| 156 | yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE ) |
| 157 | for (i in seq_len(L)) |
| 158 | { |
| 159 | plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""), |
| 160 | ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i]) |
| 161 | if (i < L) |
| 162 | par(new=TRUE) |
| 163 | } |
| 164 | yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE ) |
| 165 | for (i in seq_len(L)) |
| 166 | { |
| 167 | plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""), |
| 168 | ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i]) |
| 169 | if (i < L) |
| 170 | par(new=TRUE) |
| 171 | } |
| 172 | } |
| 173 | |
| 174 | #' @title Functional boxplot |
| 175 | #' |
| 176 | #' @description Draw the functional boxplot on the left, and bivariate plot on the right |
| 177 | #' |
| 178 | #' @param data Object return by \code{getData} |
| 179 | #' @param fiter Optional filter: return TRUE on indices to process |
| 180 | #' @param plot_bivariate Should the bivariate plot appear? |
| 181 | #' |
| 182 | #' @export |
| 183 | plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE) |
| 184 | { |
| 185 | if (!requireNamespace("rainbow", quietly=TRUE)) |
| 186 | stop("Functional boxplot requires the rainbow package") |
| 187 | |
| 188 | L = length(data$getCenteredSerie(2)) |
| 189 | series_matrix = sapply(1:data$getSize(), function(index) { |
| 190 | if (filter(index)) |
| 191 | as.matrix(data$getSerie(index)) |
| 192 | else |
| 193 | rep(NA,L) |
| 194 | }) |
| 195 | # TODO: merge with previous step: only one pass should be required |
| 196 | no_NAs_indices = sapply( 1:ncol(series_matrix), function(i) all(!is.na(series_matrix[,i])) ) |
| 197 | series_matrix = series_matrix[,no_NAs_indices] |
| 198 | |
| 199 | series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix) |
| 200 | if (plot_bivariate) |
| 201 | par(mfrow=c(1,2)) |
| 202 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5) |
| 203 | rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10", |
| 204 | plotlegend=FALSE, lwd=2) |
| 205 | if (plot_bivariate) |
| 206 | rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE) |
| 207 | } |
| 208 | |
| 209 | #' @title Functional boxplot on filaments |
| 210 | #' |
| 211 | #' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments} |
| 212 | #' |
| 213 | #' @param data Object return by \code{getData} |
| 214 | #' @param indices Indices as output by \code{computeFilaments} |
| 215 | #' |
| 216 | #' @export |
| 217 | plotFilamentsBox = function(data, indices, ...) |
| 218 | { |
| 219 | past_neighbs_indices = head(indices,-1) |
| 220 | plotFbox(data, function(i) i %in% past_neighbs_indices, plot_bivariate=FALSE) |
| 221 | par(new=TRUE) |
| 222 | # "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r |
| 223 | usr <- par("usr") |
| 224 | yr <- (usr[4] - usr[3]) / 27 |
| 225 | plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2, |
| 226 | ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="") |
| 227 | } |
| 228 | |
| 229 | #' @title Plot relative conditional variability / absolute variability |
| 230 | #' |
| 231 | #' @description Draw the relative conditional variability / absolute variability based on on |
| 232 | #' filaments obtained by \code{computeFilaments} |
| 233 | #' |
| 234 | #' @param data Object return by \code{getData} |
| 235 | #' @param indices Indices as output by \code{computeFilaments} |
| 236 | #' |
| 237 | #' @export |
| 238 | plotRelativeVariability = function(data, indices, ...) |
| 239 | { |
| 240 | #plot left / right separated by vertical line brown dotted |
| 241 | #median of 3 runs for random length(indices) series |
| 242 | ref_series = t( sapply(indices, function(i) { |
| 243 | c( data$getSerie(i), data$getSerie(i+1) ) |
| 244 | }) ) |
| 245 | ref_var = apply(ref_series, 2, sd) |
| 246 | |
| 247 | # Determine indices of no-NAs days followed by no-NAs tomorrows |
| 248 | fdays = c() |
| 249 | for (i in 1:(tail(indices,1)-1)) |
| 250 | { |
| 251 | if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) ) |
| 252 | fdays = c(fdays, i) |
| 253 | } |
| 254 | |
| 255 | # TODO: 3 == magic number |
| 256 | random_var = matrix(nrow=3, ncol=48) |
| 257 | for (mc in seq_len(nrow(random_var))) |
| 258 | { |
| 259 | random_indices = sample(fdays, length(indices)) |
| 260 | random_series = t( sapply(random_indices, function(i) { |
| 261 | c( data$getSerie(i), data$getSerie(i+1) ) |
| 262 | }) ) |
| 263 | random_var[mc,] = apply(random_series, 2, sd) |
| 264 | } |
| 265 | random_var = apply(random_var, 2, median) |
| 266 | |
| 267 | yrange = range(ref_var, random_var) |
| 268 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5) |
| 269 | plot(ref_var, type="l", col=1, lwd=3, ylim=yrange, xlab="Temps (heures)", ylab="Écart-type") |
| 270 | par(new=TRUE) |
| 271 | plot(random_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="") |
| 272 | abline(v=24, lty=2, col=colors()[56]) |
| 273 | } |