| 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$getSize())) |
| 10 | { |
| 11 | yrange = quantile( range( sapply( indices, function(i) { |
| 12 | serie = c(data$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$getSerie(indices[i]), type="l", ylim=yrange, |
| 21 | xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10","")) |
| 22 | if (i < length(indices)) |
| 23 | par(new=TRUE) |
| 24 | } |
| 25 | } |
| 26 | |
| 27 | #' @title plot measured / predicted |
| 28 | #' |
| 29 | #' @description Plot measured curve (in black) and predicted curve (in red) |
| 30 | #' |
| 31 | #' @param data Object return by \code{getData} |
| 32 | #' @param pred Object as returned by \code{getForecast} |
| 33 | #' @param index Index in forecasts |
| 34 | #' |
| 35 | #' @export |
| 36 | plotPredReal <- function(data, pred, index) |
| 37 | { |
| 38 | horizon = length(pred$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$getCenteredSerie(index) |
| 61 | if (any(is.na(ref_serie))) |
| 62 | stop("computeFilaments requires a serie without NAs") |
| 63 | L = length(ref_serie) |
| 64 | first_day = ifelse(length(data$getCenteredSerie(1)<L), 2, 1) |
| 65 | distances = sapply(first_day:(index-1), function(i) { |
| 66 | sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L ) |
| 67 | }) |
| 68 | # HACK to suppress NA effect while keeping indexation |
| 69 | |
| 70 | |
| 71 | |
| 72 | |
| 73 | |
| 74 | ##TOCONTINUE |
| 75 | |
| 76 | |
| 77 | |
| 78 | |
| 79 | distances[is.na(distances)] = max(distances,na.rm=TRUE) + 1 |
| 80 | indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))] |
| 81 | yrange = quantile( range( ref_serie, sapply( indices, function(i) { |
| 82 | ii = i - 1 + first_day |
| 83 | serie = c(data$getCenteredSerie(ii), data$getCenteredSerie(ii+1)) |
| 84 | if (!all(is.na(serie))) |
| 85 | return (range(serie, na.rm=TRUE)) |
| 86 | c() |
| 87 | }) ), probs=c(0.05,0.95) ) |
| 88 | grays = gray.colors(20, 0.1, 0.9) #TODO: 20 == magic number |
| 89 | color_values = floor( 20.5 * distances[indices] / (1+max(distances[indices])) ) |
| 90 | plot_order = sort(color_values, index.return=TRUE)$ix |
| 91 | colors = c(grays[ color_values[plot_order] ], "#FF0000") |
| 92 | if (plot) |
| 93 | { |
| 94 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2) |
| 95 | for ( i in c(plot_order,length(indices)+1) ) |
| 96 | { |
| 97 | ind = ifelse(i<=length(indices), indices[i] - first_day + 1, index) |
| 98 | plot(c(data$getCenteredSerie(ind),data$getCenteredSerie(ind+1)), |
| 99 | ylim=yrange, type="l", col=colors[i], |
| 100 | xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré","")) |
| 101 | if (i <= length(indices)) |
| 102 | par(new=TRUE) |
| 103 | } |
| 104 | } |
| 105 | list("indices"=c(indices[plot_order]-first_day+1,index), "colors"=colors) |
| 106 | } |
| 107 | |
| 108 | #' @title Plot similarities |
| 109 | #' |
| 110 | #' @description Plot histogram of similarities (weights) |
| 111 | #' |
| 112 | #' @param pred Object as returned by \code{getForecast} |
| 113 | #' @param index Index in forecasts (not in data) |
| 114 | #' |
| 115 | #' @export |
| 116 | plotSimils <- function(pred, index) |
| 117 | { |
| 118 | weights = pred$getParams(index)$weights |
| 119 | if (is.null(weights)) |
| 120 | stop("plotSimils only works on 'Neighbors' forecasts") |
| 121 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5) |
| 122 | hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif") |
| 123 | } |
| 124 | |
| 125 | #' @title Plot error |
| 126 | #' |
| 127 | #' @description Draw error graphs, potentially from several runs of \code{getForecast} |
| 128 | #' |
| 129 | #' @param err Error as returned by \code{getError} |
| 130 | #' @param cols Colors for each error (default: 1,2,3,...) |
| 131 | #' |
| 132 | #' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}} |
| 133 | #' \code{\link{plotFbox}} |
| 134 | #' |
| 135 | #' @export |
| 136 | plotError <- function(err, cols=seq_along(err)) |
| 137 | { |
| 138 | if (!is.null(err$abs)) |
| 139 | err = list(err) |
| 140 | par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2) |
| 141 | L = length(err) |
| 142 | yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE ) |
| 143 | for (i in seq_len(L)) |
| 144 | { |
| 145 | plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""), |
| 146 | ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i]) |
| 147 | if (i < L) |
| 148 | par(new=TRUE) |
| 149 | } |
| 150 | yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$indices ) ), na.rm=TRUE ) |
| 151 | for (i in seq_len(L)) |
| 152 | { |
| 153 | plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""), |
| 154 | ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i]) |
| 155 | if (i < L) |
| 156 | par(new=TRUE) |
| 157 | } |
| 158 | yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$day ) ), na.rm=TRUE ) |
| 159 | for (i in seq_len(L)) |
| 160 | { |
| 161 | plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""), |
| 162 | ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i]) |
| 163 | if (i < L) |
| 164 | par(new=TRUE) |
| 165 | } |
| 166 | yrange = range( sapply(1:L, function(index) ( err[[index]]$MAPE$indices ) ), na.rm=TRUE ) |
| 167 | for (i in seq_len(L)) |
| 168 | { |
| 169 | plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""), |
| 170 | ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i]) |
| 171 | if (i < L) |
| 172 | par(new=TRUE) |
| 173 | } |
| 174 | } |
| 175 | |
| 176 | #' @title Functional boxplot |
| 177 | #' |
| 178 | #' @description Draw the functional boxplot on the left, and bivariate plot on the right |
| 179 | #' |
| 180 | #' @param data Object return by \code{getData} |
| 181 | #' @param fiter Optional filter: return TRUE on indices to process |
| 182 | #' @param plot_bivariate Should the bivariate plot appear? |
| 183 | #' |
| 184 | #' @export |
| 185 | plotFbox <- function(data, filter=function(index) TRUE, plot_bivariate=TRUE) |
| 186 | { |
| 187 | if (!requireNamespace("rainbow", quietly=TRUE)) |
| 188 | stop("Functional boxplot requires the rainbow package") |
| 189 | |
| 190 | start_index = 1 |
| 191 | end_index = data$getSize() |
| 192 | if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2))) |
| 193 | { |
| 194 | # Shifted start (7am, or 1pm, or...) |
| 195 | start_index = 2 |
| 196 | end_index = data$getSize() - 1 |
| 197 | } |
| 198 | |
| 199 | series_matrix = sapply(start_index:end_index, function(index) { |
| 200 | as.matrix(data$getSerie(index)) |
| 201 | }) |
| 202 | # Remove NAs. + filter TODO: merge with previous step: only one pass required... |
| 203 | nas_indices = seq_len(ncol(series_matrix))[ sapply( 1:ncol(series_matrix), |
| 204 | function(index) ( !filter(index) || any(is.na(series_matrix[,index])) ) ) ] |
| 205 | series_matrix = series_matrix[,-nas_indices] |
| 206 | |
| 207 | series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix) |
| 208 | if (plot_bivariate) |
| 209 | par(mfrow=c(1,2)) |
| 210 | par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5) |
| 211 | rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10", |
| 212 | plotlegend=FALSE, lwd=2) |
| 213 | if (plot_bivariate) |
| 214 | rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE) |
| 215 | } |
| 216 | |
| 217 | #' @title Functional boxplot on filaments |
| 218 | #' |
| 219 | #' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments} |
| 220 | #' |
| 221 | #' @param data Object return by \code{getData} |
| 222 | #' @param indices Indices as output by \code{computeFilaments} |
| 223 | #' |
| 224 | #' @export |
| 225 | plotFilamentsBox = function(data, indices, ...) |
| 226 | { |
| 227 | past_neighbs_indices = head(indices,-1) |
| 228 | plotFbox(data, function(i) i %in% past_neighbs_indices, plot_bivariate=FALSE) |
| 229 | par(new=TRUE) |
| 230 | # "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r |
| 231 | usr <- par("usr") |
| 232 | yr <- (usr[4] - usr[3]) / 27 |
| 233 | plot(data$getSerie(tail(indices,1)), type="l", lwd=2, lty=2, |
| 234 | ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="") |
| 235 | } |