improve doc of computeForecast for realtime usage
[talweg.git] / 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 }