9a7a1ee55181c14704a1cf59f587d22a7ca6944b
[talweg.git] / 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=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 }