-#' @title plot curves
+#' plot curves
#'
-#' @description Plot a range of curves in data
+#' Plot a range of curves in data
#'
#' @param data Object of class Data
#' @param indices Range of indices (integers or dates)
}
}
-#' @title plot measured / predicted
+#' plot measured / predicted
#'
-#' @description Plot measured curve (in black) and predicted curve (in red)
+#' Plot measured curve (in black) and predicted curve (in red)
#'
#' @param data Object return by \code{getData}
#' @param pred Object as returned by \code{computeForecast}
plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
}
-#' @title Compute filaments
+#' Compute filaments
#'
-#' @description Get similar days in the past + "past tomorrow", as black as distances are small
+#' Get similar days in the past + "past tomorrow", as black as distances are small
#'
#' @param data Object as returned by \code{getData}
#' @param index Index in data
L = length(ref_serie)
# Determine indices of no-NAs days followed by no-NAs tomorrows
- first_day = ifelse(length(data$getCenteredSerie(1))<L, 2, 1)
- fdays_indices = c()
- for (i in first_day:(index-1))
+ fdays = c()
+ for (i in 1:(index-1))
{
if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
- fdays_indices = c(fdays_indices, i)
+ fdays = c(fdays, i)
}
- distances = sapply(fdays_indices, function(i) {
+ distances = sapply(fdays, function(i) {
sqrt( sum( (ref_serie - data$getCenteredSerie(i))^2 ) / L )
})
indices = sort(distances, index.return=TRUE)$ix[1:min(limit,length(distances))]
yrange = quantile( c(ref_serie, sapply( indices, function(i) {
- ii = fdays_indices[i]
- serie = c(data$getCenteredSerie(ii), data$getCenteredSerie(ii+1))
+ serie = c(data$getCenteredSerie(fdays[i]), data$getCenteredSerie(fdays[i]+1))
if (!all(is.na(serie)))
return (range(serie, na.rm=TRUE))
c()
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
for ( i in seq_len(length(indices)+1) )
{
- ii = ifelse(i<=length(indices), fdays_indices[ indices[plot_order[i]] ], index)
+ ii = ifelse(i<=length(indices), fdays[ indices[plot_order[i]] ], index)
plot(c(data$getCenteredSerie(ii),data$getCenteredSerie(ii+1)),
ylim=yrange, type="l", col=colors[i],
xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10 centré",""))
}
abline(v=24, lty=2, col=colors()[56])
}
- list("indices"=c(fdays_indices[ indices[plot_order] ],index), "colors"=colors)
+ list("indices"=c(fdays[ indices[plot_order] ],index), "colors"=colors)
}
-#' @title Plot similarities
+#' Plot similarities
#'
-#' @description Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights)
#'
#' @param pred Object as returned by \code{computeForecast}
#' @param index Index in forecasts (not in data)
hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
}
-#' @title Plot error
+#' Plot error
#'
-#' @description Draw error graphs, potentially from several runs of \code{computeForecast}
+#' Draw error graphs, potentially from several runs of \code{computeForecast}
#'
#' @param err Error as returned by \code{computeError}
#' @param cols Colors for each error (default: 1,2,3,...)
#'
-#' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
-#' \code{\link{plotFbox}}
+#' @seealso \code{\link{plotPredReal}},\code{\link{plotFilaments}}
+#' \code{\link{plotSimils}},\code{\link{plotFbox}},\code{\link{plotRelativeVariability}}
#'
#' @export
plotError <- function(err, cols=seq_along(err))
}
}
-#' @title Functional boxplot
+#' Functional boxplot
#'
-#' @description Draw the functional boxplot on the left, and bivariate plot on the right
+#' Draw the functional boxplot on the left, and bivariate plot on the right
#'
#' @param data Object return by \code{getData}
#' @param fiter Optional filter: return TRUE on indices to process
if (!requireNamespace("rainbow", quietly=TRUE))
stop("Functional boxplot requires the rainbow package")
- start_index = 1
- end_index = data$getSize()
- if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
- {
- # Shifted start (7am, or 1pm, or...)
- start_index = 2
- end_index = data$getSize() - 1
- }
-
L = length(data$getCenteredSerie(2))
- series_matrix = sapply(start_index:end_index, function(index) {
+ series_matrix = sapply(1:data$getSize(), function(index) {
if (filter(index))
as.matrix(data$getSerie(index))
else
rep(NA,L)
})
# TODO: merge with previous step: only one pass should be required
- no_NAs_indices = sapply( 1:ncol(series_matrix), function(i) all(!is.na(series_matrix[,i])) )
+ no_NAs_indices = sapply( 1:ncol(series_matrix),
+ function(i) all(!is.na(series_matrix[,i])) )
series_matrix = series_matrix[,no_NAs_indices]
series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
}
-#' @title Functional boxplot on filaments
+#' Functional boxplot on filaments
#'
-#' @description Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
#'
#' @param data Object return by \code{getData}
#' @param indices Indices as output by \code{computeFilaments}
ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
}
-#' @title Plot relative conditional variability / absolute variability
+#' Plot relative conditional variability / absolute variability
#'
-#' @description Draw the relative conditional variability / absolute variability based on on
-#' filaments obtained by \code{computeFilaments}
+#' Draw the relative conditional variability / absolute variability based on filaments
+#' obtained by \code{computeFilaments}
#'
#' @param data Object return by \code{getData}
#' @param indices Indices as output by \code{computeFilaments}
#' @export
plotRelativeVariability = function(data, indices, ...)
{
- #plot left / right separated by vertical line brown dotted
- #median of 3 runs for random length(indices) series
ref_series = t( sapply(indices, function(i) {
c( data$getSerie(i), data$getSerie(i+1) )
}) )
ref_var = apply(ref_series, 2, sd)
# Determine indices of no-NAs days followed by no-NAs tomorrows
- first_day = ifelse(length(data$getCenteredSerie(1))<length(ref_series[1,]), 2, 1)
- fdays_indices = c()
- for (i in first_day:(tail(indices,1)-1))
+ fdays = c()
+ for (i in 1:(tail(indices,1)-1))
{
if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
- fdays_indices = c(fdays_indices, i)
- }
-
- # TODO: 3 == magic number
- random_var = matrix(nrow=3, ncol=48)
- for (mc in seq_len(nrow(random_var)))
- {
- random_indices = sample(fdays_indices, length(indices))
- random_series = t( sapply(random_indices, function(i) {
- c( data$getSerie(i), data$getSerie(i+1) )
- }) )
- random_var[mc,] = apply(random_series, 2, sd)
+ fdays = c(fdays, i)
}
- random_var = apply(random_var, 2, median)
+ global_var = c( apply(data$getSerie(fdays),2,sd), apply(data$getSerie(fdays+1),2,sd) )
- yrange = range(ref_var, random_var)
+ yrange = range(ref_var, global_var)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- plot(ref_var, type="l", col=1, lwd=3, ylim=yrange, xlab="Temps (heures)", ylab="Écart-type")
+ plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
+ xlab="Temps (heures)", ylab="Écart-type")
par(new=TRUE)
plot(random_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
abline(v=24, lty=2, col=colors()[56])
projects / talweg.git / blobdiff