update documention, fix package to compete with 'method Bruno'

[talweg.git] / pkg / R / plot.R

#' Plot curves
#'
#' Plot a range of curves in data.
#'
#' @inheritParams computeError
#' @param indices Range of indices (integers or dates)
#'
#' @export
plotCurves <- function(data, indices=seq_len(data$getSize()))
{
	series = data$getSeries(indices)
	yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	matplot(series, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
}

#' Plot error
#'
#' 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{plotCurves}}, \code{\link{plotPredReal}},
#'   \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
#'   \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
#'
#' @export
plotError <- function(err, cols=seq_along(err))
{
	if (!is.null(err$abs))
		err = list(err)
	par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
	L = length(err)
	yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Time (hours)",""),
			ylab=ifelse(i==1,"Mean |y - y_hat|",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
	yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
			ylab=ifelse(i==1,"Mean |y - y_hat|",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
	yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Time (hours)",""),
			ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
	yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
	for (i in seq_len(L))
	{
		plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
			ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
		if (i < L)
			par(new=TRUE)
	}
}

#' Plot measured / predicted
#'
#' Plot measured curve (in black) and predicted curve (in blue).
#'
#' @inheritParams computeError
#' @param index Index in forecasts (integer or date)
#'
#' @export
plotPredReal <- function(data, pred, index)
{
	prediction = pred$getForecast(index)
	measure = data$getSerie( pred$getIndexInData(index) )[1:length(prediction)]
	yrange = range(measure, prediction)
	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
	plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
	par(new=TRUE)
	plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
}

#' Plot similarities
#'
#' Plot histogram of similarities (weights), for 'Neighbors' method.
#'
#' @inheritParams computeError
#' @param index Index in forecasts (integer or date)
#'
#' @export
plotSimils <- function(pred, index)
{
	weights = pred$getParams(index)$weights
	if (is.null(weights))
		stop("plotSimils only works on 'Neighbors' forecasts")
	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	hist(pred$getParams(index)$weights, nclass=20, main="", xlab="Weight", ylab="Count")
}

#' Functional boxplot
#'
#' Draw the functional boxplot on the left, and bivariate plot on the right.
#'
#' @inheritParams computeError
#' @inheritParams plotCurves
#'
#' @export
plotFbox <- function(data, indices=seq_len(data$getSize()))
{
	if (!requireNamespace("rainbow", quietly=TRUE))
		stop("Functional boxplot requires the rainbow package")

	series_matrix = data$getSeries(indices)
	# Remove series with NAs
	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)
	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
		plotlegend=FALSE, lwd=2)
	rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
}

#' Compute filaments
#'
#' Obtain similar days in the past, and (optionally) plot them -- as black as distances
#' are small.
#'
#' @inheritParams computeError
#' @param index Index in forecast (integer or date)
#' @param limit Number of neighbors to consider
#' @param plot Should the result be plotted?
#' @param predict_from First prediction instant
#'
#' @return A list with
#' \itemize{
#'   \item index : index of the current serie ('today')
#'   \item neighb_indices : indices of its neighbors
#'   \item colors : colors of neighbors curves (shades of gray)
#' }
#'
#' @export
computeFilaments <- function(data, pred, index, predict_from, limit=60, plot=TRUE)
{
	if (is.null(pred$getParams(index)$weights) || is.na(pred$getParams(index)$weights[1]))
		stop("computeFilaments requires a serie without NAs")

	# Compute colors for each neighbor (from darkest to lightest)
	sorted_dists = sort(-log(pred$getParams(index)$weights), index.return=TRUE)
	nn = min(limit, length(sorted_dists$x))
	min_dist = min(sorted_dists$x[1:nn])
	max_dist = max(sorted_dists$x[1:nn])
	color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
	colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number

	if (plot)
	{
		# Complete series with (past and present) tomorrows
		ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
			data$getCenteredSerie( pred$getIndexInData(index) ) )
		centered_series = rbind(
			data$getCenteredSeries( pred$getParams(index)$indices-1 ),
			data$getCenteredSeries( pred$getParams(index)$indices ) )
		yrange = range( ref_serie,
			quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
		par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
		for (i in nn:1)
		{
			plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
				xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"Centered PM10",""))
			par(new=TRUE)
		}
		# Also plot ref curve, in red
		plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
		abline(v=24+predict_from-0.5, lty=2, col=colors()[56], lwd=1)
	}

	list(
		"index"=pred$getIndexInData(index),
		"neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
		"colors"=colors)
}

#' Functional boxplot on filaments
#'
#' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
#'
#' @inheritParams computeError
#' @param fil Output of \code{computeFilaments}
#'
#' @export
plotFilamentsBox = function(data, fil, predict_from)
{
	if (!requireNamespace("rainbow", quietly=TRUE))
		stop("Functional boxplot requires the rainbow package")

	series_matrix = rbind(
		data$getSeries(fil$neighb_indices-1), data$getSeries(fil$neighb_indices) )
	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)

	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
		plotlegend=FALSE, lwd=2)

	# "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
	usr <- par("usr")
	yr <- (usr[4] - usr[3]) / 27
	par(new=TRUE)
	plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
		ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
	abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
}

#' Plot relative conditional variability / absolute variability
#'
#' Draw the relative conditional variability / absolute variability based on filaments
#' obtained by \code{computeFilaments()}.
#'
#' @inheritParams computeError
#' @inheritParams plotFilamentsBox
#'
#' @export
plotRelVar = function(data, fil, predict_from)
{
	ref_var = c( apply(data$getSeries(fil$neighb_indices-1),1,sd),
		apply(data$getSeries(fil$neighb_indices),1,sd) )
	tdays = .getNoNA2(data, 2, fil$index)
	global_var = c(
		apply(data$getSeries(tdays-1),1,sd),
		apply(data$getSeries(tdays),1,sd) )

	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="Time (hours)", ylab="Standard deviation")
	par(new=TRUE)
	plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
	abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
}