[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])
}
Commit	Line	Data
	1	#' Plot curves
	2	#'
	3	#' Plot a range of curves in data.
	4	#'
	5	#' @inheritParams computeError
	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	matplot(series, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
	15	}
	16
	17	#' Plot error
	18	#'
	19	#' Draw error graphs, potentially from several runs of \code{computeForecast()}.
	20	#'
	21	#' @param err Error as returned by \code{computeError()}
	22	#' @param cols Colors for each error (default: 1,2,3,...)
	23	#'
	24	#' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
	25	#' \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
	26	#' \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
	27	#'
	28	#' @export
	29	plotError <- function(err, cols=seq_along(err))
	30	{
	31	if (!is.null(err$abs))
	32	err = list(err)
	33	par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
	34	L = length(err)
	35	yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
	36	for (i in seq_len(L))
	37	{
	38	plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Time (hours)",""),
	39	ylab=ifelse(i==1,"Mean \|y - y_hat\|",""), ylim=yrange, col=cols[i])
	40	if (i < L)
	41	par(new=TRUE)
	42	}
	43	yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
	44	for (i in seq_len(L))
	45	{
	46	plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
	47	ylab=ifelse(i==1,"Mean \|y - y_hat\|",""), ylim=yrange, col=cols[i])
	48	if (i < L)
	49	par(new=TRUE)
	50	}
	51	yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
	52	for (i in seq_len(L))
	53	{
	54	plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Time (hours)",""),
	55	ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
	56	if (i < L)
	57	par(new=TRUE)
	58	}
	59	yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
	60	for (i in seq_len(L))
	61	{
	62	plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Time (days)",""),
	63	ylab=ifelse(i==1,"Mean MAPE",""), ylim=yrange, col=cols[i])
	64	if (i < L)
	65	par(new=TRUE)
	66	}
	67	}
	68
	69	#' Plot measured / predicted
	70	#'
	71	#' Plot measured curve (in black) and predicted curve (in blue).
	72	#'
	73	#' @inheritParams computeError
	74	#' @param index Index in forecasts (integer or date)
	75	#'
	76	#' @export
	77	plotPredReal <- function(data, pred, index)
	78	{
	79	prediction = pred$getForecast(index)
	80	measure = data$getSerie( pred$getIndexInData(index) )[1:length(prediction)]
	81	yrange = range(measure, prediction)
	82	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
	83	plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
	84	par(new=TRUE)
	85	plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
	86	}
	87
	88	#' Plot similarities
	89	#'
	90	#' Plot histogram of similarities (weights), for 'Neighbors' method.
	91	#'
	92	#' @inheritParams computeError
	93	#' @param index Index in forecasts (integer or date)
	94	#'
	95	#' @export
	96	plotSimils <- function(pred, index)
	97	{
	98	weights = pred$getParams(index)$weights
	99	if (is.null(weights))
	100	stop("plotSimils only works on 'Neighbors' forecasts")
	101	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	102	hist(pred$getParams(index)$weights, nclass=20, main="", xlab="Weight", ylab="Count")
	103	}
	104
	105	#' Functional boxplot
	106	#'
	107	#' Draw the functional boxplot on the left, and bivariate plot on the right.
	108	#'
	109	#' @inheritParams computeError
	110	#' @inheritParams plotCurves
	111	#'
	112	#' @export
	113	plotFbox <- function(data, indices=seq_len(data$getSize()))
	114	{
	115	if (!requireNamespace("rainbow", quietly=TRUE))
	116	stop("Functional boxplot requires the rainbow package")
	117
	118	series_matrix = data$getSeries(indices)
	119	# Remove series with NAs
	120	no_NAs_indices = sapply( 1:ncol(series_matrix),
	121	function(i) all(!is.na(series_matrix[,i])) )
	122	series_matrix = series_matrix[,no_NAs_indices]
	123
	124	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
	125	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	126	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
	127	plotlegend=FALSE, lwd=2)
	128	rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
	129	}
	130
	131	#' Compute filaments
	132	#'
	133	#' Obtain similar days in the past, and (optionally) plot them -- as black as distances
	134	#' are small.
	135	#'
	136	#' @inheritParams computeError
	137	#' @param index Index in forecast (integer or date)
	138	#' @param limit Number of neighbors to consider
	139	#' @param plot Should the result be plotted?
	140	#' @param predict_from First prediction instant
	141	#'
	142	#' @return A list with
	143	#' \itemize{
	144	#' \item index : index of the current serie ('today')
	145	#' \item neighb_indices : indices of its neighbors
	146	#' \item colors : colors of neighbors curves (shades of gray)
	147	#' }
	148	#'
	149	#' @export
	150	computeFilaments <- function(data, pred, index, predict_from, limit=60, plot=TRUE)
	151	{
	152	if (is.null(pred$getParams(index)$weights) \|\| is.na(pred$getParams(index)$weights[1]))
	153	stop("computeFilaments requires a serie without NAs")
	154
	155	# Compute colors for each neighbor (from darkest to lightest)
	156	sorted_dists = sort(-log(pred$getParams(index)$weights), index.return=TRUE)
	157	nn = min(limit, length(sorted_dists$x))
	158	min_dist = min(sorted_dists$x[1:nn])
	159	max_dist = max(sorted_dists$x[1:nn])
	160	color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
	161	colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
	162
	163	if (plot)
	164	{
	165	# Complete series with (past and present) tomorrows
	166	ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
	167	data$getCenteredSerie( pred$getIndexInData(index) ) )
	168	centered_series = rbind(
	169	data$getCenteredSeries( pred$getParams(index)$indices-1 ),
	170	data$getCenteredSeries( pred$getParams(index)$indices ) )
	171	yrange = range( ref_serie,
	172	quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
	173	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
	174	for (i in nn:1)
	175	{
	176	plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
	177	xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"Centered PM10",""))
	178	par(new=TRUE)
	179	}
	180	# Also plot ref curve, in red
	181	plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
	182	abline(v=24+predict_from-0.5, lty=2, col=colors()[56], lwd=1)
	183	}
	184
	185	list(
	186	"index"=pred$getIndexInData(index),
	187	"neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
	188	"colors"=colors)
	189	}
	190
	191	#' Functional boxplot on filaments
	192	#'
	193	#' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
	194	#'
	195	#' @inheritParams computeError
	196	#' @param fil Output of \code{computeFilaments}
	197	#'
	198	#' @export
	199	plotFilamentsBox = function(data, fil, predict_from)
	200	{
	201	if (!requireNamespace("rainbow", quietly=TRUE))
	202	stop("Functional boxplot requires the rainbow package")
	203
	204	series_matrix = rbind(
	205	data$getSeries(fil$neighb_indices-1), data$getSeries(fil$neighb_indices) )
	206	series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
	207
	208	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	209	rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
	210	plotlegend=FALSE, lwd=2)
	211
	212	# "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
	213	usr <- par("usr")
	214	yr <- (usr[4] - usr[3]) / 27
	215	par(new=TRUE)
	216	plot(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), type="l", lwd=2, lty=2,
	217	ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
	218	abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
	219	}
	220
	221	#' Plot relative conditional variability / absolute variability
	222	#'
	223	#' Draw the relative conditional variability / absolute variability based on filaments
	224	#' obtained by \code{computeFilaments()}.
	225	#'
	226	#' @inheritParams computeError
	227	#' @inheritParams plotFilamentsBox
	228	#'
	229	#' @export
	230	plotRelVar = function(data, fil, predict_from)
	231	{
	232	ref_var = c( apply(data$getSeries(fil$neighb_indices-1),1,sd),
	233	apply(data$getSeries(fil$neighb_indices),1,sd) )
	234	tdays = .getNoNA2(data, 2, fil$index)
	235	global_var = c(
	236	apply(data$getSeries(tdays-1),1,sd),
	237	apply(data$getSeries(tdays),1,sd) )
	238
	239	yrange = range(ref_var, global_var)
	240	par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
	241	plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
	242	xlab="Time (hours)", ylab="Standard deviation")
	243	par(new=TRUE)
	244	plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
	245	abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
	246	}