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#' Create a complete ggplot for detector-counts per second spectra.
#'
#' This function returns a ggplot object with an annotated plot of a
#' cps_spct object.
#'
#' Note that scales are expanded so as to make space for the annotations. The
#' object returned is a ggplot object, and can be further manipulated. When spct
#' has more than one column with spectral data, each of these columns is
#' normalized individually.
#'
#' @param spct a cps_spct object
#' @param w.band list of waveband objects
#' @param range an R object on which range() returns a vector of length 2, with
#' min annd max wavelengths (nm)
#' @param pc.out logical, if TRUE use percents instead of fraction of one
#' @param label.qty character string giving the type of summary quantity to use
#' for labels, one of "mean", "total", "contribution", and "relative".
#' @param span a peak is defined as an element in a sequence which is greater
#' than all other elements within a window of width span centered at that
#' element.
#' @param wls.target numeric vector indicating the spectral quantity values for
#' which wavelengths are to be searched and interpolated if need. The
#' \code{character} strings "half.maximum" and "half.range" are also accepted
#' as arguments. A list with \code{numeric} and/or \code{character} values is
#' also accepted.
#' @param annotations a character vector
#' @param geom character The name of a ggplot geometry, currently only
#' \code{"area"}, \code{"spct"} and \code{"line"}. The default \code{NULL}
#' selects between them based on \code{stacked}.
#' @param text.size numeric size of text in the plot decorations.
#' @param idfactor character Name of an index column in data holding a
#' \code{factor} with each spectrum in a long-form multispectrum object
#' corresponding to a distinct spectrum. If \code{idfactor=NULL} the name of
#' the factor is retrieved from metadata or if no metadata found, the
#' default "spct.idx" is tried.
#' @param facets logical or integer Indicating if facets are to be created for
#' the levels of \code{idfactor} when \code{spct} contain multiple spectra in
#' long form.
#' @param ylim numeric y axis limits,
#' @param na.rm logical.
#'
#' @return a \code{ggplot} object.
#'
#' @keywords internal
#'
cps_plot <- function(spct,
w.band,
range,
pc.out,
label.qty,
span,
wls.target,
annotations,
geom,
text.size,
idfactor,
facets,
ylim,
object.label,
na.rm) {
if (!is.cps_spct(spct)) {
stop("cps_plot() can only plot cps_spct objects.")
}
if (!is.null(geom) && !geom %in% c("area", "line", "spct")) {
warning("'geom = ", geom, "' not supported, using default instead.")
geom <- NULL
}
if (is.null(ylim) || !is.numeric(ylim)) {
ylim <- rep(NA_real_, 2L)
}
if (!is.null(range)) {
spct <- trim_wl(spct, range = range)
}
if (!is.null(w.band)) {
w.band <- trim_wl(w.band, range = range(spct))
}
cps.cols <- names(spct)[grep("^cps", names(spct))]
num.cps.cols <- length(cps.cols)
# if individual spectra have multiple columns we force facets
if (!as.logical(facets) && num.cps.cols > 1L && getMultipleWl(spct) > 1L) {
message("Usings facets because spectra contain multiple scans.")
facets <- TRUE
}
if (is_scaled(spct)) {
if (pc.out) {
warning("Percent scale supported only for normalized cps_spct objects.")
pc.out <- FALSE
}
s.cps.label <-
expression(Pixel~~response~~rate~~k %*% N[lambda]~~("rel."))
cps.label <- ""
} else if (is_normalized(spct)) {
norm.ls <- photobiology::getNormalization(spct)
norm.wl <- round(norm.ls[["norm.wl"]], digits = 1)
if (pc.out) {
multiplier.label <- "%"
} else {
multiplier.label <- "/1"
}
s.cps.label <-
bquote(Pixel~~response~~rate~~N[lambda]/N[lambda == .(norm.wl)]~~(.(multiplier.label)))
cps.label <- ""
} else {
if (pc.out) {
warning("Percent scale supported only for normalized cps_spct objects.")
pc.out <- FALSE
}
s.cps.label <-
expression(Pixel~~response~~rate~~N[lambda]~~(counts~~s^{-1}))
cps.label <- ""
}
if (num.cps.cols > 1L) {
spct <- photobiology::spct_wide2long(spct = spct, idfactor = "scan")
plot <- ggplot(spct, aes(x = .data[["w.length"]], y = .data[["cps"]], linetype = .data[["scan"]]))
temp <- find_idfactor(spct = spct,
idfactor = idfactor,
facets = facets,
annotations = annotations,
num.columns = num.cps.cols)
plot <- plot + temp$ggplot_comp
annotations <- temp$annotations
} else {
plot <- ggplot(spct, aes(x = .data[["w.length"]], y = .data[["cps"]]))
temp <- find_idfactor(spct = spct,
idfactor = idfactor,
facets = facets,
annotations = annotations)
plot <- plot + temp$ggplot_comp
annotations <- temp$annotations
}
if (!is.na(ylim[1])) {
y.min <- ylim[1]
spct[["cps"]] <- ifelse(spct[["cps"]] < y.min,
NA_real_,
spct[["cps"]])
} else {
y.min <- min(spct[["cps"]], 0, na.rm = TRUE)
}
if (!is.na(ylim[2])) {
y.max <- ylim[2]
spct[["cps"]] <- ifelse(spct[["cps"]] > y.max,
NA_real_,
spct[["cps"]])
} else {
y.max <- max(spct[["cps"]], y.min, 0, na.rm = TRUE)
}
# We want data plotted on top of the boundary lines
if ("boundaries" %in% annotations) {
if (y.min < (-0.01 * y.max)) {
plot <- plot + geom_hline(yintercept = 0, linetype = "dashed", colour = "red")
} else {
plot <- plot + geom_hline(yintercept = 0, linetype = "dashed", colour = "black")
}
}
if (!is.null(geom) && geom %in% c("area", "spct")) {
plot <- plot + geom_spct(fill = "black", colour = NA, alpha = 0.2)
}
plot <- plot + geom_line(na.rm = na.rm)
plot <- plot + labs(x = expression("Wavelength, "*lambda~(nm)), y = s.cps.label)
if (length(annotations) == 1 && annotations == "") {
return(plot)
}
plot <- plot + scale_fill_identity() + scale_color_identity()
plot <- plot + decoration(w.band = w.band,
y.max = y.max,
y.min = y.min,
x.max = max(spct),
x.min = min(spct),
annotations = annotations,
label.qty = label.qty,
span = span,
wls.target = wls.target,
summary.label = cps.label,
text.size = text.size,
na.rm = TRUE)
if (abs(y.max - 1) < 0.02 && abs(y.min) < 0.02) {
y.breaks <- c(0, 0.25, 0.5, 0.75, 1)
} else {
y.breaks <- scales::pretty_breaks(n = 5)
}
if (!is.null(annotations) &&
length(intersect(c("boxes", "segments", "labels", "summaries", "colour.guide", "reserve.space"), annotations)) > 0L) {
y.limits <- c(y.min, y.min + (y.max - y.min) * 1.25)
x.limits <- c(min(spct) - wl_expanse(spct) * 0.025, NA) # NA needed because of rounding errors
} else {
y.limits <- c(y.min, y.max)
x.limits <- range(spct)
}
if (pc.out) {
plot <- plot +
scale_y_continuous(labels = scales::percent,
breaks = y.breaks,
limits = y.limits)
} else {
plot <-
plot + scale_y_continuous(breaks = y.breaks,
limits = y.limits)
}
plot + scale_x_continuous(limits = x.limits, breaks = scales::pretty_breaks(n = 7))
}
#' Plot one or more detector-counts-per-second spectra.
#'
#' These methods return a ggplot object with an annotated plot of a
#' \code{cps_spct} or a \code{cps_mspct} object.
#'
#' @inheritSection decoration Plot Annotations
#' @inheritSection autotitle Title Annotations
#' @inherit autoplot.source_spct
#'
#' @param object a cps_spct object.
#' @param unit.out character IGNORED.
#'
#' @seealso \code{\link[photobiology]{normalize}},
#' \code{\link[photobiology]{cps_spct}},
#' \code{\link[photobiology]{waveband}},
#' \code{\link[photobiologyWavebands]{photobiologyWavebands-package}} and
#' \code{\link[ggplot2]{autoplot}}
#'
#' @export
#'
#' @examples
#'
#' autoplot(white_led.cps_spct)
#' autoplot(white_led.cps_spct, geom = "spct")
#' autoplot(white_led.cps_spct, norm = "max")
#'
#' two_leds.mspct <-
#' cps_mspct(list("LED 1" = white_led.cps_spct,
#' "LED 2" = white_led.cps_spct / 2))
#' autoplot(two_leds.mspct)
#' autoplot(two_leds.mspct, idfactor = "Spectra")
#' autoplot(two_leds.mspct, plot.data = "mean")
#'
#' @family autoplot methods
#'
autoplot.cps_spct <-
function(object,
...,
w.band = getOption("photobiology.plot.bands",
default = list(UVC(), UVB(), UVA(), PhR())),
range = getOption("ggspectra.wlrange", default = NULL),
norm = "skip",
unit.out = NULL,
pc.out = getOption("ggspectra.pc.out", default = FALSE),
label.qty = "mean",
span = NULL,
wls.target = "HM",
annotations = NULL,
geom = "line",
time.format = "",
tz = "UTC",
text.size = 2.5,
idfactor = NULL,
facets = FALSE,
plot.data = "as.is",
ylim = c(NA, NA),
object.label = deparse(substitute(object)),
na.rm = TRUE) {
if (is.null(idfactor)) {
idfactor <- getIdFactor(object)
}
if (is.na(idfactor) || !is.character(idfactor)) {
idfactor <- getMultipleWl(object) > 1L
}
if (plot.data != "as.is") {
return(
autoplot(object = subset2mspct(object),
w.band = w.band,
range = range,
norm = norm,
unit.out = unit.out,
pc.out = pc.out,
label.qty = label.qty,
span = span,
wls.target = wls.target,
annotations = annotations,
geom = geom,
time.format = time.format,
tz = tz,
text.size = text.size,
# chroma.type = chroma.type,
idfactor = idfactor,
facets = facets,
plot.data = plot.data,
ylim = ylim,
object.label = object.label,
na.rm = na.rm)
)
}
force(object.label)
annotations.default <-
getOption("photobiology.plot.annotations",
default = c("boxes", "labels", "colour.guide", "peaks"))
annotations <- decode_annotations(annotations,
annotations.default)
# avoid warning in 'photobiology' (== 0.10.10)
if (is.character(norm) && norm == "update" && !is_normalized(object)) {
norm <- "skip"
}
# normalization skipping is handled by normalize()
object <- photobiology::normalize(x = object,
range = range,
norm = norm,
na.rm = na.rm)
if (length(w.band) == 0) {
if (is.null(range)) {
w.band <- waveband(object)
} else if (is.waveband(range)) {
w.band <- range
} else {
w.band <- waveband(range, wb.name = "Total")
}
}
cps_plot(spct = object,
w.band = w.band,
range = range,
pc.out = pc.out,
label.qty = label.qty,
span = span,
wls.target = wls.target,
annotations = annotations,
geom = geom,
text.size = text.size,
idfactor = idfactor,
facets = facets,
ylim = ylim,
object.label = object.label,
na.rm = na.rm) +
autotitle(object = object,
time.format = time.format,
tz = tz,
object.label = object.label,
annotations = annotations)
}
#' @rdname autoplot.cps_spct
#'
#' @export
#'
autoplot.cps_mspct <-
function(object,
...,
range = getOption("ggspectra.wlrange", default = NULL),
norm = "skip",
unit.out = NULL,
pc.out = getOption("ggspectra.pc.out", default = FALSE),
idfactor = TRUE,
facets = FALSE,
plot.data = "as.is",
object.label = deparse(substitute(object)),
na.rm = TRUE) {
force(object.label)
idfactor <- validate_idfactor(idfactor = idfactor)
# We trim the spectra to avoid unnecessary computations later
if (!is.null(range)) {
object <- photobiology::trim_wl(object,
range = range,
use.hinges = TRUE,
fill = NULL)
}
# We apply the normalization to the collection if it is to be bound
# otherwise normalization is applied to the "parallel-summary" spectrum
if (plot.data == "as.is") {
object <- photobiology::normalize(object,
range = getOption("ggspectra.wlrange", default = NULL),
norm = norm,
na.rm = na.rm)
norm <- "skip"
}
# we convert the collection of spectra into a single spectrum object
# containing a summary spectrum or multiple spectra in long form.
z <- switch(plot.data,
as.is = photobiology::rbindspct(object, idfactor = idfactor),
mean = photobiology::s_mean(object),
median = photobiology::s_median(object),
sum = photobiology::s_sum(object),
prod = photobiology::s_prod(object),
var = photobiology::s_var(object),
sd = photobiology::s_sd(object),
se = photobiology::s_se(object)
)
if (is.cps_spct(z) && any(c("cps", "cps_1") %in% names(z))) {
autoplot(object = z,
range = getOption("ggspectra.wlrange", default = NULL),
norm = norm,
pc.out = pc.out,
idfactor = idfactor,
facets = facets,
object.label = object.label,
na.rm = na.rm,
...)
} else {
z <- as.generic_spct(z)
autoplot(object = z,
y.name = paste("cps", plot.data, sep = "."),
range = getOption("ggspectra.wlrange", default = NULL),
norm = norm,
pc.out = pc.out,
idfactor = idfactor,
facets = facets,
object.label = object.label,
na.rm = na.rm,
...)
}
}
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