wavelet.plot {dplR}

Plot a Continuous Wavelet Transform

Description

This function creates a filled.contour plot of a continuous wavelet transform as output from morlet.

Usage

wavelet.plot(wave.list,
             wavelet.levels = quantile(wave.list$Power,
                                       probs = (0:10)/10),
             add.coi = TRUE, add.sig = TRUE,
             x.lab = gettext("Time", domain = "R-dplR"),
             period.lab = gettext("Period", domain = "R-dplR"),
             crn.lab = gettext("RWI", domain = "R-dplR"),
             key.cols = rev(rainbow(length(wavelet.levels)-1)),
             key.lab = parse(text=paste0("\"",
                                         gettext("Power",
                                                 domain="R-dplR"),
                                         "\"^2")),
             add.spline = FALSE, f = 0.5, nyrs = NULL,
             crn.col = "black", crn.lwd = 1,coi.col='black',
             crn.ylim = range(wave.list$y) * c(0.95, 1.05),
             side.by.side = FALSE,
             useRaster = FALSE, res = 150, reverse.y = FALSE, ...)

Arguments

wave.list

A list. Output from morlet.

wavelet.levels

A numeric vector. Values for levels of the filled contours for the wavelet plot.

add.coi

A logical flag. Add cone of influence?

add.sig

A logical flag. Add contour lines for significance?

x.lab

X-axis label.

period.lab

Y-axis label for the wavelet plot.

crn.lab

Y-axis label for the time-series plot.

key.cols

A vector of colors for the wavelets and the key.

key.lab

Label for key.

add.spline

A logical flag. Add a spline to the time-series plot using caps?.

nyrs

A number giving the rigidity of the smoothing spline, defaults to 0.33 of series length if nyrs is NULL.

f

A number between 0 and 1 giving the frequency response or wavelength cutoff for the smoothing spline. Defaults to 0.5.

crn.col

Line color for the time-series plot.

crn.lwd

Line width for the time-series plot.

coi.col

Color for the COI if add.coi is TRUE.

crn.ylim

Axis limits for the time-series plot.

side.by.side

A logical flag. Plots will be in one row if TRUE.

useRaster

A logical flag. If TRUE, the filled contours are drawn as a raster image. Other parts of the plot are not affected. useRaster=TRUE can be especially useful when a pdf device is used: the size and complexity of the PDF file will probably be greatly reduced. Setting this to TRUE has negative effects when used with a bitmap device such as png. If NA, plotting of a raster image will be attempted if and only if the name of the graphics device is "pdf" or "postscript". The default is FALSE: draw directly to the graphics device without using an intermediate raster image.

res

A numeric vector of length 1. The resolution (pixels per inch) of the filled contours when a raster image is used. See useRaster.

reverse.y

A logical flag. If TRUE, the Y-axis will be reversed, i.e. period increasing towards the bottom. The default is FALSE.

...

Arguments passed to rasterPlot. Only relevant when the filled contours are drawn as a raster image. See useRaster.

Details

This produces a plot of a continuous wavelet transform and plots the original time series. Contours are added for significance and a cone of influence polygon can be added as well. Anything within the cone of influence should not be interpreted.

The time series can be plotted with a smoothing spline as well.

Value

None. This function is invoked for its side effect, which is to produce a plot.

Note

The function morlet is a port of Torrence’s IDL code, which can be accessed through the Internet Archive Wayback Machine.

Author(s)

Andy Bunn. Patched and improved by Mikko Korpela.

References

Torrence, C. and Compo, G. P. (1998) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.

See Also

morlet, caps

Examples

library(stats)
library(utils)
data(ca533)
ca533.rwi <- detrend(rwl = ca533, method = "ModNegExp")
ca533.crn <- chron(ca533.rwi, prewhiten = FALSE)
Years <- time(ca533.crn)
CAMstd <- ca533.crn[, 1]
out.wave <- morlet(y1 = CAMstd, x1 = Years, p2 = 9, dj = 0.1,
                   siglvl = 0.99)
wavelet.plot(out.wave, useRaster = NA)
## Not run: 
# Alternative palette with better separation of colors
# via: rev(RColorBrewer::brewer.pal(10, "Spectral"))
specCols <- c("#5E4FA2", "#3288BD", "#66C2A5", "#ABDDA4", "#E6F598", 
              "#FEE08B", "#FDAE61", "#F46D43", "#D53E4F", "#9E0142")
wavelet.plot(out.wave, key.cols=specCols,useRaster = NA)

# fewer colors
levs <- quantile(out.wave$Power, probs = c(0, 0.5, 0.75, 0.9, 0.99))
wavelet.plot(out.wave, wavelet.levels = levs, add.sig = FALSE,
             key.cols = c("#FFFFFF", "#ABDDA4", "#FDAE61", "#D7191C"), useRaster = NA)
             
## End(Not run)