R: Plots a wavelet power spectra

plot_wavelet {WaverideR}

R Documentation

Plots a wavelet power spectra

Description

Plot wavelet spectra using the outcome of the analyze_wavelet function.

Usage

plot_wavelet(
  wavelet = NULL,
  lowerPeriod = NULL,
  upperPeriod = NULL,
  plot.COI = TRUE,
  n.levels = 100,
  palette_name = "rainbow",
  color_brewer = "grDevices",
  useRaster = TRUE,
  periodlab = "Period (metres)",
  x_lab = "depth (metres)",
  keep_editable = FALSE,
  dev_new = TRUE,
  plot_dir = TRUE,
  add_lines = NULL,
  add_points = NULL,
  add_abline_h = NULL,
  add_abline_v = NULL,
  add_MTM_peaks = FALSE,
  add_data = TRUE,
  add_avg = FALSE,
  add_MTM = FALSE,
  siglvl = 0.95,
  demean_mtm = TRUE,
  detrend_mtm = TRUE,
  padfac_mtm = 5,
  tbw_mtm = 3,
  plot_horizontal = TRUE
)

Arguments

`wavelet`	wavelet object created using the `analyze_wavelet` function.
`lowerPeriod`	Lowest period value which will be plotted
`upperPeriod`	Highest period value which will be plotted
`plot.COI`	Option to plot the cone of influence `Default=TRUE`.
`n.levels`	Number of color levels `Default=100`.
`palette_name`	Name of the color palette which is used for plotting. The color palettes than can be chosen depends on which the R package is specified in the color_brewer parameter. The included R packages from which palettes can be chosen from are; the 'RColorBrewer', 'grDevices', 'ColorRamps' and 'Viridis' R packages. There are many options to choose from so please read the documentation of these packages `Default=rainbow`. The R package 'viridis' has the color palette options: “magma”, “plasma”, “inferno”, “viridis”, “mako”, and “rocket” and “turbo” To see the color palette options of the The R pacakge 'RColorBrewer' run the RColorBrewer::brewer.pal.info() function The R package 'colorRamps' has the color palette options:"blue2green", "blue2green2red", "blue2red", "blue2yellow", "colorRamps", "cyan2yellow", "green2red", "magenta2green", "matlab.like", "matlab.like2" and "ygobb" The R package 'grDevices' has the built in palette options:"rainbow", "heat.colors", "terrain.colors","topo.colors" and "cm.colors" To see even more color palette options of the The R pacakge 'grDevices' run the grDevices::hcl.pals() function
`color_brewer`	Name of the R package from which the color palette is chosen from. The included R packages from which palettes can be chosen are; the RColorBrewer, grDevices, ColorRamps and Viridis R packages. There are many options to choose from so please read the documentation of these packages. "`Default=grDevices`
`useRaster`	Plot as a raster or vector image `Default=TRUE`. WARNING plotting as a vector image is computationally intensive.
`periodlab`	Label for the y-axis `Default="Period (metres)"`.
`x_lab`	Label for the x-axis `Default="depth (metres)"`.
`keep_editable`	Keep option to add extra features after plotting `Default=FALSE`
`dev_new`	Opens a new plotting window to plot the plot, this guarantees a "nice" looking plot however when plotting in an R markdown document the plot might not plot `Default=TRUE`
`plot_dir`	The direction of the proxy record which is assumed for tuning if time increases with increasing depth/time values (e.g. bore hole data which gets older with increasing depth ) then plot_dir should be set to TRUE if time decreases with depth/time values (eg stratospheric logs where 0m is the bottom of the section) then plot_dir should be set to FALSE `plot_dir=TRUE`
`add_lines`	Add lines to the wavelet plot input should be matrix with first axis being depth/time the columns after that should be period values `Default=NULL`
`add_points`	Add points to the wavelet plot input should be matrix with first axis being depth/time and columns after that should be period values `Default=NULL`
`add_abline_h`	Add horizontal lines to the plot. Specify the lines as a vector e.g. c(2,3,5,6) `Default=NULL`
`add_abline_v`	Add vertical lines to the plot. Specify the lines as a vector e.g. c(2,3,5,6) `Default=NULL`
`add_MTM_peaks`	Add the MTM peak periods as horizontal lines `Default=FALSE`
`add_data`	Plot the data on top of the wavelet `Default=TRUE`
`add_avg`	Plot the average wavelet spectral power to the side of the wavelet `Default=FALSE`
`add_MTM`	Add the MTM plot next to the wavelet plot `Default=FALSE`
`siglvl`	Set the significance level for the MTM analysis (0-1) `Default=0.95`
`demean_mtm`	Remove mean from data before conducting the MTM analysis `Default=TRUE`
`detrend_mtm`	Remove mean from data before conducting the MTM analysis `Default=TRUE`
`padfac_mtm`	Pad factor for the MTM analysis `Default=5`
`tbw_mtm`	time bandwidth product of the MTM analysis `Default=3`
`plot_horizontal`	plot the wavelet horizontal or vertical eg y axis is depth or y axis power `Default=TRUE`

Value

The output is a plot of a wavelet spectra. if add_MTM_peaks = TRUE then the output of the MTM analysis will given as matrix

Author(s)

Code based on the analyze.wavelet and wt.image functions of the 'WaveletComp' R package and wt function of the 'biwavelet' R package which are based on the wavelet MATLAB code written by Christopher Torrence and Gibert P. Compo (1998). The MTM analysis is from the astrochron R package of Meyers et al., (2012)

References

Angi Roesch and Harald Schmidbauer (2018). WaveletComp: Computational Wavelet Analysis. R package version 1.1. https://CRAN.R-project.org/package=WaveletComp

Gouhier TC, Grinsted A, Simko V (2021). R package biwavelet: Conduct Univariate and Bivariate Wavelet Analyses. (Version 0.20.21), https://github.com/tgouhier/biwavelet

Torrence, C., and G. P. Compo. 1998. A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society 79:61-78. https://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf

Morlet, Jean, Georges Arens, Eliane Fourgeau, and Dominique Glard. "Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media. " Geophysics 47, no. 2 (1982): 203-221. https://pubs.geoscienceworld.org/geophysics/article/47/2/203/68601/Wave-propagation-and-sampling-theory-Part-I

J. Morlet, G. Arens, E. Fourgeau, D. Giard; Wave propagation and sampling theory; Part II, Sampling theory and complex waves. Geophysics 1982 47 (2): 222–236. https://pubs.geoscienceworld.org/geophysics/article/47/2/222/68604/Wave-propagation-and-sampling-theory-Part-II

S.R. Meyers, 2012, Seeing Red in Cyclic Stratigraphy: Spectral Noise Estimation for Astrochronology: Paleoceanography, 27, PA3228, <doi:10.1029/2012PA002307>

Examples


#Example 1. A plot of a wavelet spectra using the Total Solar Irradiance
# data set of Steinhilber et al., (2012)

TSI_wt <-
 analyze_wavelet(
   data = TSI,
   dj = 1/200,
   lowerPeriod = 16,
   upperPeriod = 8192,
   verbose = FALSE,
   omega_nr = 6
 )

plot_wavelet(
 wavelet = TSI_wt,
 lowerPeriod = NULL,
 upperPeriod = NULL,
 plot.COI = TRUE,
 n.levels = 100,
 palette_name = "rainbow",
color_brewer= "grDevices",
 useRaster = TRUE,
 periodlab = "Period (metres)",
 x_lab = "depth (metres)",
 keep_editable = FALSE,
 dev_new=TRUE,
 plot_dir = TRUE,
 add_lines = NULL,
 add_points= NULL,
 add_abline_h = NULL,
 add_abline_v = NULL,
 add_MTM_peaks = FALSE,
 add_data = TRUE,
 add_avg = TRUE,
 add_MTM = FALSE,
 siglvl = 0.95,
 demean_mtm = TRUE,
 detrend_mtm = TRUE,
 padfac_mtm = 5,
 tbw_mtm = 3,
 plot_horizontal=TRUE)

#Example 2. A plot of a wavelet spectra using the magnetic susceptibility
#data set of Pas et al., (2018)
mag_wt <-
analyze_wavelet(
data = mag,
dj = 1/100,
lowerPeriod = 0.1,
upperPeriod = 254,
verbose = FALSE,
omega_nr = 10
)

plot_wavelet(
wavelet = mag_wt,
lowerPeriod = NULL,
upperPeriod = NULL,
plot.COI = TRUE,
n.levels = 100,
palette_name = "rainbow",
color_brewer= "grDevices",
useRaster = TRUE,
periodlab = "Period (metres)",
x_lab = "depth (metres)",
keep_editable = FALSE,
dev_new=TRUE,
plot_dir = TRUE,
add_lines= NULL,
add_points= NULL,
add_abline_h = NULL,
add_abline_v = NULL,
add_MTM_peaks = FALSE,
add_data = TRUE,
add_avg = TRUE,
add_MTM = FALSE,
siglvl = 0.95,
demean_mtm = TRUE,
detrend_mtm = TRUE,
padfac_mtm = 5,
tbw_mtm = 3,
plot_horizontal=TRUE)


#Example 3. A plot of a wavelet spectra using the greyscale
# data set of Zeeden et al., (2013)
grey_wt <-
 analyze_wavelet(
   data = grey,
   dj = 1/200,
   lowerPeriod = 0.02,
   upperPeriod = 256,
   verbose = FALSE,
   omega_nr = 8
 )

plot_wavelet(
wavelet = grey_wt,
lowerPeriod = NULL,
upperPeriod = NULL,
plot.COI = TRUE,
n.levels = 100,
palette_name = "rainbow",
color_brewer= "grDevices",
useRaster = TRUE,
periodlab = "Period (metres)",
x_lab = "depth (metres)",
keep_editable = FALSE,
dev_new=TRUE,
plot_dir = TRUE,
add_lines = NULL,
add_points= NULL,
add_abline_h = NULL,
add_abline_v = NULL,
add_MTM_peaks = FALSE,
add_data = TRUE,
add_avg = TRUE,
add_MTM = FALSE,
siglvl = 0.95,
demean_mtm = TRUE,
detrend_mtm = TRUE,
padfac_mtm = 5,
tbw_mtm = 3,
plot_horizontal=TRUE)

[Package WaverideR version 0.3.2 Index]